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Habitual Ambulatory Activity Measurement Post-stroke

Permanent Link: http://ufdc.ufl.edu/UFE0024289/00001

Material Information

Title: Habitual Ambulatory Activity Measurement Post-stroke
Physical Description: 1 online resource (231 p.)
Language: english
Creator: Barak, Sharon
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009

Subjects

Subjects / Keywords: accelerometer, ambulation, gait, measurement, recovery, stroke
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: This study had four main objectives: (1) to assess the rate of and predictors for inferred compliance with lower extremity Step Activity Monitor (SAM) protocol, (2) to assess the relationship between gait speed and SAM derived home and community ambulation, (3) to examine the association of gait speed and SAM indices to mobility and social participation domains of the Stroke Impact Scale (SIS), and (4) to identify predictors of SAM derived home and community ambulation. Data in this study were derived from the Locomotor Experience Applied Post-Stroke (LEAPS) trail. Study participants consisted of individuals two-month post-stroke with moderate to severe gait impairment. The results indicated high compliance rates in the first and second measurement days (72 and 66%, respectively). However, only 54% of the participants complied with the SAM protocol in both measurement days. Older age, less severe balance control deficits, and moderate to high balance self-efficacy, predicted increased compliance. In addition, self-selected walking speed significantly correlated with step activity volume, most activity intensity indices, and one activity pattern outcome. However, balance control, walking endurance, lower extremity motor control, and balance self-efficacy distorted the association between walking speed and SAM outcomes. In addition, gait speed and certain SAM indices significantly correlated with both SIS mobility and participation. Surprisingly, gait speed association with SIS was stronger than the association of numerous SAM indices with SIS. Balance control, walking endurance, executive function, stroke location, age, balance self-efficacy, lower extremity motor control, and gait speed were all identified as home and community ambulation predictors. However, gait speed did not predict activity volume or important activity pattern outcomes such as activity randomness. Moreover, this model explained only up to 50% of the variation in SAM derived step activity. Therefore, other factors such as caregiver support may also contribute to home and community ambulation. In conclusion, walking in the real world is highly complex. Furthermore, for individuals at two months post-stroke, usual gait-speed does not associate with all the various aspects of home and community ambulation. However, the association of gait speed with home and community ambulation may be affected by the time since stroke.
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 Sharon Barak.
Thesis: Thesis (Ph.D.)--University of Florida, 2009.
Local: Adviser: Behrman, Andrea L.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2011-05-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0024289:00001

Permanent Link: http://ufdc.ufl.edu/UFE0024289/00001

Material Information

Title: Habitual Ambulatory Activity Measurement Post-stroke
Physical Description: 1 online resource (231 p.)
Language: english
Creator: Barak, Sharon
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009

Subjects

Subjects / Keywords: accelerometer, ambulation, gait, measurement, recovery, stroke
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: This study had four main objectives: (1) to assess the rate of and predictors for inferred compliance with lower extremity Step Activity Monitor (SAM) protocol, (2) to assess the relationship between gait speed and SAM derived home and community ambulation, (3) to examine the association of gait speed and SAM indices to mobility and social participation domains of the Stroke Impact Scale (SIS), and (4) to identify predictors of SAM derived home and community ambulation. Data in this study were derived from the Locomotor Experience Applied Post-Stroke (LEAPS) trail. Study participants consisted of individuals two-month post-stroke with moderate to severe gait impairment. The results indicated high compliance rates in the first and second measurement days (72 and 66%, respectively). However, only 54% of the participants complied with the SAM protocol in both measurement days. Older age, less severe balance control deficits, and moderate to high balance self-efficacy, predicted increased compliance. In addition, self-selected walking speed significantly correlated with step activity volume, most activity intensity indices, and one activity pattern outcome. However, balance control, walking endurance, lower extremity motor control, and balance self-efficacy distorted the association between walking speed and SAM outcomes. In addition, gait speed and certain SAM indices significantly correlated with both SIS mobility and participation. Surprisingly, gait speed association with SIS was stronger than the association of numerous SAM indices with SIS. Balance control, walking endurance, executive function, stroke location, age, balance self-efficacy, lower extremity motor control, and gait speed were all identified as home and community ambulation predictors. However, gait speed did not predict activity volume or important activity pattern outcomes such as activity randomness. Moreover, this model explained only up to 50% of the variation in SAM derived step activity. Therefore, other factors such as caregiver support may also contribute to home and community ambulation. In conclusion, walking in the real world is highly complex. Furthermore, for individuals at two months post-stroke, usual gait-speed does not associate with all the various aspects of home and community ambulation. However, the association of gait speed with home and community ambulation may be affected by the time since stroke.
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 Sharon Barak.
Thesis: Thesis (Ph.D.)--University of Florida, 2009.
Local: Adviser: Behrman, Andrea L.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2011-05-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0024289:00001


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1 HABITUAL AMBULATORY ACTIVITY MEASUREMENT POST -STROKE By SHARON BARAK A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHI LOSOPHY UNIVERSITY OF FLORIDA 2009

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2 2009 Sharon Barak

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3 To my family, especially my parents, who nurtured my intellectual curiosity, academic interests, and instilled in me the inspiration to set high goal s and the confidence to achieve them throughout my lifetime t hank you for making this milestone possible

