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Effect of Upper Extremity Injury on Grip Strength Effort

Permanent Link: http://ufdc.ufl.edu/UFE0021648/00001

Material Information

Title: Effect of Upper Extremity Injury on Grip Strength Effort
Physical Description: 1 online resource (274 p.)
Language: english
Creator: Sindhu, Bhagwant Singh
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: curve, effort, emg, extensor, flexor, force, forearm, frequency, grip, injury, insincere, malingering, maximal, median, pain, rehabilitation, roc, sensitivity, sincerity, slope, specificity, submaximal, time
Rehabilitation Science -- Dissertations, Academic -- UF
Genre: Rehabilitation Science thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Force-time curve (F-T curve) and electromyographic (EMG) measures have been used to differentiate between maximal and submaximal grip efforts. The Force-Time Curve Test (F-T Curve Test), which uses the slopes of the force-generation phase and the force-decay phase to detect submaximal effort, has been shown to be valid in healthy people. However, the validity of the F-T Curve Test has not been examined in people with UEMDs. The primary purpose of this study was to examine if the F-T Curve Test is valid in people with UEMDs. Another purpose of this study was to examine if other F-T Curve characteristics and EMG properties are valid sincerity of effort measures in people with UEMDs. Forty subjects participated in the study. Each subject performed 2 sessions of 2 maximal and 4 submaximal grip efforts with each hand. Each grip lasted 6-seconds. The order of the efforts (maximal versus submaximal) was randomized and the test administrator was blinded to the level of effort exerted by the subject. The force-time curve and EMG signal of each contraction were recorded and following dependent variables were measured: peak force, time-to-peak force, slopes of the force-generation phase and the force-decay phase, as well as forearm flexor and extensor EMG amplitude and MF-ratio. The dependent variable scores were subjected to the following analyses: Repeated-measures ANOVA were used to compare the dependent variables with effort, injury, and session as the within-subject variables and gender as between subject variable. Test-retest reliability was analyzed using the ICC. Sensitivity and specificity values were calculated and ROC curves were plotted to find the optimal slope cutoff values. All dependent variables identified differences between maximal and submaximal efforts. The test-retest reliability ranged from 0.3 to 0.96. The slope of the force-generation phase was the most effective in distinguishing between maximal and submaximal efforts but yielded overall error rates 55% for women and 60% for men. Despite the significant differences between maximal and submaximal efforts, we did not find acceptable combinations of sensitivity and specificity for detecting sincerity of effort. Therefore, the F-T curve and EMG measures may not be clinically valid.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Bhagwant Singh Sindhu.
Thesis: Thesis (Ph.D.)--University of Florida, 2007.
Local: Adviser: Shechtman, Orit.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2007
System ID: UFE0021648:00001

Permanent Link: http://ufdc.ufl.edu/UFE0021648/00001

Material Information

Title: Effect of Upper Extremity Injury on Grip Strength Effort
Physical Description: 1 online resource (274 p.)
Language: english
Creator: Sindhu, Bhagwant Singh
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: curve, effort, emg, extensor, flexor, force, forearm, frequency, grip, injury, insincere, malingering, maximal, median, pain, rehabilitation, roc, sensitivity, sincerity, slope, specificity, submaximal, time
Rehabilitation Science -- Dissertations, Academic -- UF
Genre: Rehabilitation Science thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Force-time curve (F-T curve) and electromyographic (EMG) measures have been used to differentiate between maximal and submaximal grip efforts. The Force-Time Curve Test (F-T Curve Test), which uses the slopes of the force-generation phase and the force-decay phase to detect submaximal effort, has been shown to be valid in healthy people. However, the validity of the F-T Curve Test has not been examined in people with UEMDs. The primary purpose of this study was to examine if the F-T Curve Test is valid in people with UEMDs. Another purpose of this study was to examine if other F-T Curve characteristics and EMG properties are valid sincerity of effort measures in people with UEMDs. Forty subjects participated in the study. Each subject performed 2 sessions of 2 maximal and 4 submaximal grip efforts with each hand. Each grip lasted 6-seconds. The order of the efforts (maximal versus submaximal) was randomized and the test administrator was blinded to the level of effort exerted by the subject. The force-time curve and EMG signal of each contraction were recorded and following dependent variables were measured: peak force, time-to-peak force, slopes of the force-generation phase and the force-decay phase, as well as forearm flexor and extensor EMG amplitude and MF-ratio. The dependent variable scores were subjected to the following analyses: Repeated-measures ANOVA were used to compare the dependent variables with effort, injury, and session as the within-subject variables and gender as between subject variable. Test-retest reliability was analyzed using the ICC. Sensitivity and specificity values were calculated and ROC curves were plotted to find the optimal slope cutoff values. All dependent variables identified differences between maximal and submaximal efforts. The test-retest reliability ranged from 0.3 to 0.96. The slope of the force-generation phase was the most effective in distinguishing between maximal and submaximal efforts but yielded overall error rates 55% for women and 60% for men. Despite the significant differences between maximal and submaximal efforts, we did not find acceptable combinations of sensitivity and specificity for detecting sincerity of effort. Therefore, the F-T curve and EMG measures may not be clinically valid.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Bhagwant Singh Sindhu.
Thesis: Thesis (Ph.D.)--University of Florida, 2007.
Local: Adviser: Shechtman, Orit.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2007
System ID: UFE0021648:00001


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EFFECT OF UPPER EXTREMITY INJURY ON GRIP STRENGTH EFFORT


By

BHAGWANT SINGH SINDHU













A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA


2007





























2007 Bhagwant Singh Sindhu


































To my parents, for their countless prayers.















ACKNOWLEDGMENTS

I would like to thank my supervisory committee. First and foremost, I thank Dr.

Orit Shechtman, my mentor and chair of my graduate study committee. Thank you for

your vision, support, and encouragement. I am especially thankful for the moments when

you lifted my spirits with your simple gestures of kindness. Thank you for polishing my

research and teaching skills and thank you for helping me become a better person. I am

lucky to have you as a mentor. I would also like to say a special thanks to Dr. Paul

Davenport, the external member on my graduate committee. Thank you for always

helping me in anyway you could, and for challenging and guiding my thought process.

Working in your lab was an enriching experience. Dr. John Rosenbek, I am grateful to

you for teaching me the meaning of being a rehabilitation scientist. Dr. Mark Bishop, I

am very grateful for all your help with the analysis of EMG data. I thank you all for

opening your hearts to me, for taking me under your wings, and for sharing your

knowledge with me.

I would also like to thank Thought Technology Ltd. and the American Society of

Hand Therapists (ASHT) for supporting this research project. I am grateful to Thought

Technology Ltd. for loaning their equipment and for providing technical support. I am

also grateful to the ASHT for partially funding this research project. I could not have

completed this project without your support.

I am grateful to the faculty and staff in the Department of Occupational Therapy at

University of Florida for their support and encouragement. I am especially grateful to









Wendy Holt for always being there to help, support, encourage and advise me. Wendy,

you have taught me so much. It has been a pleasure working with you and sharing an

office with you.

I am also very grateful to my fellow graduate students: Leigh, Rick, Patricia,

Jessica, Inga, Megan, Pey-Shan, Jia-hwa, Eric, Michelle, Cristina, Sandy, Swathy, and

Kezia. Thank you all for being so helpful and supportive at various stages of my graduate

study. Without your friendship I could not have survived the last six years in the RSD

program.

Last, but not the least, I would like to thank my grandmother, parents, aunts and

uncles, sister and brother-in-law, and my cousins for their love and support. Thank you

all for your guidance at each and every step. You inspired and motivated me to work

hard. You have dreamed of and prayed for my success. I am not sure what I would have

done without you.
















TABLE OF CONTENTS

page

A C K N O W L E D G M E N T S ................................................................................................. iv

LIST OF TABLES ....................................................... ............ .. ............ ix

LIST OF FIGURE S ......... ..................................... ........... xii

ABSTRACT ........ .............. ............. ...... ...................... xiv

CHAPTER

1 IN TR OD U CTION ............................................... .. ......................... ..

Problem Statem ent .................. ............................. .. ........... ............. ..
Sp ecific A im s ..................................................... ..................... 2
Sp ecific A im 1 ................................................ ........................ 3
Specific A im 2 ............................................... ........................ 3
Specific A im 3 ............................................... ........................ 4
B background ................................... ........................... ..... ..... ........ 4
Significant ce ...........................................................................12
Previous Study ..................................................................... ..........14
D definition of Term s ..... ...................... ....................... .... .... .. ............ 16

2 L ITE R A TU R E R E V IE W ........................................ ............................................23

Cost, Magnitude and Description of Upper Extremity Disorders .............................23
Use of Grip Strength to Assess Upper Extremity Musculoskeletal Disorders ...........25
Differences between Maximal and Submaximal Effort ..........................................26
Grip Strength Tests for Detecting Submaximal Effort .............................................27
Coefficient of V ariation (CV ) ........................................ ........................ 27
Rapid Exchange Grip Test (REG) ............... ............ ........ ......... 29
F ive R ung G rip (5R ) T est......................................................... .....................30
F orce-T im e C urve............. ............................................ ................ .......... ....... 32
R e liab ility ................................................................3 3
A athletics .......................................................................................................35
H ealthcare ............................................................................................. ....... 36
M axim al Effort ................................................................. ..... ............ 38
Biopac Dynamometer ...... ............. ..... ......... .........41
Surface Electromyographic Activity .............................. ............... 42









O rigin and Propagation................................................ ............................ 42
Signal P roperties......... ............................................................ .. .... ...... 44
Increasing Force .......................................... ................... ........ 46
F a tig u e ......................................................................................................4 8
In ju ry ............................................................................4 9
M axim al Effort .................. ...................................... ............... 5 1
P ain ............................................................................5 3
Transmission of Pain Sensation............... ........................... 54
Regulation of Pain Sensation by the Nervous System .............. ... .............57
A cute versus Chronic Pain ............................................................................ 63
Dimensions of Pain........ ................ .. ........... ...... ............ 65
Assessm ent of Pain ........ .................. .. .......... ....... .... ..... 68
Perceived M agnitude of Grip Force ........................................ ....................... 70
S u m m a ry .....................................................................................................7 1

3 M E T H O D S ........................................................................................................... 7 7

P a rtic ip a n ts ........................................................................................................... 7 7
M materials and E quipm ent ................................ ...................................................... 77
Instruments for Generating the F-T Curve ................. ................. ..........78
Instruments for Recording the EMG Signal ....... ........ ...... .................. 79
P ap er-an d-P en cil T ests .................................................................................. 82
Stu dy D esig n .....................................................83
P ro cedu re ............ ... ..... .. ..........................................................84
Participant Recruitment Phase ... .. ................. ........... 84
D ata C collection Phase..................................................... 84
Statistical A nalysis................................................... 90

4 RESULTS ........ .......... ........................ ..... 101

Subjects ............... ................................ .........101
S p e c ifi c A im 1 .................................................................................................... 1 0 1
Peak Force .............. .... .... ................. 102
Tim e-to-peak Force ................................................ ............... 102
Slope of the Force-generation Phase ............ ......................................103
Slope of the Force-decay Phase .............. ................................... ........ 103
Specific Aim 2 ..... .............. ....... .................. 104
Flexor EMG Amplitude ............. ... ......... .....................104
Extensor EMG Amplitude ..................... .............. ........... 105
Flexor Median Frequency Ratio ................ ............ ........ ......... 105
Extensor Median Frequency Ratio ..................... ............................105
S p ecifi c A im 3 .....................................................................10 6
Test-Retest Reliability ................... ......................... 106
V validity .......... .... ... ............................................................. ............. 107
Slope of force-generation phase ......... ...... .......... ........ 107
Slope of force-decay phase................................................. 108
M edian frequency ratio ....................................................... 108









Post-H oc A analysis .......................................... .. .. .... ................. 108
S u m m a ry ........................................................................................1 0 8

5 DISCUSSION .................. ................................... ........... .............. 165

Force-Tim e Curve Characteristics....................................................... ................. 167
Differences between Maximal and Submaximal Effort.................................. 168
Differences between the Injured and Uninjured Hands ..................................170
Gender Differences .............. ....... ..... .................. ......... .. 171
Electrom yographic Properties .................... ....................... ............... ... 171
Differences between Maximal and Submaximal Effort.................................. 174
Differences between Injured and Uninjured Hands .......................................175
G ender D differences ............. ......................... ...... ...... .......................176
F orce-D ecay P hase ....................... .............................................. .. .............. 177
R liability and V alidity ............ .... .................................................. .. .... .... .... .. 178
L im stations ..................................................................................................... 184
C o n c lu sio n s......................................................................................................... 1 8 5

APPENDIX

A SAMPLE SIZE CALCULATION.................................................... 186

B CORRELATION MATRIX FOR DYNAMOMETER CALIBRATION..............187

C DEMOGRAPHIC QUESTIONNAIRE...................... ... ........................ 189

D LETTER TO HEALTHCARE PROFESSIONALS WITH INCLUSION AND
EX CLU SION CR ITER IA ................................................................................. 193

E RANDOMIZATION ORDER AND SHEET..........................................................195

F CHECKLISTS USED DURING THE DATA COLLECTION PROCESS............. 197

G D A TA COLLECTION FORM ..................................................................... ...... 200

L IST O F R E FE R E N C E S ..................................................................... ..... .................228

BIOGRAPH ICAL SKETCH ...................................................... 259
















LIST OF TABLES


Table pge

1-1 F-T curve slope and EMG amplitude values from the pilot study .........................20

1-2 Summary of sensitivity and specificity values of sincerity of effort tests................20

2-1 Differences between maximal and submaximal effort...........................................73

2-2 Sensitivity and specificity values of different sincerity of effort tests...................74

2-3 Differences between second order pain neurons .............. ...................................75

2-4 Strengths and weaknesses of pain intensity assessments.............. ...................76

3-1 Schematic representation of the study protocol ......................................................94

3-2 Calculating sensitivity and specificity for the slope cut-off value of X during the
force-decay phase ......................................... ............. ... ........ .... 95

4-1 Demographic characteristics of the study sample................................................. 110

4-2 Injury related characteristics of the study sample ..................................................111

4-3 First session averages of the F-T curve characteristics............... .....................112

4-4 Second session averages of the F-T curve characteristics.................................... 113

4-5 Four-Way ANOVA on the values of peak force.............................................114

4-6 Three-Way ANOVA on first session values of the peak force............................ 115

4-7 Three-Way ANOVA on second session values of the peak force .........................116

4-8 Four-Way ANOVA on the values of time-to-peak force................... ............ 117

4-9 Three-Way ANOVA on the first session values of time-to-peak force ...............18

4-10 Four-Way ANOVA on the slopes of the force-generation phase ..........................119

4-11 Three-Way ANOVA on the first session slopes of the force-generation phase.....120









4-12 Four-Way ANOVA on the slopes of the force-decay phase............................... 121

4-13 Three-Way ANOVA on the first session slopes of the force-decay phase ............122

4-14 Three-Way ANOVA on the second session slopes of the force-decay phase........123

4-15 Average values for EM G amplitude.................................. ......................... 124

4-16 Average values of EMG median frequency for the first session..........................125

4-17 Average values of EMG median frequency for second session values..................126

4-18 Four-Way ANOVA on flexor EMG amplitude.....................................................127

4-19 Three-Way ANOVA on first session values of the flexor EMG amplitude ..........128

4-20 Four-Way ANOVA on extensor EMG amplitude ............................................. 129

4-21 Three-Way ANOVA the on first session extensor EMG amplitude .......... ......130

4-22 Three-Way ANOVA on the second session extensor EMG amplitude ...............131

4-23 Four-Way ANOVA on the flexor EMG median frequency ratio.........................132

4-24 Three-Way ANOVA on the first session values of flexor EMG median
frequ en cy ratio ................................................. ................ 13 3

4-25 Four-Way ANOVA on the extensor EMG median frequency ratio.....................134

4-26 Three-Way ANOVA on the first session values of extensor EMG median
frequ en cy ratio ................................................. ................ 13 5

4-27 Three-Way ANOVA on the second session values of extensor EMG median
frequ en cy ratio ................................................. ................ 13 6

4-28 Intraclass Correlation Coefficients for F-T curve characteristics ........................137

4-29 Intraclass Correlation Coefficients for EMG properties .....................................138

4-30 Summary of main effects of effort for force and EMG measures.......................139

4-31 Sensitivity and specificity of specific slope cutoff values for force-generation
phase .................... .......... .............. .................................. 140

4-32 Sensitivity and specificity values of specific slope cutoff values for force-decay
p h a se ...................................... ...................................................... 14 2

4-33 Sensitivity and specificity of specific flexor MF-ratio cutoff values................. 143









4-34 Sensitivity and specificity of specific extensor MF-ratio cutoff values ...............144

4-35 Summary of sensitivity and specificity values for the Force and EMG measures. 145

A-1 Range of maximal and submaximal effort slope values ....................................... 186

B-l Pearson correlation coefficients (r) between weekly voltage outputs obtained
during the dynamom eter calibration process ............................... ............... .188

E- Randomization sheet used in the study ...................................... ............... 196
















LIST OF FIGURES


Figure page

1-1 ROC curve for the slope of force-decay phase ............. ........................................21

1-2 Typical maximal and submaximal grip efforts.. .......................... ............... 22

3-1 Biomechanical instruments for recoding force and electromyographic signals.......96

3-2 Electronic Jamar dynamometer ................. .. ................. .. ............... 97

3-3 M y o Scan activ e sen sors ........................................ .............................................97

3-4 FlexC om p Infiniti encoder ............................................... ............................ 98

3-5 BioGraph Infiniti polygraph software.................................. .................................. 98

3-6 Pain Intensity Visual Analog Scale................ ....... ..... ......... .... ............... 99

3-7 Perceived Effort Visual Analog Scale................ ... .................................99

3-8 Setup used to check the dynamometer calibration.......... .................................100

4-1 Interaction between session and injury for peak force.....................................146

4-2 Average values of peak force for maximal and submaximal grip efforts ............147

4-3 Significant interactions for peak force values....................................................... 148

4-4 Average values of time-to-peak force ...........................................................149

4-5 Interaction between effort and injury for slope of force-generation phase............150

4-6 Average values of slopes of force-generation phase.............................................151

4-7 Interaction between effort and injury for slope of force-decay phase.................. 152

4-8 Interaction between session and gender for slope of force-decay phase.............. 153

4-9 Average values of slopes of force-decay phase for maximal and submaximal
grip efforts ...................................... ................................. ......... 154









4-10 Interaction between effort and injury for flexor EMG amplitude.......................155

4-11 Average values of flexor EMG amplitude for maximal and submaximal grip
effo rts .............................................................................15 6

4-12 Interaction between effort and session for extensor EMG amplitude............. 157

4-13 Average values of extensor EM G amplitude ....................... ............................ 158

4-14 Average values of flexor M F-ratio............................................... .................. 159

4-15 Interaction between injury, effort, and gender for flexor MF-ratio........................160

4-16 Average values of extensor M F-ratio.................................................................. 161

4-17 Interaction between injury and effort for the first session values of extensor
EM G M F-ratio...................................................... .......... .............. .. 162

4-18 ROC curve for slope of force-generation phase................... .......................... 163

4-19 ROC curve for MF-ratio of forearm flexor and extensor muscles......................... 164

E-1 Randomization orders used in the study ..................................... .................195

F-l Checklist used by the research assistants ................ ........................... ......... 198

F-2 Checklist used by the test administrator............................... .. ............. ........... 199















Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

EFFECT OF UPPER EXTREMITY INJURY ON GRIP STRENGTH EFFORT
By

Bhagwant Singh Sindhu

December 2007

Chair: Orit Shechtman
Major: Rehabilitation Science

Force-time curve (F-T curve) and electromyographic (EMG) measures have been

used to differentiate between maximal and submaximal grip efforts. The Force-Time

Curve Test (F-T Curve Test), which uses the slopes of the force-generation phase and the

force-decay phase to detect submaximal effort, has been shown to be valid in healthy

people. However, the validity of the F-T Curve Test has not been examined in people

with UEMDs.

The primary purpose of this study was to examine if the F-T Curve Test is valid in

people with UEMDs. Another purpose of this study was to examine if other F-T Curve

characteristics and EMG properties are valid sincerity of effort measures in people with

UEMDs.

Forty subjects participated in the study. Each subject performed 2 sessions of 2

maximal and 4 submaximal grip efforts with each hand. Each grip lasted 6-seconds. The

order of the efforts (maximal versus submaximal) was randomized and the test

administrator was blinded to the level of effort exerted by the subject. The force-time









curve and EMG signal of each contraction were recorded and following dependent

variables were measured: peak force, time-to-peak force, slopes of the force-generation

phase and the force-decay phase, as well as forearm flexor and extensor EMG amplitude

and MF-ratio.

The dependent variable scores were subjected to the following analyses: Repeated-

measures ANOVA were used to compare the dependent variables with effort, injury, and

session as the within-subject variables and gender as between subject variable. Test-retest

reliability was analyzed using the ICC. Sensitivity and specificity values were calculated

and ROC curves were plotted to find the optimal slope cutoff values.

All dependent variables identified differences between maximal and submaximal

efforts. The test-retest reliability ranged from 0.3 to 0.96. The slope of the force-

generation phase was the most effective in distinguishing between maximal and

submaximal efforts but yielded overall error rates 55% for women and 60% for men.

Despite the significant differences between maximal and submaximal efforts, we

did not find acceptable combinations of sensitivity and specificity for detecting sincerity

of effort. Therefore, the F-T curve and EMG measures may not be clinically valid.














CHAPTER 1
INTRODUCTION

Problem Statement

Upper extremity musculoskeletal disorders and injuries (UEMDs) may result in

compromised grip strength.1 Grip strength depends on the type, rate and number of

contracting muscle fibers.2 Reduced grip strength (weakness of grip) brought about by

injury may be due to either a reduction in the rate and number of contracting muscle

fibers3, changes in muscle-fiber-type3-7, or pain.s-10 Pain has been associated with

decreases in: voluntary muscle activity11'7, electromyographic (EMG) activityll' 12

motor unit discharge rates4' 15, y-motor neuron activityl6, speed of force generation 7, and

endurance time.13

Maximal voluntary grip strength scores of people with UEMDs are used by

clinicians8s to determine the extent of injuryl, disease process20, and progress in

rehabilitation.21 Grip strength is a valid indicator of musculoskeletal pathology and

recovery from such pathology only when people exert a sincere, maximal voluntary

effort.22-27 Weakness of grip strength may be brought about by an injury but could also be

due to exertion of submaximal effort. Submaximal effort may be exerted during

evaluation and treatment for a variety of reasons, either unintentional or intentional.

Unintentional submaximal effort may be exerted as a result of pain, fear of pain and fear

of re-injury. Intentional submaximal effort may be exerted for secondary gain (such as

money, benefits, or attention). To improve rehabilitative care, clinicians need to be able









to distinguish between a maximal voluntary grip effort (exerted by a client with true

weakness of grip) and a submaximal grip effort.

Force-time curve (F-T curve) characteristics and electromyographic (EMG)

properties generated during isometric grip contraction have shown promise in

differentiating between maximal and submaximal efforts.28 The F-T curve is generated by

plotting force generated by a contracting muscle over a period of time during a single

strength trial.29 The F-T curve characteristics include the slope of the force-generation

phase, the slope of the force-decay phase, and the time-to-peak force28, whereas, the

EMG properties include its amplitude and frequency.30-32 So far, these F-T curve

characteristics and EMG properties have been described in healthy people.28' 3032

However, little evidence exists regarding the effects of UEMDs on the nature of maximal

voluntary grip contraction as expressed by the F-T curve characteristics and EMG

properties. The purpose of this research proposal was to identify if selected F-T curve

characteristics and EMG properties can form valid sincerity of effort tests in people with

UEMDs. As part of this study we examined 4 force and 2 EMG measures: peak force,

time-to-peak force, slope of force-generation phase, slope of force-decay phase, EMG

amplitude, and median frequency ratio.

Specific Aims

The purpose of the study was three-fold: (1) to compare the force-time curve

characteristics of maximal and submaximal grip effort exerted by the injured versus

uninjured hand, (2) to compare the electromyographic properties of maximal and

submaximal grip effort exerted by the injured versus uninjured hand, and (3) to examine

the reliability and validity of the force-time curve test in distinguishing between maximal

and submaximal grip efforts.









Specific Aim 1

To examine the difference in force-time curve (F-T curve) characteristics between

the injured and uninjured hands as well as between maximal and submaximal efforts. The

characteristics that we examined were:

1. Peak force

2. Time-to-peak force

3. Slope of the force-generation phase

4. Slope of the force-decay phase

Hypothesis la. The peak force will be significantly greater, time-to-peak force will

be significantly faster, and slope of the force-generation phase will be significantly

steeper for the uninjured hand than for the injured hand, whereas, the slope of the force-

decay phase will be significantly steeper for the injured hand than for the uninjured hand.

Hypothesis lb. The peak force will be significantly greater, time-to-peak force will

be significantly slower, and the slope of the force-generation phase and the force-decay

phase will be significantly steeper for maximal effort than for submaximal effort.

Specific Aim 2

To examine the difference in electromyographic (EMG) properties between the

injured and uninjured hand as well as between maximal and submaximal effort. The

characteristics that we examined were:

1. The amplitude of forearm muscle EMG

2. The median frequency ratio of last to first second

Hypothesis 2a. The forearm muscle EMG amplitude will be significantly greater

for the uninjured hand than for the injured hand, whereas, the median frequency ratio will

be significantly smaller for injured hand than for the uninjured hand.









Hypothesis 2b. The forearm muscle EMG amplitude will be significantly greater,

whereas, the median frequency ratio will be significantly smaller for maximal effort than

for submaximal effort.

Specific Aim 3

To examine the reliability and validity of the force-time curve characteristics and

EMG properties in distinguishing between maximal and submaximal grip efforts. The

psychometric properties that we tested were:

1. The reliability assessed by identifying test-retest reliability

2. The validity assessed by identifying the effectiveness

Hypothesis 3a. The F-T curve characteristics and EMG properties will consistently

measure grip efforts as expressed by high test-retest reliability (r>0.9).

Hypothesis 3b. The F-T curve characteristics and EMG properties is valid for

measuring of grip efforts, i.e. effective in differentiating between maximal and

submaximal grip efforts, as expressed by a combined optimal value of 80% sensitivity

and 90% specificity.

Background

Musculoskeletal disorders and injuries have an enormous and growing impact on

American society.33 In 1996, 53.9 million Americans, or 1 in 5 Americans, reported

having at least 1 musculoskeletal condition.34 Scientists have predicted a substantial

increase in the prevalence of musculoskeletal conditions. By 2020, arthritis alone will

affect an estimated 59.4 million Americans.35 The prevalence of physical disabilities

caused by musculoskeletal conditions has been estimated at 4-5% of the population.36

Musculoskeletal impairments have been ranked number one among impairments due to

chronic conditions.33'37 Musculoskeletal and connective tissue disorders also represent









17.2% of all activity limiting conditions.38 Besides the obvious physical effects,

musculoskeletal conditions significantly affect the psychosocial status of the people with

these conditions as well as their families and caregivers.33 The economic burden of

musculoskeletal conditions is substantial: the total cost amounts to more than $250 billion

per year39 and the medical care expenditure for people with musculoskeletal conditions is

50% higher than for people with non-musculoskeletal chronic conditions.34 Specifically,

work-related musculoskeletal disorders (WMSDs) cost over $20 billion every year.40

Therefore, people with musculoskeletal disorders and injuries use a sizeable amount of

health and social care resources.41

Musculoskeletal disorders (MSDs) are caused by different pathophysiological

mechanisms42, 43 and form a diverse category of conditions.44 Among the

pathophysiological mechanisms, repeated or awkward movements have been reported to

cause or aggravate MSDs.45 MSDs encompass over 150 different diseases and syndromes

because of their anatomical links as well as by their association with pain and impaired

physical function.42 Gradually developing pain and discomfort in soft tissue structures

including nerves, muscles, tendons, blood vessels, and their related connective tissues

represents a common clinical feature of the MSDs. Moreover, MSDs that have been

associated with work activities commonly affect the upper extremities.45

Work-related musculoskeletal disorders (WMSDs) represent a cluster of conditions

that are diagnosed by symptoms of pain, numbness, and/or tingling lasting more than a

week or occurring more than 20 times in a year, without evidence of acute traumatic

onset or systemic disease.45 A large proportion of WMSDs affect the upper extremity. In

2003, 23% of the non-fatal work related injuries and illnesses affected the upper









extremity.46 The upper extremity musculoskeletal conditions that are frequently

associated with WMSDs include lateral epicondylitis (a tendon/muscle disorder) and

carpal tunnel syndrome (a nerve compression disorder).43 The muscle groups commonly

affected by the upper extremity musculoskeletal disorders and injuries (UEMDs) include

the neck and shoulder muscles4749 as well as the extensor muscles of the forearm and the

hand musculature.50 The affected muscles generally present with fatigue and stiffness,

radiating pain, increased muscle tone during passive movement, painful locations and/or

trigger points, which are defined as "palpable discrete, focal, and/or hyperirritable

spots."43

Among people with UEMDs, grip strength has been used as a measure that

indicates musculoskeletal pathology and documents recovery from such pathology.18

Grip strength scores have been frequently used to determine the extent of disability and

the amount of financial compensation for an injury, estimate physical work capacity,

match job requirements to work capacity, and assess ability to return to work after

injury.51-57 Clinicians commonly use a dynamometer to measure grip strength. Objective

measurement of grip strength is possible due to the availability of standardized testing

procedures, normative values and accurate instruments.18 A grip strength score, however,

is objective, reliable and valid only when a patient exerts a maximal voluntary effort.22-25

Some people with UEMDs may exert a less than maximal voluntary effort during

evaluation and treatment for a variety of reasons, either intentional (such as secondary

gain of money, benefits, or attention)58-61 or unintentional (such as pain, fear of pain, or

fear of re-injury).8' 9,62 A less than maximal effort has been termed as insincere, low,

submaximal, or less than honest effort.25' 27, 28, 63-67 From this point on, an intentional low









effort will be referred as an insincere effort and an unintentional low effort will be

referred as a submaximal effort. An insincere effort is a component of disability

exaggeration. Disability exaggeration has been defined as "intentional production of false

or grossly exaggerated physical or psychological symptoms, motivated by external

incentives such as avoiding military duty, avoiding work, obtaining financial

compensation, evading criminal prosecution, or obtaining drugs" (p. 245).5 The rate of

disability exaggeration is estimated to be between 25 and 30% of all personal injury

litigation, worker's compensation, or disability claims.596

A submaximal effort may be exerted when the person is in pain or has fear of pain.

Pain is one of the most commonly reported symptom by people with MSDs 43 and the

most significant symptom for the majority of people with MSDs.68 Pain signals originate

in the periphery as a result of an injury or a disease. The central nervous system (CNS)

then selects, abstracts, and synthesizes pain signals with other sensory signals.69'70 The

force exerted by a muscle decreases as the level of pain increases. Several studies have

reported this inverse relationship including region specific studies (involving a certain

area of the body)71, diagnosis specific studies (involving certain medical conditions)8, 72,

and studies involving chronic pain.9'62 However, people respond to pain differently as

their pain experience is influenced by factors such as attitude, culture, past experiences,

meaning of a situation, and other psychological variables like anxiety, stress and

depression.69,70 Therefore, pain may cause people with UEMDs to exert submaximal grip

efforts.

People with UEMDs may also develop a fear of pain. Fear of pain has been shown

to impact people with chronic musculoskeletal conditions73-75 as well as people with









acute pain.76 An elevated fear of pain has been hypothesized to induce an avoidance

behavior76-78, which includes avoidance of movement, daily activity, leisure activity and

social interaction.79 In turn, the avoidance behavior may lead to disuse syndrome, chronic

disability and exaggerated pain perception.76-78 Therefore, through the mechanism of

avoidance behavior, fear of pain may prevent people with UEMDs from exerting

maximal voluntary grip effort.

Detecting a submaximal grip effort is essential as a person cannot be effectively

rehabilitated without putting forth full effort, even on applying the most advanced

technology, equipment and therapies. Different methods based on the grip strength have

been used in the clinic to detect submaximal efforts.80-82 These methods include the five

rung grip (5R) test, the rapid exchange grip (REG) test and the coefficient of variation

(CV).27,28,64-67, 83, 84 The 5R-test uses variability in grip strength scores across the 5

handle positions of a Jamar dynamometer to identify a submaximal effort. A greater

variability in grip strength scores across the 5 handles increases the likelihood of

maximal effort.22'25'31'32, 85-89 The REG-test requires a patient to grip a dynamometer in

rapid succession. The highest score resulting from this rapid succession has been termed

as the REG-score. The REG-score is then compared to the peak score generated during a

static grip (SG) test, which has been termed as the SG-score. The REG-test score is

calculated by subtracting the REG-score from the SG-score. A negative REG-test score

(i.e. the SG-score is greater than the REG-score) increases the likelihood of a maximal

effort.62, 90, 91 The CV, which measures relative dispersion, expresses the standard

deviation (SD) of multiple grip strength trials as a percentage of their mean score.53' 64-66,

92 The premise of the CV is that a set of submaximal effort trials would result in a greater









value of the CV when compared to a set of maximal effort trials.82 The CV, 5R-test and

REG-test, however, lack standardized testing protocols, reliability and validity values,

and empirical support.27 28 64-66,80,83,89,93,94 Each of these tests has been shown to have a

high error rate for the combined sensitivity and specificity values. High error rates

indicate that using these tests inaccurately classifies a large number of people exerting

maximal effort as exerting submaximal effort, and large number of people exerting

submaximal effort as exerting maximal effort. Such high error rates deem these tests

inadequate in detecting submaximal effort in a clinical setting.26-28, 58, 64-66, 82, 83, 93

Physiologically based measures, which take into consideration muscle activity over

a period of time, such as the force-time curve and electromyography, may provide better

detection of maximal versus submaximal efforts. The force-time curve (F-T curve)

graphically represents the force generated by a contracting muscle over a period of time

during a single strength trial.29 The vertical axis (Y-axis) represents change in force of

muscular contraction and the horizontal axis (X-axis) represents change in time. The

typical isometric F-T curve consists of an initial rapid rise of force (the force-generation

phase or the initiation phase), followed by a relatively smooth peak curve (the initiation

peak), and a subsequent gradual decrease in force over time (the force-decay phase or the

maintenance phase).95,96 Various F-T curve characteristics have been used in athletics for

evaluating neuromuscular adaptations to strength-training programs. These characteristics

have been used to identify maximal effort in a variety of muscle groups.97-99 Several

characteristics have been found to be consistent for portraying age-related changes100 as

well as fatigue-related changes95 in grip strength. Submaximal grip effort was also

identified in the 1980's using the variability of force during the plateau phase of the F-T









curve.29 55, 101 However, the F-T curve is not commonly used in the clinic to evaluate

sincerity of effort because it requires specialized equipment and software, which are

currently not available commercially.

In a previous study we found that the slopes of the two phases of the F-T curve (the

force-generation phase and the force-decay phase) successfully differentiated between

maximal and submaximal grip effort in healthy subjects. The Receiver operating

characteristic (ROC) curves identified the best combinations of sensitivity and specificity

values for the slopes. We found excellent sensitivity and specificity values for the slopes,

with sensitivity values ranging from 0.8 to 0.93 and the specificity values ranging from

0.93 to 1.0. Also, the lowest overall error rates ranged between 7% and 33%.102 These

error rates are excellent when compared to the error rates of the five-rung test, rapid

exchange grip test and the coefficient of variation, which range from 47% to 69%.27, 65, 93

The grip efforts in the pilot study lasted for 5-seconds. Two studies have evaluated

sustained maximal grip efforts over 10-seconds (s).96, 103

Kamimura and Ikuta96 compared the reliability of a maximal isometric grip lasting

6-s with that lasting 10-s. Fifty healthy subjects continuously gripped a modified Jamar

dynamometer that was set at the second handle position. Subjects performed grip efforts

on two occasions separated by 2-7 days. On each occasion, subjects performed one trial

of the 6-s grip followed by the 10-s grip. Subjects rested for a minute between the two

grips. Test-retest reliability was compared for peak grip strength, time-to-peak strength,

and the momentary strength (strength at every second during a trial). The peak grip

strength and time-to-peak strength were found to have high reliability coefficients for

both grips. However, the coefficients of momentary strength were found to be higher for









the 6-s grip than the 10-s grip. Consequently, the 6-s grip was identified to be more

consistent than the 10-s grip. The study also identified that in both tests maximal strength

was achieved in the first two seconds. After achieving maximal strength, a gradual

decline in strength was observed.96

Massy-Westropp et al.103 identify age and gender-specific reference values of a 10-

s grip strength trial. The grip strengths of 476 healthy subjects were tested using the

Grippit electronic dynamometer. Each subject performed one grip trial, which was used

to calculate peak, average and final strength. The final strength was measured as an

indicator of fatigue. Peak and average grip strengths were found to be the highest in the

third and fourth decades of life, with women showing less strength than men for all age

groups. The final strength indicated that left-hand dominant adults have more equal grip

endurance between their hands than do right-hand dominant adults.103

Forearm muscle EMG properties have the potential of being a valuable adjunct for

clinicians involved in identifying submaximal grip effort.32 Surface EMG activity has

been proposed to be the best measure of overall electrical activity that drives a muscle.104

Surface EMG can also indicate the amount of voluntary effort perceived by a person.104

The EMG activity of a submaximal grip effort was found to have smaller amplitude than

that of a maximal voluntary effort.31' 32, 89 This is not surprising because less motor units

are active during submaximal compared to maximal effort resulting in lower EMG

activity. However, conflicting findings have been reported for the mean power frequency

(MPF). The MPF is the frequency of the EMG signal that represents the average power of

a power spectrum. Also, the median power frequency has been defined as the frequency

about which the power is distributed equally above and below.105 The terms power and









power spectrum have been defined on page 18. The MPF of a submaximal grip effort has

been reported to either be greater than maximal voluntary effort31 or not to be different

than that of maximal voluntary effort.32' 89

In general, it appears that F-T curve characteristics and EMG properties can be

used to develop a dependable tool for determining whether a patient exerts maximal or

submaximal effort.31'32 To date, studies investigating the potential of F-T curve28 and

EMG31, 32, 89 to determine sincerity of effort have only included healthy participants.

Furthermore, the nature of maximal voluntary effort (both F-T and EMG) has not been

studied adequately in people with UEMDs.106 Thus, there is a need to identify the nature

of maximal grip efforts and submaximal grip efforts in people with UEMDs and to

investigate the impact of current and imagined pain on grip effort.

Significance

MSDs present an enormous burden on today's society as they cost billions of

dollars annually in medical and rehabilitative care as well as in lost work time.34' 39, 40, 107,
108 This burden has increased the demands on health care professionals to correctly

identify disability exaggeration.84 One method of assessing disability exaggeration

involves determining sincerity of effort in grip strength. Commercially available sincerity

of effort tools are neither reliable nor valid.26-28' 58, 64-66, 80, 82, 83, 89, 93, 94 A reliable and valid

tool may assist in reducing the costs of misdiagnosis, rehabilitation, medical procedures,

lost work-time, and lost productivity, and thus may be of great value to the society. Such

a tool can be of benefit to rehabilitation specialists (such as occupational and physical

therapists and rehabilitation counselors), insurance companies, worker compensation

authorities, employers, and the workers themselves.









It is essential to have a reliable and valid assessment tool to identify sincerity of

effort. A reliable instrument performs with predictable consistency under set conditions.

An unreliable instrument cannot be valid as an inconsistent instrument cannot produce

meaningful measurements. A valid measurement instrument collects data in an accurate

and relevant manner.109 A sincerity of effort instrument is a diagnostic tool that screens

for the presence or absence of submaximal effort. A valid diagnostic tool has high

sensitivity and specificity values.110 An instrument with a low sensitivity value may

misclassify a person who exerts submaximal effort as exerting maximal effort.

Consequently, a person feigning disability may be mistakenly labeled as sincere. Low

sensitivity can lead to seemingly ineffective treatment, increased unnecessary procedures,

and elevated disability and health care costs.27 65, 82, 83, 93, 94 Conversely, an instrument

with a low specificity value may misclassify a person who exerts maximal effort as

exerting submaximal effort. Consequently, a person exerting sincere effort may be

erroneously labeled as feigning disability. This error can lead to inappropriate diagnosis

and treatment, reduced worker compensation settlement, withheld payments and even

loss of job.27, 65, 82, 83, 93, 94 For a sincerity of effort instrument, a low sensitivity value has

been argued to be better than a low specificity value as "it is considered more ethical to

miss subjects giving a deliberately submaximal effort rather than to misclassify as

feigning a subject giving a genuine maximal effort" (p. 1828).80 Unfairly misclassifying a

sincere person as feigning can be very damaging to the individual and may promote

clinically unfair decisions.6 Thus, there is a great need to establish a method for

identifying sincerity of effort that has high sensitivity and specificity values allowing it to

avoid mistakes in classifying patients as sincere or feigning.









The force-time curve (F-T curve) has been shown to be very effective in detecting

submaximal effort in uninjured individuals.28 Based on physiological aspects of effort,

the F-T curve has the potential to allow therapists to determine submaximal effort when

exerted by injured individuals. The F-T curve also has the ability to assist in clinical

decision-making concerning further treatment and/or referral. Moreover, the proposed

project may further the understanding of motor unit recruitment in maximal and

submaximal muscular effort of hand-injured patients, which has potential applications in

rehabilitation, ergonomics, and biomechanics.

Previous Study

In a previous study, we analyzed the force-time curves (F-T curves) of maximal

and submaximal grip strength trials exerted by healthy people for the slopes of both the

force-generation phase and the force-decay phase. We simultaneously recorded the

electromyographic (EMG) activity of the extrinsic flexor and extensor muscles of the

digits.

Methods. Thirty healthy volunteers (15 men and 15 women) performed three

maximal and three submaximal grip strength efforts with their dominant hand. We

blinded the test administrator to the nature of the effort. For force measurements, we used

a specialized dynamometer (Biopac Instruments) with a force transducer connected

through a digital oscilloscope (Gould Instruments) to an analog-to-digital (A/D) converter

(PowerLab, ADInstruments). The digital force signals were stored on a computer by a

polygraph software system (Chart, ADInstruments). For EMG activity, we placed surface

silver-silver chloride electrodes over the belly of the flexor digitorum superficialis muscle

and the extensor digitorum communis muscle. The EMG activity was amplified and

band-pass filtered in the range of 0.1-1.0 kHz (Grass Polygraph, Grass Instruments) and









led into the A/D converter (PowerLab, ADInstmments). The EMG data were digitally

smoothed and rectified by the Chart software (ADInstruments).

Data analysis. For maximal and submaximal effort, we used the Chart software to

calculate the slopes of the force-generation and the force-decay phases of the F-T curve

as well as the amplitude of the EMG activity. The Chart calculates the slope from the

least-squares line of best fit of the selected data points. The average rectified amplitude of

the EMG activity was calculated for the duration of the F-T curve.111

Statistical analysis. Paired sample t-tests were used to analyze the difference

between maximal and submaximal effort. Additionally, the sensitivity and specificity

values of the slopes of the force-generation and force-decay phases were calculated. To

find the optimal cutoff value for each of the two slopes, the receiver operating

characteristic (ROC) curves were generated from the multiple combinations cutoff

values.

Results. For the F-T curves, we found significant differences in slope between

maximal and submaximal efforts for both the force-generation phase (t=46.77; p<0.0001)

and the force-decay phase (t=79.16; p<0.0001). We also found significant differences

between maximal and submaximal effort in time-to-peak force (t=2.841; p<0.008). For

the EMG activity, we found significant differences in amplitude between maximal and

submaximal efforts for both the flexor muscles (t=4.52; p<0.0001) and the extensor

muscles (t=3.82; p<0.001). Table 1-1 presents the average and SD values of the F-T

curve slopes and EMG amplitudes.

Conclusions. This study achieved excellent combinations of sensitivity and

specificity values, especially when compared to the sensitivity and specificity of the









currently available clinical tests (Table 1-2). For the cutoff value of -0.075 during the

force-decay phase, none of the male subjects who exerted a maximal effort were wrongly

classified as exerting a submaximal effort. Of the male subjects exerting submaximal

effort, only 7% were wrongly classified as giving a maximal effort (Figure 1-1).28

On examining the concurrent EMG and force recordings of the force-decay phase

of maximal effort, starting at approximately 4.5 seconds after achieving peak force, we

found the two recordings to decompose. While force continued to decline, EMG output

actually increased, indicating that the muscles were maximally activated by the nervous

system in an attempt to maintain the maximal contraction. In contrast, during submaximal

effort, the EMG and force recordings exhibited similar trends indicating that the person

was able to maintain the maximal contraction without additional activation of motor units

(Figure 1-2).

Definition of Terms

This section defines the various terms used in this research project. When

appropriate, the conceptual and operational definitions of terms specific to the study have

been given.

1. Musculoskeletal system: Also called the locomotor system, the musculoskeletal
system consists of the skeletal system (bones and joints) and the skeletal muscle
system, and peripheral nerves that innervate the skeletal muscles. This system
performs various functions including protection of internal organs, maintain posture,
assist in movement, formation of blood cells, and storage of fats and minerals.1' 112

2. Musculoskeletal disorders:

a. Conceptual definition: Musculoskeletal disorders include a diverse
spectrum of diseases and syndromes with varied pathophysiology.
However, they are linked anatomically and by their association with pain
and impaired physical function. These conditions range from acute onset
and short duration disorders to lifelong disorders. They commonly
manifest as rheumatoid arthritis, osteoarthritis, osteoporosis, spinal









disorders, peripheral nerve injuries, major limb trauma, fibromyalgia,
gout, and sprains and strains.39'42

3. Musculoskeletal conditions:

a. Conceptual definition: Musculoskeletal conditions have been defined
differently in the literature. Some articles rely on physician provided
diagnoses, some on self-report, some include injuries to the
musculoskeletal system and some exclude injuries. The National Arthritis
Data Task Force defines musculoskeletal conditions as those that include
the International Classification of Diseases, Ninth Edition (ICD-9) codes
274 (gout) and 710.0 739.9 (diseases of musculoskeletal system and
connective tissue).34'113

4. Upper extremity musculoskeletal disorders and injuries (UEMDs):

a. Operational Definition: It is a collection of various diseases, syndromes,
and injuries that affect the musculoskeletal system (skeletal muscles,
bones, joints, blood vessels, nerves and related connective tissue) of the
upper extremity.

5. Disability exaggeration: Also called symptom magnification and malingering,
disability exaggeration has been defined as "intentional production of false or grossly
exaggerated physical or psychological symptoms, motivated by external incentives
such as avoiding military duty, avoiding work, obtaining financial compensation,
evading criminal prosecution, or obtaining drugs."58 Moreover, disability
exaggeration subsumes fraudulent persistence of symptoms. These persistent
symptoms are observed when genuine symptoms cease but a patient asserts that the
symptoms continue to exist.114-117

6. Effort: Effort is the perception of an individual regarding how much force he/she has
exerted. Effort is a psychological construct and force variables can only provide an
indirect measure of the construct.31

7. Maximal voluntary effort:

a. Conceptual definition: Also called sincere effort, maximal effort indicates
that a person consciously and voluntarily performs to the best of their
ability during an evaluation.

b. Operational definition: In relation to grip strength, maximal effort
indicates that a person consciously and voluntarily performs a grip
strength trial to the best of their ability.

8. Submaximal effort:


a. Conceptual definition: It is a less than maximal effort.









b. Operational definition: In relation to grip strength, submaximal effort
indicates that a person subconsciously or unintentionally performs a grip
strength trial in which the force generated is less than that generated
during a maximal voluntary effort.

9. Insincere effort:

a. Conceptual definition: Also termed as low, submaximal, or less than
honest effort25' 28, 63-65, 93, insincere effort indicates that a person
consciously performs at a level below the best of their ability during an
evaluation.

b. Operational definition: In relation to grip strength, insincere effort
indicates that a person consciously or intentionally performs a grip
strength trial in which the force generated is less than that generated
during a maximal voluntary effort.

10. Sincerity of effort:

a. Conceptual definition: It is a patient's conscious motivation to perform
optimally during an evaluation.82

b. Operational definition: In relation to grip strength testing, sincerity of
effort indicates exertion of maximal voluntary strength/force during a grip
strength trial.118

11. Surface electromyography (SEMG): It is a noninvasive method of measuring the
electric potential field evoked by active muscle fibers through the intact skin 119. The
SEMG signal is measured as a time-varying signal.

12. Frequency content of SEMG: Any time-varying signal can be represented by
successively adding the individual frequencies (fn) present in the signal.120' 121 The
frequencies forming the EMG signal can be identified by performing a mathematical
conversion called the Fourier Transformation.121

13. Power at frequencyfn: The Fourier Transformation of the EMG signal calculates two
Fourier coefficients (bn and c,) for each frequency (fn) in the EMG signal. The sum of
the squares of these coefficients is termed as power at frequencyf. The power
indicates how much signal is composed of the frequencyfn.121

14. Power spectrum of EMG signal: A plot of power at each frequency, that composes
the EMG signal is referred to as the power spectral density (PSD) plot or simply the
power spectrum.121

15. Mean power frequency of EMG signal: It is the frequency of the EMG signal that
represents the average power of a power spectrum.






19


16. Median power frequency of EMG signal: Sometimes know as the center frequency, it
has been defined as "the frequency about which the power is distributed equally
above and below. It is calculated as a median of a distribution" (p. 115).105









Table 1-1: F-T curve slope and EMG amplitude values from the pilot study
Slope of the F-T curve Rectified EMG Amplitude
Force-generation Force-decay Flexor muscle Extensor
phase phase muscle
Maximal 2.61+1.40 -0.16+0.08 0.08+0.03 0.18+0.08
Trials
Submaximal 0.98+0.44 -0.04+0.03 0.02+0.01 0.07+0.02
Trials

Table 1-2: Summary of sensitivity and specificity values of sincerity of effort tests
Measure Value Sensitivity Specificity Author
Slope of force- Females =1.2 0.80 0.93 Shechtman et. al, 2007
generation Males = 1.45 0.80 0.87
phase
Slope of force- Females =-0.05 0.80 0.87 Shechtman et. al, 2007
decay phase Males = -0.075 0.93 1.00
Coefficient of 11% CV cutoff 0.69 0.74
Shechtman, 2001
Variation 15% CV cutoff 0.55 0.92
Five-Rung 7.5 SD cutoff 0.7 0.83 Gutierrez &


Rapid
Exchange Grip


REG 45


0.65


Shechtman, 2003
0.66 Shechtman & Taylor,
2000





























t 0.5
1 Specificity


Figure 1-1: ROC curve for the slope of force-decay phase






















S 1 2 3 4 5 6
Time (seconds)


0 1 2 3 4 5 6
Tme seconds )


Figure 1-2: Typical maximal and submaximal grip efforts. A) F-T curve of a maximal
effort. B) Rectified EMG signal of maximal grip effort. C) F-T curve of a
submaximal effort. D) Rectified EMG signal of submaximal grip effort.


Time (seconds)


Time (seconds)














CHAPTER 2
LITERATURE REVIEW

Upper extremity musculoskeletal disorders and injuries (UEMDs) include a

heterogeneous group of soft-tissue conditions that affect muscles, tendons, ligaments,

joints, peripheral nerves and supporting blood vessels.43' 122, 123 The reported prevalence

rates of UEMDs range from 11-47%.124-129 The frequent occurrence of UEMDs has

challenged clinicians to develop new methods to improve the outcomes of rehabilitative

care.

Cost, Magnitude and Description of Upper Extremity Disorders

Work-related musculoskeletal disorders (WMSDs) of the upper extremity impose

an enormous burden on our society.40 130 WMSDs have also been described as

cumulative trauma disorders as well as repetitive strain disorders. In 1989, cumulative

trauma disorders of the upper extremity cost Americans over half-a-billion dollars in

medical and indemnity expenses.130 Since the 1980s, WMSDs of the upper extremity

have grown rapidly. 131 Cumulative trauma disorders of the upper extremity increased

from 1% in 1986 to 4% in 1993.132 For the period 1993-94, 4.4% of worker compensation

claimants had an upper extremity diagnosis.133 In 1995, upper extremity WMSDs

comprised a third of all WMSDs.40 In 2003, among the 1.3 million injuries and illnesses

occurring in the private industry, 33% were musculoskeletal disorders (MSDs) and 23%

were upper extremity conditions. Also, over 20,000 people sustained carpal tunnel

syndrome (CTS) and 7,000 people sustained tendinitis.134 CTS also resulted in the highest

lost work time (median = 32 days), which is higher than lost work time reported in 1997









(median = 25 days).46 134 Hence, the growing numbers of upper extremity WMSDs have

increased the burden of care on the society.

Work-related activities involve low to high intensity repetitive tasks as well as

awkward postures, which result in upper extremity musculoskeletal disorders and injuries

(UEMDs).135-140 WMSDs of the upper extremity have been broadly defined as "symptom

complexes characterized by pain, paraesthesia, and/or weakness affecting the upper

extremity or neck by the patient and/or their physicians to work" (p. 1279).131, 141

WMSDs of the upper extremity frequently present as either tendinitis or entrapment

neuropathy. Tendinitis involves inflammation of the muscle-tendon unit. When not

allowed to heal, this inflammatory state evolves into a degenerative condition.142

Entrapment neuropathies develop at specific points where nerves course around

anatomical structures.143, 144 Sites of entrapment distal to the elbow include the radial

tunnel, supinator muscle145148, pronator teres muscle149151, cubital tunnel52,and capral

tunnel.153-155 In the upper extremity, lateral epicondylitis is a common form of tendinitis

and carpal tunnel syndrome is a common form of entrapment neuropathy.

Lateral epicondylitis or tennis elbow involves inflammatory and degenerative

changes of the forearm extensor muscles.43' 50, 156 Lateral epicondylitis has been

associated with overuse of the elbow and is caused by force overload at the common

extensor origin of the forearm muscles.157-161 The muscle primarily affected is the

extensor carpi radialis brevis.3' 162-165 The chief clinical feature of lateral epicondylitis

includes a gradually developing pain over the lateral aspect of the elbow that radiates

distally into the forearm. The radiating pain increases with motor tasks that include









forearm pronation or supination, active wrist extension, passive wrist flexion against

resistance and gripping.156, 157

Carpal tunnel syndrome (CTS), the most pervasive entrapment neuropathy166-169

occurs when the structures within the carpal tunnel compress the median nerve.153-155 The

entrapment of the median nerve has been attributed to rheumatoid disease, pregnancy,

diabetes, renal dialysis, space occupying lesions and the bony abnormalities of the

wrist.170 It has been postulated that edema due to impaired circulation ultimately causes

CTS.171 Symptoms of CTS include nocturnal pain, paraesthesia and hypaesthesia in the

area of the hand innervated by the median nerve.172 Later stages of CTS present with

referred shoulder pain, burning pain, and wasting of thenar muscles.173

Use of Grip Strength to Assess Upper Extremity Musculoskeletal Disorders

Occupational and physical therapists frequently measure grip strength while

assessing people with upper extremity musculoskeletal disorders and injuries (UEMDs).

Grip strength scores have been used to determine the extent of injury19, disease process2,

progress in rehabilitation21 and functional integrity of an affected upper extremity.174 The

American Society of Hand Therapists (ASHT) recommends a standard method for

measuring grip strength175 as the strength output changes with factors such as positioning

of the upper extremity. Change in position of the wrist24, 176-178, forearm179, elbow180-184

and shoulder184 have been shown to affect grip strength scores. Hence, grip strength

measurements are less variable when using a standard testing protocol. However, the

ASHT's method does not control for all sources of variability. For example, the protocol

recommended by ASHT requires the tester to gently support the base of the

dynamometer.175 The dynamometer reading using this technique may become inaccurate

if a patient exerts forces that are greater than the strength of the tester as it leads to









improper stabilization of the dynamometer.185 186 To identify outcomes of rehabilitative

treatment, therapists compare grip strength scores of the injured extremity either with the

uninjured extremity21 or with the established grip strength norms.187 These comparisons

of grip strength are not accurate if a patient does not exert a maximal grip effort. Thus, a

therapist needs to distinguish a maximal effort from a submaximal effort.

Differences between Maximal and Submaximal Effort

Based on the German literature of 1950s and 1960s, Kroemer and Marraslss

presented a neurophysiological model of maximal and submaximal effort.27' 29, 64, 188, 189

According to Kroemer and Marras ls, an executive program regulates muscular

contraction on the basis of the strength output profile. This program originates in the

cerebral and cerebellar regions of the central nervous system (CNS).188 Also, when a

body part needs to generate greater effort, the CNS focuses greater mental attention on

generating that effort as well as inhibiting body systems not involved in generating that

effort.190, 191 At the level of neuromuscular junction, two strategies are used to increase

force output. Rate coding means 'frequency of motor neuron firing' whereas recruitment

coding means 'sequence of motor unit activation.' A maximal effort results in maximal

motor neuron firing and maximal recruitment of motor units. In contrast, a submaximal

effort requires motor cortex to mix and precisely control submaximal frequency of motor

neuron firing and recruitment of certain number and type of motor units., 1819, 192-194

Maximal and submaximal effort also differ in level of sensory feedback, which

influences the order of motor unit recruitment.18, 195 Sensory afferent fibers assist in

calibration and modulation of magnitude of effort.196, 197 This contribution of sensory

afferent fibers distinguishes between maximal and submaximal effort. Maximal effort,

which represents a lower order task, involves simple motor control (maximal motor unit









recruitment and firing) with minimal afferent feedback, which indicates full use of motor

units.188, 189, 192-194 In contrast, submaximal effort represents a higher order task, which

requires a more complex motor control strategy. Maintenance of submaximal effort

requires extensive and complex sensory afferent feedback.188, 193, 194 Table 2-1

summarizes the differences between maximal and submaximal effort.

Grip Strength Tests for Detecting Submaximal Effort

Therapists use a variety of tests to detect submaximal effort, for example, the

Waddell's non-organic signs, correlation between musculoskeletal evaluation and

functional capacity evaluation, documentation of pain behavior, documentation of

symptom magnification, and ratio of heart rate and pain intensity.82 These methods may

be divided into assessments that are commonly used and those not commonly used in the

clinic.64 The clinically-relevant methods can be administered easily and in a relatively

short period time, and require minimal calculations and minimal equipment, e.g. grip

strength based tests. In contrast, several tests are not commonly used in the clinic as they

involve a lengthy administration time, complicated calculations, and expensive

equipment, e.g. functional capacity evaluations and isokinetic tests. Other tests can cause

pain and discomfort, e.g. tests involving supra-maximal stimulation of muscles. Among

clinically relevant tests, the three grip strength based tests commonly used include the

coefficient of variation, rapid exchange grip test and five rung grip test, which have been

described next.

Coefficient of Variation (CV)

The CV is based on premise that submaximal exertion is more variable and less

consistent than a maximal effort. The CV identifies submaximal effort when a calculated

value is larger than a cut-off value.64-66









Physiological basis. Maximal effort can be easily replicated because it represents

a lower order task. Maximal effort requires simple motor control based on maximal

motor unit firing frequency and maximal motor unit recruitment. In contrast, submaximal

effort is difficult to replicate because it represents a higher order task. Submaximal effort

requires delicate proprioceptive feedback for grading muscle contraction, requires a

precise combination of both rate coding and recruitment coding, and involves constant

corrections of motor signals by sensory afferents.29' 51'64,66 188,189,192-194

Administration protocols. The CV uses scores of at least 3 grip strength trials.

The CV is calculated by dividing the standard deviation by the mean value of the grip

strength trials. Next, the calculated value is compared to a predetermined cut-off value. In

literature, this cut-off value ranges between 10% and 20%. A CV value that is greater

than the cut-off value is labeled as submaximal and insincere. 64

Advantages and limitations. The advantages of CV include:

1. The CV is simple to calculate.64

2. Some studies have shown that the CV differentiates between maximal and
submaximal efforts.64

3. The CV is based on a standardized grip test.64

The limitations include:

1. The CV has not been shown to distinguish between maximal and submaximal
efforts.54, 198 This could be because variability in repeated measures of maximal
effort has been reported to range from 10-24%.64 199, 200 Further, submaximal
efforts in certain isometric tasks have been reported to be reproducible.82
Furthermore, it has been suggested that psychological factors, such as fear of re-
injury and pain, can increase variability between trials.94

2. The CV can only be used for comparing dispersion of data with different units.
Since grip strength values are in same units, use of the CV as a sincerity of effort
test becomes inappropriate.92









3. For the CV to be a valid measure of sincerity of effort, the average and standard
deviation of repeated grip strength trials should increase proportionally (i.e. people
with greater average grip strength should exhibit greater variability in grip strength
trials).64,94 However, an inverse relationship has been described between grip
strength and its variability.201 Also, means and standard deviations of grip strength
do not change proportionally.66'83

4. The CV has been shown to have poor test-retest reliability.66

5. The sensitivity and specificity values of the CV do not allow it to effectively
differentiate between maximal and submaximal effort.65

Rapid Exchange Grip Test (REG)

Physiological basis. Submaximal effort, which requires the motor cortex to mix

and precisely control recruitment of motor units and their frequency of firing, requires a

longer period of processing time than maximal effort. The rapid exchange of hands

during the REG maneuver decreases the amount of time available for the cortex to

compare between contractions. Hence, when an individual feigns weakness in one hand,

the assessor expects that individual to exhibit greater REG scores than static grip (SG)

score in the weaker hand.26'90,193,194,202,203

Administration protocols. Lister developed the REG in 1983 to identify patients

exerting submaximal effort.91 The REG requires an individual to quickly grip a

dynamometer with alternating hands. The REG test involves comparing REG scores to

those of static grip (SG) test score. An SG test consists of slow, maximal grips and may

be administered using either the five-rung (5R) test or the maximal static grip test

(MSGT). In maximal effort, the clinician expects the REG scores to be less than SG

scores resulting in a negative REG test score, which indicates sincere effort. In

submaximal effort, the REG score is expected to be greater than the SG score resulting in

a positive REG test score, which indicates insincere effort.26 The testing protocols used

by therapists vary with respect to hand switch rates (varying from 45 to 100 rpm), grip









repetitions (3 and 5 repetitions), type of SG score used for comparison (the 5R and the

MSGT), and patient positioning while testing and handling of the dynamometer.26 27 84

Advantages and limitations. The advantages of REG include:

1. It is simple to administer that does not require any special equipment to administer.

2. It requires a short administration time.

The limitations of REG include:

1. Literature provides contradictory evidence for the REG as a test of sincerity of
effort.26

2. Clinicians do not use a standard protocol while administering the REG test.26, 27,
84

3. Studies performed to validate the REG provide insufficient description of testing
protocols. When described sufficiently, these protocols vary significantly.26

4. Speed of alternating grips plays an important role in the effectiveness of the REG
test.26

5. Use of different handle settings of the Jamar dynamometer can influence grip test
results.26, 27, 84

6. Clinicians do not determine sincerity of effort by just using the REG, but, by using
it in conjunction with other tests indicating difficulty in interpreting the results of
the REG.84

7. The sensitivity and specificity of the REG were not found to be sufficiently
high.27, 204

8. The concept of 'positive REG' indicating a submaximal effort works only when
comparing the peak REG scores with peak 5R scores.27, 204

Five Rung Grip (5R) Test

Physiological basis. The premise of the 5R test is based on the mechanical

advantage of the muscles involved in gripping at the mid-range of hand position

represented by rung 2 or 3 of the Jamar dynamometer. The mechanical advantage is

based on length-tension relationship, leverage, and hand size. Increased lengthening (up

to 110% of resting length) of the muscle prior to contraction produces greater muscle









forces during subsequent contraction. Lengthening the muscle beyond 110% will

generate less tension due to reduced overlap of actin and myosin filaments. During

maximal effort, the optimal resting muscle length produces the greatest contraction force.

For most people the optimal length occurs at the second or third handle-position of the

Jamar dynamometer, which also results in the best leverage. During submaximal effort,

the person exerts a controlled, less than maximal muscular contraction. Hence, the person

tends to exert approximately the same amount of force at all five rungs of the Jamar

dynamometer.85

Administration protocols. The 5R test involves maximally gripping the Jamar

dynamometer at the five available handle settings. On graphing the scores, a maximal

effort produces a skewed bell shaped curve, whereas, a submaximal effort produces a flat

line. Four different methods have been used to analyze the data from the 5R test: a) visual

analysis of grip strength curves85' 90, b) use of repeated measure analysis of variance with

two within subject factors31'32' 86,87,89, c) normalization of grip strength scores88 205, and

d) standard deviation of grip strength scores across all five trials.22

Advantages and limitations. The advantages of the 5R include:

1. It is easy to administer.

2. It requires a short administration time.

The limitations of 5R include:

1. The 5R test depends on the strength of the gripping hand. Hence, the test yields
biased results when assessing sincerity of effort in people with upper extremity
injuries93 and cannot distinguish between injured maximal effort and uninjured
submaximal effort.206

2. Multiple studies on the effectiveness of the 5R test have provided conflicting
evidence on its effectiveness as a sincerity of effort test.86, 87









3. Some studies have shown that subjects trained to feign can produce a curve that
looks like a maximal effort curve.32, 86, 87

It is clear that the commonly used clinical tests are not reliable and valid for

identifying submaximal effort. In contrast, tests based on the force-time curves28 and

EMG activity31 32, 89 can differentiate between maximal and submaximal effort (Table 2-

2). The force-time curve has been used in various research studies to investigate both

maximal97-99 and submaximal29' 55, 101 efforts. However, the force-time curve is not

commonly used in the clinic because it requires specialized equipment.28 29, 55, 96, 101, 207-209

Force-Time Curve

The force-time curve (F-T curve) graphically represents the force generated by a

contracting muscle over a period of time during a single strength trial.29 The vertical axis

(Y-axis) represents change in force of muscular contraction and the horizontal axis (X-

axis) represents time of muscular contraction. The typical F-T curve generated during a

maximal voluntary isometric contraction (MVIC) consists of three phases: 1) the force-

generation phase or the initiation phase that involves rapid or gradual development of

force, followed by 2) the initiation peak that represents a relatively smooth peak curve,

which may be followed by a secondary peak representing the maximum force value, and

finally 3) the force-decay phase or the maintenance phase involving a steady rate of force

development that may decrease gradually over time indicating onset of fatigue.95' 96, 210-212

The F-T curves of isometric100 213 as well as dynamic (concentric and eccentric )

muscle contractions214'215 have been used to evaluate skeletal muscle functioning. The F-

T curves have been used to identify differences in muscle function by age100, 213,216-218

gender209, 213,219, 220 and muscle fiber type.100,218,221









Reliability

Three studies have been performed identify test-retest reliability of various F-T

curve characteristics of grip strength trials. In general, the F-T curve characteristics have

been found to have moderate to high reliability coefficients.95' 100, 207 Bemben et al.100

identified the reliability of four different force-time curve (F-T curve) characteristics for

interpreting age related changes in muscle function and force production. The study

included 155 healthy men divided into 12 age groups ranging from 20 to 79 years. The

characteristics included maximal force, total impulse, time to maximal force and maximal

rate of force production for a 60 millisecond period. These characteristics were tested for

five muscle groups: finger flexors, thumb abductors, forearm extensors, foot dorsiflexors

and foot plantarflexors. Finger flexion force was recorded using a device similar to a

handgrip dynamometer. For each muscle group, participants performed 3 maximal

isometric contractions on 2 different days. Participants were instructed to exert maximal

effort as hard and fast as possible and were told to relax when they felt that maximal

force had been achieved. After each trial, participants rested for one minute. Day-to-day

(test-retest) reliability was identified by comparing scores recorded on 2 different days

using Pearson correlation coefficients. For finger flexors, correlation coefficients were

found to be 0.98 for maximal force, 0.93 for maximal rate of force-production and 0.91

for total impulse. This study indicated that the four maximal force characteristics were

consistently reached even by the oldest men, therefore allowing for accurate

characterization of muscle function.100

Househam et al.95 identified the reliability as well as effect of fatigue on the

variability of four different F-T curve characteristics including the maximum initiation

force, absolute maximum force, maximum maintenance force, and slope of the









maintenance phase. Six healthy men with an average age of 36 years performed 3

maximal isometric grips on 3 different days using a modified Jamar dynamometer. The

authors did not specify the handle position used in the study. The men were instructed to

squeeze the dynamometer as rapidly as possible exerting maximal effort for a 7-s period.

The grip trials were separated by a 30-s rest period, which did not eliminate the effects of

fatigue. The test-retest reliability of the force characteristics across daily sessions was

identified by calculating the coefficient of variation of the standard deviation for

corresponding trials across the 3 sessions. The presence and degree of fatigue in a grip

effort was determined by calculating slope of the maintenance phase using linear

regression. The coefficient of variation of the force characteristics ranged from 0.11 to

0.9 with the smallest value for the maintenance force. Thus, the maximal maintenance

force proved to be the most reliable parameter for quantifying maximal isometric

contraction. The mean value of the maintenance phase slopes was calculated as 13.5

N/s. There was a significant slope in 70% of the trials and when present it was negative.

However, it was not more or less likely that there would be a decline in force during trials

performed later on during a session. This indicated that intertrial and intratrial fatigue

effects are somewhat independent.95

Demura et al.207 compared the reliability of explosive and voluntary grip using 11

different F-T curve characteristics. The characteristics measured were divided into 5

categories of variables: time, average force, integrated area from the onset of exertion,

maximal rate of force development, and mechanical power. Hundred healthy men with an

average age of 17.8 + 2.5 years performed two explosive as well as voluntary grips with

their dominant hands using a digital dynamometer (ED-D100R). The men performed the









2 voluntary grips, rested for 5 minutes or longer, and finally performed 2 explosive grips.

For the voluntary grip, the men were instructed to exert maximal grip after hearing the

start signal. For the explosive grip, the men were instructed to exert maximal grip as fast

and forcefully as possible after hearing the start signal. The cross-correlation coefficients

indicated that between the two trials, the difference in explosive grip tended to be smaller

than voluntary grip. The explosive grip had greater reliability coefficients for 9 of the 11

characteristics than voluntary grip. Also, the maximal grip strength scores had highest

reliability coefficients between the two trials for explosive and voluntary grip.207 While

the F-T curves have primarily been employed in the athletics-related fields (e.g. exercise

physiology and athletics), healthcare professionals have also investigated their use for the

purposes of assessment and treatment in rehabilitation.

Athletics

In athletics, neuromuscular adaptations due to exercise have been associated with

changes in F-T curves. The F-T curves have been commonly used to assess muscular

strength97-100, endurance216, 222,223, and performance.214, 224 Strength, endurance and

performance related differences in F-T curves have been attributed to several

physiological factors including muscle fiber type composition100' 216,218,221,225, muscle

cross-sectional area226, stiffness of muscle-tendon complex227, and neural drive to the

muscle.215, 228-231

Strength training, including heavy-resistance and speed training, has been found to

change F-T curve characteristics, such as peak force and rate of force development

(RFD).98, 99,215, 229, 232 Strength training causes a muscle to undergo rapidly occurring

neural adaptations as well as gradually occurring hypertrophic adaptations. A stronger









neural drive has been associated with increases the RFD, whereas muscle hypertrophy

primarily increases peak force.98' 99, 215,228,229,232

In relation to grip strength, F-T curve characteristics based on the force-generation

phase100, 207-209 and the force-decay phase96 have been used to investigate maximal

isometric contractions. Bemben et al.100 indicated that F-T curve characteristics,

including rate of force development (force generation), can reliably identify age related

changes in explosive grip strength. Explosive strength has been defined as "the rate of

rise of contractile force at the onset of contraction, i.e. the rate of force development

(RFD) exerted within the early phase of rising muscle force."229 Bemben et al.100 also

indicated that F-T curves allow for successful implementation of strength training

programs among older men, who may be concerned with fear of injury. Demura et al.207-
209 studied explosive grip using multiple F-T curve characteristics, including the RFD

(force generation). Demura et al.207-209 found the F-T curve characteristics to be larger in

stronger subjects208, and different between males and females.209 Moreover, the force-

generation phase of explosive grip was found to be more reliable than slow maximal

grip.207 Although, F-T curve characteristics of explosive strength tests have been found

more reliable than slow strength tests, it may be safer to use slow strength tests in people

with injuries as the explosive tests may cause re-injury.

Healthcare

Among people with injuries, the F-T curve characteristics of slow grip strength

trials seem to be most appropriate for identifying muscle function. When compared to

explosive grip strength test, the slow maximal grip strength test seems to be safer.

Explosive grip requires the gripping to be performed as fast and forcefully as possible. In

contrast, the slow grip allows a person with an injury to determine their own motor unit









recruitment strategy.28'207 Two studies have been performed to identify the effect of

injury on the F-T curves of grip strength.211'212

Helliwell et al.212 measured grip strength in people with rheumatoid arthritis (RA)

using a torsion dynamometer. Study participants consisted of 33 females and 13 males

with a mean age of 57 years. The participants gripped the dynamometer 3 times with a

rest period lasting a few seconds between trials. The F-T curves generated from each grip

lasted 4.4-s and generated 6 characteristics: maximum grip strength, time to maximum

value, fatigue rate, amount of fatigue, release rate, and power factor. The study identified

adequate reproducibility of the 6 characteristics, with moderate reproducibility of time to

maximum value and fatigue rate. Helliwell et al.212 also reported 2 phases of the F-T

curve: an initial steep rise in grip strength, and a subsequent slower decline after

achieving peak strength.212 Similar phases of the F-T curve have been reported in people

without injuries.95' 96 Therefore, it appears that the shape of an F-T curve remains the

same in presence and absence of injury.

Hakkinen et al.211 studied changes in shape of knee extensor F-T curves as a result

of strength training and detraining. The study participants included 20 healthy people and

43 people with recent-onset rheumatoid arthritis (RA). Participants with RA were

randomly divided into an experimental and a control group. The experimental group

participated in a progressive strength training program for 6 months. In contrast, the

control group maintained their habitual physical activities. With the knee positioned at

1000, the participants exerted maximal effort as rapidly as possible and maintained it for

approximately 5-s. The David 200 dynamometer was used to record the maximal

voluntary isometric F-T curves at 0, 6 and 42 months. The F-T curves at 6 months









indicated the training effect and at 42 months indicated the detraining effect. The study

found that participants with RA took longer than healthy participants to produce the same

level of force at 0, 6 and 42 months. At 6 months, the shape of the F-T curve did not

change significantly in the experimental group, most likely because the participants did

not perform explosive-type training.211

Maximal Effort

Physiological basis. The F-T curves generated on exerting maximal effort differ

from curves of submaximal effort, which can be described on the basis of work by

Kroemer and Marras.188 During submaximal contractions, continuous feedback signals

control muscle output by modifying muscle fiber firing rate and muscle fiber recruitment.

During maximal contractions, central nervous system sends out the commands to recruit

all available fibers at their highest firing rates. Hence, submaximal effort involves a

slower buildup of force than in maximal effort.29' 101, 188 Further, motor units fire

asynchronously during submaximal efforts and fire synchronously during maximal or

near maximal efforts.233

Previous studies. The F-T curves generated from isokinetic234 as well as

isometric29, 55, 101 muscle contractions have been used to identify submaximal effort.

Fishbain et al.234 developed an isokinetic test using the shoulder press and pull-down

movement. The study included 34 healthy participants (18 males and 16 females) who

performed the isokinetic movement on the Ariel machine. The participants exerted 6 best

effort strokes, followed by a 1 minute rest period, and then repeating the 6 strokes giving

a faking effort. Mathematical analysis of the F-T curves generated 80 different

characteristics. Further, discriminant analysis was performed to identify the three best

characteristics for males and females. For males, the characteristics were found to be duty









cycle down, work weight/down, peak value down, and the characteristics for females

were average power up, 40% repetition down, and duty cycle up. The resulting

discriminant functions were used to identify predictive validity of the test. In a hold out

group of six males, the test classified 75% of the efforts correctly with a sensitivity value

of 0.83 and a specificity value of 0.67. This is an important study as it identified the

predictive validity of the F-T curve characteristics. Sincerity of effort tests have been

rarely tested for their predictive validity, which needs to be identified because "the

predictive models or cutoff scores obtained from validation studies should always be

cross-validated on a second sample to determine if the test criteria can be generalized

across samples" (p. 107).92 However, Fishbain et al.234 used statistical performance

instead of physiological interpretation to select F-T curve characteristics. The authors do

not describe why these F-T curve characteristics can distinguish between maximal and

submaximal effort. Hence, these characteristics should be used with caution.

Three studies have used F-T curve characteristics generated from isometric grip

contractions to identify submaximal effort.29' 55, 101 In 1983, Gilbert and Knowlton29

distinguished maximal and submaximal effort using the F-T curves. The study included

36 participants randomly assigned to either a sincere or faker group. The sincere group

performed maximal voluntary grip contraction (MVGC) and faker group performed 75%

MVGC using a specially designed grip dynamometer. The resulting F-T curves were

analyzed for the following characteristics: rate of force application (SLP), peak force

(PK), ratio of average force to peak force (DEV), and ratio of peak force to body weight

(WTRATIO). The sum of z-scores of all the variables for each subject correctly identified

effort in 87.5% of the females (N = 16) and 80% of the males (N = 20). Discriminant









analysis, performed by gender, revealed DEV to be the only significant predictor of effort

for females, and DEV, SLP and WTRATIO to be significant predictors for males. This

study suggested that F-T curves can be used to distinguish between maximal and

submaximal effort.29 This study, however, did not identify the ability of F-T curves to

distinguish between maximal and submaximal effort exerted by the same participant.

Around 1990, Smith et al.55' 101 assessed the differences between maximal and

submaximal effort using F-T curve characteristics of the plateau phase: ratio of average

and peak force, coefficient of variation, peak-average difference, and peak-average root

difference. A predictive equation combined these characteristics in order to identify

submaximal effort. The equation revealed the peak-average root difference to be the most

important characteristic.55' 101 However, this characteristic does not seem to be valid. The

peak-average root difference deals with such small variability that it requires

multiplication of 108 and is subject to a significant round-off error. Currently, these

characteristics are not used to detect submaximal effort in either the clinic or in research

studies.28

In a recent study, we found that the slopes of the force-generation phase and the

force-decay phase two phases of the F-T curve successfully differentiated between

maximal and submaximal grip effort in healthy subjects. The Receiver operating

characteristic (ROC) curves identified the best combinations of sensitivity and specificity

values for the slopes. We found excellent sensitivity and specificity values for the slopes,

with sensitivity values ranging from 0.8 to 0.93 and the specificity values ranging from

0.93 to 1.0. Also, the lowest overall error rates ranged between 7% and 33%.28 These

error rates are excellent when compared to the error rates of the five-rung test, rapid









exchange grip test and the coefficient of variation, which range from 47% to 69%.27, 65,93

The grip efforts in the pilot study were recorded using the Biopac dynamometer, which

has been identified to be reliable and valid for measuring grip force.

Biopac Dynamometer

The Biopac TSD121C hand dynamometer has a force sensor, whose reliability and

validity have been established by using precision weights235 and human participants.236

Reliability with precision weights was established using repeated measures, both within a

single testing session and over several occasions. After being spanned with a mass of

89.36 kg, masses weighing 79.2, 49.41, 29.67 and 9.59 kg were suspended from the

dynamometer to measure the output. Each testing session comprised of 8 loading

procedures, moving alternately down and up the measurement scale. This protocol was

repeated on 3 separate occasions over 3 weeks. The mean coefficient of variation (CV)

and their 95% confidence intervals (CI) assessed the reproducibility of the mass

measurements. The mass measurements were found to be highly reproducible during a

single testing period and over separate testing occasions (mean CV ranged from 0.4 to 0.8

and their CI ranged from 0.3 to 1.2). Therefore, the Biopac dynamometer has been found

to be reliable in measuring weights in the range of 0-90 kg under laboratory conditions.235

Validity was established for both single and multiple sessions by performing

repeated measures at one time and over several occasions. After being spanned with a

mass of 9.59 kg, masses weighing 9.59, 29.67, 49.41 and 79.2 kg and were suspended

from the dynamometer to measure the output. Each testing session comprised of 3

loading procedures, moving alternately up and down the measurement scale. The entire

procedure was replicated with span masses of 29.67, 49.41 and 79.2 kg. The entire

protocol was performed at 3 occasions over 3 weeks.235 Bland and Altman's 95% limits









of agreement (LOA) were used to compare the actual mass to mass measured by the

dynamometer. The LOA revealed that the span mass of 0-29.67 kg provided the most

accurate agreement between measured and actual values.235

Surface Electromyographic Activity

Electromyography can be defined as the study of electrical activity of a muscle.237

To produce muscular contraction, muscle fibers receive an impulse from a motor neuron.

The motor neuron is activated by electrical impulses that originate in the brain and travel

via the spinal cord to the motor neurons. On reaching the motor neurons, the electrical

impulses are propagated to the motor endplate resulting in ionic changes that generate the

muscle fiber action potential237, which is recorded as electromyographic (EMG) activity.

The following section describes the origin and propagation of EMG signal.

Origin and Propagation

The impulses that stimulate muscles originate in the motor cortex of the CNS. The

motor cortex, lying in front of the central sulcus in the brain, has contralateral control

over movements. Specific body parts move on the right side of the body on stimulating

different regions of the left motor cortex. This representation of the whole body in the

motor cortex has been referred to as the motor homunculus. Hence, impulses that activate

forearm muscles originate in the region that represents the forearm in the motor

homunculus. Specifically, most of these impulses originate in the pyramidal cells of layer

V of cortex. The pyramidal tract then transmits these impulses down the spinal cord.237'

238

The pyramidal tract (PT) is one of the five major tracts that descend from the brain

to the spinal cord. The PT originates in motor cortex and Betz cells (large pyramidal

cells). From the motor cortex, this tract travels through the internal capsule and the









middle of cerebral peduncles. At the level of the medulla, it forms into discrete bundles

called the pyramids and hence named the pyramidal tract.238 In the spinal cord, the

pyramidal tract divides into two separate tracts. At the lower level of the medulla, 90% of

the pyramidal tract crosses over to the opposite side forming the lateral corticospinal

tract. The remaining tract, which does not cross over, forms the anterior corticospinal

tract.238-240

The lateral and anterior corticospinal tracts ultimately end on motor neurons in the

ventral horn of the spinal cord. Afferents from intemeurons and receptors as well as

fibers from other descending tracts also end on these motor neurons. The motor neurons

represent the ultimate path through which all nervous excitation related to a motor act

must pass and thus have been called the 'final common path.' These motor neurons also

have an orderly and systematic arrangement. For example, medial neurons innervate the

trunk muscles and the most lateral neurons innervate the most distal parts of the limbs.239

The motor neuron action potential arrives at the neuromuscular junction and

releases acetylcholine (ACH). The release of ACH depolarizes the postsynaptic

membrane. By a passive process, this depolarization spreads in both directions of the

neuromuscular junction. This spread occurs in both directions along the length of the

muscle fiber. The deeper portions of the muscle fiber also require electrical stimulation,

which occurs via the transverse tubular system. Transmission of the depolarization

stimulus through the transverse tubules releases calcium in the sarcoplasmic reticulum.

Ultimately, this calcium release assists in the breakdown of ATP that provides energy for

muscle contraction. When transverse tubules and sarcoplasmic reticulum get depolarized,

it results in a depolarization wave along the direction of muscle fibers. These









depolarization waves, and subsequent repolarization waves, are observed by recording

electrodes as EMG activity.237'241

A muscle uses two different strategies to increase the muscular force output -

recruitment coding and rate coding. Recruitment coding means 'sequence of motor unit

activation.' Muscles produce higher forces by following the size principle. That is,

smaller motor units are recruited first, and successively larger motor units are recruited as

the force requirement increases.189, 192,194,237 Rate coding means 'frequency of motor

neuron firing' which represents how frequently the motor units are activated by the

nervous system. As the firing rate of the motor unit increases, it produces an increasing

amount of muscular force.189, 192, 193, 237

Signal Properties

The electromyographic (EMG) signal is a time-varying signal that conveys

information about muscle activity. Any time-varying signal has four properties:

amplitude (a), offset (ao), phase angle (0), and frequency (f). The amplitude (a) represents

the magnitude of the signal. The dimension for measuring amplitude depends on the type

of signal.121 For example, amplitude of an electrical signal may be measured in volts (V),

a unit of electrical potential or electromotive force. The offset (ao) represents average

value of the signal.121 The dimensions of the offset depend on the type of signal. For

example, the offset for an alternating current (AC) is zero volts. The phase angle (0) is

the amount of time the signal is shifted in time.121 The phase angle may be measured in

degrees (0) or radians (r). The frequency (f) represents how rapidly the signal oscillates

and is usually measured in cycles per second (s) or hertz (Hz). One hertz (Hz) equals one

cycle per second.121









Amplitude and frequency. The EMG signal properties frequently analyzed and

interpreted include its amplitude and frequency.237 The amplitude can be computed in

several different ways, including average rectified amplitude and root mean square

amplitude. The normal EMG signal is an alternating current and mean of such a signal is

zero. Therefore, to compute the averaged amplitude the signal must be rectified, which

involves converting the negative voltage to positive values. The average of all voltage

values results in the average rectified EMG amplitude.237 In contrast, the root mean

square EMG amplitude does not require rectification of the EMG signal as it integrates

the squares of EMG voltages recorded for a period of time. The square-root of the

integrated EMG voltage results in the root mean square EMG amplitude.237

The frequency of EMG signal is commonly computed using 2 different methods:

identifying turning points and zero crossings as well as by identifying mean or median

spectral frequency. "Turning points" is calculated by counting the number spike peaks

per unit time. Each peak represents an instance when the signal changes its direction.