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4 TABLE OF CONTENTS page LIST OF TABLES .............................................................................................................................. 1 0 LIST OF FIGURES ............................................................................................................................ 12 LIST OF ABBREVIATIONS ............................................................................................................ 13 ABSTRACT ........................................................................................................................................ 14 CHAPTER 1 INT RODUCTION ....................................................................................................................... 16 1.1 Overview of the Research Topic ...................................................................................... 16 1.2 Home and Community Ambulation Measurement Current Research Needs ................ 19 1.2.1 Inferred Compliance with a Step Activity Monitor Protocol to Measure Free Physical Activity in Moderate to Severe Sub -Acute Stroke Survivors .... 19 1.2.2 Clinical Based Gait Speed in Persons with Sub -Acute Stroke: Relationship to Home and Community Ambulation Activity ................................................. 20 1.2.3 Ambulatory Activity during the Sub -Acute Phas e of Stroke Recovery: Relationship to Impairment Level, Activity Limitations, and Personal Factors ................................................................................................................... 21 2 REVIEW OF THE LITERATURE ............................................................................................ 22 2.1 Stroke Definition, Epidemiology, and Sequelae ............................................................. 22 2.1.1 Definition .............................................................................................................. 22 2.1.2 Epidemiology ........................................................................................................ 22 2.1.3 Sequelae ................................................................................................................. 23 2.1.3.1 Balance post -stroke ................................................................................ 24 2.1.3.2 Skeletal muscle tissue alterations p ost -stroke ...................................... 25 2.1.3.3 Cardio respiratory system post -stroke .................................................. 25 2.2 Underlying Dimensions of Habitual Ambulation and Habitual Amb ulation in the Post -Stroke Population ...................................................................................................... 26 2.2.1 Community AmbulationDefinition .................................................................... 26 2.2.2 Major Determinants of Community Ambulati on Function Post -Stroke ........... 27 2.2.2.1 Motor control ......................................................................................... 27 2.2.2.2 Balance ................................................................................................... 27 2.2.2.3 Endurance ............................................................................................... 28 2.2.2.4 Gait speed ............................................................................................... 29 2.2.2.5 Stairs management ................................................................................. 31 2.2.2.6 Cognitive flexibility ............................................................................... 31 2.2.2.7 Balance self -efficacy ............................................................................. 32 2.2.3 Post -Stroke Community Ambulation Status ....................................................... 32

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5 2.3 The International Classification of Functioning, Disability and Health as a Conceptual Framework for Studying Home and Community Ambulation ................... 34 2.3.1 Conceptual Model Part A -Community Ambulation Barriers and Facilitators ............................................................................................................. 34 2.3.1.1 Health condition ..................................................................................... 34 2.3.1.2 Body functions and structures ............................................................... 35 2.3.1.3 Activities ................................................................................................ 35 2.3.1.4 Participation ........................................................................................... 36 2.3.1.5 Personal factors ...................................................................................... 36 2.3.1.6 Environmental factors ........................................................................... 36 2.3.2 Conceptual Model Part B Community Am bulation Descriptors ....................... 37 2.3.2.1 Community ambulation volume ........................................................... 37 2.3.2.2 Community ambulation intensity ......................................................... 37 2.3.2.3 Community ambulation pattern ............................................................ 37 2.4 Post -Stroke Commonly Used Activity, Participation, and Quality of Life Community Ambulation Outcomes ................................................................................. 38 2.4.1 Post -Stroke Activity Level Mobility Outcomes ................................................. 38 2.4.1.1 Gait speed ............................................................................................... 38 2.4.1.2 Six -minute walk test .............................................................................. 41 2.4.1.3 Timed Up and Go .................................................................................. 44 2.4.1.4 Functional Ambulation Classification .................................................. 44 2.4.1.5 Rivermead Mobility Index .................................................................... 45 2.4.1.6 Barthel Index .......................................................................................... 46 2.4.1.7 Functional Independence Measure ....................................................... 46 2.4.1.8 Frenchay Activities Index ..................................................................... 47 2.4.2 Post -Stroke Participation and Quality of Life Mobility Ou tcomes ................... 48 2.4.2.1 Stroke Impact Scale ............................................................................... 48 2.4.2.2 Medical Outcomes Study Short Form 36............................................. 49 2.4.2.3 EuroQol Quality of Life Scale .............................................................. 50 2.5 Direct Objective Measurement of Home and Community Ambulation ........................ 52 2.5.1 Doubly Labeled Water Method ........................................................................... 52 2.5.2 Pedometers ............................................................................................................ 52 2.5.3 Accelerometers ..................................................................................................... 53 2.6 Step Activity Monitor as a Home and Community Ambulation Outcome Measure Post -Stroke ......................................................................................................................... 54 2.6.1 Psychometric Properties in Persons Post -Stroke ................................................ 54 2.6.2 Protocol Compliance ............................................................................................ 56 2.6.2.1 Identification of inferred accelerometer wearing period ..................... 56 2.6.2.2 Identification of minimal wear requirement for a valid day ............... 57 2.6.2.3 Factors associated with accelerometer monitoring protocol compliance ............................................................................................. 58 2.6.2.4 Levels of compliance rates .................................................................... 61 2.6.3 Primary Aim Number One ................................................................................... 62 2.6.3.1 Specific Aim 1 ....................................................................................... 62 2.6.3.2 Hypothesis 1 ........................................................................................... 62 2.6.4 Identifying Spurious Data .................................................................................... 63

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6 2.6 .5 Data Outcomes ...................................................................................................... 64 2.6.5.1 Ambulation volume outcomes .............................................................. 64 2.6.5.2 Ambulation intensity outcomes ............................................................ 64 2.6.5.3 Ambulation pattern outcomes ............................................................... 65 2.6.6 Daily Values .......................................................................................................... 66 2.6.6.1 Ambulation volume ............................................................................... 66 2.6.6.2 Ambulation intensity ............................................................................. 67 2.6.6.3 Ambulation pattern ................................................................................ 68 2.6.7 Analysis of Between Day Patterns of Activity Differences ............................... 69 2.6.8 Relationships of Step Activity Monitor Volume, Intensity, and Pattern Indices with Impairment and Activity Measures ................................................ 69 2.6.8.1 Ambulation volume ............................................................................... 69 2.6.8.2 Ambulation intensity ............................................................................. 71 2.6.8.3 A mbulation pattern ................................................................................ 72 2.6.9 Primary Aim Number Two .................................................................................. 77 2.6.9.1 Specific Aim 2a ..................................................................................... 77 2.6.9.2 Specific Aim 2b ..................................................................................... 77 2.6.9.3 Hypothesis 2a ......................................................................................... 77 2.6.9.4 Hypothesis 2b ......................................................................................... 78 2.6.9.5 Hypothesis 2c ......................................................................................... 78 2.6.10 Primary Aim Number Three ................................................................................ 78 2.6.10.1 Specific aim 3 ......................................................................................... 78 2.6.10.2 Hypothesis 3a ......................................................................................... 79 2.6.10.3 Hypothesis 3b ......................................................................................... 79 2.6.10.4 Hypothesis 3c ......................................................................................... 79 2.6.10.5 Hypothesis 3d ......................................................................................... 79 2.7 Locomotor Experience Applied Post Stroke (LEAPS) Trial ......................................... 80 2.7.1 Objective ............................................................................................................... 80 2.7.2 Inclusion and Exclusion Criteria .......................................................................... 80 2.7.2.1 Stroke definition .................................................................................... 80 2.7.2.2 Inclusion criteria .................................................................................... 80 2.7.2.3 Exclusion criteria ................................................................................... 81 2.7.3 Assessment s .......................................................................................................... 81 3 INFERRED COMPLIANCE WITH A STEP ACTIVITY MONITOR PROTOCOL TO MEASURE FREE PHYSICAL ACTIVITY IN MODERATE TO SEVERE SUB ACUTE STROKE SURVIVORS .............................................................................................. 89 3.1 Introduction ....................................................................................................................... 89 3.2 Methods ............................................................................................................................. 92 3.2.1 Design .................................................................................................................... 92 3.2.2 Testing Procedures and Measurements ............................................................... 93 3.2.2.1 Ambulatory activity ............................................................................... 93 3.2.2.2 Accelerometer monitoring inferred compliance predictors ................ 94 3.2.3 Data Analysis ................................ ........................................................................ 97

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7 3.3 Results ................................................................................................................................ 98 3 .3.1 Study Population ................................................................................................... 98 3.3.2 Compliance Rates ................................................................................................. 98 3.3.3 Compliance Predictors ......................................................................................... 99 3.4 Discussion .......................................................................................................................... 99 3.4.1 Compliance Rates ................................................................................................. 99 3.4.2 Compliance Predictors ....................................................................................... 100 3.4.3 Study Limitations ................................................................................................ 102 3.5 Conclusion ....................................................................................................................... 103 4 CLINICAL BASED GAIT SPEED IN PERSONS WITH SUB -ACUTE STROKE: RELATIONSHIP TO HOME AND COMMUNITY AMBULATION ACTIVITY ........... 109 4.1 Introduction ..................................................................................................................... 109 4.2 Methods ........................................................................................................................... 113 4.2.1 Design .................................................................................................................. 113 4.2.2 Testing Procedures and Measurements ............................................................. 114 4.2.2.1 Ambulatory act ivity ............................................................................. 1 14 4.2.2.2 Mobility impairment ............................................................................ 117 4.2.2.3 Mobility and social participation ........................................................ 117 4.2.2.4 Potential covariates .............................................................................. 118 4.2.3 Data Analysis ...................................................................................................... 119 4.3 Results .............................................................................................................................. 120 4.3.1 Sample Characteristics ....................................................................................... 120 4.3.2 Daily Ambulatory Activity of Household and Limited Community Ambulators .......................................................................................................... 121 4.3.2.1 First measurement day all patients ..................................................... 121 4.3.2.2 First measurement day compliers only .............................................. 121 4.3.3 Correl ations between Habitual Gait Speed and Step Activity Monitor Indices .................................................................................................................. 121 4.3.3.1 First measurement day all patients ..................................................... 121 4.3.3.2 First measurement day compliers only .............................................. 122 4.3.4 Confounders to the Association between Gait Speed and Step Activity Monitor Outcomes .............................................................................................. 122 4.3.4.1 First measurement day all patients ..................................................... 122 4.3.4.2 Fist measurement day -compliers only ................................................ 123 4.3.5 Correlations of Ste p Activity Monitor Outcomes and Habitual Gait Speed with Stroke Impact Scale Mobility and Participation Domains ...................... 124 4.3.5.1 First measurement day all patients ..................................................... 124 4.3.5.2 First measurement day compliers only .............................................. 125 4.3.6 Differences between step activity monitor and gait speed strength of associations with Stroke Impact S cale .............................................................. 125 4.4 Discussion ........................................................................................................................ 126 4.4.1 Differences between All Patients and Compliers Only Analyses ................... 126 4.4.2 Daily Ambulatory Activity of Household and Limited Community Ambulators .......................................................................................................... 126