Therefore, counting the number of peaks indicates the frequency the signal. Similarly, the

number of times the EMG signal crosses zero volts in a unit time indicates the frequency

of the signal. Counts of turning points and zero crossings provide an estimate of EMG

signal frequency.237 In contrast, the frequency distribution of EMG signal can be

identified using spectral analysis. The EMG signal is a time-varying signal that can be

mathematically represented by successively adding its individual frequencies (f).120, 121

The mathematical conversion used to identify the individual EMG frequencies is termed

the Fourier Transformation.121 For each frequency (fn) in the signal, the Fourier

transformation calculates a power, which indicates the amount of signal composed of that









frequency (f,). The plot of frequency along the X-axis versus the power of the frequency

along the Y-axis results in a graph that is commonly termed as the power spectrum or the

frequency spectrum.121 The mean or median frequency of the power spectrum has been

commonly used to represent the frequency of the EMG signal. The mean frequency is a

frequency that represents the average power of the power spectrum. In contrast, the

median frequency represents the frequency that divides the power spectrum into two

regions with equal power, i.e. the parts of the spectrum above and below the median

frequency have equal distributions of power.105 Also, when compared to mean frequency,

median frequency is less susceptible to noise in the signal.242

Increasing Force

Literature provides several explanations for EMG changes with increasing muscle

force. Force-related changes in amplitude and frequency have been associated with

changes in motor unit recruitment as well as motor unit firing rates.237 Several studies

have identified a high correlation between perceived effort and amplitude of surface

EMG.2 243, 244 The amplitude of EMG signal represents magnitude of muscle activity,

which predominantly increases due to increase in the number of active motor units as

well as the motor unit firing rate. The firing rate of motor units represents the frequency

of activation of motor units by the nervous system.237 Suzuki et al.245 found that as the

force being generated by a muscle increased, the motor unit voltage increased to the same

degree as mean absolute surface EMG amplitude. This increase implies that motor unit

size and firing rate explain the increase in mean absolute surface EMG amplitude with

increasing force generation.245

The frequency of EMG signal also changes with increase in recruitment and firing

rate of motor units. Increase in active motor units results in an increase in the number of









spikes and turns in the surface EMG signal. Similarly, the frequencies of EMG signal

change with motor unit firing rate.237 According to Hermens et al.246, the EMG power

spectrum usually has a pronounced peak in the region of 10-25 Hz. Other authors have

also observed this peak.247'248 This low frequency peak, according to some models,249' 250

represents the mean firing frequency of the active motor units. During an increase in

force, this low frequency peak may change: its standard deviation increases as well as it

shifts to the right. If an increase of force does not show any shift in the peak, it is

reasonable to assume that the increase of force is mainly caused by an increase in the

number of active motor units.246

Gander et al.248 reported that the frequency spectrum shifts to higher frequencies

with an increase in muscle force. Hagberg and Ericson251 found an increase in mean

power frequency when force increases from 0 to 40%. They attributed this change to low

level of tissue filtering. That is, as contraction level increases, larger motor units closer to

the surface are recruited; the electric signal from these fibers suffers less high frequency

attenuation through the overlying tissue; and thus the power spectrum shifts to higher

frequencies.248,251 Although recruitment/tissue filtering is in part responsible for the

increase in mean power frequency, firing rate also increases with contraction level.248' 252

Also, the average firing rate of active motor units is apparently manifested as a low

frequency peak in the power spectrum. The frequency of this peak has been observed to

increase with muscle force even at low contraction levels.247'248,253 Therefore, a

combination of both recruitment and rate coding is a responsible for the increase in mean

frequency.248









Fatigue

It is well documented that a sustained forceful contraction often causes muscular

fatigue, which shifts the EMG power spectrum to a lower frequency.254-260 As early as

1912, a fatiguing contraction was found to result in a decrease in the Piper rhythm256,261

which is the tendency of motor unit potentials in steadily contracting human muscles to

group in the range of 40-60 Hz.262 Cobb and Forbes261 observed an increase in the

amplitude of the EMG signal along with a decrease in the Piper rhythm. Kogi and

Hakamada254 found that the increase in EMG amplitude was due to an increase in the

lower frequency region of the power spectrum. Furthermore, fatigue was found to result

in an increase in the lower frequency spectrum255 as well as a decrease in the higher

frequency spectrum255 263,264, which clearly indicated a shift in the power spectrum

towards the lower frequencies.260

A fatigued muscle has a reduced ability to produce tension when excited. In an

effort to compensate for the decrease in force of contraction, recruitment of motor units

takes place.105 193,265,266 An increase in number of active motor units progressively

increases the electrical activity.265 The power spectral shift to lower frequencies as a

result of fatigue has been explained using three physiological mechanisms: 1) motor unit

de-recruitment193, 265-267 and motor unit synchronization193' 255, 2) conduction velocity256,

259, 268, 269, and 3) shape of muscle action potential.269 1) The shift to lower frequencies as

explained by motor unit de-recruitment occurs as a muscle fatigues. The replacement of

some of the fast twitch motor units with lower frequency fatigue-resistant units decreases

the higher frequency spectrum.255 260 Motor unit synchronization has also been proposed

as a mechanism for the spectral shift. An increase in lower frequency content has been

suggested to result from increased synchronization of motor units.255' 260 2) Lindstrom et









al.256 identified a reduction in conduction velocity along with a downward spectral shift

during a fatiguing contraction. The velocity and spectral changes were attributed to a

strong contraction that occluded blood flow through the muscle. The subsequent lack of

oxygen resulted in anaerobic metabolism, and therefore, accumulation of lactic acid,

which, in turn reduced the intracellular pH. A decrease in pH slows down the conduction

velocity causing the action potential to become more "sluggish" and yielding a reduced

higher frequency content of the power spectrum.256 4) Kranz et al.269 examined the

median frequency of EMG spectrum as well as compound action potential (CAP) of 45-s

maximal contractions ofthenar muscles. As the contraction progressed, they found that

spectrum shifted to the lower frequency region and the CAP shape widened indicating

that the two phenomena are related. They suggested that a muscle contraction imposes a

metabolic load on the muscle, which alters its electrical properties. Altered electrical

properties slow the muscle action potential conduction velocity that causes the action

potential to widen as well as shift of power spectrum to a lower frequency region.269

Injury

Generally, the EMG patterns vary according to the disease and according to the

technique used to record EMG signal.27 Several studies have identified EMG changes as

a result of cumulative trauma disorders and central neurological disorders. Bauer and

Murray271 measured surface EMG output of forearm flexors, forearm extensors and

triceps brachii muscle to detect lateral epicondylitis. People with lateral epicondylitis,

during simulated play, had earlier, longer and greater activation of forearm extensors

when compared to individuals not suffering from the condition.

Needle EMG findings in carpal tunnel syndrome (CTS) can be useful in detecting

denervation/reinnervation of pronator quadratus (PQ), flexor pollicis longus (FPL) and









two lateral heads of flexor digitorum profundus (FDP). Presence of spontaneous activity

in form of fasciculations or positive waves indicates denervation. In contrast, polyphasic

motor unit potential (MUP) and increased amplitude and/or duration of MUP indicates

reinnervation of these muscles.272 Ogura et al.106 used power spectral analysis to assess

the compound muscle action potential (CMAP) in CTS. The study included 50 healthy

people and 24 people with CTS. The CMAP was obtained from the abductor pollicis

brevis muscle with supramaximal stimulation (rectangular waves, duration: 0.2 ms) of the

median nerve. Using the Hanning window function and fast Fourier transformation (FFT)

a power spectrum of the CMAP was obtained, which was used to calculate the mean and

peak spectral frequencies. On an average, people with CTS had smaller mean and peak

frequencies than healthy people. In people with CTS, a negative correlation was found

between distal latency of CMAP and mean frequency. The decrease in mean frequency

was associated with temporal dispersion of the CMAP at the entrapped site. In people

with muscle atrophy, the reduced frequency was associated with reduction in number

type II muscle fibers, which are associated with a high frequency component of the

spectrum.106

In stroke, on observing activity of biceps brachii and brachioradialis after sustained

exercise, median frequency of surface EMG output decreased on the non-paretic side and

not on the paretic side. This suggested that a bout of sustained activity significantly alters

ability of central nervous system to activate muscles in the paretic arm, but, not on the

273
non-paretic arm.27

Sanjak et al.210 evaluated muscle fatigue during 30 seconds of maximal voluntary

isometric contraction (MVIC) by simultaneously measuring force as well as surface









EMG output. Elbow flexor and ankle dorsiflexor muscles were evaluated in 13 people

with amyotrophic lateral sclerosis (ALS) and 13 normal controls (NC) for fatigue by

comparing the first 5-s to the last 5-s of the contraction. Mechanical fatigue, represented

by decline in force output, was expressed as the force fatigue index (FFI). Myoelectric

fatigue, represented by compression in the EMG power spectrum, was identified by

calculating the median frequency shift (MFS). People with ALS, when compared to NC,

had a greater value of FFI and a smaller value of MFS. The dissociation between FFI and

MFS was explained by selective atrophy of type II (fast glycolytic, fast oxidative) muscle

fibers and/or higher prevalence of type I (slow-twitch oxidative) muscle fibers. A shift in

the power spectrum to lower frequencies during fatigue has been suggested to primarily

occur because of a decrease in muscle fiber action potential conduction velocity (MFCV),

which is greater in type II fibers than type I fibers. Therefore, type I fibers have

inherently lower frequency content than type II fibers, which are de-recruited with

fatigue. Presence of fewer type II fibers would indicate a lower MFS.210

Maximal Effort

In 1987, Janda et al.30 used of electromyographic (EMG) recordings to characterize

normal grip patterns. Four healthy people, representing different hand sizes, performed

maximal grips at the 5 handle positions of the Jamar dynamometer. The EMG signal was

recorded from the forearm flexor area and dorsum of hand. Neither did the authors

indicate the location of recording electrodes nor did they indicate specific muscles used

for recording EMG signal. Janda et al.30 found that the forearm flexor muscles were

active at all the handle positions whereas the intrinsic hand muscles were only active

while gripping the narrower handle positions. It was also suggested that the force









recordings obtained from repeated maximal voluntary grip effort would be more

reproducible than recordings from submaximal grip efforts.30

In 1990's, Niebuhr and coauthors31 32 89 used EMG signal to distinguish between

maximal and submaximal grip effort. The EMG signal was recorded from flexor carpi

radialis (FCR) and palmaris longus (PL) muscles as they have been reported to represent

total active flexor musculature during handgrip maneuvers.30-32 The EMG activity of a

submaximal grip effort was found to have smaller amplitude than that of a maximal

voluntary effort.31' 32'89 Niebuhr et al.31' 32 89, however, reported conflicting findings

regarding the mean power frequency (MPF). The MPF of a submaximal grip effort was

reported to either be greater than maximal voluntary effort31 or not to be different than

that of maximal voluntary effort.32' 89 A primary advantage of using EMG signal for

identifying submaximal effort is that the EMG output is highly consistent over

measurement sessions.89 However, EMG signal of submaximal effort showed equal

amount of variability when compared to maximal effort. Hence, variability of EMG

cannot be associated with level of effort.89 In conclusion, forearm muscle EMG

properties have the potential of being a valuable adjunct for clinicians involved in

identifying submaximal grip effort.32

It is not clear how muscle activity is impacted by pain. There is mostly agreement

regarding effect of pain on voluntary effort. Studies have demonstrated that pain is

associated with decreased voluntary electromyographic (EMG) activity1' 12, shorter

endurance time13, decreased motor unit discharge rates14, 15 and decreased y-motor neuron

activity.16 However, there is a disagreement regarding involuntary motor activity. Pain

has been found to be associated with unaltered activity of a-motor neurons274 and y-motor









neurons.275 Yet other studies show that pain is related to increased involuntary (reflexive)

muscle activity including transient increases in resting EMG levels276 and increased

activity of y-motor neurons resulting in muscle spasm.274 276-280

Pain

Over the past century, the understanding of the behavioral, psychological and

physiological aspects of pain has been transformed. One significant transformation has

been a paradigm shift in the understanding of neural mechanisms underlying a pain

experience from a linear mechanism to a nonlinear mechanism70' 281 In other words,

earlier paradigms explained pain experience as an end product of linear sensory

transmission of noxious stimuli.282, 283 Instead, current paradigms explain pain experience

as a dynamic process that involves continuous interaction among ascending and

descending pathways of the nervous system.283 284 This dynamic process begins with an

injury or a disease that produces pain signals. Pain signals, after originating in the

periphery, enter the central nervous system (CNS). The CNS is an active system

influenced by culture, stress, anxiety and depression among other factors. The CNS

selects, abstracts, and synthesizes pain signals with other sensory signals. Therefore, pain

is a complex experience influenced by attitudes and responses of people including past

experiences, meaning of a situation, attention and other psychological variables.69' 70 The

complex nature of a pain experience has been succinctly captured in the definition of pain

by the International Association for the Study of Pain (IASP). The IASP defines pain as

"an unpleasant and emotional experience associated with actual or potential tissue

damage, or described in terms of such damage."285' 286 This definition, among other

components, describes pain as an unpleasant and unwanted experience. Nevertheless,

pain serves an important function in humans: protection.287









Pain protects humans by warning of occurrence of biologically harmful

processes.287 For example, people protect themselves from bums, bruises and wounds

primarily due to reflex activity but also because of associated emotional arousal.

Reflexes, regulated at the level of spinal cord, protect by removing a body part away

from danger.288 Quite often, associated emotional arousal, experienced as distress or fear,

may also motivate a person to move away from a painful stimulus.289 Fear of pain can

also prevent a person from moving, which in turn promotes healing of the injury resulting

in that pain.290 Additionally, from the perspective of evolutionary biology, pain may elicit

an empathic, comforting, and health promoting behavior in people observing a person in

pain. Observers react in a parental nature by taking care, assisting and consoling a person

in pain. Such pain reactions result from mammalian phylogeny, i.e. to serve in the well-

being of their young.291 Hence, pain acts as a warning system that activates protective

mechanisms in people experiencing pain and parental mechanisms in people around

them. These mechanisms promote safety and recovery. In contrast, pain can interfere

with daily functioning of a person.292 Pain may interfere with daily functioning when it

prevents people from performing their social roles, vocational roles, and impacts their

psychological well-being.290 To appreciate this duality of pain, i.e. protective and

interfering nature of pain, one must understand the CNS mechanisms of pain

transmission and regulation.

Transmission of Pain Sensation

Four specific parts of the nervous system transmit pain signals from the periphery

to the higher centers of the CNS: 1) the nociceptors, 2) the dorsal horn neurons, 3) the

ascending tracts, and 4) the supraspinal projections. Nociceptors, one type of

somatosensory receptors, are the first order neurons of pain pathways. These receptors









generate pain signals in response to harmful stimuli. Different types of nociceptors have

been identified that respond to mechanical, heat and chemical stimuli or any combination

of these stimuli. Cell bodies of the nociceptors reside in the dorsal root ganglia (DRG).

Nerve fibers leaving the DRG bifurcate and send one branch to the periphery and the

other branch to the dorsal horn (DH). The peripheral fibers conduct pain signals from the

skin, muscles, fascia, vessels, and joint capsules to the DRG.293 294 Peripheral fibers

transmitting pain and other somatosensations, and therefore called the sensory peripheral

fibers, have been classified into three types based on their diameter, myelination and

conduction velocity: the A-fibers (with four subtypes a, 0, y and 6), B-fibers and C-

fibers. The C-fibers and A-6 fibers conduct pain signals, but, at different velocities.294 A-

6 fibers conduct fast pain (a sensation immediately after an injury that indicates location

of injury) and C-fibers conduct slow pain (follows sharp pain and can be characterized as

a dull, throbbing ache with poor localization).293 294 Fibers entering the DH synapse with

the second order neurons.294

Two types of second order neurons perceive pain: nociceptive specific (NS)

neurons and wide dynamic range (WDR) neurons. The NS and WDR neurons conduct

pain signals to the brain via various ascending tracts in the spinal cord. Primarily, the NS

respond to noxious stimuli while the WDR respond to both innocuous and noxious

stimuli.292,294 Table 2-3 summarizes other differences between these neurons.

Axons of the second order neurons (the NS and WDR neurons) form the ascending

tracts, through which pain signals travel in the spinal cord. Different ascending tracts

conduct fast and slow pain signals. Fast pain travels via the neospinothalamic tracts. The

fast pain transmitting A-6 fibers predominantly terminate on the nociceptive specific









(NS) neurons in laminae I and II of the DH. The axons of the NS neurons cross the

midline of spinal cord in the anterior white commissure. The crossed NS axons ascend to

the thalamus as the neospinothalamic tract. In contrast, slow pain travels via multiple

parallel ascending pathways. The slow pain transmitting C-fibers terminate on

interneurons in laminae I, II, and/or V of the DH. The interneurons synapse with wide

dynamic range (WDR) neurons in laminae V to VIII of the DH. The WDR axons ascend

to the midbrain as spinomesencephalic tract, reticular formation as spinoreticular tract,

and thalamus as paleospinothalamic tract. Slow pain signals primarily ascend via the

paleospinothalamic tract. The other two tracts serve functions of arousal, motivation,

reflexive function, and activation of descending fibers.293

Supraspinal projections can also be divided on the basis of which fibers conduct

slow pain and which fibers conduct fast pain. The NS axons, that conduct fast pain,

mostly end in the ventral posterolateral (VPL) nucleus of the thalamus. Third order

neurons arise from the VPL nucleus and project to the primary somatosensory cortex (SI)

and the secondary somatosensory cortex (SII). These projections allow for interpreting

sensory features of pain, which includes location, intensity and quality of pain.293-295 In

contrast, the tracts conducting slow pain (the spinomesencephalic, spinoreticular, and

paleospinothalamic tracts) terminate in different areas of the brain. The

spinomesencephalic tracts conduct pain signals to the superior colliculus and

periaqueductal gray and finally to the hypothalamus and raphe nuclei. These areas assist

in turning the eyes and head towards the noxious stimulus. The spinoreticular tracts

terminate in the reticular formation in the brainstem. The paleospinothalamic tracts

project to the midline and intralaminar nuclei of the thalamus. These nuclei further









project to basal ganglia, prefrontal cortex, anterior cingulate cortex, and primary motor

cortex. Together, activity in the spinoreticular and paleospinothalamic tracts results in

arousal, withdrawal, and, autonomic and affective responses to pain.292' 293

Brain activity studies have implicated several supraspinal centers to be involved in

processing and modulating pain signals. These supraspinal centers can be divided into

subcortical and cortical areas. The subcortical areas most notably activated by pain

signals include thalamus, basal ganglia, and cerebellum. In contrast, commonly reported

cortical areas include somatosensory cortices (SI and SII), anterior cingulate cortex and

insular cortices, prefrontal cortex, and motor and pre-motor cortex. These areas serve

different purposes when a person experiences pain. Specifically, the somatosensory

cortices have been implicated in interpreting sensory features of pain. The anterior

cingulate cortex and insular cortices, both components of the limbic system, have been

implicated in affective processing of pain. Moreover, prefrontal cortical areas, as well as

parietal association areas, are also sometimes activated in response to noxious stimuli and

may be related to cognitive variables, such as memory and stimulus evaluation. Motor

and pre-motor cortical areas, also activated on occasion by pain stimuli, have been

suggested to be related to pain epiphenomena, such as suppression of movement or actual

pain evoked movements.295 Hence, a noxious stimulus originating in the periphery travels

through multiple transmission systems to reach various parts of the CNS. The CNS does

not receive a noxious stimulus passively. Rather, it processes this stimulus using various

regulatory mechanisms.

Regulation of Pain Sensation by the Nervous System

Passive transmission of noxious stimuli cannot explain how people experience

pain. Rather, their pain experience can be explained by an active process. This active









process includes several regulatory mechanisms that participate in attenuating or

accentuating the perception of a noxious stimulus.294 An accentuated pain experience can

be associated with factors such as edema, fear, anxiety, and release of endogenous

chemicals that sensitize nerve endings.296 Two spinal cord level mechanisms explain an

accentuated pain experience: 1) wind-up, and 2) central sensitization.

1) In 1965, Mendell and Wall coined the term "wind-up" to describe a gradual

increase in discharge frequency of the WDR neurons on repeatedly stimulating the C

fibers at a low frequency.294 297 Furthermore, a low frequency noxious stimulus (> 3 Hz)

results in progressively greater pain. Physiologically, this temporal summation of pain

can be explained to be similar to wind-up. Temporal summation of pain is exaggerated in

neuropathic pains and can only be evoked by activation of C fibers.

2) Central sensitization includes a complex sequence of chemical events that result

in an increased responsiveness of the nociceptive dorsal horn neurons, which results in

enhanced conduction of pain signals to the brain. For example, following cutaneous

injury, an area of undamaged skin adjacent to the damaged tissue can be stimulated to

evoke pain by either an innocuous stimulus (secondary allodynia) or more pain by a

previously painful stimulus (secondary hyperalgesia). The nociceptors supplying area of

secondary allodynia and hyperalgesia are not sensitized. However, central sensitization

occurs due to input from nociceptors that supply an area of damage. Input from these

nociceptors leads to a transient central sensitization.298 Hardy et al.299 provide a

physiological explanation of this phenomenon. According to their model, active

nociceptors produce pain signal, which in turn primarily activates the spinothalamic tracts

(STT). These nociceptors also activate neural circuits in the spinal cord dorsal horn that









also sensitize other STT cells that receive input from mechanoreceptors and nociceptors

that supply an adjacent, but uninjured, region. Therefore, enhanced response of these STT

cells to innocuous and noxious stimuli applied to uninjured skin results in secondary

allodynia and hyperalgesia.298 299

In contrast, two classic examples describe how regulatory mechanisms can

attenuate pain. First, after injuring a hand, a person may shake it vigorously to reduce

pain sensation. Second, an athlete, although injured during a game, may not feel injury

related pain until end of game. Regulatory mechanisms that attenuate pain act at four

levels of the CNS: 1) the dorsal horn (DH; includes second order neurons of ascending

pain pathways), 2) the descending fibers (from periaqueductal gray, raphe nuclei, and

locus ceruleus), 3) hormonal system (cells located in the hypothalamus, pituitary gland,

and adrenal medulla), and 4) cerebral cortex (prefrontal cortex, insular cortex, and

amygdala). The most understood mechanisms that attenuate pain occur in the substantial

gelatinosa of the DH, and, through descending fibers originating from the periaqueductal

gray, raphe nuclei, and locus ceruleus.

1) Mechanisms that attenuate pain at the dorsal horn (DH) have their basis in the

gate control theory.283 According to the gate control theory, first order pain neurons

(nociceptors) and second order pain neurons (WDR neurons) receive inhibitory signals

from non-nociceptive A-P afferents. The A-P afferents are fast, myelinated, large

diameter sensory peripheral nerves that arise from muscle spindles, golgi tendon organs,

joint receptors or cutaneous tactile receptors. Increased activity of these fibers inhibits the

WDR neurons in the DH, which are predominantly stimulated by the C fibers.292'294









Inhibition of the WDR neurons reduces pain signals reaching the brain, which reduces

level of pain perceived, and, therefore attenuates pain experience.

2) Descending fibers also attenuate pain experience.294 The axons of the raphe

nuclei, which receive information on noxious stimuli from periaqueductal gray, descend

in the spinal cord via the dorsolateral funiculus. Their axons form the descending fibers

that attenuate pain. These fibers attenuate pain experience by strongly inhibiting the

second order pain neurons in the laminae I, II or/and V of the DH. This inhibition of the

second order pain neurons reduces conduction of pain signals that travel from the

periphery to the higher centers in the brain.292

3) The action of P-endorphin (BE), which is formed by activity of the

hypothalamo-pituitary-adrenocortical (HPA) axis, attenuates pain resulting from injury in

situations such as accidents, disasters, or athletic contests. In such situations, an injured

person may have a delayed onset of pain, i.e. pain begins at the end of an emergency or a

contest. A delayed pain results partly because of BE that acts as a potent analgesic with

its effect lasting a few hours.285'296 The release of BE from the HPA axis, in presence of

noxious stimulus, can be explained by a group of neuronal projections.285 These

projections include pathways ascending from the second order pain neurons in the DH of

the spinal cord to the medial and lateral hypothalamus and several telencephalic regions,

and pathways from the medullary reticular formation via the ventral noradrenergic bundle

(VNB) to the periventricular gray of hypothalamus. The periventricular gray, which acts

as the coordinating center of the HPA axis, responds to noxious stimuli (received from

ascending pathways originating in the DH) by initiating a complex series of events

regulated by feedback mechanisms. In response to noxious stimuli, the periventricular









gray synthesizes and releases corticotropin-releasing hormone (CRH) into the portal

circulation. This CRH stimulates the anterior pituitary gland to secrete several pro-

opiomelanocortin derived neuropeptides into systemic circulation.285 These neuropeptides

include adrenocorticotrophic hormone (ACTH) and BE.285' 300 BE binds with opiate

receptors in the brain and the DH to result in analgesia.285'296,300 The amount of BE

formed is regulated by ACTH, which stimulates the adrenal cortex to release

corticosteroids such as hydrocortisone and corticosterone. These corticosteroids provide

feedback to the regulatory processes by inhibiting the anterior pituitary, which represses

the formation of pro-opiomelanocortin, thereby attenuating further secretion of BE and

ACTH.285

4) The cortical role in attenuating pain can be described in context of stimulation-

produced analgesia (SPA). SPA involves a highly specific suppression of behavioral

responses to noxious stimuli produced by electrical stimulation of specific brain sites.

Experimental SPA was first elicited by electrical stimulation of the periaqueductal gray in

rodents. Upon this electrical stimulation, rodents remained alert and active. However,

their responses to noxious stimuli (orientation, vocalization and escape) were absent.

Similarly, a SPA-like response has been elicited in humans. Subsequent research has

indicated that periaqueductal gray plays an important part in this analgesia. The

periaqueductal gray receives afferents from brainstem, diencephalon, medial prefrontal

cortex, limbic system insular cortex, and amygdala (that receives massive input from

hippocampus and neocortex). The periaqueductal gray also projects efferents rostrally to

the medial thalamus and orbital frontal cortex301 and the rostral ventromedial medulla

(RVM). The periaqueductal gray integrates inputs from various afferents with ascending









nociceptive inputs, and, in turn controls spinal nociceptive neurons through relays in the

RVM. The RVM, which consists of the raphe nuclei and surrounding reticular nuclei,

projects fibers to the DH to exert bidirectional control over nociceptive transmission.

Bidirectional control by RVM involves both inhibitory and excitatory intemeurons.294, 300

The RVM has 'off cells' and 'on cells.' The increased activity of 'off cells' has an

inhibitory effect, which attenuates pain by reducing activity of second order pain afferent

neurons in the DH. However, the increased activity of 'on cells' has an excitatory effect,

which accentuates pain by increasing activity of second order pain afferent neurons in the

DH.294, 296

Pain transmission and regulatory mechanisms have been explained using different

pain theories. Among these theories, the Gate Control Theory283 has been well accepted

as an explanation of these mechanisms. According to this theory, the substantial

gelatinosa in the dorsal horn (laminae II and III) of the spinal cord acts as a gate for pain

signals. This gate determines whether or not pain signals reach the brain. Ability of pain

signals to pass the gate appears to depend on two characteristics of somatosensory

signals: 1) the strength of signals that reach the gate, and 2) signals that first reach the

gate.302

1) Ability of pain signals to reach the brain depends on their strength at the gate.

This strength is governed by intensity of a stimulus. A noxious stimulus acts on the skin

to generate action potentials in nociceptors. These action potentials travel via the A-6 and

C fibers to reach the gate. Varying intensity of stimulus results in different events

occurring at the gate. A gentle, but sudden, pressure stimulus to the skin generates action

potentials in larger number of A-6 fibers as compared to C-fibers. Disproportionately









larger number of active A-6 fibers stimulates transporter cells in the dorsal horn that

facilitate conduction of pain signals to the brain. Active A-6 fibers also stimulate the gate

thereby shortening the activity of the transporter cells. As the intensity of stimulus on the

skin increases and gradually becomes noxious, it increases recruitment of A-6 fibers and

C fibers and also increases firing frequency of active fibers. The C fibers activate the

transporter cells and inhibit the gate. As a result, positive and negative effects of A-6

fibers and C fibers counteract each other, and therefore T cells gradually conduct more

pain signals to the brain.283'302

2) Pain signals compete with non-pain signals at the gate. An injury in a body part

generates pain signals that travel to the gate via the A-6 fibers or the C fibers. Whereas,

acts performed in an effort to reduce pain, such as massaging, vigorously moving, or

exerting deep pressure on the injured part, result in non-pain signals. Non-pain signals

travel via the A-p fibers large, myelinated nerve fibers, with a low threshold for

stimulation. The A-0 fibers conduct non-pain signals at a much faster rate than either A-6

or C fibers conduct pain signals. Due to this faster speed, non-pain signals occupy the

gate and do not allow conduction of pain signals to the brain. When pain reducing actions

stop, non-pain signals do not occupy the gate allowing for pain signals to be conducted to

the brain.283 302

Acute versus Chronic Pain

Pain has been commonly classified on the basis of its duration for which one

experiences it. Based on the duration, and extent of associated tissue damage, pain can be

classified into acute and chronic pain.294'303 304

1) Acute pain has been defined as "pain associated with tissue damage,

inflammation, or a disease process that is of relatively brief duration (i.e. hours, days, or









even weeks), regardless of its intensity."304 Usually, a serious local injury, such as a

surgical incision, activates nociceptors, their central connections and autonomic nervous

system in that region, which provokes acute pain.303 Acute pain persists until healing

takes place290 or stops long before healing has been completed.303 Healing can occur

without medical intervention as an injury with acute pain does not overwhelm the body's

reparative mechanisms. Such healing usually takes a few days to a few weeks, and

therefore acute pain lasts for the same duration.303 Additionally, acute pain has been

associated with anxiety. The clinical observation that greater the anxiety the greater the

perception of an injury as painful appears warranted. However, a clear empirical basis for

this simple proposition does not exist. Different studies indicate that anxiety enhances,

relieves or has no impact on pain.290 Acute pain has also been observed after trauma and

some diseases. Pain in these conditions, except for malignant diseases, that persists for

months or years is not considered acute pain.303

2) Chronic pain has been defined as "pain that persists for extended periods of time

(i.e. months or years), that accompanies a disease process (e.g. rheumatoid arthritis), or

that is associated with an injury that has not resolved within an expected period of time

(e.g. myofascial pain syndromes, complex regional pain syndrome, and chronic pelvic

pain)."304 Chronic pain indicates that the pain has lost its biological role of triggering

recuperative behavior.35 Chronic pain, although triggered by injury or disease, however,

has other factors associated with it that prolong its presence. These factors include

continued tissue damage, loss of a body part, extensive trauma, or damage to the nervous

system as a result of injury.303 Due to these factors, the pain persists either beyond the

expected course of disease, or beyond the time expected for an injury to heal, or it recurs









at various times for months or years.305 In such situations, the injury may exceed the

body's capability to heal. Additionally, intensity of chronic pain may be out of proportion

of original injury or damage, and syndromes, such as complex regional pain syndrome,

may occur spontaneously without any signs of injury.303 Chronic pain impairs an

individual's social, vocational and psychological well being. Among psychological

factors, chronic pain has been frequently associated with depression, which may vary

from minor to severe. Depression also appears to intensify chronic pain. While some

patients display depression, others maintain a dispassionate attitude. Patients with a

dispassionate attitude appear to have either strong personal or social resources or the pain

disorder provides a focus in life that enables them to ignore stressful life challenges,

thereby controlling depression.290 Clinically, acute and chronic pain can be distinguished

on the basis of dimensions of pain. In terms of these dimensions, described next, people

experiencing acute pain provide a clear and specific picture of their experience.296

Dimensions of Pain

Until the 1960's, researchers considered pain as purely a sensory experience with

no specific dimensions.284 Distinct dimensions of pain, having surfaced only recently,

were triggered by the gate control theory. The gate control theory allowed various

psychological factors, earlier dismissed as 'reactions to pain,' to be considered as an

integral part of pain processing.306 Currently, at least four dimensions or categories of

pain experience can be assessed: 1) pain intensity, 2) pain affect, 3) pain quality, and 4)

pain location.289

1) Pain intensity may be defined as how much a person hurts. It provides a

quantitative estimate of the severity or magnitude of the perceived pain.289 Pain

assessment tools use descriptors to describe the intensity of a painful experience varying









from 'no pain' to 'worst imaginable pain.'307 Physiologically, pain intensity is encoded

by the number of peripheral fibers that are activated by the painful stimulus and their

discharge frequency.294 The wide dynamic range (WDR) neurons assist in identifying

intensity of various noxious stimuli. These neurons receive input from both nociceptive

and non-nociceptive afferents. Non-nociceptive stimuli, such as touch, cause the WDR

neurons to discharge at lower levels and nociceptive stimuli cause them to discharge

more vigorously.292 By increasing discharge frequency in presence of noxious stimulus,

the WDR neurons assist in identifying the intensity of a noxious stimulus. The rapidly

conducting spinal systems also allow for identifying pain intensity. Intensity of pain

signals is interpreted in the primary somatosensory cortex (SI), which reach there via the

A-6 fibers and the neospinothalamic tracts.293'294

2) Pain affect has been defined as "emotional arousal and disruption engendered by

the pain experience."289 Pain affect has been identified as an intrinsic, but conceptually

and empirically distinct component of pain.70' 285, 308-310 As people can have mixed

feelings with respect to events, people in pain can have multiple emotions associated with

their painful experience.289 Pain assessment tools used to describe the affective

component of pain, use words such as distracting, depressing, dreadful, or unbearable.289

Physiologically, this affective component of pain can be described by activity of the

WDR neurons292, which is then projected by the divergent pathways to parts of the brain

for emotional arousal.293 It has been proposed that while nociceptive transmission excites

the spinothalamic pathways to generate sensory processes, the spinoreticular pathways

are used to generate affective processes. The affective dimension of pain is then produced

by activation of noradrenergic limbic structures. Also, the hormonal system, including the









HPA axis, mediates a stress response related to pain and forms a mechanism for

expressing its emotional dimension.285

3) Pain location may be defined as part of body where a person experiences pain.

This location may be same or different from where tissue injury takes place. Pain

assessment tools use line diagrams of whole body or specific parts of the body to describe

the pain location. People in pain identify location of their pain by marking these

diagrams.289 Physiologically, the nociceptive specific (NS) second order neurons, present

in the DH, allow for good localization of pain because of their small receptive fields and

being somatotopically organized in the lamina I.292 The NS neurons receive information

on pain signals via the fast conducting A-6 fibers and further project these signals via the

spinothalamic pathways to the primary somatosensory cortex (SI). Perception of pain in

the SI identifies exact location of pain in the body.293

4) Pain quality has been usually included as an aspect of the sensory-discriminative

component of pain. Melzack and Casey first described this component in 1968.311 The

sensory-discriminative component of pain can be defined as including information that

maps the sensory nature of the stimulus (thermal, mechanical, or chemical) as well as

bodily location, intensity and temporal aspects of the experience.290 Specifically, pain

quality describes sensory nature of stimulus and sensitivity to pain.289'312 Examples of

words that are used in pain assessments to describe pain quality include sharp, dull, hot,

cold, deep, superficial, sensitive and itchy.312 Physiologically, rapidly conducting spinal

systems, i.e. the neospinothalamic tracts, influence pain quality through the nociceptive

specific (NS) neurons.70 The A-6 fibers conduct pain sensation via the NS neurons,









whose axons form the neospinothalamic tracts, to the primary somatosensory cortex (SI).

The SI interprets the quality of pain sensation.293' 294

Assessment of Pain

The multidimensional nature of pain needs to be assessed accurately for improving

clinical and research outcomes. These outcomes include 1) identifying underlying cause

of pain, 2) determining most effective treatment of pain and evaluating new methods to

control pain, and 3) evaluating degree of disability or impairment of function related to

313
pain.3

1) Assessment of pain, especially identifying descriptors of its sensory qualities,

can assist in diagnosis of pain etiology. For example, a burning quality of pain may

indicate peripheral injury313, and cramping quality of pelvic pain may indicate menstrual

pain.314 Furthermore, people tend to use a constellation of descriptors to explain their

pain experience. These constellations can assist clinicians to discriminate various types of

pain.313,315

2) Accurate assessment of pain also determines the most effective treatment for

pain. For example, it has been suggested that osteoarthritis (OA) pain can be managed by

blocking newly found analgesic targets. Primary afferent neurons in affected joints

express excessive amounts of abnormally functioning sodium (Na) channels. These Na

channels may play an integral role in OA pain. Therefore, analgesics that target these Na

channels may provide relief from OA pain.316 Pain needs to be accurately assessed to

identify the efficacy of new pharmacological treatments that target these Na channels as

compared to existing treatments.

3) Pain assessment forms an important component of impairment and disability

evaluation.313 Pain commonly occurs in adults with conditions that result in physical









disability, such as spinal cord injury, cerebral palsy, multiple sclerosis and post polio

syndrome.317 In such situations, pain contributes to impairments and exacerbates

limitations.318, 319 Therefore, pain assessment needs to be a component of impairment or

disability evaluation. Many rehabilitation related situations require that the pain related

outcomes be assessed in a short duration of time.320

Until the 1960's, researchers considered pain as a purely sensory experience with

no specific dimensions.284 At present, four different dimensions of pain experience have

been identified, which include pain intensity, pain affect, pain quality, and pain

location.289 For this study, we will focus on pain intensity. Pain intensity provides a

quantitative estimate of the severity or magnitude of the perceived pain.289 In the nervous

system, pain intensity is encoded by the number and discharge frequency of peripheral

fibers that are activated by a painful stimulus.294 The wide dynamic range (WDR)

neurons, which receive input from both nociceptive and non-nociceptive afferents, assist

in identifying intensity of various noxious stimuli. Nociceptive stimuli cause the WDR

neurons to discharge more vigorously than non-nociceptive stimuli.292 The rapidly

conducting spinal systems, which include the A-6 fibers and the neospinothalamic tracts,

also allow for identifying pain intensity. Ultimately, pain intensity is interpreted in the

primary somatosensory cortex (SI) of the brain.293'294

A variety of assessments have been developed to evaluate pain intensity.304 These

assessments can be classified into three general categories: verbal rating scale (VRS),

visual analog scale (VAS), and numerical rating scale (NRS).289 These assessments tend

to have statistically similar psychometric properties but have their own strengths and

weaknesses289, 309, 321, which have been summarized in Table 2-4.









The VAS has been used extensively to assess pain intensity322, and is possibly the

most widely used pain measure.323 The VAS is non-intrusive, is easy to administer and

score, is suitable for repeated use, and has simple instructions.22 324 The VAS has also

been found to be the most sensitive measure of pain when compared to various other

methods.325, 326

The VAS that assesses pain intensity usually consists of an unbroken line, 10

centimeter (cm) long, placed horizontally on a piece of paper, with anchor points on each

end.322 One anchor of this line represents "no pain" and the other anchor represents

"maximum perceived pain intensity."289 People rate their perceived pain intensity by

placing a mark through the line.322 The distance from the "no pain" anchor to this mark

results in the overall pain intensity score.289 322 Commonly, this distance is measured in

millimeters, and therefore, the score ranges from 0 to 100.322 Since its initial development

almost 70 years ago323, many different versions of the VAS have been used to assess pain

intensity.289 For example, the words describing the anchors have been varied327, the

length of the line has been varied328, the line has been placed vertically on a piece of

paper329, and mechanical289 and electronic versions330 have been developed.

Perceived Magnitude of Grip Force

The Psychophysical Law by Stevens331 states that a power function represents the

relationship between magnitude of a sensation and its judgment by an individual. This

power function is expressed as 'the magnitude estimations of a sensation increase as a

power of the actual intensity of that sensation.'331 The exponent of the power function

varies by sensation, sensory modality and condition of stimulus presentation.332 The

Steven's Law has been shown to govern the sense of force, i.e. the magnitude of apparent

force increases as a power of the force exerted.33336 For handgrip force, the power









function exponent varies between 1.6 and 2.0.335, 336 Also, a power function with an

exponent of 0.6 describes the association between apparent grip force and duration of a

sustained grip. That is, on maintaining a handgrip at a constant force, a power function

represents an increase in apparent force with duration of the handgrip.335 Similarly,

participants maintain a constant handgrip effort by reducing the grip force over time. The

relationship between the decay in grip force and duration of grip is represented by a

double exponential function.337 Two self-report scales have been used to identify the

level of perceived intensity of force, which include the Rating of Perceived Exertion

(RPE) Scale338 and the Category Ratio (CR-10) Scale.339 We will use the CR-10 Scale as

it has been shown to be effective in assessing perceived exertion of grip strength.340

Further, the CR-10 Scale has been used to describe the relationship between perceived

effort and associated EMG changes during a sustained isometric grip.243 Furthermore, the

CR-10 Scale has attributes of a ratio scale.339'341 This ratio scale will allow study

participants to report the perceived level of force applied during submaximal grip efforts

as a percentage of their maximal grip efforts.

Summary

The present cost of managing musculoskeletal disorders (MSDs) stands at an

estimated $20 billion per year.40 By 2020, an estimated 59.4 million Americans (18.4%)

will suffer from MSDs35, which would further increase the financial burden. MSDs

commonly affect the upper extremities, whose rehabiltative outcomes are commonly

assessed using grip strength. Grip strength is a valid method of assessing rehabilitative

outcomes only when a person exerts maximal effort. A person may exert submaximal

effort either intentionally (e.g. financial gain) or unintentionally (e.g. injury-related pain).

Various grip strength based tests have been used to distinguish between maximal and






72


submaximal effort. However, these methods have been shown to have poor reliability and

validity. In contrast, a recent pilot study indicated that the force-time curve (F-T curve)

characteristics and electromyographic (EMG) properties can accurately identify maximal

effort. The pilot study was performed using healthy participants. Therefore, the current

study will assess the ability of F-T curve characteristics and EMG properties to identify

maximal voluntary effort of isometric grip in people with upper extremity disorders and

injuries.









Table 2-1: Differences between maximal and submaximal effort


Characteristic
Order of task
Somatosensory
system
Afferent activity

Cerebral cortex
Metabolic uptake
ratio
Mental effort
Inhibition of
uninvolved systems
Motor system
Motor unit
recruitment
Motor unit firing
Variability in effort

Onset of Fatigue
Force production
Motor unit
recruitment
Motor unit firing
EMG frequency
Brain activity
(fMRI)


Maximal Effort
Lower order task


Indicates full utilization of
motor recruitment and firing

Decreases in the first minutes
of recovery
Large
Increased inhibition


Maximal

Synchronous
Less variability


Declines
Cannot be further increased

Shifts from high to low value
Decreases
Increases with decreased
muscle activity


Submaximal Effort
Higher order task


Assists in calibration and
modulation of effort

No significant change

Small
Less inhibition than in
maximal effort

Increases with level of
effort
Asynchronous
Maximum variability at
60% of MVC

Maintained
Increases

Stays constant
Maintained
Not reported









Table 2-2: Sensitivity and specificity values of different sincerity of effort tests
Measure Value Sensitivity Specificity Author


CV


Five-Rung


Rapid Exchange Grip


Slope of force-
generation phase
Slope of force-decay
phase


11% CV
cutoff
15% CV
cutoff
7.5 SD cutoff


REG 45


Females =1.2
Males = 1.45
Females = -
0.05
Males = -
0.075


0.69

0.55

0.7


0.65


0.80
0.80
0.80

0.93


0.74

0.92

0.83


0.66


0.93
0.87
0.87

1.00


Shechtman,
2001

Gutierrez &
Shechtman,
2003
Shechtman
& Taylor,
2000
Shechtman,
et al, 2007
Shechtman,
et al, 2007









Table 2-3: Differences between second order pain neurons
Difference Nociceptive Specific (NS) Wide Dynamic Range (WDR)
Neurons Neurons


Activating fibers
Activating stimuli


Location

Lamina I

Pain receptive field


Discharge strength



Contribution to
Spinothalamic
Tract
Function






Pain theory
supported


A-6 and C fibers 42
Nociceptive (fast and slow
pain) 342

Mostly in Lamina I of spinal
cord 344, 345
Somatotopically organized 342

Restricted to relatively small
292
areas

Vigorous increase in discharge
as a result of noxious stimuli
(e.g. pinching and strong
compression) 292
Make up 20-25% of tract 342


Involved in sensory-
discriminative aspects of pain
(localization of pain 292; nature
of pain stimulus 292, 344)


Specificity theory: presence of
specific neurons activated only
by noxious stimuli 345,346


A-p, A-6 and C fibers 22
Innocuous cutaneouss touch and
pressure) and Nociceptive (fast
and slow pain) 343
Mostly in Lamina V and VII of
spinal cord 343
Not somatotopically organized
342
Vary with stimulus strength;
much larger than those of NS
342
neurons
Discharge at lower levels in
response to innocuous stimuli;
discharge more vigorously in
response to noxious stimuli 343
Make up about 75% of tract 342


Involved in affective-
motivational aspects of pain
(intensity; differences in
noxious stimuli intensities;
initiation of complex behavioral
responses to pain) 292
Pattern theory: presence of
second order neurons that
discharge differently to noxious
and innocuous stimuli 292










Table 2-4: Strengths and weaknesses of pain intensity assessments
Strength Weakness
Verbal Rating Scale (VRS)
Simple, complete and a usable pain Some adjectives may be ambiguous323
assessment347 Familiarity with adjectives required289
Adjectives may convey more subtle meanings Equal intervals may not exist between
of pain348 Ijdc 'l -
Easy to administer289 Cross modality matching reduces patient
Easy to scoic- compliance289
Usually easy to comprehend289 Longer lists have long response tlit.s'
Good compliance rates289 Included adjectives may not describe level
Have internal consistency and temporal of pain experience289
stability349 Adjectives pose literacy challenges289
Cross-modality related ratio scores are valid, Pain affect and intensity assessments are
reliable and objective mIisJ.i s- ;0 not always distinct289
Responsive to change in pain state289 Single adjective may not describe pain
Ability to discriminate between different types experience323 347
of pain351 352 Use of adjectives varies with ethnic groups
and gender353
Visual Analog Scale (VAS)
Simple and easy to construct324 Scoring is more time-consuming and
Easily grasped322 involves more steps than other measures289
Require little motivation to complete and The respondent needs to have minimum
quickly filled out322 level of motor abilities to use the s. Ic '
Suitable for use by untrained staff324 Cognitive difficulties make it harder to
Linear scak ulS -i
Ratio qualitieL- -' May have increased measurement error
Valid measures of pain state289 due to freedom in reporting356
More responsive than other measures289
Suitable for repeated use322
Numerical Rating Scale (NRS)
Valid & correlates with other pain intensity Scores cannot be necessarily treated as
measures289 ratio data357
Sensitive to treatments that impact pain
111011NS1l1 -
Easy to administer and scoic-
Can be administered over the phone289














CHAPTER 3
METHODS

Participants

Forty participants (20 males and 20 females) who currently had upper extremity

musculoskeletal disorders and injuries (UEMDs) were recruited for this study. The

sample size was calculated based on the data from a preliminary study involving healthy

participants (Appendix A).

We used convenience sampling to recruit the study participants (the recruitment

process is described in the "procedure" section on page 84). Specific inclusion and

exclusion criteria were used to select the participants. The inclusion criteria were as

follows: Participants were 1) aged between 18 and 65 years, 2) treated for unilateral

UEMDs involving the elbow or distally in the last 1 year. The exclusion criteria were:

People who 1) had bilateral UEMDs, 2) had UEMDs proximal to the elbow, 3) were

unable to safely perform 4 maximal and 8 submaximal grip trials with their affected

extremity, 4) verbally report their pain intensity to be greater than 7 on a scale of 0 to 10,

5) were currently ill and/or taking medication which would compromise their grip

strength, 6) had impaired cognition.

Materials and Equipment

This section discusses the equipment we used for recording the force-time curve

(F-T curve) and EMG activity, as well as for reporting participant demographics,

perceived exertion, and current and imagined level of pain. The instruments used to

record the F-T curve characteristics and EMG properties of the isometric grips included:









1) signal sensors: a transducer for recording grip force and surface electrodes for

recording the muscles EMG activity, 2) signal conditioner for amplifying, filtering and

processing the EMG activity signal, 3) an analog-to-digital (A/D) converter for

transforming a continuous electrical signal into a discrete electrical signal, and 4) a

computer with polygraph software for processing the discrete signal and for generating

the F-T curve and EMG activity. A diagram of the equipment setup has been presented in

Figure 3-1.

The paper-and-pencil tests included: 1) demographic questionnaire, 2) visual

analog scale (VAS) for measuring current pain intensity and for assigning imagined pain,

and 3) VAS for rating perceived grip effort. The specific equipment is discussed next.

Instruments for Generating the F-T Curve

Hand dynamometer. The force characteristics of the grip efforts were captured

using a force transducer in the form of an electronic Jamar dynamometer (Thought

Technology Ltd; Figure 3-2). A transducer is an electrical device that converts one form

of energy to another.358 The transducer in the modified Jamar dynamometer converts grip

pressure (measured in Kilograms; kg) into an electrical signal (measured in Volts; V).

The modified Jamar dynamometer has an operating range of 0-90.72 kg (0-200 lbs.) and

converts 1kg of external force into an electrical potential difference of 23.11 mV. This

conversion factor was calculated by suspending known weights (10, 20, and 25kg) from

the dynamometer prior to beginning data collection on 3 consecutive days. The observed

voltage readings were used to calculate 3 linear equations, Equation 3-1 for day 1,

Equation 3-2 for day 2, and Equation 3-3 for day 3. In the 3 equations, x represents the

weight of a load in kilograms and y represents the voltage output observed as a result of









suspending a load. Using these equations, we calculated a conversion factor for each day,

which when averaged resulted in a value of 23.11 mV.

y = 20.356x + 551.16 (3-1)
y = 20.631x + 547.63 (3-2)
y = 20.669x + 545.08 (3-3)

The calibration of the dynamometer was checked once a week by measuring the

electrical output on suspending known weights (10, 20, and 25kg). A linear relationship

between the suspended loads and electrical activity indicated a calibrated transducer

because the electrical output should increase proportionally to the load of the suspended

weights. We examined the linear relationship between the suspended loads and the

electrical activity by performing regression analysis and calculating the coefficient of

determination (r2) for each week. For the duration of the study, the average r2 value was

calculated as 0.999. To identify differences in weekly calibration, we correlated the

voltage outputs using Pearson product-moment correlation coefficient (Pearson r) as well

as the Intraclass correlation coefficient (ICC 3, 1). We found perfect correlations between

the weekly voltage (Pearson r = 1.0, Appendix B). We also found perfect test-retest

reliability between the first and last weekly voltage outputs (ICC 3,1 = 1.0). Large

coefficients of determination as well as perfect correlation coefficients indicate that the

dynamometer maintained its calibration. The electrical signal from the Jamar

dynamometer was amplified and fed into the FlexComp analog-to-digital converter for

analog display of the signal.

Instruments for Recording the EMG Signal

Surface EMG electrodes. The electromyographic (EMG) signal of two groups of

gripping muscles was captured using the MyoScan active sensor (Model # SA9401M,

Thought Technology Ltd., Montreal, QC; Figure 3-3) and the Triode electrode featuring a









bi-metal design, where silver-silver chloride (Ag-AgC1) contacts the skin and conducts

the captured signal via nickel-plated brass dome to the MyoScan sensor. The forearm has

2 muscle groups that play an important role in gripping.359 The recording electrodes were

placed over the belly of the flexor digitorum superficialis muscle (forearm flexor area)

and over the belly of the extensor digitorum communis muscle (forearm extensor area).360

Alcohol swabs were used to cleanse the skin before applying the electrodes. The signal

from the recording electrodes was transmitted to the signal conditioner.

Signal conditioner. The EMG output was amplified and filtered using the

MyoScan active sensor and the FlexComp Infiniti encoder (Model # SA7550, Thought

Technology Ltd., Montreal, QC; Figure 3-4). An amplifier takes a small analog signal

and increases its magnitude.358 The MyoScan sensor detects EMG signal in the range of+

1600V and uses a gain value of 500 to amplify it. The amplified signal was band-pass

filtered at 20-500 Hz (high-passed to 20Hz by the MyoScan sensor and low-passed to

500Hz by the FlexComp Infiniti), which eliminated the frequencies that mostly represent

noise.237 The filtered and amplified EMG signal and the force signal were led into the

analog-to-digital (A/D) converter.

Analog-to-digital (A/D) converter. The A/D converter (FlexComp Infiniti

encoder) transformed the analog data (F-T curve and EMG signal) into digital form,

which was stored and used for data processing. The FlexComp A/D converter sampled a

continuous/analog voltage signal and converted it into discrete voltage values. The

discrete voltage values were further translated into numerical values with a scale called

'A/D units' and stored in the computer for data analysis purposes.358









For the present study, we used a channel bandwidth of 20-500Hz at a sampling rate

of 2048 samples/second. It has been recommended that for proper analog-to-digital

conversion, the amplified EMG output of a maximal voluntary isometric contraction

(MVIC) be less than half the range of voltage accepted by the A/D converter.241 Also, the

Nyquist theorem states that the data should be sampled at least at twice the rate of the

highest frequency that is present in the signal.237 The amplified EMG signal from the

MyoScan sensor has an active range of+ 0.8V, which is half the voltage range of the

FlexComp A/D converter (+ 1.7V centered around a 2.8V offset).361 This range was

appropriate for digital conversion of our amplified analog signal. Also, the FlexComp

A/D converter samples data at 2kHz, which met the requirement of the Nyquist theorem

as the highest frequency in our signal was 500Hz.361

Computer with polygraph software. The BioGraph Infiniti software (Version 3.1,

Thought Technology; Figure 3-5) was used to generate the F-T curve characteristics and

EMG properties. For the F-T curves, we employed the BioGraph Infiniti's linear

transformer to convert force values from volts to kilograms. The slopes of the F-T curve

were calculated by exporting force values, sampled at a rate of 2048 samples/second, into

Microsoft Excel (Version 2003) and employing its function of the least-square line of

best fit. For the amplified and band-passed EMG signal, we rejected the 60Hz hum

(power-line noise) by employing BioGraph Inifiniti's notch filter. The notch filtered

signal was used to calculate the amplitude and median power frequency. The amplitude

of the EMG signal was calculated as average rectified amplitude for the duration of the

grip. The frequency spectrum was generated by applying a Fourier transformation

algorithm. To achieve a resolution of 1 Hz, a 2048-point Fast Fourier Transformation









(FFT) with the Hanning window function was applied to the EMG signal. The resulting

power spectrum was used to calculate the median power frequency, which was smoothed

using an averaging factor of 40. The median frequency was computed for two separate 1-

second intervals, the first interval beginning at peak force (called the median frequency of

the first second, or MF first second) and the second interval forming the last second of

the force decay phase (called the median frequency of the last second, or MF last

second). We also computed the ratio of last to first second values of MF, or the MF-ratio.

Paper-and-Pencil Tests

1) Demographic Questionnaire. A demographic questionnaire was used to collect

participant information on demographic variables such as age and gender. The

questionnaire also included questions on UEMD-related variables such as diagnosis and

site of condition (Appendix C).

2) Visual Analog Scale (VAS) for pain intensity. For the present study, a VAS

was used to assess current pain intensity. The VAS consists of a 10 cm line anchored by 2

extremes of pain, i.e., 'no pain' (numerical score of 0) and 'pain as bad as it could be'

(numerical score of 10; Figure 3-6). Participants were instructed to mark the VAS at a

point that identified their current pain level. The VAS was administered at the beginning

of the testing session and before each gripping effort in both hands to ensure that pain

returned to pre-injury level. Also, based on the initial VAS, an imagined level of pain

intensity was verbally assigned as 2-3 cm above the initial perceived pain level.

3) Visual Analog Scale (VAS) for perceived grip effort. The perceived exertion

of grip effort was rated using a VAS (Figure 3-7). It consisted of a 10 cm line anchored

by 2 extremes of effort, i.e., 'no grip force' (numerical score of 0) and 'strongest grip

force' (numerical score of 10). We used the effort scale to examine how imagined pain









can affect the level of effort. The effort scale was used to compute perceived submaximal

effort as a percentage of perceived maximal effort. In the present study, the effort scale

was given immediately after each grip trial for the participant to report his or her

perceived grip effort.

Study Design

The present study employed a repeated measures design. Each participant served as

their own control for two variables levels of grip efforts (maximal vs. submaximal) and

levels of injury (injured vs. uninjured hand). The participants were divided into two

groups on the basis of gender (male vs. female).

Rationale for the Study Design. Stringent controls have been applied to the

research design. The stringent controls would identify any significant differences

between maximal and submaximal effort as well as to identify their association with pain.

The steps taken to make the study design conservative and stringent include:

* Appropriate sample size was calculated based on previous data and was sufficient
to indicate if the force-time curve (F-T curve) characteristics and EMG properties
truly differentiate between maximal and submaximal grip efforts.

* A repeated measures design provides the ability to control for potential influence of
individual differences. We can safely assume that important participant
characteristics, such as age, gender and disability related to the upper extremity
condition remained constant through the course of the experiment.92

* One disadvantage of a repeated measures design is the potential for carryover
effects when a participant is exposed to multiple-treatment conditions.
Carryover/residual effects, such as fatigue due to grip strength trials, can be
reduced by allotting sufficient time between successive treatment conditions to
allow for complete dissipation of previous effects.92 To dissipate carryover effects,
study participants were provided with a rest break lasting a minute after each grip
trial96 362, and were also provided with a 10 minute break between the two
362-364
sessions.36364

* This design also controls for order effects by randomizing the sequence of maximal
and submaximal effort and which extremity was used to begin the grip efforts.365









Procedure

Participant Recruitment Phase

Participants with upper extremity conditions were recruited from various hand

therapy clinics and rehabilitation clinics in the cities of Gainesville, and St. Augustine,

Florida. Health care professionals, including physical therapists and occupational

therapists, were provided with inclusion/exclusion criteria and a standard script for

recruiting participants. The criteria and directions were provided to the healthcare

professionals as part of a letter (Appendix D). The script is as follows:

"A study is being conducted to identify how pain affects grip strength among
people with upper extremity musculoskeletal conditions. Your condition makes you
eligible to participate in this study. This study involves gripping a hand
dynamometer 12 times with each hand and rating your pain and perceived grip
effort. If you agree to participate, you will attend one session lasting approximately
45 minutes and will be paid $20.00 for participating in the study. Please let me
know if you are interested in participating and I can provide you with information
to contact the research group."

These health care professionals communicated the information on the study to their

patients who they judged to be able to safely perform 4 maximal and 8 submaximal

efforts with their injured extremity. Interested participants were asked to call or email the

investigators indicating their interest in participating in the study and to setup an

appointment for collecting data.

Data Collection Phase

1) Instrument calibration. The Jamar dynamometer and the FlexComp Infiniti

were calibrated prior to the testing session. The calibration of the dynamometer was

checked by measuring the electrical output on suspending known weights (10, 20, and

25kg). The setup used to check the calibration of the dynamometer has been presented in

Figure 3-8. The FlexComp Infiniti includes a built-in voltage reference that possesses









good temperature stability. This reference voltage was used to self-calibrate the unit. The

self-calibration process sets the gain and offset of each channel of the unit to a value

361
within their preset specifications.3

2) Participant preparation. All participants first read and signed the informed

consent form approved by the Institutional Review Board at the University of Florida.

The participants then filled out a demographic questionnaire (Appendix C). While

completing the questionnaire, the participants were also provided with instructions on

how to complete the pain-intensity VAS. Next, the participants were prepared for EMG

data collection from forearm flexor and extensor muscles by cleaning the forearm skin

using alcohol swabs. The forearm flexor compartment was represented by the flexor

digitorum superficialis (FDS) muscle and the extensor compartment by the extensor

digitorum communis (EDC) muscle. The location of the recording electrode on the FDS

muscle was identified as follows360:

1. Place the participant's forearm in supination.

2. Ask the participant to close and open their fist 3-5 times.

3. Look and feel for the muscle belly while the participant performs the movement.

4. Place the electrode on the muscle's belly, approximately 1-2 inches below the
cubital fossa.

The location of the recording electrode on the EDC muscle was identified as follows360:

1. Place the forearm of the participant in pronation, and ask the participant to close
and open their fist.

2. Feel for the bulge of the muscle in the upper forearm.

3. To confirm the muscle's location, ask the participant to flex and extend the
metacarpophalangeal joint of the long/middle finger while the rest of the fingers are
in flexion.









4. Place the electrode on the bulge, approximately 3 inches below the lateral
epicondyle.

3) Protocol. Each participant participated in a total of 2 sessions of gripping. In

each session, a participant exerted 2 maximal and 4 submaximal grip efforts with each

hand. Hence, a participant exerted a total of 12 grips with each hand. Each grip lasted 6

seconds. After each grip effort, the participant rested for a period of 1 minute. Between

the 2 sessions, the participant received a rest break lasting 10 minutes. For all grips, the

participant was seated in an adjustable chair without arm rests. The participant assumed

the testing position recommended by the American Society of Hand Therapists. 17 The

participant's feet were fully resting on the floor and the hips were as far back in the chair

as possible, with the hips and knees positioned at approximately 900. The shoulder of the

tested extremity was adducted and neutrally rotated, the elbow flexed to 90, and the

forearm and wrist held in a neutral position.

After each grip effort, the participant rested for a period of 1 minute. At the

beginning of the rest period, the test administrator asked the participant to complete the

effort VAS for perceived exertion of grip strength. At the end of the rest period, the

participant completed the pain intensity VAS for pain resulting from the grip. If the

reported level of pain was more than 1-point higher than the range of pain usually

experienced then the participant continued to rest until the level of pain returned to within

1-point of the initial level of pain. This time was recorded exactly on the checklist used

by the test administrator (Figure F-1). Before the first session, the participant also

performed a practice grip with each hand to get used to the dynamometer and to check if

the force and EMG signal were being recorded properly. The participant also practiced

marking the pain and effort VAS.









A data collection form was used to record the perceived grip force and pain

intensity. The form recorded effort and pain associated with the practice trial as well as

with maximal and submaximal grip effort trials. The form was compiled prior to

beginning of the data collection phase of the study and followed the same order that was

assigned to a participant. An example of a data collection form has been provided in

Appendix G. Table 3-1 presents an example of the study protocol.

To control for order effects, the sequence of maximal vs. submaximal effort and

injured vs. uninjured extremity was randomly assigned. The random assignment was

performed prior to the beginning of the study and was implemented by an assistant. An

assistant used the randomization sheet (Appendix E) to assign the order (sequence) of

gripping. Each participant was assigned 1 of 4 possible gripping sequences based on

starting with one of the hands (injured vs. uninjured) and one of the levels of effort

(maximal vs. submaximal) (Appendix E). To reduce measurement bias, the test

administrator was blinded to the level of effort. An assistant implemented the

randomization by providing participants with standard instructions (see next section).

4) Instructions. When maximal effort was assigned, the instructions were be as

follows:

"In this session, I want you to give maximal effort with your injured/uninjured
hand for all 2 grip trials. Follow the directions of the test administrator to exert full
effort. Do you have any questions? This task will test your grip strength. When I
say go, give your maximum effort in a smooth manner. Be careful not to jerk the
tool while gripping. You will exert a maximal effort for 6 seconds. You will be
given a rest period after each grip. Before each trial I will ask you 'Are you ready?'
and then the computer will tell you 'Are you ready? Go!' The computer will tell
you to stop after 6 seconds. If you experience any unusual pain or discomfort at any
point during testing, stop immediately. Do you have any questions?"

The submaximal effort instructions for the injured extremity assigned on the basis of

imagined pain level were as follows:









"In this session, I want you to imagine that the level of pain that you are
experiencing is affecting your grip. I want you to imagine that your pain is 2 points
higher on the VAS and is at a level of ( number) out of 10. Imagine that this
higher intensity pain causes your grip to be weaker. I want you to perform the grip
trials in such a way that you convince me that you are more affected by pain than
you really are, in other words, to exert less than a maximal effort. When I say go,
give your submaximal effort in a smooth manner. Be careful not to jerk the tool
while gripping. You will exert a submaximal effort for 6 seconds. You will be
given a rest period after each grip. Before each trial I will ask you 'Are you ready?'
and then the computer will tell you 'Are you ready? Go!' The computer will tell
you to stop after 6 seconds. If you experience any unusual pain or discomfort at any
point during testing, stop immediately. Do you have any questions?"

The submaximal effort instructions for the uninjured extremity assigned on the basis of

imagined pain level were as follows:

"In this session, I want you to imagine that the level of pain that you are
experiencing is affecting your grip. I want you to pretend that you are experiencing
pain that equals the intensity of pain you experienced in your injured extremity at
the beginning of the session and is at a level of ( number) out of 10. Imagine that
this pain causes your grip to be weaker. The test administrator will ask you to exert
your maximal effort. I want you to perform the grip trials in such a way that you
convince the administrator that you are more affected by pain than you really are,
in other words, to exert less than a maximal effort when gripping. Try to be
consistent in repeating the force of your grip. The test administrator will ask you,
throughout the testing session, to give maximal effort, but you need to ignore his
instructions. Do you have any questions? When I say go, give your submaximal
effort in a smooth manner. Be careful not to jerk the tool while gripping. You will
exert a submaximal effort for 6 seconds. You will be given a rest period after each
grip. Before each trial I will ask you 'Are you ready?' and then tell you to 'Go!' I
will tell you to stop after 6 seconds. If you experience any unusual pain or
discomfort at any point during testing, stop immediately. Do you have any
questions?"

The submaximal effort instructions for the injured as well as uninjured extremity

assigned on the basis of 50% of maximal effort were as follows:

"In this session, I want you to perform the grip trials in such a way that you exert
50% of your maximal effort. When I say go, give your submaximal effort in a
smooth manner. Be careful not to jerk the tool while gripping. You will exert a
submaximal effort for 6 seconds. You will be given a rest period after each grip.
Before each trial I will ask you 'Are you ready?' and then tell you to 'Go!' I will
tell you to stop after 6 seconds. If you experience any unusual pain or discomfort at
any point during testing, stop immediately. Do you have any questions?"









The test administrator, who was blinded to the level of effort, instructed the participant to

exert maximal grip strength effort regardless of whether the assigned effort was maximal

or submaximal. The grip effort instructions given before each session were as follows:

"This task will test your grip strength. When I say go, give your maximum effort in
a smooth manner. Be careful not to jerk the tool while gripping. You will exert a
maximal effort for 6 seconds. You will be given a rest period after each grip.
Before each trial I will ask you 'Are you ready?' and then tell you to 'Go!' I will
tell you to stop after 6 seconds. If you experience any unusual pain or discomfort at
any point during testing, stop immediately. Do you have any questions?"

Before each grip trial, the grip effort instructions to the participant were as follows:

"During the next grip, give your maximum/submaximal effort. Are you ready? Go!
(After 6 seconds) Stop!"

A practice trial was given before gripping begins. The instructions for the practice trial

are as follows:

"This is a practice trial so you can get used to gripping the dynamometer and
practice marking the pain and effort scales. Please do not exert maximal effort
during this practice trial so that you don't fatigue. This is just a practice trial. Do
you have any questions? Are you ready? Go! (After 6 seconds) Stop!"

Before the practice trial, the participant practiced marking the pain VAS and after the

practice trial, the participant was instructed on how to complete the effort VAS on the

"Practice Trial" sheet of the Data Collection Form (Appendix F) as follows:

"Refer to the Effort Scale on the practice trial page of the data collection form. You
will use the Effort Scale for recording the amount of effort you think you exerted
during that grip. On this scale, 0 means no grip force and 10 means strongest grip
force. Mark a vertical line at a point that indicates the level of effort you just
exerted. Do you have any questions?"

Instructions for completing the effort VAS immediately after each grip trial were as

follows:

"Now please complete the effort scale. Mark a vertical line at a point that indicates
the level of effort you just exerted."









The participant was also instructed on how to complete the VAS for pain intensity. While

completing the demographic questionnaire, the instructions for answering questions 14

and 15 were as follows:

"You will use the pain scale for recording the pain that you are currently
experiencing in your injured upper extremity. On this scale, 0 means no pain and
10 means pain as bad as it could be. Mark a vertical line between 0 and 10 at a
point that indicates your pain level. Do you have any questions?"

Instructions for completing the pain VAS before each grip trial (at the end of the 1 minute

rest period) were as follows:

"Now please complete the pain scale. Mark a vertical line at a point that indicates
your pain level."

Statistical Analysis

The statistical analysis varied according to the specific aims of the study. For

specific aims 1 and 2, we used repeated measures analysis of variance (ANOVA) for

identifying differences between maximal and submaximal effort. All tests were

considered significant at the p < 0.05. Due to exploratory nature of the study, we did not

adjust the p-value for inflation in Type I error resulting from performing multiple

comparisons. For specific aim 3, we used Intraclass correlation coefficients (ICC) to

examine test-retest reliability and sensitivity and specificity analysis to assess validity

and effectiveness of identifying maximal vs. submaximal efforts. All tests were

performed using SPSS 15.0.366

Specific Aims 1 and 2. We used the General Linear Model (GLM) Repeated

Measures Analysis of Variance (ANOVA) to compare between maximal and submaximal

efforts. The ANOVA consisted of three within-subject variables, effort (maximal vs.

submaximal), injury (injured vs. uninjured hand), and session (first vs. second), and one

between-subject variable, gender (male vs. female). Repeated Measures ANOVA









analyzes a group of related dependent variables that represent different measurements of

the same attribute.366 The dependent variables for Aim 1 were the peak force, time-to-

peak force, slope of the force-generation phase and slope of the force-decay phase of the

F-T curve. The dependent variables for Aim 2 were the flexor and extensor EMG

amplitude and median frequency ratio between first and last second of a 6-second grip.

Specific Aim 3. We examined the validity of the F-T curve characteristics and

EMG properties in differentiating between maximal and submaximal efforts. However, a

valid test first needs to be reliable.92 The test-retest reliability of F-T curve characteristics

and EMG properties was examined using the Intraclass Correlation Coefficient (ICC 3,

1). The ICC 3, 1 has become the preferred index for testing rater reliability as it reflects

both correlation (correspondence) and agreement. Correlation indicates how scores vary

together, whereas, agreement identifies any significant differences between scores.92 In

the designation "ICC 3, 1", 3 represents model 3 and 1 represents a single rater. The ICC

3,1 has been suggested to be appropriate for testing intrarater reliability with multiple

scores from the same rater.92'367 ICC r values range from 0.00 to 1.00. According to

Portney and Watkins92, "reliability coefficients of measurements used for decision

making or diagnosis of individuals need to be higher, perhaps at least 0.9 to ensure valid

interpretations of findings" (p. 65). Portney and Watkins92 further suggest that an index

greater than 0.9 is a guideline and not an absolute standard.92 For the present study, the

ICC 3, 1 was used to compare the mean scores of the two trials of the first and second

sessions. We expected coefficients ofr > 0.9 to indicate that an F-T curve characteristic

or EMG property had good reliability.









The validity indicators in the present study include: 1) significant differences in

aims 1 and 2, 2) calculating sensitivity and specificity values, and 3) generating ROC

curves to identify optimal cutoff value as well as effectiveness of the test. Significant

differences were examined in aims 1 and 2. Sensitivity and specificity was calculated for

different cutoff values. A cutoff value can be any value in the range of measures of an F-

T curve characteristic or EMG property. For each cutoff value, sensitivity and specificity

can be calculated by finding the number of true and false positives and negatives.27' 64, 65
109 For example, for the slopes of the force-decay phase, let a cutoff value be denoted by

X. Steeper slopes have been associated with maximal effort.28 Therefore, any slope value

greater than X is considered positive (indicating submaximal effort). A true-positive

exists when submaximal effort has really occurred. We calculated sensitivity by dividing

the number of true-positives by the total number of times submaximal effort was exerted.

Likewise, any slope value less than X is considered negative (indicating maximal effort).

A true-negative exists when submaximal effort has really occurred. We calculated

specificity by dividing the number of true-negatives by the total number of times

maximal effort was exerted (Table 3-2). We also calculated the overall error rate by using

the formula (1-sensitivity) + (1-specificity). The false-positive rates will be calculated by

subtracting the specificity values from 1.0 (1-specificity).92

Sensitivity and specificity values change with the cutoff value, as an inverse

relationship exists between the two, i.e., as sensitivity increases the specificity decreases

and vice versa.27 64 65 One way to evaluate how different cutoff values affect sensitivity

and specificity is by plotting the receiver operating characteristic (ROC) curve.110 The

ROC curve is a plot of true-positive rates (sensitivity) against false-positive rates (1-









specificity). It can be used to identify the optimal combination of sensitivity and

specificity values.110 The area under the ROC curve, or the area under the curve (AUC),

is an index of separation of signal and noise distributions. A higher value of AUC

indicates a more effective test.110 The AUC was calculated by plotting the ROC curve on

a graph paper, counting the number of squares below the curve and dividing it by the

total number of squares. Sensitivity and specificity analyses were performed using the

first session values of the injured hands only.

Post-hoc analysis. We wanted to examine if pain significantly affected grip and if

there were significant differences in pain between males and females and the four

different orders of testing. Repeated measures ANOVA was conducted with the within

subjects variable as pain (baseline pain vs. pain after last maximal grip) and the between

subjects variables were gender (male vs. female) and order (1 vs. 2 vs. 3 vs. 4).







94





Table 3-1: Schematic representation of the study protocol
Order Event
Grip 1
Rest 1


Sub-pain injured


Sub-pain uninjured


Sub-percent injured


Grip 2
Rest 2


Grip 1
Rest 1


Grip 2
Rest 2


Grip 1
Rest 1


Grip 2
Rest 2


Grip 1
Rest 1


Grip 2
Rest 2


Grip 1
Rest 1


Grip 2
Rest 2


Sub-percent uninjured


Maximal injured


Complete Effort VAS
Complete Pain VAS


Complete Effort VAS
Complete Pain VAS


Complete Effort VAS
Complete Pain VAS


Complete Effort VAS
Complete Pain VAS


Complete Effort VAS
Complete Pain VAS


Complete Effort VAS
Complete Pain VAS


Complete Effort VAS
Complete Pain VAS


Complete Effort VAS
Complete Pain VAS


Complete Effort VAS
Complete Pain VAS


Complete Effort VAS
Complete Pain VAS


Time


6 sec

1 min

6 sec

1 min

6 sec

1 min

6 sec

1 min

6 sec

1 min

6 sec

1 min

6 sec

1 min

6 sec

1 min

6 sec

1 min

6 sec

1 min


Grip 1 6 sec
Rest 1
Complete Effort VAS 2 min
Maximal uninjured Complete Pain VAS
Grip 2 6 sec
Rest 2
Complete Effort VAS 1 min
Complete Pain VAS
Rest 10 min
Repeat









Table 3-2: Calculating sensitivity and specificity for the slope cut-off value of X during
the force-decay phase


EFFORT


(Submaximal Effort)


(Maximal Effort)


+ a b
a+b
(Slope>X) True-positives False-positives






c d
c+d
(Slope


Total a + c b + d N


Sensitivity = a/(a + c)
Specificity = d/(b + d)


True-positives/Total Submaximal Effort
True-negatives/Total Maximal Effort


Total


SLOPE

TEST











Type of Equipment EMG Equipment Force Equipment


Signal sensors


Signal conditioner


Analog-to-digital
converter


Digital output
display


Figure 3-1: Biomechanical instruments for recoding force and electromyographic signals





























Figure 3-2: Electronic Jamar dynamometer


Figure 3-3: MyoScan active sensors











m!h


Figure 3-4: FlexComp Infiniti encoder


Figure 3-5: BioGraph Infiniti polygraph software










Pain


No pain


Pain as bad as
it could be


Please mark a vertical line at a point that indicates the level of pain that you are
currently experiencing.


Figure 3-6: Pain Intensity Visual Analog Scale






Effort


0%


No Grip
Force


100%


Strongest
Grip Force


Please mark a vertical line at a point that indicates the level of effort you just exerted.


Figure 3-7: Perceived Effort Visual Analog Scale



































Figure 3-8: Setup used to check the dynamometer calibration














CHAPTER 4
RESULTS

Subjects

Forty subjects (20 males and 20 females) participated in the study. Average age

was 37 + 12 years. At the time of the study, over half (N=23) of the subjects were

employed, with the most being employed by educational institutions (N=7). All subjects

had unilateral upper extremity musculoskeletal conditions, with two-thirds (N=26)

experiencing injury to their dominant side (Table 4-1). The most common location of the

injury was to the hands (N=16). Almost all men (N=19) experienced traumatic injuries.

In contrast, equal number of women experienced traumatic injuries (N=9) and cumulative

trauma disorders (N=10). Injury-related baseline pain as measured by the VAS ranged

from 0-6 cm with an average and SD of 1.6 + 1.8 cm. Three-eights of the subjects (N=15)

experienced reduced ability to independently perform activities of daily living due to

their injury (Table 4-2).

Specific Aim 1

For the specific aim 1, differences were examined for 4 force-time curve (F-T

curve) characteristics including peak force, time-to-peak force, slope of force-generation

phase and slope of force-decay phase using Repeated Measures ANOVA. The within-

subjects variables were 1) effort (maximal vs. submaximal), 2) session (1 vs. 2), and 3)

injury (injured vs. uninjured), while the between-subjects variable was gender (male vs.

female). When significant differences existed between the two sessions, we computed

separate ANOVAs for the first and second sessions. When there were no significant









differences between sessions, we considered only the values of the first session. All

differences were deemed significant at the 0.05 alpha levels. Average values of the four

F-T curve characteristics for males and females are presented in Tables 4-3 and 4-4.

Peak Force

Peak force indicates strength of an isometric contraction. We found a significant

interaction between session and injury [F (1, 38)= 5.05, p < 0.03] (Table 4-5). When

compared to the first session, peak force during second session increased for injured

hands but decreased for uninjured hands (Figure 4-1). For both sessions, there were

significant main effects for injury, effort, and gender. Peak force was significantly greater

for uninjured vs. injured hand, maximal vs. submaximal effort, and males vs. females

(Table 4-6, Table 4-7, Figure 4-2). When analyzed separately, both sessions showed

significant interaction effects between gender and effort as well as between injury and

effort. The difference in peak force between maximal and submaximal efforts was greater

for males than for females and for the uninjured hands vs. the injured hands (Figure 4-3).

Time-to-peak Force

Time-to-peak force indicates the time required to reach the highest force exerted

during an isometric contraction. Time-to-peak force values were not significantly

different between the first and second session [F (1, 38) = 1.93, p < 0.1] (Table 4-8). For

the first session time values, the main effect was not significant for gender [F (1, 38) =

0.32, p < 0.57] and injury [F (1, 38) = 0.24, p < 0.62] but was significant for effort [F (1,

38) = 33.64, p < 0.0001] (Table 4-9, Figure 4-4). Time-to-peak force for maximal effort

was greater than submaximal effort by 0.53 seconds.









Slope of the Force-generation Phase

The slope of force-generation phase indicates the rate of force development during

the initial phase of an isometric contraction. A significant interaction effect existed

between injury and effort [F (1, 38) = 5.77, p < 0.02] (Table 4-10). When compared to

submaximal effort, the slopes of maximal effort were steeper for uninjured than for the

injured hands (Figure 4-5). The main effect for session was not significant [F (1, 38) =

0.37, p < 0.54] (Table 4-10). For the first session slopes, significant main effects existed

for injury [F (1, 38) = 10.0, p < 0.003], effort [F (1, 38) = 55.77, p < 0.0001], and gender

[F (1, 38) = 8.37, p < 0.006] (Table 4-11, Figure 4-6). In other words, the slopes of force-

generation phase were steeper for the uninjured hand than the injured hand (by 0.24 V/s),

for the maximal effort than the submaximal effort (by 0.61 V/s), and for males than

females (by 0.46 V/s).

Slope of the Force-decay Phase

The slope of the force-decay phase indicates the extent of fatigue during an

isometric contraction. A significant interaction effect existed between injury and effort [F

(1, 38) = 4.03, p < 0.052] (Table 4-12). In other words, the decrease in slope between

maximal effort and submaximal effort was greater for the uninjured hand compared to the

injured hand (Figure 4-7). A significant interaction effect also existed between session

and gender [F (1, 38) = 9.47, p < 0.005] (Table 4-12). That is, the steepness of the slopes

during the second session (as compared to the first session) increased for males but

decreased for females (Figure 4-8).

For both sessions, significant main effects existed for injury and effort. A

significant main effect for gender existed for the second session and not for the first

session (Tables 4-13 and 4-14). That is, the slopes of force-decay phase were









significantly steeper for the uninjured than the injured hand, the maximal vs. submaximal

effort, and males vs. females (Figure 4-9).

Specific Aim 2

For the specific aim 2, differences were examined for 2 electromyographic (EMG)

properties, namely amplitude and median frequency ratio (MF-ratio) using Repeated

Measures ANOVA. The within-subjects variables were 1) effort (maximal vs.

submaximal), 2) session (1 vs. 2), and 3) injury (injured vs. uninjured), while the

between-subjects variable was gender (male vs. female). When significant differences

existed between the two sessions, we performed separate ANOVAs for the first and

second session. When there were no significant differences between sessions, we

considered only the values of the first session. All differences were deemed significant at

the 0.05 alpha levels. Average values of the EMG properties for males and females are

presented in Tables 4-15, 4-16 and 4-17.

Flexor EMG Amplitude

The amplitude of the EMG signal represents the magnitude of the muscle activity.

There were no significant differences in flexor EMG amplitude between the first and

second session [F (1, 38) = 0.02, p < 0.87] (Table 4-18). For the first session, significant

main effects existed for injury [F (1, 38) = 6.29, p < 0.01] and effort [F (1, 38) = 91.35, p

< 0.0001] but not for gender [F (1, 38) = 0.18, p < 0.6] (Table 4-19). In other words,

flexor EMG amplitude was significantly greater for the uninjured vs. injured hands, and

for maximal vs. submaximal efforts (Figure 4-11). The first session revealed a significant

interaction effect between injury and effort [F (1, 38) = 7.81, p < 0.01] (Table 4-19). That

is, flexor EMG amplitude was similar for the injured and uninjured hands during

submaximal effort but greater for uninjured hand during maximal effort (Figure 4-10).









Extensor EMG Amplitude

A significant interaction effect existed between effort and session [F (1, 38) = 5.89,

p < 0.02] (Table 4-20). That is, the decrease in extensor EMG amplitude between the first

and second session was greater for maximal than for submaximal efforts (Figure 4-12).

For both sessions, significant main effects existed for effort but not for gender and injury

(Tables 4-21 and 4-22). In other words, extensor EMG amplitude was significantly

greater for maximal vs. submaximal efforts (Figure 4-13).

Flexor Median Frequency Ratio

The median frequency ratio (MF-ratio) represents the extent of fatigue or motor

unit de-recruitment during an isometric contraction. The flexor MF-ratio was not

significantly different between the first and second session (Table 4-23). For the MF-

ratios in the first session, significant main effects existed for effort [F (1, 38) = 30.27, p <

0.0001] but not for gender [F (1, 38) = 0.43, p < 0.52] or injury [F (1, 38) = 0.02, p < 0.9]

(Table 4-24). In other words, MF-ratio was significantly smaller for maximal vs.

submaximal efforts (Figure 4-14). Also, a significant interaction effect existed between

injury, effort, and gender (Tables 4-23 and 4-24). The decrease in MF-ratio between

submaximal and maximal efforts was greater for uninjured vs. injured hands in males but

not in females (uninjured and injured hands showed the same decrease in MF-ratio)

(Figure 4-15).

Extensor Median Frequency Ratio

A significant main effect existed for session [F (1, 38) = 4.61, p < 0.04] (Table 4-

25). Therefore, we performed separate ANOVAs for each session. For both sessions,

main effects were significant for effort but not significant for gender and injury (Tables

4-26 and 4-27). That is, MF-ratio was significantly smaller for maximal vs. submaximal









efforts (Figure 4-16). During the first session, a significant interaction effect existed

between injury and effort (Table 4-26). In other words, the decrease in MF-ratio between

submaximal and maximal efforts was greater for uninjured hand when compared to the

injured hand (Figure 4-17).

Specific Aim 3

To examine the validity of the various force and EMG measures we first examined

the test-retest reliability and then the effectiveness of the most highly significant

variables. The test-retest reliability of the F-T curve characteristics and EMG properties

was analyzed using the Intraclass Correlation Coefficient (ICC 3, 1). Generally,

coefficients below 0.50 represent poor reliability, coefficients between 0.50 and 0.75

represent moderate reliability, and values above 0.75 represent good reliability.

Moreover, to ensure valid interpretations, reliability should exceed 0.90.109 The validity

of the F-T curve characteristics and EMG properties was examined by calculating

sensitivity and specificity values for the four measures that showed the most significant

differences between maximal and submaximal efforts. In addition, ROC curves were

generated to identify the optimal cutoff values of these measures. The sensitivity and

specificity analyses were performed using injured hands only.