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8 4.4.3 Confounders to the Association between Gait Speed and Step Activity M onitor Outcomes .............................................................................................. 128 4.4.4 Correlations between Step Activity Monitor Outcomes and Habitual Gait Speed to Stroke Impact Scale Mobility and Participation Domains ............... 129 4.4.5 Differences between Step Activity Monitor and Gait Speed Strength of Associations with Stroke Impact Scale Mobility and Participation ................ 130 4.4.6 Study Limitations ................................................................................................ 131 4.5 Conclusion ....................................................................................................................... 131 5 AMBULATORY ACTIVITY DURING THE SUB -ACUTE PHASE OF STROKE RECOVERY: RELATIONSHIP TO IMPAIRME NT LEVEL, ACTIVITY LIMITATIONS, AND PERSONAL FACTORS .................................................................... 143 5.1 Introduction ..................................................................................................................... 143 5.2 Methods ........................................................................................................................... 145 5.2.1 Design .................................................................................................................. 145 5.2.2 Testing Procedures and Measurements ............................................................. 146 5.2.2.1 Dependent variables (habitu al ambulatory activity) .......................... 146 5.2.2.2 Independent variables .......................................................................... 149 5.2.3 Data Analysis ...................................................................................................... 152 5.3 Results .............................................................................................................................. 153 5.3.1 Sample Characteristics ....................................................................................... 153 5.3.2 Step Activity Monitor Volume, Intensity, and Patter n Predictors ................... 154 5.3.2.1 Activity volume predictors .................................................................. 154 5.3.2.2 Activity intensity predictors ................................................................ 155 5.3.2.3 Activity pattern predictors ................................................................... 156 5.4 Discussion ........................................................................................................................ 158 5.4.1 Post -Stroke Step Activity Pro files ..................................................................... 158 5.4.2 Step Activity Predictors ...................................................................................... 159 5.4.2.1 Lower extremity motor control as a home and community ambulation predictor ............................................................................ 159 5.4.2.2 Balance control as a home and community ambulation predictor .... 160 5.4.2.3 Balance self -efficacy as a home and communi ty ambulation predictor ................................................................................................ 161 5.4.2.4 Walking endurance as a home and community ambulation predictor ................................................................................................ 161 5.4.2.5 Habitual gait spe ed as a home and community ambulation predictor ................................................................................................ 161 5.4.2.6 Executive function as a home and community ambulation predictor ................................................................................................ 163 5.4.2.7 Age as a home and community ambulation predictor ....................... 164 5.4.2.8 Body composition as a home and community ambulation predictor ................................................................................................ 165 5.4.2.9 Depression as a home and community ambulation predictor ........... 165 5.4.2.10 Stroke location as a home and community ambulation predictor ..... 166 5.4.3 Study Limitations ................................................................................................ 167 5.5 Conclusion ....................................................................................................................... 168

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9 6 CONCLUSION ......................................................................................................................... 178 APPENDIX A STEP ACTIVITY MONITOR VALUES IN DIFFERENT POPULATIONS ..................... 182 B PILOT WORK: INFERRED COMPLIANCE WITH ACCELEROMETER MONITORING PROTOCOL .................................................................................................. 189 B.1 Methods ........................................................................................................................... 189 B.1.1 Design ............................................................................................................... 189 B.1.2 Testing Procedures and Measurements ............................................................. 189 B.1.2.1 Ambulatory activity ............................................................................. 189 B.1.2.2 Quantification of inferred accelerometer compliance rate ................ 190 B.1.3 Data Analysis ...................................................................................................... 191 B.2 Results .............................................................................................................................. 191 B.3 Discussion ........................................................................................................................ 192 C PILOT WORK: COMPUTING ACCELEROMETER OUTCOME MEASURES ............. 194 C.1 Methods ........................................................................................................................... 194 C.1.1 Design ............................................................................................................... 194 C.1.2 Testing Procedures and Measurements ............................................................. 194 C.1.2.1 Ambulatory activity ............................................................................ 194 C.1.2.2 Outcome measures .............................................................................. 195 C.1.3 Data Analysis ...................................................................................................... 196 C.2 Results .............................................................................................................................. 196 C.3 Discussion ........................................................................................................................ 197 LIST OF REFERENCES ................................................................................................................. 204 BIOGRAPHICAL SKETCH ........................................................................................................... 231

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10 LIST OF TABLES Table page 2 1 Trial of Org 10172 in Acute Stroke Treatment Classification of subtypes of ischemic stroke ....................................................................................................................................... 82 2 2 Level of functional walking ability at home and in the community ................................... 83 2 3 Gait speed classification ........................................................................................................ 83 2 4 Community ambulation classification .................................................................................. 83 2 5 Activity level post -stroke outcomes summary table ............................................................ 84 2 6 Participation and quality of life post -stroke outcomes su mmary table ............................... 85 2 7 Locomotor Experience Applied Post Stroke enrollment, screening, and baseline assessments ............................................................................................................................. 86 3 1 Demographics and clinical characteristics of the participants by compliance status ...... 105 3 2 Second day predictors for compliance with step activity monitor .................................... 107 3 3 First and second days predictors for compliance with step activity monitor ................... 107 4 1 First and second days step activity monitor characteristics ............................................... 133 4 2 Summary statistics of step activity monit or indices by functional groups (all patients) 134 4 3 Summary statistics of step activity monitor indices by functional groups (compliers only) ...................................................................................................................................... 135 4 4 Spearman correlation coefficients between habitual gait speed and step activity monitor in dices ..................................................................................................................... 136 4 5 Comparisons of the correlations between gait speed and step activity monitor indices based on categories of balance, walking endurance, lower extremity motor control impairment, and balance self -efficacy (all patients). ......................................................... 137 4 6 Comparisons of the correlations between gait speed and step activity monitor indices based on categories of balance, walking endurance, lower extremity motor control impairment, and balance self -efficacy (compliers only). .................................................. 138 4 7 Step activity of two individuals with a similar gait speed ................................................. 139 4 8 Clinical characteristics of two study participants with a similar gait speed ..................... 139

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11 4 9 Spearman correlation coefficients between habitual gait speed, step activity monitor indices, and Stroke Impact Scale mobility and participation domains ............................. 140 4 10 Differences between step activity monitor and gait speed strength of associations with Stroke Impact Scale mobility an d participation. ........................................................ 141 5 1 All subjects and compliers only demographic and clinical characteristics ...................... 170 5 2 Sample step activity monitor characteristics ...................................................................... 171 5 3 Step activity volume predictors ........................................................................................... 171 5 4 Step activity volume summary statistics based on subjects characteristics .................... 172 5 5 Step activity intensity predictors ......................................................................................... 173 5 6 Step activity intensity summary sta tistics based on subjects characteristics .................. 174 5 7 Step activity pattern predictors ............................................................................................ 176 5 8 Step activity pattern summary statistics based on subjects characteristics ..................... 177 B1 Inferred compliance rate with the accelerometer monitoring protocol ............................. 193 C1 Step activity volume, intensity, and pattern outcomes ...................................................... 201 C2 Functional Ambulation Classification scale ....................................................................... 202

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12 LIST OF FIGURES Figure page 2 1 The International Classification of Functioning, Disability and Health as a conceptual framework for studying community Ambulation ............................................. 87 2 2 One day of step acti vity data. ............................................................................................... 88 3 1 Inferred wearing activity based on the windows method .................................................. 107 3 2 Compliance with t he step activity monitor by day ............................................................. 108 4 1 Data analyses decision making flow chart .......................................................................... 142 4 2 Step activity of two individuals with a similar gait speed ................................................. 142 C1 Twenty -four hours ambulatory activity data of two subjects who took different number of steps. ................................................................................................................... 202 C2 Twenty -four hours ambulatory activity data of two subjects who took similar number of steps. ................................................................................................................................. 203

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13 LIST OF ABBREVIATIONS 6MWT Six Minute Walk Test ABC Activities Specific Balance scale BBS Berg Balance Scale BI Barthel Index CV Coefficient of Variation EQ 5D EuroQ ol Quality of Life Scale FAC Functional Ambulation Classification FIM Functional Independence Measure FMA Fugl -Meyer Assessment FM -LE Fugl -Meyer Assessment Lower Extremity motor function FM -UE Fugl -Meyer Assessment -Upper Extremity motor function ICC Intra -class Correlation Coefficients ICF International Classification of Functioning, Disability and Health IDEEA Intelligent Device for Energy Expenditure and Activity LEAPS Locomotor Experience Applied Post Stroke MMSE Mini Mental State Examination MOS -SF36 Me dical Outcomes Study Short Form 36 NIHSS National Institutes of Health Stroke Scale PHQ 9 Patient Health Questionnaire Nine item Depression Scale SAM Step Activity Monitor SD Standard Deviation SIS Stroke Impact Scale TMT B Trail Making Test part B

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14 Abs tract 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 HABITUAL AMBULATORY ACTIVITY MEASUREMENT POST -STROKE By Sharon Barak Ma y 2009 Chair: Andrea Behrman Major: Rehabilitation Science This study had four main objectives: (1) to assess the rate of and predictors for inferred compliance with lower extremity Step Activity Monitor (SAM) protocol, (2) to assess the relationship between gait speed and SAM derived home and community ambulation, (3) to examine the association of gait speed and SAM indices to mobility and social participation domains of the Stroke Impact Scale (SIS), and (4) to identify predictors of SAM derived home and community ambulation. Data in this study were derived from the Locomotor Experience Applied Post -Stroke (LEAPS) trail. Study participants consisted of individuals two -month post stroke with moderate to severe gait impairment. The results indicated high compliance rates in the first and second measurement days (72 and 66%, respectively). However, only 54% of the participants complied with the SAM protocol in both measurement days. Older age, less severe balance control deficits, and moderate to high bal ance self -efficacy, predicted increased compliance. In addition, self -selected walking speed significantly correlated with step activity volume, most activity intensity indices, and one activity pattern outcome. However, balance control, walking endurance, lower extremity motor control, and balance self -efficacy distorted the association between walking speed and SAM outcomes. In addition, gait speed and certain SAM indices significantly correlated with both SIS mobility

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15 and participation. Surprisingly, gai t speed association with SIS was stronger than the association of numerous SAM indices with SIS. Balance control, walking endurance, executive function, stroke location, age, balance self efficacy, lower extremity motor control, and gait speed were all i dentified as home and community ambulation predictors. However, gait speed did not predict activity volume or important activity pattern outcomes such as activity randomness. Moreover, this model explained only up to 50% of the variation in SAM derived ste p activity. Therefore, other factors such as caregiver support may also contribute to home and community ambulation. In conclusion, walking in the real world is highly complex. Furthermore, for individuals at two months post -stroke, usual gait -speed does not associate with all the various aspects of home and community ambulation. However, the association of gait speed with home and community ambulation may be affected by the time since stroke.