Test-Retest Reliability

When examining the test-retest reliability, we will discuss only the values of

maximal effort because it has been documented that submaximal effort is less

consistent. 188 The test-retest reliability of F-T curve characteristics ranged from r = 0.3 to

r = 0.96 (Table 4-28). The test-retest reliability of EMG properties ranged from r = 0.7 to

r = 0.96 (Table 4-29).









Validity

The measures that showed in the greatest significance between maximal and

submaximal efforts were time-to-peak force, slope of force-generation phase, flexor MF-

ratio, and extensor MF-ratio (Table 4-30). Sensitivity and specificity values as well as

overall error rates for multiple cutoff values were calculated and are shown for slopes of

force-generation phase (Table 4-31), slopes of force-decay phase (Table 4-32), flexor

MF-ratio (Table 4-33), and extensor MF-ratio (Table 4-34). We did not calculate the

sensitivity and specificity values for time-to-peak force because it had poor test-retest

reliability rendering it as an invalid measure. Using the sensitivity and specificity values

for various cutoff values, we created ROC curves. When significant differences in gender

existed, we generated separate ROC curves for males and females. We did not create

ROC curves for the slope of force-decay phase because it had poor sensitivity and

specificity values. The optimal cutoff values for slope of force-generation phase, flexor

MF-ratio, and extensor MF-ratio are presented in Table 4-35.

Slope of force-generation phase

The ROC curve revealed that for the force-generation phase, the slope cutoff value

of 1.5 V/s for men yielded the most optimal combination of sensitivity (0.85) and

specificity (0.55) and the lowest overall error rate (0.6). For women, the slope cutoff

value of 0.5 V/s yielded the most optimal combination of sensitivity (0.6) and specificity

(0.85) and the lowest overall error rate (0.55) (Table 4-31). The proportional area under

the ROC curve was greater for women (76%) than for men (72%) (Figure 4-18).









Slope of force-decay phase

The slope cutoff value of -0.04V/s yielded the most optimal combination of

sensitivity (0.85) and specificity (0.27) and the lowest overall error rate (0.87) (Table 4-

32).

Median frequency ratio

The ROC curve for flexor MF-ratio revealed that the ratio cutoff value of 102%

yielded the most optimal combination of sensitivity (0.53) and specificity (0.78) and the

lowest overall error rate (0.70) (Table 4-33, Figure 4-19). The proportional area under the

ROC curve for flexor frequency ratio was. For the ratio of extensor median frequency,

the ratio cutoff value of 100% yielded the most optimal combination of sensitivity (0.63)

and specificity (0.7) producing the lowest overall error rate (0.68) (Table 4-34). The

proportional area under the ROC curve was the same for the frequency ratio for forearm

flexors (66.25%) as well as extensors (71%) (Figure 4-19).

Post-Hoc Analysis

There were no significant differences between baseline pain and pain after last

maximal effort grip [F (1, 38) = 0.33, p < 0.56]. There were no significant differences in

pain between males and females [F (1, 38) = 0.008, p < 0.92] and between the four orders

of testing [F (1, 38) = 1.37, p < 0.26].

Summary

1. Session differences were identified by peak force, slope of force-decay phase,
extensor amplitude, and extensor MF-ratio.
2. Differences between injured and uninjured hands were identified by peak force,
slopes of force-generation phase and force-decay phase, flexor EMG amplitude,
flexor MF-ratio, and extensor MF-ratio.
3. Differences between maximal and submaximal efforts were identified by all F-T
curve characteristics and EMG properties.
4. Gender differences were identified by peak force, slope of the force-generation
phase, slope of force-decay phase, and flexor MF-ratio.






109


5. The test-retest reliability of force variables ranged from r = 0.3 to r = 0.96 and
EMG variables ranged from r = 0.7 to r = 0.96.
6. Based on the area under the ROC curve, the slope of the force-generation phase
was the most effective in distinguishing between maximal and submaximal efforts.
Yet, 15% of the men who exerted submaximal effort were misclassified as exerting
a maximal effort and 45% of the men who exerted maximal effort were
misclassified as exerting a submaximal effort. Further, 40% of women who exerted
submaximal effort were misclassified as exerting a maximal effort and 15% of
women who exerted maximal effort were misclassified as exerting maximal effort.






110


Table 4-1: Demographic characteristics of the study sample
Men (N=20) Women (N=20) All (N=40)


Mean
or
Number


Age (years)
Height (inches)
Weight (lbs.)
Race
European
African
Hispanic
Asian
Occupation
Classification
Business and
Financial
Education
Healthcare
Food and Service
Sales
Office and
Administrative
Construction
Installation, and
Maintenance
Production
Transportation
Sports Occupations
Retired/Not
working
Current work status
Full-time
Part-time
Not working
Dominant extremity
Left
Right
Injured extremity
Left
Right


SD
or %

11.8
2.81
36


Mean
or
Number
39
66
172


SD or
%

12.73
3.6
46


Mean
or
Number
37.7
63
190


SD or
%

12.14
4.04
44


75
12.5
10
2.5


2 10


2 5


25
17.5
7.5
5
12.5

2.5
2.5

5
5
5
7.5


37.5
20
32.5

17.5
82.5

42.5
57.5











Table 4-2: Injury related characteristics of the study sample
Men (N=20) Women (N=20) All (N = 41
Ave/Num SD/% Ave/Num SD/% Ave/Num SD/%
Location of injury


Hand
Wrist
Forearm
Elbow
Duration of injury (months)
Etiology
Cause of injury
Traumatic
Motor vehicle accident
Sports injury
Violence-related injury
Falls
Occupational injury
Other
Cumulative Trauma
Sports injury
Occupational injury
House maintenance
Do not know
Signs/Symptoms
Pain intensity in past week
(cm.)
Current pain intensity (cm.)
Injury limits ADL
Management
Currently taking pain
medications

Undergone surgical
intervention
Benefited from surgery
Length of rehabilitative care
Duration (weeks)
Times per week (median)
Success of rehabilitative
care
Somewhat successful
Successful
Very successful


10 50
4 20
1 5
5 25
8 15


19 95
3 15
6 30
2 10
3 15
1 5
4 20
1 5
1 5


3.15 2.1

1.57 1.87
6 30


5 25


12 60

12 60


6.05 7.01
2 --
18 90

2 10
10 50
6 30


6 30
7 35
5 25
2 10
16.15 29


9 45
5 25


1 5
10 50
3 15
6 30
1 5
1 5


3.46 2.2

1.61 1.94
9 45


6 30


10 50

9 45


30.82 4.62
2 --
16 80


3 15
7 35
6 30


16 40
11 27.5
6 15
7 17.5
19.7 41


28 70
8 20
6 15
2 5
6 15
1 2.5
5 12.5
11 27.5
4 10
6 15
1 2.5
1 2.5


3.3 2.1

1.5 1.8
15 37.5


11 27.5


22 55

21 52.5


11.1 21.6
2 --
34 85

5 12.5
17 42.5
12 30












Table 4-3: First session averages of the F-T curve characteristics
Males (N=20) Females (N=20)
Injured Hand Uninjured Hand Injured Hand Uninjured Hand
Average SD Average SD Average SD Average SD


Peak Force (kg)
Maximal effort 29.90 15
Sub-pain effort 16.03 10
Sub-percent effort 15.60 8
Time-to-peak force (s)
Maximal effort 1.75 1
Sub-pain effort 0.96 0
Sub-percent effort 0.93 0
Slope of force-generation phase (V/s)
Maximal effort 1.690 1.
Sub-pain effort 0.883 0.'
Sub-nercent effort 0.863 0.


Slope of force-decay phase (V/s)
Maximal effort -0.030
Sub-pain effort -0.016
Sub-percent effort -0.022


.44
.86
.92

.08
.41
.34

343
762
548


0.064
0.037
0.028


39.67
21.01
20.22

1.44
1.07
0.94

1.973
0.930
1.221

-0.043
-0.030
-0.032


10.50
9.92
9.20

0.58
0.44
0.36

1.061
0.602
0.848

0.043
0.042
0.036


20.14
10.88
10.43

1.35
1.12
0.86

0.936
0.412
0.522

-0.024
-0.015
-0.020


10.22
7.15
6.39

0.63
0.42
0.33

0.589
0.271
0.357

0.019
0.015
0.014


26.87
13.31
14.49

1.29
1.10
0.99

1.354
0.626
0.631

-0.046
-0.029
-0.027


8.86
7.50
7.66

0.55
0.45
0.34

0.710
0.407
0.367

0.023
0.029
0.028












Table 4-4: Second session averages of the F-T curve characteristics
Males (N=20) Females (N=20)
Injured Hand Uninjured Hand Injured Hand Uninjured Hand
Average SD Average SD Average SD Average SD


Peak Force (kg)
Maximal effort 29.68 15
Sub-pain effort 16.76 9
Sub-percent effort 16.95 8
Time to peak force (s)
Maximal effort 1.57 1
Sub-pain effort 0.92 0
Sub-percent effort 0.85 0
Slope of force-generation phase (V/s)
Maximal effort 1.622 1.
Sub-pain effort 0.832 0.
Sub-nercent effort 1.021 0.t


Slope of force-decay phase (V/s)
Maximal effort -0.035
Sub-pain effort -0.030
Sub-percent effort -0.034


.21
.43
.90

.08
.30
.35

292
589
865


0.049
0.034
0.031


38.40
21.00
20.28

1.36
1.06
0.93

1.996
1.074
1.150

-0.051
-0.040
-0.037


10.75
9.27
7.26

0.54
0.44
0.30

1.530
0.872
0.738

0.045
0.039
0.028


20.35
11.26
10.75

1.27
1.04
0.95

0.949
0.474
0.526

-0.023
-0.016
-0.015


9.96
6.31
6.04

0.70
0.36
0.38

0.692
0.301
0.465

0.014
0.017
0.012


25.59
13.82
13.10

1.38
0.90
0.82

1.455
0.663
0.652

-0.040
-0.022
-0.022


8.44
7.98
6.13

0.70
0.28
0.25

0.925
0.417
0.344

0.023
0.022
0.019












Table 4-5: Four-Way ANOVA on the values of peak force
Sum of Mean
Source Squares DF Square F p-value
Between-Subjects
Gender 7446.76 1 7446.76 9.82 0.003*
Within-Subjects
Injured 2906.15 1 2906.15 41.77 0.0001*
Effort 14894.06 1 14894.06 183.69 0.0001*
Session 0.32 1 0.32 0.02 0.883
Injured x Gender 125.51 1 125.51 1.80 0.187
Effort x Gender 425.23 1 425.23 5.24 0.028*
Session x Gender 2.92 1 2.92 0.20 0.656
Injured x Effort 340.31 1 340.31 15.49 0.0001*
Injured x Session 31.45 1 31.45 5.05 0.031*
Effort x Session 13.47 1 13.47 1.32 0.257
Injured x Effort x Gender 11.31 1 11.31 0.51 0.477
Injured x Session x Gender 0.00 1 0.00 0.00 0.997
Effort x Session x Gender 1.10 1 1.10 0.11 0.745
Injured x Effort x Session 0.01 1 0.01 0.00 0.970
Injured x Effort x Session x Gender 4.97 1 4.97 0.70 0.409
Indicates significant differences at the p < 0.05 alpha level
Gender = Male vs. Female


Maximal vs. Submaximal according to imagined pain vs.
= Injured hand vs. Uninjured hand
= Session 1 vs. Session 2


Fifty percent of maximal effort


Effort =
Injured
Session












Table 4-6: Three-Way ANOVA on first session values of the peak force
Sum of Mean
Source Squares DF Square F p-value
Between- Subjects
Gender 3577.37 1 3577.37 9.01 0.005*
Within-Subjects
Injured 1771.10 1 1771.10 39.82 0.0001*
Effort 7901.72 1 7901.72 156.37 0.0001*
Injured x Gender 62.65 1 62.65 1.41 0.243
Effort x Gender 234.76 1 234.76 4.65 0.038*
Injured x Effort 168.33 1 168.33 9.57 0.004*
Injured x Effort x Gender 0.64 1 0.64 0.04 0.849
Indicates significant differences at the p < 0.05 alpha level
Gender = Male vs. Female
Effort = Maximal vs. Submaximal according to imagined pain vs. Fifty percent of maximal effort
Injured = Injured hand vs. Uninjured hand












Table 4-7: Three-Way ANOVA on second session values of the peak force
Sum of Mean
Source Squares DF Square F p-value
Between- Subjects
Gender 3872.31 1 3872.31 10.31 0.003*
Within-Subjects
Injured 1166.49 1 1166.49 37.24 0.0001*
Effort 7005.81 1 7005.81 171.99 0.0001*
Injured x Gender 62.86 1 62.86 2.01 0.165
Effort x Gender 191.56 1 191.56 4.70 0.036*
Injured x Effort 172.00 1 172.00 14.95 0.0001*
Injured x Effort x Gender 15.64 1 15.64 1.36 0.251
Indicates significant differences at the p < 0.05 alpha level
Gender = Male vs. Female
Effort = Maximal vs. Submaximal according to imagined pain vs. Fifty percent of maximal effort
Injured = Injured hand vs. Uninjured hand












Table 4-8: Four-Way ANOVA on the values of time-to-peak force
Sum of Mean
Source Squares DF Square F p-value
Between-Subjects
Gender 0.42 1 0.42 0.33 0.570
Within-Subjects
Injured 0.07 1 0.07 0.37 0.545
Effort 21.50 1 21.50 58.14 0.0001*
Session 0.45 1 0.45 1.93 0.173
Injured x Gender 0.01 1 0.01 0.04 0.845
Effort x Gender 0.77 1 0.77 2.09 0.157
Session x Gender 0.00 1 0.00 0.01 0.904
Injured x Effort 0.38 1 0.38 1.72 0.197
Injured x Session 0.00 1 0.00 0.00 0.993
Effort x Session 0.01 1 0.01 0.04 0.842
Injured x Effort x Gender 0.53 1 0.53 2.37 0.132
Injured x Session x Gender 0.11 1 0.11 0.97 0.331
Effort x Session x Gender 0.09 1 0.09 0.32 0.577
Injured x Effort x Session 0.27 1 0.27 1.64 0.207
Injured x Effort x Session x Gender 0.20 1 0.20 1.20 0.281
Indicates significant differences at the p < 0.05 alpha level
Gender = Male vs. Female
Effort = Maximal vs. Submaximal according to imagined pain vs. Fifty percent of maximal effort
Injured = Injured hand vs. Uninjured hand
Session = Session 1 vs. Session 2












Table 4-9: Three-Way ANOVA on the first session values of time-to-peak force
Sum of Mean
Source Squares DF Square F p-value
Between-Subj ects
Gender 0.25 1.00 0.25 0.32 0.572
Within-Subj ects
Injured 0.03 1.00 0.03 0.24 0.629
Effort 11.25 1.00 11.25 33.64 0.0001*
Injured x Gender 0.09 1.00 0.09 0.63 0.434
Effort x Gender 0.69 1.00 0.69 2.08 0.158
Injured x Effort 0.65 1.00 0.65 2.95 0.094
Injured x Effort x Gender 0.04 1.00 0.04 0.18 0.672
Indicates significant differences at the p < 0.05 alpha level
Gender = Male vs. Female
Effort = Maximal vs. Submaximal according to imagined pain vs. Fifty percent of maximal effort
Injured = Injured hand vs. Uninjured hand












Table 4-10: Four-Way ANOVA on the slopes of the force-generation phase
Sum of Mean
Source Squares DF Square F p-value
Between-Subj ects
Gender 30.56 1 30.56 6.99 0.012*
Within-Subjects
Injured 7.49 1 7.49 18.61 0.0001*
Effort 36.28 1 36.28 52.06 0.0001*
Session 0.12 1 0.12 0.37 0.548
Injured x Gender 0.01 1 0.01 0.03 0.854
Effort x Gender 0.55 1 0.55 0.78 0.382
Session x Gender 0.01 1 0.01 0.03 0.869
Injured x Effort 0.92 1 0.92 5.77 0.021*
Injured x Session 0.02 1 0.02 0.07 0.786
Effort x Session 0.00 1 0.00 0.01 0.916
Injured x Effort x Gender 0.34 1 0.34 2.10 0.155
Injured x Session x Gender 0.00 1 0.00 0.00 0.965
Effort x Session x Gender 0.06 1 0.06 0.30 0.588
Injured x Effort x Session 0.19 1 0.19 1.26 0.268
Injured x Effort x Session x Gender 0.08 1 0.08 0.52 0.476
Indicates significant differences at the p < 0.05 alpha level
Gender = Male vs. Female
Effort = Maximal vs. Submaximal according to imagined pain vs. Fifty percent of maximal effort
Injured = Injured hand vs. Uninjured hand
Session = Session 1 vs. Session 2












Table 4-11: Three-Way ANOVA on the first session slopes of the force-generation phase
Source Sum of Squares DF Mean F p-value
Square
Between-Subjects
Gender 15.80 1 15.80 8.37 0.006*
Within-Subjects
Injured 3.40 1 3.40 10.00 0.003*
Effort 18.43 1 18.43 55.77 0.0001*
Injured x Gender 0.00 1 0.00 0.01 0.907
Effort x Gender 0.48 1 0.48 1.46 0.234
Injured x Effort 0.14 1 0.14 0.88 0.355
Injured x Effort x Gender 0.37 1 0.37 2.36 0.133
* Indicates significant differences at the p < 0.05 alpha level
Gender = Male vs. Female
Effort = Maximal vs. Submaximal according to imagined pain vs. Fifty percent of maximal effort
Injured = Injured hand vs. Uninjured hand












Table 4-12: Four-Way ANOVA on the slopes of the force-decay phase


Sum of
Squares


Source
Between-Subjects


Gender 0.00855 1
Within-Subjects
Injured 0.01665 1
Effort 0.00860 1
Session 0.00082 1
Injured x Gender 0.00004 1
Effort x Gender 0.00031 1
Session x Gender 0.00484 1
Injured x Effort 0.00225 1
Injured x Session 0.00035 1
Effort x Session 0.00000 1
Injured x Effort x Gender 0.00011 1
Injured x Session x Gender 0.00003 1
Effort x Session x Gender 0.00007 1
Injured x Effort x Session 0.00002 1
Injured x Effort x Session x Gender 0.00019 1
* Indicates significant differences at the p < 0.05 alpha level
Gender = Male vs. Female


DF Mean Square


0.00855 1.25


0.01665
0.00860
0.00082
0.00004
0.00031
0.00484
0.00225
0.00035
0.00000
0.00011
0.00003
0.00007
0.00002
0.00019


32.56
8.38
1.60
0.07
0.30
9.47
4.03
0.72
0.00
0.20
0.07
0.17
0.05
0.45


Effort = Maximal vs. Submaximal according to imagined pain vs. Fifty percent of maximal effort
Injured = Injured hand vs. Uninjured hand
Session = Session 1 vs. Session 2


p-value


0.271


0.0001*
0.006*
0.213
0.788
0.587
0.004*
0.052
0.401
0.957
0.655
0.795
0.685
0.821
0.507












Table 4-13: Three-Way ANOVA on the first session slopes of the force-decay phase
Sum of Mean
Source Squares DF Square F p-value
Between-Subjects
Gender 0.00026 1 0.00026 0.06239 0.804
Within-Subjects
Injured 0.01091 1 0.01091 15.96264 0.0001*
Effort 0.00440 1 0.00440 5.62535 0.023*
Injured x Gender 0.00007 1 0.00007 0.10293 0.750
Effort x Gender 0.00004 1 0.00004 0.05665 0.813
Injured x Effort 0.00091 1 0.00091 1.93942 0.172
Injured x Effort x Gender 0.00030 1 0.00030 0.62882 0.433
* Indicates significant differences at the p < 0.05 alpha level
Gender = Male vs. Female
Effort = Maximal vs. Submaximal according to imagined pain vs. Fifty percent of maximal
effort
Injured = Injured hand vs. Uninjured hand












Table 4-14: Three-Way ANOVA on the second session slopes of the force-decay phase
Sum of Mean
Source Squares DF Square F p-value
Between-Subj ects
Gender 0.013126 1 0.013126 4.1709 0.048*
Within-Subjects
Injured 0.006091 1 0.006091 19.5771 0.0001*
Effort 0.004200 1 0.004200 6.5429 0.015*
Injured x Gender 0.000000 1 0.000000 0.0002 0.989
Effort x Gender 0.000330 1 0.000330 0.5142 0.478
Injured x Effort 0.001356 1 0.001356 2.6824 0.110
Injured x Effort x Gender 0.000005 1 0.000005 0.0093 0.924
Indicates significant differences at the p < 0.05 alpha level
Gender = Male vs. Female
Effort = Maximal vs. Submaximal according to imagined pain vs. Fifty percent of maximal
effort
Injured = Injured hand vs. Uninjured hand












Table 4-15: Average values for EMG amplitude
Males (N=20) Females (N=20)
Injured Hand Uninjured Hand Injured Hand Uninjured Hand
Average SD Average SD Average SD Average SD
First Session
Flexor EMG amplitude (iLV)
Maximal effort 59.57 35.38 69.43 34.52 47.61 24.51 62.56 34.76
Sub-pain effort 24.37 15.16 24.26 9.99 23.58 11.50 28.52 18.49
Sub-percent effort 23.08 8.92 22.52 8.83 22.10 12.93 26.00 17.15
Extensor EMG amplitude (iLV)
Maximal effort 126.94 85.38 150.12 106.90 101.51 82.76 101.93 44.58
Sub-pain effort 68.68 54.14 64.38 52.29 45.36 33.20 49.86 22.12
Sub-percent effort 56.86 33.80 62.08 39.04 41.78 30.68 42.60 21.33
Second Session
Flexor EMG amplitude (iLV)
Maximal effort 58.26 33.96 65.93 34.95 49.73 30.77 61.36 30.37
Sub-pain effort 25.00 10.63 23.47 10.25 24.21 13.25 29.55 23.82
Sub-percent effort 24.30 12.94 21.68 7.58 22.02 10.11 26.58 19.64
Extensor EMG amplitude (iLV)
Maximal effort 120.66 83.20 139.24 82.24 97.63 83.02 100.61 47.44
Sub-pain effort 62.78 48.38 64.86 46.32 48.13 37.53 45.47 23.72
Sub-percent effort 60.87 34.35 62.08 31.59 44.66 35.58 42.15 21.52














Table 4-16: Average values of EMG median frequency for the first session
Males (N=20) Females (N=20)
Injured Hand Uninjured Hand Injured Hand Uninjured Hand
Average SD Average SD Average SD Average SD


First second
Flexor Median Frequency (Hz)
Maximal effort
Sub-pain effort
Sub-percent effort
Extensor Median Frequency (Hz)
Maximal effort
Sub-pain effort
Sub-percent effort
Last second
Flexor Median Frequency (Hz)
Maximal effort
Sub-pain effort
Sub-percent effort
Extensor Median Frequency (Hz)
Maximal effort
Sub-pain effort
Sub-percent effort
Flexor Median Frequency Ratio (% initial MF)
Maximal effort
Sub-pain effort
Sub-percent effort
Extensor Median Frequency Ratio (% initial MF)
Maximal effort
Sub-pain effort
Sub-percent effort


110.46
110.13
109.18

132.74
130.59
134.84



107.56
112.54
111.81

132.79
135.11
135.63

97.75
103.10
102.25

100.43
103.61
100.59


15.60
17.80
13.26

14.24
12.43
13.22



16.03
16.82
17.48

18.87
16.51
15.33

2.05
2.67
1.75

3.31
2.23
1.31


120.62
115.54
117.25

141.75
137.58
139.86



111.33
119.19
122.98

137.42
142.84
145.44

92.22
103.36
105.02

96.95
103.79
103.96


10.49
11.90
10.64

14.39
13.12
16.34



13.98
13.36
12.53

16.24
15.63
18.38


116.52
115.21
116.10

136.86
137.96
136.16



113.64
119.36
117.95

131.45
138.25
138.43


7.06 97.84
7.64 104.11
7.08 102.27

5.77 96.02
5.14 100.40
4.71 101.88


10.45
13.57
15.61

14.20
12.97
15.71



9.83
14.92
12.60

15.56
13.88
15.41

7.93
12.40
9.08

5.09
7.34
5.83


116.77
116.28
114.96

138.52
137.18
137.72



115.30
119.37
118.68

133.37
139.82
141.19

98.95
102.95
103.66

96.18
101.99
102.55


12.35
11.96
12.12

11.38
11.85
10.19



12.16
11.31
10.81

14.95
12.72
12.41

7.20
6.75
7.78

5.87
4.81
5.60














Table 4-17: Average values of EMG median frequency for second session values
Males (N=20) Females (N=20)
Injured Hand Uninjured Hand Injured Hand Uninjured Hand
Average SD Average SD Average SD Average SD


First second
Flexor Median Power Frequency (Hz)
Maximal effort
Sub-pain effort
Sub-percent effort
Extensor Median Power Frequency (Hz)
Maximal effort
Sub-pain effort
Sub-percent effort
Last second
Flexor Median Power Frequency (Hz)
Maximal effort
Sub-pain effort
Sub-percent effort
Extensor Median Power Frequency (Hz)
Maximal effort
Sub-pain effort
Sub-percent effort
Flexor Median Frequency Ratio (% initial MF)
Maximal effort
Sub-pain effort
Sub-percent effort
Extensor Median Frequency Ratio (% initial MF)
Maximal effort
Sub-pain effort
Sub-percent effort


111.56
109.01
109.51

136.65
135.06
136.51



108.21
112.09
110.02

134.07
135.73
138.22

96.88
103.05
99.54

98.72
100.72
101.34


14.68
15.15
21.29

17.27
16.34
17.36



17.79
17.78
25.28

17.80
18.06
18.72

7.31
10.14
10.69

13.26
9.51
6.76


121.27
118.20
119.15

143.57
143.12
144.70



112.32
119.65
121.76

139.25
144.78
145.03

92.75
101.43
102.46

97.13
101.11
100.33


11.39
13.11
10.72

14.54
13.25
14.62



13.70
12.88
13.29

16.15
16.87
14.57


116.04
117.03
115.40

138.26
138.58
138.99



113.52
117.36
118.88

133.16
140.15
141.66


8.59 98.21
6.01 100.77
9.69 103.84

7.20 96.27
6.82 101.06
5.14 101.93


10.44
14.43
14.95

14.59
13.44
13.84



9.01
12.83
10.82

16.59
15.85
14.72

7.96
8.96
9.79

5.41
4.68
3.70


117.35
113.73
115.31

138.47
138.47
137.68



115.40
118.86
119.55

133.82
137.87
141.17

98.55
104.80
104.16

96.68
99.44
102.55


10.76
9.66
12.65

10.66
10.16
10.41



10.82
11.44
11.43

12.95
15.66
12.13

6.85
8.92
8.05

6.35
6.81
4.47












Table 4-18: Four-Way ANOVA on flexor EMG amplitude
Sum of Mean p-
Source Squares DF Square F value
Between-Subj ects
Gender 271.25 1 271.25 0.09 0.772
Within-Subj ects
Injured 2805.68 1 2805.68 4.72 0.036*
Effort 102376.24 1 102376.24 102.07 0.000*
Session 1.87 1 1.87 0.02 0.877
Injured x Gender 885.46 1 885.46 1.49 0.230
Effort x Gender 1716.29 1 1716.29 1.71 0.199
Session x Gender 48.88 1 48.88 0.63 0.432
Injured x Effort 1885.15 1 1885.15 7.16 0.011*
Injured x Session 52.08 1 52.08 2.52 0.121
Effort x Session 28.51 1 28.51 0.56 0.457
Injured x Effort x Gender 8.49 1 8.49 0.03 0.858
Injured x Session x Gender 9.67 1 9.67 0.47 0.498
Effort x Session x Gender 39.26 1 39.26 0.78 0.384
Injured x Effort x Session 21.13 1 21.13 0.48 0.492
Injured x Effort x Session x Gender 18.79 1 18.79 0.43 0.517
Indicates significant differences at the p < 0.05 alpha level
Gender = Male vs. Female
Effort = Maximal vs. Submaximal according to imagined pain vs. Fifty percent of maximal effort


Injured:
Session


= Injured hand vs. Uninjured hand
= Session 1 vs. Session 2












Table 4-19: Three-Way ANOVA on first session values of the flexor EMG amplitude
Sum of Mean p-
Source Squares DF Square F value
Between-Subj ects
Gender 275.21 1 275.21 0.18 0.674
Within-Subj ects
Injured 1811.12 1 1811.12 6.29 0.017*
Effort 52910.89 1 52910.89 91.35 0.000*
Injured x Gender 355.01 1 355.01 1.23 0.274
Effort x Gender 1137.34 1 1137.34 1.96 0.169
Injured x Effort 1152.70 1 1152.70 7.81 0.008*
Injured x Effort x Gender 1.01 1 1.01 0.01 0.935
Indicates significant differences at the p < 0.05 alpha level
Gender = Male vs. Female
Effort = Maximal vs. Submaximal according to imagined pain vs. Fifty percent of maximal
effort
Injured = Injured hand vs. Uninjured hand












Table 4-20: Four-Way ANOVA on extensor EMG amplitude
Sum of Mean
Source Squares DF Square F p-value
Between-Subj ects
Gender 64338.83 1 64338.83 2.92 0.096
Within-Subj ects
Injured 2045.07 1 2045.07 0.45 0.507
Effort 345249.89 1 345249.89 61.35 0.0001*
Session 439.71 1 439.71 1.00 0.323
Injured x Gender 1501.12 1 1501.12 0.33 0.569
Effort x Gender 5212.78 1 5212.78 0.93 0.342
Session x Gender 167.83 1 167.83 0.38 0.540
Injured x Effort 2042.04 1 2042.04 3.13 0.085
Injured x Session 86.64 1 86.64 0.51 0.480
Effort x Session 1037.29 1 1037.29 5.89 0.020*
Injured x Effort x Gender 1144.00 1 1144.00 1.75 0.193
Injured x Session x Gender 27.07 1 27.07 0.16 0.693
Effort x Session x Gender 229.29 1 229.29 1.30 0.261
Injured x Effort x Session 35.09 1 35.09 0.19 0.662
Injured x Effort x Session x Gender 52.27 1 52.27 0.29 0.594
Indicates significant differences at the p < 0.05 alpha level
Gender = Male vs. Female


Effort = Maximal vs. Submaximal according to imagined pain vs.
Injured = Injured hand vs. Uninjured Hand
Session = Session 1 vs. Session 2


Fifty percent of maximal effort












Table 4-21: Three-Way ANOVA the on first session extensor EMG amplitude
Mean
Source Sum of Squares DF Square F p-value
Between-Subj ects
Gender 35539.32 1 35539.32 2.99 0.092
Within-Subjects
Injured 1486.78 1 1486.78 0.61 0.439
Effort 192067.72 1 192067.72 55.76 0.0001*
Injured x Gender 562.52 1 562.52 0.23 0.633
Effort x Gender 3814.31 1 3814.31 1.11 0.299
Injured x Effort 770.88 1 770.88 1.99 0.167
Injured x Effort x Gender 842.68 1 842.68 2.17 0.149
Indicates significant differences at the p < 0.05 alpha level


Gender
Effort =
Injured


= Male vs. Female
Maximal vs. Submaximal according to imagined pain vs. Fifty percent of maximal effort
= Injured hand vs. Uninjured Hand












Table 4-22: Three-Way ANOVA on the second session extensor EMG amplitude
Mean
Source Sum of Squares DF Square F p-value
Between-Subj ects
Gender 28967.34 1 28967.34 2.74 0.106
Within-Subjects
Injured 644.93 1 644.93 0.28 0.599
Effort 154219.45 1 154219.45 65.37 0.0001*
Injured x Gender 965.67 1 965.67 0.42 0.520
Effort x Gender 1627.76 1 1627.76 0.69 0.411
Injured x Effort 1306.25 1 1306.25 2.93 0.095
Injured x Effort x Gender 353.59 1 353.59 0.79 0.379
Indicates significant differences at the p < 0.05 alpha level


Gender
Effort =
Injured


= Male vs. Female
Maximal vs. Submaximal according to imagined pain vs. Fifty percent of maximal effort
= Injured hand vs. Uninjured Hand












Table 4-23: Four-Way ANOVA on the flexor EMG median frequency ratio
Sum of Mean
Source Squares DF Square F p-value
Between-Subj ects
Gender 343.13 1 343.13 1.26 0.269
Within-Subjects
Injured 0.41 1 0.41 0.00 0.967
Effort 3133.33 1 3133.33 36.68 0.0001*
Session 41.25 1 41.25 1.14 0.292
Injured x Gender 107.52 1 107.52 0.44 0.510
Effort x Gender 108.02 1 108.02 1.26 0.268
Session x Gender 55.06 1 55.06 1.52 0.225
Injured x Effort 304.34 1 304.34 7.08 0.011*
Injured x Session 7.63 1 7.63 0.22 0.642
Effort x Session 10.04 1 10.04 0.41 0.525
Injured x Effort x Gender 284.58 1 284.58 6.62 0.014*
Injured x Session x Gender 11.37 1 11.37 0.33 0.570
Effort x Session x Gender 62.19 1 62.19 2.56 0.118
Injured x Effort x Session 2.95 1 2.95 0.11 0.745
Injured x Effort x Session x Gender 1.18 1 1.18 0.04 0.837
Indicates significant differences at the p < 0.05 alpha level
Gender = Male vs. Female
Effort = Maximal vs. Submaximal according to imagined pain vs. Fifty percent of maximal effort
Injured = Injured hand vs. Uninjured hand
Session = Session 1 vs. Session 2












Table 4-24: Three-Way ANOVA on the first session values of flexor EMG median frequency ratio
Sum of Mean


Source Squares DF Square F p-va]
Between-Subj ects
Gender 61.64 1 61.64 0.43 0
Within-Subjects
Injured 2.25 1 2.25 0.02 0.S
Effort 1749.05 1 1749.05 30.27 0.00(
Injured x Gender 24.48 1 24.48 0.17 0.(
Effort x Gender 167.07 1 167.07 2.89 0.(
Injured x Effort 183.60 1 183.60 8.04 0.0(
Injured x Effort x Gender 161.23 1 161.23 7.06 0.01
* Indicates significant differences at the p < 0.05 alpha level
Gender = Male vs. Female
Effort = Maximal vs. Submaximal according to imagined pain vs. Fifty percent of maximal
effort
Injured = Injured hand vs. Uninjured hand


lue

.52

)01
)1*
382
)97
)7*
11*












Table 4-25: Four-Way ANOVA on the extensor EMG median frequency ratio
Sum of Mean
Source Squares DF Square F p-value
Between-Subj ects
Gender 114.58 1 114.58 0.47 0.499
Within-Subjects
Injured 0.08 1 0.08 0.00 0.981
Effort 1687.78 1 1687.78 26.77 0.0001*
Session 102.07 1 102.07 4.61 0.038*
Injured x Gender 13.25 1 13.25 0.09 0.760
Effort x Gender 145.26 1 145.26 2.30 0.137
Session x Gender 65.39 1 65.39 2.95 0.094
Injured x Effort 82.87 1 82.87 2.32 0.136
Injured x Session 23.51 1 23.51 1.31 0.259
Effort x Session 5.24 1 5.24 0.44 0.513
Injured x Effort x Gender 56.60 1 56.60 1.59 0.216
Injured x Session x Gender 0.47 1 0.47 0.03 0.873
Effort x Session x Gender 0.51 1 0.51 0.04 0.838
Injured x Effort x Session 54.02 1 54.02 5.40 0.026*
Injured x Effort x Session x Gender 44.56 1 44.56 4.46 0.041*
Indicates significant differences at the p < 0.05 alpha level
Gender = Male vs. Female
Effort = Maximal vs. Submaximal according to imagined pain vs. Fifty percent of maximal
effort
Injured = Injured hand vs. Uninjured Hand
Session = Session 1 vs. Session 2












Table 4-26: Three-Way ANOVA on the first session values of extensor EMG median frequency ratio
Sum of Mean


Source Squares DF Square F p-value
Between-Subj ects
Gender 176.55 1 176.55 1.33 0.256
Within-Subjects
Injured 10.41 1 10.41 0.14 0.710
Effort 940.59 1 940.59 21.16 0.0001*
Injured x Gender 9.34 1 9.34 0.13 0.725
Effort x Gender 64.31 1 64.31 1.45 0.236
Injured x Effort 135.36 1 135.36 5.07 0.030*
Injured x Effort x Gender 100.80 1 100.80 3.78 0.059
* Indicates significant differences at the p < 0.05 alpha level
Gender = Male vs. Female
Effort = Maximal vs. Submaximal according to imagined pain vs. Fifty percent of maximal
effort
Injured = Injured hand vs. Uninjured Hand












Table 4-27: Three-Way ANOVA on the second session values of extensor EMG median frequency ratio
Sum of Mean


Source Squares DF Square F p-value
Between-Subj ects
Gender 3.43 1 3.43 0.03 0.875
Within-Subjects
Injured 13.18 1 13.18 0.16 0.693
Effort 752.43 1 752.43 24.58 0.0001*
Injured x Gender 4.37 1 4.37 0.05 0.820
Effort x Gender 81.46 1 81.46 2.66 0.111
Injured x Effort 1.54 1 1.54 0.08 0.778
Injured x Effort x Gender 0.36 1 0.36 0.02 0.891
* Indicates significant differences at the p < 0.05 alpha level
Gender = Male vs. Female
Effort = Maximal vs. Submaximal according to imagined pain vs. Fifty percent of maximal
effort
Injured = Injured hand vs. Uninjured Hand












Table 4-28: Intraclass Correlation Coefficients for F-T curve
Injured hand
r-value


Peak Force
Maximal effort
Sub-pain effort
Sub-percent effort
Time-to-peak force
Maximal effort
Sub-pain effort
Sub-percent effort
Slope of force-generation phase
Maximal effort
Sub-pain effort
Sub-percent effort
Slope of force-decay phase
Maximal effort
Sub-pain effort
Sub-percent effort


characteristics
Uninjured hand
r-value


0.957
0.904
0.859

0.306
0.539
0.414

0.822
0.783
0.711

0.579
0.677
0.610


0.950
0.919
0.873

0.368
0.257
0.586

0.598
0.788
0.706

0.592
0.713
0.604









Table 4-29: Intraclass Correlation Coefficients for EMG properties
Injured Uninjured
hand hand
r-value r-value
Flexor EMG amplitude
Maximal effort 0.926 0.933
Sub-pain effort 0.824 0.874
Sub-percent effort 0.792 0.938
Extensor EMG amplitude
Maximal effort 0.967 0.930
Sub-pain effort 0.822 0.915
Sub-percent effort 0.917 0.914
Flexor MF-ratio
Maximal effort 0.702 0.740
Sub-pain effort 0.618 0.715
Sub-percent effort 0.298 0.777
Extensor MF-ratio
Maximal effort 0.893 0.710
Sub-pain effort 0.518 0.481
Sub-percent effort 0.496 0.535












Table 4-30: Summary of main effects of effort for force and EMG measures
Both Sessions First Session Second Session
F- p-
F-value p-value F-value p-value value value


F-T Curve
Peak force
Time-to-peak force
Slope of force-generation
phase
Slope of force-decay phase
EMG
Flexor amplitude
Extensor amplitude
Flexor MF-ratio
Extensor MF-ratio


183.68
58.14

52.06
8.38

102.07
61.35
36.68
26.77


0.0001
0.0001

0.0001
0.006

0.0001
0.0001
0.0001
0.0001


156.37
33.64

55.77
5.62

91.35
55.76
30.27
21.16


0.0001
0.0001

0.0001
0.023

0.0001
0.0001
0.0001
0.0001


171.99 0.0001


6.54



65.37

24.58


0.015



0.0001

0.0001













Table 4-31: Sensitivity and specificity of specific slope cutoff values for force-generation phase
Slope 0.1 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6


Males
Sensitivity 0.00
Specificity 1.00
Overall error
rate 1.00
Females
Sensitivity 0.00
Specificity 1.00
Overall error
rate 1.00
Table 4-31: Continued
Slope 1.7
Males
Sensitivity 0.90
Specificity 0.50
Overall error
rate 0.60
Females
Sensitivity 1.00
Specificity 0.15
Overall error
rate 0.85


0.00 0.20
1.00 0.95

1.00 0.85


0.05 0.10
1.00 1.00

0.95 0.90

1.8 1.9


0.90 0.95
0.50 0.40

0.60 0.65


1.00 1.00
0.15 0.10

0.85 0.90


0.30 0.35 0.45 0.45 0.50 0.55 0.55 0.60 0.60 0.60 0.65 0.70 0.85 0.85
0.85 0.80 0.80 0.75 0.75 0.70 0.60 0.55 0.55 0.55 0.55 0.55 0.55 0.50

0.85 0.85 0.75 0.80 0.75 0.75 0.85 0.85 0.85 0.85 0.80 0.75 0.60 0.65


0.35 0.50 0.60 0.70 0.75 0.75 0.85 0.90 0.95 0.95 0.95 0.95 1.00 1.00
0.95 0.85 0.85 0.60 0.60 0.55 0.45 0.35 0.25 0.20 0.15 0.15 0.15 0.15

0.70 0.65 0.55 0.70 0.65 0.70 0.70 0.75 0.80 0.85 0.90 0.90 0.85 0.85

2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0 3.1 3.2 3.3


0.95 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
0.25 0.25 0.25 0.25 0.20 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15

0.80 0.75 0.75 0.75 0.80 0.85 0.85 0.85 0.85 0.85 0.85 0.85 0.85 0.85


1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
0.10 0.10 0.05 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.90 0.90 0.95 0.95 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00













Table 4-31: Continued
Slope 3.4 3.5 3.6 3.7 3.8 3.9 4.0 4.1 4.2 4.3 4.4 4.5 4.6 4.7
Males


Sensitivity
Specificity
Overall error
rate
Females
Sensitivity
Specificity
Overall error
rate


1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.10 0.10 0.05 0.05 0.05 0.05 0.00

0.85 0.85 0.85 0.85 0.85 0.85 0.85 0.90 0.90 0.95 0.95 0.95 0.95 1.00


1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00












Table 4-32: Sensitivity and specificity values of specific slope cutoff values for force-decay phase
Slope -0.2 -0.18 -0.16 -0.14 -0.12 -0.1 -0.08 -0.06 -0.04 -0.02 0.07
Sensitivity 1 1 1 1 1 1 0.975 0.95 0.85 0.575 0
Specificity 0 0.025 0.025 0.025 0.075 0.075 0.075 0.125 0.275 0.425 1
Overall error rate 1 0.975 0.975 0.975 0.925 0.925 0.95 0.925 0.875 1 1












Table 4-33: Sensitivity and specificity of specific flexor MF-ratio cutoff values
Frequency Ratio
(%) 75 80 85 90 92 95 96 97 98 99 100
Sensitivity 1.00 0.98 0.95 0.95 0.93 0.88 0.83 0.78 0.73 0.63 0.58
Specificity 0.00 0.03 0.08 0.20 0.28 0.35 0.38 0.45 0.48 0.50 0.58
Overall error rate 1.00 1.00 0.98 0.85 0.80 0.78 0.80 0.78 0.80 0.88 0.85
Table 4-33: Continued
Frequency Ratio
(%) 102 103 105 107 108 110 115 120 125 130
Sensitivity 0.53 0.48 0.35 0.25 0.20 0.15 0.05 0.03 0.03 0.00
Specificity 0.78 0.80 0.85 0.88 0.90 0.93 0.98 0.98 1.00 1.00
Overall error rate 0.70 0.73 0.80 0.88 0.90 0.93 0.98 1.00 0.98 1.00












Table 4-34: Sensitivity and specificity of specific extensor MF-ratio cutoff values
Frequency Ratio
(%) 75 80 85 90 92 95 96 97 98 99 100 102 103 105 107 108 110 160
Sensitivity 1.00 1.00 1.00 0.98 0.93 0.90 0.85 0.75 0.68 0.68 0.63 0.45 0.38 0.25 0.18 0.15 0.08 0.00
Specificity 0.00 0.00 0.03 0.13 0.20 0.35 0.45 0.50 0.58 0.60 0.70 0.80 0.85 0.90 0.95 0.95 0.98 1.00
Overall error
rate 1.00 1.00 0.98 0.90 0.88 0.75 0.70 0.75 0.75 0.73 0.68 0.75 0.78 0.85 0.88 0.90 0.95 1.00












Table 4-35: Summary of sensitivity and specificity values for the Force and EMG measures
Area under
Optimal Overall the curve
Characteristic Cutoff Sensitivity Specificity error (%) (%)
Slope of force-
generation phase (V/s)
Males 1.5 0.85 0.55 60 76
Females 0.5 0.60 0.85 55 72
Flexor MF-ratio (%) 102 0.57 0.78 70 66.25
Extensor MF-ratio (%) 100 0.63 0.70 67 71














23-




22-




A
C 21-




20




" 19-
CUl-
E

LIJ

18-




17-


I I
First Second
Session

Figure 4-1: Interaction between session and injury for peak force


Uninjured hands




















Injured hands .




















First Session Second Session


Maximal effort
Sub-pain effort
Sub-percent effort


Injured Hand Uninjured Injured Hand Uninjured Injured Hand
Males Hand Males Females Hand Females Males


Uninjured
Hand Males


Injured Hand Uninjured
Females Hand Females


Figure 4-2: Average values of peak force for maximal and submaximal grip efforts


45.00


40.00


35.00



30.00



, 25.00


. 20.00
15.00
Li-




10.00



5.00



0.00
5.00



0.00





































|Sub-pain|




Males Females
Gender





















Subpain


ISub-percent


Injured hand


Injured


Unnjured hand


MaSul F ale



Gender


Injured hands


Injured


Unnjured hands


Figure 4-3: Significant interactions for peak force values. A) First session values for males and females. B) Second session values for

males and females. C) First session values for injured and uninjured hands. D) Second session values for injured and

uninjured hands.


ISub-pain



|Sub-percent |


















First Session


Source p-value
Gender 0.572
Injured 0.629
Effort 0. "')


* Maximal effort
O Sub-pain effort
E Sub-percent effort


- J A _J L L_~A~ L A


Injured Hand Males


Uninjured Hand Males


Injured Hand Females


Uninjured Hand Females


Figure 4-4: Average values of time-to-peak force















---- Max effort
----Sub-pain effort
------ Sub-percent effort
2
1.8
1.6
1.4
1.2
S1



0 ~---------------------------------------------A
0.8
0.6 _
0.4
0.2


Injured Uninjured

Injury

Figure 4-5: Interaction between effort and injury for slope of force-generation phase

















2.500





2.000





1.500

5.


1.000





0.500





0.000


Injured Hand Males


First Session

Source p-value
Gender 0.006

Inli.ie 1 i 1 I111
Fi. It i, ,.,


Uninjured Hand Males


Injured Hand Females


* Maximal effort
1 Sub-pain effort
1 Sub-percent effort


Uninjured Hand Females


Figure 4-6: Average values of slopes of force-generation phase
















Injury


-0.015


-0.02


-0.025


-0.03


-0.035


-0.04


-0.045


-0.05


Figure 4-7: Interaction between effort and injury for slope of force-decay phase














Sessions


First session


Second session


-- Males
-- Females


-0.04'


Figure 4-8: Interaction between session and gender for slope of force-decay phase


-0.015



-0.02



-0.025



-0.03



-0.035


II















Injured Hand Uninjured Hand Injured Hand Uninjured Hand Injured Hand Uninjured Hand Injured Hand Uninjured Hand
Males Males Females Females Males Males Females Females
0.000



-0.010 -



-0.020



-0.030



-0.040



-0.050



-0.060 Source p-value Source p-value
Gender 0.804 Gender 0.048
,J ,L ,[ I .. ...... I ,ii ,- I .. ......
Etft IT '' Efi IT "" 1' Maximale
-0.070 iihnin


First Session


Second Session u ll II
El Sub-percent effort


Figure 4-9: Average values of slopes of force-decay phase for maximal and submaximal grip efforts


effort
-ff rt
















I.-LLIIIIL C IVIQIl llllCI IVIc il. VI I I 1.L 3.C I.V l I I IcAvI E iEivilJ fliiiiiLu. C

70-





60-





-50-
c




S40-



LU
30-
3sub-pain|




20 Sub-percent


Injured hands Uninjured hands
Injury



Figure 4-10: Interaction between effort and injury for flexor EMG amplitude

















First Session

Source p-value
Gender 0.674


* Maximal effort
O Sub-pain effort
2 Sub-percent effort


70.00


" 60.00

2
o

. 50.00
E
-c-

. 40.00
E

2 30.00


20.00


10.00


0.00


Injured Hand Males


Injured
Effort


0.017
0.000


Injured x Effort 0008


------ -- -


Uninjured Hand Males


Injured Hand Females


-L


Uninjured Hand Females


Figure 4-11: Average values of flexor EMG amplitude for maximal and submaximal grip efforts


90.00


80.00


2~


























Max


















|Sub-pain|
----.-. --.












[Sub-percent
I I
First Second
Session


Figure 4-12: Interaction between effort and session for extensor EMG amplitude


120-




Cu
I ,-



100-






80-
E

LU
6.I




60-
















Second Session IB Maximal effort


Injured Hand Uninjured Hand
Males Males


Injured Hand Uninjured Hand Injured Hand Uninjured Hand
Females Females Males Males


Injured Hand Uninjured Hand
Females Females


Figure 4-13: Average values of extensor EMG amplitude


First Session


200.00


180.00


160.00


140.00


> 120.00
.2
E


E
100.00


S80.00
w


60.00


40.00


20.00


0.00














First Session


110.00


Source p-value
Gender 0 52
Injured 0.901
Effort 0.000


o Max effort
0 Sub-pain effort
I Sub-percent effort


Injured Hand Males


Uninjured Hand Males


Injured Hand Females
Injre-Had-emaes


Uninjured Hand Females


Figure 4-14: Average values of flexor MF-ratio


108.00


106.00


104.00


. 102.00


> 100.00
ci
0"
! 98.00
LL


96.00


94.00


92.00


90.00


J


L


I
















Males Females




ISub-pain |Sub-percent|

102 50- 10250-
6 |Sub-pain

10000- Subpercent 10000-


9750- 9750-


S9500- 9500-


9250- 9250-

Injured hands Uninjured hands Injured hands Uninjured hands
Injury A Injury B


Figure 4-15: Interaction between injury, effort, and gender for flexor MF-ratio. A) Estimated marginal means for males. B) Estimated
marginal means for females.


















O Max effort
] Sub-pain effort
0 Sub-percent effort


Second Session


Irr IT
Ii_ iir


I l 0.



I o.oooll


Injured Hand Uninjured Hand Injured Hand Uninjured Hand Injured Hand Uninjured Hand Injured Hand Uninjured Hand
Males Males Females Females Males Males Females Females


Figure 4-16: Average values of extensor MF-ratio


First Session


I I
ErI IT


0.0001


110.00


108.00


106.00


104.00


S102.00
tc


> 100.00


! 98.00
LL.

96.00


94.00


92.00


90.00


........





























102-



TO-






E
C)





O 100-



LI


98-







96-


Sub-percent




Sub-pain



















~- ^ |Max|


Injured hands


Uninjured hands


Injury


Figure 4-17: Interaction between injury and effort for the first session values of extensor EMG MF-ratio



















1.00


0.90


0.80


0.70


0.60


0.50


0.40


0.30


0.20 -


0.10


0.00
0.00


0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90
1-Specificity


Figure 4-18: ROC curve for slope of force-generation phase






















0.9






0.7 99 /












0.3
0.2 100
0102









0.1
-0Flexors
& Extensors

0 01 02 03 04 05 06 07 08 09
1 -Specificity


Figure 4-19: ROC curve for MF-ratio of forearm flexor and extensor muscles














CHAPTER 5
DISCUSSION

Upper extremity musculoskeletal disorders and injuries (UEMDs) may result in

compromised grip strength.1 Grip strength depends on the size, type, rate and number of

contracting muscle fibers.2 Reduced grip strength (weakness of grip) brought about by

injury may be due to either a reduction in the rate and number of contracting muscle

fibers3, or changes in muscle-fiber-type.3-7 Reduced grip strength may also occur in

presence of pain.8-10 Pain has been associated with decreases in: voluntary muscle

activity11-17, electromyographic (EMG) activity1' 12, motor unit discharge rates4' 15, -

motor neuron activity16, speed of force generation17, and endurance time.13

Maximal voluntary grip strength scores of people with UEMDs are used by

clinicians18 to determine the extent of injury19, disease process20, and progress in

rehabilitation.21 Grip strength is a valid indicator of musculoskeletal pathology and

recovery from such pathology only when people exert a sincere, maximal voluntary

effort.2227, 64-66, 83, 84, 102, 206 Weakness of grip strength may be brought about by an injury

but also could be due to exertion of submaximal effort. Submaximal effort may be

exerted during evaluation and treatment for a variety of reasons, either intentional or

unintentional. Unintentional submaximal effort may be exerted as a result of pain, fear of

pain and fear of re-injury. Intentional submaximal effort may be exerted for secondary

gain, such as money, benefits, or attention. To improve rehabilitative care, clinicians need

to be able to distinguish between a maximal voluntary grip effort (exerted by a client with

true weakness of grip) and a submaximal grip effort.









Force-time curve (F-T curve) characteristics28, 29, 55, 101, 102 and electromyographic

(EMG) properties3032 have been used to differentiate between maximal and submaximal

grip effort. Furthermore, the Force-Time Curve test (F-T Curve Test)102 has shown

promise in determining sincerity of effort. The F-T Curve Test includes the slopes of the

force-generation phase and the force-decay phase. So far, the F-T Curve Test has been

shown to be valid in healthy people.102 However, the validity of the F-T Curve Test has

not been examined in people with UEMDs. Therefore, the primary purpose of this

research project was to examine if the F-T Curve Test is valid in people with UEMDs.

Another purpose of this study was to examine other F-T Curve characteristics and EMG

properties are valid sincerity of effort measures in people with UEMDs.

We examined the ability of four F-T curve characteristics (peak force, time-to-peak

force, slope of force-generation phase, and slope of force-decay phase) and two EMG

properties (amplitude and median frequency ratio) to differentiate between maximal and

submaximal effort in people with UEMDs. A valid test has to first be reliable.92

Therefore, we examined the test-retest reliability of the various measures mentioned

above and found that they ranged from r = 0.3 to r = 0.97, with time-to-peak force having

the worst reliability. A valid test also means that it can differentiate between maximal and

submaximal effort. We found all six measures to be significantly different between

maximal and submaximal effort. However, to be clinically valid a test must be effective;

i.e. it should not misclassify many patients. We examined the effectiveness of the above

measures by identifying the optimal combination of sensitivity and specificity based on

the overall error rates, receiver operating characteristic (ROC) curve, and the area under









the ROC curve. Based on these analyses, the most valid and effective measure of

sincerity of effort in this study was the slope of the force-generation phase.

We found significant gender differences for the slope of the force-generation phase.

Therefore, we calculated separate ROC curves for males and females. The area under the

curve was greater for women (76%) than for men (72%), which indicated a greater ability

to discriminate between maximal and submaximal efforts for women. In a previous study

on healthy subjects, we found that the slope of the force-generation phase was more

effective for women than for men.102 For healthy women, the slope cutoff value of 1.2

V/s yielded the most optimal combination of sensitivity (0.8) and specificity (0.93) and

the lowest overall error rate (0.27). Also, the area under the curve was 92%.102 In

contrast, in the present study for women with UEMDs, the slope cutoff value of 0.5 V/s

yielded the most optimal combination of sensitivity (0.6) and specificity (0.85) and the

lowest overall error rate (0.55). Also, the area under the curve was 76%.

Despite the high reliability and significant differences between maximal and

submaximal efforts, the slope of the force-generation phase does not possess adequate

sensitivity and specificity values to be considered clinically valid. Based on previous

findings102, our hypothesis was that a sensitivity value of at least 80% combined with a

specificity value of at least 90% would be adequate clinically.

Force-Time Curve Characteristics

In the present study, we examined the ability of Force-Time curve (F-T curve)

characteristics to identify differences between maximal and submaximal effort. The F-T

curve graphically represents the force generated by a contracting muscle over a period of

time during a single strength trial.29 The vertical axis (Y-axis) represents change in force

of muscular contraction and the horizontal axis (X-axis) represents time of muscular









contraction. The typical F-T curve generated during a maximal voluntary isometric

contraction (MVIC) consists of three phases: 1) the force-generation phase or the

initiation phase that involves rapid or gradual development of force, followed by 2) the

peak force, and finally 3) the force-decay phase or the maintenance phase that involves a

steady rate of force that may decrease gradually over time indicating onset of fatigue.95 96,

210-212 The characteristics of the F-T curve Test that we examined were its peak force,

time-to-peak force as well as its slopes of the force-generation phase and force-decay

phase.

Some F-T curve characteristics, such as peak force and the slope of the force-

generation phase, have been found to change with strength training.98' 99'215, 229, 232

Strength training causes a muscle to undergo both rapid neural adaptations and gradual

hypertrophic adaptations. Increases in slope of the force-generation phase was associated

with a stronger neural drive, whereas, increases in peak force were primarily associated

with muscle hypertrophy.98' 99, 215,228, 229, 232

Differences between Maximal and Submaximal Effort

For the F-T curve characteristics, we expected 1) peak force to be greater for

maximal versus submaximal effort, 2) the time-to-peak force be greater for maximal

versus submaximal effort, and 3) slopes of the force-generation phase as well as force-

decay phase to be steeper for maximal versus submaximal effort. The findings of the

present study confirmed our hypotheses. We assigned submaximal effort using 2 different

ways. One was to instruct the subject to exert 50% of maximal effort. The other was to

exert submaximal effort based on an imagined level of pain. Because no significant

differences were found between the 2 submaximal efforts, we discuss both at once as

submaximal effort.









We found peak force to be greater, time-to-peak force to be longer, and slope of

force-generation phase to be steeper for maximal than for submaximal efforts. Although

peak force showed significant differences between maximal and submaximal efforts, it is

not a good measure of sincerity of effort because peak force indicates strength of a

contraction.98' 99,215,229, 232 Obviously, maximal effort is stronger than submaximal effort

and injured hand is weaker than uninjured hand. Time-to-peak force and the slope of

force-generation phase were found to be significantly different between maximal and

submaximal efforts and thus may be valid sincerity of effort tests. According to Kroemer

and Marras188, during maximal contractions, the central nervous system recruits all

available fibers at their highest firing rates. Conversely, during submaximal contractions,

continuous feedback signals control muscle output by modifying muscle fiber firing rate

and muscle fiber recruitment. Further, motor units fire synchronously during maximal or

near maximal efforts and fire asynchronously during submaximal efforts.233 Therefore, a

faster buildup of force occurs in maximal effort than submaximal effort.29' 101,188

Consequently, a maximal grip effort results in a greater peak force as well as a steeper

slope of force-generation phase. Also, greater peak force exerted during maximal effort

requires a longer time to reach this higher force, which results in a longer time-to-peak

force.

We also found the slope of force-decay phase to be steeper for maximal versus

submaximal effort. The differences in steepness of slope of force-decay phase can be

explained on the basis of the onset of fatigue.95' 96, 210-212 During maximal effort all motor

units are activated maximally and simultaneously. Consequently, when motor units

fatigue there are no "fresh" motor units that can be activated to take over.188' 189, 192-194 In









contrast, a submaximal force can be maintained by activation of fewer motor units that

fire asynchronously. In other words, as one motor unit is deactivated, another is being

activated and there is a reserve of "fresh" motor units to maintain the required

submaximal muscle tension. Therefore, there is a greater drop in force during maximal

effort than submaximal effort, which results in a steeper slope during the force-decay

phase of a maximal effort than of a submaximal effort.215' 368

Differences between the Injured and Uninjured Hands

For the F-T curve characteristics, we expected 1) the slope of the force-generation

phase to be steeper for uninjured versus injured hands, 2) the slope of the force-decay

phase to be steeper for injured versus uninjured hands, 3) peak force to be greater for

uninjured versus injured hands, and 4) time-to-peak force faster for uninjured versus

injured hands. Our findings for the slope of force-generation phase and peak force were

as expected but for time-to-peak force and slope of force-decay phase were not as

expected.

We found that the uninjured hands had steeper slopes for both the force-generation

phase and the force-decay phase. The injured hands exhibited gentler slopes of force-

generation phase probably because people with injuries have a slower rate of force

production.211 We expected a steeper slope during the force-decay phase for the injured

hands because people with musculoskeletal conditions experience greater fatigue and

inability to maintain force.369-373 However, we found that the uninjured hand showed

greater fatigue as indicated by a steeper slope during the force-decay phase. We propose

an explanation that is based on the assumption that people with injuries are protective of

their injured hand. They may experience pain, fear of pain, and/or fear of re-injury and

thus they may not exert true maximal voluntary contraction with their injured hand. The









interaction showing that uninjured hand force decreases during the second session but

injured hand increases supports this notion.

Gender Differences

We found that the slopes of force-generation phase were steeper for males than

females. Gender differences in the slope of force-generation phase may be due to

differences in muscle strength. Demura et al.207-209 found the F-T curve characteristics

(such as rate of force development) to be larger in stronger subjects208, and different

between males and females.209 Males also produce greater forces374-376 at faster rates.213

Gender differences in F-T curve characteristics may be due to larger muscle cross-

sectional area, higher concentration of anabolic hormones, and higher voluntary neural

activity of muscles.220, 377

For the slope of the force-decay phase, gender differences in slope were found only

for the second session and not for the first session. During the second session, we found

steeper slopes for males versus females. Gender differences in the slopes of the force-

decay phase may be due to gender differences in ability to maintain static grip force.

Yamaji et al.378 examined gender differences in ability to maintain grip force over six

minutes at different effort levels (20-100% of maximal voluntary contraction, MVC).The

authors reported that for efforts greater than 40% MVC, females maintained the effort

level for a longer time or at a higher force level. It is also possible that females are less

motivated to exert true maximal voluntary contraction and thus fatigue less as they have

more "fresh" motor units to recruit.

Electromyographic Properties

In the present study, we evaluated the electromyographic (EMG) properties of

forearm flexor and extensor muscles during isometric grip contractions. Both forearm









flexor and extensor muscles have been reported participate in isometric grip.359, 379-384 The

forearm flexors generate the gripping force, while the extensors stabilize the wrist.359, 383,

385 The properties of the EMG signal that we examined were its amplitude and median

frequency ratio (MF-ratio).

The amplitude of the EMG signal represents the magnitude of muscle activity. As

the force being generated by a muscle increases, it results in an increase in the EMG

amplitude. Increase in amplitude predominantly occurs due to increases in number of

active motor units.237'245 Also, an injury to the hand or arm decreases the EMG amplitude

by reducing the ability to produce force.

The frequency of the EMG signal represents how rapidly motor units are firing.

One method of describing the frequency of EMG signal involves using spectral analysis

to compute its median frequency (MF). The MF represents which motor units are

predominantly active. Fast twitch motor units dominate the higher frequency spectrum

and slow twitch dominate the lower frequency spectrum.255 260, 386 In other words,

muscles with a greater percentage of type II fibers or fast twitch motor units exhibit

greater values of MF.386 An increase in muscle force results in an increase in MF, and

therefore a shift of the power spectrum to a higher frequency region. That is, as

contraction level increases, larger motor units are recruited, and thus the power spectrum

shifts to a higher frequency region.248 251 A sustained forceful contraction often causes

muscular fatigue, which shifts the power spectrum to a lower frequency region.254-260 The

shift of the power spectrum to a lower frequency occurs due to motor unit de-recruitment.

The replacement of fast twitch motor units, which fatigue more quickly, with lower

frequency fatigue-resistant units causes a decrease in the higher frequency spectrum.255'









260 Fatigue has been shown to result in an increase in the lower frequency spectrum255 and

a decrease in the higher frequency spectrum,255 263,264 which translates into a shift of the

power spectrum towards the lower frequencies.260 Further, muscles with a greater

percentage of fast twitch motor units exhibit a greater reduction in MF.386 Thus, MF can

indicate an increase in muscular force when it shifts to the higher frequency spectrum and

muscular fatigue when it shifts to lower frequency spectrum.

The process of calculating median frequency (MF) involves a mathematical

conversion called Fourier Transformation, which identifies different frequencies forming

the EMG signal. The power of each frequency, i.e. the quantity of each frequency in the

signal, can also be identified using Fourier Transformation.120, 121 The plot of frequency

along the X-axis versus the power of the frequency along the Y-axis results in a graph

that is commonly termed as the power spectrum or the frequency spectrum.121 The MF

represents the frequency that divides the power spectrum into two regions with equal

power, i.e. the parts of the spectrum above and below the MF have equal distributions of
105
power.

In presence of pathological conditions, the decline in MF with development of

fatigue varies markedly with the type of motor units recruited.210' 387-389 A smaller shift to

the lower frequency region as a result of fatigue has been observed in people with

Amyotrophic lateral sclerosis210 and Parkinson's disease.390 The smaller shift has been

attributed to selective atrophy of type II (fast glycolytic, fast oxidative) muscle fibers,

which fatigue more quickly, and/or higher prevalence of type I (slow twitch oxidative)

muscle fibers.210 390 In contrast, a greater shift in MF associated with earlier onset of

fatigue has been observed in people with chronic heart failure391, peripheral arterial









disease392, and chronic neck pain.393 The greater shift in MF has been attributed to

selective atrophy of type I muscle fibers or hypertrophy of type II muscle fibers.391-393

Therefore, a greater or smaller shift in MF may be observed in people with a pathological

condition than healthy people as expressed by changes in muscle fiber type.

For the current study, we calculated the amplitude of the EMG signal as the

average rectified amplitude for the duration of the grip. We computed the MF for two

separate 1-second intervals, the first interval beginning at peak force (called the median

frequency of the first interval) and the second interval forming the last second of the

force-decay phase of the F-T curve (called the median frequency of the last interval). We

then computed the MF-ratio of the last to first intervals, to reflect changes in median

frequency between the beginning and end of the grip contraction. The MF-ratio

represents the extent of fatigue or de-recruitment of motor units.

Differences between Maximal and Submaximal Effort

For the EMG properties, we expected 1) the amplitude to be greater for maximal

versus submaximal effort, and 2) the MF-ratio to be smaller for maximal than for

submaximal effort. The findings of the present study confirmed our hypotheses.

The EMG amplitude was significantly greater for maximal versus submaximal

efforts. EMG amplitude is not a valid measure of sincerity of effort because it correlates

to the amount of force exerted by a muscle. Obviously, maximal effort results in greater

muscle force than submaximal effort and an injury may reduce the ability of a muscle to

produce force.

We examined the shift in the MF spectrum during 6-seconds of isometric grip

contraction. MF indicates which motor units are predominantly firing.255' 260 We found

both the flexor and extensor MF-ratios were smaller for maximal effort than for









submaximal effort. During maximal effort, the MF-ratio actually decreased probably due

to replacement of some of the fast twitch motor units with lower frequency fatigue-

resistant units.255 260 Conversely, during submaximal effort, the MF-ratio increased,

probably as a result of an increase in recruitment and firing rate of motor units. The

maintenance of a submaximal muscle force over 6 seconds gradually requires a greater

number of motor units or increased firing of already recruited motor units. As the

contraction persists, larger (and more) motor units are recruited, and thus the power

spectrum shifts to a higher frequency region.248 251 Therefore, a combination of both

recruitment and rate coding results in shift of the power spectrum to the higher frequency

region.248 MF-ratio has the potential to form a valid measure of sincerity of effort as it

indicates a shift of MF during a grip strength trial.

Differences between Injured and Uninjured Hands

For the EMG properties, we expected 1) the amplitude to be greater for uninjured

versus injured hands, and 2) the MF-ratio to be smaller for injured versus uninjured

hands. The findings of the present study did not confirm all our hypotheses.

Flexor EMG amplitude was significantly greater for the uninjured hands. Uninjured

hands can produce greater force as they have greater number of motor units available.

Therefore, uninjured hands have a greater EMG amplitude.245 However, extensor EMG

amplitude was unexpectedly not significantly different between the 2 hands. One reason

could be because of differences in diagnosis. But, we did not collect information on

diagnosis. Therefore, we are not certain regarding the cause of no difference in extensor

amplitude.

Regarding MF-ratio, the interaction effects of injury were significant but the main

effects were not significant. The decrease in flexor MF-ratio between submaximal and









maximal efforts was greater for uninjured vs. injured hands in males but not in females.

Also, the decrease in extensor MF-ratio between submaximal and maximal efforts was

greater for uninjured vs. injured hands. It was unexpected that the decrease in MF-ratio

was greater for the uninjured hand than the injured hand. The difference in MF-ratio

between injured and uninjured hands may be present because subjects did not exert their

maximal voluntary contraction with their injured hands as a protective mechanism. A

smaller shift to the lower frequency as result of fatigue has been attributed to selective

atrophy of type II (fast glycolytic, fast oxidative) muscle fibers and/or higher prevalence

of type I (slow twitch oxidative) muscle fibers.210' 390 However, our study participants had

a diverse group of musculoskeletal conditions. It is not clear if these conditions resulted

in selective atrophy of type II fibers and/or higher prevalence of type I fibers. Therefore,

a smaller decrease in MF-ratio with injured hands most likely occurred because people

with injuries may be protective and not exert maximal voluntary contraction with their

injured hands.

Gender Differences

Gender differences existed for flexor MF-ratio but not for flexor amplitude,

extensor amplitude, and extensor MF-ratio. For flexor MF-ratio, the interaction effect of

gender was significant but the main effect was not significant. The decrease in flexor

MF-ratio between submaximal and maximal efforts was greater for uninjured vs. injured

hands in males but not in females. In other words, males fatigued more with the uninjured

hands, whereas, females fatigued the same with injured as well as uninjured hands. The

cause of these differences is not clear. It is possible that females are less motivated to

exert true maximal voluntary effort and thus fatigue to the same extent with injured and

uninjured hands, as they have more "fresh" motor units to recruit. In contrast, it seems









that males are protective of their injured hands. Males may experience pain, fear of pain,

and/or fear of re-injury and thus may not exert true maximal voluntary effort with their

injured hand and thus fatigue less with their injured hands.

On examining maximal effort exerted with the uninjured hands, we found that the

flexor MF-ratios were smaller for males versus females. This difference in uninjured

hands suggests an existence of gender differences in forearm flexor muscle fatigability,

i.e. males fatigue more than females. Gender differences in elbow flexor fatigability have

been related to the level of absolute force exerted during an isometric contraction.394

Hunter et al.394 found that women had longer endurance times, which indicates less

fatigability, because the maximal voluntary contraction force was smaller for females.

Indeed, in our study, greater peak forces with uninjured hands were exerted by males

versus females. Therefore, gender differences in fatigability of forearm flexor muscles

during maximal isometric grips seem to be related to force exerted during a maximal

voluntary contraction.

Force-Decay Phase

When examining the region of the F-T curve from peak force to the end of

contraction, we found that the changes in the slope of the force in the slope of the force-

decay phase corresponded to the changes in EMG signal as expressed by the flexor MF-

ratio. We found steeper slopes of force-decay phase and smaller MF-ratios for 1)

maximal versus submaximal effort, 2) uninjured versus injured hands, and 3) males

versus females. Steeper slopes and smaller flexor MF-ratios for maximal versus

submaximal effort may be explained on the basis of differences in onset of fatigue, which

has been associated with ability of a muscle to maintain force95 96, 210-212 as well as shift

in the median frequency to a lower frequency region.254-260 Further, steeper slopes and









smaller MF-ratios for uninjured versus injured hands suggest that uninjured hands

fatigued more than injured hands. One possible explanation is that people exert less effort

with their injured hands as a protective mechanism and therefore experience less fatigue.

Furthermore, steeper slopes and smaller flexor MF-ratios for males versus females

suggest that males fatigue more than females, which could be because females are better

able to maintain forces than males.378 It is also possible that females are less motivated to

exert true maximal voluntary contraction and thus fatigue less as they have more "fresh"

motor units to recruit.

Reliability and Validity

The usefulness of an assessment depends on its reliability, i.e., its ability to

measure an attribute or behavior consistently and free of error. Test-retest reliability of an

assessment indicates that an assessment obtains the same results with repeated

administrations of the test.92 According to Portney and Watkins92, "reliability coefficients

of measurements used for decision making or diagnosis of individuals need to be higher,

perhaps at least 0.9 to ensure valid interpretations of findings" (p. 65). Portney and

Watkins92 also suggest that an index greater than 0.9 is a guideline and not an absolute

standard. Further, as a general guideline, coefficients below 0.5 represent poor reliability,

coefficients from 0.5 to 0.75 represent moderate reliability, and coefficients above 0.75

represent good reliability.92

We found that the slope of the force-generation phase and the MF-ratios to have

acceptable levels of test-retest reliability. We expected the F-T curve characteristics and

EMG properties to consistently measure grip efforts as expressed by high test-retest

reliability (r>0.9). Based on the guidelines provided by Portney and Watkins92, we found

acceptable levels of reliability only for peak force, slope of force-generation phase, and









almost all EMG properties (r>0.7). Therefore, the slope of the force-generation phase, as

well as the flexor and extensor MF-ratios are sufficiently reliable measures of sincerity of

effort. A reliable sincerity of effort test is appropriate for clinical use only if it is valid.92

A valid sincerity of effort test should reveal significant differences between

maximal and submaximal efforts. We found significant differences between maximal and

submaximal effort for the following measures: time-to-peak force, slopes of the force-

generation phase and force-decay phase, and MF-ratios of EMG signal for flexors and

extensors.

A sincerity of effort assessment could be classified as a "diagnostic" test as its

purpose is to distinguish between the presence and absence of a feigned effort.102

According to Portney and Watkins92, "the validity of a diagnostic test is evaluated in

terms of its ability to accurately assess the presence and absence of the target condition"

(p. 93).92 To be valid, a diagnostic test must also be effective; i.e., possess acceptable

levels of sensitivity and specificity.92 In absence of adequate sensitivity and specificity

values, either a feigning individual may be incorrectly classified as sincere (low

sensitivity) or a sincere individual may be wrongly identified of being insincere (low

specificity). Specificity becomes more important than sensitivity when the risks

associated with misdiagnosing maximal effort are substantial.92 As results of a sincerity

of effort test impact continuation of rehabilitative services and workers compensation, it

is better to make a mistake in the direction of low sensitivity. Unfairly misclassifying a

sincere person as feigning can be very damaging to the individual and promote clinically

unfair decisions.28' 64-67, 83, 93, 102, 206 It seems less damaging to misclassify people giving a









deliberate feigned effort than to mistakenly classify a person giving a true maximal effort

f 80
as feigning.8

The sensitivity of a sincerity of effort test indicates the percentage of people who

were classified as exerting a submaximal effort and really exerted a submaximal effort

(true positives). The specificity indicates the percentage of people who were classified as

exerting a maximal effort and really exerted a maximal effort (true-negatives). An inverse

relationship exists between specificity and sensitivity: increasing the specificity (by

reducing the false-positive rate) results in a decrease in sensitivity and vise versa.

Therefore, when interpreting sensitivity and specificity results, one has to find a cutoff

value that yields the most optimal combination of sensitivity and specificity.27'28, 64-67, 83,

93, 102, 206

One method of finding the best combination of sensitivity and specificity involves

calculating the overall error rate by using the formula (1-sensitivity) + (1-specificity). In

other words, the overall error rate for a specific cutoff value represents the percentage of

combined errors (false-positive plus false-negatives). Therefore, the lowest overall error

rate identifies the cutoff value with the best combination of sensitivity and specificity.27'

28, 64-67, 83, 93, 102, 206 In the present study, we found that the overall error rate for the slope

of force-generation phase of the force-time curve ranged from 55% to 60%. We also

found the overall error rates for the MF-ratios ranged from 68% to 70%. These error rates

are too large to render these measures valid in detecting submaximal effort. These error

rates are just as bad as the error rates identified for the clinically relevant sincerity of

effort tests including the five rung grip test, coefficient of variation, and rapid exchange

grip test (Table 1-1). The error rates of the clinically relevant tests range from 47% to









69%.27, 65,93 They are also larger than the slopes of the force-time curve for healthy

people, which ranged from 7% to 33%.102 Therefore, the force-time curve characteristics

and the EMG properties of a 6-second grip exertion do not seem to provide an effective

means of distinguishing between maximal and submaximal efforts in people with upper

extremity injuries.

Another method of finding the best combination of sensitivity and specificity is

plotting a receiver operating characteristic (ROC) curve.110 The ROC curve is a plot of

false-positive rates (1-specificity) along the X-axis against true-positive rates (sensitivity)

along the Y-axis resulting from application of many arbitrarily chosen cutoff points.

Therefore, the ROC curve demonstrates the effectiveness of using different cutoff

values.110 That is, the ROC curve shows the accuracy of detecting sincerity of effort over

a range of cutoff values.27' 28, 64-67, 83, 93, 102, 206 For the present study, the cutoff values were

different values of the slopes of the force-generation phase, as well as flexor and extensor

MF-ratios. The ROC curve allows a researcher to decide which cutoff point is the most

beneficial for a certain diagnostic test.92 Using the ROC curve facilitates choosing a

cutoff value that is not arbitrary, but rather is based on the best combination of sensitivity

and specificity.92 When using the ROC curve to choose the best cutoff value for a

sincerity of effort test, it is better to err in the direction of lower sensitivity and higher

specificity so that a true maximal effort will not be misclassified as a submaximal

effort.27, 28, 64-67, 83, 93, 102, 206 Due to significant gender differences in the slope of the force-

generation phase, we generated separate ROC curves for males and females. ROC curves

were not generated for time-to-peak force due to low reliability and slope of force-decay

phase due to poor sensitivity and specificity values.









The ROC curve for the force-generation phase in the present study revealed that the

most optimal combination of specificity and sensitivity values for the injured hand of

men was at the slope cutoff value of 1.5 V/s. When using the slope of 1.5 V/s as a

criterion for determining sincerity of effort, 15% of the men who exerted submaximal

effort were incorrectly identified as exerting a maximal effort (1-sensitivity) and 45% of

the men who exerted maximal effort were mistakenly identified as exerting a submaximal

effort (1-specificity). The overall error rate for this criterion was 60%. In a previous

study, we also found the slope of 1.5 V/s as the optimal slope value among healthy men.

However, only 20% of the men who exerted submaximal effort were incorrectly

identified as exerting a maximal effort and only 13% of the men who exerted maximal

effort were mistakenly identified as exerting a submaximal effort. The overall error rate

for this criterion was 33%.102 The most optimal cutoff value for injured women in the

present study was 0.5 V/s. When using this cutoff value as a criterion for determining

sincerity of effort, 40% of those who exerted submaximal effort were misclassified as

exerting a maximal effort and 15% of those who exerted maximal effort were mistakenly

identified as exerting a submaximal effort. The overall error rate was 55%. With healthy

subjects, we found an optimal cutoff value of 1.2 V/s for women, which resulted in

incorrectly identifying 20% of those who exerted submaximal effort and 7% of those who

exerted maximal effort with an overall error rate was 27%.102 In the present study, the

overall error rate was almost double for both men and women with UEMDs than the error

rate reported for healthy people.102 This discrepancy between the two studies could be

explained by the probable protective response of injured people who may avoid exerting

maximal voluntary contraction due to pain, fear of pain and/or fear of re-injury. This is









supported by the significant session by injury interaction term for peak force (Figure 4-1),

which indicated that the uninjured hand force exertion decreased in the second session

while the injured hand's force exertion increased. This suggests that while the uninjured

hand experienced fatigue the injured hand did not. This further suggests that the force

exertion of the injured hand in the first session was not maximal.

The ROC curves generated for the EMG properties revealed similar effectiveness

for the median frequency ratio of flexor and extensor muscles. High overall error rates,

however, do not deem them to be good sincerity of effort tests. The ROC curve for the

flexor muscle MF-ratio revealed that the most optimal combination of specificity and

sensitivity values for the injured hand was for the MF-ratio cutoff value of 102%. When

using this cutoff as a criterion for determining sincerity of effort, 47% of the people who

exerted submaximal effort were incorrectly identified as exerting a maximal effort and

22% of the people who exerted maximal effort were mistakenly identified as exerting a

submaximal effort with an overall error rate of 70%.

The ROC curve for the extensor muscle MF-ratio revealed that the most optimal

combination of specificity and sensitivity values was at the MF-ratio cutoff value of

100%. When using this cutoff as a criterion for determining sincerity of effort, 37% of

the people who exerted submaximal effort were incorrectly identified as exerting a

maximal effort and 30% of the people who exerted maximal effort were mistakenly

identified as exerting a submaximal effort. The overall error rate for this criterion was

68%. Based on our findings, if a therapist used either the flexor or extensor MF-ratio as a

sincerity of effort test, he or she would incorrectly classify a large proportion of people









that they tested, which makes these measures inappropriate for clinical use as a sincerity

of effort test.

Using the ROC curve, the proportional area under the curve is calculated and used

as a measure of its discriminability, i.e., the ability of the test to discriminate between

maximal and submaximal efforts. The area under the ROC curve is an index of the degree

of separation (or overlap) between the distributions of true-positives (signal) and false-

positives (noise).110 An ideal diagnostic test has an area of 100%.92 The greater the area

under the curve, the better the ability to discriminate between maximal and submaximal

efforts. In the present study, the area under the curve for the force-generation phase was

greater for women (76%) than for men (72%), indicating greater ability to discriminate

between maximal and submaximal efforts in women. The area under the curve for the

flexor MF-ratio was 66.25% and extensor MF-ratio was 71%. In a previous study, the

proportional area under the ROC curve for the force-generation phase was 92.5% for men

and 92% for women.102 Therefore, the force-time curve characteristics and the EMG

properties of a 6-second grip exertion do not seem to provide an effective means of

distinguishing between maximal and submaximal efforts in people with upper extremity

injuries. This discrepancy between the two studies is that people with injuries are

protective of their injured hand. They may experience pain, fear of pain, and/or fear of re-

injury and thus they may not exert true maximal voluntary contraction with their injured

hand. The interaction showing that uninjured hand force decreases during the second

session but injured hand increases supports this notion.

Limitations

The limitations of this study include a heterogeneous patient population and only

measuring EMG activity of forearm flexor and extensor muscles. Our findings are based









on people with upper extremity musculoskeletal conditions, with various etiologies

including acute versus cumulative trauma. Further, the etiologies were different for men

and women. Almost all men experienced acute trauma, whereas, half of the women

experienced acute trauma and the other half experienced cumulative trauma. Therefore,

the etiology could be a confounding variable for this study. Furthermore, we did not

gather information regarding diagnosis because the injury-related information was

provided by the patients and thus could have been inaccurate. Also, due to technical

limitations, we were unable to measure the EMG activity of the intrinsic hand muscles,

which participate in grip and may have identified differences between maximal and

submaximal efforts. Future studies should focus on using a homogeneous patient

population and examining EMG activity of intrinsic hand muscles. It is possible that

testing EMG activity of intrinsic hand muscles as well as a homogeneous patient

population would yield different results.

Conclusions

We found significant differences in the time-to-peak force, slope of force-

generation phase as well as flexor and extensor MF-ratio between maximal and

submaximal efforts. However, we did not find acceptable combinations of sensitivity and

specificity for detecting sincerity of effort using these characteristics. Sensitivity and

specificity analysis revealed that the slope of the force-generation phase had the best

effectiveness, with the slope being a more effective assessment of sincerity of effort for

women than for men. These measures yielded overall error rates of 55% to 70%.

Therefore, these measures may not possess adequate sensitivity and specificity values to

justify their use in the clinic.














APPENDIX A
SAMPLE SIZE CALCULATION

The primary hypothesis identified for sample size calculation was that the force-

decay phase of a force-time curve (FT-curve) possesses a significantly steeper slope for

maximal effort than for submaximal effort. Table A-i lists the range of maximal and

submaximal effort slope values in the preliminary study.

The extreme slope values result in the largest and smallest differences:

Largest difference = 0.39 0.009 = 0.381 (A-1)
Smallest difference = 0.014 0.16 = -0.146 (A-2)
Largest possible range of differences (Range) = 0.381 (-0.146) = 0.527 (A-3)
We estimated the standard deviation (ad) by dividing the range by 4:

ad = Range/4 = 0.131 V/s (A-4)
We decided to keep the bound (B), i.e. the mean difference between maximal and
submaximal effort slope that is important to detect, as 0.1 V/s. We computed DELTA as:
DELTA = B/d = 0.1/0.131 = 0.76. (A-5)
We also chose a 2-tailed a = 0.05 and P = 0.2. Although our hypothesis is

unidirectional, we chose a 2-tailed a to be more conservative. Using these values, we

calculated the sample size as 24. However, to take into consideration attrition due to

pain/fatigue, we increased sample size to 30. Thus, 30 participants are required to detect

an average difference between maximal and submaximal effort slopes of 0.1 V/s, if that

difference truly exists.

Table A-i: Range of maximal and submaximal effort slope values
Maximal Effort Submaximal Effort
Minimum slope (V/s) 0.014 0.009
Maximum slope (V/s) 0.390 0.160














APPENDIX B
CORRELATION MATRIX FOR DYNAMOMETER CALIBRATION

Prior to beginning data collection, the calibration of the Jamar dynamometer was

checked on 3 consecutive days, which when averaged resulted in Pre-data collection

values. During the data collection phase, the calibration of the dynamometer was checked

weekly, resulting in values Wk. 1-17. The Pearson moment correlation coefficients (r)

between the Pre-data collection values and weekly values have been reported in Table B-

1.