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16 CHAPTER 1 INTRODUCTION 1. 1 Overview of the Research Topic Stroke is a significant contributor to long term adult disability and burden of care,1, 2 due to the consequences of residual neurologic deficits on mobility.3 Inability to independently ambulate at home and in the community is a common mobility problem post -stroke4 and one of the most disabling aspects for stroke survivors.5 Consequently, a return to home and communitybased ambulatory activity is a major rehabilitation goal for many stroke survivors.3 Due to the importance of ambulation in people who have survived a stroke, an accurate assessment of mobility at home and in the community is an important outcome measure in treatment and rehabilitation.6 A variety of methods for estimating the ability to independently amb ulate at home and in the community post -stroke are available. However, selecting post stroke outcomes is a difficult process as stroke has various etiologies, heterogeneity of symptoms, and variability of severity.7 Incorporating a framework of health and disability can maximize clinical relevance in post -stroke outcome measu res. The World Health Organization International Classification of Functioning, Disability and Health (ICF) model provides a standard language and a framework to describe consequences of disease (e.g., stroke), and physical and social environment on differ ent health related domains.8 Thus, the ICF can be used to classify post -stroke walking outcome measures. Within the ICF system there are two different domains to assess or estimate stroke impact on walking and community ambulation. Walking and community ambulation can be estimated via activity and/or participation measures. Quality of life outco me measures may contain items that reflect community ambulation as well. Measures such as gait speed and walking distance are examples of activity level measures. These measures are simple, fast, and related to the

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17 severity of disability in the home and t he community.9 However, such measures evaluate the individual in a non -familiar environment,10 attempt to derive mobility from brief snapshots of walking performan ce,11 may not be sensitive to subtle changes in ambulatory recovery across the subacute stroke rehabilitation period (first 3 months at home),11, 12 and overall, do not directly assess persons free living ambulation or mobility -driven social participation.12 Participation level measures may be ascertained with self report of mobility via instruments such as the SIS. The SIS is a 64 item scale covering eight domains that assess dimensions of health related to quality of life. The mobility and participati on domains include items which represent community ambulation and mobility aspects (e.g., the ability to climb flights of stairs and to participate in active recreation).13 Self report approaches are especially useful in large population studies.14, 15 However, such instruments do not take direct measures of individuals mobility level and rely on factors related to recall and cognitive ability which contribute to the inaccuracy of these approaches.16 An alternative approach to participation level community ambulation measurement involves direct recording of ambulatory activity.14 Pedome ters and microprocessor -based accelerometers, such as the SAM, enable direct and long -term monitoring of individuals normal activities conducted in their own environments.17, 18 Pedometers count the number of steps taken during ambulatory activity (an indication of volume of physical activity). However, pedometers are unable to breakdown activity minute by-minute, therefore, they do not provide information on activity intensity and pattern. Furthermore, within the stroke population, pedometers do not provide accurate and reliable measure of stride counts.19 Pedometers inaccuracy within the stroke population may be due to stroke survivors slow gait speeds and irregular gait patterns. Pedometers are typically worn at the waist. Therefore, they tend to function best during vigorous

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18 walking (gait speed > 0.9 m/s), when the motion of the hips is more pronounced.2022 In addition, pedometers respond to vertical (upand -down) displacement only and are not capable to detect movements in the sagittal (forward/backward) and frontal (right/left) planes. Therefore, pedometers tend to underestimate steps taken in irregular gait patterns.20, 21 Unlike th e pedometers, SAM was found to be valid, reliable,19, 23 and sensitive to change12 when used with individuals post -stroke. In addition, SAM provides minute -by -minute measurements. The resulting temporal series of data can be analyzed in a variety of ways:24 (1) Activit y volume, refers to activity quantity and/or duration (e.g., the number of steps an individual took in 24 hour period),25 (2) Activity intensity, as determined via step vigor (steps/min). Step vigor can be used to determine time spent in bouts of specific intensity categories (e.g. light, moderate, vigorous),26, 27 and (3) Activity pattern, represents the degree to which activity (e.g., number of steps) fluctuates minute to -minute, and whether these minute -to minute fluctuations o ccur in an orderly, repetitive, or predictable manner. Measurement of ambulation volume, intensity, and pattern is beneficial as they are potentially useful indicators of age and health associated walking function.14 Measures of activity intensity and pattern are especially valuable as they illustrate the manner in which a person accumulated their steps (two subjects with nearly the same step count could accumulate their steps in very different ways in terms of activity intensity and patt ern). Consequently, the use of SAM as a post -stroke home and community ambulation outcome measure is promising. Nevertheless, habitual ambulation measurement post -stroke is not without its challenges and additional research is needed to provide recommendat ions on the best practice of home and community ambulation measurement post -stroke.

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19 1. 2 Home and Community Ambulation Measurement Current Research Needs Gait speed is commonly used as a measure of walking ability in both research and clinical settings. Cor relations of gait speed with impairment severity, activity limitations, and participation have been established. Such correlations, including inferences to activities beyond the clinic, have validated its utility as an outcome measure for rehabilitation in terventions aimed at improving walking ability after neurologic injury or disease. Unlike gait speed, recording community -based step activity with an accelerometer provides a window into actual walking behavior at home and in the community. However, simila r to the course of scientific inquiry that established gait speed as a meaningful outcome measure, there is a need to systematically examine the use of step activity as an outcome measure. Towards that goal, I propose t o address three critical steps. First we need to establish the compliance rate for use of an accelerometer protocol by individuals post -stroke. Second, as gait speed has been the predominant outcome measure for rehabilitation research and walking, it will be valuable to understand its relati onship to actual step activity at home and the community. Lastly, as with gait speed, we will examine factors that may influence step activity at home and the community. 1. 2.1 Inferred Compliance with a Step Activity Monitor Protocol to Measure Free Physic al Activity in Moderate to Severe Sub Acute Stroke Survivors SAM is a highly accurate microprocessor based instrument that directly and continuously counts and records individuals number of steps taken while ambulating in their natural environment.14, 18 However, in order for the SAM to be a valuable tool in clinica l trials, determining the inferred rate of compliance with SAMs protocol in persons with moderate to severe sub acute stroke is essential, as participant compliance in wearing the monitor is critical for obtaining accurate physical activity measurement.15 In addition, it is vital to examine factors that are associated with ac celerometer monitor protocol compliance within this population.

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20 Understanding these factors may aid researchers in determining ways to ensure participant compliance in wearing the monitor. 1. 2.2 Clinical Based Gait Speed in Persons with SubAcute Stroke: R elationship to Home and Community Ambulation Activity A speed -based classification system has often been used as a proxy measure for community ambulation28 in post -stroke clinical trials, as is a reliable and valid measure of walking performance29 31 and recovery of walking ability.1 However, the use of gait speed to predict home and community ambulation post -stroke and clinical meaningfulness has been mainly validated with questionnaires.9, 32 Bowden et al. 33 validated the clinical based gait speed classification system against a quantitative and objective measure of home and community ambulation (SAM). However, this study recruited individuals with chronic stroke (more than 6 months post -stroke) and validated the gait speed classification system against steps activity volume only. In order to validate speed -based classification system as a home and communityambulation outcome measure in the subacute ph ase of stroke recovery, there is a need to establish the relationship between speed -based classification system and actual home and community ambulation activity volume, intensity, and pattern. Moreover, it is important to establish whether other variable s (e.g., motor and sensory impairments, balance, walking endurance, and balance self -efficacy) significantly confound the relationship between speed -based classification and actual habitual ambulation. Finally, both SAM and gait speed do not provide inform ation on what type of activities the person is actually doing in the community. Therefore, it will be useful to evaluate the association between gait speed classification system and the SAM with the commonly used SIS, as it provides information on how stro ke has impacted the participants ability to conduct activities such as climb flights of stairs and to participate in active recreation.13

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21 1. 2.3 Ambulatory Activity during the SubAcute Phase of Stroke Recovery: Relationship to Impairment Level, Activity Limitations, and Personal Factors A myriad of factors contribute to home and community ambulation activity level. Quantifying post -stroke home an d community ambulation activity and determining the relationship of patients impairment level, activity limitations, and personal factors with SAM derived home and community ambulation is important, as these relationships may reveal home and community ambulation underlying mechanisms. Thus, it may be beneficial in development of interventions resulting in better carryover of everyday ambulatory activity.34 In addition, these relationships will shed light on which clinically -based outcome measu res best predict home and community ambulation level, and thus would be beneficial clinically and in research. The next section provides a literature review to orient the reader to the knowledge underlying the overall object of this project. The literatur e review is composed of the following seven sections: 1) stroke definition, epidemiology, and sequelae; 2) underlying dimensions of habitual ambulation and habitual ambulation in the post -stroke population; 3) the ICF as a conceptual framework for studying home and community ambulation; 4) post -stroke commonly used activity, participation, and quality of life home and community ambulation outcomes; 5) direct objective measurement of home and community ambulation; 6) SAM as a home and community ambulation ou tcome measure post -stroke; and 7) the LEAPS trial.

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22 CHAPTER 2 REVIEW OF THE LITERA TURE 2. 1 Stroke Definition, Epidemiology, and Sequelae 2. 1.1 Definition The World Health Organization defines stroke as a rapid onset event of vascular origin reflecting a focal disturbance of cerebral function, excluding isolated impairments of higher function and persisting longer than 24 hours.35 Stroke diagnosis can be confirmed by Magnetic Resonance Imaging or Computed Tomography. If scan is not available, stroke diagnosis can be confirmed also by clinical criteria.36 2. 1.2 Epidemiology Stroke is the most common serious neurologic disorder,36 and the primary cause of adult disability in the United States.37 Every year approximately 700,000 Americans suffer a stroke.9 About 500,000 of these are first or new strokes, and 200,000 occur in people who have already had a stroke.38 Strokes can and do occur at any age. However, stroke incidence is related to age, being uncommon un der age 50, and doubling each decade past age 55.36 In addition, stroke is the third leading cause of death after heart disease and cancer,36 with over 160,000 people dying each year from stroke in the United States.38 Most patients who die from acute stroke succumb in the first 30 days. The overall 30 day survival following a new stroke is reported to be 20% to 85%, survival being largely dependent on stroke type. There are two main types of stroke: ischemic and hemorrhagic. Ischemic stroke is the most common type of stroke accounting for approximately 80% of all strokes. Ischemic stroke is caused by the presence of a blood clot or other blockage within an artery leading to the brain.39 According to the Trial of Org 10172 in Acute Stroke Treatment, Ischemic stroke can be further divided into five subtypes (see table 2 1). The 30 days survival of patients with cerebral infarction is around