Table B-l: Pearson correlation coefficients (r) between weekly voltage outputs obtained during the dynamometer calibration process

Pre-data
collection Wk. Wk. Wk. Wk. Wk. Wk. Wk. Wk. Wk. Wk. Wk. Wk. Wk. Wk. Wk. Wk. Wk.
values 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Pre-data
collection
values 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Wk. 1 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Wk. 2 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Wk. 3 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Wk. 4 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Wk. 5 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Wk. 6 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Wk. 7 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Wk. 8 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Wk. 9 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Wk. 10 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Wk. 11 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Wk. 12 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Wk. 13 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Wk. 14 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Wk. 15 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

Wk. 16 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Wk. 17 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00















APPENDIX C
DEMOGRAPHIC QUESTIONNAIRE

University of Florida
Department of Occupational Therapy


Demographic Questionnaire


Participant ID#:


Date Completed:


A. DEMOGRAPHIC INFORMATION
1. Please fill out or circle the correct answers) for the following questions about
yourself.


Year of birth?
Height?
Dominant hand/arm?


Inches
R L


b. Gender? M
d. Weight?
f. Injured hand/arm? R


B. INJURY-RELATED INFORMATION
1. What injury/condition are you in therapy for?




2. Do you think your condition was caused by work? (Please circle one option)
YES NO
If so, please explain:


3. Do you think your condition is aggravated by work? (Please circle one option)
YES NO
If so, please explain:


4. What do you think is the cause of your injury?


Time:











5. How long have you had this condition (in years and months)?
Years Months

6. How long have you been in therapy?
Weeks Times per week


7. Do you experience similar symptoms on the uninjured side? (Please circle one
option)
YES NO

8. Do you have any other condition that affects your hand grip? (Please circle one
option)
YES NO
If so, please explain:



9. Are you taking any pain medications?
YES NO

10. Do you have any limitations in Activities of Daily Living, such as dressing,
bathing, etc.?
YES NO


11. Have you had surgery for your injury? YES NO
If yes, did you benefit from the surgery? YES NO


12. Have you seen any improvement with therapy? YES NO


13. How successful is (was) your therapy? (Please circle one option)
a. Very successful
b. Successful (average)
c. Somewhat successful (less than average)
d. Not successful at all







191


14. What was the average range of pain over the last week on a scale of 0 to 10?
(Please cross the line below at the most appropriate point)

0 1 2 3 4 5 6 7 8 9 10


No pain Pain as bad as
it could be

15. What is the level of your current pain on a scale of 0 to 10? (Please cross the line
below at the most appropriate point)
0 1 2 3 4 5 6 7 8 9 10
I I I I I I I I I I I
No pain Pain as bad as
it could be


C. JOB-RELATED INFORMATION
5. What was your occupation when you were injured?




6. How long have you held that position?



7. Please describe your duties at that position.


8. Are you currently working?
Part-time


YES NO


If part-time, how many hours?


9. Are you performing the same job duties as prior to your injury?


YES
If no, describe changes.


Full-time











For Office Use Only
Order #














APPENDIX D
LETTER TO HEALTHCARE PROFESSIONALS WITH INCLUSION AND
EXCLUSION CRITERIA


Orit Shechtman, Ph.D., OTR/L
Associate Professor
College of Public Health and Health Professions
Department of Occupational Therapy
Box 100164
University of Florida
Department: (352) 273-6817
Office: (352) 273-6021
Fax: (352) 273-6024
E-mail: oshechtm@phhp.ufl.edu

[Date]

[Contact information of healthcare professional]

Dear [Name of Healthcare Professional]:

Subject: Study on association between pain and grip strength, [IRB #]

It was a pleasure to talk to you on [date]. Thank you for agreeing to help us recruit
participants for our study to identify an association between upper extremity pain and
grip strength. This study has been approved by the Institutional Review Board at the
University of Florida ([IRB Study #]). In order to be compliant with the Health Insurance
Portability and Accountability Act (HIPPA), you will need to use your clinical judgment
to identify individuals who meet the study criteria. Please use the following criteria to
identify study participants:

The participant should have experienced a unilateral traumatic or non-traumatic
injury involving the elbow or distally in the last 1 year but not necessarily
diagnosed in the last year.
The uninjured extremity should have not experienced any injury in the last 5
years and currently should not be experiencing any injury-related
signs/symptoms.
The participant should be aged between 18 and 65 years.
The participant should be able to perform 4 maximal and 8 submaximal grip
efforts with their injured extremity.









The participant should not be suffering from extreme pain. When asked verbally
the level of pain generally experienced by the participant on a scale of 0-10, the
participant should not experience pain intensity greater than 7.
The participant should not have any associated illness that would compromise
their grip strength.
The participant should not be taking any medications that would compromise
their grip strength.
The participant should not have impaired cognition.

Once you have identified a study participant, please brief the individual regarding the
study using the following standard instructions:

"A study is being conducted to identify how pain affects grip strength among
people with upper extremity musculoskeletal conditions. Your condition makes
you eligible to participate in this study. This study involves gripping a hand
dynamometer 12 times with each hand and rating your pain and perceived grip
effort. If you agree to participate, you will attend one session lasting
approximately 45 minutes and will be paid $20.00 for participating in the study.
Please let me know if you are interested in participating and I can provide you
with information to contact the research group."

If an individual agrees to participate in the study, please ask them to either contact me or
Bhagwant Sindhu (Phone Number: (352)273-6057, Email: bsindhu@phhp.ufl.edu).
Bhagwant is my doctoral student and he will be conducting this study. If we do not
answer the phone please ask them to leave a message with their name and phone number.
If you have questions regarding this study, please feel free to contact us at anytime.

We appreciate your help with this study.

Sincerely,


Orit Shechtman
















APPENDIX E
RANDOMIZATION ORDER AND SHEET


Order 1 Order 2 Order 3 Order 4
Effort 1 IM IS UM US
UM US IM IS
Effort 2 IS IS50 US US50
US US50 IS IS50
Effort 3 IS50 IM US50 UM
US50 UM IS50 IM




For submaximal effort, always start with
submaximal effort according to imagined pain


Injured-Submaximum
IS (Imagined Pain)
Injured-Submaximum
IS50 (50% max)

IM Injured--Maximum
US Uninjured-
Submaximum
(Imagined Pain)
Uninjured-
US50 Submaximum (50% max)

UM Uninjured--Maximum
Figure E-l: Randomization orders used in the study








196



Table E-1: Randomization sheet used in the study


Participant Pre/Post Dominant Injured
# Name Sex Order Date Time Therapy hand hand
1 Male 3
'.lale 1
'3 r.lale 2
4 Male 2
5 r.ldale 4
6 Male 4
r.lale 3
5 .Male 3
9 Male 3
10 r.lale 2
11 Male 1
1 2F.rale 2
1 r.lale 1
14 Male 4

1 Male 3
16 Male1
1- r.1ale 4
Male3

19 rF.ale 2
C0 Male1 4

21 Female 4
l_ Female 4
Female 2
"4 Female 1
-5 Female 4
26 Female 4

2T Female 3
2, Female 1
29 Female 3
30 Female 3
31 Female 2
32 Female 3
33 Female 1
34 Female 2
35 Female 1
36 Female 4
37 Female 2
3,F Female 2

39 Female 3
40 Female 1














APPENDIX F
CHECKLISTS USED DURING THE DATA COLLECTION PROCESS

During the data collection phase, we used checklists to assist us in following the

correct experimental procedure. To maintain blinding, we used two different checklists.

The checklist used by the research assistants indicated the level of grip effort being

exerted by the study participant (Figure F-l). In contrast, the checklist used by the test

administrator did not indicate the level of grip effort. The test administrator's checklist

was also used to note the length of the rest period between hand grips (Figure F-2).

























Participant #
Order 2
Session 1


Check
when done

Pain VAS Pain VAS Practice Practice Grip Effort Injured njed d Uninjured Uninjure Ijured Injure Uinjured Uninjured
Test- Participant Test- Grp Gnp Instructon Sub Pain Sub Pain Sub Pain Sub Pain Sub 50% Sul 50% Sub 50% Sub 50% Injured Injured Uninjured Uninjured
Task Forms Retest Setup Retest Uninjured Injured s 1 2 1 2 1 2 1 2 Max 1 Max 2 Ma Max2

i,-, i, :r r .. r .in.. I :. Instruction Instruction
Sub-tasks '. -: : ::, c. Cue Card Cue Card ue Card s Cue Card Cue Card Cueard s Ca e Card Cue Card Cue Card

'.i,,, i. -i .. .. I I. i -,] Er ,.
FI.- -i ...] ... l- :. I Grip2 Grip3 Grip4 Ginp5 Grip6 Grip7 Gp G rip 9 Grip10 Grip11 Grip12
rl.:n

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r ] I..' Scale -cae Scale Sale Scale c ale Scale Sale scale Scale Scale
II 1 Pain Pain Pin a Pain Pain Pain Pan Pain Pa in Pain
F. .T Scale Scale Scale Scale Scale Scale Scale Scale Scale Scale Scale
Time Time e ime Time Timmeme Time Time Time Time

Break


Check
when done
Task Forms
Sub tasks Payment

Session 2


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when done

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Participant f
Session 1

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Session 2

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Figure F-2: Checklist used by the test administrator


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APPENDIX G
DATA COLLECTION FORM


Participant ID#:


Order #: 1


Session 1
Practice 1

What is the level of your current pain on a scale of 0 to 10?


Pain

0 10
I I
Pain as bad as
No pain
it could be

Please mark a vertical line at a point that indicates the level of pain that you are
currently experiencing.










Time (between practice 1 and 2):






Practice 2


What is the level of your current pain on a scale of 0 to 10?


Pain

0 10


Pain as bad as
No pain
it could be

Please mark a vertical line at a point that indicates the level of pain that you are
currently experiencing.







202


Practice Trial Uninjured Hand


Pain


0 10


Pain as bad as
No pain
it could be

Please mark a vertical line at a point that indicates the level of pain that you are
currently experiencing.


Effort


0% 100%



No Grip Strongest
Force Grip Force

Please mark a vertical line at a point that indicates the level of effort you just exerted.











Practice Trial Sheet Injured Hand


Effort


0% 100%



No Grip Strongest
Force Grip Force

Please mark a vertical line at a point that indicates the level of effort you just exerted.


Pain


0 10

I I
Pain as bad as
No pain
it could be

Please mark a vertical line at a point that indicates the level of pain that you are
currently experiencing.










Injured- Maximum

Grip 1


Effort


0% 100%



No Grip Strongest
Force Grip Force

Please mark a vertical line at a point that indicates the level of effort you just exerted.


Pain


0 10

I I
Pain as bad as
No pain
it could be

Please mark a vertical line at a point that indicates the level of pain that you are
currently experiencing.










Injured- Maximum

Grip 2


Effort


0% 100%



No Grip Strongest
Force Grip Force

Please mark a vertical line at a point that indicates the level of effort you just exerted.


Pain


0 10

I I
Pain as bad as
No pain
it could be

Please mark a vertical line at a point that indicates the level of pain that you are
currently experiencing.







206


Uninjured- Maximum

Grip 1


Effort


0% 100%



No Grip Strongest
Force Grip Force

Please mark a vertical line at a point that indicates the level of effort you just exerted.


Pain


0 10

I I
Pain as bad as
No pain
it could be

Please mark a vertical line at a point that indicates the level of pain that you are
currently experiencing.







207


Uninjured- Maximum

Grip 2


Effort


0% 100%



No Grip Strongest
Force Grip Force

Please mark a vertical line at a point that indicates the level of effort you just exerted.


Pain


0 10

I I
Pain as bad as
No pain
it could be

Please mark a vertical line at a point that indicates the level of pain that you are
currently experiencing.










Injured- Submaximum (Pain)

Grip 1


Effort


0% 100%



No Grip Strongest
Force Grip Force

Please mark a vertical line at a point that indicates the level of effort you just exerted.


Pain


0 10

I I
Pain as bad as
No pain
it could be

Please mark a vertical line at a point that indicates the level of pain that you are
currently experiencing.







209


Injured- Submaximum (Pain)

Grip 2


Effort


0% 100%



No Grip Strongest
Force Grip Force

Please mark a vertical line at a point that indicates the level of effort you just exerted.


Pain


0 10

I I
Pain as bad as
No pain
it could be

Please mark a vertical line at a point that indicates the level of pain that you are
currently experiencing.







210


Uninjured- Submaximum (Pain)

Grip 1


Effort


0% 100%



No Grip Strongest
Force Grip Force

Please mark a vertical line at a point that indicates the level of effort you just exerted.


Pain


0 10

I I
Pain as bad as
No pain
it could be

Please mark a vertical line at a point that indicates the level of pain that you are
currently experiencing.










Uninjured- Submaximum (Pain)

Grip 2


Effort


0% 100%



No Grip Strongest
Force Grip Force

Please mark a vertical line at a point that indicates the level of effort you just exerted.


Pain


0 10

I I
Pain as bad as
No pain
it could be

Please mark a vertical line at a point that indicates the level of pain that you are
currently experiencing.







212


Injured- Submaximum (50% Maximum)

Grip 1


Effort


0% 100%



No Grip Strongest
Force Grip Force

Please mark a vertical line at a point that indicates the level of effort you just exerted.


Pain


0 10

I I
Pain as bad as
No pain
it could be

Please mark a vertical line at a point that indicates the level of pain that you are
currently experiencing.










Injured- Submaximum (50% Maximum)

Grip 2


Effort


0% 100%



No Grip Strongest
Force Grip Force

Please mark a vertical line at a point that indicates the level of effort you just exerted.


Pain


0 10

I I
Pain as bad as
No pain
it could be

Please mark a vertical line at a point that indicates the level of pain that you are
currently experiencing.










Uninjured- Submaximum (50% Maximum)

Grip 1


Effort


0% 100%



No Grip Strongest
Force Grip Force

Please mark a vertical line at a point that indicates the level of effort you just exerted.


Pain


0 10

I I
Pain as bad as
No pain
it could be

Please mark a vertical line at a point that indicates the level of pain that you are
currently experiencing.










Uninjured- Submaximum (50% Maximum)

Grip 2


Effort


0% 100%



No Grip Strongest
Force Grip Force

Please mark a vertical line at a point that indicates the level of effort you just exerted.


Pain


0 10

I I
Pain as bad as
No pain
it could be

Please mark a vertical line at a point that indicates the level of pain that you are
currently experiencing.







216


Participant ID#:


Order #: 1


Session 2


Injured- Maximum

Grip 1


Effort


0% 100%



No Grip Strongest
Force Grip Force

Please mark a vertical line at a point that indicates the level of effort you just exerted.


Pain


0 10

I I
Pain as bad as
No pain
it could be

Please mark a vertical line at a point that indicates the level of pain that you are
currently experiencing.







217


Injured- Maximum

Grip 2


Effort


0% 100%



No Grip Strongest
Force Grip Force

Please mark a vertical line at a point that indicates the level of effort you just exerted.


Pain


0 10

I I
Pain as bad as
No pain
it could be

Please mark a vertical line at a point that indicates the level of pain that you are
currently experiencing.










Uninjured- Maximum

Grip 1


Effort


0% 100%



No Grip Strongest
Force Grip Force

Please mark a vertical line at a point that indicates the level of effort you just exerted.


Pain


0 10

I I
Pain as bad as
No pain
it could be

Please mark a vertical line at a point that indicates the level of pain that you are
currently experiencing.







219


Uninjured- Maximum

Grip 2


Effort


0% 100%



No Grip Strongest
Force Grip Force

Please mark a vertical line at a point that indicates the level of effort you just exerted.


Pain


0 10

I I
Pain as bad as
No pain
it could be

Please mark a vertical line at a point that indicates the level of pain that you are
currently experiencing.







220


Injured- Submaximum (Pain)

Grip 1


Effort


0% 100%



No Grip Strongest
Force Grip Force

Please mark a vertical line at a point that indicates the level of effort you just exerted.


Pain


0 10

I I
Pain as bad as
No pain
it could be

Please mark a vertical line at a point that indicates the level of pain that you are
currently experiencing.










Injured- Submaximum (Pain)

Grip 2


Effort


0% 100%



No Grip Strongest
Force Grip Force

Please mark a vertical line at a point that indicates the level of effort you just exerted.


Pain


0 10

I I
Pain as bad as
No pain
it could be

Please mark a vertical line at a point that indicates the level of pain that you are
currently experiencing.







222


Uninjured- Submaximum (Pain)

Grip 1


Effort


0% 100%



No Grip Strongest
Force Grip Force

Please mark a vertical line at a point that indicates the level of effort you just exerted.


Pain


0 10

I I
Pain as bad as
No pain
it could be

Please mark a vertical line at a point that indicates the level of pain that you are
currently experiencing.










Uninjured- Submaximum (Pain)

Grip 2


Effort


0% 100%



No Grip Strongest
Force Grip Force

Please mark a vertical line at a point that indicates the level of effort you just exerted.


Pain


0 10

I I
Pain as bad as
No pain
it could be

Please mark a vertical line at a point that indicates the level of pain that you are
currently experiencing.










Injured- Submaximum (50% Maximum)

Grip 1


Effort


0% 100%



No Grip Strongest
Force Grip Force

Please mark a vertical line at a point that indicates the level of effort you just exerted.


Pain


0 10

I I
Pain as bad as
No pain
it could be

Please mark a vertical line at a point that indicates the level of pain that you are
currently experiencing.










Injured- Submaximum (50% Maximum)

Grip 2


Effort


0% 100%



No Grip Strongest
Force Grip Force

Please mark a vertical line at a point that indicates the level of effort you just exerted.


Pain


0 10

I I
Pain as bad as
No pain
it could be

Please mark a vertical line at a point that indicates the level of pain that you are
currently experiencing.







226


Uninjured- Submaximum (50% Maximum)

Grip 1


Effort


0% 100%



No Grip Strongest
Force Grip Force

Please mark a vertical line at a point that indicates the level of effort you just exerted.


Pain


0 10

I I
Pain as bad as
No pain
it could be

Please mark a vertical line at a point that indicates the level of pain that you are
currently experiencing.







227


Uninjured- Submaximum (50% Maximum)

Grip 2


Effort


0% 100%


No Grip Strongest
Force Grip Force

Please mark a vertical line at a point that indicates the level of effort you just exerted.


Pain


0 10

I I
Pain as bad as
No pain
it could be

Please mark a vertical line at a point that indicates the level of pain that you are
currently experiencing.















LIST OF REFERENCES


1. Salter RB. Reactions of musculoskeletal tissues to disorders and injuries. In:
Salter RB, ed. Textbook of Disorders and Injuries of the Musculoskeletal System.
3rd ed. Baltimore: Williams and Wilkins; 1999:29-49.

2. Chaffin DB, Lee M, Freivalds A. Muscle strength assessment from EMG
analysis. Med Sci Sports Exerc. 1980;12(3):205-211.

3. Ljung BO, Lieber RL, Friden J. Wrist extensor muscle pathology in lateral
epicondylitis. JHandSurg [Br]. Apr 1999;24(2):177-183.

4. Kadi F, Waling K, Ahlgren C, et al. Pathological mechanisms implicated in
localized female trapezius myalgia. Pain. Dec 1998;78(3):191-196.

5. Larsson SE, Bodegard L, Henriksson KG, Oberg PA. Chronic trapezius myalgia.
Morphology and blood flow studied in 17 patients. Acta Orthop Scand. Oct
1990;61(5):394-398.

6. Dennett X, Fry HJ. Overuse syndrome: a muscle biopsy study. Lancet. Apr 23
1988;1(8591):905-908.

7. Hagg GM. Human muscle fibre abnormalities related to occupational load. Eur J
ApplPhysiol. Oct 2000;83(2-3):159-165.

8. Pienimaki T, Tarvainen T, Siira P, Malmivaara A, Vanharanta H. Associations
between pain, grip strength, and manual tests in the treatment evaluation of
chronic tennis elbow. Clin JPain. May-Jun 2002; 18(3):164-170.

9. van Wilgen CP, Akkerman L, Wieringa J, Dijkstra PU. Muscle strength in
patients with chronic pain. Clin Rehabil. Dec 2003;17(8):885-889.

10. Nordenskiold U, Grimby G. Assessments of disability in women with rheumatoid
arthritis in relation to grip force and pain. DisabilRehabil. Jan 1997; 19(1):13-19.

11. Farina D, Arendt-Nielsen L, Graven-Nielsen T. Experimental muscle pain
decreases voluntary EMG activity but does not affect the muscle potential evoked
by transcutaneous electrical stimulation. Clin Neurophysiol. Jul
2005;116(7):1558-1565.


228






229


12. Graven-Nielsen T, Svensson P, Arendt-Nielsen L. Effects of experimental muscle
pain on muscle activity and co-ordination during static and dynamic motor
function. Electroencephalogr Clin Neurophysiol. Apr 1997; 105(2):156-164.

13. Ciubotariu A, Arendt-Nielsen L, Graven-Nielsen T. The influence of muscle pain
and fatigue on the activity of synergistic muscles of the leg. Eur JApplPhysiol.
May 2004;91(5-6):604-614.

14. Farina D, Arendt-Nielsen L, Merletti R, Graven-Nielsen T. Effect of experimental
muscle pain on motor unit firing rate and conduction velocity. JNeurophysiol.
Mar 2004;91(3): 1250-1259.

15. Sohn MK, Graven-Nielsen T, Arendt-Nielsen L, Svensson P. Effects of
experimental muscle pain on mechanical properties of single motor units in
human masseter. Clin Neurophysiol. Jan 2004; 115(1):76-84.

16. Mense S, Skeppar P. Discharge behaviour of feline gamma-motoneurones
following induction of an artificial myositis. Pain. Aug 1991;46(2):201-210.

17. Yamaji S, Demura S, Nagasawa Y, Nakada M. The influence of different target
values and measurement times on the decreasing force curve during sustained
static gripping work. JPhysiolAnthropol. Jan 2006;25(1):23-28.

18. Richards LG, Palmiter-Thomas P. Grip strenght measurement: A critical review
of tools, methods, and clinical utility. Critical Reviews in Physical and
Rehabilitation Medicine. 1996;8(1&2):87-109.

19. Kirkpatrick JE. Evaluation of grip loss. CalifMed. Nov 1956;85(5):314-320.

20. Spiegel JS, Paulus HE, Ward NB, Spiegel TM, Leake B, Kane RL. What are we
measuring? An examination of walk time and grip strength. JRheumatol. Feb
1987;14(1):80-86.

21. Petersen P, Petrick M, Connor H, Conklin D. Grip strength and hand dominance:
challenging the 10% rule. Am J Occup Ther. Jul 1989;43(7):444-447.

22. Stokes HM, Landrieu KW, Domangue B, Kunen S. Identification of low-effort
patients through dynamometry. The Journal ofHand Surgery. 1995;20A(6):1047-
1056.

23. Rothstein JN, Lamb RL, Mayhew TP. Clinical uses of isokinetic measurements:
Critical issues. Physical Therapy. 1987;67:1840-1844.

24. Mathiowetz V, Weber K, Volland G, Kashman N. Reliability and validity of grip
and pinch strength evaluations. JHand Surg [Am]. Mar 1984;9(2):222-226.






230


25. Tredgett M, Davis TR. Rapid repeat testing of grip strength for detection of faked
hand weakness. Journal ofHand Surgery (British and European Volume).
2000;25B(4):372-375.

26. Taylor C, Shechtman O. The use of the rapid exchange grip test in detecting
sincerity of effort, part i: Administration of the test. Journal of Hand Therapy.
2000;13:195-202.

27. Shechtman O, Taylor C. The use of the rapid exchange grip test in detecting
sincerity of effort, part ii: Validity of the test. Journal of Hand Therapy.
2000;13:203-210.

28. Shechtman O, Sindhu B. Using the force-time curve to detect submaximal effort.
Journal of Hand Therapy. Sept 22 2005; 18(4):461-462.

29. Gilbert JC, Knowlton RG. Simple method to determine sincerity of effort during a
maximal isometric test of grip strength. Am JPhys Med. Jun 1983;62(3):135-144.

30. Janda DH, Geiringer, S. R., Hankin, F. M., Barry, D. T. Objective evaluation of
grip strength. Journal of Occupational Medicine. 1987;29(7):569-571.

31. Niebuhr BR, Marion R, Hasson SM. Electromyographic analysis of effort in grip
strength assessment. Electromyogr Clin Neurophysiol. Apr-May 1993;33(3):149-
156.

32. Niebuhr BR. Detecting submaximal grip exertions of variable effort by
electromyography. Electromyogr Clin Neurophysiol. Mar 1996;36(2): 113-120.

33. Woolf AD, Akesson K. Understanding the burden of musculoskeletal conditions.
The burden is huge and not reflected in national health priorities. Bmj. May 5
2001;322(7294):1079-1080.

34. Yelin E, Herrndorf A, Trupin L, Sonneborn D. A national study of medical care
expenditures for musculoskeletal conditions: the impact of health insurance and
managed care. Arthritis Rheum. May 2001;44(5): 1160-1169.

35. Lawrence RC, Helmick CG, Arnett FC, et al. Estimates of the prevalence of
arthritis and selected musculoskeletal disorders in the United States. At ilh itin
Rheum. May 1998;41(5):778-799.

36. Reynolds DL, Chambers LW, Badley EM, et al. Physical disability among
Canadians reporting musculoskeletal diseases. JRheumatol. Jul 1992;19(7):1020-
1030.

37. National Center for Health Statistics. National health interview survey, 1995. US
Department of Health and Human Services.









38. LaPlante M. Health conditions and impairments causing disability. Disability
Statistics Center [Website]. Available at:
http://dsc.ucsf.edu/publisting.php?pub type=abstract. Accessed October 1, 2005.

39. Lidgren L. The Bone and Joint Decade and the global economic and healthcare
burden of musculoskeletal disease. JRheumatol Suppl. Aug 2003;67:4-5.

40. Bernard BP. Musculoskeletal Disorders and Workplace Factors. Cincinnati, OH:
National Institue for Occupational Safety and Health; 1997.

41. Woolf AD. How to assess musculoskeletal conditions. History and physical
examination. Best Pract Res Clin Rheumatol. Jun 2003;17(3):381-402.

42. Woolf AD, Pfleger B. Burden of major musculoskeletal conditions. Bull World
Health Organ. 2003;81(9):646-656.

43. Visser B, van Dieen JH. Pathophysiology of upper extremity muscle disorders. J
Electromyogr Kinesiol. Feb 2006; 16(1): 1-16.

44. Lidgren L. The bone and joint decade 2000-2010. Bull World Health Organ.
2003;81(9):629.

45. Sanders MJ. The Medical Context. In: Sanders MJ, ed. Management of
cumulative trauma disorders. Boston: Butterworth-Heinemann; 1997:21-26.

46. United States Department of Labor. Lost-worktime injuries and illnesses:
Characteristics and resulting days away from work, 1997. Washington, D.C.:
Bureau of Labor Statistics; April 22, 1999 1999.

47. Kuorinka I, Koskinen P. Occupational rheumatic diseases and upper limb strain in
manual jobs in a light mechanical industry. ScandJ Work Environ Health. 1979;5
supply 3:39-47.

48. Luopajarvi T, Kuorinka I, Virolainen M, Holmberg M. Prevalence of
tenosynovitis and other injuries of the upper extremities in repetitive work. Scand
J Work Environ Health. 1979;5 supply 3:48-55.

49. Viikari-Juntura E. Neck and upper limb disorders among slaughterhouse workers.
An epidemiologic and clinical study. ScandJ Work Environ Health. Jun
1983;9(3):283-290.

50. Ranney D, Wells R, Moore A. Upper limb musculoskeletal disorders in highly
repetitive industries: precise anatomical physical findings. Ergonomics. Jul
1995;38(7):1408-1423.






232


51. Niemeyer LO. The issue of abnormal illness behavior in work hardening. In:
Niemeyer LO, Jacobs K, eds. Work Hardening: State of the art. Thorafare, N.J.:
Slack; 1989.

52. Shultz-Johnson K. Assessment of upper extremity-injured persons' return to work
potential. Journal ofHand Surgery. 1987; 12A:950-957.

53. Simonsen JC. Coefficient of variation as a measure of subject effort. Archives of
Physical Medicine and Rehabilitation. 1995;76:516-520.

54. Ashford RF, Nagelburg S, Adkins R. Sensitivity of the Jamar dynamometer in
detecting submaximal grip effort. Journal of Hand Surgery. 1996;21A:402-405.

55. Chengalur SN, Smith GA, Nelson RC, Sadoff AM. Assessing sincerity of effort in
maximal grip strength tests. Am JPhys MedRehabil. Jun 1990;69(3): 148-153.

56. King JW, Berryhill BH. Assessing maximum effort in upper-extremity functional
testing. Work. 1991;1(3):65-76.

57. Patterson HM. Grip measurements as a part of pre-placement evaluation.
Industrial Medicine and Surgery. 1965;34(7):555-557.

58. Fishbain DA, Cutler RB, RosomoffHL, RosomoffRS. Chronic pain disability
exaggeration/malingering and submaximal effort research. Clinical Journal of
Pain. 1999; 15(4):244-274.

59. Mittenberg W, Patton C, Canyock EM, Condit DC. Base rates of malingering and
symptom exaggeration. Journal of Clinical and Experimental Neuropsychology.
2002;24(8):1094-1102.

60. Green P, Rohling ML, Lees-Haley PR, Allen L, M. Effort has a greater effect on
test scores than severe brain injury in compensation claimants. Brain Injury.
2001;15:1045-1060.

61. Lees-Haley PR. MMPI-2 base rates for 492 personal injury plaintiffs:
Implications and challenges for forensic assessment. Journal of Clinical
Psychology. 1997;53(7):745-755.

62. Czitrom AA, Lister GD. Measurement of grip strength in the diagnosis of wrist
pain. JHandSurg [Am]. Jan 1988;13(1):16-19.

63. Mitterhauser MD, Muse VL, Dellon AL, Jetzer TC. Detection of Submaximal
Effort With Computer-Assisted Grip Strength Measurements. The Journal of
Occupational and Environmental Medicine. 1997;39(1220-1227).









64. Shechtman O. Using the coefficient of variation to detect sincerity of effort of
grip strength: A literature review. JHand Ther. Jan-Mar 2000;13(1):25-32.

65. Shechtman O. The coefficient of variation as a measure of sincerity of effort of
grip strength, part ii: Sensitivity and Specificity. Journal ofHand Therapy.
2001;14:188-194.

66. Shechtman O. The coefficient of variation as a measure of sincerity of effort of
grip strength, part i: The statistical principle. Journal ofHand Therapy.
2001;14:180-187.

67. Shechtman O, Davenport R, Malcolm M, Nabavi D. Reliability and validity of the
BTE-Primus grip tool. Journal of Hand Therapy. 2003;16(1):36-42.

68. Dieppe P. The relationships of musculoskeletal disease to age, pain, poverty and
behaviour. Rheumatology (Oxford). Mar 2006;45(3):248-249.

69. Kucharski A, Todd EM. Pain: Historical perspectives. In: Warfield CA, Bajwa
ZH, eds. Principles and practice ofpain medicine. 2nd ed. New York: McGraw-
Hill; 2004:1-10.

70. Melzack R, Katz J. The McGill Pain Questionnaire: Appraisal and Current Status.
In: Turk DC, Melzack R, eds. Handbook ofpain assessment. 2nd ed. New York:
The Guilford Press; 2001:35-52.

71. Reinking MF, Bockrath-Pugliese K, Worrell T, Kegerreis RL, Miller-Sayers K,
Farr J. Assessment of quadriceps muscle performance by hand-held, isometric,
and isokinetic dynamometry in patients with knee dysfunction. JOrthop Sports
Phys Ther. Sep 1996;24(3):154-159.

72. Lysholm J. The relation between pain and torque in an isokinetic strength test of
knee extension. Arthroscopy. 1987;3(3):182-184.

73. Heuts PH, Vlaeyen JW, Roelofs J, et al. Pain-related fear and daily functioning in
patients with osteoarthritis. Pain. Jul 2004; 110(1-2):228-235.

74. Turk DC, Robinson JP, Burwinkle T. Prevalence of fear of pain and activity in
patients with fibromyalgia syndrome. JPain. Nov 2004;5(9):483-490.

75. Boersma K, Linton S, Overmeer T, Jansson M, Vlaeyen J, de Jong J. Lowering
fear-avoidance and enhancing function through exposure in vivo. A multiple
baseline study across six patients with back pain. Pain. Mar 2004;108(1-2):8-16.

76. George SZ, Dannecker EA, Robinson ME. Fear of pain, not pain catastrophizing,
predicts acute pain intensity, but neither factor predicts tolerance or blood









pressure reactivity: An experimental investigation in pain-free individuals. Eur J
Pain. Aug 8 2005.

77. Slade PD, Troup JD, Lethem J, Bentley G. The Fear-Avoidance Model of
exaggerated pain perception--II. Behav Res Ther. 1983;21(4):409-416.

78. Lethem J, Slade PD, Troup JD, Bentley G. Outline of a Fear-Avoidance Model of
exaggerated pain perception--I. Behav Res Ther. 1983;21(4):401-408.

79. Samwel HJ, Evers AW, Crul BJ, Kraaimaat FW. The role of helplessness, fear of
pain, and passive pain-coping in chronic pain patients. Clin JPain. Mar-Apr
2006;22(3):245-251.

80. Hamilton Fairfax A, Balnave R, Adams RD. Variability of grip strength during
isometric contraction. Ergonomics. 1995;38:1819-1830.

81. King PM. Analysis of approaches to detection of sincerity of effort through grip
strength measurement. Work. 1998;10:9-13.

82. Lechner DE, Bradbury SF, Bradley LA. Detecting sincerity of effort: a summary
of methods and approaches. Phys Ther. Aug 1998;78(8):867-888.

83. Shechtman O. Is the coefficient of variation a valid measure for detecting
sincerity of effort of grip strength? Work. 1999;13(2):163-169.

84. Shechtman O, Taylor C. How do therapists administer the rapid exchange grip
test? A survey. Journal ofHand Therapy. 2002;15(1):53-61.

85. Stokes HM. The seriously uninjured hand -- Weakness of grip. Journal of
Occupational Medicine. 1983;25(9):683-684.

86. Niebuhr BR, Marion R. Detecting sincerity of effort when measuring grip
strength. American Journal ofPhysical Medicine. 1987;66(1): 16-24.

87. Niebuhr BR, Marion R. Voluntary control of submaximal grip strength. American
Journal ofPhysical Medicine and Rehabilitation. 1990;69(2):96-101.

88. Goldman S, Cahalan T, An K. The injured upper extremity and the jamar five-
handle position grip test. American Journal of Physical Medicine and
Rehabilitation. 1991;70(6):306-308.

89. Hoffmaster E, Lech R, Niebuhr BR. Consistency of sincere and feigned grip
exertions with repeated testing. Journal of Occupational Medicine.
1993;35(8):788-794.









90. Hildreth DH, Breidenbach WC, Lister GD, Hodges AD. Detection of submaximal
effort by use of the rapid exchange grip. JHand Surg [Am]. Jul 1989;14(4):742-
745.

91. Lister G. The hand: Diagnosis and indications. 2nd ed. New York: Churchill
Livingstone; 1984.

92. Portney LG, Watkins MP. Foundations of Clinical Research: Applications to
Practice. 2nd ed. Norwalk, Connecticut: Appleton & Lange; 2000.

93. Gutierrez Z, Shechtman O. Effectiveness of the five-handle position grip strength
test in detecting sincerity of effort in men and women. American Journal of
Physical Medicine and Rehabilitation. 2003;82:847-855.

94. Robinson ME, Geisser ME, Hanson CS, O'Connor PD. Detecting submaximal
efforts in grip strength testing with the coefficient of variation. Journal of
Occupational Rehabilitation. 1993;3(1):45-50.

95. Househam E, McAuley J, Charles T, Lightfoot T, Swash M. Analysis of force
profile during a maximum voluntary isometric contraction task. Muscle Nerve.
Mar 2004;29(3):401-408.

96. Kamimura T, Ikuta Y. Evaluation of grip strength with a sustained maximal
isometric contraction for 6 and 10 seconds. JRehabilMed. Sep 2001;33(5):225-
229.

97. Viitasalo JT, Komi PV. Interrelationships between Electro-Myographic,
Mechanical, Muscle Structure and Reflex Time Measurements in Man. Acta
Physiologica Scandinavica. 1981; 111(1):97-103.

98. Hakkinen A, Komi PV. The effects of explosive type strength training on
electromyographic and force production characteristics of leg extensor muscles
during concentric and various stretch shortening cycle exercises. Scandinavian
Journal of Sports Science. 1985;7:65-76.

99. Hakkinen A, Komi PV. Changes in electrical and mechanical behavior of leg
extensor muscle during heavy resistance strength training. Scandinavian Journal
of Sports Science. 1985;7:55-64.

100. Bemben MG, Massey BH, Boileau RA, Misner JE. Reliability of isometric force-
time curve parameters for men aged 20 to 79 years. Journal of Applied Sports
Science Research. 1992;6(3):158-164.

101. Smith GA, Nelson RC, Sadoff SJ, Sadoff AM. Assessing sincerity of effort in
maximal grip strength tests. Am JPhysMedRehabil. Apr 1989;68(2):73-80.






236


102. Shechtman O, Sindhu BS, Davenport PW. Using the force-time curve to detect
maximal grip strength effort. JHand Ther. Jan-Mar 2007;20(1):37-48.

103. Massy-Westropp N, Rankin W, Ahern M, Krishnan J, Hearn TC. Measuring grip
strength in normal adults: reference ranges and a comparison of electronic and
hydraulic instruments. JHand Surg [Am]. May 2004;29(3):514-519.

104. Cafarelli E. Force sensation in fresh and fatigued human skeletal muscle. Exerc
Sport Sci Rev. 1988;16:139-168.

105. Redfern M. Functional muscle: effects on electromyographic output. In:
Soderberg GL, ed. Selected Topics in Surface Electromyography for the Use in
the Occupational Setting: Expert Perspectives. Cincinnati, OH: US Department of
Health and Human Services, Public Health Service; 1992:104-120.

106. Ogura T, Kubo T, Okuda Y, et al. Power spectrum analysis of compound muscle
action potential in carpal tunnel syndrome patients. J Orthop Surg (Hong Kong).
Jun 2002;10(1):67-71.

107. Lidgren L. The bone and joint decade 2000-2010. Acta Orthopaedica
Scandinavica. 2000;71(1):3-6.

108. Frymoyer J, Durett C. The economics of spinal disorders. In: Frymoyer J, ed. The
Adult Spine: Principles and Practice. 2 ed. Philadephia: Lippincott-Raven;
1997:143-150.

109. Portney LG, Watkins MP. Foundations of Clinical Research: Applications to
Practice. 1st ed. Norwalk, Connecticut: Appleton & Lange; 1993.

110. McNicol D. A Primer of Signal Detection Theory. London: Lawrence Erlbaum
Associates, Publishers; 2005.

111. Chart for Windows [computer program]. Version 4.2. Colorado Springs, CO:
ADInstruments; 2002.

112. Rose LP. The Muscular System (Musculoskeletal System). Partners in Assistive
Technology Training and Services [Website]. Available at:
http://webschoolsolutions.com/patts/systems/muscles.htm. Accessed March 14,
2006.

113. Yelin E, Callahan LF. The economic cost and social and psychological impact of
musculoskeletal conditions. National Arthritis Data Work Groups. At /th iti,
Rheum. Oct 1995;38(10):1351-1362.






237


114. Iverson GL, Binder LM. Detecting exaggeration and malingering in
neuropsychological assessment. Journal ofHead Trauma Rehabilitation.
2000;15(2):829-858.

115. Main CJ, Spanswick CC. 'Functional Overlay', and illness behaviour in chronic
pain: Distress or Malingering? Conceptual difficulties in medico-legal assessment
of personal injury claims. Journal of Psychometric Research. 1995;39(6):737-
753.

116. Matheson LN. Symptom Magnification Syndrome. In: Isernhagen SJ, ed. Work
Injury: Management and Prevention. New York: Apen Publishers; 1988.

117. Lipman FD. Malingering in personal injury cases. Temple Law Quarterly.
1962;35:141-162.

118. Hamilton Fairfax A, Balnave R, Adams R. Review of sincerity of effort testing.
Safety Science. 1997;25:237-245.

119. Zwarts MJ, Stegeman DF. Multichannel surface EMG: basic aspects and clinical
utility. Muscle Nerve. Jul 2003;28(1):1-17.

120. Winter DA. Biomechanics and motor control of human movement. 2nd ed.
Toronto: John Wiley & Sons; 1990.

121. Derrick TR. Signal processing. In: Robertson DGE, Caldwell GE, Hamill J,
Kamen G, Whittlesey SN, eds. Research Methods in Biomechanics. 1st ed.
Champaign: Human Kinetics; 2004:227-238.

122. Sluiter JK, Rest KM, Frings-Dresen MH. Criteria document for evaluating the
work-relatedness of upper-extremity musculoskeletal disorders. ScandJ Work
Environ Health. 2001;27 Suppl 1:1-102.

123. Keller K, Corbett J, Nichols D. Repetitive strain injury in computer keyboard
users: pathomechanics and treatment principles in individual and group
intervention. JHand Ther. Jan-Mar 1998;11(1):9-26.

124. Morse T, Punnett L, Warren N, Dillon C, Warren A. The relationship of unions to
prevalence and claim filing for work-related upper-extremity musculoskeletal
disorders. Am JlndMed. Jul 2003;44(1):83-93.

125. Hales TR, Sauter SL, Peterson MR, et al. Musculoskeletal disorders among visual
display terminal users in a telecommunications company. Ergonomics. Oct
1994;37(10):1603-1621.









126. McCormack RR, Jr., Inman RD, Wells A, Berntsen C, Imbus HR. Prevalence of
tendinitis and related disorders of the upper extremity in a manufacturing
workforce. JRheumatol. Jul 1990;17(7):958-964.

127. Bernard B, Sauter S, Fine L, Petersen M, Hales T. Job task and psychosocial risk
factors for work-related musculoskeletal disorders among newspaper employees.
Scand J Work Environ Health. Dec 1994;20(6):417-426.

128. Stockstill JW, Harn SD, Strickland D, Hruska R. Prevalence of upper extremity
neuropathy in a clinical dentist population. JAm Dent Assoc. Aug
1993;124(8):67-72.

129. Huisstede BM, Bierma-Zeinstra SM, Koes BW, Verhaar JA. Incidence and
prevalence of upper-extremity musculoskeletal disorders. A systematic appraisal
of the literature. BMC Musculoskelet Disord. 2006;7:7.

130. Webster BS, Snook SH. The cost of compensable upper extremity cumulative
trauma disorders. J Occup Med. Jul 1994;36(7):713-717.

131. Fabrizio AJ. Work-related upper extremity injuries: prevalence, cost and risk
factors in military and civilian populations. Work. 2002;18(2):115-121.

132. Brogmus GE, Sorock GS, Webster BS. Recent trends in work-related cumulative
trauma disorders of the upper extremities in the United States: an evaluation of
possible reasons. J Occup Environ Med. Apr 1996;38(4):401-411.

133. Feuerstein M, Miller VL, Burrell LM, Berger R. Occupational upper extremity
disorders in the federal workforce. Prevalence, health care expenditures, and
patterns of work disability. J Occup Environ Med. Jun 1998;40(6):546-555.

134. United States Department of Labor. Lost-worktime injuries and illnesses:
Characteristics and resulting days away from work, 2003. Washington, D.C.:
Bureau of Labor Statistics; March 30, 2005 2005.

135. Barbe MF, Barr AE, Gorzelany I, Amin M, Gaughan JP, Safadi FF. Chronic
repetitive reaching and grasping results in decreased motor performance and
widespread tissue responses in a rat model of MSD. J Orthop Res. Jan
2003;21(1):167-176.

136. Barr AE, Safadi FF, Gorzelany I, Amin M, Popoff SN, Barbe MF. Repetitive,
negligible force reaching in rats induces pathological overloading of upper
extremity bones. JBone Miner Res. Nov 2003;18(11):2023-2032.

137. Barr AE, Barbe MF. Pathophysiological tissue changes associated with repetitive
movement: a review of the evidence. Phys Ther. Feb 2002;82(2):173-187.






239


138. Armstrong RB, Ogilvie RW, Schwane JA. Eccentric exercise-induced injury to
rat skeletal muscle. JAppl Physiol. Jan 1983;54(1):80-93.

139. Jarvinen M, Jozsa L, Kannus P, Jarvinen TL, Kvist M, Leadbetter W.
Histopathological findings in chronic tendon disorders. ScandJMedSci Sports.
Apr 1997;7(2):86-95.

140. Stauber WT, Smith CA. Cellular responses in exertion-induced skeletal muscle
injury. Mol Cell Biochem. Feb 1998;179(1-2):189-196.

141. Himmelstein JS, Feuerstein M, Stanek EJ, 3rd, et al. Work-related upper-
extremity disorders and work disability: clinical and psychosocial presentation. J
Occup Environ Med. Nov 1995;37(11):1278-1286.

142. Duff SV. Tendinitis, entrapment neuropathies and related conditions. In: Sanders
MJ, ed. Management of cumulative trauma disorders. Boston: Butterworth-
Heinemann; 1997:41-64.

143. Salter RB. Neuromuscular disorders. In: Salter RB, ed. Textbook of Disorders and
Injuries of the Musculoskeletal System. 3rd ed. Baltimore: Williams and Wilkins;
1999:303-337.

144. Pratt N. Anatomy of nerve entrapment sites in the upper quarter. JHand Ther.
Apr-Jun 2005;18(2):216-229.

145. Fuss FK, Wurzl GH. Radial nerve entrapment at the elbow: surgical anatomy. J
Hand Surg [Am]. Jul 1991; 16(4):742-747.

146. Lister GD, Belsole RB, Kleinert HE. The radial tunnel syndrome. JHandSurg
[Am]. Jan 1979;4(1):52-59.

147. Prasartritha T, Liupolvanish P, Rojanakit A. A study of the posterior interosseous
nerve (PIN) and the radial tunnel in 30 Thai cadavers. JHand Surg [Am]. Jan
1993;18(1):107-112.

148. Sponseller PD, Engber WD. Double-entrapment radial tunnel syndrome. JHand
Surg [Am]. Jul 1983;8(4):420-423.

149. Fuss FK, Wurzl GH. Median nerve entrapment. Pronator teres syndrome. Surgical
anatomy and correlation with symptom patterns. SurgRadiolAnat.
1990;12(4):267-271.

150. Spinner M. The anterior interosseous-nerve syndrome, with special attention to its
variations. JBone Joint Surg Am. Jan 1970;52(1):84-94.






240


151. Fearn CB, Goodfellow JW. Anterior Interosseous Nerve Palsy. JBone Joint Surg
Br. Feb 1965;47:91-93.

152. McPherson SA, Meals RA. Cubital tunnel syndrome. Orthop Clin North Am. Jan
1992;23(1):111-123.

153. Stack RE. Carpal tunnel syndrome. American Family Physician. 1973;8:88.

154. Phalen G. The carpal tunnel syndrome. Journal of Bone and Joint Surgery.
1966;48A(2):211-228.

155. Pickett JB. The carpal tunnel syndrome. Journal of Sonlu Carolina Medical
Association. 1984;80:298-301.

156. Salter RB. Degenerative disorders of joints and related tissues. In: Salter RB, ed.
Textbook of Disorders and Injuries of the Musculoskeletal System. 3rd ed.
Baltimore: Williams and Wilkins; 1999:257-302.

157. Wuori JL, Overend TJ, Kramer JF, MacDermid J. Strength and pain measures
associated with lateral epicondylitis bracing. Arch Phys MedRehabil. Jul
1998;79(7):832-837.

158. Nirschl RP. Tennis elbow. Orthop Clin North Am. Jul 1973;4(3):787-800.

159. Nirschl RP. Soft-tissue injuries about the elbow. Clin Sports Med. Oct
1986;5(4):637-652.

160. Safran MR. Elbow injuries in athletes. A review. Clin Orthop Relat Res. Jan
1995(310):257-277.

161. Nirschl RP, Pettrone FA. Tennis elbow. The surgical treatment of lateral
epicondylitis. JBone Joint Surg Am. Sep 1979;61(6A):832-839.

162. Regan W, Wold LE, Coonrad R, Morrey BF. Microscopic histopathology of
chronic refractory lateral epicondylitis. Am J Sports Med. Nov-Dec
1992;20(6):746-749.

163. Goldie I. Epicondylitis Lateralis Humeri (Epicondylalgia or Tennis Elbow). A
Pathogenetical Study. Acta Chir ScandSuppl. 1964;57:SUPPL 339:331+.

164. Coonrad RW, Hooper WR. Tennis elbow: its course, natural history, conservative
and surgical management. JBone Joint Surg Am. Sep 1973;55(6):1177-1182.

165. Greenbaum B, Itamura J, Vangsness CT, Tibone J, Atkinson R. Extensor carpi
radialis brevis. An anatomical analysis of its origin. JBone Joint Surg Br. Sep
1999;81(5):926-929.









166. Eversmann W. Entrapment and compression neuropathies. In: Green DP, ed.
Operative Hand Surgery. 3 ed. New York: Churchill Livingstone; 1993:1341-
1385.

167. Graham R. Carpal tunnel syndrome: A statistical analysis of 214 cases.
Orthopaedics. 1983;6:1283-1287.

168. Katz R. Carpal tunnel syndrome: A practical review. American Family Physician.
1994;49:1371-1379.

169. Omer GE. Median nerve compression at the wrist. Hand Clinics. 1992;8(2):317-
324.

170. Erdmann MWH. Endoscopic carpal tunnel decompression. Journal ofHand
Surgery [Br]. 1994;19B:5-13.

171. Faithfull DK, Moir DH. The micropathology of the typical carpal tunnel
syndrome. Journal ofHand Surgery [Am]. 1986; 11B: 131-132.

172. Gerritsen AAM, de Krom M, Struijs MA, Scholten R, de Vet H, Bouter LM.
Conservative treatment options for carpal tunnel syndrome: a systematic review
of randomised controlled trials. Journal ofNeurology. 2002;249:272-280.

173. Sunderland S. Nerves and nerve injuries. Baltimore: Williams & Wilkins; 1968.

174. Richards LG. Posture effects on grip strength. Arch Phys MedRehabil. Oct
1997;78(10):1154-1156.

175. Fess EE. Grip Strength. In: Casanova JS, ed. Clinical assessment
recommendations. 2nd ed. Chicago, IL: The American Society of Hand
Therapists; 1992:41-45.

176. Kraft GH, Detels PE. Position of function of the wrist. Arch Phys MedRehabil.
Jun 1972;53(6):272-275.

177. Pryce JC. The wrist position between neutral and ulnar deviation that facilitates
the maximum power grip strength. JBiomech. 1980;13(6):505-511.

178. O'Driscoll SW, Horii E, Ness R, Cahalan TD, Richards RR, An KN. The
relationship between wrist position, grasp size, and grip strength. JHandSurg
[Am]. Jan 1992;17(1):169-177.

179. Richards LG, Olson B, Palmiter-Thomas P. How forearm position affects grip
strength. Am J Occup Ther. Feb 1996;50(2):133-138.






242


180. Balogun JA, Akomolafe CT, Amusa LO. Grip strength: effects of testing posture
and elbow position. Arch Phys MedRehabil. Apr 1991;72(5):280-283.

181. Mathiowetz V, Rennells C, Donahoe L. Effect of elbow position on grip and key
pinch strength. JHand Surg [Am]. Sep 1985;10(5):694-697.

182. Kuzala EA, Vargo MC. The relationship between elbow position and grip
strength. Am J Occup Ther. Jun 1992;46(6):509-512.

183. Ferraz MB, Ciconelli RM, Araujo PM, Oliveira LM, Atra E. The effect of elbow
flexion and time of assessment on the measurement of grip strength in rheumatoid
arthritis. JHand Surg [Am]. Nov 1992; 17(6): 1099-1103.

184. Su CY, Lin JH, Chien TH, Cheng KF, Sung YT. Grip strength in different
positions of elbow and shoulder. Arch Phys Med Rehabil. Jul 1994;75(7):812-
815.

185. Bohannon RW. Intertester reliability of hand-held dynamometry: a concise
summary of published research. Percept Mot .ill//, Jun 1999;88(3 Pt 1):899-902.

186. Bohannon RW. Hand-held dynamometry: factors influencing reliability and
validity. Clin Rehabil. Aug 1997; 11(3):263-264.

187. Mathiowetz V, Kashman N, Volland G, Weber K, Dowe M, Rogers S. Grip and
Pinch Strength Normative Data for Adults. Archives ofPhysical Medicine and
Rehabilitation. 1985;66(2):69-74.

188. Kroemer KH, Marras WS. Towards an objective assessment of the "maximal
voluntary contraction" component in routine muscle strength measurements. Eur
JAppl Physiol Occup Physiol. 1980;45(1):1-9.

189. Astrand PO, Rodahl K, eds. Textbook ofwork physiology. 2nd ed. New York:
McGraw-Hill; 1977.

190. Sust M, Schmalz T, Beyer L, Rost R, Hansen E, Weiss T. Assessment of
isometric contractions performed with maximal subjective effort: corresponding
results for EEG changes and force measurements. Int JNeurosci. Nov 1997;92(1-
2):103-118.

191. Dalsgaard MK, Ide K, Cai Y, Quistorff B, Secher NH. The intent to exercise
influences the cerebral O(2)/carbohydrate uptake ratio in humans. JPhysiol. Apr
15 2002;540(Pt 2):681-689.

192. Edgerton VR. Mammalian Muscle-Fiber Types and Their Adaptability. American
Zoologist. 1978;18(1):113-125.









193. Milner-Brown HS, Stein RB, Yemm R. Changes in firing rate of human motor
units during linearly changing voluntary contractions. JPhysiol. Apr
1973;230(2):371-390.

194. Milner-Brown HS, Stein RB, Yemm R. The orderly recruitment of human motor
units during voluntary isometric contractions. JPhysiol. Apr 1973;230(2):359-
370.

195. Grimby L, Hannerz J. Recruitment order of motor units on voluntary contraction:
changes induced by proprioceptive afferent activity. JNeurol Neurosurg
Psychiatry. Dec 1968;31(6):565-573.

196. Kilbreath SL, Refshauge K, Gandevia SC. Differential control of the digits of the
human hand: evidence from digital anaesthesia and weight matching. Exp Brain
Res. Dec 1997;117(3):507-511.

197. Lafargue G, Paillard J, Lamarre Y, Sirigu A. Production and perception of grip
force without proprioception: is there a sense of effort in deafferented subjects?
Eur JNeurosci. Jun 2003;17(12):2741-2749.

198. Robinson ME, Mac Millan M, O'Connor P, Fuller A, Cassisi JE. Reproducibility
of maximal versus submaximal efforts in an isometric lumbar extension task. J
SpinalDisord. Dec 1991;4(4):444-448.

199. Bechtol CO. Grip test; the use of a dynamometer with adjustable handle spacings.
JBone Joint Surg Am. Jul 1954;36-A(4):820-824; passim.

200. Young VL, Pin P, Kraemer BA, Gould RB, Nemergut L, Pellowski M.
Fluctuation in grip and pinch strength among normal subjects. JHand Surg [Am].
Jan 1989;14(1):125-129.

201. Krombholz H. On the association of effort and force of handgrip. Percept Mot
,N\,d/A Feb 1985;60(1):161-162.

202. Caldwell LS, Chaffin DB, Dukes-Dobos FN, et al. A proposed standard procedure
for static muscle strength testing. Am IndHyg Assoc J Apr 1974;35(4):201-206.

203. Ramos MU, Mundale MO, Awad EA, et al. Cardiovascular effects of spread of
excitation during prolonged isometric exercise. Arch Phys MedRehabil. Nov
1973;54(11):496-504 passim.

204. Joughin K, Gulati P, Mackinnon SE, et al. An evaluation of rapid exchange and
simultaneous grip tests. JHand Surg [Am]. Mar 1993; 18(2):245-252.









205. Tredgett M, Pimble LJ, Davis TR. The detection of feigned hand weakness using
the five position grip strength. Journal ofHand Surgery (British and European
Volume). 1999;24B(4):426-428.

206. Shechtman O, Gutierrez Z, Kokendofer E. Analysis of the statistical methods used
to detect submaximal effort with the five-rung grip strength test. JHand Ther.
Jan-Mar 2005;18(1):10-18.

207. Demura S, Yamaji S, Nagasawa Y, Ikemoto Y, Shimada S. Force developmental
phase and reliability in explosive and voluntary grip exertions. Percept Mot .,\kill
Jun 2001;92(3 Pt 2):1009-1021.

208. Demura S, Yamaji S, Nagasawa Y, Minami M, Kita I. Examination of force-
production properties during static explosive grip based on force-time curve
parameters. Percept Mot .,\ki// Dec 2000;91(3 Pt 2): 1209-1220.

209. Nagasawa Y, Demura S, Nakada M. Reliability of a computerized target-pursuit
system for measuring coordinated exertion of force. Percept Mot .\kill, Jun
2003;96(3 Pt 2):1071-1085.

210. Sanjak M, Konopacki R, Capasso R, et al. Dissociation between mechanical and
myoelectrical manifestation of muscle fatigue in amyotrophic lateral sclerosis.
Amyotroph Lateral Scler Other Motor Neuron Disord. Mar 2004;5(1):26-32.

211. Hakkinen A, Malkia E, Hakkinen K, Jappinen I, Laitinen L, Hannonen P. Effects
of detraining subsequent to strength training on neuromuscular function in
patients with inflammatory arthritis. Br JRheumatol. Oct 1997;36(10):1075-1081.

212. Helliwell P, Howe A, Wright V. Functional assessment of the hand:
reproducibility, acceptability, and utility of a new system for measuring strength.
Ann Rheum Dis. Mar 1987;46(3):203-208.

213. Valkeinen H, Ylinen J, Malkia E, Alen M, Hakkinen K. Maximal force,
force/time and activation/coactivation characteristics of the neck muscles in
extension and flexion in healthy men and women at different ages. Eur JAppl
Physiol. Dec 2002;88(3):247-254.

214. Haff GG, Carlock JM, Hartman MJ, et al. Force-time curve characteristics of
dynamic and isometric muscle actions of elite women olympic weightlifters. J
Suenigih CondRes. Nov 2005;19(4):741-748.

215. Aagaard P, Simonsen EB, Andersen JL, Magnusson SP, Halkjaer-Kristensen J,
Dyhre-Poulsen P. Neural inhibition during maximal eccentric and concentric
quadriceps contraction: effects of resistance training. JApplPhysiol. Dec
2000;89(6):2249-2257.









216. Bemben MG, Massey BH, Bemben DA, Misner JE, Boileau RA. Isometric
intermittent endurance of four muscle groups in men aged 20-74 yr. Med Sci
Sports Exerc. Jan 1996;28(1): 145-154.

217. Bemben MG, Massey BH, Bemben DA, Misner JE, Boileau RA. Isometric
muscle force production as a function of age in healthy 20- to 74-yr-old men. Med
Sci Sports Exerc. Nov 1991;23(11):1302-1310.

218. Izquierdo M, Ibanez J, Gorostiaga E, et al. Maximal strength and power
characteristics in isometric and dynamic actions of the upper and lower
extremities in middle-aged and older men. Acta Physiol Scand. Sep
1999;167(1):57-68.

219. Demura S, Yamaji S, Nagasawa Y, Sato S, Minami M, Yoshimura Y. Reliability
and gender differences of static explosive grip parameters based on force-time
curves. JSportsMedPhys Fitness. Mar 2003;43(1):28-35.

220. Ryushi T, Hakkinen K, Kauhanen H, Komi PV. Muscle fiber characteristics,
muscle cross-sectional area and force production in strength athletes, physically
active males and females. ScandJSports Sci. 1988;10:7-15.

221. Harridge SD, Bottinelli R, Canepari M, et al. Whole-muscle and single-fibre
contractile properties and myosin heavy chain isoforms in humans. Pflugers Arch.
Sep 1996;432(5):913-920.

222. Nakada M, Demura S, Yamaji S, Nagasawa Y. Examination of the reproducibility
of grip force and muscle oxygenation kinetics on maximal repeated rhythmic grip
exertion. JPhysiolAnthropol Appl Human Sci. Jan 2005;24(1): 1-6.

223. Yamaji S, Demura S, Nagasawa Y, Nakada M, Kitabayashi T. The effect of
measurement time when evaluating static muscle endurance during sustained
static maximal gripping. JPhysiol Anthropol Appl Human Sci. May
2002;21(3):151-158.

224. Haff GG, Stone M, OBryant HS, et al. Force-time dependent characteristics of
dynamic and isometric muscle actions. Journal of Sii engthl and Conditioning
Research. NOV 1997;11(4):269-272.

225. Aagaard P, Andersen JL. Correlation between contractile strength and myosin
heavy chain isoform composition in human skeletal muscle. Med Sci Sports
Exerc. Aug 1998;30(8):1217-1222.

226. Aagaard P, Thorstensson A. Neuromuscular aspects of exercise-adaptive
responses evoked by strength training. In: Kjaer M, ed. Textbook of sport
medicine. London: Blackwell; 2003:70-106.






246


227. Bojsen-Moller J, Magnusson SP, Rasmussen LR, Kjaer M, Aagaard P. Muscle
performance during maximal isometric and dynamic contractions is influenced by
the stiffness of the tendinous structures. JApplPhysiol. Sep 2005;99(3):986-994.

228. Sale DG. Neural adaptation to resistance training. Med Sci Sports Exerc. Oct
1988;20(5 Suppl):S135-145.

229. Aagaard P, Simonsen EB, Andersen JL, Magnusson P, Dyhre-Poulsen P.
Increased rate of force development and neural drive of human skeletal muscle
following resistance training. JAppl Physiol. Oct 2002;93(4): 1318-1326.

230. Grimby L, Hannerz J, Hedman B. The fatigue and voluntary discharge properties
of single motor units in man. JPhysiol. Jul 1981;316:545-554.

231. Andersen LL, Aagaard P. Influence of maximal muscle strength and intrinsic
muscle contractile properties on contractile rate of force development. Eur JAppl
Physiol. Jan 2006;96(1):46-52.

232. Aagaard P, Simonsen EB, Trolle M, Bangsbo J, Klausen K. Effects of different
strength training regimes on moment and power generation during dynamic knee
extensions. Eur JAppl Physiol Occup Physiol. 1994;69(5):382-386.

233. Siff M. Biomechanical foundations of strength and power training. In: Zatsiorsky
V, ed. Biomechanics in Sport. London: Blackwell Scientific Ltd.; 2001:103-139.

234. Fishbain DA, Abdel-Moty E, Cutler RB, RosomoffHL, Steele-RosomoffR.
Detection of a "faked" strength task effort in volunteers using a computerized
exercise testing system. Am JPhysMedRehabil. May-Jun 1999;78(3):222-227.

235. Wiles JD, Boyson H, Balmer J, Bird SR. Validity and reliability of a new
isometric hand dynamometer. Sports Engineering. 2001/08// 2001;4(3):147-152.

236. Watts PB, Jensen RL. Reliability of peak forces during a finger curl motion
common in rock climbing. Measurement in Physical Education and Exercise
Science. 2003;7(4):263-267.

237. Kamen G. Electromyographic kinesiology. In: Robertson DGE, Caldwell GE,
Hamill J, Kamen G, Whittlesey SN, eds. Research Methods in Biomechanics. 1st
ed. Champaign: Human Kinetics; 2004:163-181.

238. Carpenter RHS. Global Motor Control. In: Carpenter RHS, ed. Neurophysiology.
New York: Oxford University Press; 1996:226-243.

239. Carpenter RHS. Local Motor Control. In: Carpenter RHS, ed. Neurophysiology.
New York: Oxford University Press; 1996:200-225.






247


240. Gilman S, Winans S. Motor Pathways. In: Gilman S, Winans S, eds. Manter and
Gatz's Essentials of Clinical Neuroanatomy and Neurophysiology. 10th ed.
Philadelphia: F. A. Davis; 2003:60-67.

241. Chow J. Electromyography (EMG). Gainesville: University of Florida; 2005:PET
6347, Biomechanical Instrumentation, lecture notes.

242. Bilodeau M, Arsenault AB, Gravel D, Bourbonnais D. EMG power spectrum of
elbow extensors: a reliability study. Electromyogr Clin Neurophysiol. Apr-May
1994;34(3):149-158.

243. Hasson SM, Williams JH, Signorile JF. Fatigue-induced changes in myoelectric
signal characteristics and perceived exertion. Can JSport Sci. Vol 14; 1989:99-
102.

244. Jones LA, Hunter IW. Effect of fatigue on force sensation. Exp Neurol. Sep
1983;81(3):640-650.

245. Suzuki H, Conwit RA, Stashuk D, Santarsiero L, Metter EJ. Relationships
between surface-detected EMG signals and motor unit activation. Med Sci Sports
Exerc. Sep 2002;34(9):1509-1517.

246. Hermens HJ, Boon KL, Zilvold G. The clinical use of surface EMG.
Electromyogr Clin Neurophysiol. May 1984;24(4):243-265.

247. Lago PJ, Jones NB. Low-frequency spectral analysis of the e.m.g. MedBiolEng
Comput. Nov 1981;19(6):779-782.

248. Gander RE, Hudgins BS. Power spectral density of the surface myoelectric signal
of the biceps brachii as a function of static load. Electromyogr Clin Neurophysiol.
Nov-Dec 1985;25(7-8):469-478.

249. De Luca CJ. Towards understanding the EMG signal. Muscles Alive. 4th ed.
Baltimore: Williams & Wilkinson; 1978.

250. Guha K, Anand S. Simulation linking EMG power spectra and rate coding.
Comput. Biol. Med. 1979;9:213-221.

251. Hagberg M, Ericson BE. Myoelectric power spectrum dependence on muscular
contraction level of elbow flexors. Eur JAppl Physiol Occup Physiol.
1982;48(2):147-156.

252. Gydikov A, Kosarov D. Some features of different motor units in human biceps
brachii. Pflugers Arch. Feb 18 1974;347(1):75-88.









253. Blinowska A, Verroust J, Cannet G. An analysis of synchronization and double
discharge effects on low frequency electromyographic power spectra.
Electromyogr Clin Neurophysiol. Oct-Dec 1980;20(6):465-480.

254. Kogi K, Hakamada T. Slowing of surface electromyogram and muscle strength in
muscle fatigue. Rep. Inst. Sc. Lab. 1962;60:27-41.

255. Kadefors R, Kaiser E, Petersen I. Dynamic spectrum analysis of myo-potentials
and with special reference to muscle fatigue. Electromyography. Jan-Apr
1968;8(1):39-74.

256. Lindstrom L, Magnusson R, Petersen I. Muscular fatigue and action potential
conduction velocity changes studied with frequency analysis of EMG signals.
Electromyography. Nov-Dec 1970;10(4):341-356.

257. Petrofsky JS, Lind AR. Frequency analysis of the surface electromyogram during
sustained isometric contractions. Eur JAppl Physiol Occup Physiol.
1980;43(2):173-182.

258. Mills KR. Power spectral analysis of electromyogram and compound muscle
action potential during muscle fatigue and recovery. JPhysiol. May
1982;326:401-409.

259. Sadoyama T, Miyano H. Frequency analysis of surface EMG to evaluation of
muscle fatigue. Eur JAppl Physiol Occup Physiol. 1981;47(3):239-246.

260. Winter D. EMG interpretation. In: Kumar S, Mital A, eds. Electromyography in
Ergonomics. London: Taylor and Francis; 1996:109-125.

261. Cobb S, Forbes A. Electromyographic studies of muscular fatigue in man.
American Journal of Physiology. 1923 ;65:234-251.

262. Hagbarth KE, Jessop J, Eklund G, Wallin EU. The Piper rhythm--a phenomenon
related to muscle resonance characteristics? Acta Physiol Scand. Feb
1983;117(2):263-271.

263. Kaiser E, Petersen I. Frequency analysis of muscle action potentials during tetanic
contraction. Electromyography. Jan-Apr 1963;3:5-17.

264. Sato M. Some problems in the quantitative evaluation of muscle fatigue by
frequency analysis of the electromyogram. JAnthropol. Soc. Nippon. Jan-Apr
1965;73:20-27.

265. Edwards RG, Lippold OC. The relation between force and integrated electrical
activity in fatigued muscle. JPhysiol. Jun 28 1956;132(3):677-681.






249


266. Maton B. Human motor unit activity during the onset of muscle fatigue in
submaximal isometric isotonic contraction. Eur JAppl Physiol Occup Physiol.
1981;46(3):271-281.

267. Solomonow M, Baten C, Smit J, et al. Electromyogram power spectra frequencies
associated with motor unit recruitment strategies. JAppl Physiol. Mar
1990;68(3):1177-1185.

268. Bigland-Ritchie B, Donovan EF, Roussos CS. Conduction velocity and EMG
power spectrum changes in fatigue of sustained maximal efforts. JAppl Physiol.
Nov 1981;51(5):1300-1305.

269. Kranz H, Williams AM, Cassell J, Caddy DJ, Silberstein RB. Factors determining
the frequency content of the electromyogram. JAppl Physiol. Aug
1983;55(2):392-399.

270. Stalberg E, Daube JR. Electromyographic methods. In: Stalberg E, ed. Clinical
Neurophysiology of Disorders of Muscle and Neuromuscular Junction, Including
Fatigue. Vol 2. 1st ed. Amsterdam: Elsevier; 2003.

271. Bauer JA, Murray RD. Electromyographic patterns of individuals suffering from
lateral tennis elbow. JElectromyogr Kinesiol. Aug 1999;9(4):245-252.

272. Shechtman O. Upper extremity musculoskeletal disorders: Electrodiagnosis.
Gainesville: University of Florida; 2003:6-8, RSD 6930, Musculoskeletal
disorders of upper extremity, lecture notes.

273. Riley NA, Bilodeau M. Changes in upper limb joint torque patterns and EMG
signals with fatigue following a stroke. DisabilRehabil. Dec 15 2002;24(18):961-
969.

274. Matre DA, Sinkjaer T, Svensson P, Arendt-Nielsen L. Experimental muscle pain
increases the human stretch reflex. Pain. Apr 1998;75(2-3):331-339.

275. Kang YM, Wheeler JD, Pickar JG. Stimulation of chemosensitive afferents from
multifidus muscle does not sensitize multifidus muscle spindles to vertebral loads
in the lumbar spine of the cat. Spine. Jul 15 2001;26(14):1528-1536.

276. Svensson P, Graven-Nielsen T, Matre D, Arendt-Nielsen L. Experimental muscle
pain does not cause long-lasting increases in resting electromyographic activity.
Muscle Nerve. Nov 1998;21(11):1382-1389.

277. Djupsjobacka M, Johansson H, Bergenheim M. Influences on the gamma-muscle-
spindle system from muscle afferents stimulated by increased intramuscular
concentrations of arachidonic acid. Brain Res. Nov 14 1994;663(2):293-302.






250


278. Ljubisavljevic M, Jovanovic K, Anastasijevic R. Changes in discharge rate of
fusimotor neurones provoked by fatiguing contractions of cat triceps surae
muscles. JPhysiol. Jan 1992;445:499-513.

279. Pedersen J, Sjolander P, Wenngren BI, Johansson H. Increased intramuscular
concentration of bradykinin increases the static fusimotor drive to muscle spindles
in neck muscles of the cat. Pain. Mar 1997;70(1):83-91.

280. Pedersen J, Ljubisavljevic M, Bergenheim M, Johansson H. Alterations in
information transmission in ensembles of primary muscle spindle afferents after
muscle fatigue in heteronymous muscle. Neuroscience. Jun 1998;84(3):953-959.

281. Haeri M, Asemani D, Gharibzadeh S. Modeling of pain using artificial neural
networks. Journal of Theoretical Biology. FEB 7 2003;220(3):277-284.

282. Sweet WH. Pain. In: Field J, Magoun HW, Hall WE, eds. Handbook of
Physiology. 1st ed. Washington, D. C.: Amer. Physiol. Sec.; 1959:459-506.

283. Melzack R, Wall PD. Pain mechanisms: A new theory. Science.
1965;150(3699):971-979.

284. Melzack R, Katz J. Pain measurement in persons in pain. In: Wall PD, Melzack
R, eds. Textbook ofPain. 4th ed. New York: Churchill Livingstone; 1999:409-
426.

285. Chapman CR. The affective dimension of pain: A model. In: Bromm B, Desmedt
JE, eds. Pain and the brain: From nociception to cognition. Vol 22. New York:
Raven Press; 1995:283-301.

286. Merskey H, Bogduk N, eds. IASP Task Force on Taxonomy. Classification of
chronic pain. 2nd ed. Seattle, WA: IASP Press; 1994.

287. Melzack R. The perception of pain. Scientific American. 1961;204(2):41-49.

288. Sherrington CS. The integrative action of the nervous system. New Haven: Yale
University Press; 1906.

289. Jensen MP, Karoly P. Self-report scales and procedures for assessing pain in
adults. In: Turk DC, Melzack R, eds. Handbook ofpain assessment. 2nd ed. New
York: The Guilford Press; 2001:15-34.

290. Craig KD. Emotions and psychobiology. In: Wall PD, Melzack R, eds. Textbook
ofPain. 4th ed. New York: Churchill Livingstone; 1999:331-343.

291. Schiefenhovel W. Perception, expression, and social function of pain: a human
ethological view. Sci Context. Spring 1995;8(1):31-46.









292. Robinson AJ. Central nervous system pathways for pain transmission an pain
control: Issues relevant to the practicing clinician. Journal of Hand Therapy.
1997;10(2):64-77.

293. Lundy-Ekman L. Somatosensory System. In: Lundy-Ekman L, ed. Neuroscience.
Fundamentals for Rehabilitation. 2nd ed. Philadelphia: W.B. Saunders Company;
2002:99-122.

294. Covington EC. The biological basis of pain. International Review ofPsychiatry.
MAY 2000; 12(2):128-147.

295. Apkarian AV, Bushnell MC, Treede RD, Zubieta JK. Human brain mechanisms
of pain perception and regulation in health and disease. Eur JPain. Aug
2005;9(4):463-484.

296. Lundy-Ekman L. Somatosensation: Clinical Applications. In: Lundy-Ekman L,
ed. Neuroscience. Fundamentals for Rehabilitation. 2nd ed. Philadelphia: W.B.
Saunders Company; 2002:123-152.

297. Mendell LM, Wall PD. Responses of Single Dorsal Cord Cells to Peripheral
Cutaneous Unmyelinated Fibres. Nature. Apr 3 1965;206:97-99.

298. Willis WD. Role of neurotransmitters in sensitization of pain responses. Ann N Y
AcadSci. Mar 2001;933:142-156.

299. Hardy JD, Woolf HG, Goodell H. Pain sensations and reactions. New York:
Hafner Pub; 1967.

300. Fields HL, Basbaum AI. Central nervous system mechanisms of pain modulation.
In: Wall PD, Melzack R, eds. Textbook ofPain. 3rd ed. New York: Churchill
Livingstone; 1994:243-257.

301. Coffield JA, Bowen KK, Miletic V. Retrograde tracing of projections between the
nucleus submedius, the ventrolateral orbital cortex, and the midbrain in the rat. J
Comp Neurol. Jul 15 1992;321(3):488-499.

302. Aronson PA. Pain theories -- A review for application in athletic training and
therapy. Athletic Therapy Today. 2002;7(4):8-13.

303. Loeser JD, Melzack R. Pain: an overview. The Lancet. 1999;353(1607-1609).

304. Turk DC, Melzack R. The measurement of pain and the assessment of people
experiencing. In: Turk DC, Melzack R, eds. Handbook ofpain assessment. 2nd
ed. New York: The Guilford Press; 2001:3-11.






252


305. Reed KL. Quick reference to occupational therapy. Gaithersburg: Aspen
Publishers; 1991.

306. Melzack R. From the gate to the neuromatrix. Pain. 1999;Supplement 6:S1210-
S1126.

307. Dionne RA, Bartoshuk L, Mogil J, Witter J. Individual responder analyses for
pain: does one pain scale fit all? Trends Pharmacol Sci. Mar 2005;26(3):125-130.

308. Gracely RH, McGrath P, Dubner R. Ratio scales of sensory and affective verbal
pain descriptors. Pain. 1978;5:5-18.

309. Jensen MP, Karoly P, O'Riordan EF, Bland F, Jr., Burns RS. The subjective
experience of acute pain: An assessment of the utility of 10 indices. Clinical
Journal ofPain. 1989;5:153-159.

310. Jensen MP, Karoly P, Harris P. Assessing the affective component of chronic
pain: development of the Pain Discomfort Scale. JPsychosom Res. 1991;35(2-
3):149-154.

311. Melzack R, Casey KL. Sensory, motivational, and central control determinants of
pain: A new conceptual model. In: Kenshalo D, ed. The skin senses. Springfield,
IL: Charles C Thomas; 1968:423-443.

312. Jensen MP, Dworkin RH, Gammaitoni AR, Olaleye DO, Oleka N, Galer BS.
Assessment of pain quality in chronic neuropathic and nociceptive pain clinical
trials with the Neuropathic Pain Scale. JPain. Feb 2005;6(2):98-106.

313. Ong KS, Seymour RA. Pain measurement in humans. Surgeon. Feb 2004;2(1):15-
27.

314. Durain D. Primary dysmenorrhea: assessment and management update. Journal of
Midwifery & Womens Health. NOV-DEC 2004;49(6):520-528.

315. Dubuisson D, Melzack R. Classification of clinical pain descriptions by multiple
group discriminant analysis. Exp Neurol. May 1976;51(2):480-487.

316. Galer BS, Sheldon E, Patel N, Codding C, Burch F, Gammaitoni AR. Topical
lidocaine patch 5% may target a novel underlying pain mechanism in
osteoarthritis. Current Medical Research and Opinion. SEP 2004;20(9):1455-
1458.

317. Ehde DM, Jensen MP, Engel JM, Turner JA, Hoffman AJ, Cardenas DD. Chronic
pain secondary to disability: A review. Clinical Journal ofPain. JAN-FEB
2003;19(1):3-17.









318. Benrud-Larson LM, Wegener ST. Chronic pain in neurorehabilitation
populations: Prevalence, severity and impact. NeuroRehabilitation.
2000;14(3):127-137.

319. Dudgeon BJ, Ehde DM, Cardenas DD, Engel JM, Hoffman AJ, Jensen MP.
Describing pain with physical disability: narrative interviews and the McGill Pain
Questionnaire. Arch Phys MedRehabil. Jan 2005;86(1): 109-115.

320. Beattie PF, Dowda M, Feuerstein M. Differentiating sensory and affective-
sensory pain descriptions in patients undergoing magnetic resonance imaging for
persistent low back pain. Pain. Jul 2004; 110(1-2):189-196.

321. Jensen MP, Karoly P, Braver S. The measurement of clinical pain intensity: a
comparison of six methods. Pain. Oct 1986;27(1):117-126.

322. Butler PV. Linear analogue self-assessment and procrustean measurement: A
critical review of visual analogue scaling in pain assessment. Journal of Clinical
Psychology in Medical Settings. MAR 1997;4(1):111-129.

323. Coll AM, Ameen JR, Mead D. Postoperative pain assessment tools in day
surgery: literature review. JAdv Nurs. Apr 2004;46(2):124-133.

324. McCormack HM, Home DJ, Sheather S. Clinical applications of visual analogue
scales: a critical review. PsycholMed. Nov 1988;18(4):1007-1019.

325. Huskisson EC. Measurement of pain. Lancet. Nov 9 1974;2(7889):1127-1131.

326. Scott J, Huskisson EC. Graphic representation of pain. Pain. Jun 1976;2(2):175-
184.

327. Melzack R. The short-form McGill Pain Questionnaire. Pain. Aug
1987;30(2):191-197.

328. Price DD, McGrath PA, Rafii A, Buckingham B. The validation of visual
analogue scales as ratio scale measures for chronic and experimental pain. Pain.
Sep 1983;17(1):45-56.

329. Stephenson NL, Herman JA. Pain measurement: a comparison using horizontal
and vertical visual analogue scales. ApplNurs Res. Aug 2000;13(3):157-158.

330. Cook AJ, Roberts DA, Henderson MD, Van Winkle LC, Chastain DC, Hamill-
Ruth RJ. Electronic pain questionnaires: a randomized, crossover comparison
with paper questionnaires for chronic pain assessment. Pain. Jul 2004; 110(1-
2):310-317.

331. Stevens SS. On the psychophysical law. Psychol Rev. May 1957;64(3):153-181.









332. Jones LA. Perception of force and weight: theory and research. Psychol Bull. Jul
1986;100(1):29-42.

333. Eisler H. Subjective scale of force for a large muscle group. JExp Psychol. Sep
1962;64:253-257.

334. Eisler H. The Ceiling of Psychophysical Power Functions. Am JPsychol. Sep
1965;78:506-509.

335. Stevens JC, Cain WS. Effort in Isometric Muscular Contractions Related to Force
Level and Duration. Perception & Psychophysics. 1970;8(4):240-&.

336. Stevens JC, Mack JD. Scales of Apparent Force. Journal ofExperimental
Psychology. 1959;58(5):405-413.

337. Cain WS, Stevens JC. Effort in sustained and phasic handgrip contractions. Am J
Psychol. Mar 1971;84(1):52-65.

338. Borg G. Perceived exertion as an indicator of somatic stress. ScandJRehabil
Med. 1970;2(2):92-98.

339. Borg GA. Psychophysical bases of perceived exertion. Med Sci Sports Exerc.
1982;14(5):377-381.

340. Spielholz P. Calibrating Borg scale ratings of hand force exertion. ApplErgon.
Sep 2006;37(5):615-618.

341. Borg E, Kaij ser L. A comparison between three rating scales for perceived
exertion and two different work tests. ScandJMedSci Sports. Feb 2006;16(1):57-
69.

342. Price DD. Psychological and neural mechanisms ofpain. New York: Raven
Press; 1988.

343. Willis WD. The pain system. The neuronal basis of nociceptive transmission in
the mammalian nervous system. Basel: Karger; 1985.

344. Christensen BN, Perl ER. Spinal neurons specifically excited by noxious or
thermal stimuli: marginal zone of the dorsal horn. JNeurophysiol. Mar
1970;33(2):293-307.

345. Han ZS, Zhang ET, Craig AD. Nociceptive and thermoreceptive lamina I neurons
are anatomically distinct. Nat Neurosci. Jul 1998; 1(3):218-225.









346. Craig AD, Krout K, Andrew D. Quantitative response characteristics of
thermoreceptive and nociceptive lamina I spinothalamic neurons in the cat. J
Neurophysiol. Sep 2001;86(3):1459-1480.

347. Flaherty SA. Pain measurement tools for clinical practice and research. Aana J.
Apr 1996;64(2):133-140.

348. Morley S, Pallin V. Scaling the affective domain of pain: a study of the
dimensionality of verbal descriptors. Pain. Jul 1995;62(1):39-49.

349. Morley S, Hassard A. The development of a self-administered psychophysical
scaling method: internal consistency and temporal stability in chronic pain
patients. Pain. Apr 1989;37(1):33-39.

350. Heft MW, Gracely RH, Dubner R, McGrath PA. A validation model for verbal
description scaling of human clinical pain. Pain. Dec 1980;9(3):363-373.

351. Jamner LD, Tursky B. Syndrome-specific descriptor profiling: a
psychophysiological and psychophysical approach. Health Psychol.
1987;6(5):417-430.

352. Macfarlane TV, Blinkhorn AS, Craven R, et al. Can one predict the likely specific
orofacial pain syndrome from a self-completed questionnaire? Pain. Oct
2004;111(3):270-277.

353. Campbell TS, Hughes JW, Girdler SS, Maixner W, Sherwood A. Relationship of
ethnicity, gender, and ambulatory blood pressure to pain sensitivity: effects of
individualized pain rating scales. JPain. Apr 2004;5(3):183-191.

354. Myles PS. The pain visual analog scale: linear or nonlinear? Anesthesiology. Mar
2004; 100(3):744; author reply 745.

355. Myles PS, Troedel S, Boquest M, Reeves M. The pain visual analog scale: is it
linear or nonlinear? Anesth Analg. Dec 1999;89(6):1517-1520.

356. Downie WW, Leatham PA, Rhind VM, Wright V, Branco JA, Anderson JA.
Studies with pain rating scales. Annals of Rheumatic Diseases. 1978;37:378-381.

357. Price DD, Bush FM, Long S, Harkins SW. A comparison of pain measurement
characteristics or mechanical visual analogue and simple numerical rating scales.
Pain. 1994;217-226.

358. Robertson DGE, Caldwell GE, Hamill J, Kamen G, Whittlesey SN, eds. Research
Methods in Biomechanics. 1st ed. Champaign: Human Kinetics; 2004.






256


359. Hagg GM, Milerad E. Forearm extensor and flexor muscle exertion during
simulated gripping work -- an electromyographic study. Clin Biomech (Bristol,
Avon). Jan 1997;12(1):39-43.

360. Chu-Andrews J, Johnson RJ. Electrodiagnosis: An anatomical and clinical
approach. Philadelphia: Lippincott; 1986.

361. Thought Technology Ltd. FlexComp Infiniti Hardware Manual. Montreal:
Thought Technology Ltd.; 2006:50.

362. Trossman PB, Li PW. The Effect of the Duration of Intertrial Rest Periods on
Isometric Grip Strength Performance in Young-Adults. Occupational Therapy
Journal ofResearch. NOV-DEC 1989;9(6):362-378.

363. Stull GA, Clarke DH. Patterns of recovery following isometric and isotonic
strength decrement. Med Sci Sports. Fall 1971;3(3):135-139.

364. Sahlin K. Metabolic changes limiting muscle performance. In: Saltin B, ed.
Biochemistry of exercise VI. Champaign, IL: Human Kinetics; 1986:323-343.

365. Maxwell SE, Delaney HD. Designing experiments and analyzing data: A model
comparison perspective. Mahwah, NJ: Lawrence Erlbaum Associates, Publishers;
2000.

366. SPSSfor Windows [computer program]. Version Rel. 15.0.1.1. Chicago; 2007.

367. Shrout PE, Fleiss JL. Intraclass Correlations Uses in Assessing Rater Reliability.
Psychological Bulletin. 1979;86(2):420-428.

368. Semmler JG, Enoka RM. Neural contributions to changes in muscle strength. In:
Zatsiorsky V, ed. Biomechanics in Sport. London: Blackwell Scientific Ltd.;
2001.

369. Schwid SR, Thornton CA, Pandya S, et al. Quantitative assessment of motor
fatigue and strength in MS. Neurology. Sep 11 1999;53(4):743-750.

370. Sanjak M, Brinkmann J, Belden DS, et al. Quantitative assessment of motor
fatigue in amyotrophic lateral sclerosis. JNeurol Sci. Oct 15 2001;191(1-2):55-
59.

371. Nicklin J, Karni Y, Wiles CM. Shoulder abduction fatiguability. JNeurol
Neurosurg Psychiatry. Apr 1987;50(4):423-427.

372. Moore JS. Biomechanical models for the pathogenesis of specific distal upper
extremity disorders. Am JIndMed. May 2002;41(5):353-369.






257


373. Sokk J, Gapeyeva H, Ereline J, Kolts I, Paasuke M. Shoulder muscle strength and
fatigability in patients with frozen shoulder syndrome: the effect of 4-week
individualized rehabilitation. Electromyogr Clin Neurophysiol. Jul 2007;47(4-
5):205-213.

374. Backman E, Johansson V, Hager B, Sjoblom P, Henriksson KG. Isometric muscle
strength and muscular endurance in normal persons aged between 17 and 70
years. ScandJRehabilMed. Jun 1995;27(2):109-117.

375. Laubach LL. Comparative muscular strength of men and women: a review of the
literature. Aviat Space Environ Med. May 1976;47(5):534-542.

376. Miller AE, MacDougall JD, Tarnopolsky MA, Sale DG. Gender differences in
strength and muscle fiber characteristics. Eur JAppl Physiol Occup Physiol.
1993;66(3):254-262.

377. Hakkinen K, Pakarinen A. Muscle strength and serum testosterone, cortisol and
SHBG concentrations in middle-aged and elderly men and women. Acta Physiol
Scand. Jun 1993;148(2):199-207.

378. Yamaji S, Demura S, Nakada M. Sex differences and properties of the decreasing
force during sustained static grip at various target forces. Percept Mot .\kd// Aug
2006;103(1):29-39.

379. Bystrom S, Fransson-Hall C. Acceptability of intermittent handgrip contractions
based on physiological response. Hum Factors. Mar 1994;36(1):158-171.

380. Kilbom A, Makarainen M, Sperling L, Kadefors R, Liedberg L. Tool design, user
characteristics and performance: a case study on plate-shears. ApplErgon. Jun
1993;24(3):221-230.

381. Bystrom SE, Kilbom A. Physiological response in the forearm during and after
isometric intermittent handgrip. Eur JAppl Physiol Occup Physiol.
1990;60(6):457-466.

382. Bystrom SE, Mathiassen SE, Fransson-Hall C. Physiological effects of
micropauses in isometric handgrip exercise. Eur JAppl Physiol Occup Physiol.
1991;63(6):405-411.

383. Snijders CJ, Volkers AC, Mechelse K, Vleeming A. Provocation of
epicondylalgia lateralis (tennis elbow) by power grip or pinching. Med Sci Sports
Exerc. Oct 1987;19(5):518-523.

384. Mogk JP, Keir PJ. The effects of posture on forearm muscle loading during
gripping. Ergonomics. Jul 15 2003;46(9):956-975.









385. De Serres SJ, Milner TE. Wrist muscle activation patterns and stiffness associated
with stable and unstable mechanical loads. Exp Brain Res. 1991;86(2):451-458.

386. Kupa EJ, Roy SH, Kandarian SC, De Luca CJ. Effects of muscle fiber type and
size on EMG median frequency and conduction velocity. JAppl Physiol. Jul
1995;79(1):23-32.

387. Krivickas LS, Taylor A, Maniar RM, Mascha E, Reisman SS. Is spectral analysis
of the surface electromyographic signal a clinically useful tool for evaluation of
skeletal muscle fatigue? JCin Neurophysiol. Mar 1998;15(2): 138-145.

388. Fuglsang-Frederiksen A, Ronager J. EMG power spectrum, turns-amplitude
analysis and motor unit potential duration in neuromuscular disorders. JNeurol
Sci. Jun 1990;97(1):81-91.

389. Ronager J, Christensen H, Fuglsang-Frederiksen A. Power spectrum analysis of
the EMG pattern in normal and diseased muscles. JNeurol Sci. Dec 1989;94(1-
3):283-294.

390. Rossi B, Siciliano G, Carboncini MC, et al. Muscle modifications in Parkinson's
disease: myoelectric manifestations. Electroencephalogr Clin Neurophysiol. Jun
1996;101(3):211-218.

391. Buonocore M, Opasich C, Casale R. Early development of EMG localized muscle
fatigue in hand muscles of patients with chronic heart failure. Arch Phys Med
Rehabil. Jan 1998;79(1):41-45.

392. Casale R, Buonocore M, Di Massa A, Setacci C. Electromyographic signal
frequency analysis in evaluating muscle fatigue of patients with peripheral arterial
disease. Arch PhysMedRehabil. Oct 1994;75(10):1118-1121.

393. Falla D, Rainoldi A, Merletti R, Jull G. Myoelectric manifestations of
sternocleidomastoid and anterior scalene muscle fatigue in chronic neck pain
patients. Clin Neurophysiol. Mar 2003;114(3):488-495.

394. Hunter SK, Enoka RM. Sex differences in the fatigability of arm muscles depends
on absolute force during isometric contractions. JApplPhysiol. Dec
2001;91(6):2686-2694.















BIOGRAPHICAL SKETCH

Bhagwant Singh Sindhu was born on July 9, 1976 in New Delhi, India. He grew up

in New Delhi, graduating from Springdales Public School in 1994. He earned his B.Sc.

(H) in Occupational Therapy from University of Delhi 1998 and his M.S. in Occupational

Therapy from the University of Wisconsin-Milwaukee in 2002.


259





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EFFECT OF UPPER EXTREMITY INJU RY ON GRIP STRE NGTH EFFORT By BHAGWANT SINGH SINDHU A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2007

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2007 Bhagwant Singh Sindhu

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To my parents, for their countless prayers.

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iv ACKNOWLEDGMENTS I would like to thank my supervisory co mmittee. First and foremost, I thank Dr. Orit Shechtman, my mentor and chair of my graduate study committee. Thank you for your vision, support, and encouragement. I am especially thankful for the moments when you lifted my spirits with your simple gestures of kindness. Thank you for polishing my research and teaching skills and thank you fo r helping me become a better person. I am lucky to have you as a mentor. I would also like to say a special thanks to Dr. Paul Davenport, the external member on my gr aduate committee. Thank you for always helping me in anyway you could, and for ch allenging and guiding my thought process. Working in your lab was an enriching experi ence. Dr. John Rosenbek, I am grateful to you for teaching me the meaning of being a re habilitation scientist. Dr. Mark Bishop, I am very grateful for all your help with the analysis of EMG data. I thank you all for opening your hearts to me, for taking me under your wings, and for sharing your knowledge with me. I would also like to than k Thought Technology Ltd. and the American Society of Hand Therapists (ASHT) for s upporting this research project. I am grateful to Thought Technology Ltd. for loaning their equipment a nd for providing technical support. I am also grateful to the ASHT for partially fundi ng this research project. I could not have completed this project without your support. I am grateful to the faculty and staff in the Department of O ccupational Therapy at University of Florida for their support and en couragement. I am espe cially grateful to

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v Wendy Holt for always being there to help, support, encourage and advise me. Wendy, you have taught me so much. It has been a pleasure working w ith you and sharing an office with you. I am also very grateful to my fellow gr aduate students: Leigh, Rick, Patricia, Jessica, Inga, Megan, Pey-Shan, Jia-hwa, Er ic, Michelle, Cristin a, Sandy, Swathy, and Kezia. Thank you all for being so helpful and supportive at various stages of my graduate study. Without your friendship I could not have survived the last six years in the RSD program. Last, but not the least, I w ould like to thank my grandm other, parents, aunts and uncles, sister and brother-in-la w, and my cousins for their love and support. Thank you all for your guidance at each and every step. You inspired and motivated me to work hard. You have dreamed of and prayed for my success. I am not sure what I would have done without you.

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vi TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iv LIST OF TABLES.............................................................................................................ix LIST OF FIGURES..........................................................................................................xii ABSTRACT.....................................................................................................................xi v CHAPTER 1 INTRODUCTION........................................................................................................1 Problem Statement........................................................................................................1 Specific Aims................................................................................................................2 Specific Aim 1.......................................................................................................3 Specific Aim 2.......................................................................................................3 Specific Aim 3.......................................................................................................4 Background...................................................................................................................4 Significance................................................................................................................12 Previous Study............................................................................................................14 Definition of Terms....................................................................................................16 2 LITERATURE REVIEW...........................................................................................23 Cost, Magnitude and Description of Upper Extremity Disorders..............................23 Use of Grip Strength to Assess Upper Extremity Musculoskeletal Disorders...........25 Differences between Maximal and Submaximal Effort.............................................26 Grip Strength Tests for Detecting Submaximal Effort...............................................27 Coefficient of Variation (CV).............................................................................27 Rapid Exchange Grip Test (REG).......................................................................29 Five Rung Grip (5R) Test....................................................................................30 Force-Time Curve.......................................................................................................32 Reliability............................................................................................................33 Athletics...............................................................................................................35 Healthcare............................................................................................................36 Maximal Effort....................................................................................................38 Biopac Dynamometer.................................................................................................41 Surface Electromyographic Activity..........................................................................42

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vii Origin and Propagation........................................................................................42 Signal Properties..................................................................................................44 Increasing Force..................................................................................................46 Fatigue.................................................................................................................48 Injury...................................................................................................................49 Maximal Effort....................................................................................................51 Pain........................................................................................................................... ..53 Transmission of Pain Sensation...........................................................................54 Regulation of Pain Sensa tion by the Nervous System........................................57 Acute versus Chronic Pain..................................................................................63 Dimensions of Pain..............................................................................................65 Assessment of Pain..............................................................................................68 Perceived Magnitude of Grip Force...........................................................................70 Summary.....................................................................................................................71 3 METHODS.................................................................................................................77 Participants.................................................................................................................77 Materials and Equipment............................................................................................77 Instruments for Generating the F-T Curve..........................................................78 Instruments for Recording the EMG Signal........................................................79 Paper-and-Pencil Tests........................................................................................82 Study Design...............................................................................................................83 Procedure....................................................................................................................84 Participant Recruitment Phase.............................................................................84 Data Collection Phase..........................................................................................84 Statistical Analysis......................................................................................................90 4 RESULTS.................................................................................................................101 Subjects.....................................................................................................................10 1 Specific Aim 1..........................................................................................................101 Peak Force.........................................................................................................102 Time-to-peak Force...........................................................................................102 Slope of the Force-generation Phase.................................................................103 Slope of the Force-decay Phase.........................................................................103 Specific Aim 2..........................................................................................................104 Flexor EMG Amplitude.....................................................................................104 Extensor EMG Amplitude.................................................................................105 Flexor Median Frequency Ratio........................................................................105 Extensor Median Frequency Ratio....................................................................105 Specific Aim 3..........................................................................................................106 Test-Retest Reliability.......................................................................................106 Validity..............................................................................................................107 Slope of force-generation phase.................................................................107 Slope of force-decay phase.........................................................................108 Median frequency ratio..............................................................................108

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viii Post-Hoc Analysis....................................................................................................108 Summary...................................................................................................................108 5 DISCUSSION...........................................................................................................165 Force-Time Curve Characteristics............................................................................167 Differences between Maximal and Submaximal Effort....................................168 Differences between the Injured and Uninjured Hands....................................170 Gender Differences............................................................................................171 Electromyographic Properties..................................................................................171 Differences between Maximal and Submaximal Effort....................................174 Differences between Injured and Uninjured Hands..........................................175 Gender Differences............................................................................................176 Force-Decay Phase...................................................................................................177 Reliability and Validity.............................................................................................178 Limitations................................................................................................................184 Conclusions...............................................................................................................185 APPENDIX A SAMPLE SIZE CALCULATION............................................................................186 B CORRELATION MATRIX FOR DYNAMOMETER CALIBRATION................187 C DEMOGRAPHIC QUESTIONNAIRE....................................................................189 D LETTER TO HEALTHCARE PROFESSIONALS WITH INCLUSION AND EXCLUSION CRITERIA........................................................................................193 E RANDOMIZATION ORDER AND SHEET...........................................................195 F CHECKLISTS USED DURING TH E DATA COLLECTION PROCESS.............197 G DATA COLLECTION FORM.................................................................................200 LIST OF REFERENCES.................................................................................................228 BIOGRAPHICAL SKETCH...........................................................................................259

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ix LIST OF TABLES Table page 1-1 F-T curve slope and EMG amplitude values from the pilot study...........................20 1-2 Summary of sensitivity a nd specificity values of sincerity of effort tests................20 2-1 Differences between maximal and submaximal effort.............................................73 2-2 Sensitivity and specificity values of different sincerity of effort tests.....................74 2-3 Differences between second order pain neurons......................................................75 2-4 Strengths and weaknesses of pain intensity assessments.........................................76 3-1 Schematic representation of the study protocol.......................................................94 3-2 Calculating sensitivity a nd specificity for the slope cut-off value of X during the force-decay phase.....................................................................................................95 4-1 Demographic characteristics of the study sample..................................................110 4-2 Injury related characte ristics of the study sample..................................................111 4-3 First session averages of the F-T curve characteristics..........................................112 4-4 Second session averages of the F-T curve characteristics......................................113 4-5 Four-Way ANOVA on the values of peak force....................................................114 4-6 Three-Way ANOVA on first sessi on values of the peak force..............................115 4-7 Three-Way ANOVA on second session values of the peak force.........................116 4-8 Four-Way ANOVA on the values of time-to-peak force.......................................117 4-9 Three-Way ANOVA on the first sessi on values of time-to-peak force.................118 4-10 Four-Way ANOVA on the slopes of the force-generation phase..........................119 4-11 Three-Way ANOVA on the first session slopes of the force-generation phase.....120

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x 4-12 Four-Way ANOVA on the slopes of the force-decay phase..................................121 4-13 Three-Way ANOVA on the first sessi on slopes of the force-decay phase............122 4-14 Three-Way ANOVA on the second session slopes of the force-decay phase........123 4-15 Average values for EMG amplitude.......................................................................124 4-16 Average values of EMG median frequency for the first session............................125 4-17 Average values of EMG median frequency for second session values..................126 4-18 Four-Way ANOVA on flexor EMG amplitude......................................................127 4-19 Three-Way ANOVA on first session va lues of the flexor EMG amplitude..........128 4-20 Four-Way ANOVA on extensor EMG amplitude..................................................129 4-21 Three-Way ANOVA the on first session extensor EMG amplitude......................130 4-22 Three-Way ANOVA on the second session extensor EMG amplitude.................131 4-23 Four-Way ANOVA on the flexor EMG median frequency ratio...........................132 4-24 Three-Way ANOVA on the first sessi on values of flexor EMG median frequency ratio........................................................................................................133 4-25 Four-Way ANOVA on the extensor EMG median frequency ratio.......................134 4-26 Three-Way ANOVA on the first sessi on values of extensor EMG median frequency ratio........................................................................................................135 4-27 Three-Way ANOVA on the second session values of extensor EMG median frequency ratio........................................................................................................136 4-28 Intraclass Correlation Coefficients for F-T curve characteristics..........................137 4-29 Intraclass Correlation Coefficients for EMG properties........................................138 4-30 Summary of main effects of e ffort for force and EMG measures..........................139 4-31 Sensitivity and specificity of specific slope cutoff values for force-generation phase.......................................................................................................................140 4-32 Sensitivity and specificity values of specific slope cutoff values for force-decay phase.......................................................................................................................142 4-33 Sensitivity and specificity of speci fic flexor MF-ratio cutoff values.....................143

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xi 4-34 Sensitivity and specificity of speci fic extensor MF-ratio cutoff values.................144 4-35 Summary of sensitivity and specificity values for the Force and EMG measures.145 A-1 Range of maximal and submaximal effort slope values........................................186 B-1 Pearson correlation coefficients ( r ) between weekly voltage outputs obtained during the dynamometer calibration process.........................................................188 E-1 Randomization sheet used in the study..................................................................196

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xii LIST OF FIGURES Figure page 1-1 ROC curve for the slope of force-decay phase.........................................................21 1-2 Typical maximal and submaximal grip efforts........................................................22 3-1 Biomechanical instruments for recodi ng force and electromyographic signals.......96 3-2 Electronic Jamar dynamometer................................................................................97 3-3 MyoScan active sensors...........................................................................................97 3-4 FlexComp Infiniti encoder.......................................................................................98 3-5 BioGraph Infiniti polygraph software......................................................................98 3-6 Pain Intensity Visual Analog Scale..........................................................................99 3-7 Perceived Effort Visual Analog Scale......................................................................99 3-8 Setup used to check the dynamometer calibration.................................................100 4-1 Interaction between session and injury for peak force...........................................146 4-2 Average values of peak force for maximal and submaximal grip efforts..............147 4-3 Significant interactions for peak force values........................................................148 4-4 Average values of time-to-peak force....................................................................149 4-5 Interaction between effort and inju ry for slope of force-generation phase............150 4-6 Average values of slopes of force-generation phase..............................................151 4-7 Interaction between effort and in jury for slope of force-decay phase....................152 4-8 Interaction between session and ge nder for slope of force-decay phase................153 4-9 Average values of slopes of force-decay phase for maximal and submaximal grip efforts..............................................................................................................154

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xiii 4-10 Interaction between effort and injury for flexor EMG amplitude..........................155 4-11 Average values of flexor EMG amplitude for maximal and submaximal grip efforts.....................................................................................................................156 4-12 Interaction between effort and session for extensor EMG amplitude....................157 4-13 Average values of extensor EMG amplitude.........................................................158 4-14 Average values of flexor MF-ratio........................................................................159 4-15 Interaction between injury, effort and gender for flexor MF-ratio........................160 4-16 Average values of extensor MF-ratio.....................................................................161 4-17 Interaction between injury and effort for the first session va lues of extensor EMG MF-ratio........................................................................................................162 4-18 ROC curve for slope of force-generation phase.....................................................163 4-19 ROC curve for MF-ratio of forear m flexor and extensor muscles.........................164 E-1 Randomization orders used in the study................................................................195 F-1 Checklist used by the research assistants...............................................................198 F-2 Checklist used by the test administrator.................................................................199

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xiv Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy EFFECT OF UPPER EXTREMITY INJU RY ON GRIP STRE NGTH EFFORT By Bhagwant Singh Sindhu December 2007 Chair: Orit Shechtman Major: Rehabilitation Science Force-time curve (F-T curve) and elec tromyographic (EMG) measures have been used to differentiate between maximal and submaximal grip efforts. The Force-Time Curve Test (F-T Curve Test), which uses the slopes of the force-generation phase and the force-decay phase to detect submaximal effo rt, has been shown to be valid in healthy people. However, the validity of the F-T Cu rve Test has not been examined in people with UEMDs. The primary purpose of this study was to exam ine if the F-T Curve Test is valid in people with UEMDs. Another purpose of this study was to examine if other F-T Curve characteristics and EMG propertie s are valid sincerity of effo rt measures in people with UEMDs. Forty subjects participated in the study. Each subject performed 2 sessions of 2 maximal and 4 submaximal grip efforts with each hand. Each grip lasted 6-seconds. The order of the efforts (maximal versus submaximal) was randomized and the test administrator was blinded to th e level of effort exerted by the subject. The force-time

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xv curve and EMG signal of each contraction were recorded and following dependent variables were measured: peak force, time-to-peak force, slopes of the force-generation phase and the force-decay phase, as well as forearm flexor and extensor EMG amplitude and MF-ratio. The dependent variable scores were subjected to the fo llowing analyses: Repeatedmeasures ANOVA were used to compare the de pendent variables with effort, injury, and session as the within-subject va riables and gender as between subject variable. Test-retest reliability was analyzed using the ICC. Sensitiv ity and specificity values were calculated and ROC curves were plotted to fi nd the optimal slope cutoff values. All dependent variables identified diffe rences between maximal and submaximal efforts. The test-retest relia bility ranged from 0.3 to 0.96. The slope of the forcegeneration phase was the most effective in distinguishing between maximal and submaximal efforts but yielded overall e rror rates 55% for women and 60% for men. Despite the significant differences between maximal and submaximal efforts, we did not find acceptable combinations of sensitiv ity and specificity for detecting sincerity of effort. Therefore, the F-T curve and EM G measures may not be clinically valid.

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1 CHAPTER 1 INTRODUCTION Problem Statement Upper extremity musculoskeletal disorder s and injuries (UEMDs) may result in compromised grip strength.1 Grip strength depends on the type, rate and number of contracting muscle fibers.2 Reduced grip strength (weakne ss of grip) brought about by injury may be due to either a reduction in the rate and number of contracting muscle fibers3, changes in muscle-fiber-type3-7, or pain.8-10 Pain has been associated with decreases in: voluntary muscle activity11-17, electromyographic (EMG) activity11, 12, motor unit discharge rates14, 15, -motor neuron activity16, speed of force generation17, and endurance time.13 Maximal voluntary grip strength scores of people with UEMDs are used by clinicians18 to determine the extent of injury19, disease process20, and progress in rehabilitation.21 Grip strength is a valid indicato r of musculoskeletal pathology and recovery from such pathology only when people exert a sincere, maximal voluntary effort.22-27 Weakness of grip strength may be brought about by an injury but could also be due to exertion of submaximal effort. Submaximal effort may be exerted during evaluation and treatment for a variety of r easons, either unintentional or intentional. Unintentional submaximal effort may be exerted as a result of pain, f ear of pain and fear of re-injury. Intentional submax imal effort may be exerted for secondary gain (such as money, benefits, or attention). To improve reha bilitative care, clinicians need to be able

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2 to distinguish between a maximal voluntary grip effort (exerted by a client with true weakness of grip) and a submaximal grip effort. Force-time curve (F-T curve) charac teristics and elec tromyographic (EMG) properties generated during isometric grip contraction have shown promise in differentiating between maximal and submaximal efforts.28 The F-T curve is generated by plotting force generated by a contracting mu scle over a period of time during a single strength trial.29 The F-T curve characteristics include the slope of the force-generation phase, the slope of the force-decay phase, and the time-to-peak force28, whereas, the EMG properties include its amplitude and frequency.30-32 So far, these F-T curve characteristics and EMG properties have been described in healthy people.28, 30-32 However, little evidence exists regarding the effects of UEMDs on th e nature of maximal voluntary grip contraction as expressed by the F-T curve characteristics and EMG properties. The purpose of this research propos al was to identify if selected F-T curve characteristics and EMG properties can form vali d sincerity of effort tests in people with UEMDs. As part of this study we examined 4 force and 2 EMG measures: peak force, time-to-peak force, slope of force-genera tion phase, slope of fo rce-decay phase, EMG amplitude, and median frequency ratio. Specific Aims The purpose of the study was three-fold: (1) to compare the force-time curve characteristics of maximal and submaximal grip effort exerted by the injured versus uninjured hand, (2) to compare the elect romyographic properties of maximal and submaximal grip effort exerted by the injure d versus uninjured hand, and (3) to examine the reliability and validity of the force-time curve test in distinguishing between maximal and submaximal grip efforts.

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3 Specific Aim 1 To examine the difference in force-time cu rve (F-T curve) characteristics between the injured and uninjured hands as well as between maximal and submaximal efforts. The characteristics that we examined were: 1. Peak force 2. Time-to-peak force 3. Slope of the force-generation phase 4. Slope of the force-decay phase Hypothesis 1a. The peak force will be significantly greater, time-to-peak force will be significantly faster, and slope of the fo rce-generation phase will be significantly steeper for the uninjured hand than for the injured hand, whereas, the slope of the forcedecay phase will be significantly steeper for the injured hand than for the uninjured hand. Hypothesis 1b. The peak force will be significan tly greater, time-to-peak force will be significantly slower, and the slope of the force-generation phase and the force-decay phase will be significantly steeper for maximal effort than for submaximal effort. Specific Aim 2 To examine the difference in electromyographi c (EMG) properties between the injured and uninjured hand as well as between maximal and submaximal effort. The characteristics that we examined were: 1. The amplitude of forearm muscle EMG 2. The median frequency ratio of last to first second Hypothesis 2a. The forearm muscle EMG amplitude will be significantly greater for the uninjured hand than for the injured hand, whereas, the median frequency ratio will be significantly smaller for injure d hand than for the uninjured hand.

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4 Hypothesis 2b. The forearm muscle EMG amplitude will be significantly greater, whereas, the median frequency ratio will be si gnificantly smaller for maximal effort than for submaximal effort. Specific Aim 3 To examine the reliability and validity of the force-time curve characteristics and EMG properties in distinguish ing between maximal and submaximal grip efforts. The psychometric properties that we tested were: 1. The reliability assessed by identifying test-retest reliability 2. The validity assessed by identifying the effectiveness Hypothesis 3a. The F-T curve characteristics and EMG properties will consistently measure grip efforts as expressed by high test-retest reliability (r> 0.9). Hypothesis 3b. The F-T curve characteristics and EMG properties is valid for measuring of grip efforts, i.e. effec tive in differentiating between maximal and submaximal grip efforts, as expressed by a combined optimal value of 80% sensitivity and 90% specificity. Background Musculoskeletal disorders and injuries ha ve an enormous and growing impact on American society.33 In 1996, 53.9 million Americans, or 1 in 5 Americans, reported having at least 1 musculoskeletal condition.34 Scientists have pr edicted a substantial increase in the prevalence of musculoske letal conditions. By 2020, arthritis alone will affect an estimated 59.4 million Americans.35 The prevalence of physical disabilities caused by musculoskeletal conditions has been estimated at 4-5% of the population.36 Musculoskeletal impairments have been ra nked number one among impairments due to chronic conditions.33, 37 Musculoskeletal and connective tissue disorders also represent

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5 17.2% of all activity limiting conditions.38 Besides the obvious physical effects, musculoskeletal conditions signifi cantly affect the psychosocial status of the people with these conditions as well as their families and caregivers.33 The economic burden of musculoskeletal conditions is substantial: the total cost amounts to more than $250 billion per year39 and the medical care expenditure for pe ople with musculoskeletal conditions is 50% higher than for people with non -musculoskeletal chronic conditions.34 Specifically, work-related musculoskeletal disorders (W MSDs) cost over $20 billion every year.40 Therefore, people with musculoskeletal diso rders and injuries us e a sizeable amount of health and social care resources.41 Musculoskeletal disorders (MSDs) ar e caused by different pathophysiological mechanisms42, 43 and form a diverse category of conditions.44 Among the pathophysiological mechanisms, repeated or aw kward movements have been reported to cause or aggravate MSDs.45 MSDs encompass over 150 different diseases and syndromes because of their anatomical links as well as by their association with pain and impaired physical function.42 Gradually developing pain and disc omfort in soft tissue structures including nerves, muscles, tendons, blood vesse ls, and their related connective tissues represents a common clinical feature of th e MSDs. Moreover, MSDs that have been associated with work activities commonly affect the upper extremities.45 Work-related musculoskeletal disorders (WMSDs) represen t a cluster of conditions that are diagnosed by symptoms of pain, num bness, and/or tingling lasting more than a week or occurring more than 20 times in a year, without evidence of acute traumatic onset or systemic disease.45 A large proportion of WMSDs a ffect the upper extremity. In 2003, 23% of the non-fatal work related in juries and illnesses affected the upper

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6 extremity.46 The upper extremity musculoskeleta l conditions that are frequently associated with WMSDs include latera l epicondylitis (a tendon/muscle disorder) and carpal tunnel syndrome (a nerve compression disorder).43 The muscle groups commonly affected by the upper extremity musculoskeleta l disorders and injuries (UEMDs) include the neck and shoulder muscles47-49 as well as the extensor muscles of the forearm and the hand musculature.50 The affected muscles generally pr esent with fatigue and stiffness, radiating pain, increased musc le tone during passive moveme nt, painful locations and/or trigger points, which are defined as “pal pable discrete, focal, and/or hyperirritable spots.”43 Among people with UEMDs, grip strengt h has been used as a measure that indicates musculoskeletal pathology and doc uments recovery from such pathology.18 Grip strength scores have been frequently used to determine the exte nt of disability and the amount of financial compensation for an injury, estimate physical work capacity, match job requirements to work capacity, and assess ability to return to work after injury.51-57 Clinicians commonly use a dynamometer to measure grip strength. Objective measurement of grip strength is possible due to the availability of standardized testing procedures, normative values and accurate instruments.18 A grip strength score, however, is objective, reliable and valid only when a patient exerts a maximal voluntary effort.22-25 Some people with UEMDs may exert a le ss than maximal voluntary effort during evaluation and treatment for a variety of reasons, either in tentional (such as secondary gain of money, benefits, or attention)58-61 or unintentional (such as pain, fear of pain, or fear of re-injury).8, 9, 62 A less than maximal effort has been termed as insincere, low, submaximal, or less than honest effort.25, 27, 28, 63-67 From this point on, an intentional low

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7 effort will be referred as an insincere effort and an unintentional low effort will be referred as a submaximal effort. An insin cere effort is a component of disability exaggeration. Disability exagge ration has been defined as “i ntentional production of false or grossly exaggerated physic al or psychological sympto ms, motivated by external incentives such as avoiding military duty, avoiding work, obtaining financial compensation, evading criminal prosecu tion, or obtaining drugs” (p. 245).58 The rate of disability exaggeration is estimated to be between 25 and 30% of all personal injury litigation, worker’s compensation, or disability claims.59-61 A submaximal effort may be exerted when th e person is in pain or has fear of pain. Pain is one of the most commonly reported symptom by people with MSDs 43 and the most significant symptom for the majority of people with MSDs.68 Pain signals originate in the periphery as a result of an injury or a disease. The central nervous system (CNS) then selects, abstracts, a nd synthesizes pain signals with other sensory signals.69, 70 The force exerted by a muscle decreases as the leve l of pain increases. Several studies have reported this inverse relationship including region speci fic studies (involving a certain area of the body)71, diagnosis specific studies (in volving certain medical conditions)8, 72, and studies involving chronic pain.9, 62 However, people respond to pain differently as their pain experience is influenced by factors such as attitude, culture, past experiences, meaning of a situation, and other psychol ogical variables like anxiety, stress and depression.69, 70 Therefore, pain may cause people w ith UEMDs to exert submaximal grip efforts. People with UEMDs may also develop a fear of pain. Fear of pain has been shown to impact people with chr onic musculoskeletal conditions73-75 as well as people with

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8 acute pain.76 An elevated fear of pain has been hypothesized to induce an avoidance behavior76-78, which includes avoidance of movement daily activity, leisure activity and social interaction.79 In turn, the avoidance behavior ma y lead to disuse syndrome, chronic disability and exaggerated pain perception.76-78 Therefore, through the mechanism of avoidance behavior, fear of pain may prevent people with UEMDs from exerting maximal voluntary grip effort. Detecting a submaximal grip effort is e ssential as a person cannot be effectively rehabilitated without putting forth full e ffort, even on applying the most advanced technology, equipment and therapies. Different methods based on the gr ip strength have been used in the clinic to detect submaximal efforts.80-82 These methods include the five rung grip (5R) test, the rapid exchange grip (REG) test and the coefficient of variation (CV).27, 28, 64-67, 83, 84 The 5R-test uses variability in grip strength scores across the 5 handle positions of a Jamar dynamometer to identify a submaximal effort. A greater variability in grip strength scores acro ss the 5 handles increa ses the likelihood of maximal effort.22, 25, 31, 32, 85-89 The REG-test requires a pati ent to grip a dynamometer in rapid succession. The highest score resulting from this rapid succession has been termed as the REG-score. The REG-score is then co mpared to the peak score generated during a static grip (SG) test, which has been term ed as the SG-score. The REG-test score is calculated by subtracting the REG-score from the SG-score. A negative REG-test score (i.e. the SG-score is greater than the REGscore) increases the likelihood of a maximal effort.62, 90, 91 The CV, which measures relative dispersion, expresses the standard deviation (SD) of multiple grip strength trials as a percentage of their mean score.53, 64-66, 92 The premise of the CV is that a set of submaximal effort trials would result in a greater

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9 value of the CV when compared to a set of maximal effort trials.82 The CV, 5R-test and REG-test, however, lack standardized testi ng protocols, reliability and validity values, and empirical support.27, 28, 64-66, 80, 83, 89, 93, 94 Each of these tests has been shown to have a high error rate for the combined sensitivity and specificity values. High error rates indicate that using these tests inaccurately classifies a large number of people exerting maximal effort as exerting submaximal ef fort, and large number of people exerting submaximal effort as exerting maximal effo rt. Such high error rates deem these tests inadequate in detecting submaximal effort in a clinical setting.26-28, 58, 64-66, 82, 83, 93 Physiologically based measures, which take into consideration muscle activity over a period of time, such as the force-time curve and electromyography, may provide better detection of maximal versus submaximal efforts. The force-time curve (F-T curve) graphically represents the force generated by a contracting muscle over a period of time during a single strength trial.29 The vertical axis (Y-axis) re presents change in force of muscular contraction and the horizontal axis (X-axis) repr esents change in time. The typical isometric F-T curve consists of an initi al rapid rise of force (the force-generation phase or the initiation phase), followed by a re latively smooth peak curve (the initiation peak), and a subsequent gradual decrease in fo rce over time (the forc e-decay phase or the maintenance phase).95, 96 Various F-T curve characteristics have been used in athletics for evaluating neuromuscular adaptations to stre ngth-training programs. These characteristics have been used to identify maximal effort in a variety of muscle groups.97-99 Several characteristics have been found to be cons istent for portraying age-related changes100 as well as fatigue-related changes95 in grip strength. Submaxim al grip effort was also identified in the 1980’s using the variability of force during the plat eau phase of the F-T

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10 curve.29, 55, 101 However, the F-T curve is not commonl y used in the clinic to evaluate sincerity of effort because it requires specialized equipm ent and software, which are currently not available commercially. In a previous study we found that the slopes of the two phases of the F-T curve (the force-generation phase and the force-decay phase) successfully differentiated between maximal and submaximal grip effort in healthy subjects. The Receiver operating characteristic (ROC) curves identified the best combinations of sensitivity and specificity values for the slopes. We found excellent sensi tivity and specificity values for the slopes, with sensitivity values rangi ng from 0.8 to 0.93 and the specificity values ranging from 0.93 to 1.0. Also, the lowest overall erro r rates ranged betw een 7% and 33%.102 These error rates are excellent when compared to the error rates of the five-rung test, rapid exchange grip test and the coefficient of variation, which range from 47% to 69%.27, 65, 93 The grip efforts in the pilot study lasted for 5-seconds. Two studies have evaluated sustained maximal grip e fforts over 10-seconds (s).96, 103 Kamimura and Ikuta96 compared the reliability of a maximal isometric grip lasting 6-s with that lasting 10-s. Fifty healthy s ubjects continuously gri pped a modified Jamar dynamometer that was set at the second handl e position. Subjects performed grip efforts on two occasions separated by 2-7 days. On each occasion, subjects performed one trial of the 6-s grip followed by the 10-s grip. S ubjects rested for a minute between the two grips. Test-retest reliability was compared for peak grip strength, time-to-peak strength, and the momentary strength (strength at ev ery second during a trial). The peak grip strength and time-to-peak strength were found to have high reliability coefficients for both grips. However, the coefficients of mo mentary strength were found to be higher for

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11 the 6-s grip than the 10-s grip. Consequentl y, the 6-s grip was identified to be more consistent than the 10-s grip. Th e study also identified that in both tests maximal strength was achieved in the first two seconds. Af ter achieving maximal strength, a gradual decline in strength was observed.96 Massy-Westropp et al.103 identify age and gender-specific reference values of a 10s grip strength trial. The gr ip strengths of 476 healthy s ubjects were tested using the Grippit electronic dynamometer. Each subject performed one grip trial, which was used to calculate peak, average and final streng th. The final strength was measured as an indicator of fatigue. Peak and average grip st rengths were found to be the highest in the third and fourth decades of life, with wome n showing less strength than men for all age groups. The final strength indicated that left-h and dominant adults have more equal grip endurance between their hands than do right-hand dominant adults.103 Forearm muscle EMG properties have the pot ential of being a valuable adjunct for clinicians involved in identifying submaximal grip effort.32 Surface EMG activity has been proposed to be the best measure of overa ll electrical activity th at drives a muscle.104 Surface EMG can also indicate the amount of voluntary effort perceived by a person.104 The EMG activity of a submaximal grip effort was found to have smaller amplitude than that of a maximal voluntary effort.31, 32, 89 This is not surprising because less motor units are active during submaximal compared to maximal effort resulting in lower EMG activity. However, conflicting findings have been reported for the mean power frequency (MPF). The MPF is the frequency of the EMG si gnal that represents the average power of a power spectrum. Also, the median power fr equency has been defined as the frequency about which the power is distri buted equally above and below.105 The terms power and

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12 power spectrum have been defined on page 18. The MPF of a submaximal grip effort has been reported to either be grea ter than maximal voluntary effort31 or not to be different than that of maximal voluntary effort.32, 89 In general, it appears that F-T curve ch aracteristics and EMG properties can be used to develop a dependable tool for dete rmining whether a patient exerts maximal or submaximal effort.31, 32 To date, studies investigating the potential of F-T curve28 and EMG31, 32, 89 to determine sincerity of effort ha ve only included healthy participants. Furthermore, the nature of maximal voluntar y effort (both F-T and EMG) has not been studied adequately in people with UEMDs.106 Thus, there is a need to identify the nature of maximal grip efforts and submaximal gr ip efforts in people with UEMDs and to investigate the impact of current and imagined pain on grip effort. Significance MSDs present an enormous burden on today’ s society as they cost billions of dollars annually in medical and rehabilitative care as well as in lost work time.34, 39, 40, 107, 108 This burden has increased the demands on health care professionals to correctly identify disability exaggeration.84 One method of assessing disability exaggeration involves determining sincerity of effort in gr ip strength. Commercially available sincerity of effort tools are neith er reliable nor valid.26-28, 58, 64-66, 80, 82, 83, 89, 93, 94 A reliable and valid tool may assist in reducing the costs of misdiagnosis, rehabilitation, medical procedures, lost work-time, and lost productivity, and thus may be of great value to the society. Such a tool can be of benefit to rehabilitation specialists (such as occupational and physical therapists and rehabilitation counselors), insurance companies, worker compensation authorities, employers, and the workers themselves.

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13 It is essential to have a reliable and valid assessment tool to identify sincerity of effort. A reliable instrument performs with predictable consistency under set conditions. An unreliable instrument cannot be valid as an inconsistent in strument cannot produce meaningful measurements. A valid measuremen t instrument collects data in an accurate and relevant manner.109 A sincerity of effort instrument is a diagnostic tool that screens for the presence or absence of submaximal effort. A valid diagnostic tool has high sensitivity and specificity values.110 An instrument with a low sensitivity value may misclassify a person who exerts submaxim al effort as exerting maximal effort. Consequently, a person feigning disability ma y be mistakenly labeled as sincere. Low sensitivity can lead to seemingly ineffectiv e treatment, increased unnecessary procedures, and elevated disability and health care costs.27, 65, 82, 83, 93, 94 Conversely, an instrument with a low specificity value may misclassif y a person who exerts maximal effort as exerting submaximal effort. Consequently, a person exerting sincere effort may be erroneously labeled as feigning disability. This error can lead to inappropriate diagnosis and treatment, reduced worker compensation settlement, withheld payments and even loss of job.27, 65, 82, 83, 93, 94 For a sincerity of effort instrument, a low sensitivity value has been argued to be better than a low specificity value as “it is consid ered more ethical to miss subjects giving a deliberately submaximal effort rather than to misclassify as feigning a subject giving a genui ne maximal effort” (p. 1828).80 Unfairly misclassifying a sincere person as feigning can be very da maging to the individual and may promote clinically unfair decisions.65 Thus, there is a great need to establish a method for identifying sincerity of effort that has high se nsitivity and specificity values allowing it to avoid mistakes in classifying pa tients as sincere or feigning.

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14 The force-time curve (F-T curve) has been shown to be very effective in detecting submaximal effort in uninjured individuals.28 Based on physiological aspects of effort, the F-T curve has the potential to allow therapists to determine submaximal effort when exerted by injured individuals. Th e F-T curve also has the ability to assist in clinical decision-making concerning further treatment and/or referral. Moreover, the proposed project may further the unde rstanding of motor unit recruitment in maximal and submaximal muscular effort of hand-injured patients, which has potential applications in rehabilitation, ergonomics, and biomechanics. Previous Study In a previous study, we analyzed the for ce-time curves (F-T curves) of maximal and submaximal grip strength trials exerted by healthy people for th e slopes of both the force-generation phase and the force-decay phase. We simultaneously recorded the electromyographic (EMG) activity of the extrin sic flexor and extensor muscles of the digits. Methods. Thirty healthy volunteers (15 me n and 15 women) performed three maximal and three submaximal grip streng th efforts with their dominant hand. We blinded the test administrator to the nature of the effort. For force measurements, we used a specialized dynamometer (Biopac Instrument s) with a force transducer connected through a digital oscilloscope (Gould Instruments) to an analog-to-digital (A/D) converter (PowerLab, ADInstruments). The digital for ce signals were stored on a computer by a polygraph software system (Chart, ADInstruments). For EMG activity, we placed surface silver-silver chloride electrodes over the belly of the flexor digitorum superficialis muscle and the extensor digitorum communis mu scle. The EMG activity was amplified and band-pass filtered in the range of 0.1-1.0 kHz (Grass Polygra ph, Grass Instruments) and

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15 led into the A/D converter (PowerLab, ADIns truments). The EMG data were digitally smoothed and rectified by the Ch art software (ADInstruments). Data analysis. For maximal and submaximal effort we used the Chart software to calculate the slopes of the fo rce-generation and the force-decay phases of the F-T curve as well as the amplitude of the EMG activit y. The Chart calculates the slope from the least-squares line of best fit of the selected data points. The average rectified amplitude of the EMG activity was calculated fo r the duration of the F-T curve.111 Statistical analysis. Paired sample t-tests were used to analyze the difference between maximal and submaximal effort. A dditionally, the sensitivity and specificity values of the slopes of the force-generation and force-decay phases were calculated. To find the optimal cutoff value for each of the two slopes, the receiver operating characteristic (ROC) curves were generated from the multiple combinations cutoff values. Results. For the F-T curves, we found signifi cant differences in slope between maximal and submaximal efforts for both th e force-generation phase (t=46.77; p<0.0001) and the force-decay phase (t=79.16; p<0.0001). We also found significant differences between maximal and submaximal effort in time-to-peak force (t=2.841; p< 0.008). For the EMG activity, we found significant diffe rences in amplitude between maximal and submaximal efforts for both the flexor muscles (t=4.52; p<0.0001) and the extensor muscles (t=3.82; p<0.001). Table 1-1 presents the average and SD values of the F-T curve slopes and EMG amplitudes. Conclusions. This study achieved excellent co mbinations of sensitivity and specificity values, especially when compared to the sensitivity and specificity of the

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16 currently available c linical tests (Table 1-2). For the cutoff value of –0.075 during the force-decay phase, none of the male subjects who exerted a maximal effort were wrongly classified as exerting a submaximal effort. Of the male subjects exerting submaximal effort, only 7% were wrongly classified as giving a maximal effort (Figure 1-1).28 On examining the concurrent EMG and for ce recordings of the force-decay phase of maximal effort, starting at approximately 4.5 seconds afte r achieving peak force, we found the two recordings to decompose. While force continued to decline, EMG output actually increased, indicating that the muscle s were maximally activated by the nervous system in an attempt to maintain the maxima l contraction. In contra st, during submaximal effort, the EMG and force recordings exhibite d similar trends indicating that the person was able to maintain the maximal contracti on without additional activ ation of motor units (Figure 1-2). Definition of Terms This section defines the various terms used in this research project. When appropriate, the conceptual and operational definitions of terms specific to the study have been given. 1. Musculoskeletal system: Also called the locomotor system, the musculoskeletal system consists of the skeletal system (bones and joints) and the skeletal muscle system, and peripheral nerves that innerv ate the skeletal muscles. This system performs various functions including protec tion of internal organs, maintain posture, assist in movement, formation of blood cel ls, and storage of fats and minerals.1, 112 2. Musculoskeletal disorders: a. Conceptual definition: Musculoskeletal disorders include a diverse spectrum of diseases and syndrom es with varied pathophysiology. However, they are linked anatomically and by their associ ation with pain and impaired physical function. These conditions range from acute onset and short duration diso rders to lifelong disorders. They commonly manifest as rheumatoid arthritis, osteoarthritis, osteoporosis, spinal

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17 disorders, peripheral nerve injuries major limb trauma, fibromyalgia, gout, and sprains and strains.39, 42 3. Musculoskeletal conditions: a. Conceptual definition: Musculoskele tal conditions have been defined differently in the literature. Some articles rely on physician provided diagnoses, some on self-report, so me include injuries to the musculoskeletal system and some excl ude injuries. The National Arthritis Data Task Force defines musculoskele tal conditions as those that include the International Classification of Diseases, Ninth Edition (ICD-9) codes 274 (gout) and 710.0 – 739.9 (diseases of musculoskeletal system and connective tissue).34, 113 4. Upper extremity musculoskeletal di sorders and injuries (UEMDs): a. Operational Definition: It is a coll ection of various diseases, syndromes, and injuries that affect the musculoskeletal system (skeletal muscles, bones, joints, blood vessels, nerves a nd related connective tissue) of the upper extremity. 5. Disability exaggeration: Also calle d symptom magnification and malingering, disability exaggeration has been defined as “intentional production of false or grossly exaggerated physical or psychological symp toms, motivated by external incentives such as avoiding military duty, avoiding work, obtaining financial compensation, evading criminal prosecution, or obtaining drugs.”58 Moreover, disability exaggeration subsumes fraudulent persis tence of symptoms. These persistent symptoms are observed when genuine sympto ms cease but a patient asserts that the symptoms continue to exist.114-117 6. Effort: Effort is the perception of an indi vidual regarding how much force he/she has exerted. Effort is a psychol ogical construct and force va riables can only provide an indirect measure of the construct.31 7. Maximal voluntary effort: a. Conceptual definition: Also called sin cere effort, maximal effort indicates that a person consciously and voluntar ily performs to the best of their ability during an evaluation. b. Operational definition: In relation to grip strength, maximal effort indicates that a person consciously and voluntarily performs a grip strength trial to the best of their ability. 8. Submaximal effort: a. Conceptual definition: It is a less than maximal effort.

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18 b. Operational definition: In relation to grip strength, submaximal effort indicates that a person subconsciously or unintentionally performs a grip strength trial in which the force ge nerated is less than that generated during a maximal voluntary effort. 9. Insincere effort: a. Conceptual definition: Also termed as low, submaximal, or less than honest effort25, 28, 63-65, 93, insincere effort indicates that a person consciously performs at a level below the best of their ability during an evaluation. b. Operational definition: In relation to grip strength, insincere effort indicates that a person consciously or intentionally performs a grip strength trial in which the force ge nerated is less than that generated during a maximal voluntary effort. 10. Sincerity of effort: a. Conceptual definition: It is a patient’s conscious motivation to perform optimally during an evaluation.82 b. Operational definition: In relation to grip strength testing, sincerity of effort indicates exertion of maximal voluntary strength/force during a grip strength trial.118 11. Surface electromyography (SEMG): It is a noninvasive method of measuring the electric potential field evoked by active mu scle fibers through the intact skin 119. The SEMG signal is measured as a time-varying signal. 12. Frequency content of SEMG: Any time -varying signal can be represented by successively adding the individual frequencies ( fn) present in the signal.120, 121 The frequencies forming the EMG signal can be identified by performing a mathematical conversion called the Fourier Transformation.121 13. Power at frequency fn: The Fourier Transformation of the EMG signal calculates two Fourier coefficients ( bn and cn) for each frequency ( fn) in the EMG signal. The sum of the squares of these coefficients is termed as power at frequency fn. The power indicates how much signal is composed of the frequency fn.121 14. Power spectrum of EMG signal: A plot of power at each frequency fn that composes the EMG signal is referred to as the powe r spectral density (PSD) plot or simply the power spectrum.121 15. Mean power frequency of EMG signal: It is the frequency of the EMG signal that represents the average power of a power spectrum.

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19 16. Median power frequency of EMG signal: So metimes know as the center frequency, it has been defined as “the frequency about which the power is distributed equally above and below. It is calculated as a median of a di stribution” (p. 115).105

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20 Table 1-1: F-T curve slope and EMG amplitude values from the pilot study Slope of the F-T curve Rectified EMG Amplitude Force-generation phase Force-decay phase Flexor muscle Extensor muscle Maximal Trials 2.61+ 1.40-0.16+ 0.080.08+ 0.03 0.18+ 0.08 Submaximal Trials 0.98+ 0.44-0.04+ 0.030.02+ 0.01 0.07+ 0.02 Table 1-2: Summary of sensitivity and specifi city values of sincerity of effort tests Measure Value Sensitivity SpecificityAuthor Females =1.2 0.800.93 Slope of forcegeneration phase Males = 1.45 0.800.87 Shechtman et. al, 2007 Females = -0.05 0.800.87 Slope of forcedecay phase Males = -0.075 0.931.00 Shechtman et. al, 2007 11% CV cutoff 0.690.74 Coefficient of Variation 15% CV cutoff 0.550.92 Shechtman, 2001 Five-Rung 7.5 SD cutoff 0.70.83Gutierrez & Shechtman, 2003 Rapid Exchange Grip REG 45 0.650.66Shechtman & Taylor, 2000

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21 Figure 1-1: ROC curve for the slope of force-decay phase 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1 SpecificitySensitivity -.2 -.137 -.075 .1625 .05 -.075 -.1 -.125 Males Females

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22 A C B D Figure 1-2: Typical maximal and submaximal grip efforts. A) F-T curve of a maximal effort. B) Rectified EMG signal of maxi mal grip effort. C) F-T curve of a submaximal effort. D) Rectified EMG signal of submaximal grip effort.

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23 CHAPTER 2 LITERATURE REVIEW Upper extremity musculoskeletal disord ers and injuries (UEMDs) include a heterogeneous group of soft-tissue conditions that affect muscles, tendons, ligaments, joints, peripheral nerves and supporting blood vessels.43, 122, 123 The reported prevalence rates of UEMDs range from 11-47%.124-129 The frequent occurrence of UEMDs has challenged clinicians to develop new methods to improve the outcomes of rehabilitative care. Cost, Magnitude and Description of Upper Extremity Disorders Work-related musculoskeletal disorders (WMSDs) of the upper extremity impose an enormous burden on our society.40, 130 WMSDs have also been described as cumulative trauma disorders as well as repe titive strain di sorders. In 1989, cumulative trauma disorders of the upper extremity cost Americans over half-a-billion dollars in medical and indemnity expenses.130 Since the 1980s, WMSDs of the upper extremity have grown rapidly. 131 Cumulative trauma disorders of the upper extremity increased from 1% in 1986 to 4% in 1993.132 For the period 1993-94, 4.4% of worker compensation claimants had an upper extremity diagnosis.133 In 1995, upper extremity WMSDs comprised a third of all WMSDs.40 In 2003, among the 1.3 million injuries and illnesses occurring in the private indus try, 33% were musculoskeleta l disorders (MSDs) and 23% were upper extremity conditions. Also, over 20,000 people sustained carpal tunnel syndrome (CTS) and 7,000 people sustained tendinitis.134 CTS also resulted in the highest lost work time (median = 32 days), which is higher than lost work time reported in 1997

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24 (median = 25 days).46, 134 Hence, the growing numbers of upper extremity WMSDs have increased the burden of care on the society. Work-related activities involve low to high intensity repetitive tasks as well as awkward postures, which result in upper extr emity musculoskeletal disorders and injuries (UEMDs).135-140 WMSDs of the upper extremity have been broadly defined as “symptom complexes characterized by pain, paraesth esia, and/or weakness affecting the upper extremity or neck by the patient and/ or their physicians to work” (p. 1279).131, 141 WMSDs of the upper extremity frequently pres ent as either tendinitis or entrapment neuropathy. Tendinitis involve s inflammation of the muscle-tendon unit. When not allowed to heal, this inflammatory stat e evolves into a degenerative condition.142 Entrapment neuropathies develop at sp ecific points where nerves course around anatomical structures.143, 144 Sites of entrapment distal to the elbow include the radial tunnel, supinator muscle145-148, pronator teres muscle149-151, cubital tunnel152,and capral tunnel.153-155 In the upper extremity, lateral epicondy litis is a common form of tendinitis and carpal tunnel syndrome is a comm on form of entrapment neuropathy. Lateral epicondylitis or tennis elbow i nvolves inflammatory and degenerative changes of the forearm extensor muscles.43, 50, 156 Lateral epicondylitis has been associated with overuse of the elbow a nd is caused by force overload at the common extensor origin of the forearm muscles.157-161 The muscle primarily affected is the extensor carpi radialis brevis.3, 162-165 The chief clinical feat ure of lateral epicondylitis includes a gradually developing pain over the lateral aspect of the elbow that radiates distally into the forearm. The radiating pa in increases with motor tasks that include

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25 forearm pronation or supination, active wrist extension, passive wrist flexion against resistance and gripping.156, 157 Carpal tunnel syndrome (CTS), the mo st pervasive entrapment neuropathy166-169, occurs when the structures within the carpal tunnel compress the median nerve.153-155 The entrapment of the median nerve has been attr ibuted to rheumatoid disease, pregnancy, diabetes, renal dialysis, space occupyi ng lesions and the bony abnormalities of the wrist.170 It has been postulated that edema due to impaired circulation ultimately causes CTS.171 Symptoms of CTS include nocturnal pai n, paraesthesia and hypaesthesia in the area of the hand innervated by the median nerve.172 Later stages of CTS present with referred shoulder pain, burning pai n, and wasting of thenar muscles.173 Use of Grip Strength to Assess Upper Extremity Musculoskeletal Disorders Occupational and physical therapists fre quently measure grip strength while assessing people with upper ex tremity musculoskeletal disord ers and injuries (UEMDs). Grip strength scores have been used to determine the extent of injury19, disease process20, progress in rehabilitation21 and functional integrity of an affected upper extremity.174 The American Society of Hand Therapists (ASHT) recommends a standard method for measuring grip strength175 as the strength output changes wi th factors such as positioning of the upper extremity. Change in position of the wrist24, 176-178, forearm179, elbow180-184 and shoulder184 have been shown to affect grip st rength scores. Hence, grip strength measurements are less variable when using a standard testing protocol. However, the ASHT’s method does not control for all sources of variability. For example, the protocol recommended by ASHT requires the tester to gently support the base of the dynamometer.175 The dynamometer reading using this technique may become inaccurate if a patient exerts forces that are greater than the strength of the tester as it leads to

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26 improper stabilization of the dynamometer.185, 186 To identify outcomes of rehabilitative treatment, therapists compare grip strength scor es of the injured extremity either with the uninjured extremity21 or with the establishe d grip strength norms.187 These comparisons of grip strength are not accura te if a patient does not exert a maximal grip effort. Thus, a therapist needs to distinguish a maximal effort from a submaximal effort. Differences between Maximal and Submaximal Effort Based on the German literature of 1950s and 1960s, Kroemer and Marras188 presented a neurophysiological model of maximal and submaximal effort.27, 29, 64, 188, 189 According to Kroemer and Marras188, an executive program regulates muscular contraction on the basis of th e strength output profile. This program originates in the cerebral and cerebellar regions of the central nervous system (CNS).188 Also, when a body part needs to generate greater effort, the CNS focuses greater mental attention on generating that effort as we ll as inhibiting body systems not involved in generating that effort.190, 191 At the level of neuromuscular junctio n, two strategies are used to increase force output. Rate coding means ‘frequency of motor neuron firing’ whereas recruitment coding means ‘sequence of moto r unit activation.’ A maximal effort results in maximal motor neuron firing and maximal recruitment of motor units. In contrast, a submaximal effort requires motor cortex to mix and preci sely control submaximal frequency of motor neuron firing and recruitment of certain number and type of motor units.188, 189, 192-194 Maximal and submaximal effort also diffe r in level of sensory feedback, which influences the order of motor unit recruitment.188, 195 Sensory afferent fibers assist in calibration and modulation of magnitude of effort.196, 197 This contribution of sensory afferent fibers distinguishe s between maximal and submaximal effort. Maximal effort, which represents a lower order task, involve s simple motor control (maximal motor unit

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27 recruitment and firing) with minimal afferent feedback, which indicates full use of motor units.188, 189, 192-194 In contrast, submaximal effort re presents a higher order task, which requires a more complex motor control stra tegy. Maintenance of submaximal effort requires extensive and complex sensory afferent feedback.188, 193, 194 Table 2-1 summarizes the differences between maximal and submaximal effort. Grip Strength Tests for Detecting Submaximal Effort Therapists use a variety of tests to det ect submaximal effort, for example, the Waddell’s non-organic signs, correlation between musculoskeletal evaluation and functional capacity evaluation, documentati on of pain behavior, documentation of symptom magnification, a nd ratio of heart rate and pain intensity.82 These methods may be divided into assessments that are commonly used and t hose not commonly used in the clinic.64 The clinically-relevant methods can be administered easily and in a relatively short period time, and require minimal calcul ations and minimal e quipment, e.g. grip strength based tests. In contra st, several tests are not commonly used in the clinic as they involve a lengthy administration time, co mplicated calculations, and expensive equipment, e.g. functional capacity evaluations and isokinetic tests. Other tests can cause pain and discomfort, e.g. tests involving supr a-maximal stimulation of muscles. Among clinically relevant tests, the three grip st rength based tests commonly used include the coefficient of variation, rapid exchange grip te st and five rung grip test, which have been described next. Coefficient of Variation (CV) The CV is based on premise that submaximal exertion is more variable and less consistent than a maximal effort. The CV id entifies submaximal effort when a calculated value is larger than a cut-off value.64-66

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28 Physiological basis. Maximal effort can be easily re plicated because it represents a lower order task. Maximal effort require s simple motor control based on maximal motor unit firing frequency and maximal motor unit recruitment. In contrast, submaximal effort is difficult to replicate because it repr esents a higher order task. Submaximal effort requires delicate proprioceptive feedback for grading muscle contraction, requires a precise combination of both rate coding and recruitment coding, and involves constant corrections of motor signals by sensory afferents.29, 51, 64, 66, 188, 189, 192-194 Administration protocols. The CV uses scores of at l east 3 grip strength trials. The CV is calculated by dividing the standard deviation by the mean value of the grip strength trials. Next, the calculated value is co mpared to a predetermined cut-off value. In literature, this cut-off value ranges between 10% and 20%. A CV value that is greater than the cut-off value is labele d as submaximal and insincere. 64 Advantages and limitations. The advantages of CV include: 1. The CV is simple to calculate.64 2. Some studies have shown that the CV differentiates between maximal and submaximal efforts.64 3. The CV is based on a standardized grip test.64 The limitations include: 1. The CV has not been shown to distin guish between maximal and submaximal efforts.54, 198 This could be because variability in repeated measures of maximal effort has been reported to range from 10-24%.64, 199, 200 Further, submaximal efforts in certain isometric tasks have been reported to be reproducible.82 Furthermore, it has been suggested that psyc hological factors, su ch as fear of reinjury and pain, can increase variability between trials.94 2. The CV can only be used for comparing di spersion of data with different units. Since grip strength values are in same units use of the CV as a sincerity of effort test becomes inappropriate.92

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29 3. For the CV to be a valid measure of sin cerity of effort, the average and standard deviation of repeated grip strength tria ls should increase propo rtionally (i.e. people with greater average grip strength should e xhibit greater variability in grip strength trials).64, 94 However, an inverse relationship has been described between grip strength and its variability.201 Also, means and standard deviations of grip strength do not change proportionally.66, 83 4. The CV has been shown to have poor test-retest reliability.66 5. The sensitivity and specificity values of the CV do not allow it to effectively differentiate between maximal and submaximal effort.65 Rapid Exchange Grip Test (REG) Physiological basis. Submaximal effort, which requi res the motor cortex to mix and precisely control recruitment of motor un its and their frequency of firing, requires a longer period of processing time than maxi mal effort. The rapid exchange of hands during the REG maneuver decr eases the amount of time available for the cortex to compare between contractions. Hence, when an individual feigns weakness in one hand, the assessor expects that indivi dual to exhibit greater REG sc ores than static grip (SG) score in the weaker hand.26, 90, 193, 194, 202, 203 Administration protocols. Lister developed the REG in 1983 to identify patients exerting submaximal effort.91 The REG requires an individual to quickly grip a dynamometer with alternating hands. The REG test involves comparing REG scores to those of static grip (SG) test score. An SG test consists of slow, maximal grips and may be administered using either the five-rung (5 R) test or the maximal static grip test (MSGT). In maximal effort, the clinician e xpects the REG scores to be less than SG scores resulting in a negative REG test sc ore, which indicates sincere effort. In submaximal effort, the REG score is expected to be greater than the SG score resulting in a positive REG test score, which indicates insincere effort.26 The testing protocols used by therapists vary with respect to hand sw itch rates (varying from 45 to 100 rpm), grip

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30 repetitions (3 and 5 repetitions ), type of SG score used for comparison (the 5R and the MSGT), and patient positioning while te sting and handling of the dynamometer.26, 27, 84 Advantages and limitations. The advantages of REG include: 1. It is simple to administer that does not require any special equipment to administer. 2. It requires a short administration time. The limitations of REG include: 1. Literature provides contradictory evidence for the REG as a test of sincerity of effort.26 2. Clinicians do not use a standard protoc ol while administer ing the REG test.26, 27, 84 3. Studies performed to validate the REG provi de insufficient description of testing protocols. When described sufficientl y, these protocols vary significantly.26 4. Speed of alternating grips plays an importa nt role in the effectiveness of the REG test.26 5. Use of different handle settings of the Jamar dynamometer can influence grip test results.26, 27, 84 6. Clinicians do not determine sincerity of effort by just using the REG, but, by using it in conjunction with other te sts indicating difficulty in interpreting the results of the REG.84 7. The sensitivity and specifi city of the REG were not found to be sufficiently high.27, 204 8. The concept of ‘positive REG’ indicating a submaximal effort works only when comparing the peak REG scores with peak 5R scores.27, 204 Five Rung Grip (5R) Test Physiological basis. The premise of the 5R test is based on the mechanical advantage of the muscles involved in gr ipping at the mid-range of hand position represented by rung 2 or 3 of the Jamar dyna mometer. The mechanical advantage is based on length-tension relationship, leverage and hand size. Incr eased lengthening (up to 110% of resting length) of the muscle pr ior to contraction produces greater muscle

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31 forces during subsequent contraction. Lengthening the muscle beyond 110% will generate less tension due to reduced overlap of actin and myosin filaments. During maximal effort, the optimal resting muscle leng th produces the greatest contraction force. For most people the optimal length occurs at the second or third handle-position of the Jamar dynamometer, which also results in th e best leverage. During submaximal effort, the person exerts a controlled, less than maxi mal muscular contraction. Hence, the person tends to exert approximately the same amount of force at all five rungs of the Jamar dynamometer.85 Administration protocols. The 5R test involves ma ximally gripping the Jamar dynamometer at the five available handle se ttings. On graphing the scores, a maximal effort produces a skewed bell shaped curve, whereas, a submaximal effort produces a flat line. Four different methods have been used to analyze the data from the 5R test: a) visual analysis of grip strength curves85, 90, b) use of repeated measure analysis of variance with two within subject factors31, 32, 86, 87, 89, c) normalization of grip strength scores88, 205, and d) standard deviation of grip streng th scores across all five trials.22 Advantages and limitations. The advantages of the 5R include: 1. It is easy to administer. 2. It requires a short administration time. The limitations of 5R include: 1. The 5R test depends on the strength of the gripping hand. Hence, the test yields biased results when assessing sincerity of effort in people with upper extremity injuries93 and cannot distinguish between injured maximal effort and uninjured submaximal effort.206 2. Multiple studies on the effectiveness of the 5R test have provided conflicting evidence on its effectiveness as a sincerity of effort test.86, 87

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32 3. Some studies have shown that subjects trained to feign can produce a curve that looks like a maximal e ffort curve.32, 86, 87 It is clear that the commonly used clin ical tests are not reliable and valid for identifying submaximal effort. In contra st, tests based on the force-time curves28 and EMG activity31, 32, 89 can differentiate between maximal and submaximal effort (Table 22). The force-time curve has been used in various research studies to investigate both maximal97-99 and submaximal29, 55, 101 efforts. However, the force-time curve is not commonly used in the clinic because it requires specialized equipment.28, 29, 55, 96, 101, 207-209 Force-Time Curve The force-time curve (F-T curve) graphica lly represents the force generated by a contracting muscle over a period of time during a single strength trial.29 The vertical axis (Y-axis) represents change in force of musc ular contraction and th e horizontal axis (Xaxis) represents time of muscular contrac tion. The typical F-T curve generated during a maximal voluntary isometric contraction (MVIC) consists of three phases: 1) the forcegeneration phase or the initiation phase that involves rapid or gr adual development of force, followed by 2) the initiation peak that represents a relatively smooth peak curve, which may be followed by a secondary peak representing the maximum force value, and finally 3) the force-decay phase or the main tenance phase involving a steady rate of force development that may decrease gradually over time indicating onset of fatigue.95, 96, 210-212 The F-T curves of isometric100, 213 as well as dynamic (concentric and eccentric ) muscle contractions214, 215 have been used to evaluate skeletal muscle functioning. The FT curves have been used to identify differences in muscle function by age100, 213, 216-218, gender209, 213, 219, 220 and muscle fiber type.100, 218, 221

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33 Reliability Three studies have been performed identify test-retest reliability of various F-T curve characteristics of grip strength trials. In general, the F-T curve characteristics have been found to have moderate to high reliability coefficients.95, 100, 207 Bemben et al.100 identified the reliability of f our different force-time curve (F -T curve) characteristics for interpreting age related ch anges in muscle function and force production. The study included 155 healthy men divided into 12 ag e groups ranging from 20 to 79 years. The characteristics included maximal force, total impulse, time to maximal force and maximal rate of force production for a 60 millisecond pe riod. These characteristics were tested for five muscle groups: finger flexors, thumb a bductors, forearm extensors, foot dorsiflexors and foot plantarflexors. Finger flexion for ce was recorded using a device similar to a handgrip dynamometer. For each muscle group, participants performed 3 maximal isometric contractions on 2 different days. Pa rticipants were instructed to exert maximal effort as hard and fast as possible and were told to relax when they felt that maximal force had been achieved. After each trial, par ticipants rested for one minute. Day-to-day (test-retest) reliability was identified by comp aring scores recorded on 2 different days using Pearson correlation coefficients. For fi nger flexors, correlation coefficients were found to be 0.98 for maximal force, 0.93 for ma ximal rate of force-production and 0.91 for total impulse. This study indicated that the four maximal force characteristics were consistently reached even by the oldest men, therefore allowing for accurate characterization of muscle function.100 Househam et al.95 identified the reliability as we ll as effect of fatigue on the variability of four different F-T curve ch aracteristics including the maximum initiation force, absolute maximum force, maximu m maintenance force, and slope of the

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34 maintenance phase. Six healthy men with an average age of 36 years performed 3 maximal isometric grips on 3 different days using a modified Jamar dynamometer. The authors did not specify the handle position used in the study. The men were instructed to squeeze the dynamometer as rapidly as possibl e exerting maximal effort for a 7-s period. The grip trials were separated by a 30-s rest period, which did not eliminate the effects of fatigue. The test-retest reliability of the fo rce characteristics across daily sessions was identified by calculating the coefficient of variation of the standard deviation for corresponding trials across the 3 sessions. The presence and degree of fatigue in a grip effort was determined by calculating slope of the maintenan ce phase using linear regression. The coefficient of variation of the force characteristics ranged from 0.11 to 0.9 with the smallest value for the maintenance force. Thus, the maximal maintenance force proved to be the most reliable pa rameter for quantifying maximal isometric contraction. The mean value of the mainte nance phase slopes was calculated as – 13.5 N/s. There was a significant slope in 70% of th e trials and when present it was negative. However, it was not more or less likely that there would be a decline in force during trials performed later on during a session. This indicat ed that intertrial and intratrial fatigue effects are somewhat independent.95 Demura et al.207 compared the reliability of expl osive and voluntary grip using 11 different F-T curve characteristics. The char acteristics measured were divided into 5 categories of variables: time, average force, integrated area from the onset of exertion, maximal rate of force development, and mech anical power. Hundred healthy men with an average age of 17.8 + 2.5 years performed two explosive as well as volunt ary grips with their dominant hands using a digital dynamo meter (ED-D100R). The men performed the

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35 2 voluntary grips, rested for 5 minutes or longer, and finally performed 2 explosive grips. For the voluntary grip, the men were instruct ed to exert maximal grip after hearing the start signal. For the explosive gr ip, the men were instructed to exert maximal grip as fast and forcefully as possible after hearing the st art signal. The cross-correlation coefficients indicated that between the two trials, the differe nce in explosive grip tended to be smaller than voluntary grip. The explos ive grip had greater reliability coefficients for 9 of the 11 characteristics than voluntar y grip. Also, the maximal grip strength scores had highest reliability coefficients between the two trials for explosive and voluntary grip.207 While the F-T curves have primarily been employed in the athletics-related fields (e.g. exercise physiology and athletics), healthca re professionals have also i nvestigated their use for the purposes of assessment and treatment in rehabilitation. Athletics In athletics, neuromuscular adaptations due to exercise have been associated with changes in F-T curves. The F-T curves have been commonly used to assess muscular strength97-100, endurance216, 222, 223, and performance.214, 224 Strength, endurance and performance related differences in F-T cu rves have been attributed to several physiological factors including mu scle fiber type composition100, 216, 218, 221, 225, muscle cross-sectional area226, stiffness of muscle-tendon complex227, and neural drive to the muscle.215, 228-231 Strength training, including h eavy-resistance and speed tr aining, has been found to change F-T curve characteristics, such as peak force and rate of force development (RFD).98, 99, 215, 229, 232 Strength training causes a musc le to undergo rapidly occurring neural adaptations as well as gradually occurring hypertr ophic adaptations. A stronger

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36 neural drive has been associated with in creases the RFD, whereas muscle hypertrophy primarily increases peak force.98, 99, 215, 228, 229, 232 In relation to grip strength, F-T curve ch aracteristics based on the force-generation phase100, 207-209 and the force-decay phase96 have been used to investigate maximal isometric contractions. Bemben et al.100 indicated that F-T curve characteristics, including rate of force development (force ge neration), can reliably identify age related changes in explosive grip strength. Explosive strength has been define d as “the rate of rise of contractile force at the onset of contract ion, i.e. the rate of force development (RFD) exerted within the early phase of rising muscle force.”229 Bemben et al.100 also indicated that F-T curves allow for succe ssful implementation of strength training programs among older men, who may be concerne d with fear of injury. Demura et al.207209 studied explosive grip using multiple F-T curve characteristics, including the RFD (force generation). Demura et al.207-209 found the F-T curve characte ristics to be larger in stronger subjects208, and different between males and females.209 Moreover, the forcegeneration phase of explosive grip was found to be more reliable than slow maximal grip.207 Although, F-T curve character istics of explosive strength tests have been found more reliable than slow strength tests, it may be safer to use slow st rength tests in people with injuries as the explosive tests may cause re-injury. Healthcare Among people with injuries, the F-T curve characteristics of slow grip strength trials seem to be most appropriate for id entifying muscle function. When compared to explosive grip strength test, the slow maxima l grip strength test seems to be safer. Explosive grip requires the gripping to be perf ormed as fast and forcefully as possible. In contrast, the slow grip allows a person with an injury to determine their own motor unit

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37 recruitment strategy.28, 207 Two studies have been performed to identify the effect of injury on the F-T curves of grip strength.211, 212 Helliwell et al.212 measured grip strength in people with rheumatoid arthritis (RA) using a torsion dynamometer. Study participan ts consisted of 33 females and 13 males with a mean age of 57 years. The participan ts gripped the dynamometer 3 times with a rest period lasting a few seconds between trials. The F-T curves generated from each grip lasted 4.4-s and generated 6 characteristics: maximum grip strength, time to maximum value, fatigue rate, amount of fatigue, rele ase rate, and power factor. The study identified adequate reproducibility of the 6 characteristics, with modera te reproducibility of time to maximum value and fatigue rate. Helliwell et al.212 also reported 2 phases of the F-T curve: an initial steep rise in grip stre ngth, and a subsequent slower decline after achieving peak strength.212 Similar phases of the F-T curve have been reported in people without injuries.95, 96 Therefore, it appears that the shape of an F-T curve remains the same in presence and absence of injury. Hakkinen et al.211 studied changes in shape of knee extensor F-T curves as a result of strength training a nd detraining. The study participants included 20 healthy people and 43 people with recent-onset rheumatoid arth ritis (RA). Participan ts with RA were randomly divided into an experimental and a control group. The experimental group participated in a progressive strength training program for 6 months. In contrast, the control group maintained their habitual phys ical activities. With the knee positioned at 100, the participants exerted maximal effort as rapidly as possible and maintained it for approximately 5-s. The David 200 dynamome ter was used to record the maximal voluntary isometric F-T curves at 0, 6 and 42 months. The F-T curves at 6 months

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38 indicated the training effect and at 42 mont hs indicated the detraining effect. The study found that participants with RA took longer than healthy part icipants to produce the same level of force at 0, 6 and 42 months. At 6 months, the shape of the F-T curve did not change significantly in the experimental gr oup, most likely because the participants did not perform explosive-type training.211 Maximal Effort Physiological basis. The F-T curves generated on exerting maximal effort differ from curves of submaximal effort, which can be described on the basis of work by Kroemer and Marras.188 During submaximal contractions continuous feedback signals control muscle output by modifying muscle fibe r firing rate and muscle fiber recruitment. During maximal contractions, central nervous system sends out the commands to recruit all available fibers at their highest firing rates. Hence, submaximal effort involves a slower buildup of force than in maximal effort.29, 101, 188 Further, motor units fire asynchronously during submaximal efforts a nd fire synchronously during maximal or near maximal efforts.233 Previous studies. The F-T curves generated from isokinetic234 as well as isometric29, 55, 101 muscle contractions have been us ed to identify submaximal effort. Fishbain et al.234 developed an isokinetic test us ing the shoulder press and pull-down movement. The study included 34 healthy part icipants (18 males and 16 females) who performed the isokinetic movement on the Ariel machine. The participants exerted 6 best effort strokes, followed by a 1 minute rest pe riod, and then repeati ng the 6 strokes giving a faking effort. Mathematical analysis of the F-T curves generated 80 different characteristics. Further, disc riminant analysis was performe d to identify the three best characteristics for males and females. For males, the characteristics were found to be duty

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39 cycle down, work weight/down, peak value down, and the characteristics for females were average power up, 40% repetiti on down, and duty cycle up. The resulting discriminant functions were used to identify pr edictive validity of the test. In a hold out group of six males, the test classified 75% of the efforts correctly with a sensitivity value of 0.83 and a specificity value of 0.67. This is an important study as it identified the predictive validity of the F-T curve characteristics. Sincerity of effort tests have been rarely tested for their predictive validity, which needs to be identified because “the predictive models or cutoff scores obtained from validation studies should always be cross-validated on a second sample to determine if the test criteria can be generalized across samples” (p. 107).92 However, Fishbain et al.234 used statistical performance instead of physiological interpretation to sel ect F-T curve characteristics. The authors do not describe why these F-T curve characteris tics can distinguish between maximal and submaximal effort. Hence, these char acteristics should be used with caution. Three studies have used F-T curve charac teristics generated from isometric grip contractions to identify submaximal effort.29, 55, 101 In 1983, Gilbert and Knowlton29 distinguished maximal and submaximal effort using the F-T curves. The study included 36 participants randomly assigned to either a sincere or faker group. The sincere group performed maximal voluntary grip contrac tion (MVGC) and faker group performed 75% MVGC using a specially designed grip dyna mometer. The resulting F-T curves were analyzed for the following characteristics: ra te of force application (SLP), peak force (PK), ratio of average force to peak force (D EV), and ratio of peak force to body weight (WTRATIO). The sum of z-scores of all the va riables for each subject correctly identified effort in 87.5% of the females (N = 16) a nd 80% of the males (N = 20). Discriminant

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40 analysis, performed by gender, revealed DEV to be the only significant predictor of effort for females, and DEV, SLP and WTRATIO to be significant predictors for males. This study suggested that F-T curves can be us ed to distinguish between maximal and submaximal effort.29 This study, however, did not identify the ability of F-T curves to distinguish between maximal and submaximal effort exerted by the same participant. Around 1990, Smith et al.55, 101 assessed the differences between maximal and submaximal effort using F-T curve characteri stics of the plateau pha se: ratio of average and peak force, coefficient of variation, peak -average difference, and peak-average root difference. A predictive equation combined these characteristics in order to identify submaximal effort. The equation revealed the pe ak-average root difference to be the most important characteristic.55, 101 However, this characteristic do es not seem to be valid. The peak-average root difference deals with such small variability that it requires multiplication of 108 and is subject to a significant round-off error. Currently, these characteristics are not used to detect submaximal effort in either the clinic or in research studies.28 In a recent study, we found that the slopes of the force-generation phase and the force-decay phase two phases of the F-T curve successfully differentiated between maximal and submaximal grip effort in healthy subjects. The Receiver operating characteristic (ROC) curves identified the best combinations of sensitivity and specificity values for the slopes. We found excellent sensi tivity and specificity values for the slopes, with sensitivity values rangi ng from 0.8 to 0.93 and the specificity values ranging from 0.93 to 1.0. Also, the lowest overall erro r rates ranged betw een 7% and 33%.28 These error rates are excellent when compared to the error rates of the five-rung test, rapid

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41 exchange grip test and the coefficient of variation, which range from 47% to 69%.27, 65, 93 The grip efforts in the pilot study were re corded using the Biopac dynamometer, which has been identified to be reliable and valid for measuring grip force. Biopac Dynamometer The Biopac TSD121C hand dynamometer has a force sensor, whose reliability and validity have been establishe d by using precision weights235 and human participants.236 Reliability with precision weights was establ ished using repeated measures, both within a single testing session and over several occasions. After being spanned with a mass of 89.36 kg, masses weighing 79.2, 49.41, 29.67 and 9.59 kg were suspended from the dynamometer to measure the output. Each testing session comprised of 8 loading procedures, moving alternately down and up th e measurement scale. This protocol was repeated on 3 separate occasions over 3 week s. The mean coefficient of variation (CV) and their 95% confidence intervals (CI) assessed the reproducibility of the mass measurements. The mass measurements were found to be highly reproducible during a single testing period and over se parate testing occasions (mean CV ranged from 0.4 to 0.8 and their CI ranged from 0.3 to 1.2). Theref ore, the Biopac dynamometer has been found to be reliable in measuring weights in th e range of 0-90 kg under laboratory conditions.235 Validity was established for both single and multiple sessions by performing repeated measures at one time and over seve ral occasions. After being spanned with a mass of 9.59 kg, masses weighing 9.59, 29.67, 49.41 and 79.2 kg and were suspended from the dynamometer to measure the output Each testing session comprised of 3 loading procedures, moving alternately up and down the measurement scale. The entire procedure was replicated with span masses of 29.67, 49.41 and 79.2 kg. The entire protocol was performed at 3 occasions over 3 weeks.235 Bland and Altman’s 95% limits

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42 of agreement (LOA) were used to compar e the actual mass to mass measured by the dynamometer. The LOA revealed that the span mass of 0-29.67 kg provided the most accurate agreement between m easured and actual values.235 Surface Electromyographic Activity Electromyography can be defined as the st udy of electrical activity of a muscle.237 To produce muscular contraction, muscle fibe rs receive an impulse from a motor neuron. The motor neuron is activated by electrical impul ses that originate in the brain and travel via the spinal cord to the motor neurons. On reaching the motor neurons, the electrical impulses are propagated to the motor endplate re sulting in ionic changes that generate the muscle fiber action potential237, which is recorded as el ectromyographic (EMG) activity. The following section describes the or igin and propagation of EMG signal. Origin and Propagation The impulses that stimulate muscles originat e in the motor cortex of the CNS. The motor cortex, lying in front of the central sulcus in the br ain, has contralateral control over movements. Specific body parts move on the right side of the body on stimulating different regions of the left motor cortex. This representation of the whole body in the motor cortex has been referred to as the moto r homunculus. Hence, impulses that activate forearm muscles originate in the region th at represents the forearm in the motor homunculus. Specifically, most of these impulse s originate in the pyramidal cells of layer V of cortex. The pyramidal tract then tran smits these impulses down the spinal cord.237, 238 The pyramidal tract (PT) is one of the five major tracts that descend from the brain to the spinal cord. The PT originates in mo tor cortex and Betz cells (large pyramidal cells). From the motor cortex, this tract travels through the internal capsule and the

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43 middle of cerebral peduncles. At the level of the medulla, it forms into discrete bundles called the pyramids and hence named the pyramidal tract.238 In the spinal cord, the pyramidal tract divides into tw o separate tracts. At the lowe r level of the medulla, 90% of the pyramidal tract crosses over to the opposit e side forming the lateral corticospinal tract. The remaining tract, which does not cr oss over, forms the anterior corticospinal tract.238-240 The lateral and anterior cort icospinal tracts ultimately end on motor neurons in the ventral horn of the spinal cord. Afferents fr om interneurons and receptors as well as fibers from other descending tracts also e nd on these motor neurons. The motor neurons represent the ultimate path through which all nervous excita tion related to a motor act must pass and thus have been called the ‘fin al common path.’ These motor neurons also have an orderly and systematic arrangement. For example, medial neurons innervate the trunk muscles and the most lateral neurons inne rvate the most distal parts of the limbs.239 The motor neuron action potential arrive s at the neuromuscular junction and releases acetylcholine (ACH). The rel ease of ACH depolarizes the postsynaptic membrane. By a passive process, this depolar ization spreads in both directions of the neuromuscular junction. This spread occurs in both directions along the length of the muscle fiber. The deeper portions of the musc le fiber also require electrical stimulation, which occurs via the transverse tubular system. Transmission of the depolarization stimulus through the transverse tubules releas es calcium in the sarcoplasmic reticulum. Ultimately, this calcium release assists in th e breakdown of ATP that provides energy for muscle contraction. When transverse tubules and sarcoplasmic retic ulum get depolarized, it results in a depolarization wave along the direction of muscle fibers. These

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44 depolarization waves, and subsequent repo larization waves, are observed by recording electrodes as EMG activity.237, 241 A muscle uses two different strategies to increase the muscular force output – recruitment coding and rate coding. Recruitm ent coding means ‘sequence of motor unit activation.’ Muscles produce higher forces by following the size principle. That is, smaller motor units are recruited first, and su ccessively larger motor units are recruited as the force requirement increases.189, 192, 194, 237 Rate coding means ‘frequency of motor neuron firing’ which represents how freque ntly the motor units are activated by the nervous system. As the firing rate of the mo tor unit increases, it produces an increasing amount of muscular force.189, 192, 193, 237 Signal Properties The electromyographic (EMG) signal is a time-varying signal that conveys information about muscle activity. Any tim e-varying signal has four properties: amplitude ( a ), offset ( a0), phase angle ( ), and frequency ( f ). The amplitude ( a ) represents the magnitude of the signal. The dimension for measuring amplitude depends on the type of signal.121 For example, amplitude of an electrical signal may be measured in volts (V), a unit of electrical potential or electromotive force. The offset ( a0) represents average value of the signal.121 The dimensions of the offset de pend on the type of signal. For example, the offset for an alternating curre nt (AC) is zero volts. The phase angle ( ) is the amount of time the signal is shifted in time.121 The phase angle may be measured in degrees () or radians (r). The frequency ( f ) represents how rapidl y the signal oscillates and is usually measured in cycles per second (s) or hertz (Hz). One hertz (Hz) equals one cycle per second.121

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45 Amplitude and frequency. The EMG signal properties frequently analyzed and interpreted include its amplitude and frequency.237 The amplitude can be computed in several different ways, including average r ectified amplitude and root mean square amplitude. The normal EMG signal is an alternat ing current and mean of such a signal is zero. Therefore, to compute the averaged am plitude the signal must be rectified, which involves converting the negative voltage to pos itive values. The average of all voltage values results in the average rectified EMG amplitude.237 In contrast, the root mean square EMG amplitude does not require rectif ication of the EMG signal as it integrates the squares of EMG voltages recorded for a period of time. The square-root of the integrated EMG voltage results in th e root mean square EMG amplitude.237 The frequency of EMG signal is commonl y computed using 2 different methods: identifying turning points and zero crossings as well as by identifying mean or median spectral frequency. “Turning points” is calc ulated by counting the number spike peaks per unit time. Each peak represents an inst ance when the signal changes its direction. Therefore, counting the number of peaks indicat es the frequency the signal. Similarly, the number of times the EMG signal crosses zero volts in a unit time i ndicates the frequency of the signal. Counts of turning points and zero crossings provide an estimate of EMG signal frequency.237 In contrast, the frequency di stribution of EMG signal can be identified using spectral analysis. The EMG signal is a time-varying signal that can be mathematically represented by successively adding its individual frequencies ( fn).120, 121 The mathematical conversion used to identify the individual EMG frequencies is termed the Fourier Transformation.121 For each frequency ( fn) in the signal, the Fourier transformation calculates a power, which indicat es the amount of signal composed of that

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46 frequency ( fn). The plot of frequency along the X-ax is versus the power of the frequency along the Y-axis results in a graph that is co mmonly termed as the power spectrum or the frequency spectrum.121 The mean or median frequency of the power spectrum has been commonly used to represent the frequency of the EMG signal. The mean frequency is a frequency that represents the average power of the power spectrum. In contrast, the median frequency represents the frequency that divides the power spectrum into two regions with equal power, i.e. the parts of the spectrum above and below the median frequency have equal distributions of power.105 Also, when compared to mean frequency, median frequency is less sus ceptible to noise in the signal.242 Increasing Force Literature provides several explanations for EMG changes with increasing muscle force. Force-related changes in amplitude and frequency have been associated with changes in motor unit recruitment as well as motor unit firing rates.237 Several studies have identified a high correlation between perceived effort and amplitude of surface EMG.2, 243, 244 The amplitude of EMG signal represents magnitude of muscle activity, which predominantly increases due to increa se in the number of active motor units as well as the motor unit firing rate. The firing ra te of motor units represents the frequency of activation of motor un its by the nervous system.237 Suzuki et al.245 found that as the force being generated by a muscle increased, the motor unit voltage increased to the same degree as mean absolute surface EMG amplitude. This increase implies that motor unit size and firing rate explain th e increase in mean absolute surface EMG amplitude with increasing force generation.245 The frequency of EMG signal also changes with increase in recruitment and firing rate of motor units. Increase in active motor un its results in an increase in the number of

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47 spikes and turns in the surface EMG signal. Similarly, the frequencies of EMG signal change with motor unit firing rate.237 According to Hermens et al.246, the EMG power spectrum usually has a pronounced peak in th e region of 10-25 Hz. Other authors have also observed this peak.247, 248 This low frequency peak, according to some models,249, 250 represents the mean firing fr equency of the active motor units. During an increase in force, this low frequency peak may change: it s standard deviation increases as well as it shifts to the right. If an in crease of force does not show any shift in the peak, it is reasonable to assume that the increase of force is mainly caused by an increase in the number of active motor units.246 Gander et al.248 reported that the frequency spectr um shifts to higher frequencies with an increase in muscle force. Hagberg and Ericson251 found an increase in mean power frequency when force increases from 0 to 40%. They attributed this change to low level of tissue filtering. That is, as contraction level increases, larger motor units closer to the surface are recruited; the electric signal from these fibers suffers less high frequency attenuation through the overlyi ng tissue; and thus the power spectrum shifts to higher frequencies.248, 251 Although recruitment/tissue filtering is in part responsible for the increase in mean power fre quency, firing rate also increa ses with contraction level.248, 252 Also, the average firing rate of active motor units is apparently manifested as a low frequency peak in the power spectrum. The fr equency of this peak has been observed to increase with muscle force ev en at low contraction levels.247, 248, 253 Therefore, a combination of both recruitment and rate codi ng is a responsible for the increase in mean frequency.248

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48 Fatigue It is well documented that a sustained fo rceful contraction often causes muscular fatigue, which shifts the EMG power spectrum to a lower frequency.254-260 As early as 1912, a fatiguing contraction was found to resu lt in a decrease in the Piper rhythm256, 261, which is the tendency of motor unit potentials in steadily contracting human muscles to group in the range of 40-60 Hz.262 Cobb and Forbes261 observed an increase in the amplitude of the EMG signal along with a de crease in the Piper rhythm. Kogi and Hakamada254 found that the increase in EMG amplitu de was due to an increase in the lower frequency region of the power spectrum. Furthermore, fatigue was found to result in an increase in the lower frequency spectrum255 as well as a decrease in the higher frequency spectrum255, 263, 264, which clearly indicated a sh ift in the power spectrum towards the lower frequencies.260 A fatigued muscle has a reduced ability to produce tension when excited. In an effort to compensate for the decrease in fo rce of contraction, recr uitment of motor units takes place.105, 193, 265, 266 An increase in number of ac tive motor units progressively increases the electrical activity.265 The power spectral shift to lower frequencies as a result of fatigue has been explained using three physiological mechanisms: 1) motor unit de-recruitment193, 265-267 and motor unit synchronization193, 255, 2) conduction velocity256, 259, 268, 269, and 3) shape of muscle action potential.269 1) The shift to lower frequencies as explained by motor unit de-recr uitment occurs as a muscle fatigues. The replacement of some of the fast twitch motor units with lowe r frequency fatigue-resistant units decreases the higher frequency spectrum.255, 260 Motor unit synchronization has also been proposed as a mechanism for the spectral shift. An in crease in lower frequency content has been suggested to result from increa sed synchronization of motor units.255, 260 2) Lindstrom et

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49 al.256 identified a reduction in c onduction velocity along with a downward spectral shift during a fatiguing contraction. The velocity a nd spectral changes were attributed to a strong contraction that occlude d blood flow through the muscle The subsequent lack of oxygen resulted in anaerobic metabolism, and therefore, accumulati on of lactic acid, which, in turn reduced the intracellular pH. A decrease in pH slows down the conduction velocity causing the action potential to beco me more “sluggish” and yielding a reduced higher frequency content of the power spectrum.256 4) Kranz et al.269 examined the median frequency of EMG spectrum as well as compound action pote ntial (CAP) of 45-s maximal contractions of thenar muscles. As the contraction progressed, they found that spectrum shifted to the lower frequency re gion and the CAP shape widened indicating that the two phenomena are related. They sugge sted that a muscle contraction imposes a metabolic load on the muscle, which alters its electrical properties. Altered electrical properties slow the muscle ac tion potential conduction veloc ity that causes the action potential to widen as well as shift of power spectrum to a lower frequency region.269 Injury Generally, the EMG patterns vary accordi ng to the disease and according to the technique used to record EMG signal.270 Several studies have identified EMG changes as a result of cumulative trauma disorders a nd central neurological disorders. Bauer and Murray271 measured surface EMG output of fore arm flexors, forearm extensors and triceps brachii muscle to de tect lateral epicondy litis. People with la teral epicondylitis, during simulated play, had earlier, longer a nd greater activation of forearm extensors when compared to individuals not suffering from the condition. Needle EMG findings in carpal tunnel syndr ome (CTS) can be useful in detecting denervation/reinnervat ion of pronator quadratus (PQ), fl exor pollicis longus (FPL) and

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50 two lateral heads of flexor digitorum prof undus (FDP). Presence of spontaneous activity in form of fasciculations or positive waves indicates denervation. In contrast, polyphasic motor unit potential (MUP) and increased am plitude and/or duration of MUP indicates reinnervation of these muscles.272 Ogura et al.106 used power spectral analysis to assess the compound muscle action potential (CMAP) in CTS. The study included 50 healthy people and 24 people with CTS. The CMAP was obtained from the abductor pollicis brevis muscle with supramaximal stimulati on (rectangular waves, duration: 0.2 ms) of the median nerve. Using the Hanning window func tion and fast Fourier transformation (FFT) a power spectrum of the CMAP was obtained, which was used to calculate the mean and peak spectral frequencies. On an average, people with CTS had smaller mean and peak frequencies than healthy people. In people w ith CTS, a negative correlation was found between distal latency of CMAP and mean frequency. The decrease in mean frequency was associated with temporal dispersion of the CMAP at the entra pped site. In people with muscle atrophy, the reduced frequency was associated with reduction in number type II muscle fibers, which are associated with a high frequency component of the spectrum.106 In stroke, on observing activity of biceps brachii and brachioradial is after sustained exercise, median frequency of surface EMG output decreased on the non-paretic side and not on the paretic side. This suggested that a bout of sustained activ ity significantly alters ability of central nervous syst em to activate muscles in the paretic arm, but, not on the non-paretic arm.273 Sanjak et al.210 evaluated muscle fatigue during 30 seconds of maximal voluntary isometric contraction (MVIC) by simultane ously measuring force as well as surface

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51 EMG output. Elbow flexor and ankle dorsiflexor muscles we re evaluated in 13 people with amyotrophic lateral scle rosis (ALS) and 13 normal controls (NC) for fatigue by comparing the first 5-s to the last 5-s of th e contraction. Mechanical fatigue, represented by decline in force output, was expressed as the force fatigue index (FFI). Myoelectric fatigue, represented by compression in th e EMG power spectrum, was identified by calculating the median frequency shift (MFS). People with ALS, when compared to NC, had a greater value of FFI a nd a smaller value of MFS. Th e dissociation between FFI and MFS was explained by selective atrophy of type II (fast glycolytic, fast oxidative) muscle fibers and/or higher prevalence of type I (slow-twitch oxidative ) muscle fibers. A shift in the power spectrum to lower frequencies duri ng fatigue has been suggested to primarily occur because of a decrease in muscle fibe r action potential conduction velocity (MFCV), which is greater in type II fibers than type I fibers. Th erefore, type I fibers have inherently lower frequency c ontent than type II fibers, which are de-recruited with fatigue. Presence of fewer type II fibers would indicate a lower MFS.210 Maximal Effort In 1987, Janda et al.30 used of electromyogr aphic (EMG) recordings to characterize normal grip patterns. Four healthy people, representing different hand sizes, performed maximal grips at the 5 handle positions of the Jamar dynamometer. The EMG signal was recorded from the forearm flexor area and dorsum of hand. Neither did the authors indicate the location of recording electrodes nor did they indicate specific muscles used for recording EMG signal. Janda et al.30 found that the forearm flexor muscles were active at all the handle positions whereas the intrinsic hand muscles were only active while gripping the narrower handle positions. It was also suggested that the force

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52 recordings obtained from repeated maxi mal voluntary grip effort would be more reproducible than recordings fr om submaximal grip efforts.30 In 1990’s, Niebuhr and coauthors31, 32, 89 used EMG signal to distinguish between maximal and submaximal grip effort. The EM G signal was recorded from flexor carpi radialis (FCR) and palmaris l ongus (PL) muscles as they have been reported to represent total active flexor musculat ure during handgrip maneuvers.30-32 The EMG activity of a submaximal grip effort was found to have smaller amplitude than that of a maximal voluntary effort.31, 32, 89 Niebuhr et al.31, 32, 89, however, reported conflicting findings regarding the mean power frequency (MPF). Th e MPF of a submaximal grip effort was reported to either be greater than maximal voluntary effort31 or not to be different than that of maximal voluntary effort.32, 89 A primary advantage of using EMG signal for identifying submaximal effort is that the EMG output is highly consistent over measurement sessions.89 However, EMG signal of submaximal effort showed equal amount of variability when compared to ma ximal effort. Hence, variability of EMG cannot be associated with level of effort.89 In conclusion, forearm muscle EMG properties have the potential of being a valuable adjunct for clinicians involved in identifying submaximal grip effort.32 It is not clear how muscle activity is im pacted by pain. There is mostly agreement regarding effect of pain on voluntary effort. Studies have demonstrated that pain is associated with decreased volunt ary electromyographic (EMG) activity11, 12, shorter endurance time13, decreased motor unit discharge rates14, 15 and decreased -motor neuron activity.16 However, there is a disagreement rega rding involuntary mo tor activity. Pain has been found to be associat ed with unaltered activity of -motor neurons274 and -motor

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53 neurons.275 Yet other studies show that pain is related to incr eased involuntary (reflexive) muscle activity including transient increases in resting EMG levels276 and increased activity of -motor neurons resulting in muscle spasm.274, 276-280 Pain Over the past century, the understandi ng of the behavioral, psychological and physiological aspects of pain has been tr ansformed. One significant transformation has been a paradigm shift in the understanding of neural mechanisms underlying a pain experience – from a linear mechanism to a nonlinear mechanism70, 281 In other words, earlier paradigms explained pain experien ce as an end product of linear sensory transmission of noxious stimuli.282, 283 Instead, current paradigms explain pain experience as a dynamic process that involves c ontinuous interaction among ascending and descending pathways of the nervous system.283, 284 This dynamic process begins with an injury or a disease that produces pain signa ls. Pain signals, after originating in the periphery, enter the central nervous system (CNS). The CNS is an active system influenced by culture, stress, anxiety a nd depression among other factors. The CNS selects, abstracts, and synthesi zes pain signals with other se nsory signals. Therefore, pain is a complex experience influenced by att itudes and responses of people including past experiences, meaning of a s ituation, attention and othe r psychological variables.69, 70 The complex nature of a pain experience has been succinctly captured in the definition of pain by the International A ssociation for the Study of Pain (IASP). The IASP defines pain as “an unpleasant and emotional experience asso ciated with actual or potential tissue damage, or described in terms of such damage.”285, 286 This definition, among other components, describes pain as an unpleas ant and unwanted experience. Nevertheless, pain serves an important f unction in humans: protection.287

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54 Pain protects humans by warning of occurrence of biologically harmful processes.287 For example, people protect themselv es from burns, bruises and wounds primarily due to reflex activity but also because of associated emotional arousal. Reflexes, regulated at the level of spinal cord, protect by rem oving a body part away from danger.288 Quite often, associated emotional arous al, experienced as distress or fear, may also motivate a person to move away from a painful stimulus.289 Fear of pain can also prevent a person from moving, which in tu rn promotes healing of the injury resulting in that pain.290 Additionally, from the perspective of evolutionary biology, pain may elicit an empathic, comforting, and health promoti ng behavior in people observing a person in pain. Observers react in a parental nature by taking care, assisting and consoling a person in pain. Such pain reactions result from mammalian phylogeny, i.e. to serve in the wellbeing of their young.291 Hence, pain acts as a warning system that activates protective mechanisms in people experiencing pain and parental mechanisms in people around them. These mechanisms promote safety and recovery. In contrast, pain can interfere with daily functioning of a person.292 Pain may interfere with daily functioning when it prevents people from performing their social roles, vocational roles, and impacts their psychological well-being.290 To appreciate this duality of pain, i.e. protective and interfering nature of pain, one must understand the CNS mechanisms of pain transmission and regulation. Transmission of Pain Sensation Four specific parts of the nervous system transmit pain signals from the periphery to the higher centers of the CNS: 1) the noc iceptors, 2) the dorsa l horn neurons, 3) the ascending tracts, and 4) the supraspinal projections. Nociceptors, one type of somatosensory receptors, are the first order neurons of pain pathways. These receptors

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55 generate pain signals in response to harmful stimuli. Different types of nociceptors have been identified that respond to mechanical, h eat and chemical stimuli or any combination of these stimuli. Cell bodies of the nociceptors reside in th e dorsal root ganglia (DRG). Nerve fibers leaving the DRG bifurcate and send one branch to the periphery and the other branch to the dorsal horn (DH). The pe ripheral fibers conduct pa in signals from the skin, muscles, fascia, vessels, and joint capsules to the DRG.293, 294 Peripheral fibers transmitting pain and other somatosensations, and therefore called the sensory peripheral fibers, have been classified into three t ypes based on their diameter, myelination and conduction velocity: the A-fibers (with four subtypes – and ), B-fibers and Cfibers. The C-fibers and Afibers conduct pain signals, but, at different velocities.294 Afibers conduct fast pain (a sensation immediately after an injury that indicates location of injury) and C-fibers conduct slow pain (follows sharp pain and can be characterized as a dull, throbbing ache with poor localization).293, 294 Fibers entering the DH synapse with the second order neurons.294 Two types of second order neurons perc eive pain: nociceptiv e specific (NS) neurons and wide dynamic range (WDR) ne urons. The NS and WDR neurons conduct pain signals to the brain via various ascending tracts in the spinal cord. Primarily, the NS respond to noxious stimuli while the WDR respond to both innocuous and noxious stimuli.292, 294 Table 2-3 summarizes other differences between these neurons. Axons of the second order neurons (the NS and WDR neurons) form the ascending tracts, through which pain signals travel in the spinal cord. Diffe rent ascending tracts conduct fast and slow pain signals. Fast pain travels via the neospi nothalamic tracts. The fast pain transmitting Afibers predominantly terminat e on the nociceptive specific

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56 (NS) neurons in laminae I a nd II of the DH. The axons of the NS neurons cross the midline of spinal cord in the anterior wh ite commissure. The crossed NS axons ascend to the thalamus as the neospinothalamic tract. In contrast, slow pain travels via multiple parallel ascending pathways. The slow pain transmitting C-fibers terminate on interneurons in laminae I, II, and/or V of the DH. The interneurons synapse with wide dynamic range (WDR) neurons in laminae V to VIII of the DH. The WDR axons ascend to the midbrain as spinomesencephalic tract, re ticular formation as spinoreticular tract, and thalamus as paleospinothalamic tract. Sl ow pain signals primarily ascend via the paleospinothalamic tract. The other two tr acts serve functions of arousal, motivation, reflexive function, and activa tion of descending fibers.293 Supraspinal projections can also be divided on the basi s of which fibers conduct slow pain and which fibers conduct fast pa in. The NS axons, that conduct fast pain, mostly end in the ventral posterolateral (V PL) nucleus of the th alamus. Third order neurons arise from the VPL nucleus and projec t to the primary somatosensory cortex (SI) and the secondary somatosensory cortex (SI I). These projections allow for interpreting sensory features of pain, which includes location, intensity an d quality of pain.293-295 In contrast, the tracts conducting slow pain (the spinomesencephalic, spinoreticular, and paleospinothalamic tracts) terminate in different areas of the brain. The spinomesencephalic tracts conduct pain si gnals to the superior colliculus and periaqueductal gray and finally to the hypothalamus and raphe nuclei. These areas assist in turning the eyes and head towards the noxious stimulus. The spinoreticular tracts terminate in the reticular formation in the brainstem. The paleospinothalamic tracts project to the midline and intralaminar nuclei of the thalamus. These nuclei further

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57 project to basal ganglia prefrontal cortex, anterior ci ngulate cortex, and primary motor cortex. Together, activity in the spinoreticul ar and paleospinothalamic tracts results in arousal, withdrawal, and, autonomic and affective responses to pain.292, 293 Brain activity studies have implicated seve ral supraspinal center s to be involved in processing and modulating pain signals. Thes e supraspinal centers can be divided into subcortical and cortical areas The subcortical areas most notably activated by pain signals include thalamus, basal ganglia, a nd cerebellum. In contrast, commonly reported cortical areas include somatosensory cortices (SI and SII), anterior cingulate cortex and insular cortices, prefrontal cortex, and mo tor and pre-motor cortex. These areas serve different purposes when a person experien ces pain. Specifically, the somatosensory cortices have been implicated in interpre ting sensory features of pain. The anterior cingulate cortex and insular co rtices, both components of th e limbic system, have been implicated in affective processing of pain. More over, prefrontal cortical areas, as well as parietal association areas, are also sometimes activated in response to noxious stimuli and may be related to cognitive variables, such as memory and stimulus evaluation. Motor and pre-motor cortical areas, also activated on occasion by pain stimuli, have been suggested to be related to pain epiphenomena, such as suppression of movement or actual pain evoked movements.295 Hence, a noxious stimulus origin ating in the periphery travels through multiple transmission systems to reac h various parts of the CNS. The CNS does not receive a noxious stimulus passively. Rather it processes this stimulus using various regulatory mechanisms. Regulation of Pain Sensation by the Nervous System Passive transmission of noxious stimuli cannot explain how people experience pain. Rather, their pain experience can be explained by an active process. This active

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58 process includes several regulatory mechanis ms that participate in attenuating or accentuating the perception of a noxious stimulus.294 An accentuated pain experience can be associated with factors such as edem a, fear, anxiety, and release of endogenous chemicals that sensitize nerve endings.296 Two spinal cord level mechanisms explain an accentuated pain experience: 1) wind -up, and 2) central sensitization. 1) In 1965, Mendell and Wall coined the te rm “wind-up” to describe a gradual increase in discharge frequency of the WD R neurons on repeatedly stimulating the C fibers at a low frequency.294, 297 Furthermore, a low frequency noxious stimulus (> 3 Hz) results in progressively greater pain. Physiologically, this temporal summation of pain can be explained to be similar to wind-up. Temp oral summation of pain is exaggerated in neuropathic pains and can only be evoked by activation of C fibers. 2) Central sensitization incl udes a complex sequence of ch emical events that result in an increased responsivene ss of the nociceptive dorsal horn neurons, which results in enhanced conduction of pain signals to the brain. For example, following cutaneous injury, an area of undamaged skin adjacent to the damaged tissue can be stimulated to evoke pain by either an innocuous stimulus (secondary allodynia) or more pain by a previously painful stimulus (secondary hyperalg esia). The nociceptors supplying area of secondary allodynia and hyperalgesia are not sensitized. However, cen tral sensitization occurs due to input from noci ceptors that supply an area of damage. Input from these nociceptors leads to a transient central sensitization.298 Hardy et al.299 provide a physiological explanation of this phenomenon. According to their model, active nociceptors produce pain signal, which in turn primarily activates the spinothalamic tracts (STT). These nociceptors also activate neural circ uits in the spinal cord dorsal horn that

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59 also sensitize other STT cells that receiv e input from mechanoreceptors and nociceptors that supply an adjacent, but uninjured, region. Therefore, enhanced response of these STT cells to innocuous and noxious stimuli applied to uninjured skin results in secondary allodynia and hyperalgesia.298, 299 In contrast, two classic examples de scribe how regulatory mechanisms can attenuate pain. First, afte r injuring a hand, a person may shake it vigorously to reduce pain sensation. Second, an athlete, although in jured during a game, ma y not feel injury related pain until end of game. Regulatory mech anisms that attenuate pain act at four levels of the CNS: 1) the dorsal horn (DH; includes second order neurons of ascending pain pathways), 2) the descending fibers (from periaqueductal gray, raphe nuclei, and locus ceruleus), 3) hormonal sy stem (cells located in the hypothalamus, pituitary gland, and adrenal medulla), and 4) cerebral cortex (prefrontal cortex, insular cortex, and amygdala). The most understood mechanisms that attenuate pain occu r in the substantia gelatinosa of the DH, and, through descending fi bers originating from the periaqueductal gray, raphe nuclei, and locus ceruleus. 1) Mechanisms that attenuate pain at the dorsal horn (DH) have their basis in the gate control theory.283 According to the gate control theory, first order pain neurons (nociceptors) and second order pain neurons (WDR neurons) receive inhibitory signals from non-nociceptive Aafferents. The Aafferents are fast, myelinated, large diameter sensory peripheral ne rves that arise from muscle spindles, golgi tendon organs, joint receptors or cutaneous tac tile receptors. Increased activity of these fibers inhibits the WDR neurons in the DH, which are pre dominantly stimulated by the C fibers.292, 294

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60 Inhibition of the WDR neurons reduces pain signals reaching the brain, which reduces level of pain perceived, and, theref ore attenuates pain experience. 2) Descending fibers also attenuate pain experience.294 The axons of the raphe nuclei, which receive information on noxious stimuli from periaqueductal gray, descend in the spinal cord via the dorsolateral funicu lus. Their axons form the descending fibers that attenuate pain. These fi bers attenuate pain experien ce by strongly inhibiting the second order pain neurons in the laminae I, II or/and V of the DH. This inhibition of the second order pain neurons reduces conduction of pain signals that travel from the periphery to the higher centers in the brain.292 3) The action of -endorphin (BE), which is formed by activity of the hypothalamo-pituitary-adrenocortical (HPA) axis, attenuates pain resulting from injury in situations such as accidents, di sasters, or athletic contests. In such s ituations, an injured person may have a delayed onset of pain, i.e. pa in begins at the end of an emergency or a contest. A delayed pain results partly because of BE that acts as a potent analgesic with its effect lasting a few hours.285, 296 The release of BE from the HPA axis, in presence of noxious stimulus, can be explained by a group of neuronal projections.285 These projections include pathways ascending from the second order pain neurons in the DH of the spinal cord to the medial and lateral hypothalamus and se veral telencephalic regions, and pathways from the medullary reticular fo rmation via the ventra l noradrenergic bundle (VNB) to the periventricular gray of hypothala mus. The periventricular gray, which acts as the coordinating center of the HPA axis responds to noxious stimuli (received from ascending pathways originating in the DH) by initiating a complex series of events regulated by feedback mechanisms. In res ponse to noxious stimuli, the periventricular

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61 gray synthesizes and releases corticotropi n-releasing hormone ( CRH) into the portal circulation. This CRH stimulates the anteri or pituitary gland to secrete several proopiomelanocortin derived neuropeptid es into systemic circulation.285 These neuropeptides include adrenocorticotrophi c hormone (ACTH) and BE.285, 300 BE binds with opiate receptors in the brain and the DH to result in analgesia.285, 296, 300 The amount of BE formed is regulated by ACTH, which stim ulates the adrenal cortex to release corticosteroids such as hydrocortisone and corticosterone. These corticosteroids provide feedback to the regulatory pr ocesses by inhibiting the anteri or pituitary, which represses the formation of pro-opiomelanocortin, there by attenuating further se cretion of BE and ACTH.285 4) The cortical role in at tenuating pain can be describe d in context of stimulationproduced analgesia (SPA). SPA involves a highly specific suppression of behavioral responses to noxious stimuli produced by electri cal stimulation of specific brain sites. Experimental SPA was first elicited by electri cal stimulation of the periaqueductal gray in rodents. Upon this electrical stimulation, ro dents remained alert and active. However, their responses to noxious s timuli (orientation, vocalizati on and escape) were absent. Similarly, a SPA-like response has been elicit ed in humans. Subsequent research has indicated that periaqueductal gray plays an important part in this analgesia. The periaqueductal gray receives afferents from brainstem, diencephalon, medial prefrontal cortex, limbic system insular cortex, and am ygdala (that receives massive input from hippocampus and neocortex). The periaqueductal gray also projects efferents rostrally to the medial thalamus and orbital frontal cortex301 and the rostral ventromedial medulla (RVM). The periaqueductal gray integrates inputs from various afferents with ascending

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62 nociceptive inputs, and, in turn controls spin al nociceptive neurons th rough relays in the RVM. The RVM, which consists of the ra phe nuclei and surrounding reticular nuclei, projects fibers to the DH to exert bidir ectional control over nociceptive transmission. Bidirectional control by RVM involves both in hibitory and excitatory interneurons.294, 300 The RVM has ‘off cells’ and ‘on cells.’ The increased activity of ‘off cells’ has an inhibitory effect, which attenuates pain by re ducing activity of second order pain afferent neurons in the DH. However, the increased activ ity of ‘on cells’ has an excitatory effect, which accentuates pain by increasing activity of second order pain afferent neurons in the DH.294, 296 Pain transmission and regulatory mechanisms have been explained using different pain theories. Among these theo ries, the Gate Control Theory283 has been well accepted as an explanation of these mechanisms. A ccording to this theory, the substantia gelatinosa in the dorsal horn (laminae II and III) of the spinal cord acts as a gate for pain signals. This gate determines whether or not pain signals reach the brain. Ability of pain signals to pass the gate a ppears to depend on two characteristics of somatosensory signals: 1) the strength of signa ls that reach the gate, and 2) signals that first reach the gate.302 1) Ability of pain signals to reach the br ain depends on their strength at the gate. This strength is governed by intensity of a stimulus. A noxious stimulus acts on the skin to generate action potentials in nociceptor s. These action potentials travel via the Aand C fibers to reach the gate. Varying intensity of stimulus results in different events occurring at the gate. A gentle, but sudden, pre ssure stimulus to the skin generates action potentials in larger number of Afibers as compared to C-fibers. Disproportionately

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63 larger number of active Afibers stimulates transporter cells in the do rsal horn that facilitate conduction of pain signals to the brain. Active Afibers also stimulate the gate thereby shortening the activity of the transporte r cells. As the intensity of stimulus on the skin increases and gradually becomes noxious, it increases recruitment of Afibers and C fibers and also increases firing frequency of active fibers. The C fibers activate the transporter cells and inhibit the gate. As a result, positive and negative effects of Afibers and C fibers counteract each other, and therefore T cells gradually conduct more pain signals to the brain.283, 302 2) Pain signals compete with non-pain signals at the gate. An injury in a body part generates pain signals that tr avel to the gate via the Afibers or the C fibers. Whereas, acts performed in an effort to reduce pa in, such as massaging, vigorously moving, or exerting deep pressure on the injured part, result in non-pain signals. Non-pain signals travel via the Afibers – large, myelinated nerv e fibers, with a low threshold for stimulation. The Afibers conduct non-pain signals at a much faster rate than either Aor C fibers conduct pain signals. Due to th is faster speed, non-pain signals occupy the gate and do not allow conduction of pain signals to the brai n. When pain reducing actions stop, non-pain signals do not occupy the gate al lowing for pain signals to be conducted to the brain.283, 302 Acute versus Chronic Pain Pain has been commonly classified on the basis of its duration for which one experiences it. Based on the duration, and extent of associated tissue damage, pain can be classified into acute and chronic pain.294, 303, 304 1) Acute pain has been defined as “pain associated with tissue damage, inflammation, or a disease process that is of relatively brief duration (i.e. hours, days, or

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64 even weeks), regardless of its intensity.”304 Usually, a serious local injury, such as a surgical incision, activates noc iceptors, their central connec tions and autonomic nervous system in that region, which provokes acute pain.303 Acute pain persists until healing takes place290 or stops long before he aling has been completed.303 Healing can occur without medical intervention as an injury with acute pain does not overwhelm the body’s reparative mechanisms. Such healing usually takes a few days to a few weeks, and therefore acute pain last s for the same duration.303 Additionally, acute pain has been associated with anxiety. The clinical observa tion that greater the anxiety the greater the perception of an injury as painful appears wa rranted. However, a clear empirical basis for this simple proposition does not exist. Differe nt studies indicate th at anxiety enhances, relieves or has no impact on pain.290 Acute pain has also been observed after trauma and some diseases. Pain in these conditions, excep t for malignant diseases, that persists for months or years is not considered acute pain.303 2) Chronic pain has been defined as “pain that persists for extended periods of time (i.e. months or years), that accompanies a dis ease process (e.g. rheumatoid arthritis), or that is associated with an in jury that has not resolved with in an expected period of time (e.g. myofascial pain syndromes, complex re gional pain syndrome, and chronic pelvic pain).”304 Chronic pain indicates that the pain ha s lost its biological role of triggering recuperative behavior.305 Chronic pain, although triggered by injury or disease, however, has other factors associated with it that prolong its presence. These factors include continued tissue damage, loss of a body part, ex tensive trauma, or damage to the nervous system as a result of injury.303 Due to these factors, the pa in persists either beyond the expected course of disease, or beyond the time expected for an injury to heal, or it recurs

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65 at various times for months or years.305 In such situations, the injury may exceed the body’s capability to heal. Additi onally, intensity of chronic pa in may be out of proportion of original injury or damage, and syndromes such as complex regional pain syndrome, may occur spontaneously wit hout any signs of injury.303 Chronic pain impairs an individual’s social, vocat ional and psychological we ll being. Among psychological factors, chronic pain has b een frequently associated w ith depression, which may vary from minor to severe. Depression also app ears to intensify chr onic pain. While some patients display depression, others maintain a dispassionate attitude. Patients with a dispassionate attitude appear to have either strong personal or social resources or the pain disorder provides a focus in life that enab les them to ignore stressful life challenges, thereby controlling depression.290 Clinically, acute and chroni c pain can be distinguished on the basis of dimensions of pain. In terms of these dimensions, described next, people experiencing acute pain provide a clear and specific picture of their experience.296 Dimensions of Pain Until the 1960’s, researchers considered pain as purely a sensory experience with no specific dimensions.284 Distinct dimensions of pain, having surfaced only recently, were triggered by the gate c ontrol theory. The gate cont rol theory allowed various psychological factors, earlier di smissed as ‘reactions to pain,’ to be considered as an integral part of pain processing.306 Currently, at least four di mensions or categories of pain experience can be assessed: 1) pain intens ity, 2) pain affect, 3) pain quality, and 4) pain location.289 1) Pain intensity may be defined as how much a person hur ts. It provides a quantitative estimate of the severity or magnitude of the perceived pain.289 Pain assessment tools use descriptors to describe th e intensity of a painful experience varying

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66 from ‘no pain’ to ‘worst imaginable pain.’307 Physiologically, pain intensity is encoded by the number of peripheral fibers that are activated by the painful stimulus and their discharge frequency.294 The wide dynamic range (WDR) ne urons assist in identifying intensity of various noxious stimuli. These neurons receiv e input from both nociceptive and non-nociceptive afferents. Non-nociceptiv e stimuli, such as touch, cause the WDR neurons to discharge at lowe r levels and nociceptive stim uli cause them to discharge more vigorously.292 By increasing discharge frequenc y in presence of noxious stimulus, the WDR neurons assist in identifying the intensity of a noxious stimulus. The rapidly conducting spinal systems also allow for iden tifying pain intensity. Intensity of pain signals is interpreted in the primary somatose nsory cortex (SI), which reach there via the Afibers and the neospinothalamic tracts.293, 294 2) Pain affect has been defined as “e motional arousal and disruption engendered by the pain experience.”289 Pain affect has been identified as an intrinsic, but conceptually and empirically distinct component of pain.70, 285, 308-310 As people can have mixed feelings with respect to events, people in pa in can have multiple emotions associated with their painful experience.289 Pain assessment tools used to describe the affective component of pain, use words such as dist racting, depressing, dr eadful, or unbearable.289 Physiologically, this affective component of pain can be described by activity of the WDR neurons292, which is then projected by the diverg ent pathways to parts of the brain for emotional arousal.293 It has been proposed that while nociceptive transmission excites the spinothalamic pathways to generate sens ory processes, the spi noreticular pathways are used to generate affective processes. The affective dimension of pain is then produced by activation of noradrenergic limbic structures. Also, the ho rmonal system, including the

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67 HPA axis, mediates a stress response rela ted to pain and forms a mechanism for expressing its emotional dimension.285 3) Pain location may be defined as part of body where a person experiences pain. This location may be same or different fr om where tissue injury takes place. Pain assessment tools use line diagrams of whole body or specific parts of the body to describe the pain location. People in pain identify location of their pa in by marking these diagrams.289 Physiologically, the nociceptive specif ic (NS) second order neurons, present in the DH, allow for good localization of pain because of their small receptive fields and being somatotopically organized in the lamina I.292 The NS neurons receive information on pain signals via the fast conducting Afibers and further proj ect these signals via the spinothalamic pathways to the primary somato sensory cortex (SI). Pe rception of pain in the SI identifies exact lo cation of pain in the body.293 4) Pain quality has been usually included as an aspect of the sensory-discriminative component of pain. Melzack and Casey first described this component in 1968.311 The sensory-discriminative component of pain can be defined as including information that maps the sensory nature of the stimulus (t hermal, mechanical, or chemical) as well as bodily location, intensity and tem poral aspects of the experience.290 Specifically, pain quality describes sensory nature of stimulus and sensitivity to pain.289, 312 Examples of words that are used in pain assessments to de scribe pain quality include sharp, dull, hot, cold, deep, superficia l, sensitive and itchy.312 Physiologically, rapidly conducting spinal systems, i.e. the neospinothalamic tracts, influence pain quality through the nociceptive specific (NS) neurons.70 The Afibers conduct pain sensation via the NS neurons,

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68 whose axons form the neospinothalamic tracts, to the primary somatosensory cortex (SI). The SI interprets the quality of pain sensation.293, 294 Assessment of Pain The multidimensional nature of pain needs to be assessed accurately for improving clinical and research outcomes. These outco mes include 1) identif ying underlying cause of pain, 2) determining most effective trea tment of pain and evaluating new methods to control pain, and 3) evaluating degree of disa bility or impairment of function related to pain.313 1) Assessment of pain, especially identifyi ng descriptors of its sensory qualities, can assist in diagnosis of pa in etiology. For example, a burning quality of pain may indicate peripheral injury313, and cramping quality of pelvic pain may indicate menstrual pain.314 Furthermore, people tend to use a conste llation of descriptor s to explain their pain experience. These constellations can assist clinicians to discrimi nate various types of pain.313, 315 2) Accurate assessment of pain also dete rmines the most effective treatment for pain. For example, it has been suggested that osteoarthritis (OA) pain can be managed by blocking newly found analgesic targets. Primar y afferent neurons in affected joints express excessive amounts of abnormally func tioning sodium (Na) channels. These Na channels may play an integral role in OA pain. Therefore, anal gesics that target these Na channels may provide relief from OA pain.316 Pain needs to be accurately assessed to identify the efficacy of new pha rmacological treatments that ta rget these Na channels as compared to existing treatments. 3) Pain assessment forms an important component of impairment and disability evaluation.313 Pain commonly occurs in adults with conditions that result in physical

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69 disability, such as spinal cord injury, cer ebral palsy, multiple sclerosis and post polio syndrome.317 In such situations, pain contribut es to impairments and exacerbates limitations.318, 319 Therefore, pain assessment needs to be a component of impairment or disability evaluation. Many rehabi litation related situations re quire that the pain related outcomes be assessed in a short duration of time.320 Until the 1960’s, researchers considered pain as a purely sensory experience with no specific dimensions.284 At present, four different dime nsions of pain experience have been identified, which include pain intens ity, pain affect, pain quality, and pain location.289 For this study, we will focus on pain intensity. Pain intensity provides a quantitative estimate of the severity or magnitude of the perceived pain.289 In the nervous system, pain intensity is encoded by the num ber and discharge frequency of peripheral fibers that are activated by a painful stimulus.294 The wide dynamic range (WDR) neurons, which receive input from both nocic eptive and non-nociceptive afferents, assist in identifying intensity of various noxious stimuli. Noci ceptive stimuli cause the WDR neurons to discharge more vigor ously than non-nociceptive stimuli.292 The rapidly conducting spinal systems, which include the Afibers and the neos pinothalamic tracts, also allow for identifying pain intensity. Ultimately, pain intensity is interpreted in the primary somatosensory cortex (SI) of the brain.293, 294 A variety of assessments have been de veloped to evaluate pain intensity.304 These assessments can be classified into three ge neral categories: verbal rating scale (VRS), visual analog scale (VAS), a nd numerical rating scale (NRS).289 These assessments tend to have statistically similar psychometric properties but have their own strengths and weaknesses289, 309, 321, which have been summarized in Table 2-4.

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70 The VAS has been used extens ively to assess pain intensity322, and is possibly the most widely used pain measure.323 The VAS is non-intrusive, is easy to administer and score, is suitable for repeated us e, and has simple instructions.322, 324 The VAS has also been found to be the most sensitive measure of pain when compared to various other methods.325, 326 The VAS that assesses pain intensity us ually consists of an unbroken line, 10 centimeter (cm) long, placed horizontally on a pi ece of paper, with anchor points on each end.322 One anchor of this line represents “ no pain” and the other anchor represents “maximum perceived pain intensity.”289 People rate their perceived pain intensity by placing a mark through the line.322 The distance from the “no pain” anchor to this mark results in the overall pain intensity score.289, 322 Commonly, this distance is measured in millimeters, and therefore, the score ranges from 0 to 100.322 Since its initial development almost 70 years ago323, many different versions of the VAS have been used to assess pain intensity.289 For example, the words describi ng the anchors have been varied327, the length of the line has been varied328, the line has been placed vertically on a piece of paper329, and mechanical289 and electronic versions330 have been developed. Perceived Magnitude of Grip Force The Psychophysical Law by Stevens331 states that a power function represents the relationship between magnitude of a sensation and its judgment by an individual. This power function is expressed as ‘the magnitude estimations of a sensation increase as a power of the actual intensity of that sensation.’331 The exponent of the power function varies by sensation, sensory modality a nd condition of stimulus presentation.332 The Steven’s Law has been shown to govern the sens e of force, i.e. the magnitude of apparent force increases as a power of the force exerted.333-336 For handgrip force, the power

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71 function exponent varies between 1.6 and 2.0.335, 336 Also, a power function with an exponent of 0.6 describes the a ssociation between apparent gr ip force and duration of a sustained grip. That is, on ma intaining a handgrip at a cons tant force, a power function represents an increase in apparent force with duration of the handgrip.335 Similarly, participants maintain a constant handgrip e ffort by reducing the grip force over time. The relationship between the decay in grip for ce and duration of grip is represented by a double exponential function.337 Two self-report scales have been used to identify the level of perceived intensity of force, whic h include the Rating of Perceived Exertion (RPE) Scale338 and the Category Ratio (CR-10) Scale.339 We will use the CR-10 Scale as it has been shown to be eff ective in assessing perceived exertion of grip strength.340 Further, the CR-10 Scale has been used to describe the relationship between perceived effort and associated EMG changes during a sustained isometric grip.243 Furthermore, the CR-10 Scale has attributes of a ratio scale.339, 341 This ratio scale will allow study participants to report the perc eived level of force applied du ring submaximal grip efforts as a percentage of their maximal grip efforts. Summary The present cost of managing musculoske letal disorders (MSDs) stands at an estimated $20 billion per year.40 By 2020, an estimated 59.4 million Americans (18.4%) will suffer from MSDs35, which would further increas e the financial burden. MSDs commonly affect the upper extremities, whos e rehabiltative outcomes are commonly assessed using grip strength. Grip strength is a valid method of assessing rehabilitative outcomes only when a person exerts maximal effort. A person may exert submaximal effort either intentionally (e.g. financial gain ) or unintentionally (e.g. injury-related pain). Various grip strength based tests have been used to distinguish between maximal and

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72 submaximal effort. However, these methods have been shown to have poor reliability and validity. In contrast, a recent pilot study indicated that the force-time curve (F-T curve) characteristics and electromyographic (EMG) properties can accurate ly identify maximal effort. The pilot study was performed using he althy participants. Therefore, the current study will assess the ability of F-T curve ch aracteristics and EMG properties to identify maximal voluntary effort of isometric grip in people with upper extremity disorders and injuries.

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73 Table 2-1: Differences between maximal and submaximal effort Characteristic Maximal Effort Submaximal Effort Order of task Lower order task Higher order task Somatosensory system Afferent activity Indica tes full utilization of motor recruitment and firing Assists in calibration and modulation of effort Cerebral cortex Metabolic uptake ratio Decreases in the first minutes of recovery No significant change Mental effort Large Small Inhibition of uninvolved systems Increased inhibition Less inhibition than in maximal effort Motor system Motor unit recruitment Maximal Increases with level of effort Motor unit firing Synchronous Asynchronous Variability in effort Less variability Maximum variability at 60% of MVC Onset of Fatigue Force production Declines Maintained Motor unit recruitment Cannot be further increased Increases Motor unit firing Shifts from hi gh to low value Stays constant EMG frequency Decreases Maintained Brain activity (fMRI) Increases with decreased muscle activity Not reported

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74 Table 2-2: Sensitivity and specificity values of different sincerity of effort tests Measure Value Sensitivity SpecificityAuthor 11% CV cutoff 0.690.74 CV 15% CV cutoff 0.550.92 Shechtman, 2001 Five-Rung 7.5 SD cutoff 0.70.83Gutierrez & Shechtman, 2003 Rapid Exchange Grip REG 45 0.650.66Shechtman & Taylor, 2000 Females =1.2 0.800.93 Slope of forcegeneration phase Males = 1.45 0.800.87 Shechtman, et al, 2007 Females = 0.05 0.800.87 Slope of force-decay phase Males = 0.075 0.931.00 Shechtman, et al, 2007

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75 Table 2-3: Differences between second order pain neurons Difference Nociceptive Specific (NS) Neurons Wide Dynamic Range (WDR) Neurons Activating fibers Aand C fibers 342 A, Aand C fibers 292 Activating stimuli Nociceptive (fast and slow pain) 342 Innocuous (cutaneous touch and pressure) and Nociceptive (fast and slow pain) 343 Location Mostly in Lamina I of spinal cord 344, 345 Mostly in Lamina V and VII of spinal cord 343 Lamina I Somatotopically organized 342 Not somatotopically organized 342 Pain receptive field Restricted to relatively small areas 292 Vary with stimulus strength; much larger than those of NS neurons 342 Discharge strength Vigorous increase in discharge as a result of noxious stimuli (e.g. pinching and strong compression) 292 Discharge at lower levels in response to innocuous stimuli; discharge more vigorously in response to noxious stimuli 343 Contribution to Spinothalamic Tract Make up 20–25% of tract 342 Make up about 75% of tract 342 Function Involved in sensorydiscriminative aspects of pain (localization of pain 292; nature of pain stimulus 292, 344) Involved in affectivemotivational aspects of pain (intensity; differences in noxious stimuli intensities; initiation of complex behavioral responses to pain) 292 Pain theory supported Specificity theory: presence of specific neurons activated only by noxious stimuli 345, 346 Pattern theory: presence of second order neurons that discharge differently to noxious and innocuous stimuli 292

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76 Table 2-4: Strengths and weaknesse s of pain intensity assessments Strength Weakness Verbal Rating Scale (VRS) Simple, complete and a usable pain assessment347 Adjectives may convey more subtle meanings of pain348 Easy to administer289 Easy to score289 Usually easy to comprehend289 Good compliance rates289 Have internal consistency and temporal stability349 Cross-modality related ratio scores are valid, reliable and objective measures289, 308, 350 Responsive to change in pain state289 Ability to discriminate between different types of pain351, 352 Some adjectives may be ambiguous323 Familiarity with adjectives required289 Equal intervals may not exist between adjectives289 Cross modality matching reduces patient compliance289 Longer lists have long response times289 Included adjectives may not describe level of pain experience289 Adjectives pose literacy challenges289 Pain affect and intensity assessments are not always distinct289 Single adjective may not describe pain experience323, 347 Use of adjectives vari es with ethnic groups and gender353 Visual Analog Scale (VAS) Simple and easy to construct324 Easily grasped322 Require little motivation to complete and quickly filled out322 Suitable for use by untrained staff324 Linear scale354, 355 Ratio qualities289, 324 Valid measures of pain state289 More responsive than other measures289 Suitable for repeated use322 Scoring is more time-consuming and involves more steps than other measures289 The respondent needs to have minimum level of motor abilities to use the scale289 Cognitive difficulties make it harder to use289 May have increased measurement error due to freedom in reporting356 Numerical Rating Scale (NRS) Valid & correlates with other pain intensity measures289 Sensitive to treatments that impact pain intensity289 Easy to administer and score289 Can be administered over the phone289 Scores cannot be necessarily treated as ratio data357

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77 CHAPTER 3 METHODS Participants Forty participants (20 males and 20 fema les) who currently had upper extremity musculoskeletal disorders and injuries (U EMDs) were recruited for this study. The sample size was calculated based on the data from a preliminary study involving healthy participants (Appendix A). We used convenience sampling to recruit the study participants (the recruitment process is described in the “procedure” se ction on page 84). Specific inclusion and exclusion criteria were used to select the participants. The inclusion criteria were as follows: Participants were 1) aged between 18 and 65 years, 2) treated for unilateral UEMDs involving the elbow or di stally in the last 1 year. The exclusion criteria were: People who 1) had bilateral UEMDs, 2) ha d UEMDs proximal to the elbow, 3) were unable to safely perform 4 maximal and 8 subm aximal grip trials with their affected extremity, 4) verbally report thei r pain intensity to be greate r than 7 on a scale of 0 to 10, 5) were currently ill and/or taking medicat ion which would compromise their grip strength, 6) had impaired cognition. Materials and Equipment This section discusses the equipment we used for recording the force-time curve (F-T curve) and EMG activity, as well as for reporting participant demographics, perceived exertion, and current and imagined level of pain. The instruments used to record the F-T curve characteristics and EMG properties of the isom etric grips included:

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78 1) signal sensors: a transducer for record ing grip force and surface electrodes for recording the muscles EMG activity, 2) signal conditioner for amplifying, filtering and processing the EMG activity signal, 3) an analog-to-digital (A/D) converter for transforming a continuous electr ical signal into a discrete electrical signal, and 4) a computer with polygraph software for proces sing the discrete signa l and for generating the F-T curve and EMG activity. A diagram of the equipment setup has been presented in Figure 3-1. The paper-and-pencil tests included: 1) demographic questionnaire, 2) visual analog scale (VAS) for measuring current pain intensity and for assigning imagined pain, and 3) VAS for rating perceived grip effort The specific equipment is discussed next. Instruments for Generating the F-T Curve Hand dynamometer. The force characteristics of the grip efforts were captured using a force transducer in the form of an electronic Jamar dynamometer (Thought Technology Ltd; Figure 3-2). A tr ansducer is an electrical de vice that converts one form of energy to another.358 The transducer in the modified Jamar dynamometer converts grip pressure (measured in Kilograms; kg) into an electrical signal (measured in Volts; V). The modified Jamar dynamometer has an operating range of 0-90.72 kg (0-200 lbs.) and converts 1kg of external force into an elec trical potential diffe rence of 23.11 mV. This conversion factor was calculated by susp ending known weights (10, 20, and 25kg) from the dynamometer prior to beginning data co llection on 3 consecutive days. The observed voltage readings were used to calculate 3 linear equations, Equation 3-1 for day 1, Equation 3-2 for day 2, and Equation 3-3 for da y 3. In the 3 equations, x represents the weight of a load in kilograms and y represen ts the voltage output observed as a result of

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79 suspending a load. Using these equations, we calculated a conversion factor for each day, which when averaged resulted in a value of 23.11 mV. y = 20.356x + 551.16 (3-1) y = 20.631x + 547.63 (3-2) y = 20.669x + 545.08 (3-3) The calibration of the dynamometer was checked once a week by measuring the electrical output on suspe nding known weights (10, 20, a nd 25kg). A linear relationship between the suspended loads and electrical activity indicated a cal ibrated transducer because the electrical output should increase proportionally to the load of the suspended weights. We examined the linear relationship between the suspended loads and the electrical activity by performing regression an alysis and calculating the coefficient of determination ( r2) for each week. For the duratio n of the study, the average r2 value was calculated as 0.999. To identify differences in weekly calibration, we correlated the voltage outputs using Pearson product-moment correlation coefficient (Pearson r) as well as the Intraclass corre lation coefficient (ICC 3, 1). We found perfect correlations between the weekly voltage (Pearson r = 1.0, Appendi x B). We also found perfect test-retest reliability between the first and last w eekly voltage outputs (ICC 3,1 = 1.0). Large coefficients of determination as well as perf ect correlation coefficients indicate that the dynamometer maintained its calibration. Th e electrical signal from the Jamar dynamometer was amplified and fed into the FlexComp analog-to-dig ital converter for analog display of the signal. Instruments for Recording the EMG Signal Surface EMG electrodes. The electromyographic (EMG) signal of two groups of gripping muscles was captured using the MyoScan active sens or (Model # SA9401M, Thought Technology Ltd., Montreal, QC; Figure 3-3) and the Triode electrode featuring a

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80 bi-metal design, where silver-silver chloride (Ag-AgCl) contacts the skin and conducts the captured signal via nickel-plated brass dom e to the MyoScan sensor. The forearm has 2 muscle groups that play an important role in gripping.359 The recording electrodes were placed over the belly of the flexor digitorum superficialis muscle (forearm flexor area) and over the belly of the extensor digitoru m communis muscle (forearm extensor area).360 Alcohol swabs were used to cleanse the skin before applying the electrodes. The signal from the recording electrodes was transmitted to the signal conditioner. Signal conditioner. The EMG output was amplified and filtered using the MyoScan active sensor and the FlexComp Infiniti encoder (Model # SA7550, Thought Technology Ltd., Montreal, QC; Fi gure 3-4). An amplifier takes a small analog signal and increases its magnitude.358 The MyoScan sensor detects EMG signal in the range of + 1600V and uses a gain value of 500 to amp lify it. The amplified signal was band-pass filtered at 20-500 Hz (high-passed to 20Hz by the MyoScan sensor and low-passed to 500Hz by the FlexComp Infiniti), which eliminat ed the frequencies that mostly represent noise.237 The filtered and amplified EMG signal and the force signal were led into the analog-to-digital (A/D) converter. Analog-to-digital (A/D) converter. The A/D converter (FlexComp Infiniti encoder) transformed the analog data (F-T curve and EMG signal) into digital form, which was stored and used for data proces sing. The FlexComp A/D converter sampled a continuous/analog voltage signa l and converted it into disc rete voltage values. The discrete voltage values were further translat ed into numerical values with a scale called ‘A/D units’ and stored in the comp uter for data analysis purposes.358

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81 For the present study, we used a channel bandwidth of 20-500Hz at a sampling rate of 2048 samples/second. It has been recomm ended that for proper analog-to-digital conversion, the amplified EMG output of a maximal voluntary isometric contraction (MVIC) be less than half the range of voltage accepted by the A/D converter.241 Also, the Nyquist theorem states that the data should be sampled at least at twice the rate of the highest frequency that is present in the signal.237 The amplified EMG signal from the MyoScan sensor has an active range of + 0.8V, which is half the voltage range of the FlexComp A/D converter (+ 1.7V centered around a 2.8V offset).361 This range was appropriate for digital conversion of our am plified analog signal. Also, the FlexComp A/D converter samples data at 2kHz, which met the requirement of the Nyquist theorem as the highest frequency in our signal was 500Hz.361 Computer with polygraph software. The BioGraph Infiniti software (Version 3.1, Thought Technology; Figure 3-5) was used to ge nerate the F-T curve characteristics and EMG properties. For the F-T curves, we employed the BioGraph Infiniti’s linear transformer to convert force values from volts to kilograms. The sl opes of the F-T curve were calculated by exporting force values, samp led at a rate of 2048 samples/second, into Microsoft Excel (Version 2003) and employing its function of the least-square line of best fit. For the amplified and band-passe d EMG signal, we rejected the 60Hz hum (power-line noise) by employing BioGraph Inifin iti’s notch filter. The notch filtered signal was used to calculate the amplitude and median power frequency. The amplitude of the EMG signal was calculated as average rectified amplitude for the duration of the grip. The frequency spectrum was genera ted by applying a Fourier transformation algorithm. To achieve a resolution of 1 Hz a 2048-point Fast Fourier Transformation

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82 (FFT) with the Hanning window function was ap plied to the EMG si gnal. The resulting power spectrum was used to calculate the median power frequency, which was smoothed using an averaging factor of 40. The median frequency was computed for two separate 1second intervals, the first interval beginning at peak force (called the median frequency of the first second, or MF first se cond) and the second interval forming the last second of the force decay phase (called the median fr equency of the last second, or MF last second). We also computed the ratio of last to first second values of MF, or the MF-ratio. Paper-and-Pencil Tests 1) Demographic Questionnaire. A demographic questionnaire was used to collect participant information on demographic variables such as age and gender. The questionnaire also included questions on UEMD-related variab les such as diagnosis and site of condition (Appendix C). 2) Visual Analog Scale (VAS) for pain intensity. For the present study, a VAS was used to assess current pain intensity. Th e VAS consists of a 10 cm line anchored by 2 extremes of pain, i.e., ‘no pain’ (numerical score of 0) and ‘pain as bad as it could be’ (numerical score of 10; Figure 3-6). Participants were instru cted to mark the VAS at a point that identified their current pain leve l. The VAS was administered at the beginning of the testing session and before each gripping effort in both hands to ensure that pain returned to pre-injury level. Also, based on the initial VAS, an imagined level of pain intensity was verbally assigned as 2-3 cm above the initial per ceived pain level. 3) Visual Analog Scale (VAS ) for perceived grip effort. The perceived exertion of grip effort was rated using a VAS (Figure 3-7). It consisted of a 10 cm line anchored by 2 extremes of effort, i.e., ‘no grip force’ (numerical score of 0) and ‘strongest grip force’ (numerical score of 10). We used the effort scale to examine how imagined pain

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83 can affect the level of effort. The effort sc ale was used to compute perceived submaximal effort as a percentage of perceived maximal effort. In the present study, the effort scale was given immediately after each grip trial for the participant to report his or her perceived grip effort. Study Design The present study employed a repeated measur es design. Each participant served as their own control for two variab les – levels of grip efforts (maximal vs. submaximal) and levels of injury (injured vs. uninjured ha nd). The participants were divided into two groups on the basis of gender (male vs. female). Rationale for the Study Design. Stringent controls have been applied to the research design. The stringent controls w ould identify any significant differences between maximal and submaximal effort as well as to identify their association with pain. The steps taken to make the study desi gn conservative and st ringent include: Appropriate sample size was calculated ba sed on previous data and was sufficient to indicate if the force-time curve (F-T curve) characteristics and EMG properties truly differentiate between maximal and submaximal grip efforts. A repeated measures design provides the abili ty to control for potential influence of individual differences. We can safely assume that important participant characteristics, such as age, gender and disability related to the upper extremity condition remained constant through the course of the experiment.92 One disadvantage of a repeated measures design is the potential for carryover effects when a participant is exposed to multiple-treatment conditions. Carryover/residual effects, such as fatigue due to grip strength trials, can be reduced by allotting sufficient time between successive treatment conditions to allow for complete dissipa tion of previous effects.92 To dissipate carryover effects, study participants were provided with a rest break lasting a minute after each grip trial96, 362, and were also provided with a 10 minute break between the two sessions.362-364 This design also controls for order eff ects by randomizing the sequence of maximal and submaximal effort and which extremity was used to begin the grip efforts.365

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84 Procedure Participant Recruitment Phase Participants with upper extremity cond itions were recruited from various hand therapy clinics and rehabilitati on clinics in the cities of Ga inesville, and St. Augustine, Florida. Health care professionals, incl uding physical therapists and occupational therapists, were provided with inclusion/exclusion criteria and a standard script for recruiting participants. The criteria and di rections were provided to the healthcare professionals as part of a letter (A ppendix D). The script is as follows: “A study is being conducted to identify how pain affects grip strength among people with upper extremity musculoskele tal conditions. Your condition makes you eligible to participate in this st udy. This study involves gripping a hand dynamometer 12 times with each hand and ra ting your pain and perceived grip effort. If you agree to participate, you w ill attend one session lasting approximately 45 minutes and will be paid $20.00 for part icipating in the study. Please let me know if you are interested in participating and I can pr ovide you with information to contact the research group.” These health care professionals communicat ed the information on the study to their patients who they judged to be able to sa fely perform 4 maximal and 8 submaximal efforts with their injured extremity. Interested participants were asked to call or email the investigators indicating their interest in pa rticipating in the study and to setup an appointment for collecting data. Data Collection Phase 1) Instrument calibration. The Jamar dynamometer and the FlexComp Infiniti were calibrated prior to th e testing session. The calibration of the dynamometer was checked by measuring the electrical output on suspending known weights (10, 20, and 25kg). The setup used to check the calibration of the dynamometer has been presented in Figure 3-8. The FlexComp Infiniti includes a built-in voltage reference that possesses

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85 good temperature stability. This reference voltage was used to self-calibrate the unit. The self-calibration process sets the gain and o ffset of each channel of the unit to a value within their preset specifications.361 2) Participant preparation. All participants first read and signed the informed consent form approved by the Institutional Revi ew Board at the University of Florida. The participants then filled out a dem ographic questionnaire (Appendix C). While completing the questionnaire, th e participants were also pr ovided with instructions on how to complete the pain-int ensity VAS. Next, the participants were prepared for EMG data collection from forearm flexor and ex tensor muscles by cleaning the forearm skin using alcohol swabs. The forearm flexor co mpartment was represented by the flexor digitorum superficialis (FDS) muscle and the extensor compartment by the extensor digitorum communis (EDC) muscle. The locatio n of the recording electrode on the FDS muscle was identified as follows360: 1. Place the participant's forearm in supination. 2. Ask the participant to close and open their fist 3-5 times. 3. Look and feel for the muscle belly while the participant performs the movement. 4. Place the electrode on the muscle’s belly, approximately 1-2 inches below the cubital fossa. The location of the recording electrode on the EDC muscle was identified as follows360: 1. Place the forearm of the participant in pr onation, and ask the participant to close and open their fist. 2. Feel for the bulge of the mu scle in the upper forearm. 3. To confirm the muscle’s location, ask the participan t to flex and extend the metacarpophalangeal joint of the long/middle fi nger while the rest of the fingers are in flexion.

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86 4. Place the electrode on the bulge, approximately 3 inches below the lateral epicondyle. 3) Protocol. Each participant participated in a total of 2 sessions of gripping. In each session, a participant exerted 2 maxima l and 4 submaximal grip efforts with each hand. Hence, a participant exer ted a total of 12 grips with each hand. Each grip lasted 6 seconds. After each grip effort, the participan t rested for a period of 1 minute. Between the 2 sessions, the participant received a rest break lasting 10 minutes For all grips, the participant was seated in an adjustable chai r without arm rests. The participant assumed the testing position recommended by the American Society of Hand Therapists.175 The participant’s feet were fully resting on the floor and the hips were as far back in the chair as possible, with the hips and knees positioned at approximately 90 The shoulder of the tested extremity was adducted and neut rally rotated, the el bow flexed to 90 and the forearm and wrist held in a neutral position. After each grip effort, the participant re sted for a period of 1 minute. At the beginning of the rest period, the test administ rator asked the participant to complete the effort VAS for perceived exertion of grip st rength. At the end of the rest period, the participant completed the pain intensity VAS for pain resulting from the grip. If the reported level of pain was more than 1-point higher than the range of pain usually experienced then the participant continued to re st until the level of pain returned to within 1-point of the initial level of pain. This time was recorded exactly on the checklist used by the test administrator (Figure F-1). Before the first session, the participant also performed a practice grip with each hand to ge t used to the dynamometer and to check if the force and EMG signal were being recorded properly. The particip ant also practiced marking the pain and effort VAS.

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87 A data collection form was used to reco rd the perceived grip force and pain intensity. The form recorded effort and pain a ssociated with the practice trial as well as with maximal and submaximal grip effort trials. The form was compiled prior to beginning of the data collection phase of the study and followed the same order that was assigned to a participant. An example of a data collection form has been provided in Appendix G. Table 3-1 presents an example of the study protocol. To control for order effect s, the sequence of maximal vs. submaximal effort and injured vs. uninjured extremity was randomly assigned. The random assignment was performed prior to the beginning of the study and was implemented by an assistant. An assistant used the randomization sheet (Appendi x E) to assign the order (sequence) of gripping. Each participant was assigned 1 of 4 possible gripping sequences based on starting with one of the hands (injured vs. uninjured) and one of the levels of effort (maximal vs. submaximal) (Appendix E). To reduce measurement bias, the test administrator was blinded to the level of effort. An assistant implemented the randomization by providing participants with st andard instructions (see next section). 4) Instructions. When maximal effort was assigne d, the instructions were be as follows: “In this session, I want you to give ma ximal effort with your injured/uninjured hand for all 2 grip trials. Follow the directions of the test administrator to exert full effort. Do you have any questions? This ta sk will test your grip strength. When I say go, give your maximum effo rt in a smooth manner. Be careful not to jerk the tool while gripping. You will exert a maxi mal effort for 6 seconds. You will be given a rest period after each grip. Before each trial I will ask you ‘Are you ready?’ and then the computer will tell you ‘Are you ready? Go!’ The computer will tell you to stop after 6 seconds. If you experien ce any unusual pain or discomfort at any point during testing, stop immediat ely. Do you have any questions?” The submaximal effort instructions for th e injured extremity assigned on the basis of imagined pain level were as follows:

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88 “In this session, I want you to imagin e that the level of pain that you are experiencing is affecting your grip. I want you to imagine that your pain is 2 points higher on the VAS and is at a level of ( number) out of 10. Imagine that this higher intensity pain causes your grip to be weaker. I want you to perform the grip trials in such a way that you convince me that you are more affected by pain than you really are, in other words, to exert less than a maximal effort. When I say go, give your submaximal effort in a smooth manner. Be careful not to jerk the tool while gripping. You will exert a submaxim al effort for 6 seconds. You will be given a rest period after each grip. Before each trial I will ask you ‘Are you ready?’ and then the computer will tell you ‘Are you ready? Go!’ The computer will tell you to stop after 6 seconds. If you experien ce any unusual pain or discomfort at any point during testing, stop immediat ely. Do you have any questions?” The submaximal effort instructions for the uninjured extremity assigned on the basis of imagined pain level were as follows: “In this session, I want you to imagin e that the level of pain that you are experiencing is affecting your grip. I wa nt you to pretend that you are experiencing pain that equals the intens ity of pain you experienced in your injured extremity at the beginning of the session a nd is at a level of ( number) out of 10. Imagine that this pain causes your grip to be weaker. Th e test administrator will ask you to exert your maximal effort. I want you to perform the grip trials in such a way that you convince the administrator that you are more affected by pain than you really are, in other words, to exert less than a maximal effort when gripping. Try to be consistent in repeating th e force of your grip. The te st administrator will ask you, throughout the testing session, to give maxi mal effort, but you need to ignore his instructions. Do you have any questions? When I say go, give your submaximal effort in a smooth manner. Be careful not to jerk the tool while gripping. You will exert a submaximal effort for 6 seconds. Y ou will be given a rest period after each grip. Before each trial I will ask you ‘Are you ready?’ and then tell you to ‘Go!’ I will tell you to stop after 6 seconds. If you experience any unusual pain or discomfort at any point during testin g, stop immediately. Do you have any questions?” The submaximal effort instructions for th e injured as well as uninjured extremity assigned on the basis of 50% of ma ximal effort were as follows: “In this session, I want you to perform the grip trials in such a way that you exert 50% of your maximal effort. When I say go, give your submaximal effort in a smooth manner. Be careful not to jerk the tool while gripping. You will exert a submaximal effort for 6 seconds. You will be given a rest period after each grip. Before each trial I will ask you ‘Are you ready?’ and then te ll you to ‘Go!’ I will tell you to stop after 6 seconds. If you experi ence any unusual pain or discomfort at any point during testing, stop immedi ately. Do you have any questions?”

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89 The test administrator, who was blinded to the level of effort, instructed the participant to exert maximal grip strength effort regardless of whether the assigned effort was maximal or submaximal. The grip effort instructions given before each session were as follows: “This task will test your grip strength. When I say go, give your maximum effort in a smooth manner. Be careful not to jerk the tool while gripping. You will exert a maximal effort for 6 seconds. You will be given a rest period after each grip. Before each trial I will ask you ‘Are you ready?’ and then te ll you to ‘Go!’ I will tell you to stop after 6 seconds. If you experi ence any unusual pain or discomfort at any point during testing, stop immedi ately. Do you have any questions?” Before each grip trial, the grip effort inst ructions to the participant were as follows: “During the next grip, give your maximum/ submaximal effort. Are you ready? Go! (After 6 seconds) Stop!” A practice trial was given before gripping begi ns. The instructions for the practice trial are as follows: “This is a practice trial so you can get used to gripping the dynamometer and practice marking the pain and effort scales. Please do not exert maximal effort during this practice trial so that you don’t fatigue. This is just a practice trial. Do you have any questions? Are you ready? Go! (After 6 seconds) Stop!” Before the practice trial, the participant pr acticed marking the pain VAS and after the practice trial, the participant was instructed on how to complete the effort VAS on the “Practice Trial” sheet of the Data Co llection Form (Appendix F) as follows: “Refer to the Effort Scale on the practice tr ial page of the data collection form. You will use the Effort Scale for recording the amount of effort you think you exerted during that grip. On this scale, 0 means no grip force and 10 means strongest grip force. Mark a vertical line at a point th at indicates the level of effort you just exerted. Do you have any questions?” Instructions for completing the effort VAS immediately after each grip trial were as follows: “Now please complete the effort scale. Mark a vertical line at a point that indicates the level of effort you just exerted.”

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90 The participant was also instructed on how to complete the VAS for pain intensity. While completing the demographic questionnaire, th e instructions for answering questions 14 and 15 were as follows: “You will use the pain scale for record ing the pain that you are currently experiencing in your injured upper extremit y. On this scale, 0 means no pain and 10 means pain as bad as it could be. Mark a vertical line between 0 and 10 at a point that indicates your pain le vel. Do you have any questions?” Instructions for completing the pain VAS before each grip trial (at th e end of the 1 minute rest period) were as follows: “Now please complete the pain scale. Mark a vertical line at a point that indicates your pain level.” Statistical Analysis The statistical analysis varied accordi ng to the specific aims of the study. For specific aims 1 and 2, we used repeated m easures analysis of variance (ANOVA) for identifying differences between maximal and submaximal effort. All tests were considered significant at the p < 0.05. Due to exploratory natu re of the study, we did not adjust the p-value for inflation in Type I error resulting from performing multiple comparisons. For specific aim 3, we used Intr aclass correlation coefficients (ICC) to examine test-retest reliability and sensitivity and specificity analysis to assess validity and effectiveness of identifying maximal vs submaximal efforts. All tests were performed using SPSS 15.0.366 Specific Aims 1 and 2. We used the General Linear Model (GLM) Repeated Measures Analysis of Variance (ANOVA) to compare between maximal and submaximal efforts. The ANOVA consisted of three within -subject variables, effort (maximal vs. submaximal), injury (injured vs. uninjured ha nd), and session (first vs. second), and one between-subject variable, gender (male vs. female). Repeated Measures ANOVA

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91 analyzes a group of related dependent variable s that represent different measurements of the same attribute.366 The dependent variables for Aim 1 were the peak force, time-topeak force, slope of the force-generation phase and slope of the force-decay phase of the F-T curve. The dependent variables for Ai m 2 were the flexor and extensor EMG amplitude and median frequency ratio between first and last sec ond of a 6-second grip. Specific Aim 3. We examined the validity of the F-T curve characteristics and EMG properties in differentiating between maxi mal and submaximal efforts. However, a valid test first needs to be reliable.92 The test-retest reliability of F-T curve characteristics and EMG properties was examined using the Intraclass Correlati on Coefficient (ICC 3, 1). The ICC 3, 1 has become the preferred index for testing rater reliability as it reflects both correlation (correspondence) and agreement. Correlation indicates how scores vary together, whereas, agreement identifies a ny significant differences between scores.92 In the designation “ICC 3, 1”, 3 represents model 3 and 1 represents a single rater. The ICC 3,1 has been suggested to be appropriate for testing intrarater reliability with multiple scores from the same rater.92, 367 ICC r values range from 0.00 to 1.00. According to Portney and Watkins92, “reliability coefficients of measurements used for decision making or diagnosis of individuals need to be higher, perhaps at least 0.9 to ensure valid interpretations of findings” (p. 65). Portney and Watkins92 further suggest that an index greater than 0.9 is a guideline a nd not an absolute standard.92 For the present study, the ICC 3, 1 was used to compare the mean scores of the two trials of the first and second sessions. We expected coefficients of r > 0.9 to indicate that an F-T curve characteristic or EMG property had good reliability.

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92 The validity indicators in th e present study include: 1) significant differences in aims 1 and 2, 2) calculating sensitivity and specificity values, and 3) generating ROC curves to identify optimal cutoff value as we ll as effectiveness of the test. Significant differences were examined in aims 1 and 2. Sensitivity and specificity was calculated for different cutoff values. A cutoff value can be an y value in the range of measures of an FT curve characteristic or EMG property. For each cutoff value, sensitivity and specificity can be calculated by finding the number of true and false positives and negatives.27, 64, 65, 109 For example, for the slopes of the force-d ecay phase, let a cutoff value be denoted by X. Steeper slopes have been associated with maximal effort.28 Therefore, any slope value greater than X is considered positive (indi cating submaximal effort). A true-positive exists when submaximal effort has really occurred. We calculated sensitivity by dividing the number of true-positives by the total number of times submaximal effort was exerted. Likewise, any slope value less than X is considered negative (indicating maximal effort). A true-negative exists when submaximal effort has really occurred. We calculated specificity by dividing the number of true -negatives by the total number of times maximal effort was exerted (Table 3-2). We also calculated the overa ll error rate by using the formula (1-sensitivity) + (1-specificity). The false-positive rates will be calculated by subtracting the specificity va lues from 1.0 (1-specificity).92 Sensitivity and specificity values change with the cutoff value, as an inverse relationship exists between the two, i.e., as sensitivity increases the specificity decreases and vice versa.27, 64, 65 One way to evaluate how different cutoff values affect sensitivity and specificity is by plotting the receiv er operating characteristic (ROC) curve.110 The ROC curve is a plot of true-positive rates (sensitivity) against false-positive rates (1-

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93 specificity). It can be used to identify the optimal combination of sensitivity and specificity values.110 The area under the ROC curve, or the area under the curve (AUC), is an index of separation of signal and noise distributions. A higher value of AUC indicates a more effective test.110 The AUC was calculated by plotting the ROC curve on a graph paper, counting the number of squa res below the curve and dividing it by the total number of squares. Sensitivity and specificity analyses were performed using the first session values of the injured hands only. Post-hoc analysis. We wanted to examine if pain significantly affected grip and if there were significant differences in pain between males and females and the four different orders of testing. Repeated meas ures ANOVA was conducted with the within subjects variable as pain (baseline pain vs. pain after last maximal grip) and the between subjects variables were gender (male vs. female) and order (1 vs. 2 vs. 3 vs. 4).

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94 Table 3-1: Schematic representa tion of the study protocol Order Event Time Grip 1 6 sec Rest 1 Complete Effort VAS 1 min Sub-pain injured Complete Pain VAS Grip 2 6 sec Rest 2 Complete Effort VAS 1 min Complete Pain VAS Grip 1 6 sec Rest 1 Complete Effort VAS 1 min Sub-pain uninjured Complete Pain VAS Grip 2 6 sec Rest 2 Complete Effort VAS 1 min Complete Pain VAS Grip 1 6 sec Rest 1 Complete Effort VAS 1 min Sub-percent injured Complete Pain VAS Grip 2 6 sec Rest 2 Complete Effort VAS 1 min Complete Pain VAS Grip 1 6 sec Rest 1 Complete Effort VAS 1 min Sub-percent uninjured Complete Pain VAS Grip 2 6 sec Rest 2 Complete Effort VAS 1 min Complete Pain VAS Grip 1 6 sec Rest 1 Complete Effort VAS 1 min Maximal injured Complete Pain VAS Grip 2 6 sec Rest 2 Complete Effort VAS 1 min Complete Pain VAS Grip 1 6 sec Rest 1 Complete Effort VAS 2 min Maximal uninjured Complete Pain VAS Grip 2 6 sec Rest 2 Complete Effort VAS 1 min Complete Pain VAS Rest 10 min Repeat

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95 Table 3-2: Calculating sensitivity and specific ity for the slope cut-off value of X during the force-decay phase EFFORT + (Submaximal Effort) (Maximal Effort) Total + (Slope> X) a True-positives b False-positives a + b SLOPE TEST (Slope
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96 Figure 3-1: Biomechanical instruments for r ecoding force and electromyographic signals EMG Electrodes Force Transducer FlexComp Infiniti Encoder Computer with BioGraph Infiniti software MyoScan Pro EMG Sensor (Amplifier and filter) Signal sensors Signal conditioner Amplifier Analog-to-digital converter Type of Equipment EMG Equipment Force Equipment Digital output display

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97 Figure 3-2: Electroni c Jamar dynamometer Figure 3-3: MyoScan active sensors

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98 Figure 3-4: FlexComp Infiniti encoder Figure 3-5: BioGraph Infiniti polygraph software

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99 Figure 3-6: Pain Intens ity Visual Analog Scale Figure 3-7: Percei ved Effort Visual Analog Scale

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100 Figure 3-8: Setup used to ch eck the dynamometer calibration

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101 CHAPTER 4 RESULTS Subjects Forty subjects (20 males and 20 females) participated in the study. Average age was 37 + 12 years. At the time of the study, over half (N=23) of the subjects were employed, with the most being employed by edu cational institutions (N=7). All subjects had unilateral upper extremity musculoskele tal conditions, with two-thirds (N=26) experiencing injury to their dominant side (Table 4-1). The most common location of the injury was to the hands (N=16). Almost all men (N=19) experienced traumatic injuries. In contrast, equal number of women experien ced traumatic injuries (N=9) and cumulative trauma disorders (N=10). Injury-related base line pain as measured by the VAS ranged from 0-6 cm with an average and SD of 1.6 + 1.8 cm. Three-eights of the subjects (N=15) experienced reduced ability to independently perform activiti es of daily living due to their injury (Table 4-2). Specific Aim 1 For the specific aim 1, differences were examined for 4 force-time curve (F-T curve) characteristics including peak force, tim e-to-peak force, slope of force-generation phase and slope of force-decay phase us ing Repeated Measures ANOVA. The withinsubjects variables were 1) effort (maximal vs submaximal), 2) session (1 vs. 2), and 3) injury (injured vs. uninjured), while the be tween-subjects variable was gender (male vs. female). When significant differences exis ted between the two sessions, we computed separate ANOVAs for the first and second se ssions. When there were no significant

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102 differences between sessions, we considered only the values of the first session. All differences were deemed significant at the 0.05 alpha levels. Average values of the four F-T curve characteristics for males and fema les are presented in Tables 4-3 and 4-4. Peak Force Peak force indicates strength of an is ometric contraction. We found a significant interaction between se ssion and injury [ F (1, 38) = 5.05, p < 0.03] (Table 4-5). When compared to the first session, peak force during second session increased for injured hands but decreased for uninjured hands (F igure 4-1). For both sessions, there were significant main effects for injury, effort, and gender. Peak force was significantly greater for uninjured vs. injured hand, maximal vs. submaximal effort, and males vs. females (Table 4-6, Table 4-7, Figure 4-2). When an alyzed separately, both sessions showed significant interaction effects between gender and effort as well as between injury and effort. The difference in peak force between maximal and submaximal efforts was greater for males than for females and for the uninjur ed hands vs. the injure d hands (Figure 4-3). Time-to-peak Force Time-to-peak force indicates the time re quired to reach the highest force exerted during an isometric contraction. Time-to-p eak force values were not significantly different between the first and second session [ F (1, 38) = 1.93, p < 0.1] (Table 4-8). For the first session time values, the main effect was not significant for gender [ F (1, 38) = 0.32, p < 0.57] and injury [ F (1, 38) = 0.24, p < 0.62] but was signifi cant for effort [ F (1, 38) = 33.64, p < 0.0001] (Table 4-9, Figure 4-4). Time-t o-peak force for maximal effort was greater than submaximal effort by 0.53 seconds.

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103 Slope of the Force-generation Phase The slope of force-generation phase indicat es the rate of force development during the initial phase of an isom etric contraction. A significan t interaction effect existed between injury and effort [ F (1, 38) = 5.77, p < 0.02] (Table 4-10). When compared to submaximal effort, the slopes of maximal effo rt were steeper for uninjured than for the injured hands (Figure 4-5). The main e ffect for session was not significant [ F (1, 38) = 0.37, p < 0.54] (Table 4-10). For the first session slopes, significant main effects existed for injury [ F (1, 38) = 10.0, p < 0.003], effort [ F (1, 38) = 55.77, p < 0.0001], and gender [ F (1, 38) = 8.37, p < 0.006] (Table 4-11, Figure 4-6). In other words, the slopes of forcegeneration phase were steeper for the uninjur ed hand than the injured hand (by 0.24 V/s), for the maximal effort than the submaximal effort (by 0.61 V/s), and for males than females (by 0.46 V/s). Slope of the Force-decay Phase The slope of the force-decay phase indi cates the extent of fatigue during an isometric contraction. A significan t interaction effect existed between injury and effort [ F (1, 38) = 4.03, p < 0.052] (Table 4-12). In other word s, the decrease in slope between maximal effort and submaximal effort was greater for the uninjured hand compared to the injured hand (Figure 4-7). A significant inte raction effect also existed between session and gender [ F (1, 38) = 9.47, p < 0.005] (Table 4-12). That is, the steepness of the slopes during the second session (as co mpared to the first sessi on) increased for males but decreased for females (Figure 4-8). For both sessions, significant main eff ects existed for injury and effort. A significant main effect for ge nder existed for the second se ssion and not for the first session (Tables 4-13 and 4-14). That is, the slopes of force-decay phase were

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104 significantly steeper for the uninjured than the injured hand, the maximal vs. submaximal effort, and males vs. females (Figure 4-9). Specific Aim 2 For the specific aim 2, differences were examined for 2 electromyographic (EMG) properties, namely amplitude and median fr equency ratio (MF-ratio) using Repeated Measures ANOVA. The within-subjects vari ables were 1) effort (maximal vs. submaximal), 2) session (1 vs. 2), and 3) injury (injured vs. uninjured), while the between-subjects variable was gender (male vs. female). When significant differences existed between the two sessions, we perf ormed separate ANOVAs for the first and second session. When there were no signifi cant differences between sessions, we considered only the values of the first session. All differen ces were deemed significant at the 0.05 alpha levels. Average values of th e EMG properties for males and females are presented in Tables 4-15, 4-16 and 4-17. Flexor EMG Amplitude The amplitude of the EMG signal represents the magnitude of the muscle activity. There were no significant differences in flexor EMG amplitude between the first and second session [ F (1, 38) = 0.02, p < 0.87] (Table 4-18). For the first session, significant main effects existed for injury [ F (1, 38) = 6.29, p < 0.01] and effort [ F (1, 38) = 91.35, p < 0.0001] but not for gender [ F (1, 38) = 0.18, p < 0.6] (Table 4-19). In other words, flexor EMG amplitude was significantly greater for the uninjured vs. injured hands, and for maximal vs. submaximal efforts (Figure 411). The first session re vealed a significant interaction effect between injury and effort [ F (1, 38) = 7.81, p < 0.01] (Table 4-19). That is, flexor EMG amplitude was similar for the injured and uninjured hands during submaximal effort but greater for uninjured hand during maximal effort (Figure 4-10).

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105 Extensor EMG Amplitude A significant interaction effect exis ted between effort and session [ F (1, 38) = 5.89, p < 0.02] (Table 4-20). That is, the decrease in extensor EM G amplitude between the first and second session was greater for maximal than for submaximal efforts (Figure 4-12). For both sessions, significant main effects exis ted for effort but not for gender and injury (Tables 4-21 and 4-22). In other words, extensor EMG amplitude was significantly greater for maximal vs. submaximal efforts (Figure 4-13). Flexor Median Frequency Ratio The median frequency ratio (MF-ratio) repr esents the extent of fatigue or motor unit de-recruitment during an isometric co ntraction. The flexor MF-ratio was not significantly different between the first and second session (Table 4-23). For the MFratios in the first session, significan t main effects existed for effort [ F (1, 38) = 30.27, p < 0.0001] but not for gender [ F (1, 38) = 0.43, p < 0.52] or injury [ F (1, 38) = 0.02, p < 0.9] (Table 4-24). In other words, MF-ratio was significantly smaller for maximal vs. submaximal efforts (Figure 4-14). Also, a sign ificant interaction effect existed between injury, effort, and gender (Tables 4-23 and 4-24). The decrease in MF-ratio between submaximal and maximal efforts was greater fo r uninjured vs. injured hands in males but not in females (uninjured and injured hands showed the same decrease in MF-ratio) (Figure 4-15). Extensor Median Frequency Ratio A significant main effect existed for session [ F (1, 38) = 4.61, p < 0.04] (Table 425). Therefore, we performed separate ANOVAs for each session. For both sessions, main effects were significant for effort but not significant for gende r and injury (Tables 4-26 and 4-27). That is, MF-ra tio was significantly smaller for maximal vs. submaximal

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106 efforts (Figure 4-16). During the first sessi on, a significant interaction effect existed between injury and effort (Table 4-26). In other words, the decrea se in MF-ratio between submaximal and maximal efforts was greater for uninjured hand when compared to the injured hand (Figure 4-17). Specific Aim 3 To examine the validity of the various fo rce and EMG measures we first examined the test-retest reliability and then the e ffectiveness of the most highly significant variables. The test-retest reliability of the F-T curve characteristics and EMG properties was analyzed using the Intraclass Corre lation Coefficient (ICC 3, 1). Generally, coefficients below 0.50 represent poor reli ability, coefficients between 0.50 and 0.75 represent moderate reliability, and va lues above 0.75 repres ent good reliability. Moreover, to ensure valid interpre tations, reliability should exceed 0.90.109 The validity of the F-T curve characteristics and EM G properties was examined by calculating sensitivity and specificity values for the four measures that showed the most significant differences between maximal and submaximal efforts. In addition, ROC curves were generated to identify the optimal cutoff values of these measures. The sensitivity and specificity analyses were perf ormed using injured hands only. Test-Retest Reliability When examining the test-retest reliabilit y, we will discuss on ly the values of maximal effort because it has been documented that submaximal effort is less consistent.188 The test-retest reliability of FT curve characteristics ranged from r = 0.3 to r = 0.96 (Table 4-28). The test-retest reli ability of EMG properties ranged from r = 0.7 to r = 0.96 (Table 4-29).

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107 Validity The measures that showed in the grea test significance be tween maximal and submaximal efforts were time-to-peak force, slope of force-generation phase, flexor MFratio, and extensor MF-ratio (Table 4-30). Se nsitivity and specificit y values as well as overall error rates for multiple cutoff values were calculated and are shown for slopes of force-generation phase (Table 4-31), slopes of force-decay phase (Table 4-32), flexor MF-ratio (Table 4-33), and extensor MF-rati o (Table 4-34). We did not calculate the sensitivity and specificity values for timeto-peak force because it had poor test-retest reliability rendering it as an invalid measure. Using the sensitivity and specificity values for various cutoff values, we created ROC curv es. When significant differences in gender existed, we generated separate ROC curves for males and females. We did not create ROC curves for the slope of force-decay phase because it had poor sensitivity and specificity values. The optimal cutoff values for slope of force-generation phase, flexor MF-ratio, and extensor MF-ratio are presented in Table 4-35. Slope of force-generation phase The ROC curve revealed that for the for ce-generation phase, the slope cutoff value of 1.5 V/s for men yielded the most optimal combination of sensitivity (0.85) and specificity (0.55) and the lowest overall er ror rate (0.6). For women, the slope cutoff value of 0.5 V/s yielded the most optimal comb ination of sensitivity (0.6) and specificity (0.85) and the lowest overall error rate (0.55) (Table 4-31) The proportional area under the ROC curve was greater for women ( 76%) than for men (72%) (Figure 4-18).

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108 Slope of force-decay phase The slope cutoff value of –0.04V/s yielde d the most optimal combination of sensitivity (0.85) and specificity (0.27) and th e lowest overall error ra te (0.87) (Table 432). Median frequency ratio The ROC curve for flexor MF-ratio reveal ed that the ratio cutoff value of 102% yielded the most optimal combination of se nsitivity (0.53) and specificity (0.78) and the lowest overall error rate ( 0.70) (Table 4-33, Figure 4-19). The proportional area under the ROC curve for flexor frequency ratio was. Fo r the ratio of extensor median frequency, the ratio cutoff value of 100% yielded the most optimal combination of sensitivity (0.63) and specificity (0.7) produci ng the lowest overall error rate (0.68) (Table 4-34). The proportional area under the ROC curve was the sa me for the frequency ratio for forearm flexors (66.25%) as well as extensors (71%) (Figure 4-19). Post-Hoc Analysis There were no significant di fferences between baseline pain and pain after last maximal effort grip [ F (1, 38) = 0.33, p < 0.56]. There were no signi ficant differences in pain between males and females [ F (1, 38) = 0.008, p < 0.92] and between the four orders of testing [ F (1, 38) = 1.37, p < 0.26]. Summary 1. Session differences were iden tified by peak force, sl ope of force-decay phase, extensor amplitude, and extensor MF-ratio. 2. Differences between injured and uninjured hands were identified by peak force, slopes of force-generation phase and fo rce-decay phase, flexor EMG amplitude, flexor MF-ratio, and extensor MF-ratio. 3. Differences between maximal and submaximal efforts were identified by all F-T curve characteristics and EMG properties. 4. Gender differences were identified by p eak force, slope of the force-generation phase, slope of force-decay phase, and flexor MF-ratio.

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109 5. The test-retest reliability of force variables ranged from r = 0.3 to r = 0.96 and EMG variables ranged from r = 0.7 to r = 0.96. 6. Based on the area under the ROC curve, th e slope of the force-generation phase was the most effective in distinguishing between maximal and submaximal efforts. Yet, 15% of the men who exerted submaximal effort were misclassified as exerting a maximal effort and 45% of the men who exerted maximal effort were misclassified as exerting a submaximal e ffort. Further, 40% of women who exerted submaximal effort were misclassified as exerting a maximal effort and 15% of women who exerted maximal effort were mi sclassified as exerting maximal effort.

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110 Table 4-1: Demographic characte ristics of the study sample Men (N=20) Women (N=20) All (N=40) Mean or Number SD or % Mean or Number SD or % Mean or Number SD or % Age (years) 3711.83912.7337.7 12.14 Height (inches) 712.81663.663 4.04 Weight (lbs.) 2083617246190 44 Race European 1365178530 75 African 420155 12.5 Hispanic 2102104 10 Asian 15----1 2.5 Occupation Classification Business and Financial ----2102 5 Education 73531510 25 Healthcare 3154207 17.5 Food and Service ----3153 7.5 Sales 15152 5 Office and Administrative 154205 12.5 Construction 15----1 2.5 Installation, and Maintenance 15----1 2.5 Production 210----2 5 Transportation 210----2 5 Sports Occupations ----2102 5 Retired/Not working 210153 7.5 Current work status Full-time 420115515 37.5 Part-time 3155258 20 Not working 94542013 32.5 Dominant extremity Left 4203157 17.5 Right 1680178533 82.5 Injured extremity Left 105073517 42.5 Right 1050136523 57.5

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111 Table 4-2: Injury related characteristics of the study sample Men (N=20) Women (N=20) All (N=40) Ave/Num SD/% Ave/Num SD/% Ave/Num SD/% Location of injury Hand 10 50 6 30 16 40 Wrist 4 20 7 35 11 27.5 Forearm 1 5 5 25 6 15 Elbow 5 25 2 10 7 17.5 Duration of injury (months) 8 15 16.15 29 19.7 41 Etiology Cause of injury Traumatic 19 95 9 45 28 70 Motor vehicle accident 3 15 5 25 8 20 Sports injury 6 30 --6 15 Violence-related injury 2 10 --2 5 Falls 3 15 3 15 6 15 Occupational injury 1 5 --1 2.5 Other 4 20 1 5 5 12.5 Cumulative Trauma 1 5 10 50 11 27.5 Sports injury 1 5 3 15 4 10 Occupational injury -6 30 6 15 House maintenance --1 5 1 2.5 Do not know --1 5 1 2.5 Signs/Symptoms Pain intensity in past week (cm.) 3.15 2.1 3.46 2.2 3.3 2.1 Current pain intensity (cm.) 1.57 1.87 1.61 1.94 1.5 1.8 Injury limits ADL 6 30 9 45 15 37.5 Management Currently taking pain medications 5 25 6 30 11 27.5 Undergone surgical intervention 12 60 10 50 22 55 Benefited from surgery 12 60 9 45 21 52.5 Length of rehabilitative care Duration (weeks) 6.05 7.01 30.82 4.62 11.1 21.6 Times per week (median) 2 -2 -2 -Success of rehabilitative care 18 90 16 80 34 85 Somewhat successful 2 10 3 15 5 12.5 Successful 10 50 7 35 17 42.5 Very successful 6 30 6 30 12 30

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112Table 4-3: First session averages of the F-T curve characteristics Males (N=20) Females (N=20) Injured Hand Uninjured Hand Injured Hand Uninjured Hand AverageSD AverageSD Average SD AverageSD Peak Force (kg) Maximal effort 29.9015.4439.6710.5020.14 10.2226.878.86 Sub-pain effort 16.0310.8621.019.9210.88 7.1513.317.50 Sub-percent effort 15.608.9220.229.2010.43 6.3914.497.66 Time-to-peak force (s) Maximal effort 1.751.081.440.581.35 0.631.290.55 Sub-pain effort 0.960.411.070.441.12 0.421.100.45 Sub-percent effort 0.930.340.940.360.86 0.330.990.34 Slope of force-generation phase (V/s) Maximal effort 1.6901.3431.9731.0610.936 0.5891.3540.710 Sub-pain effort 0.8830.7620.9300.6020.412 0.2710.6260.407 Sub-percent effort 0.8630.6481.2210.8480.522 0.3570.6310.367 Slope of force-decay phase (V/s) Maximal effort -0.0300.064-0.0430.043-0.024 0.019-0.0460.023 Sub-pain effort -0.0160.037-0.0300.042-0.015 0.015-0.0290.029 Sub-percent effort -0.0220.028-0.0320.036-0.020 0.014-0.0270.028

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113Table 4-4: Second session averages of the F-T curve characteristics Males (N=20) Females (N=20) Injured Hand Uninjured Hand Injured Hand Uninjured Hand AverageSD AverageSD Average SD AverageSD Peak Force (kg) Maximal effort 29.6815.2138.4010.7520.35 9.9625.598.44 Sub-pain effort 16.769.4321.009.2711.26 6.3113.827.98 Sub-percent effort 16.958.9020.287.2610.75 6.0413.106.13 Time to peak force (s) Maximal effort 1.571.081.360.541.27 0.701.380.70 Sub-pain effort 0.920.301.060.441.04 0.360.900.28 Sub-percent effort 0.850.350.930.300.95 0.380.820.25 Slope of force-generation phase (V/s) Maximal effort 1.6221.2921.9961.5300.949 0.6921.4550.925 Sub-pain effort 0.8320.5891.0740.8720.474 0.3010.6630.417 Sub-percent effort 1.0210.8651.1500.7380.526 0.4650.6520.344 Slope of force-decay phase (V/s) Maximal effort -0.0350.049-0.0510.045-0.023 0.014-0.0400.023 Sub-pain effort -0.0300.034-0.0400.039-0.016 0.017-0.0220.022 Sub-percent effort -0.0340.031-0.0370.028-0.015 0.012-0.0220.019

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114Table 4-5: Four-Way ANOVA on the values of peak force Source Sum of Squares DF Mean Square F p-value Between-Subjects Gender 7446.7617446.769.820.003* Within-Subjects Injured 2906.1512906.1541.770.0001* Effort 14894.06114894.06183.690.0001* Session 0.3210.320.020.883 Injured x Gender 125.511125.511.800.187 Effort x Gender 425.231425.235.240.028* Session x Gender 2.9212.920.200.656 Injured x Effort 340.311340.3115.490.0001* Injured x Session 31.45131.455.050.031* Effort x Session 13.47113.471.320.257 Injured x Effort x Gender 11.31111.310.510.477 Injured x Session x Gender 0.0010.000.000.997 Effort x Session x Gender 1.1011.100.110.745 Injured x Effort x Session 0.0110.010.000.970 Injured x Effort x Session x Gender 4.9714.970.700.409 Indicates significant differences at the p < 0.05 alpha level Gender = Male vs. Female Effort = Maximal vs. Submaximal according to imag ined pain vs. Fifty percent of maximal effort Injured = Injured hand vs. Uninjured hand Session = Session 1 vs. Session 2

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115Table 4-6: Three-Way ANOVA on first session values of the peak force Source Sum of Squares DF Mean Square F p-value Between-Subjects Gender 3577.3713577.379.010.005* Within-Subjects Injured 1771.1011771.1039.820.0001* Effort 7901.7217901.72156.370.0001* Injured x Gender 62.65162.651.410.243 Effort x Gender 234.761234.764.650.038* Injured x Effort 168.331168.339.570.004* Injured x Effort x Gender 0.6410.640.040.849 Indicates significant differences at the p < 0.05 alpha level Gender = Male vs. Female Effort = Maximal vs. Submaximal according to imag ined pain vs. Fifty percent of maximal effort Injured = Injured hand vs. Uninjured hand

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116Table 4-7: Three-Way ANOVA on second session values of the peak force Source Sum of Squares DF Mean Square F p-value Between-Subjects Gender 3872.3113872.3110.310.003* Within-Subjects Injured 1166.4911166.4937.240.0001* Effort 7005.8117005.81171.990.0001* Injured x Gender 62.86162.862.010.165 Effort x Gender 191.561191.564.700.036* Injured x Effort 172.001172.0014.950.0001* Injured x Effort x Gender 15.64115.641.360.251 Indicates significant differences at the p < 0.05 alpha level Gender = Male vs. Female Effort = Maximal vs. Submaximal according to imag ined pain vs. Fifty percent of maximal effort Injured = Injured hand vs. Uninjured hand

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117Table 4-8: Four-Way ANOVA on the values of time-to-peak force Source Sum of Squares DF Mean Square F p-value Between-Subjects Gender 0.4210.420.330.570 Within-Subjects Injured 0.0710.070.370.545 Effort 21.50121.5058.140.0001* Session 0.4510.451.930.173 Injured x Gender 0.0110.010.040.845 Effort x Gender 0.7710.772.090.157 Session x Gender 0.0010.000.010.904 Injured x Effort 0.3810.381.720.197 Injured x Session 0.0010.000.000.993 Effort x Session 0.0110.010.040.842 Injured x Effort x Gender 0.5310.532.370.132 Injured x Session x Gender 0.1110.110.970.331 Effort x Session x Gender 0.0910.090.320.577 Injured x Effort x Session 0.2710.271.640.207 Injured x Effort x Session x Gender 0.2010.201.200.281 Indicates significant differences at the p < 0.05 alpha level Gender = Male vs. Female Effort = Maximal vs. Submaximal according to imag ined pain vs. Fifty percent of maximal effort Injured = Injured hand vs. Uninjured hand Session = Session 1 vs. Session 2

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118Table 4-9: Three-Way ANOVA on the firs t session values of time-to-peak force Source Sum of Squares DF Mean Square F p-value Between-Subjects Gender 0.251.000.250.320.572 Within-Subjects Injured 0.031.000.030.240.629 Effort 11.251.0011.2533.640.0001* Injured x Gender 0.091.000.090.630.434 Effort x Gender 0.691.000.692.080.158 Injured x Effort 0.651.000.652.950.094 Injured x Effort x Gender 0.041.000.040.180.672 Indicates significant differences at the p < 0.05 alpha level Gender = Male vs. Female Effort = Maximal vs. Submaximal according to imag ined pain vs. Fifty percent of maximal effort Injured = Injured hand vs. Uninjured hand

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119Table 4-10: Four-Way ANOVA on the sl opes of the force-generation phase Source Sum of Squares DF Mean Square F p-value Between-Subjects Gender 30.56130.56 6.990.012* Within-Subjects Injured 7.4917.49 18.610.0001* Effort 36.28136.28 52.060.0001* Session 0.1210.12 0.370.548 Injured x Gender 0.0110.01 0.030.854 Effort x Gender 0.5510.55 0.780.382 Session x Gender 0.0110.01 0.030.869 Injured x Effort 0.9210.92 5.770.021* Injured x Session 0.0210.02 0.070.786 Effort x Session 0.0010.00 0.010.916 Injured x Effort x Gender 0.3410.34 2.100.155 Injured x Session x Gender 0.0010.00 0.000.965 Effort x Session x Gender 0.0610.06 0.300.588 Injured x Effort x Session 0.1910.19 1.260.268 Injured x Effort x Session x Gender 0.0810.08 0.520.476 Indicates significant differences at the p < 0.05 alpha level Gender = Male vs. Female Effort = Maximal vs. Submaximal according to imag ined pain vs. Fifty percent of maximal effort Injured = Injured hand vs. Uninjured hand Session = Session 1 vs. Session 2

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120Table 4-11: Three-Way ANOVA on the first sess ion slopes of the force-generation phase Source Sum of Squares DF Mean Square F p-value Between-Subjects Gender 15.80 1 15.80 8.37 0.006* Within-Subjects Injured 3.40 1 3.40 10.00 0.003* Effort 18.43 1 18.43 55.77 0.0001* Injured x Gender 0.00 1 0.00 0.01 0.907 Effort x Gender 0.48 1 0.48 1.46 0.234 Injured x Effort 0.14 1 0.14 0.88 0.355 Injured x Effort x Gender 0.37 1 0.37 2.36 0.133 Indicates significant differences at the p < 0.05 alpha level Gender = Male vs. Female Effort = Maximal vs. Submaximal according to imag ined pain vs. Fifty percent of maximal effort Injured = Injured hand vs. Uninjured hand

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121Table 4-12: Four-Way ANOVA on the slopes of the force-decay phase Source Sum of Squares DF Mean Square F p-value Between-Subjects Gender 0.0085510.008551.250.271 Within-Subjects Injured 0.0166510.0166532.560.0001* Effort 0.0086010.008608.380.006* Session 0.0008210.000821.600.213 Injured x Gender 0.0000410.000040.070.788 Effort x Gender 0.0003110.000310.300.587 Session x Gender 0.0048410.004849.470.004* Injured x Effort 0.0022510.002254.030.052 Injured x Session 0.0003510.000350.720.401 Effort x Session 0.0000010.000000.000.957 Injured x Effort x Gender 0.0001110.000110.200.655 Injured x Session x Gender 0.0000310.000030.070.795 Effort x Session x Gender 0.0000710.000070.170.685 Injured x Effort x Session 0.0000210.000020.050.821 Injured x Effort x Session x Gender 0.0001910.000190.450.507 Indicates significant differences at the p < 0.05 alpha level Gender = Male vs. Female Effort = Maximal vs. Submaximal according to imag ined pain vs. Fifty percent of maximal effort Injured = Injured hand vs. Uninjured hand Session = Session 1 vs. Session 2

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122Table 4-13: Three-Way ANOVA on the first se ssion slopes of the force-decay phase Source Sum of Squares DF Mean Square F p-value Between-Subjects Gender 0.0002610.000260.06239 0.804 Within-Subjects Injured 0.0109110.0109115.96264 0.0001* Effort 0.0044010.004405.62535 0.023* Injured x Gender 0.0000710.000070.10293 0.750 Effort x Gender 0.0000410.000040.05665 0.813 Injured x Effort 0.0009110.000911.93942 0.172 Injured x Effort x Gender 0.0003010.000300.62882 0.433 Indicates significant differences at the p < 0.05 alpha level Gender = Male vs. Female Effort = Maximal vs. Submaximal according to imagined pain vs. Fifty percent of maximal effort Injured = Injured hand vs. Uninjured hand

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123Table 4-14: Three-Way ANOVA on the second session slopes of the force-decay phase Source Sum of Squares DF Mean Square F p-value Between-Subjects Gender 0.01312610.0131264.17090.048* Within-Subjects Injured 0.00609110.00609119.57710.0001* Effort 0.00420010.0042006.54290.015* Injured x Gender 0.00000010.0000000.00020.989 Effort x Gender 0.00033010.0003300.51420.478 Injured x Effort 0.00135610.0013562.68240.110 Injured x Effort x Gender 0.00000510.0000050.00930.924 Indicates significant differences at the p < 0.05 alpha level Gender = Male vs. Female Effort = Maximal vs. Submaximal according to imagined pain vs. Fifty percent of maximal effort Injured = Injured hand vs. Uninjured hand

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124Table 4-15: Average values for EMG amplitude Males (N=20) Females (N=20) Injured Hand Uninjured Hand Injured Hand Uninjured Hand Average SD Average SD AverageSD AverageSD First Session Flexor EMG amplitude (V) Maximal effort 59.5735.3869.4334.52 47.6124.5162.5634.76 Sub-pain effort 24.3715.1624.269.99 23.5811.5028.5218.49 Sub-percent effort 23.088.9222.528.83 22.1012.9326.0017.15 Extensor EMG amplitude (V) Maximal effort 126.9485.38150.12106.90 101.5182.76101.9344.58 Sub-pain effort 68.6854.1464.3852.29 45.3633.2049.8622.12 Sub-percent effort 56.8633.8062.0839.04 41.7830.6842.6021.33 Second Session Flexor EMG amplitude (V) Maximal effort 58.2633.9665.9334.95 49.7330.7761.3630.37 Sub-pain effort 25.0010.6323.4710.25 24.2113.2529.5523.82 Sub-percent effort 24.3012.9421.687.58 22.0210.1126.5819.64 Extensor EMG amplitude (V) Maximal effort 120.6683.20139.2482.24 97.6383.02100.6147.44 Sub-pain effort 62.7848.3864.8646.32 48.1337.5345.4723.72 Sub-percent effort 60.8734.3562.0831.59 44.6635.5842.1521.52

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125Table 4-16: Average values of EMG me dian frequency for the first session Males (N=20) Females (N=20) Injured Hand Uninjured Hand Injured Hand Uninjured Hand AverageSD AverageSD AverageSD Average SD First second Flexor Median Frequency (Hz) Maximal effort 110.46 15.60 120.62 10.49 116.52 10.45 116.77 12.35 Sub-pain effort 110.13 17.80 115.54 11.90 115.21 13.57 116.28 11.96 Sub-percent effort 109.18 13.26 117.25 10.64 116.10 15.61 114.96 12.12 Extensor Median Frequency (Hz) Maximal effort 132.74 14.24 141.75 14.39 136.86 14.20 138.52 11.38 Sub-pain effort 130.59 12.43 137.58 13.12 137.96 12.97 137.18 11.85 Sub-percent effort 134.84 13.22 139.86 16.34 136.16 15.71 137.72 10.19 Last second Flexor Median Frequency (Hz) Maximal effort 107.56 16.03 111.33 13.98 113.64 9.83 115.30 12.16 Sub-pain effort 112.54 16.82 119.19 13.36 119.36 14.92 119.37 11.31 Sub-percent effort 111.81 17.48 122.98 12.53 117.95 12.60 118.68 10.81 Extensor Median Frequency (Hz) Maximal effort 132.79 18.87 137.42 16.24 131.45 15.56 133.37 14.95 Sub-pain effort 135.11 16.51 142.84 15.63 138.25 13.88 139.82 12.72 Sub-percent effort 135.63 15.33 145.44 18.38 138.43 15.41 141.19 12.41 Flexor Median Frequency Ratio (% initial MF) Maximal effort 97.75 2.05 92.22 7.06 97.84 7.93 98.95 7.20 Sub-pain effort 103.10 2.67 103.36 7.64 104.11 12.40 102.95 6.75 Sub-percent effort 102.25 1.75 105.02 7.08 102.27 9.08 103.66 7.78 Extensor Median Frequency Ratio (% initial MF) Maximal effort 100.43 3.31 96.95 5.77 96.02 5.09 96.18 5.87 Sub-pain effort 103.61 2.23 103.79 5.14 100.40 7.34 101.99 4.81 Sub-percent effort 100.59 1.31 103.96 4.71 101.88 5.83 102.55 5.60

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126Table 4-17: Average values of EMG medi an frequency for second session values Males (N=20) Females (N=20) Injured Hand Uninjured Hand Injured Hand Uninjured Hand AverageSD AverageSD AverageSD Average SD First second Flexor Median Power Frequency (Hz) Maximal effort 111.56 14.68 121.27 11.39 116.04 10.44 117.35 10.76 Sub-pain effort 109.01 15.15 118.20 13.11 117.03 14.43 113.73 9.66 Sub-percent effort 109.51 21.29 119.15 10.72 115.40 14.95 115.31 12.65 Extensor Median Power Frequency (Hz) Maximal effort 136.65 17.27 143.57 14.54 138.26 14.59 138.47 10.66 Sub-pain effort 135.06 16.34 143.12 13.25 138.58 13.44 138.47 10.16 Sub-percent effort 136.51 17.36 144.70 14.62 138.99 13.84 137.68 10.41 Last second Flexor Median Power Frequency (Hz) Maximal effort 108.21 17.79 112.32 13.70 113.52 9.01 115.40 10.82 Sub-pain effort 112.09 17.78 119.65 12.88 117.36 12.83 118.86 11.44 Sub-percent effort 110.02 25.28 121.76 13.29 118.88 10.82 119.55 11.43 Extensor Median Power Frequency (Hz) Maximal effort 134.07 17.80 139.25 16.15 133.16 16.59 133.82 12.95 Sub-pain effort 135.73 18.06 144.78 16.87 140.15 15.85 137.87 15.66 Sub-percent effort 138.22 18.72 145.03 14.57 141.66 14.72 141.17 12.13 Flexor Median Frequency Ratio (% initial MF) Maximal effort 96.88 7.31 92.75 8.59 98.21 7.96 98.55 6.85 Sub-pain effort 103.05 10.14 101.43 6.01 100.77 8.96 104.80 8.92 Sub-percent effort 99.54 10.69 102.46 9.69 103.84 9.79 104.16 8.05 Extensor Median Frequency Ratio (% initial MF) Maximal effort 98.72 13.26 97.13 7.20 96.27 5.41 96.68 6.35 Sub-pain effort 100.72 9.51 101.11 6.82 101.06 4.68 99.44 6.81 Sub-percent effort 101.34 6.76 100.33 5.14 101.93 3.70 102.55 4.47

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127Table 4-18: Four-Way ANOV A on flexor EMG amplitude Source Sum of Squares DF Mean Square F pvalue Between-Subjects Gender 271.251271.25 0.090.772 Within-Subjects Injured 2805.6812805.68 4.720.036* Effort 102376.241102376.24 102.070.000* Session 1.8711.87 0.020.877 Injured x Gender 885.461885.46 1.490.230 Effort x Gender 1716.2911716.29 1.710.199 Session x Gender 48.88148.88 0.630.432 Injured x Effort 1885.1511885.15 7.160.011* Injured x Session 52.08152.08 2.520.121 Effort x Session 28.51128.51 0.560.457 Injured x Effort x Gender 8.4918.49 0.030.858 Injured x Session x Gender 9.6719.67 0.470.498 Effort x Session x Gender 39.26139.26 0.780.384 Injured x Effort x Session 21.13121.13 0.480.492 Injured x Effort x Session x Gender 18.79118.79 0.430.517 Indicates significant differences at the p < 0.05 alpha level Gender = Male vs. Female Effort = Maximal vs. Submaximal according to imag ined pain vs. Fifty percent of maximal effort Injured = Injured hand vs. Uninjured hand Session = Session 1 vs. Session 2

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128Table 4-19: Three-Way ANOVA on first sessi on values of the flexor EMG amplitude Source Sum of Squares DF Mean Square F pvalue Between-Subjects Gender 275.211275.210.18 0.674 Within-Subjects Injured 1811.1211811.126.29 0.017* Effort 52910.89152910.8991.35 0.000* Injured x Gender 355.011355.011.23 0.274 Effort x Gender 1137.3411137.341.96 0.169 Injured x Effort 1152.7011152.707.81 0.008* Injured x Effort x Gender 1.0111.010.01 0.935 Indicates significant differences at the p < 0.05 alpha level Gender = Male vs. Female Effort = Maximal vs. Submaximal according to imagined pain vs. Fifty percent of maximal effort Injured = Injured hand vs. Uninjured hand

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129Table 4-20: Four-Way ANOVA on extensor EMG amplitude Source Sum of Squares DF Mean Square F p-value Between-Subjects Gender 64338.83164338.832.920.096 Within-Subjects Injured 2045.0712045.070.450.507 Effort 345249.891345249.8961.350.0001* Session 439.711439.711.000.323 Injured x Gender 1501.1211501.120.330.569 Effort x Gender 5212.7815212.780.930.342 Session x Gender 167.831167.830.380.540 Injured x Effort 2042.0412042.043.130.085 Injured x Session 86.64186.640.510.480 Effort x Session 1037.2911037.295.890.020* Injured x Effort x Gender 1144.0011144.001.750.193 Injured x Session x Gender 27.07127.070.160.693 Effort x Session x Gender 229.291229.291.300.261 Injured x Effort x Session 35.09135.090.190.662 Injured x Effort x Session x Gender 52.27152.270.290.594 Indicates significant differences at the p < 0.05 alpha level Gender = Male vs. Female Effort = Maximal vs. Submaximal according to imag ined pain vs. Fifty percent of maximal effort Injured = Injured hand vs. Uninjured Hand Session = Session 1 vs. Session 2

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130Table 4-21: Three-Way ANOVA the on fi rst session extensor EMG amplitude Source Sum of Squares DF Mean Square F p-value Between-Subjects Gender 35539.32135539.322.990.092 Within-Subjects Injured 1486.7811486.780.610.439 Effort 192067.721192067.7255.760.0001* Injured x Gender 562.521562.520.230.633 Effort x Gender 3814.3113814.311.110.299 Injured x Effort 770.881770.881.990.167 Injured x Effort x Gender 842.681842.682.170.149 Indicates significant differences at the p < 0.05 alpha level Gender = Male vs. Female Effort = Maximal vs. Submaximal according to imag ined pain vs. Fifty percent of maximal effort Injured = Injured hand vs. Uninjured Hand

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131Table 4-22: Three-Way ANOVA on the s econd session extensor EMG amplitude Source Sum of Squares DF Mean Square F p-value Between-Subjects Gender 28967.34128967.342.740.106 Within-Subjects Injured 644.931644.930.280.599 Effort 154219.451154219.4565.370.0001* Injured x Gender 965.671965.670.420.520 Effort x Gender 1627.7611627.760.690.411 Injured x Effort 1306.2511306.252.930.095 Injured x Effort x Gender 353.591353.590.790.379 Indicates significant differences at the p < 0.05 alpha level Gender = Male vs. Female Effort = Maximal vs. Submaximal according to imag ined pain vs. Fifty percent of maximal effort Injured = Injured hand vs. Uninjured Hand

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132Table 4-23: Four-Way ANOVA on the fl exor EMG median frequency ratio Source Sum of Squares DF Mean Square F p-value Between-Subjects Gender 343.131343.13 1.260.269 Within-Subjects Injured 0.4110.41 0.000.967 Effort 3133.3313133.33 36.680.0001* Session 41.25141.25 1.140.292 Injured x Gender 107.521107.52 0.440.510 Effort x Gender 108.021108.02 1.260.268 Session x Gender 55.06155.06 1.520.225 Injured x Effort 304.341304.34 7.080.011* Injured x Session 7.6317.63 0.220.642 Effort x Session 10.04110.04 0.410.525 Injured x Effort x Gender 284.581284.58 6.620.014* Injured x Session x Gender 11.37111.37 0.330.570 Effort x Session x Gender 62.19162.19 2.560.118 Injured x Effort x Session 2.9512.95 0.110.745 Injured x Effort x Session x Gender 1.1811.18 0.040.837 Indicates significant differences at the p < 0.05 alpha level Gender = Male vs. Female Effort = Maximal vs. Submaximal according to imag ined pain vs. Fifty percent of maximal effort Injured = Injured hand vs. Uninjured hand Session = Session 1 vs. Session 2

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133Table 4-24: Three-Way ANOVA on the first session values of flexor EMG median frequency ratio Source Sum of Squares DF Mean Square F p-value Between-Subjects Gender 61.64161.640.43 0.52 Within-Subjects Injured 2.2512.250.02 0.901 Effort 1749.0511749.0530.27 0.0001* Injured x Gender 24.48124.480.17 0.682 Effort x Gender 167.071167.072.89 0.097 Injured x Effort 183.601183.608.04 0.007* Injured x Effort x Gender 161.231161.237.06 0.011* Indicates significant differences at the p < 0.05 alpha level Gender = Male vs. Female Effort = Maximal vs. Submaximal according to imagined pain vs. Fifty percent of maximal effort Injured = Injured hand vs. Uninjured hand

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134Table 4-25: Four-Way ANOVA on the ex tensor EMG median frequency ratio Source Sum of Squares DF Mean Square F p-value Between-Subjects Gender 114.581114.580.47 0.499 Within-Subjects Injured 0.0810.080.00 0.981 Effort 1687.7811687.7826.77 0.0001* Session 102.071102.074.61 0.038* Injured x Gender 13.25113.250.09 0.760 Effort x Gender 145.261145.262.30 0.137 Session x Gender 65.39165.392.95 0.094 Injured x Effort 82.87182.872.32 0.136 Injured x Session 23.51123.511.31 0.259 Effort x Session 5.2415.240.44 0.513 Injured x Effort x Gender 56.60156.601.59 0.216 Injured x Session x Gender 0.4710.470.03 0.873 Effort x Session x Gender 0.5110.510.04 0.838 Injured x Effort x Session 54.02154.025.40 0.026* Injured x Effort x Session x Gender 44.56144.564.46 0.041* Indicates significant differences at the p < 0.05 alpha level Gender = Male vs. Female Effort = Maximal vs. Submaximal according to imagined pain vs. Fifty percent of maximal effort Injured = Injured hand vs. Uninjured Hand Session = Session 1 vs. Session 2

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135Table 4-26: Three-Way ANOVA on the first session values of extens or EMG median frequency ratio Source Sum of Squares DF Mean Square F p-value Between-Subjects Gender 176.551176.551.33 0.256 Within-Subjects Injured 10.41110.410.14 0.710 Effort 940.591940.5921.16 0.0001* Injured x Gender 9.3419.340.13 0.725 Effort x Gender 64.31164.311.45 0.236 Injured x Effort 135.361135.365.07 0.030* Injured x Effort x Gender 100.801100.803.78 0.059 Indicates significant differences at the p < 0.05 alpha level Gender = Male vs. Female Effort = Maximal vs. Submaximal according to imagined pain vs. Fifty percent of maximal effort Injured = Injured hand vs. Uninjured Hand

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136Table 4-27: Three-Way ANOVA on the second session valu es of extensor EMG median frequency ratio Source Sum of Squares DF Mean Square F p-value Between-Subjects Gender 3.4313.430.03 0.875 Within-Subjects Injured 13.18113.180.16 0.693 Effort 752.431752.4324.58 0.0001* Injured x Gender 4.3714.370.05 0.820 Effort x Gender 81.46181.462.66 0.111 Injured x Effort 1.5411.540.08 0.778 Injured x Effort x Gender 0.3610.360.02 0.891 Indicates significant differences at the p < 0.05 alpha level Gender = Male vs. Female Effort = Maximal vs. Submaximal according to imagined pain vs. Fifty percent of maximal effort Injured = Injured hand vs. Uninjured Hand

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137 Table 4-28: Intraclass Correlation Coeffi cients for F-T curve characteristics Injured hand Uninjured hand r-value r-value Peak Force Maximal effort 0.9570.950 Sub-pain effort 0.9040.919 Sub-percent effort 0.8590.873 Time-to-peak force Maximal effort 0.3060.368 Sub-pain effort 0.5390.257 Sub-percent effort 0.4140.586 Slope of force-generation phase Maximal effort 0.8220.598 Sub-pain effort 0.7830.788 Sub-percent effort 0.7110.706 Slope of force-decay phase Maximal effort 0.5790.592 Sub-pain effort 0.6770.713 Sub-percent effort 0.6100.604

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138 Table 4-29: Intraclass Correlation Coefficients for EMG properties Injured hand Uninjured hand r-value r-value Flexor EMG amplitude Maximal effort 0.9260.933 Sub-pain effort 0.8240.874 Sub-percent effort 0.7920.938 Extensor EMG amplitude Maximal effort 0.9670.930 Sub-pain effort 0.8220.915 Sub-percent effort 0.9170.914 Flexor MF-ratio Maximal effort 0.7020.740 Sub-pain effort 0.6180.715 Sub-percent effort 0.2980.777 Extensor MF-ratio Maximal effort 0.8930.710 Sub-pain effort 0.5180.481 Sub-percent effort 0.4960.535

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139Table 4-30: Summary of main effects of effort for force and EMG measures Both Sessions First Session Second Session F-valuep-valueF-value p-value Fvalue pvalue F-T Curve Peak force 183.680.0001156.370.0001171.99 0.0001 Time-to-peak force 58.140.000133.640.0001--Slope of force-generation phase 52.060.000155.770.0001--Slope of force-decay phase 8.380.0065.620.0236.54 0.015 EMG Flexor amplitude 102.070.000191.350.0001--Extensor amplitude 61.350.000155.760.000165.37 0.0001 Flexor MF-ratio 36.680.000130.270.0001--Extensor MF-ratio 26.770.000121.160.000124.58 0.0001

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140Table 4-31: Sensitivity and specificity of specific slope cutoff values for force-generation phase Slope 0.1 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 Males Sensitivity 0.00 0.00 0.20 0.30 0.35 0.45 0.45 0.50 0.55 0.55 0.60 0.60 0.60 0.65 0.70 0.85 0.85 Specificity 1.00 1.00 0.95 0.85 0.80 0.80 0.75 0.75 0.70 0.60 0.55 0.55 0.55 0.55 0.55 0.55 0.50 Overall error rate 1.00 1.00 0.85 0.85 0.85 0.75 0.80 0.75 0.75 0.85 0.85 0.85 0.85 0.80 0.75 0.60 0.65 Females Sensitivity 0.00 0.05 0.10 0.35 0.50 0.60 0.70 0.75 0.75 0.85 0.90 0.95 0.95 0.95 0.95 1.00 1.00 Specificity 1.00 1.00 1.00 0.95 0.85 0.85 0.60 0.60 0.55 0.45 0.35 0.25 0.20 0.15 0.15 0.15 0.15 Overall error rate 1.00 0.95 0.90 0.70 0.65 0.55 0.70 0.65 0.70 0.70 0.75 0.80 0.85 0.90 0.90 0.85 0.85 Table 4-31: Continued Slope 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0 3.1 3.2 3.3 Males Sensitivity 0.90 0.90 0.95 0.95 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Specificity 0.50 0.50 0.40 0.25 0.25 0.25 0.25 0.20 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 Overall error rate 0.60 0.60 0.65 0.80 0.75 0.75 0.75 0.80 0.85 0.85 0.85 0.85 0.85 0.85 0.85 0.85 0.85 Females Sensitivity 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Specificity 0.15 0.15 0.10 0.10 0.10 0.05 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Overall error rate 0.85 0.85 0.90 0.90 0.90 0.95 0.95 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

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141Table 4-31: Continued Slope 3.4 3.5 3.6 3.7 3.8 3.9 4.0 4.1 4.2 4.3 4.4 4.5 4.6 4.7 Males Sensitivity 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Specificity 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.10 0.10 0.05 0.05 0.05 0.05 0.00 Overall error rate 0.85 0.85 0.85 0.85 0.85 0.85 0.85 0.90 0.90 0.95 0.95 0.95 0.95 1.00 Females Sensitivity 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Specificity 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Overall error rate 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

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142Table 4-32: Sensitivity and specificity values of specific slope cutoff values for force-decay phase Slope -0.2 -0.18-0.16-0.14-0.12-0.1-0.08-0.06-0.04-0.020.07 Sensitivity 1 11111 0.9750.95 0.85 0.5750 Specificity 0 0.0250.0250.0250.0750.0750.0750.125 0.275 0.4251 Overall error rate 1 0.9750.9750.9750.9250.9250.950.925 0.875 11

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143Table 4-33: Sensitivity and specificity of specific flexor MF-ratio cutoff values Frequency Ratio (%) 75 80 85 90 92 95 96 97 98 99 100 Sensitivity 1.00 0.98 0.95 0.95 0.93 0.88 0.83 0.78 0.73 0.63 0.58 Specificity 0.00 0.03 0.08 0.20 0.28 0.35 0.38 0.45 0.48 0.50 0.58 Overall error rate 1.00 1.00 0.98 0.85 0.80 0.78 0.80 0.78 0.80 0.88 0.85 Table 4-33: Continued Frequency Ratio (%) 102 103 105 107 108 110 115 120 125 130 Sensitivity 0.53 0.48 0.35 0.25 0.20 0.15 0.05 0.03 0.03 0.00 Specificity 0.78 0.80 0.85 0.88 0.90 0.93 0.98 0.98 1.00 1.00 Overall error rate 0.70 0.73 0.80 0.88 0.90 0.93 0.98 1.00 0.98 1.00

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144Table 4-34: Sensitivity and specificity of specific extensor MF-ratio cutoff values Frequency Ratio (%) 75 80 8590929596979899100 102103105107108110160 Sensitivity 1.00 1.00 1.00 0.98 0.93 0.90 0.85 0.75 0.68 0.68 0.63 0.45 0.38 0.25 0.18 0.15 0.08 0.00 Specificity 0.00 0.00 0.03 0.13 0.20 0.35 0.45 0.50 0.58 0.60 0.70 0.80 0.85 0.90 0.95 0.95 0.98 1.00 Overall error rate 1.00 1.00 0.98 0.90 0.88 0.75 0.70 0.75 0.75 0.73 0.68 0.75 0.78 0.85 0.88 0.90 0.95 1.00

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145Table 4-35: Summary of sensitiv ity and specificity values fo r the Force and EMG measures Characteristic Optimal Cutoff SensitivitySpecificity Overall error (%) Area under the curve (%) Slope of forcegeneration phase (V/s) Males 1.5 0.850.556076 Females 0.5 0.600.855572 Flexor MF-ratio (%) 102 0.570.787066.25 Extensor MF-ratio (%) 100 0.630.706771

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146 SessionSecond First Estimated Marginal Means (kg)23 22 21 20 19 18 17 Injured hands Uninjured hands Estimated Marginal Means of Peak Force Figure 4-1: Interaction between session and injury for peak force

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147 0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 Injured Hand Males Uninjured Hand Males Injured Hand Females Uninjured Hand Females Injured Hand Males Uninjured Hand Males Injured Hand Females Uninjured Hand Females Peak Force (kg) Maximal effort Sub-pain effort Sub-percent effort First Session Second Session Figure 4-2: Average values of peak force for maximal and submaximal grip efforts

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148 GenderFemales Males Estimated Marginal Means35 30 25 20 15 10 Max Sub-pain Sub-percentA GenderFemales Males Estimated Marginal Means35 30 25 20 15 10 Max Sub-pain Sub-percent Estimated Marginal Means of Peak Force Second Session B InjuredUninjured hand Injured hand Estimated Marginal Means35 30 25 20 15 10 Max Sub-pain Sub-percent Estimated Marginal Means of Peak Force First Session C InjuredUninjured hands Injured hands Estimated Marginal Means35 30 25 20 15 10 Max Sub-pain Sub-percentD Figure 4-3: Significant interactions for peak force values. A) First session valu es for males and females. B) Second session va lues for males and females. C) First session values for injured and uninjured hands. D) Second session values for injured and uninjured hands.

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149 0.00 0.50 1.00 1.50 2.00 2.50 Injured Hand Males Uninjured Hand Males Injured Hand Females Uninjured Hand Females Time (s) Maximal effort Sub-pain effort Sub-percent effort First Session Figure 4-4: Average values of time-to-peak force

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150 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 InjuredUninjured InjurySlope (V/s) Max effort Sub-pain effort Sub-percent effort Figure 4-5: Interaction between effort and injury for slope of force-generation phase

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151 0.000 0.500 1.000 1.500 2.000 2.500 Injured Hand Males Uninjured Hand Males Injured Hand Females Uninjured Hand Females Slope (V/s) Maximal effort Sub-pain effort Sub-percent effort First Session Figure 4-6: Average values of slopes of force-generation phase

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152 Figure 4-7: Interaction between effort a nd injury for slope of force-decay phase Uninjured Injured -0.05 -0.045 -0.04 -0.035 -0.03 -0.025 -0.02 -0.015 InjuryEstimated marginal means of Slope Max Sub-pain Sub-percent

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153 Figure 4-8: Interaction between session a nd gender for slope of force-decay phase First session Second session -0.04 -0.035 -0.03 -0.025 -0.02 -0.015 Sessions Males FemalesEstimated marginal means of Slope

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154 Injured Hand Males Uninjured Hand Females Injured Hand Females Uninjured Hand Males Injured Hand Females Injured Hand Males Uninjured Hand Males Uninjured Hand Females -0.070 -0.060 -0.050 -0.040 -0.030 -0.020 -0.010 0.000Slope (V/s) Maximal effort Sub-pain effort Sub-percent effort First Session Second Session Figure 4-9: Average values of slopes of force-decay phase for maximal and submaximal grip efforts

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155 InjuryUninjured hands Injured hands Estimated Marg inal Means70 60 50 40 30 20 Max Sub-pain Sub-percent Estimated Marginal Means of First Session Flexor EMG Amplitude Figure 4-10: Interaction between effort and injury for flexor EMG amplitude

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156 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 Injured Hand MalesUninjured Hand MalesInjured Hand Females Uninjured Hand Females EMG Amplitude (micro Volts) Maximal effort Sub-pain effort Sub-percent effort First Session Figure 4-11: Average values of flexor EMG amp litude for maximal and submaximal grip efforts

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157 SessionSecond First Estimated Marginal Means120 100 80 60 Max Sub-pain Sub-percent statedagaeasotesoGptude Figure 4-12: Interaction between effort and session for extensor EMG amplitude

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158 0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00 160.00 180.00 200.00 Injured Hand Males Uninjured Hand Males Injured Hand Females Uninjured Hand Females Injured Hand Males Uninjured Hand Males Injured Hand Females Uninjured Hand Females EMG Amplitude (micro Volts) Maximal effort Sub-pain effort Sub-percent effort First Session Second Session Figure 4-13: Average values of extensor EMG amplitude

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159 90.00 92.00 94.00 96.00 98.00 100.00 102.00 104.00 106.00 108.00 110.00 Injured Hand Males Uninjured Hand Males Injured Hand Females Uninjured Hand FemalesFrequency (% initial) Max effort Sub-pain effort Sub-percent effort First Session Figure 4-14: Average values of flexor MF-ratio

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160 InjuryUninjured hands Injured hands Estimated Marginal Means102.50 100.00 97.50 95.00 92.50 Max Sub-pain Sub-percentMalesA InjuryUninjured hands Injured hands g 102.50 100.00 97.50 95.00 92.50 Max Sub-pain Sub-percentFemalesB Figure 4-15: Interaction between in jury, effort, and gender for flexor MF-ratio. A) Estimated marginal means for males. B) Esti mated marginal means for females.

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161 90.00 92.00 94.00 96.00 98.00 100.00 102.00 104.00 106.00 108.00 110.00 Injured Hand Males Uninjured Hand Males Injured Hand Females Uninjured Hand Females Injured Hand Males Uninjured Hand Males Injured Hand Females Uninjured Hand Females Frequency (% initial) Max effort Sub-pain effort Sub-percent effort First Session Second Session Figure 4-16: Average values of extensor MF-ratio

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162 InjuryUninjured hands Injured hands Estimated Marginal Means104 102 100 98 96 Max Sub-pain Sub-percent Estimated Marginal Means of Extensor Median Frequency Ratio Figure 4-17: Interaction betw een injury and effort for the first se ssion values of extensor EMG MF-ratio

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163 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 0.000.100.200.300.400.500.600.700.800.901.00 1-SpecificitySensitivity Males Females 1.7 1 0.7 1.5 0.5 1.0 0.9 0.8 Figure 4-18: ROC curve for slope of force-generation phase

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164 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 00.10.20.30.40.50.60.70.80.91 1-SpecificitySensitivit y Flexors Extensors 99 97 100 102 100 96 102 Figure 4-19: ROC curve for MF-ratio of forearm flexor and extensor muscles

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165 CHAPTER 5 DISCUSSION Upper extremity musculoskeletal disorder s and injuries (UEMDs) may result in compromised grip strength.1 Grip strength depends on the si ze, type, rate and number of contracting muscle fibers.2 Reduced grip strength (weakne ss of grip) brought about by injury may be due to either a reduction in the rate and number of contracting muscle fibers3, or changes in muscle-fiber-type.3-7 Reduced grip strength may also occur in presence of pain.8-10 Pain has been associated with decreases in: voluntary muscle activity11-17, electromyographic (EMG) activity11, 12, motor unit discharge rates14, 15, motor neuron activity16, speed of force generation17, and endurance time.13 Maximal voluntary grip strength scores of people with UEMDs are used by clinicians18 to determine the extent of injury19, disease process20, and progress in rehabilitation.21 Grip strength is a valid indicato r of musculoskeletal pathology and recovery from such pathology only when people exert a sincere, maximal voluntary effort.22-27, 64-66, 83, 84, 102, 206 Weakness of grip strength may be brought about by an injury but also could be due to exertion of subm aximal effort. Submaximal effort may be exerted during evaluation and treatment for a va riety of reasons, either intentional or unintentional. Unintentional submaximal effort may be exerted as a re sult of pain, fear of pain and fear of re-injury. Intentional subm aximal effort may be exerted for secondary gain, such as money, benefits, or attention. To improve rehabilitative care, clinicians need to be able to distinguish between a maximal vol untary grip effort (exe rted by a client with true weakness of grip) and a submaximal grip effort.

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166 Force-time curve (F-T curve) characteristics28, 29, 55, 101, 102 and electromyographic (EMG) properties30-32 have been used to differentiate between maximal and submaximal grip effort. Furthermore, the ForceTime Curve test (F-T Curve Test)102 has shown promise in determining sincerity of effort. The F-T Curve Test includes the slopes of the force-generation phase and the force-decay pha se. So far, the F-T Curve Test has been shown to be valid in healthy people.102 However, the validity of the F-T Curve Test has not been examined in people with UEMDs. Therefore, the primary purpose of this research project was to examine if the F-T Curve Test is valid in people with UEMDs. Another purpose of this study was to examin e other F-T Curve characteristics and EMG properties are valid sincerity of effo rt measures in pe ople with UEMDs. We examined the ability of four F-T curv e characteristics (peak force, time-to-peak force, slope of force-gene ration phase, and slope of fo rce-decay phase) and two EMG properties (amplitude and median frequency ra tio) to differentiate between maximal and submaximal effort in people with UEMDs. A valid test has to first be reliable.92 Therefore, we examined the test-retest reliability of th e various measures mentioned above and found that they ranged from r = 0.3 to r = 0.97, with time-to-peak force having the worst reliability. A valid test also means that it can differentiate between maximal and submaximal effort. We found all six measur es to be significantly different between maximal and submaximal effort. However, to be clinically valid a test must be effective; i.e. it should not misclassify many patients. We examined the effec tiveness of the above measures by identifying the optimal combination of sensitivity and specificity based on the overall error rates, receiver operating char acteristic (ROC) curv e, and the area under

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167 the ROC curve. Based on these analyses, th e most valid and effective measure of sincerity of effort in this study was the slope of the force-generation phase. We found significant gender differences for the slope of the force-generation phase. Therefore, we calculated separate ROC curves for males and females. The area under the curve was greater for women (76%) than for me n (72%), which indicated a greater ability to discriminate between maximal and submax imal efforts for women. In a previous study on healthy subjects, we found that the slope of the for ce-generation phase was more effective for women than for men.102 For healthy women, the slope cutoff value of 1.2 V/s yielded the most optimal combination of sensitivity (0.8) and specificity (0.93) and the lowest overall error rate (0.27). Also, the area under the curve was 92%.102 In contrast, in the present study for women with UEMDs, the slope cutoff value of 0.5 V/s yielded the most optimal combination of se nsitivity (0.6) and specificity (0.85) and the lowest overall error rate (0.55). Als o, the area under the curve was 76%. Despite the high reliability and signifi cant differences between maximal and submaximal efforts, the slope of the for ce-generation phase does not possess adequate sensitivity and specificity values to be c onsidered clinically valid. Based on previous findings102, our hypothesis was that a se nsitivity value of at l east 80% combined with a specificity value of at least 90% would be adequate clinically. Force-Time Curve Characteristics In the present study, we examined the abil ity of Force-Time curve (F-T curve) characteristics to identify differences betw een maximal and submaximal effort. The F-T curve graphically represents the force generate d by a contracting muscle over a period of time during a single strength trial.29 The vertical axis (Y-axis) represents change in force of muscular contraction and the horizontal axis (X-axis) represents time of muscular

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168 contraction. The typical F-T curve genera ted during a maximal voluntary isometric contraction (MVIC) consists of three phases: 1) the fo rce-generation phase or the initiation phase that involves rapid or gra dual development of force, followed by 2) the peak force, and finally 3) the force-decay pha se or the maintenance phase that involves a steady rate of force that may decrease gra dually over time indicating onset of fatigue.95, 96, 210-212 The characteristics of the F-T curve Test that we examined were its peak force, time-to-peak force as well as its slopes of the force-generation phase and force-decay phase. Some F-T curve characteristics, such as peak force and the slope of the forcegeneration phase, have been found to change with strength training.98, 99, 215, 229, 232 Strength training causes a muscle to undergo both rapid neural adaptations and gradual hypertrophic adaptations. Increa ses in slope of the force-ge neration phase was associated with a stronger neural drive, whereas, increa ses in peak force were primarily associated with muscle hypertrophy.98, 99, 215, 228, 229, 232 Differences between Maximal and Submaximal Effort For the F-T curve characteristics, we exp ected 1) peak force to be greater for maximal versus submaximal effort, 2) the time-to-peak force be greater for maximal versus submaximal effort, and 3) slopes of the force-generation phase as well as forcedecay phase to be steeper for maximal versus submaximal effort. The findings of the present study confirmed our hypotheses. We a ssigned submaximal effort using 2 different ways. One was to instruct the subject to ex ert 50% of maximal effort. The other was to exert submaximal effort based on an imag ined level of pain. Because no significant differences were found between the 2 submaxim al efforts, we discuss both at once as submaximal effort.

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169 We found peak force to be greater, time-t o-peak force to be longer, and slope of force-generation phase to be steeper for maxi mal than for submaximal efforts. Although peak force showed significant differences betw een maximal and submaximal efforts, it is not a good measure of sincerity of effort b ecause peak force indicates strength of a contraction.98, 99, 215, 229, 232 Obviously, maximal effort is st ronger than submaximal effort and injured hand is weaker than uninjured hand. Time-to-peak force and the slope of force-generation phase were found to be si gnificantly different between maximal and submaximal efforts and thus may be valid sinc erity of effort tests. According to Kroemer and Marras188, during maximal contractions, the cen tral nervous syst em recruits all available fibers at their highe st firing rates. Conversely, duri ng submaximal contractions, continuous feedback signals control muscle output by modifying muscle fiber firing rate and muscle fiber recruitment. Further, moto r units fire synchronously during maximal or near maximal efforts and fire asynchronously during submaximal efforts.233 Therefore, a faster buildup of force occurs in maximal effort than submaximal effort.29, 101, 188 Consequently, a maximal grip effort results in a greater peak force as well as a steeper slope of force-generation pha se. Also, greater peak force exerted during maximal effort requires a longer time to reach this higher fo rce, which results in a longer time-to-peak force. We also found the slope of force-decay pha se to be steeper for maximal versus submaximal effort. The differences in steepne ss of slope of for ce-decay phase can be explained on the basis of the onset of fatigue.95, 96, 210-212 During maximal effort all motor units are activated maximally and simulta neously. Consequently, when motor units fatigue there are no “fresh” motor units that can be activated to take over.188, 189, 192-194 In

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170 contrast, a submaximal force can be maintain ed by activation of fewer motor units that fire asynchronously. In other words, as one motor unit is deactivated, another is being activated and there is a reserve of “fresh ” motor units to maintain the required submaximal muscle tension. Therefore, there is a greater drop in force during maximal effort than submaximal effort, which result s in a steeper slope during the force-decay phase of a maximal effort than of a submaximal effort.215, 368 Differences between the Injured and Uninjured Hands For the F-T curve characteristics, we exp ected 1) the slope of the force-generation phase to be steeper for uninjured versus in jured hands, 2) the slope of the force-decay phase to be steeper for injured versus uninjur ed hands, 3) peak force to be greater for uninjured versus injured hands, and 4) timeto-peak force faster for uninjured versus injured hands. Our findings for the slope of force-generation phase and peak force were as expected but for time-to-peak force and slope of force-decay phase were not as expected. We found that the uninjured hands had steep er slopes for both the force-generation phase and the force-decay phase. The injured hands exhibited gentler slopes of forcegeneration phase probably because people with injuries have a slower rate of force production.211 We expected a steeper slope during the force-decay phase for the injured hands because people with musculoskeletal conditions experience greater fatigue and inability to maintain force.369-373 However, we found that the uninjured hand showed greater fatigue as indicated by a steeper slope during the force-decay phase. We propose an explanation that is based on the assumption that people with injuries are protective of their injured hand. They may experience pain, fe ar of pain, and/or fe ar of re-injury and thus they may not exert true maximal volunt ary contraction with their injured hand. The

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171 interaction showing that uninj ured hand force decreases du ring the second session but injured hand increases supports this notion. Gender Differences We found that the slopes of force-genera tion phase were steeper for males than females. Gender differences in the slope of force-generation phase may be due to differences in muscle strength. Demura et al.207-209 found the F-T curve characteristics (such as rate of force development) to be larger in stronger subjects208, and different between males and females.209 Males also produce greater forces374-376 at faster rates.213 Gender differences in F-T curve characteris tics may be due to larger muscle crosssectional area, higher concentr ation of anabolic hormones, and higher voluntary neural activity of muscles.220, 377 For the slope of the force-decay phase, gender differences in slope were found only for the second session and not for the first session. During the second session, we found steeper slopes for males versus females. Ge nder differences in the slopes of the forcedecay phase may be due to gender differences in ability to maintain static grip force. Yamaji et al.378 examined gender differences in ability to maintain grip force over six minutes at different effort levels (20-100% of maximal vol untary contraction, MVC).The authors reported that for efforts greater th an 40% MVC, females maintained the effort level for a longer time or at a higher force le vel. It is also possible that females are less motivated to exert true maximal voluntary cont raction and thus fatigue less as they have more “fresh” motor units to recruit. Electromyographic Properties In the present study, we evaluated the electromyographic (EMG) properties of forearm flexor and extensor muscles during isometric grip contractions. Both forearm

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172 flexor and extensor muscles have been reported participate in isometric grip.359, 379-384 The forearm flexors generate the gripping force, while the ex tensors stabilize the wrist.359, 383, 385 The properties of the EMG signal that we examined were its amplitude and median frequency ratio (MF-ratio). The amplitude of the EMG signal represents the magnitude of muscle activity. As the force being generated by a muscle increas es, it results in an increase in the EMG amplitude. Increase in amplitude predominantly occurs due to incr eases in number of active motor units.237, 245 Also, an injury to the hand or arm decreases the EMG amplitude by reducing the ability to produce force. The frequency of the EMG signal represen ts how rapidly motor units are firing. One method of describing the frequency of EMG signal involves using spectral analysis to compute its median frequency (MF). The MF represents which motor units are predominantly active. Fast twitch motor uni ts dominate the higher frequency spectrum and slow twitch dominate the lower frequency spectrum.255, 260, 386 In other words, muscles with a greater percentage of type II fibers or fast twitch motor units exhibit greater values of MF.386 An increase in muscle force resu lts in an increase in MF, and therefore a shift of the pow er spectrum to a higher fr equency region. That is, as contraction level increases, la rger motor units are recruited, and thus the power spectrum shifts to a higher frequency region.248, 251 A sustained forceful contraction often causes muscular fatigue, which shifts the powe r spectrum to a lower frequency region.254-260 The shift of the power spectrum to a lower freque ncy occurs due to motor unit de-recruitment. The replacement of fast twitch motor units which fatigue more quickly, with lower frequency fatigue-resistant units causes a de crease in the higher frequency spectrum.255,

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173 260 Fatigue has been shown to result in an increase in the lower frequency spectrum255 and a decrease in the higher frequency spectrum,255, 263, 264 which translates into a shift of the power spectrum towards the lower frequencies.260 Further, muscles with a greater percentage of fast twitch mo tor units exhibit a greater reduction in MF.386 Thus, MF can indicate an increase in muscular force when it shifts to the higher frequency spectrum and muscular fatigue when it shifts to lower frequency spectrum. The process of calculating median fre quency (MF) involves a mathematical conversion called Fourier Transf ormation, which identifies di fferent frequencies forming the EMG signal. The power of each frequency, i.e. the quantity of each frequency in the signal, can also be identified using Fourier Transformation.120, 121 The plot of frequency along the X-axis versus the power of the fr equency along the Y-axis results in a graph that is commonly termed as the power spectrum or the frequency spectrum.121 The MF represents the frequency that divides the power spectrum into two regions with equal power, i.e. the parts of the spectrum above a nd below the MF have equal distributions of power.105 In presence of pathological conditions, the decline in MF with development of fatigue varies markedly with the type of motor units recruited.210, 387-389 A smaller shift to the lower frequency region as a result of fatigue has been observed in people with Amyotrophic lateral sclerosis210 and Parkinson’s disease.390 The smaller shift has been attributed to selective atrophy of type II (fast glycolytic, fast oxidative) muscle fibers, which fatigue more quickly, and/or higher pr evalence of type I (slow twitch oxidative) muscle fibers.210, 390 In contrast, a greater shift in MF associated with earlier onset of fatigue has been observed in pe ople with chronic heart failure391, peripheral arterial

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174 disease392, and chronic neck pain.393 The greater shift in MF has been attributed to selective atrophy of type I muscle fibers or hypertrophy of type II muscle fibers.391-393 Therefore, a greater or smaller shift in MF may be observed in people with a pathological condition than healthy people as expre ssed by changes in muscle fiber type. For the current study, we calculated the amplitude of the EMG signal as the average rectified amplitude for the duration of the grip. We computed the MF for two separate 1-second intervals, th e first interval beginning at p eak force (called the median frequency of the first interval) and the sec ond interval forming th e last second of the force-decay phase of the F-T curve (called the median frequency of the last interval). We then computed the MF-ratio of the last to fi rst intervals, to reflect changes in median frequency between the beginning and end of the grip contraction. The MF-ratio represents the extent of fatigue or de-recruitment of motor units. Differences between Maximal and Submaximal Effort For the EMG properties, we expected 1) the amplitude to be greater for maximal versus submaximal effort, and 2) the MF-ra tio to be smaller for maximal than for submaximal effort. The findings of the present study confirmed our hypotheses. The EMG amplitude was significantly gr eater for maximal versus submaximal efforts. EMG amplitude is not a valid measure of sincerity of effort because it correlates to the amount of force exerted by a muscle. O bviously, maximal effort results in greater muscle force than submaximal effort and an injury may reduce the ability of a muscle to produce force. We examined the shift in the MF spectr um during 6-seconds of isometric grip contraction. MF indicates which mo tor units are predominantly firing.255, 260 We found both the flexor and extensor MF-ratios were smaller for maximal effort than for

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175 submaximal effort. During maximal effort, th e MF-ratio actually decreased probably due to replacement of some of the fast twitch motor units with lower frequency fatigueresistant units.255, 260 Conversely, during submaximal effort, the MF-ratio increased, probably as a result of an increase in r ecruitment and firing rate of motor units. The maintenance of a submaximal muscle force over 6 seconds gradually requires a greater number of motor units or increased firing of already recruited motor units. As the contraction persists, larger (and more) moto r units are recruited, and thus the power spectrum shifts to a higher frequency region.248, 251 Therefore, a combination of both recruitment and rate coding results in shift of the power spectrum to the higher frequency region.248 MF-ratio has the potential to form a valid measure of sincerity of effort as it indicates a shift of MF duri ng a grip strength trial. Differences between Injured and Uninjured Hands For the EMG properties, we expected 1) the amplitude to be greater for uninjured versus injured hands, and 2) the MF-ratio to be smaller for injured versus uninjured hands. The findings of the present stu dy did not confirm all our hypotheses. Flexor EMG amplitude was significantly gr eater for the uninjured hands. Uninjured hands can produce greater force as they have greater number of motor units available. Therefore, uninjured hands have a greater EMG amplitude.245 However, extensor EMG amplitude was unexpectedly not significantl y different between the 2 hands. One reason could be because of differences in diagnos is. But, we did not collect information on diagnosis. Therefore, we are not certain rega rding the cause of no difference in extensor amplitude. Regarding MF-ratio, the interaction effects of injury were significant but the main effects were not significant. The decrease in flexor MF-ratio between submaximal and

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176 maximal efforts was greater for uninjured vs. injured hands in males but not in females. Also, the decrease in extensor MF-ratio be tween submaximal and maximal efforts was greater for uninjured vs. injured hands. It was unexpected that the decrease in MF-ratio was greater for the uninjured hand than th e injured hand. The difference in MF-ratio between injured and uninjured hands may be pr esent because subjects did not exert their maximal voluntary contraction with their in jured hands as a protective mechanism. A smaller shift to the lower frequency as result of fatigue has been at tributed to selective atrophy of type II (fast glycoly tic, fast oxidative) muscle fi bers and/or higher prevalence of type I (slow twitch oxidative) muscle fibers.210, 390 However, our study participants had a diverse group of musculoskeleta l conditions. It is not clear if these conditions resulted in selective atrophy of type II fi bers and/or higher prevalence of type I fibers. Therefore, a smaller decrease in MF-ratio with injured hands most likely occurred because people with injuries may be protec tive and not exert maximal volunt ary contraction with their injured hands. Gender Differences Gender differences existed for flexor MF-ratio but not for flexor amplitude, extensor amplitude, and extensor MF-ratio. For flexor MF-ratio, the interaction effect of gender was significant but the main effect wa s not significant. The decrease in flexor MF-ratio between submaximal and maximal effo rts was greater for uninjured vs. injured hands in males but not in females. In other words, males fatigued more with the uninjured hands, whereas, females fatigued the same with injured as well as uninjured hands. The cause of these differences is not clear. It is possible that females are less motivated to exert true maximal voluntary effo rt and thus fatigue to the sa me extent with injured and uninjured hands, as they have more “fresh” mo tor units to recruit. In contrast, it seems

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177 that males are protective of their injured ha nds. Males may experience pain, fear of pain, and/or fear of re-injury and thus may not ex ert true maximal voluntary effort with their injured hand and thus fatigue less with their injured hands. On examining maximal effort exerted with the uninjured hands, we found that the flexor MF-ratios were smaller for males vers us females. This difference in uninjured hands suggests an existence of gender differen ces in forearm flexor muscle fatigability, i.e. males fatigue more than females. Gender differences in elbow fl exor fatigability have been related to the level of absolute fo rce exerted during an isometric contraction.394 Hunter et al.394 found that women had longer endu rance times, which indicates less fatigability, because the maximal voluntary co ntraction force was smaller for females. Indeed, in our study, greater peak forces w ith uninjured hands were exerted by males versus females. Therefore, gender differences in fatigability of forearm flexor muscles during maximal isometric grips seem to be related to force exer ted during a maximal voluntary contraction. Force-Decay Phase When examining the region of the F-T cu rve from peak force to the end of contraction, we found that the ch anges in the slope of the force in the slope of the forcedecay phase corresponded to the changes in EMG signal as expressed by the flexor MFratio. We found steeper slopes of force-decay phase and smaller MF-ratios for 1) maximal versus submaximal effort, 2) uninj ured versus injured hands, and 3) males versus females. Steeper slopes and sma ller flexor MF-ratios for maximal versus submaximal effort may be explained on the basi s of differences in onset of fatigue, which has been associated with ability of a muscle to maintain force95, 96, 210-212 as well as shift in the median frequency to a lower frequency region.254-260 Further, steeper slopes and

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178 smaller MF-ratios for uninjured versus in jured hands suggest that uninjured hands fatigued more than injured hands. One possible e xplanation is that people exert less effort with their injured hands as a protective m echanism and therefore experience less fatigue. Furthermore, steeper slopes and smaller fl exor MF-ratios for males versus females suggest that males fatigue more than females, which could be because females are better able to maintain forces than males.378 It is also possible that females are less motivated to exert true maximal voluntary contraction and th us fatigue less as they have more “fresh” motor units to recruit. Reliability and Validity The usefulness of an assessment depends on its reliability, i.e., its ability to measure an attribute or behavior consistently and free of error. Test-retest reliability of an assessment indicates that an assessment obtains the same results with repeated administrations of the test.92 According to Portney and Watkins92, “reliability coefficients of measurements used for decision making or di agnosis of individuals need to be higher, perhaps at least 0.9 to ensure valid interpretations of findings” (p. 65). Portney and Watkins92 also suggest that an index greater th an 0.9 is a guideline and not an absolute standard. Further, as a general guideline, co efficients below 0.5 re present poor reliability, coefficients from 0.5 to 0.75 represent modera te reliability, and coefficients above 0.75 represent good reliability.92 We found that the slope of the force-generation phase and the MF-ratios to have acceptable levels of test-retest reliability. We expected the F-T curve characteristics and EMG properties to consistently measure grip efforts as expresse d by high test-retest reliability (r> 0.9). Based on the guidelines pr ovided by Portney and Watkins92, we found acceptable levels of reliability only for peak force, slope of force-generation phase, and

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179 almost all EMG properties (r> 0.7). Therefore, the slope of the force-generation phase, as well as the flexor and extensor MF-ratios are su fficiently reliable meas ures of sincerity of effort. A reliable sincerity of effort test is appropriate for clinical use only if it is valid.92 A valid sincerity of effort test should reveal significant differences between maximal and submaximal efforts. We found significant differences between maximal and submaximal effort for the following measures: time-to-peak force, slopes of the forcegeneration phase and force-decay phase, a nd MF-ratios of EMG signal for flexors and extensors. A sincerity of effort assessment could be classified as a “dia gnostic” test as its purpose is to distinguish be tween the presence and absence of a feigned effort.102 According to Portney and Watkins92, “the validity of a diagnos tic test is evaluated in terms of its ability to accurately assess the presence and absence of the target condition” (p. 93).92 To be valid, a diagnostic te st must also be effective; i.e., possess acceptable levels of sensitivity and specificity.92 In absence of adequate sensitivity and specificity values, either a feigning indi vidual may be incorrectly cl assified as sincere (low sensitivity) or a sincere i ndividual may be wrongly identif ied of being insincere (low specificity). Specificity becomes more im portant than sensitivity when the risks associated with misdiagnosing maximal effort are substantial.92 As results of a sincerity of effort test impact continuation of rehabi litative services and workers compensation, it is better to make a mistake in the directi on of low sensitivity. Unfairly misclassifying a sincere person as feigning can be very damagi ng to the individual a nd promote clinically unfair decisions.28, 64-67, 83, 93, 102, 206 It seems less damaging to misclassify people giving a

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180 deliberate feigned effort than to mistakenly classify a person giving a true maximal effort as feigning.80 The sensitivity of a sincerity of effort te st indicates the percentage of people who were classified as exerting a submaximal effo rt and really exerted a submaximal effort (true positives). The specificity indicates the percentage of people who were classified as exerting a maximal effort and really exerted a maximal effort (true-negatives). An inverse relationship exists between specificity and sensitivity: increasing the specificity (by reducing the false-positive rate) results in a decrease in sensitivity and vise versa. Therefore, when interpreting sensitivity and specificity results, one has to find a cutoff value that yields the most optimal combination of sensitivity and specificity.27, 28, 64-67, 83, 93, 102, 206 One method of finding the best combination of sensitivity and specificity involves calculating the overall error rate by using the formula (1-sensitivity) + (1-specificity). In other words, the overall error rate for a specifi c cutoff value represents the percentage of combined errors (false-positive plus false-nega tives). Therefore, the lowest overall error rate identifies the cutoff value with the best combination of sensitivity and specificity.27, 28, 64-67, 83, 93, 102, 206 In the present study, we found that the overall error ra te for the slope of force-generation phase of the force-time curve ranged from 55% to 60%. We also found the overall error rates fo r the MF-ratios ranged from 68 % to 70%. These error rates are too large to render these measures valid in detecting submaximal effort. These error rates are just as bad as the e rror rates identified for the clinically relevant sincerity of effort tests including the five rung grip test, coefficient of variation, and rapid exchange grip test (Table 1-1). The erro r rates of the clinically relevant tests range from 47% to

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181 69%.27, 65, 93 They are also larger than the slopes of the force-time curve for healthy people, which ranged from 7% to 33%.102 Therefore, the force-time curve characteristics and the EMG properties of a 6-second grip ex ertion do not seem to provide an effective means of distinguishing between maximal and submaximal efforts in people with upper extremity injuries. Another method of finding the best combin ation of sensitivity and specificity is plotting a receiver operating ch aracteristic (ROC) curve.110 The ROC curve is a plot of false-positive rates (1-specificity) along the Xaxis against true-positive rates (sensitivity) along the Y-axis resulting from application of many arbitrarily chosen cutoff points. Therefore, the ROC curve demonstrates th e effectiveness of using different cutoff values.110 That is, the ROC curve shows the accuracy of detecting sincerity of effort over a range of cutoff values.27, 28, 64-67, 83, 93, 102, 206 For the present study, th e cutoff values were different values of the slopes of the force-ge neration phase, as well as flexor and extensor MF-ratios. The ROC curve allows a researcher to decide which cutoff point is the most beneficial for a certain diagnostic test.92 Using the ROC curve facilitates choosing a cutoff value that is not arbitrary, but rather is based on the best combination of sensitivity and specificity.92 When using the ROC curve to choose the best cutoff value for a sincerity of effort test, it is better to err in the directi on of lower sensitivity and higher specificity so that a true maximal effort will not be misclassified as a submaximal effort.27, 28, 64-67, 83, 93, 102, 206 Due to significant gender differences in the slope of the forcegeneration phase, we generated separate ROC curves for males and females. ROC curves were not generated for time-to-peak force due to low reliability and slope of force-decay phase due to poor sensitivity and specificity values.

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182 The ROC curve for the force-generation phase in the present study revealed that the most optimal combination of specificity and sensitivity values for the injured hand of men was at the slope cutoff value of 1.5 V/ s. When using the slope of 1.5 V/s as a criterion for determining sincerity of effo rt, 15% of the men who exerted submaximal effort were incorrectly identified as exerting a maximal effort (1-sensitivity) and 45% of the men who exerted maximal effort were mist akenly identified as exerting a submaximal effort (1-specificity). The overall error rate for this criterion wa s 60%. In a previous study, we also found the slope of 1.5 V/s as the optimal slope value among healthy men. However, only 20% of the men who exerte d submaximal effort were incorrectly identified as exerting a maximal effort a nd only 13% of the men who exerted maximal effort were mistakenly identified as exerting a submaximal effort. The overall error rate for this criterion was 33%.102 The most optimal cutoff value for injured women in the present study was 0.5 V/s. When using this cutoff value as a criterion for determining sincerity of effort, 40% of those who exerte d submaximal effort were misclassified as exerting a maximal effort and 15% of those w ho exerted maximal effort were mistakenly identified as exerting a submaximal effort. The overall error rate was 55%. With healthy subjects, we found an optimal cutoff valu e of 1.2 V/s for women, which resulted in incorrectly identifying 20% of those who exerted submaximal effort and 7% of those who exerted maximal effort with an overall error rate was 27%.102 In the present study, the overall error rate was almost double for both men and women with UEMDs than the error rate reported for healthy people.102 This discrepancy between the two studies could be explained by the probable protective response of injured people who may avoid exerting maximal voluntary contraction due to pain, fear of pain and/or fear of re-injury. This is

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183 supported by the significant sess ion by injury interaction term for peak force (Figure 4-1), which indicated that the uninjured hand for ce exertion decreased in the second session while the injured hand’s force exertion increa sed. This suggests that while the uninjured hand experienced fatigue the injured hand did not. This further suggests that the force exertion of the injured hand in the first session was not maximal. The ROC curves generated for the EMG pr operties revealed similar effectiveness for the median frequency ratio of flexor a nd extensor muscles. Hi gh overall error rates, however, do not deem them to be good sincerity of effort tests. The ROC curve for the flexor muscle MF-ratio revealed that the mo st optimal combination of specificity and sensitivity values for the injured hand was for the MF-ratio cutoff value of 102%. When using this cutoff as a criterion for determini ng sincerity of effort, 47% of the people who exerted submaximal effort were incorrectly identified as exerting a maximal effort and 22% of the people who exerted maximal effort were mistakenly identified as exerting a submaximal effort with an overall error rate of 70%. The ROC curve for the extensor muscle MF-ratio revealed that the most optimal combination of specificity and sensitivity values was at the MF-ratio cutoff value of 100%. When using this cutoff as a criterion fo r determining sincerity of effort, 37% of the people who exerted submaximal effort were incorrectly identified as exerting a maximal effort and 30% of the people who exerted maximal effort were mistakenly identified as exerting a submaximal effort. Th e overall error rate for this criterion was 68%. Based on our findings, if a therapist used ei ther the flexor or ex tensor MF-ratio as a sincerity of effort test, he or she would in correctly classify a large proportion of people

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184 that they tested, which makes these measures inappropriate for clinical use as a sincerity of effort test. Using the ROC curve, the proportional area under the curve is ca lculated and used as a measure of its discriminability, i.e., the ability of the test to discriminate between maximal and submaximal efforts. The area under the ROC curve is an index of the degree of separation (or overlap) between the distributions of true-positives (signal) and falsepositives (noise).110 An ideal diagnostic te st has an area of 100%.92 The greater the area under the curve, the better th e ability to discriminate be tween maximal and submaximal efforts. In the present stu dy, the area under the curve for th e force-generation phase was greater for women (76%) than for men (72%), indicating greater abil ity to discriminate between maximal and submaximal efforts in women. The area under the curve for the flexor MF-ratio was 66.25% and extensor MF-ratio was 71%. In a previous study, the proportional area under the ROC curve for the force-generation phase was 92.5% for men and 92% for women.102 Therefore, the force-time curve characteristics and the EMG properties of a 6-second grip exertion do not seem to provide an effective means of distinguishing between maximal and submaxim al efforts in people with upper extremity injuries. This discrepancy between the two st udies is that people with injuries are protective of their injured hand. They may experience pain, fear of pain, and/or fear of reinjury and thus they may not exert true maxi mal voluntary contraction with their injured hand. The interaction showing that uninjure d hand force decreases during the second session but injured hand incr eases supports this notion. Limitations The limitations of this study include a he terogeneous patient population and only measuring EMG activity of forearm flexor a nd extensor muscles. Our findings are based

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185 on people with upper extremity musculoskele tal conditions, with various etiologies including acute versus cumulative trauma. Furt her, the etiologies were different for men and women. Almost all men experienced acu te trauma, whereas, half of the women experienced acute trauma and the other half experienced cumulative trauma. Therefore, the etiology could be a conf ounding variable for this study. Furthermore, we did not gather information regarding diagnosis b ecause the injury-related information was provided by the patients and thus could have been inaccurate. Also, due to technical limitations, we were unable to measure the EMG activity of the intrinsic hand muscles, which participate in grip and may have id entified differences between maximal and submaximal efforts. Future studies s hould focus on using a homogeneous patient population and examining EMG activity of intr insic hand muscles. It is possible that testing EMG activity of intr insic hand muscles as well as a homogeneous patient population would yield different results. Conclusions We found significant differences in the time-to-peak force, slope of forcegeneration phase as well as flexor an d extensor MF-ratio between maximal and submaximal efforts. However, we did not fi nd acceptable combinations of sensitivity and specificity for detecting sincerity of effort using these characteristics. Sensitivity and specificity analysis revealed that the slope of the force-generati on phase had the best effectiveness, with the slope being a more e ffective assessment of sincerity of effort for women than for men. These measures yiel ded overall error ra tes of 55% to 70%. Therefore, these measures may not possess adeq uate sensitivity and specificity values to justify their use in the clinic.

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186 APPENDIX A SAMPLE SIZE CALCULATION The primary hypothesis identified for sample size calculation was that the forcedecay phase of a force-time curve (FT-curve) possesses a significantly steeper slope for maximal effort than for submaximal effort Table A-1 lists the range of maximal and submaximal effort slope valu es in the preliminary study. The extreme slope values result in the largest and smallest differences: Largest difference = 0.39 – 0.009 = 0.381 (A-1) Smallest difference = 0.014 – 0.16 = -0.146 (A-2) Largest possible range of differences (Range) = 0.381 – (0.146) = 0.527 (A-3) We estimated the standard deviation ( d) by dividing the range by 4: d = Range/4 = 0.131 V/s (A-4) We decided to keep the bound (B), i.e. the mean difference between maximal and submaximal effort slope that is important to detect, as 0.1 V/s. We computed DELTA as: DELTA = B/ d = 0.1/0.131 = 0.76. (A-5) We also chose a 2-tailed = 0.05 and = 0.2. Although our hypothesis is unidirectional, we chose a 2-tailed to be more conservative Using these values, we calculated the sample size as 24. However, to take into consideration attrition due to pain/fatigue, we increased sample size to 30. Th us, 30 participants are required to detect an average difference between maximal and subm aximal effort slopes of 0.1 V/s, if that difference truly exists. Table A-1: Range of maximal and submaximal effort slope values Maximal Effort Submaximal Effort Minimum slope (V/s) 0.0140.009 Maximum slope (V/s) 0.3900.160

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187 APPENDIX B CORRELATION MATRIX FOR DYNAMOMETER CALIBRATION Prior to beginning data collection, the calibration of the Jamar dynamometer was checked on 3 consecutive days, which when averaged resulted in Pre-data collection values. During the data collection phase, th e calibration of the dynamometer was checked weekly, resulting in values Wk. 1-17. The Pearson moment correlation coefficients ( r ) between the Pre-data collection values and week ly values have been reported in Table B1.

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188Table B-1: Pearson correlation coefficients ( r ) between weekly voltage outputs obtaine d during the dynamomete r calibration process Pre-data collection values Wk. 1 Wk. 2 Wk. 3 Wk. 4 Wk. 5 Wk. 6 Wk. 7 Wk. 8 Wk. 9 Wk. 10 Wk. 11 Wk. 12 Wk. 13 Wk. 14 Wk. 15 Wk. 16 Wk. 17 Pre-data collection values 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Wk. 1 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Wk. 2 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Wk. 3 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Wk. 4 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Wk. 5 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Wk. 6 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Wk. 7 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Wk. 8 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Wk. 9 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Wk. 10 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Wk. 11 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Wk. 12 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Wk. 13 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Wk. 14 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Wk. 15 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Wk. 16 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Wk. 17 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

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189 APPENDIX C DEMOGRAPHIC QUESTIONNAIRE University of Florida Department of Occupational Therapy Demographic Questionnaire Participant ID#: Date Completed: Time: A. DEMOGRAPHIC INFORMATION 1. Please fill out or circle the correct an swer(s) for the following questions about yourself a. Year of birth? _____ b. Gender? M F c. Height? _____ Inches d. Weight? ______ lbs e. Dominant hand/arm? R L f. Injured hand/arm? R L B. INJURY-RELATED INFORMATION 1. What injury/condition are you in therapy for? ____________________________________________________________ ____________________________________________________________ 2. Do you think your condition was caused by work? (Please circle one option) YES NO If so, please explain: _________________________________________________________ 3. Do you think your condition is aggravated by work? (Please circle one option) YES NO If so, please explain: _________________________________________________________ 4. What do you think is the cause of your injury? ____________________________________________________________ ____________________________________________________________

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190 5. How long have you had this cond ition (in years and months)? Years Months 6. How long have you been in therapy? ___________ Weeks ___________ Times per week 7. Do you experience similar symptoms on the uninjured side? (Please circle one option) YES NO 8. Do you have any other condition that affects your hand grip? (Please circle one option) YES NO If so, please explain: _________________________________________________________ 9. Are you taking any pain medications? YES NO 10. Do you have any limitations in Activities of Daily Living, such as dressing, bathing, etc.? YES NO 11. Have you had surgery for your injury? YES NO If yes, did you benefit from the surgery? YES NO 12. Have you seen any improvement with therapy? YES NO 13. How successful is (was) your therapy? (Please circle one option) a. Very successful b. Successful (average) c. Somewhat successful (less than average) d. Not successful at all

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191 9 8 7 6 5 4 3 2 1 0 10 No pain Pain as bad as it could b e 9 8 7 6 5 4 3 2 1 0 10 No pain Pain as bad as it could b e14. What was the average range of pain ove r the last week on a scale of 0 to 10? (Please cross the line below at the most appropriate point) 15. What is the level of your current pain on a scale of 0 to 10? (Please cross the line below at the most appropriate point) C. JOB-RELATED INFORMATION 5. What was your occupation when you were injured? _______________________________________________________ _______________________________________________________ 6. How long have you held that position? ________________________________________________________ 7. Please describe your duties at that position. ________________________________________________________ _________________________________________________________ 8. Are you currently working? YES NO Full-time Part-time If part-time, how many hours? _________________________________________ 9. Are you performing the same job duties as prior to your injury? YES NO If no, describe changes. _____________________ ____________________

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192 For Office Use Only Order #

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193 APPENDIX D LETTER TO HEALTHCARE PROFESSIONALS WITH INCLUSION AND EXCLUSION CRITERIA Orit Shechtman, Ph.D., OTR/L Associate Professor College of Public Health and Health Professions Department of Occupational Therapy Box 100164 University of Florida Department: (352) 273-6817 Office: (352) 273-6021 Fax: (352) 273-6024 E-mail: oshechtm@phhp.ufl.edu [Date] [Contact information of healthcare professional] Dear [Name of Health care Professional]: Subject: Study on associa tion between pain and gr ip strength, [IRB #] It was a pleasure to talk to you on [date] Thank you for agreeing to help us recruit participants for our study to identify an association betw een upper extremity pain and grip strength. This study ha s been approved by the Institu tional Review Board at the University of Florida ([IRB Study #]). In orde r to be compliant with the Health Insurance Portability and Accountability Act (HIPPA), you will need to use your clinical judgment to identify individuals who meet the study criteria. Pleas e use the following criteria to identify study participants: The participant should have experienced a unilateral traumatic or non-traumatic injury involving the elbow or distally in the last 1 year but not necessarily diagnosed in the last year. The uninjured extremity should have not experienced any injury in the last 5 years and currently should not be experiencing any injury-related signs/symptoms. The participant should be aged between 18 and 65 years. The participant should be able to perform 4 maximal and 8 submaximal grip efforts with their injured extremity.

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194 The participant should not be suffering fr om extreme pain. When asked verbally the level of pain generally experienced by the participant on a scale of 0-10, the participant should not experience pain intens ity greater than 7. The participant should not have any asso ciated illness that would compromise their grip strength. The participant should not be taking a ny medications that would compromise their grip strength. The participant should not have impaired cognition. Once you have identified a study participant, please brief the indi vidual regarding the study using the following st andard instructions: “A study is being conducted to identify how pain affects grip strength among people with upper extremity musculoske letal conditions. Your condition makes you eligible to participat e in this study. This st udy involves gripping a hand dynamometer 12 times with each hand and ra ting your pain and perceived grip effort. If you agree to participate, you will attend one session lasting approximately 45 minutes and will be pa id $20.00 for participating in the study. Please let me know if you are interested in participating and I can provide you with information to contact the research group.” If an individual agrees to pa rticipate in the study, please ask th em to either contact me or Bhagwant Sindhu (Phone Number: (352)273-6057, Email: bsindhu@phhp.ufl.edu ). Bhagwant is my doctoral student and he will be conducting this study. If we do not answer the phone please ask them to leave a message with their name and phone number. If you have questions regarding this study, please feel free to contact us at anytime. We appreciate your help with this study. Sincerely, Orit Shechtman

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195 APPENDIX E RANDOMIZATION ORDER AND SHEET Order 1 Order 2 Order 3 Order 4 Effort 1 IM IS UM US UM US IM IS Effort 2 IS IS50 US US50 US US50 IS IS50 Effort 3 IS50 IM US50 UM US50 UM IS50 IM IS Injured—Submaximum (Imagined Pain) IS50 Injured—Submaximum (50% max) IM Injured--Maximum US Uninjured— Submaximum (Imagined Pain) US50 Uninjured— Submaximum (50% max) UM Uninjured--Maximum Figure E-1: Randomization or ders used in the study For submaximal effort, always start with submaximal effort according to imagined pain

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196 Table E-1: Randomization sh eet used in the study Participant # Name Sex Order Date Time Pre/Post Therapy Dominant hand Injured hand 1 Male 3 2 Male 1 3 Male 2 4 Male 2 5 Male 4 6 Male 4 7 Male 3 8 Male 3 9 Male 3 10 Male 2 11 Male 1 12 Male 2 13 Male 1 14 Male 4 15 Male 3 16 Male 1 17 Male 4 18 Male 1 19 Male 2 20 Male 4 21 Female 4 22 Female 4 23 Female 2 24 Female 1 25 Female 4 26 Female 4 27 Female 3 28 Female 1 29 Female 3 30 Female 3 31 Female 2 32 Female 3 33 Female 1 34 Female 2 35 Female 1 36 Female 4 37 Female 2 38 Female 2 39 Female 3 40 Female 1

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197 APPENDIX F CHECKLISTS USED DURING TH E DATA COLLECTION PROCESS During the data collection phase, we used checklists to assist us in following the correct experimental procedure. To maintain blinding, we used two different checklists. The checklist used by the research assistants indicated the level of grip effort being exerted by the study participant (Figure F-1). In contrast, th e checklist used by the test administrator did not indicate th e level of grip effort. The te st administrator’s checklist was also used to note the length of the re st period between hand grips (Figure F-2).

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198 Figure F-1: Checklist used by the research assistants

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199 Figure F-2: Checklist used by the test administrator

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200 APPENDIX G DATA COLLECTION FORM Participant ID#: Order #: 1 Session 1 Practice 1 What is the level of your current pain on a scale of 0 to 10? 0 10 No pain Please mark a vertical line at a point that indicates the level of pain that you are currently experiencing. Pain as bad as it could be Pain

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201 Time (between practice 1 and 2): Practice 2 What is the level of your current pain on a scale of 0 to 10? 0 10 No pain Please mark a vertical line at a point that indicates the level of pain that you are currently experiencing. Pain as bad as it could be Pain

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202 Practice Trial – Uninjured Hand 0% 100% No Grip Force Please mark a vertical line at a point that indicates the level of effort you just exerted. Strongest Grip Force Effort 0 10 No pain Please mark a vertical line at a point that indicates the level of pain that you are currently experiencing. Pain as bad as it could be Pain

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203 Practice Trial Sheet – Injured Hand 0% 100% No Grip Force Please mark a vertical line at a point that indicates the level of effort you just exerted. Strongest Grip Force Effort 0 10 No pain Please mark a vertical line at a point that indicates the level of pain that you are currently experiencing. Pain as bad as it could be Pain

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204 InjuredMaximum Grip 1 0% 100% No Grip Force Please mark a vertical line at a point that indicates the level of effort you just exerted. Strongest Grip Force Effort 0 10 No pain Please mark a vertical line at a point that indicates the level of pain that you are currently experiencing. Pain as bad as it could be Pain

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205 InjuredMaximum Grip 2 0% 100% No Grip Force Please mark a vertical line at a point that indicates the level of effort you just exerted. Strongest Grip Force Effort 0 10 No pain Please mark a vertical line at a point that indicates the level of pain that you are currently experiencing. Pain as bad as it could be Pain

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206 UninjuredMaximum Grip 1 0% 100% No Grip Force Please mark a vertical line at a point that indicates the level of effort you just exerted. Strongest Grip Force Effort 0 10 No pain Please mark a vertical line at a point that indicates the level of pain that you are currently experiencing. Pain as bad as it could be Pain

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207 UninjuredMaximum Grip 2 0% 100% No Grip Force Please mark a vertical line at a point that indicates the level of effort you just exerted. Strongest Grip Force Effort 0 10 No pain Please mark a vertical line at a point that indicates the level of pain that you are currently experiencing. Pain as bad as it could be Pain

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208 InjuredSubmaximum (Pain) Grip 1 0% 100% No Grip Force Please mark a vertical line at a point that indicates the level of effort you just exerted. Strongest Grip Force Effort 0 10 No pain Please mark a vertical line at a point that indicates the level of pain that you are currently experiencing. Pain as bad as it could be Pain

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209 InjuredSubmaximum (Pain) Grip 2 0% 100% No Grip Force Please mark a vertical line at a point that indicates the level of effort you just exerted. Strongest Grip Force Effort 0 10 No pain Please mark a vertical line at a point that indicates the level of pain that you are currently experiencing. Pain as bad as it could be Pain

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210 UninjuredSubmaximum (Pain) Grip 1 0% 100% No Grip Force Please mark a vertical line at a point that indicates the level of effort you just exerted. Strongest Grip Force Effort 0 10 No pain Please mark a vertical line at a point that indicates the level of pain that you are currently experiencing. Pain as bad as it could be Pain

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211 UninjuredSubmaximum (Pain) Grip 2 0% 100% No Grip Force Please mark a vertical line at a point that indicates the level of effort you just exerted. Strongest Grip Force Effort 0 10 No pain Please mark a vertical line at a point that indicates the level of pain that you are currently experiencing. Pain as bad as it could be Pain

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212 InjuredSubmaximum (50% Maximum) Grip 1 0% 100% No Grip Force Please mark a vertical line at a point that indicates the level of effort you just exerted. Strongest Grip Force Effort 0 10 No pain Please mark a vertical line at a point that indicates the level of pain that you are currently experiencing. Pain as bad as it could be Pain

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213 InjuredSubmaximum (50% Maximum) Grip 2 0% 100% No Grip Force Please mark a vertical line at a point that indicates the level of effort you just exerted. Strongest Grip Force Effort 0 10 No pain Please mark a vertical line at a point that indicates the level of pain that you are currently experiencing. Pain as bad as it could be Pain

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214 UninjuredSubmaximum (50% Maximum) Grip 1 0% 100% No Grip Force Please mark a vertical line at a point that indicates the level of effort you just exerted. Strongest Grip Force Effort 0 10 No pain Please mark a vertical line at a point that indicates the level of pain that you are currently experiencing. Pain as bad as it could be Pain

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215 UninjuredSubmaximum (50% Maximum) Grip 2 0% 100% No Grip Force Please mark a vertical line at a point that indicates the level of effort you just exerted. Strongest Grip Force Effort 0 10 No pain Please mark a vertical line at a point that indicates the level of pain that you are currently experiencing. Pain as bad as it could be Pain

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216 Participant ID#: Order #: 1 Session 2 InjuredMaximum Grip 1 0% 100% No Grip Force Please mark a vertical line at a point that indicates the level of effort you just exerted. Strongest Grip Force Effort 0 10 No pain Please mark a vertical line at a point that indicates the level of pain that you are currently experiencing. Pain as bad as it could be Pain

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217 InjuredMaximum Grip 2 0% 100% No Grip Force Please mark a vertical line at a point that indicates the level of effort you just exerted. Strongest Grip Force Effort 0 10 No pain Please mark a vertical line at a point that indicates the level of pain that you are currently experiencing. Pain as bad as it could be Pain

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218 UninjuredMaximum Grip 1 0% 100% No Grip Force Please mark a vertical line at a point that indicates the level of effort you just exerted. Strongest Grip Force Effort 0 10 No pain Please mark a vertical line at a point that indicates the level of pain that you are currently experiencing. Pain as bad as it could be Pain

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219 UninjuredMaximum Grip 2 0% 100% No Grip Force Please mark a vertical line at a point that indicates the level of effort you just exerted. Strongest Grip Force Effort 0 10 No pain Please mark a vertical line at a point that indicates the level of pain that you are currently experiencing. Pain as bad as it could be Pain

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220 InjuredSubmaximum (Pain) Grip 1 0% 100% No Grip Force Please mark a vertical line at a point that indicates the level of effort you just exerted. Strongest Grip Force Effort 0 10 No pain Please mark a vertical line at a point that indicates the level of pain that you are currently experiencing. Pain as bad as it could be Pain

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221 InjuredSubmaximum (Pain) Grip 2 0% 100% No Grip Force Please mark a vertical line at a point that indicates the level of effort you just exerted. Strongest Grip Force Effort 0 10 No pain Please mark a vertical line at a point that indicates the level of pain that you are currently experiencing. Pain as bad as it could be Pain

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222 UninjuredSubmaximum (Pain) Grip 1 0% 100% No Grip Force Please mark a vertical line at a point that indicates the level of effort you just exerted. Strongest Grip Force Effort 0 10 No pain Please mark a vertical line at a point that indicates the level of pain that you are currently experiencing. Pain as bad as it could be Pain

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223 UninjuredSubmaximum (Pain) Grip 2 0% 100% No Grip Force Please mark a vertical line at a point that indicates the level of effort you just exerted. Strongest Grip Force Effort 0 10 No pain Please mark a vertical line at a point that indicates the level of pain that you are currently experiencing. Pain as bad as it could be Pain

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224 InjuredSubmaximum (50% Maximum) Grip 1 0% 100% No Grip Force Please mark a vertical line at a point that indicates the level of effort you just exerted. Strongest Grip Force Effort 0 10 No pain Please mark a vertical line at a point that indicates the level of pain that you are currently experiencing. Pain as bad as it could be Pain

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225 InjuredSubmaximum (50% Maximum) Grip 2 0% 100% No Grip Force Please mark a vertical line at a point that indicates the level of effort you just exerted. Strongest Grip Force Effort 0 10 No pain Please mark a vertical line at a point that indicates the level of pain that you are currently experiencing. Pain as bad as it could be Pain

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226 UninjuredSubmaximum (50% Maximum) Grip 1 0% 100% No Grip Force Please mark a vertical line at a point that indicates the level of effort you just exerted. Strongest Grip Force Effort 0 10 No pain Please mark a vertical line at a point that indicates the level of pain that you are currently experiencing. Pain as bad as it could be Pain

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227 227 UninjuredSubmaximum (50% Maximum) Grip 2 0% 100% No Grip Force Please mark a vertical line at a point that indicates the level of effort you just exerted. Strongest Grip Force Effort 0 10 No pain Please mark a vertical line at a point that indicates the level of pain that you are currently experiencing. Pain as bad as it could be Pain

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228 LIST OF REFERENCES 1. Salter RB. Reactions of musculoskeletal tissues to disorders and injuries. In: Salter RB, ed. Textbook of Disorders and Injuries of the Musculoskeletal System 3rd ed. Baltimore: Williams and Wilkins; 1999:29-49. 2. Chaffin DB, Lee M, Freivalds A. Muscle strength assessment from EMG analysis. Med Sci Sports Exerc. 1980;12(3):205-211. 3. Ljung BO, Lieber RL, Friden J. Wrist extensor muscle pathology in lateral epicondylitis. J Hand Surg [Br]. Apr 1999;24(2):177-183. 4. Kadi F, Waling K, Ahlgren C, et al. Pathological mechanisms implicated in localized female trapezius myalgia. Pain. Dec 1998;78(3):191-196. 5. Larsson SE, Bodegard L, Henriksson KG, Oberg PA. Chronic trapezius myalgia. Morphology and blood flow studied in 17 patients. Acta Orthop Scand. Oct 1990;61(5):394-398. 6. Dennett X, Fry HJ. Overuse s yndrome: a muscle biopsy study. Lancet. Apr 23 1988;1(8591):905-908. 7. Hagg GM. Human muscle fibre abnormalities related to occupational load. Eur J Appl Physiol. Oct 2000;83(2-3):159-165. 8. Pienimaki T, Tarvainen T, Siira P, Ma lmivaara A, Vanharanta H. Associations between pain, grip strength, and manual tests in the treatment evaluation of chronic tennis elbow. Clin J Pain. May-Jun 2002;18(3):164-170. 9. van Wilgen CP, Akkerman L, Wieringa J, Dijkstra PU. Muscle strength in patients with chronic pain. Clin Rehabil. Dec 2003;17(8):885-889. 10. Nordenskiold U, Grimby G. Assessments of disability in women with rheumatoid arthritis in relation to grip force and pain. Disabil Rehabil. Jan 1997;19(1):13-19. 11. Farina D, Arendt-Nielsen L, Graven-N ielsen T. Experimental muscle pain decreases voluntary EMG activity but does not affect the musc le potential evoked by transcutaneous electrical stimulation. Clin Neurophysiol. Jul 2005;116(7):1558-1565.

PAGE 244

229 12. Graven-Nielsen T, Svensson P, Arendt-Nie lsen L. Effects of experimental muscle pain on muscle activity and co-ordina tion during static and dynamic motor function. Electroencephalogr Clin Neurophysiol. Apr 1997;105(2):156-164. 13. Ciubotariu A, Arendt-Nielsen L, Graven-N ielsen T. The influence of muscle pain and fatigue on the activity of s ynergistic muscles of the leg. Eur J Appl Physiol. May 2004;91(5-6):604-614. 14. Farina D, Arendt-Nielsen L, Merletti R, Graven-Nielsen T. Effect of experimental muscle pain on motor unit firi ng rate and conduction velocity. J Neurophysiol. Mar 2004;91(3):1250-1259. 15. Sohn MK, Graven-Nielsen T, Arendt-N ielsen L, Svensson P. Effects of experimental muscle pain on mechanical properties of single motor units in human masseter. Clin Neurophysiol. Jan 2004;115(1):76-84. 16. Mense S, Skeppar P. Discharge beha viour of feline gamma-motoneurones following induction of an artificial myositis. Pain. Aug 1991;46(2):201-210. 17. Yamaji S, Demura S, Nagasawa Y, Nakada M. The influence of different target values and measurement times on the d ecreasing force curve during sustained static gripping work. J Physiol Anthropol. Jan 2006;25(1):23-28. 18. Richards LG, Palmiter-Thomas P. Grip strenght measurement: A critical review of tools, methods, and clinical utility. Critical Reviews in Physical and Rehabilitation Medicine. 1996;8(1&2):87-109. 19. Kirkpatrick JE. Evaluation of grip loss. Calif Med. Nov 1956;85(5):314-320. 20. Spiegel JS, Paulus HE, Ward NB, Spiege l TM, Leake B, Kane RL. What are we measuring? An examination of walk time and grip strength. J Rheumatol. Feb 1987;14(1):80-86. 21. Petersen P, Petrick M, Connor H, Conk lin D. Grip strength and hand dominance: challenging the 10% rule. Am J Occup Ther. Jul 1989;43(7):444-447. 22. Stokes HM, Landrieu KW, Domangue B, K unen S. Identification of low-effort patients through dynamometry. The Journal of Hand Surgery. 1995;20A(6):10471056. 23. Rothstein JN, Lamb RL, Mayhew TP. Clin ical uses of isokinetic measurements: Critical issues. Physical Therapy. 1987;67:1840-1844. 24. Mathiowetz V, Weber K, Volland G, Kash man N. Reliability a nd validity of grip and pinch strength evaluations. J Hand Surg [Am]. Mar 1984;9(2):222-226.

PAGE 245

230 25. Tredgett M, Davis TR. Rapid repeat testing of grip stre ngth for detection of faked hand weakness. Journal of Hand Surgery (British and European Volume). 2000;25B(4):372-375. 26. Taylor C, Shechtman O. The use of the rapid exchange grip test in detecting sincerity of effort, part i: Administration of the test. Journal of Hand Therapy. 2000;13:195-202. 27. Shechtman O, Taylor C. The use of the rapid exchange grip test in detecting sincerity of effort, part ii: Validity of the test. Journal of Hand Therapy. 2000;13:203-210. 28. Shechtman O, Sindhu B. Using the force-time curve to detect submaximal effort. Journal of Hand Therapy. Sept 22 2005;18(4):461-462. 29. Gilbert JC, Knowlton RG. Simple method to determine sincerity of effort during a maximal isometric test of grip strength. Am J Phys Med. Jun 1983;62(3):135-144. 30. Janda DH, Geiringer, S. R., Hankin, F. M., Barry, D. T. Objective evaluation of grip strength. Journal of Occupational Medicine. 1987;29(7):569-571. 31. Niebuhr BR, Marion R, Hasson SM. Electromy ographic analysis of effort in grip strength assessment. Electromyogr Clin Neurophysiol. Apr-May 1993;33(3):149156. 32. Niebuhr BR. Detecting submaximal gr ip exertions of variable effort by electromyography. Electromyogr Clin Neurophysiol. Mar 1996;36(2):113-120. 33. Woolf AD, Akesson K. Understanding th e burden of musculos keletal conditions. The burden is huge and not reflected in national health priorities. Bmj. May 5 2001;322(7294):1079-1080. 34. Yelin E, Herrndorf A, Trupin L, Sonnebor n D. A national stu dy of medical care expenditures for musculoskeletal conditions : the impact of health insurance and managed care. Arthritis Rheum. May 2001;44(5):1160-1169. 35. Lawrence RC, Helmick CG, Arnett FC, et al. Estimates of the prevalence of arthritis and selected musculoskele tal disorders in the United States. Arthritis Rheum. May 1998;41(5):778-799. 36. Reynolds DL, Chambers LW, Badley EM et al. Physical disability among Canadians reporting musculoskeletal diseases. J Rheumatol. Jul 1992;19(7):10201030. 37. National Center for Health Statistics National health interview survey, 1995. US Department of Health and Human Services

PAGE 246

231 38. LaPlante M. Health conditions and impairments causing disability. Disability Statistics Center [Website]. Available at: http://dsc.ucsf.edu/pub_lis ting.php?pub_type=abstract Accessed October 1, 2005. 39. Lidgren L. The Bone and Joint Decade and the global economic and healthcare burden of musculoskeletal disease. J Rheumatol Suppl. Aug 2003;67:4-5. 40. Bernard BP. Musculoskeletal Disorde rs and Workplace Factors. Cincinnati, OH: National Institue for Occupati onal Safety and Health; 1997. 41. Woolf AD. How to assess musculoske letal conditions. History and physical examination. Best Pract Res Clin Rheumatol. Jun 2003;17(3):381-402. 42. Woolf AD, Pfleger B. Burden of major musculoskeletal conditions. Bull World Health Organ. 2003;81(9):646-656. 43. Visser B, van Dieen JH. Pathophysiology of upper extremity muscle disorders. J Electromyogr Kinesiol. Feb 2006;16(1):1-16. 44. Lidgren L. The bone an d joint decade 2000-2010. Bull World Health Organ. 2003;81(9):629. 45. Sanders MJ. The Medical C ontext. In: Sanders MJ, ed. Management of cumulative trauma disorders Boston: Butterworth-Heinemann; 1997:21-26. 46. United States Department of Labor. Lost-worktime injuries and illnesses: Characteristics and resulting days away from work, 1997. Washington, D.C.: Bureau of Labor Sta tistics; April 22, 1999 1999. 47. Kuorinka I, Koskinen P. Occupational rh eumatic diseases and upper limb strain in manual jobs in a light mechanical industry. Scand J Work Environ Health. 1979;5 suppl 3:39-47. 48. Luopajarvi T, Kuorinka I, Virolain en M, Holmberg M. Prevalence of tenosynovitis and other injuries of th e upper extremities in repetitive work. Scand J Work Environ Health. 1979;5 suppl 3:48-55. 49. Viikari-Juntura E. Neck and upper limb disorders among slaughterhouse workers. An epidemiologic and clinical study. Scand J Work Environ Health. Jun 1983;9(3):283-290. 50. Ranney D, Wells R, Moore A. Upper li mb musculoskeletal disorders in highly repetitive industries: precis e anatomical physical findings. Ergonomics. Jul 1995;38(7):1408-1423.

PAGE 247

232 51. Niemeyer LO. The issue of abnormal illness behavior in work hardening. In: Niemeyer LO, Jacobs K, eds. Work Hardening: State of the art Thorafare, N.J.: Slack; 1989. 52. Shultz-Johnson K. Assessment of upper ex tremity-injured persons' return to work potential. Journal of Hand Surgery. 1987;12A:950-957. 53. Simonsen JC. Coefficient of variat ion as a measure of subject effort. Archives of Physical Medicine and Rehabilitation. 1995;76:516-520. 54. Ashford RF, Nagelburg S, Adkins R. Sensitivity of the Jamar dynamometer in detecting submaximal grip effort. Journal of Hand Surgery. 1996;21A:402-405. 55. Chengalur SN, Smith GA, Nelson RC, Sadoff AM. Assessing sincerity of effort in maximal grip strength tests. Am J Phys Med Rehabil. Jun 1990;69(3):148-153. 56. King JW, Berryhill BH. Assessing maximum effort in upper-extremity functional testing. Work. 1991;1(3):65-76. 57. Patterson HM. Grip measurements as a part of pre-placement evaluation. Industrial Medicine and Surgery. 1965;34(7):555-557. 58. Fishbain DA, Cutler RB, Rosomoff HL, Rosomoff RS. Chronic pain disability exaggeration/malingering and submaximal effort research. Clinical Journal of Pain. 1999;15(4):244-274. 59. Mittenberg W, Patton C, Canyock EM, C ondit DC. Base rates of malingering and symptom exaggeration. Journal of Clinical and Ex perimental Neuropsychology. 2002;24(8):1094-1102. 60. Green P, Rohling ML, Lees-Haley PR, Alle n L, M. Effort has a greater effect on test scores than severe brain in jury in compensation claimants. Brain Injury. 2001;15:1045-1060. 61. Lees-Haley PR. MMPI-2 base rates for 492 personal injury plaintiffs: Implications and challenges for forensic assessment. Journal of Clinical Psychology. 1997;53(7):745-755. 62. Czitrom AA, Lister GD. Measurement of grip strength in the diagnosis of wrist pain. J Hand Surg [Am]. Jan 1988;13(1):16-19. 63. Mitterhauser MD, Muse VL, Dellon AL, Jetzer TC. Detection of Submaximal Effort With Computer-Assisted Grip Strength Measurements. The Journal of Occupational and Environmental Medicine. 1997;39(1220-1227).

PAGE 248

233 64. Shechtman O. Using the coefficient of vari ation to detect sincerity of effort of grip strength: A literature review. J Hand Ther. Jan-Mar 2000;13(1):25-32. 65. Shechtman O. The coefficient of variation as a measure of sincerity of effort of grip strength, part ii: Sensitivity and Specificity. Journal of Hand Therapy. 2001;14:188-194. 66. Shechtman O. The coefficient of variation as a measure of sincerity of effort of grip strength, part i: The statistical principle. Journal of Hand Therapy. 2001;14:180-187. 67. Shechtman O, Davenport R, Malcolm M, Na bavi D. Reliability and validity of the BTE-Primus grip tool. Journal of Hand Therapy. 2003;16(1):36-42. 68. Dieppe P. The relationships of musculos keletal disease to age, pain, poverty and behaviour. Rheumatology (Oxford). Mar 2006;45(3):248-249. 69. Kucharski A, Todd EM. Pain: Historical perspectives. In: Warfield CA, Bajwa ZH, eds. Principles and practice of pain medicine 2nd ed. New York: McGrawHill; 2004:1-10. 70. Melzack R, Katz J. The McGill Pain Qu estionnaire: Appraisal and Current Status. In: Turk DC, Melzack R, eds. Handbook of pain assessment 2nd ed. New York: The Guilford Press; 2001:35-52. 71. Reinking MF, Bockrath-Pugliese K, Worre ll T, Kegerreis RL, Miller-Sayers K, Farr J. Assessment of quadriceps muscle performance by hand-held, isometric, and isokinetic dynamometry in patients with knee dysfunction. J Orthop Sports Phys Ther. Sep 1996;24(3):154-159. 72. Lysholm J. The relation between pain and to rque in an isokine tic strength test of knee extension. Arthroscopy. 1987;3(3):182-184. 73. Heuts PH, Vlaeyen JW, Roelofs J, et al. Pain-related fear and daily functioning in patients with osteoarthritis. Pain. Jul 2004;110(1-2):228-235. 74. Turk DC, Robinson JP, Burwinkle T. Preval ence of fear of pa in and activity in patients with fibromyalgia syndrome. J Pain. Nov 2004;5(9):483-490. 75. Boersma K, Linton S, Overmeer T, Jan sson M, Vlaeyen J, de Jong J. Lowering fear-avoidance and enhancing functi on through exposure in vivo. A multiple baseline study across six patients with back pain. Pain. Mar 2004;108(1-2):8-16. 76. George SZ, Dannecker EA, Robinson ME. Fear of pain, not pain catastrophizing, predicts acute pain intensity, but neith er factor predicts tolerance or blood

PAGE 249

234 pressure reactivity: An experimental i nvestigation in pain -free individuals. Eur J Pain. Aug 8 2005. 77. Slade PD, Troup JD, Lethem J, Bent ley G. The Fear-Avoidance Model of exaggerated pain perception--II. Behav Res Ther. 1983;21(4):409-416. 78. Lethem J, Slade PD, Troup JD, Bentley G. Outline of a Fear-Avoidance Model of exaggerated pain perception--I. Behav Res Ther. 1983;21(4):401-408. 79. Samwel HJ, Evers AW, Crul BJ, Kraaimaat FW. The role of helplessness, fear of pain, and passive pain-coping in chronic pain patients. Clin J Pain. Mar-Apr 2006;22(3):245-251. 80. Hamilton Fairfax A, Balnave R, Adams RD Variability of grip strength during isometric contraction. Ergonomics. 1995;38:1819-1830. 81. King PM. Analysis of approaches to detec tion of sincerity of effort through grip strength measurement. Work. 1998;10:9-13. 82. Lechner DE, Bradbury SF, Bradley LA. De tecting sincerity of effort: a summary of methods and approaches. Phys Ther. Aug 1998;78(8):867-888. 83. Shechtman O. Is the coefficient of variation a valid measure for detecting sincerity of effort of grip strength? Work. 1999;13(2):163-169. 84. Shechtman O, Taylor C. How do therapis ts administer the rapid exchange grip test? A survey. Journal of Hand Therapy. 2002;15(1):53-61. 85. Stokes HM. The seriously uninjured hand -Weakness of grip. Journal of Occupational Medicine. 1983;25(9):683-684. 86. Niebuhr BR, Marion R. Detecting sincer ity of effort when measuring grip strength. American Journal of Physical Medicine. 1987;66(1):16-24. 87. Niebuhr BR, Marion R. Voluntary cont rol of submaximal grip strength. American Journal of Physical Medicine and Rehabilitation. 1990;69(2):96-101. 88. Goldman S, Cahalan T, An K. The injured upper extremity and the jamar fivehandle position grip test. American Journal of Physical Medicine and Rehabilitation. 1991;70(6):306-308. 89. Hoffmaster E, Lech R, Niebuhr BR. C onsistency of sincere and feigned grip exertions with repeated testing. Journal of Occupational Medicine. 1993;35(8):788-794.

PAGE 250

235 90. Hildreth DH, Breidenbach WC, Lister GD, Hodges AD. Detection of submaximal effort by use of the rapid exchange grip. J Hand Surg [Am]. Jul 1989;14(4):742745. 91. Lister G. The hand: Diagnosis and indications 2nd ed. New York: Churchill Livingstone; 1984. 92. Portney LG, Watkins MP. Foundations of Clinical Re search: Applications to Practice 2nd ed. Norwalk, Connecticut: Appleton & Lange; 2000. 93. Gutierrez Z, Shechtman O. Effectiveness of the five-handle position grip strength test in detecting sincerity of effort in men and women. American Journal of Physical Medicine and Rehabilitation. 2003;82:847-855. 94. Robinson ME, Geisser ME, Hanson CS, O'Connor PD. Detecting submaximal efforts in grip strength testing w ith the coefficient of variation. Journal of Occupational Rehabilitation. 1993;3(1):45-50. 95. Househam E, McAuley J, Charles T, Li ghtfoot T, Swash M. Analysis of force profile during a maximum voluntar y isometric contraction task. Muscle Nerve. Mar 2004;29(3):401-408. 96. Kamimura T, Ikuta Y. Evaluation of gr ip strength with a sustained maximal isometric contraction for 6 and 10 seconds. J Rehabil Med. Sep 2001;33(5):225229. 97. Viitasalo JT, Komi PV. Interrelati onships between Electro-Myographic, Mechanical, Muscle Structure and Re flex Time Measurements in Man. Acta Physiologica Scandinavica. 1981;111(1):97-103. 98. Hakkinen A, Komi PV. The effects of explosive type strength training on electromyographic and force production char acteristics of leg extensor muscles during concentric and various stre tch shortening cycle exercises. Scandinavian Journal of Sports Science. 1985;7:65-76. 99. Hakkinen A, Komi PV. Changes in electr ical and mechanical behavior of leg extensor muscle during heavy resistance strength training. Scandinavian Journal of Sports Science. 1985;7:55-64. 100. Bemben MG, Massey BH, Boileau RA, Misn er JE. Reliability of isometric forcetime curve parameters for men aged 20 to 79 years. Journal of Applied Sports Science Research. 1992;6(3):158-164. 101. Smith GA, Nelson RC, Sadoff SJ, Sadoff AM. Assessing sincerity of effort in maximal grip strength tests. Am J Phys Med Rehabil. Apr 1989;68(2):73-80.

PAGE 251

236 102. Shechtman O, Sindhu BS, Davenport PW. Using the force-time curve to detect maximal grip strength effort. J Hand Ther. Jan-Mar 2007;20(1):37-48. 103. Massy-Westropp N, Rankin W, Ahern M, Kr ishnan J, Hearn TC. Measuring grip strength in normal adults: reference ranges and a comparison of electronic and hydraulic instruments. J Hand Surg [Am]. May 2004;29(3):514-519. 104. Cafarelli E. Force sensation in fres h and fatigued human skeletal muscle. Exerc Sport Sci Rev. 1988;16:139-168. 105. Redfern M. Functional muscle: e ffects on electromyographic output. In: Soderberg GL, ed. Selected Topics in Surface Elec tromyography for the Use in the Occupational Setting: Expert Perspectives Cincinnati, OH: US Department of Health and Human Services, P ublic Health Se rvice; 1992:104–120. 106. Ogura T, Kubo T, Okuda Y, et al. Po wer spectrum analysis of compound muscle action potential in carpal tunnel syndrome patients. J Orthop Surg (Hong Kong). Jun 2002;10(1):67-71. 107. Lidgren L. The bone an d joint decade 2000-2010. Acta Orthopaedica Scandinavica. 2000;71(1):3-6. 108. Frymoyer J, Durett C. The economics of spinal disorders. In: Frymoyer J, ed. The Adult Spine: Principles and Practice 2 ed. Philadephia: Lippincott-Raven; 1997:143-150. 109. Portney LG, Watkins MP. Foundations of Clinical Re search: Applications to Practice 1st ed. Norwalk, Connectic ut: Appleton & Lange; 1993. 110. McNicol D. A Primer of Signal Detection Theory London: Lawrence Erlbaum Associates, Publishers; 2005. 111. Chart for Windows [computer program]. Versi on 4.2. Colorado Springs, CO: ADInstruments; 2002. 112. Rose LP. The Muscular System (Musculoskeletal System). Partners in Assistive Technology Training and Services [Website]. Available at: http://webschoolsolutions.co m/patts/systems/muscles.htm Accessed March 14, 2006. 113. Yelin E, Callahan LF. The economic cost and social and ps ychological impact of musculoskeletal conditions. Nati onal Arthritis Data Work Groups. Arthritis Rheum. Oct 1995;38(10):1351-1362.

PAGE 252

237 114. Iverson GL, Binder LM. Detecti ng exaggeration and malingering in neuropsychological assessment. Journal of Head Trauma Rehabilitation. 2000;15(2):829-858. 115. Main CJ, Spanswick CC. 'Functional Over lay', and illness behaviour in chronic pain: Distress or Malingering? Conceptual difficulties in medico-legal assessment of personal injury claims. Journal of Psychometric Research. 1995;39(6):737753. 116. Matheson LN. Symptom Magnification Syndrome. In: Isernhagen SJ, ed. Work Injury: Management and Prevention New York: Apen Publishers; 1988. 117. Lipman FD. Malingering in personal injury cases. Temple Law Quarterly. 1962;35:141-162. 118. Hamilton Fairfax A, Balnave R, Adams R. Review of sincerity of effort testing. Safety Science. 1997;25:237-245. 119. Zwarts MJ, Stegeman DF. Multichannel su rface EMG: basic aspects and clinical utility. Muscle Nerve. Jul 2003;28(1):1-17. 120. Winter DA. Biomechanics and motor control of human movement 2nd ed. Toronto: John Wiley & Sons; 1990. 121. Derrick TR. Signal processing. In: R obertson DGE, Caldwell GE, Hamill J, Kamen G, Whittlesey SN, eds. Research Methods in Biomechanics 1st ed. Champaign: Human Kinetics; 2004:227-238. 122. Sluiter JK, Rest KM, Frings-Dresen MH Criteria document for evaluating the work-relatedness of upper-extremity musculoskeletal disorders. Scand J Work Environ Health. 2001;27 Suppl 1:1-102. 123. Keller K, Corbett J, Nichols D. Repetitiv e strain injury in computer keyboard users: pathomechanics and treatment principles in i ndividual and group intervention. J Hand Ther. Jan-Mar 1998;11(1):9-26. 124. Morse T, Punnett L, Warren N, Dillon C, Warren A. The relationship of unions to prevalence and claim filing for work-re lated upper-extremity musculoskeletal disorders. Am J Ind Med. Jul 2003;44(1):83-93. 125. Hales TR, Sauter SL, Peterson MR, et al Musculoskeletal disorders among visual display terminal users in a telecommunications company. Ergonomics. Oct 1994;37(10):1603-1621.

PAGE 253

238 126. McCormack RR, Jr., Inman RD, Wells A, Berntsen C, Imbus HR. Prevalence of tendinitis and related di sorders of the upper extremity in a manufacturing workforce. J Rheumatol. Jul 1990;17(7):958-964. 127. Bernard B, Sauter S, Fine L, Petersen M, Hales T. Job task and psychosocial risk factors for work-related musculoskeleta l disorders among newspaper employees. Scand J Work Environ Health. Dec 1994;20(6):417-426. 128. Stockstill JW, Harn SD, St rickland D, Hruska R. Prev alence of upper extremity neuropathy in a clinic al dentist population. J Am Dent Assoc. Aug 1993;124(8):67-72. 129. Huisstede BM, Bierma-Zeinstra SM, Koes BW, Verhaar JA. Incidence and prevalence of upper-extremity musculoskele tal disorders. A systematic appraisal of the literature. BMC Musculoskelet Disord. 2006;7:7. 130. Webster BS, Snook SH. The cost of compensable upper extremity cumulative trauma disorders. J Occup Med. Jul 1994;36(7):713-717. 131. Fabrizio AJ. Work-related upper extremity injuries: pr evalence, cost and risk factors in military and civilian populations. Work. 2002;18(2):115-121. 132. Brogmus GE, Sorock GS, Webster BS. Recent trends in work-related cumulative trauma disorders of the upper extremities in the United States: an evaluation of possible reasons. J Occup Environ Med. Apr 1996;38(4):401-411. 133. Feuerstein M, Miller VL, Burrell LM, Berger R. Occupational upper extremity disorders in the federal workforce. Pr evalence, health care expenditures, and patterns of work disability. J Occup Environ Med. Jun 1998;40(6):546-555. 134. United States Department of Labor. Lost-worktime injuries and illnesses: Characteristics and resulting days away from work, 2003. Washington, D.C.: Bureau of Labor Sta tistics; March 30, 2005 2005. 135. Barbe MF, Barr AE, Gorzelany I, Amin M, Gaughan JP, Safadi FF. Chronic repetitive reaching and grasping results in decreased motor performance and widespread tissue responses in a rat model of MSD. J Orthop Res. Jan 2003;21(1):167-176. 136. Barr AE, Safadi FF, Gorzelany I, Amin M, Popoff SN, Barbe MF. Repetitive, negligible force reaching in rats induces pathological overloading of upper extremity bones. J Bone Miner Res. Nov 2003;18(11):2023-2032. 137. Barr AE, Barbe MF. Pathophysiological tissu e changes associated with repetitive movement: a review of the evidence. Phys Ther. Feb 2002;82(2):173-187.

PAGE 254

239 138. Armstrong RB, Ogilvie RW, Schwane JA. Eccentric exercise-i nduced injury to rat skeletal muscle. J Appl Physiol. Jan 1983;54(1):80-93. 139. Jarvinen M, Jozsa L, Kannus P, Ja rvinen TL, Kvist M, Leadbetter W. Histopathological findings in chronic tendon disorders. Scand J Med Sci Sports. Apr 1997;7(2):86-95. 140. Stauber WT, Smith CA. Cellu lar responses in exertioninduced skeletal muscle injury. Mol Cell Biochem. Feb 1998;179(1-2):189-196. 141. Himmelstein JS, Feuerstein M, Stan ek EJ, 3rd, et al. Work-related upperextremity disorders and work disability : clinical and psychosocial presentation. J Occup Environ Med. Nov 1995;37(11):1278-1286. 142. Duff SV. Tendinitis, entrapment neuropa thies and related conditions. In: Sanders MJ, ed. Management of cumulative trauma disorders Boston: ButterworthHeinemann; 1997:41-64. 143. Salter RB. Neuromuscular disorders. In: Salter RB, ed. Textbook of Disorders and Injuries of the Mu sculoskeletal System 3rd ed. Baltimore: Williams and Wilkins; 1999:303-337. 144. Pratt N. Anatomy of nerve entr apment sites in the upper quarter. J Hand Ther. Apr-Jun 2005;18(2):216-229. 145. Fuss FK, Wurzl GH. Radial nerve entrap ment at the elbow: surgical anatomy. J Hand Surg [Am]. Jul 1991;16(4):742-747. 146. Lister GD, Belsole RB, Kleinert HE. The radial tunnel syndrome. J Hand Surg [Am]. Jan 1979;4(1):52-59. 147. Prasartritha T, Liupolvanish P, Rojanak it A. A study of the posterior interosseous nerve (PIN) and the radial tunnel in 30 Thai cadavers. J Hand Surg [Am]. Jan 1993;18(1):107-112. 148. Sponseller PD, Engber WD. Double-en trapment radial tunnel syndrome. J Hand Surg [Am]. Jul 1983;8(4):420-423. 149. Fuss FK, Wurzl GH. Median nerve entrap ment. Pronator teres syndrome. Surgical anatomy and correlation with symptom patterns. Surg Radiol Anat. 1990;12(4):267-271. 150. Spinner M. The anterior interosseous-nerv e syndrome, with special attention to its variations. J Bone Joint Surg Am. Jan 1970;52(1):84-94.

PAGE 255

240 151. Fearn CB, Goodfellow JW. Anteri or Interosseous Nerve Palsy. J Bone Joint Surg Br. Feb 1965;47:91-93. 152. McPherson SA, Meals RA. Cubital tunnel syndrome. Orthop Clin North Am. Jan 1992;23(1):111-123. 153. Stack RE. Carpal tunnel syndrome. American Family Physician. 1973;8:88. 154. Phalen G. The carpal tunnel syndrome. Journal of Bone and Joint Surgery. 1966;48A(2):211-228. 155. Pickett JB. The carpal tunnel syndrome. Journal of South Carolina Medical Association. 1984;80:298-301. 156. Salter RB. Degenerative disorders of join ts and related tissues. In: Salter RB, ed. Textbook of Disorders and Injuries of the Musculoskeletal System 3rd ed. Baltimore: Williams and Wilkins; 1999:257-302. 157. Wuori JL, Overend TJ, Kramer JF, MacD ermid J. Strength and pain measures associated with late ral epicondylitis bracing. Arch Phys Med Rehabil. Jul 1998;79(7):832-837. 158. Nirschl RP. Tennis elbow. Orthop Clin North Am. Jul 1973;4(3):787-800. 159. Nirschl RP. Soft-tissue injuries about the elbow. Clin Sports Med. Oct 1986;5(4):637-652. 160. Safran MR. Elbow injuries in athletes. A review. Clin Orthop Relat Res. Jan 1995(310):257-277. 161. Nirschl RP, Pettrone FA. Tennis elbow. The surgical treatment of lateral epicondylitis. J Bone Joint Surg Am. Sep 1979;61(6A):832-839. 162. Regan W, Wold LE, Coonrad R, Morre y BF. Microscopic histopathology of chronic refractory lateral epicondylitis. Am J Sports Med. Nov-Dec 1992;20(6):746-749. 163. Goldie I. Epicondylitis Lateralis Hume ri (Epicondylalgia or Tennis Elbow). A Pathogenetical Study. Acta Chir Scand Suppl. 1964;57:SUPPL 339:331+. 164. Coonrad RW, Hooper WR. Tennis elbow: its course, natural hist ory, conservative and surgical management. J Bone Joint Surg Am. Sep 1973;55(6):1177-1182. 165. Greenbaum B, Itamura J, Vangsness CT, Tibone J, Atkinson R. Extensor carpi radialis brevis. An anatomi cal analysis of its origin. J Bone Joint Surg Br. Sep 1999;81(5):926-929.

PAGE 256

241 166. Eversmann W. Entrapment and compre ssion neuropathies. In: Green DP, ed. Operative Hand Surgery 3 ed. New York: Churchill Livingstone; 1993:13411385. 167. Graham R. Carpal tunnel syndrome: A statistical analysis of 214 cases. Orthopaedics. 1983;6:1283-1287. 168. Katz R. Carpal tunnel syndrome: A practical review. American Family Physician. 1994;49:1371-1379. 169. Omer GE. Median nerve compression at the wrist. Hand Clinics. 1992;8(2):317324. 170. Erdmann MWH. Endoscopic carpal tunnel decompression. Journal of Hand Surgery [Br]. 1994;19B:5-13. 171. Faithfull DK, Moir DH. The micropathology of the typical carpal tunnel syndrome. Journal of Hand Surgery [Am]. 1986;11B:131-132. 172. Gerritsen AAM, de Krom M, Struijs MA Scholten R, de Vet H, Bouter LM. Conservative treatment options for carpal tunnel syndrome: a systematic review of randomised controlled trials. Journal of Neurology. 2002;249:272-280. 173. Sunderland S. Nerves and nerve injuries Baltimore: Williams & Wilkins; 1968. 174. Richards LG. Posture effects on grip strength. Arch Phys Med Rehabil. Oct 1997;78(10):1154-1156. 175. Fess EE. Grip Strength. In: Casanova JS, ed. Clinical assessment recommendations 2nd ed. Chicago, IL: The American Society of Hand Therapists; 1992:41-45. 176. Kraft GH, Detels PE. Position of function of the wrist. Arch Phys Med Rehabil. Jun 1972;53(6):272-275. 177. Pryce JC. The wrist position between neut ral and ulnar deviati on that facilitates the maximum power grip strength. J Biomech. 1980;13(6):505-511. 178. O'Driscoll SW, Horii E, Ness R, Cahalan TD, Richards RR, An KN. The relationship between wrist position, grasp size, and grip strength. J Hand Surg [Am]. Jan 1992;17(1):169-177. 179. Richards LG, Olson B, Palmiter-Thomas P. How forearm position affects grip strength. Am J Occup Ther. Feb 1996;50(2):133-138.

PAGE 257

242 180. Balogun JA, Akomolafe CT, Amusa LO. Grip strength: effects of testing posture and elbow position. Arch Phys Med Rehabil. Apr 1991;72(5):280-283. 181. Mathiowetz V, Rennells C, Donahoe L. Effect of elbow position on grip and key pinch strength. J Hand Surg [Am]. Sep 1985;10(5):694-697. 182. Kuzala EA, Vargo MC. The relationshi p between elbow position and grip strength. Am J Occup Ther. Jun 1992;46(6):509-512. 183. Ferraz MB, Ciconelli RM, Araujo PM, Oliv eira LM, Atra E. The effect of elbow flexion and time of assessment on the meas urement of grip strength in rheumatoid arthritis. J Hand Surg [Am]. Nov 1992;17(6):1099-1103. 184. Su CY, Lin JH, Chien TH, Cheng KF, Sung YT. Grip strength in different positions of elbow and shoulder. Arch Phys Med Rehabil. Jul 1994;75(7):812815. 185. Bohannon RW. Intertester reliability of hand-held dynamometry: a concise summary of published research. Percept Mot Skills. Jun 1999;88(3 Pt 1):899-902. 186. Bohannon RW. Hand-held dynamometry: f actors influencing reliability and validity. Clin Rehabil. Aug 1997;11(3):263-264. 187. Mathiowetz V, Kashman N, Volland G, Weber K, Dowe M, Rogers S. Grip and Pinch Strength Normative Data for Adults. Archives of Physical Medicine and Rehabilitation. 1985;66(2):69-74. 188. Kroemer KH, Marras WS. Towards an objective assessment of the "maximal voluntary contraction" component in r outine muscle strength measurements. Eur J Appl Physiol Occup Physiol. 1980;45(1):1-9. 189. Astrand PO, Rodahl K, eds. Textbook of work physiology. 2nd ed. New York: McGraw-Hill; 1977. 190. Sust M, Schmalz T, Beyer L, Rost R, Hansen E, Weiss T. Assessment of isometric contractions performed with maximal subjective effort: corresponding results for EEG changes and force measurements. Int J Neurosci. Nov 1997;92(12):103-118. 191. Dalsgaard MK, Ide K, Cai Y, Quistorff B, Secher NH. The intent to exercise influences the cerebral O(2)/carboh ydrate uptake ratio in humans. J Physiol. Apr 15 2002;540(Pt 2):681-689. 192. Edgerton VR. Mammalian Muscle-Fib er Types and Their Adaptability. American Zoologist. 1978;18(1):113-125.

PAGE 258

243 193. Milner-Brown HS, Stein RB, Yemm R. Changes in firing rate of human motor units during linearly changi ng voluntary contractions. J Physiol. Apr 1973;230(2):371-390. 194. Milner-Brown HS, Stein RB, Yemm R. The orderly recruitment of human motor units during voluntary isometric contractions. J Physiol. Apr 1973;230(2):359370. 195. Grimby L, Hannerz J. Recruitment order of motor units on voluntary contraction: changes induced by proprioceptive afferent activity. J Neurol Neurosurg Psychiatry. Dec 1968;31(6):565-573. 196. Kilbreath SL, Refshauge K, Gandevia SC. Di fferential control of the digits of the human hand: evidence from digital anaesthesia and weight matching. Exp Brain Res. Dec 1997;117(3):507-511. 197. Lafargue G, Paillard J, Lamarre Y, Si rigu A. Production and perception of grip force without propriocep tion: is there a sense of effo rt in deafferented subjects? Eur J Neurosci. Jun 2003;17(12):2741-2749. 198. Robinson ME, Mac Millan M, O'Connor P, Fuller A, Cassisi JE. Reproducibility of maximal versus submaximal efforts in an isometric lumbar extension task. J Spinal Disord. Dec 1991;4(4):444-448. 199. Bechtol CO. Grip test; th e use of a dynamometer with adjustable handle spacings. J Bone Joint Surg Am. Jul 1954;36-A(4):820-824; passim. 200. Young VL, Pin P, Kraemer BA, Goul d RB, Nemergut L, Pellowski M. Fluctuation in grip and pinch strength among normal subjects. J Hand Surg [Am]. Jan 1989;14(1):125-129. 201. Krombholz H. On the association of effort and force of handgrip. Percept Mot Skills. Feb 1985;60(1):161-162. 202. Caldwell LS, Chaffin DB, Dukes-Dobos FN et al. A proposed standard procedure for static muscle strength testing. Am Ind Hyg Assoc J. Apr 1974;35(4):201-206. 203. Ramos MU, Mundale MO, Awad EA, et al. Cardiovascular effe cts of spread of excitation during prolonged isometric exercise. Arch Phys Med Rehabil. Nov 1973;54(11):496-504 passim. 204. Joughin K, Gulati P, Mackinnon SE, et al An evaluation of rapid exchange and simultaneous grip tests. J Hand Surg [Am]. Mar 1993;18(2):245-252.

PAGE 259

244 205. Tredgett M, Pimble LJ, Davis TR. The detection of feigned hand weakness using the five position grip strength. Journal of Hand Surgery (British and European Volume). 1999;24B(4):426-428. 206. Shechtman O, Gutierrez Z, Kokendofer E. An alysis of the statistical methods used to detect submaximal effort with the five-rung grip strength test. J Hand Ther. Jan-Mar 2005;18(1):10-18. 207. Demura S, Yamaji S, Nagasawa Y, Ikemoto Y, Shimada S. Force developmental phase and reliability in explos ive and voluntary grip exertions. Percept Mot Skills. Jun 2001;92(3 Pt 2):1009-1021. 208. Demura S, Yamaji S, Nagasawa Y, Minami M, Kita I. Examination of forceproduction properties during st atic explosive grip based on force-time curve parameters. Percept Mot Skills. Dec 2000;91(3 Pt 2):1209-1220. 209. Nagasawa Y, Demura S, Nakada M. Relia bility of a computerized target-pursuit system for measuring coordi nated exertion of force. Percept Mot Skills. Jun 2003;96(3 Pt 2):1071-1085. 210. Sanjak M, Konopacki R, Capasso R, et al. Dissociation between mechanical and myoelectrical manifestation of muscle fa tigue in amyotrophic lateral sclerosis. Amyotroph Lateral Scler Other Motor Neuron Disord. Mar 2004;5(1):26-32. 211. Hakkinen A, Malkia E, Hakkinen K, Ja ppinen I, Laitinen L, Hannonen P. Effects of detraining subsequent to strength training on neuromuscular function in patients with inflammatory arthritis. Br J Rheumatol. Oct 1997;36(10):1075-1081. 212. Helliwell P, Howe A, Wright V. Functional assessment of the hand: reproducibility, acceptability, and utility of a new system for measuring strength. Ann Rheum Dis. Mar 1987;46(3):203-208. 213. Valkeinen H, Ylinen J, Malkia E, Alen M, Hakkinen K. Maximal force, force/time and activation/coactivation ch aracteristics of the neck muscles in extension and flexion in healthy men and women at different ages. Eur J Appl Physiol. Dec 2002;88(3):247-254. 214. Haff GG, Carlock JM, Hartman MJ, et al Force-time curve characteristics of dynamic and isometric muscle actions of elite women olympic weightlifters. J Strength Cond Res. Nov 2005;19(4):741-748. 215. Aagaard P, Simonsen EB, Andersen JL, Magnusson SP, Halkjaer-Kristensen J, Dyhre-Poulsen P. Neural inhibition du ring maximal eccentric and concentric quadriceps contraction: eff ects of resistance training. J Appl Physiol. Dec 2000;89(6):2249-2257.

PAGE 260

245 216. Bemben MG, Massey BH, Bemben DA, Misner JE, Boileau RA. Isometric intermittent endurance of four musc le groups in men aged 20-74 yr. Med Sci Sports Exerc. Jan 1996;28(1):145-154. 217. Bemben MG, Massey BH, Bemben DA, Misner JE, Boileau RA. Isometric muscle force production as a function of age in healthy 20to 74-yr-old men. Med Sci Sports Exerc. Nov 1991;23(11):1302-1310. 218. Izquierdo M, Ibanez J, Gorostiaga E, et al. Maximal strength and power characteristics in isometric and dyna mic actions of the upper and lower extremities in middle-aged and older men. Acta Physiol Scand. Sep 1999;167(1):57-68. 219. Demura S, Yamaji S, Nagasawa Y, Sato S, Minami M, Yoshimura Y. Reliability and gender differences of static explos ive grip parameters based on force-time curves. J Sports Med Phys Fitness. Mar 2003;43(1):28-35. 220. Ryushi T, Hakkinen K, Kauhanen H, Ko mi PV. Muscle fiber characteristics, muscle cross-sectional area and force pr oduction in strength athletes, physically active males and females. Scand J Sports Sci. 1988;10:7-15. 221. Harridge SD, Bottinelli R, Canepari M, et al. Whole-muscle and single-fibre contractile properties and myosin heavy chain isoforms in humans. Pflugers Arch. Sep 1996;432(5):913-920. 222. Nakada M, Demura S, Yamaji S, Nagasawa Y. Examination of the reproducibility of grip force and muscle oxygenation kineti cs on maximal repeated rhythmic grip exertion. J Physiol Anthropol Appl Human Sci. Jan 2005;24(1):1-6. 223. Yamaji S, Demura S, Nagasawa Y, Nakada M, Kitabayashi T. The effect of measurement time when evaluating sta tic muscle endurance during sustained static maximal gripping. J Physiol Anthropol Appl Human Sci. May 2002;21(3):151-158. 224. Haff GG, Stone M, OBryant HS, et al. Force-time dependent characteristics of dynamic and isometric muscle actions. Journal of Strength and Conditioning Research. NOV 1997;11(4):269-272. 225. Aagaard P, Andersen JL. Correlation be tween contractile strength and myosin heavy chain isoform composition in human skeletal muscle. Med Sci Sports Exerc. Aug 1998;30(8):1217-1222. 226. Aagaard P, Thorstensson A. Neuromus cular aspects of exercise—adaptive responses evoked by strength tr aining. In: Kjr M, ed. Textbook of sport medicine London: Blackwell; 2003:70–106.

PAGE 261

246 227. Bojsen-Moller J, Magnusson SP, Rasmussen LR, Kjaer M, Aagaard P. Muscle performance during maximal isometric and dynamic contractions is influenced by the stiffness of the tendinous structures. J Appl Physiol. Sep 2005;99(3):986-994. 228. Sale DG. Neural adaptati on to resistance training. Med Sci Sports Exerc. Oct 1988;20(5 Suppl):S135-145. 229. Aagaard P, Simonsen EB, Andersen JL, Magnusson P, Dyhre-Poulsen P. Increased rate of force development and neural drive of human skeletal muscle following resistance training. J Appl Physiol. Oct 2002;93(4):1318-1326. 230. Grimby L, Hannerz J, Hedman B. The fatigue and voluntary discharge properties of single motor units in man. J Physiol. Jul 1981;316:545-554. 231. Andersen LL, Aagaard P. Influence of maximal muscle strength and intrinsic muscle contractile properties on contr actile rate of force development. Eur J Appl Physiol. Jan 2006;96(1):46-52. 232. Aagaard P, Simonsen EB, Trolle M, Ba ngsbo J, Klausen K. Effects of different strength training regimes on moment and power generation during dynamic knee extensions. Eur J Appl Physiol Occup Physiol. 1994;69(5):382-386. 233. Siff M. Biomechanical foundations of st rength and power training. In: Zatsiorsky V, ed. Biomechanics in Sport London: Blackwell Scientific Ltd.; 2001:103-139. 234. Fishbain DA, Abdel-Moty E, Cutler RB, Rosomoff HL, Steele-Rosomoff R. Detection of a "faked" strength task effo rt in volunteers using a computerized exercise testing system. Am J Phys Med Rehabil. May-Jun 1999;78(3):222-227. 235. Wiles JD, Boyson H, Balmer J, Bird SR. Validity and reliability of a new isometric hand dynamometer. Sports Engineering. 2001/08// 2001;4(3):147-152. 236. Watts PB, Jensen RL. Reliability of p eak forces during a finger curl motion common in rock climbing. Measurement in Physical Education and Exercise Science. 2003;7(4):263-267. 237. Kamen G. Electromyographic kinesiology. In: Robertson DGE, Caldwell GE, Hamill J, Kamen G, Whittlesey SN, eds. Research Methods in Biomechanics 1st ed. Champaign: Human Kinetics; 2004:163-181. 238. Carpenter RHS. Global Motor Control. In: Carpenter RHS, ed. Neurophysiology New York: Oxford University Press; 1996:226-243. 239. Carpenter RHS. Local Motor Control. In: Carpenter RHS, ed. Neurophysiology New York: Oxford University Press; 1996:200-225.

PAGE 262

247 240. Gilman S, Winans S. Motor Pathwa ys. In: Gilman S, Winans S, eds. Manter and Gatz's Essentials of Clinic al Neuroanatomy and Neurophysiology 10th ed. Philadelphia: F. A. Davis; 2003:60-67. 241. Chow J. Electromyography (EMG). Gaines ville: University of Florida; 2005:PET 6347, Biomechanical Instrume ntation, lecture notes. 242. Bilodeau M, Arsenault AB, Gravel D, Bourbonnais D. EMG power spectrum of elbow extensors: a reliability study. Electromyogr Clin Neurophysiol. Apr-May 1994;34(3):149-158. 243. Hasson SM, Williams JH, Signorile JF. Fa tigue-induced changes in myoelectric signal characteristics and perceived exertion. Can J Sport Sci. Vol 14; 1989:99102. 244. Jones LA, Hunter IW. Effect of fatigue on force sensation. Exp Neurol. Sep 1983;81(3):640-650. 245. Suzuki H, Conwit RA, Stashuk D, Sant arsiero L, Metter EJ. Relationships between surface-detected EMG si gnals and motor unit activation. Med Sci Sports Exerc. Sep 2002;34(9):1509-1517. 246. Hermens HJ, Boon KL, Zilvold G. The clinical use of surface EMG. Electromyogr Clin Neurophysiol. May 1984;24(4):243-265. 247. Lago PJ, Jones NB. Low-frequency spectral analysis of the e.m.g. Med Biol Eng Comput. Nov 1981;19(6):779-782. 248. Gander RE, Hudgins BS. Power spectral de nsity of the surface myoelectric signal of the biceps brachii as a function of static load. Electromyogr Clin Neurophysiol. Nov-Dec 1985;25(7-8):469-478. 249. De Luca CJ. Towards understanding the EMG signal. Muscles Alive 4th ed. Baltimore: Williams & Wilkinson; 1978. 250. Guha K, Anand S. Simulation linking EMG power spectra and rate coding. Comput. Biol. Med. 1979;9:213-221. 251. Hagberg M, Ericson BE. Myoelectric power spectrum dependence on muscular contraction level of elbow flexors. Eur J Appl Physiol Occup Physiol. 1982;48(2):147-156. 252. Gydikov A, Kosarov D. Some features of different motor units in human biceps brachii. Pflugers Arch. Feb 18 1974;347(1):75-88.

PAGE 263

248 253. Blinowska A, Verroust J, Cannet G. An analysis of synchronization and double discharge effects on low frequency electromyographic power spectra. Electromyogr Clin Neurophysiol. Oct-Dec 1980;20(6):465-480. 254. Kogi K, Hakamada T. Slowing of surf ace electromyogram and muscle strength in muscle fatigue. Rep. Inst. Sc. Lab. 1962;60:27-41. 255. Kadefors R, Kaiser E, Petersen I. D ynamic spectrum analysis of myo-potentials and with special reference to muscle fatigue. Electromyography. Jan-Apr 1968;8(1):39-74. 256. Lindstrom L, Magnusson R, Petersen I. Muscular fatigue and action potential conduction velocity changes studied with frequency an alysis of EMG signals. Electromyography. Nov-Dec 1970;10(4):341-356. 257. Petrofsky JS, Lind AR. Frequency analys is of the surface el ectromyogram during sustained isometric contractions. Eur J Appl Physiol Occup Physiol. 1980;43(2):173-182. 258. Mills KR. Power spectral analysis of electromyogram and compound muscle action potential during musc le fatigue and recovery. J Physiol. May 1982;326:401-409. 259. Sadoyama T, Miyano H. Frequency analysis of surface EMG to evaluation of muscle fatigue. Eur J Appl Physiol Occup Physiol. 1981;47(3):239-246. 260. Winter D. EMG interpretation. In: Kumar S, Mital A, eds. Electromyography in Ergonomics London: Taylor and Francis; 1996:109-125. 261. Cobb S, Forbes A. Electromyographic studies of muscular fatigue in man. American Journal of Physiology. 1923;65:234-251. 262. Hagbarth KE, Jessop J, Eklund G, Wa llin EU. The Piper rhythm--a phenomenon related to muscle resonance characteristics? Acta Physiol Scand. Feb 1983;117(2):263-271. 263. Kaiser E, Petersen I. Frequency analysis of muscle action pot entials during tetanic contraction. Electromyography. Jan-Apr 1963;3:5-17. 264. Sato M. Some problems in the quantit ative evaluation of muscle fatigue by frequency analysis of the electromyogram. J Anthropol. Soc. Nippon. Jan-Apr 1965;73:20-27. 265. Edwards RG, Lippold OC. The relation be tween force and integrated electrical activity in fatigued muscle. J Physiol. Jun 28 1956;132(3):677-681.

PAGE 264

249 266. Maton B. Human motor unit activity dur ing the onset of muscle fatigue in submaximal isometric isotonic contraction. Eur J Appl Physiol Occup Physiol. 1981;46(3):271-281. 267. Solomonow M, Baten C, Smit J, et al. Electromyogram power spectra frequencies associated with motor unit recruitment strategies. J Appl Physiol. Mar 1990;68(3):1177-1185. 268. Bigland-Ritchie B, Donovan EF, Rou ssos CS. Conduction velocity and EMG power spectrum changes in fatigue of sustained maximal efforts. J Appl Physiol. Nov 1981;51(5):1300-1305. 269. Kranz H, Williams AM, Cassell J, Caddy DJ, Silberstein RB. Factors determining the frequency content of the electromyogram. J Appl Physiol. Aug 1983;55(2):392-399. 270. Stalberg E, Daube JR. Electromyographic methods. In: Stalberg E, ed. Clinical Neurophysiology of Disord ers of Muscle and Neurom uscular Junction, Including Fatigue. Vol 2. 1st ed. Amsterdam: Elsevier; 2003. 271. Bauer JA, Murray RD. Electromyographic pa tterns of individuals suffering from lateral tennis elbow. J Electromyogr Kinesiol. Aug 1999;9(4):245-252. 272. Shechtman O. Upper extremity muscul oskeletal disorders: Electrodiagnosis. Gainesville: University of Florid a; 2003:6-8, RSD 6930, Musculoskeletal disorders of upper extremity, lecture notes. 273. Riley NA, Bilodeau M. Changes in uppe r limb joint torque patterns and EMG signals with fatigue following a stroke. Disabil Rehabil. Dec 15 2002;24(18):961969. 274. Matre DA, Sinkjaer T, Svensson P, Arendt -Nielsen L. Experimental muscle pain increases the human stretch reflex. Pain. Apr 1998;75(2-3):331-339. 275. Kang YM, Wheeler JD, Pickar JG. Stimulation of chemosensitive afferents from multifidus muscle does not sensitize multifidus muscle spindles to vertebral loads in the lumbar spine of the cat. Spine. Jul 15 2001;26(14):1528-1536. 276. Svensson P, Graven-Nielsen T, Matre D, Arendt-Nielsen L. Experimental muscle pain does not cause long-lasting increase s in resting electr omyographic activity. Muscle Nerve. Nov 1998;21(11):1382-1389. 277. Djupsjobacka M, Johansson H, Bergenheim M. Influences on the gamma-musclespindle system from muscle afferents stimulated by increased intramuscular concentrations of arachidonic acid. Brain Res. Nov 14 1994;663(2):293-302.

PAGE 265

250 278. Ljubisavljevic M, Jovanovic K, Anastasi jevic R. Changes in discharge rate of fusimotor neurones provoked by fatiguing contractions of cat triceps surae muscles. J Physiol. Jan 1992;445:499-513. 279. Pedersen J, Sjolander P, Wenngren BI Johansson H. Increased intramuscular concentration of bradykinin increases the st atic fusimotor drive to muscle spindles in neck muscles of the cat. Pain. Mar 1997;70(1):83-91. 280. Pedersen J, Ljubisavljevic M, Berge nheim M, Johansson H. Alterations in information transmission in ensembles of primary muscle spindle afferents after muscle fatigue in heteronymous muscle. Neuroscience. Jun 1998;84(3):953-959. 281. Haeri M, Asemani D, Gharibzadeh S. M odeling of pain using artificial neural networks. Journal of Theoretical Biology. FEB 7 2003;220(3):277-284. 282. Sweet WH. Pain. In: Field J, Magoun HW, Hall WE, eds. Handbook of Physiology 1st ed. Washington, D. C.: Amer. Physiol. Sec.; 1959:459-506. 283. Melzack R, Wall PD. Pain mechanisms: A new theory. Science. 1965;150(3699):971-979. 284. Melzack R, Katz J. Pain measurement in persons in pain. In: Wall PD, Melzack R, eds. Textbook of Pain 4th ed. New York: Churchill Livingstone; 1999:409426. 285. Chapman CR. The affective dimension of pain: A model. In: Bromm B, Desmedt JE, eds. Pain and the brain: From nociception to cognition. Vol 22. New York: Raven Press; 1995:283-301. 286. Merskey H, Bogduk N, eds. IASP Task Force on Taxonomy. Classification of chronic pain. 2nd ed. Seattle, WA: IASP Press; 1994. 287. Melzack R. The perception of pain. Scientific American. 1961;204(2):41-49. 288. Sherrington CS. The integrative action of the nervous system New Haven: Yale University Press; 1906. 289. Jensen MP, Karoly P. Self-report scales and procedures for assessing pain in adults. In: Turk DC, Melzack R, eds. Handbook of pain assessment 2nd ed. New York: The Guilford Press; 2001:15-34. 290. Craig KD. Emotions and psychobiology. In: Wall PD, Melzack R, eds. Textbook of Pain 4th ed. New York: Church ill Livingstone; 1999:331-343. 291. Schiefenhovel W. Perception, expression, a nd social function of pain: a human ethological view. Sci Context. Spring 1995;8(1):31-46.

PAGE 266

251 292. Robinson AJ. Central nervous system pa thways for pain transmission an pain control: Issues relevant to the practicing clinician. Journal of Hand Therapy. 1997;10(2):64-77. 293. Lundy-Ekman L. Somatosensory System. In: Lundy-Ekman L, ed. Neuroscience. Fundamentals for Rehabilitation 2nd ed. Philadelphia: W.B. Saunders Company; 2002:99-122. 294. Covington EC. The biological basis of pain. International Review of Psychiatry. MAY 2000;12(2):128-147. 295. Apkarian AV, Bushnell MC, Treede RD, Zubieta JK. Human brain mechanisms of pain perception and regulat ion in health and disease. Eur J Pain. Aug 2005;9(4):463-484. 296. Lundy-Ekman L. Somatosensation: Clin ical Applications. In: Lundy-Ekman L, ed. Neuroscience. Fundamentals for Rehabilitation 2nd ed. Philadelphia: W.B. Saunders Company; 2002:123-152. 297. Mendell LM, Wall PD. Responses of Si ngle Dorsal Cord Cells to Peripheral Cutaneous Unmyelinated Fibres. Nature. Apr 3 1965;206:97-99. 298. Willis WD. Role of neurotransmitters in sensitization of pain responses. Ann N Y Acad Sci. Mar 2001;933:142-156. 299. Hardy JD, Woolf HG, Goodell H. Pain sensations and reactions New York: Hafner Pub; 1967. 300. Fields HL, Basbaum AI. Central nervous system mechanisms of pain modulation. In: Wall PD, Melzack R, eds. Textbook of Pain 3rd ed. New York: Churchill Livingstone; 1994:243-257. 301. Coffield JA, Bowen KK, Miletic V. Retr ograde tracing of projections between the nucleus submedius, the ventrolateral orbi tal cortex, and the midbrain in the rat. J Comp Neurol. Jul 15 1992;321(3):488-499. 302. Aronson PA. Pain theories -A review fo r application in athletic training and therapy. Athletic Therapy Today. 2002;7(4):8-13. 303. Loeser JD, Melzack R. Pain: an overview. The Lancet. 1999;353(1607-1609). 304. Turk DC, Melzack R. The measurement of pain and the assessment of people experiencing. In: Turk DC, Melzack R, eds. Handbook of pain assessment 2nd ed. New York: The Guilford Press; 2001:3-11.

PAGE 267

252 305. Reed KL. Quick reference to occupational therapy Gaithersburg: Aspen Publishers; 1991. 306. Melzack R. From the gate to the neuromatrix. Pain. 1999;Supplement 6:S1210S1126. 307. Dionne RA, Bartoshuk L, Mogil J, Witter J. Individual responder analyses for pain: does one pain scale fit all? Trends Pharmacol Sci. Mar 2005;26(3):125-130. 308. Gracely RH, McGrath P, Dubner R. Ratio sc ales of sensory and affective verbal pain descriptors. Pain. 1978;5:5-18. 309. Jensen MP, Karoly P, O'Riordan EF, Bl and F, Jr., Burns RS. The subjective experience of acute pain: An assessment of the utility of 10 indices. Clinical Journal of Pain. 1989;5:153-159. 310. Jensen MP, Karoly P, Harris P. Asse ssing the affective co mponent of chronic pain: development of the Pain Discomfort Scale. J Psychosom Res. 1991;35(23):149-154. 311. Melzack R, Casey KL. Sensory, motivationa l, and central control determinants of pain: A new conceptual model. In: Kenshalo D, ed. The skin senses Springfield, IL: Charles C Thomas; 1968:423-443. 312. Jensen MP, Dworkin RH, Gammaitoni AR, Olaleye DO, Oleka N, Galer BS. Assessment of pain quality in chronic ne uropathic and nociceptive pain clinical trials with the Neuropathic Pain Scale. J Pain. Feb 2005;6(2):98-106. 313. Ong KS, Seymour RA. Pain measurement in humans. Surgeon. Feb 2004;2(1):1527. 314. Durain D. Primary dysmenorrhea: assessment and management update. Journal of Midwifery & Womens Health. NOV-DEC 2004;49(6):520-528. 315. Dubuisson D, Melzack R. Classification of clinical pain descriptions by multiple group discriminant analysis. Exp Neurol. May 1976;51(2):480-487. 316. Galer BS, Sheldon E, Patel N, Codding C, Burch F, Garnmaitoni AR. Topical lidocaine patch 5% may target a novel underlying pain mechanism in osteoarthritis. Current Medical Research and Opinion. SEP 2004;20(9):14551458. 317. Ehde DM, Jensen MP, Engel JM, Turner JA, Hoffman AJ, Cardenas DD. Chronic pain secondary to disability: A review. Clinical Journal of Pain. JAN-FEB 2003;19(1):3-17.

PAGE 268

253 318. Benrud-Larson LM, Wegener ST. Chr onic pain in neurorehabilitation populations: Prevalence, severity and impact. NeuroRehabilitation. 2000;14(3):127-137. 319. Dudgeon BJ, Ehde DM, Cardenas DD, Engel JM, Hoffman AJ, Jensen MP. Describing pain with physical disability: narrative interviews and the McGill Pain Questionnaire. Arch Phys Med Rehabil. Jan 2005;86(1):109-115. 320. Beattie PF, Dowda M, Feuerstein M. Differentiating sensory and affectivesensory pain descriptions in patients undergoing magnetic resonance imaging for persistent low back pain. Pain. Jul 2004;110(1-2):189-196. 321. Jensen MP, Karoly P, Braver S. The m easurement of clinical pain intensity: a comparison of six methods. Pain. Oct 1986;27(1):117-126. 322. Butler PV. Linear analogue self-assessment and procrustean measurement: A critical review of visual anal ogue scaling in pain assessment. Journal of Clinical Psychology in Medical Settings. MAR 1997;4(1):111-129. 323. Coll AM, Ameen JR, Mead D. Postoperative pain assessment tools in day surgery: literature review. J Adv Nurs. Apr 2004;46(2):124-133. 324. McCormack HM, Horne DJ, Sheather S. Clin ical applications of visual analogue scales: a critical review. Psychol Med. Nov 1988;18(4):1007-1019. 325. Huskisson EC. Measurement of pain. Lancet. Nov 9 1974;2(7889):1127-1131. 326. Scott J, Huskisson EC. Graphic representation of pain. Pain. Jun 1976;2(2):175184. 327. Melzack R. The short-form McGill Pain Questionnaire. Pain. Aug 1987;30(2):191-197. 328. Price DD, McGrath PA, Rafii A, Buck ingham B. The validation of visual analogue scales as ratio scale measures for chronic and experimental pain. Pain. Sep 1983;17(1):45-56. 329. Stephenson NL, Herman JA. Pain meas urement: a comparison using horizontal and vertical visual analogue scales. Appl Nurs Res. Aug 2000;13(3):157-158. 330. Cook AJ, Roberts DA, Henderson MD, Van Winkle LC, Chastain DC, HamillRuth RJ. Electronic pain questionnaire s: a randomized, crossover comparison with paper questionnaires for chronic pain assessment. Pain. Jul 2004;110(12):310-317. 331. Stevens SS. On the psychophysical law. Psychol Rev. May 1957;64(3):153-181.

PAGE 269

254 332. Jones LA. Perception of force a nd weight: theory and research. Psychol Bull. Jul 1986;100(1):29-42. 333. Eisler H. Subjective scale of force for a large muscle group. J Exp Psychol. Sep 1962;64:253-257. 334. Eisler H. The Ceiling of Psychophysical Power Functions. Am J Psychol. Sep 1965;78:506-509. 335. Stevens JC, Cain WS. Effort in Isometri c Muscular Contractions Related to Force Level and Duration. Perception & Psychophysics. 1970;8(4):240-&. 336. Stevens JC, Mack JD. Scal es of Apparent Force. Journal of Experimental Psychology. 1959;58(5):405-413. 337. Cain WS, Stevens JC. Effort in sust ained and phasic handgrip contractions. Am J Psychol. Mar 1971;84(1):52-65. 338. Borg G. Perceived exertion as an indicator of somatic stress. Scand J Rehabil Med. 1970;2(2):92-98. 339. Borg GA. Psychophysical bases of perceived exertion. Med Sci Sports Exerc. 1982;14(5):377-381. 340. Spielholz P. Calibrating Borg s cale ratings of hand force exertion. Appl Ergon. Sep 2006;37(5):615-618. 341. Borg E, Kaijser L. A comparison betw een three rating scales for perceived exertion and two different work tests. Scand J Med Sci Sports. Feb 2006;16(1):5769. 342. Price DD. Psychological and neural mechanisms of pain New York: Raven Press; 1988. 343. Willis WD. The pain system. The neuronal basis of nociceptive transmission in the mammalian nervous system Basel: Karger; 1985. 344. Christensen BN, Perl ER. Spinal ne urons specifically ex cited by noxious or thermal stimuli: marginal zone of the dorsal horn. J Neurophysiol. Mar 1970;33(2):293-307. 345. Han ZS, Zhang ET, Craig AD. Nociceptive and thermoreceptive lamina I neurons are anatomically distinct. Nat Neurosci. Jul 1998;1(3):218-225.

PAGE 270

255 346. Craig AD, Krout K, Andrew D. Quan titative response characteristics of thermoreceptive and nociceptive lamina I spinothalamic neurons in the cat. J Neurophysiol. Sep 2001;86(3):1459-1480. 347. Flaherty SA. Pain measurement tool s for clinical practice and research. Aana J. Apr 1996;64(2):133-140. 348. Morley S, Pallin V. Scaling the a ffective domain of pain: a study of the dimensionality of verbal descriptors. Pain. Jul 1995;62(1):39-49. 349. Morley S, Hassard A. The developmen t of a self-adminis tered psychophysical scaling method: internal consistency a nd temporal stability in chronic pain patients. Pain. Apr 1989;37(1):33-39. 350. Heft MW, Gracely RH, Dubner R, McGr ath PA. A validation model for verbal description scaling of human clinical pain. Pain. Dec 1980;9(3):363-373. 351. Jamner LD, Tursky B. Syndrome-sp ecific descriptor profiling: a psychophysiological and ps ychophysical approach. Health Psychol. 1987;6(5):417-430. 352. Macfarlane TV, Blinkhorn AS, Craven R, et al. Can one predict the likely specific orofacial pain syndrome from a self-completed questionnaire? Pain. Oct 2004;111(3):270-277. 353. Campbell TS, Hughes JW, Girdler SS, Ma ixner W, Sherwood A. Relationship of ethnicity, gender, and ambulatory blood pre ssure to pain sensitivity: effects of individualized pain rating scales. J Pain. Apr 2004;5(3):183-191. 354. Myles PS. The pain visual an alog scale: linear or nonlinear? Anesthesiology. Mar 2004;100(3):744; author reply 745. 355. Myles PS, Troedel S, Boquest M, Reeves M. The pain visual analog scale: is it linear or nonlinear? Anesth Analg. Dec 1999;89(6):1517-1520. 356. Downie WW, Leatham PA, Rhind VM, Wright V, Branco JA, Anderson JA. Studies with pain rating scales. Annals of Rheumatic Diseases. 1978;37:378-381. 357. Price DD, Bush FM, Long S, Harkins SW. A comparison of pain measurement characteristics or mechanical visual anal ogue and simple numerical rating scales. Pain. 1994;217-226. 358. Robertson DGE, Caldwell GE, Hamill J, Kamen G, Whittlesey SN, eds. Research Methods in Biomechanics. 1st ed. Champaign: Human Kinetics; 2004.

PAGE 271

256 359. Hagg GM, Milerad E. Forearm extensor and flexor muscle exertion during simulated gripping work -an electromyographic study. Clin Biomech (Bristol, Avon). Jan 1997;12(1):39-43. 360. Chu-Andrews J, Johnson RJ. Electrodiagnosis: An anat omical and clinical approach Philadelphia: Lippincott; 1986. 361. Thought Technology Ltd. FlexComp Infi niti Hardware Manual. Montreal: Thought Technology Ltd.; 2006:50. 362. Trossman PB, Li PW. The Effect of the Duration of Intertri al Rest Periods on Isometric Grip Strength Pe rformance in Young-Adults. Occupational Therapy Journal of Research. NOV-DEC 1989;9(6):362-378. 363. Stull GA, Clarke DH. Patterns of rec overy following isometric and isotonic strength decrement. Med Sci Sports. Fall 1971;3(3):135-139. 364. Sahlin K. Metabolic changes limiting muscle performance. In: Saltin B, ed. Biochemistry of exercise VI Champaign, IL: Human Kinetics; 1986:323-343. 365. Maxwell SE, Delaney HD. Designing experiments and analyzing data: A model comparison perspective Mahwah, NJ: Lawrence Erlbaum Associates, Publishers; 2000. 366. SPSS for Windows [computer program]. Vers ion Rel. 15.0.1.1. Chicago; 2007. 367. Shrout PE, Fleiss JL. Intraclass Correlations Uses in Assessing Rater Reliability. Psychological Bulletin. 1979;86(2):420-428. 368. Semmler JG, Enoka RM. Neural contributi ons to changes in muscle strength. In: Zatsiorsky V, ed. Biomechanics in Sport London: Blackwell Scientific Ltd.; 2001. 369. Schwid SR, Thornton CA, Pandya S, et al. Quantitative asse ssment of motor fatigue and strength in MS. Neurology. Sep 11 1999;53(4):743-750. 370. Sanjak M, Brinkmann J, Belden DS, et al. Quantitative assessment of motor fatigue in amyotrophic lateral sclerosis. J Neurol Sci. Oct 15 2001;191(1-2):5559. 371. Nicklin J, Karni Y, Wiles CM. Shoulder abduction fatiguability. J Neurol Neurosurg Psychiatry. Apr 1987;50(4):423-427. 372. Moore JS. Biomechanical models for the pathogenesis of specific distal upper extremity disorders. Am J Ind Med. May 2002;41(5):353-369.

PAGE 272

257 373. Sokk J, Gapeyeva H, Ereline J, Kolts I, Paasuke M. Shoulder muscle strength and fatigability in patients with frozen shoulder syndrome: the effect of 4-week individualized rehabilitation. Electromyogr Clin Neurophysiol. Jul 2007;47(45):205-213. 374. Backman E, Johansson V, Hager B, Sjobl om P, Henriksson KG. Isometric muscle strength and muscular endurance in no rmal persons aged between 17 and 70 years. Scand J Rehabil Med. Jun 1995;27(2):109-117. 375. Laubach LL. Comparative muscular streng th of men and women: a review of the literature. Aviat Space Environ Med. May 1976;47(5):534-542. 376. Miller AE, MacDougall JD, Tarnopolsky MA, Sale DG. Gender differences in strength and muscle fiber characteristics. Eur J Appl Physiol Occup Physiol. 1993;66(3):254-262. 377. Hakkinen K, Pakarinen A. Muscle strengt h and serum testosterone, cortisol and SHBG concentrations in middleaged and elderly men and women. Acta Physiol Scand. Jun 1993;148(2):199-207. 378. Yamaji S, Demura S, Nakada M. Sex di fferences and propertie s of the decreasing force during sustained static gr ip at various target forces. Percept Mot Skills. Aug 2006;103(1):29-39. 379. Bystrom S, Fransson-Hall C. Acceptability of intermittent handgrip contractions based on physiological response. Hum Factors. Mar 1994;36(1):158-171. 380. Kilbom A, Makarainen M, Sperling L, Ka defors R, Liedberg L. Tool design, user characteristics and performance: a case study on plate-shears. Appl Ergon. Jun 1993;24(3):221-230. 381. Bystrom SE, Kilbom A. Physiological re sponse in the forearm during and after isometric intermittent handgrip. Eur J Appl Physiol Occup Physiol. 1990;60(6):457-466. 382. Bystrom SE, Mathiassen SE, FranssonHall C. Physiological effects of micropauses in isometric handgrip exercise. Eur J Appl Physiol Occup Physiol. 1991;63(6):405-411. 383. Snijders CJ, Volkers AC, Mechelse K, Vleeming A. Provocation of epicondylalgia lateralis (tennis el bow) by power grip or pinching. Med Sci Sports Exerc. Oct 1987;19(5):518-523. 384. Mogk JP, Keir PJ. The effects of pos ture on forearm muscle loading during gripping. Ergonomics. Jul 15 2003;46(9):956-975.

PAGE 273

258 385. De Serres SJ, Milner TE. Wrist muscle act ivation patterns and stiffness associated with stable and unstable mechanical loads. Exp Brain Res. 1991;86(2):451-458. 386. Kupa EJ, Roy SH, Kandarian SC, De Luca CJ. Effects of musc le fiber type and size on EMG median frequenc y and conduction velocity. J Appl Physiol. Jul 1995;79(1):23-32. 387. Krivickas LS, Taylor A, Maniar RM, Masc ha E, Reisman SS. Is spectral analysis of the surface electromyographic signal a c linically useful tool for evaluation of skeletal muscle fatigue? J Clin Neurophysiol. Mar 1998;15(2):138-145. 388. Fuglsang-Frederiksen A, Ronager J. EMG power spectrum, turns-amplitude analysis and motor unit potential du ration in neuromuscular disorders. J Neurol Sci. Jun 1990;97(1):81-91. 389. Ronager J, Christensen H, Fuglsang-Fred eriksen A. Power spectrum analysis of the EMG pattern in normal and diseased muscles. J Neurol Sci. Dec 1989;94(13):283-294. 390. Rossi B, Siciliano G, Carboncini MC, et al. Muscle modifications in Parkinson's disease: myoelectric manifestations. Electroencephalogr Clin Neurophysiol. Jun 1996;101(3):211-218. 391. Buonocore M, Opasich C, Casale R. Earl y development of EMG localized muscle fatigue in hand muscles of patient s with chronic heart failure. Arch Phys Med Rehabil. Jan 1998;79(1):41-45. 392. Casale R, Buonocore M, Di Massa A, Setacci C. Electromyographic signal frequency analysis in evalua ting muscle fatigue of patient s with peripheral arterial disease. Arch Phys Med Rehabil. Oct 1994;75(10):1118-1121. 393. Falla D, Rainoldi A, Merletti R, Jull G. Myoelectric manifestations of sternocleidomastoid and anterior scalene muscle fatigue in chronic neck pain patients. Clin Neurophysiol. Mar 2003;114(3):488-495. 394. Hunter SK, Enoka RM. Sex differences in the fatigability of arm muscles depends on absolute force during isometric contractions. J Appl Physiol. Dec 2001;91(6):2686-2694.

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259 BIOGRAPHICAL SKETCH Bhagwant Singh Sindhu was born on July 9, 197 6 in New Delhi, India. He grew up in New Delhi, graduating from Springdales Public School in 1994. He earned his B.Sc. (H) in Occupational Therapy fr om University of Delhi 1998 a nd his M.S. in Occupational Therapy from the University of Wisconsin-Milwaukee in 2002.