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23 85%.36 The highest 2 -year survival is in persons with lacuner stroke and the lowest is in persons with cardioembolism.40 Hemorrhagic stroke is due to rupture of a blood vessel or an abnormal vascular structure. There are two types of hemorrhagic stroke: intracerebral and subarachnoid. In intracerebral hemorrhagic stroke the blood from the ruptured artery is released into the brain. A subarachnoid stroke differs from intracerebral stroke in that the blood from the ruptured artery fills the space surrounding the brain rather than into the brain.39 The 30 -day survival of patients with hemorrhagic stroke is only about 20%, most of the deaths occurring in the first 3 days.36 Side of stroke is another importan t factor which has an impact on post -stroke outcomes as left -hemispheric and right -hemispheric strokes have different characteristics and symptoms. A left -hemispheric stroke often causes paralysis of the right side of the body. In addition, individuals wit h left -hemispheric stroke may suffer from an inability to solve problems, have impaired retention of information, are often more easily frustrated and angered, and may have apraxia or language difficulties.42 Language difficulties may transfer into difficulty understanding and following therapy directions. Right -hemispheric stroke frequently causes paralysis in the left side of the body. Moreover, persons with right -hemispheric stroke often experience difficulty with spatial perceptual tasks, develop impulsive style, tend to have greater balance problems, and may experience left -side neglect.42 Despite the differences between left and right -hemispheric stroke, side of stroke does not appear to be associated with independence in activities of daily living outcome4347 or gait.48 Moreover, the available literature suggests that unilateral spatial neglect is a poor predictor of activities of daily living45, 46, 49 and gait speed.50 2. 1.3 Sequelae Approximately two thirds of stroke survivors experience residual neurologic deficits,3 which impair, among others, the ability to sense, speak, understand, and remem ber.3 In addition,

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24 stroke often results in impairments su ch as muscle weakness51, 52 and balance disorders53, 54 which may lead to functional disability.1, 5558 Accordingly, about 90% of stroke survivo rs suffer from functional disability, with mobility being a major impairment.59 Gait abnormalities are common mobility impairments post -stroke, which commo nly persist through the chronic stages of stroke. Such abnormalities have a negative impact on activities and instrumental activities of daily living, and the ability to function safely and effectively at home and within the community.60, 61 In addition, post -stroke impairments and gait abnormalities commonly lead to low levels of physical activity,34 which may predispose st roke survivors to common post -stroke secondary complications, such as osteoporosis, heart disease,62 skeletal muscle tissue alterations,63 and profound cardio respiratory deconditioning.64 These secondary complications may further contribute to post -stroke gait abnormalities. Below is a review of post -stroke balance deficits, skeletal muscle tissue alteration, an d cardiovascular deconditioning. 2. 1.3.1 Balance post -stroke Balance problems are common after stroke.65 68 Tyson et al.69 reported that 83% of first time stroke survivors during the acute phase of stroke recovery had a balance disability. Of these, 27% could sit but not stand, 40% could stand but not step, and 33% could step and walk but still had limited balance. Other studies of balance impairments post -stroke have shown that compared to age matched hea lthy volunteers, stroke survivors have greater postural sway,7074 altered weight distribution patterns, and smaller excursions when moving weight around the base of support.75 83 In addition, a positive relationship between balance disability and other aspects of function, such as activities of daily living, mobility, and falls has been reported.83, 84 Moreover, numerous researchers reported a significant positive correlation between balance disability and strength, lower limb control69, 85, 86 and sensation.69, 85, 86 On the other hand, it has been reported that age, gender or side of stroke are not related to balance disability.69, 8 5

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25 2. 1.3.2 Skeletal muscle tissue alterations post -stroke Stroke patients commonly experience a number of changes in skeletal muscle including, gross muscular atrophy,63 fiber phenotype shift,87 and increase in insulin resistance.88 Extreme gross muscular atrophy in paretic thigh has been reported, showing 20% lower muscle area compared to the non-paretic thigh. I n addition, intramuscular fat area is 25% greater in the paretic thigh compared to the non-paretic thigh.63 Reduced muscle mass decreases stroke survivors ability to use oxygen because the amount of muscle tissue is partially responsible for the amount and rate of oxygen a person can utelize.64 Accordingly, Ryan and associates89 reported a strong relationship between th igh muscle mass and peak oxygen consumption, with lean mass predicting over 40% of the variance in peak aerobic fitness. Moreover, muscular atrophy in the paretic limb is a strong predictor of gait deficit severity.64 Paretic le g muscle in stroke survivors show elevated proportions of fast type myosin heavy chain II fibers.87 Neurological alterations may be partially responsible for the shift of muscle phe notype. A shift to fast myosin heavy chain fibers can also occur as a result of muscle unloading or disuse. The shift to fast myosin heavy chain fibers in the stroke paretic leg muscle would be expected to result in a more fatigable and insulin resistant m uscle fiber type.90 It was reported that in the paretic limb, the proportion of fast myosin heavy chain isoform is strongly inversely related to self -selected walking speed and that gait deficit severity may account for approximately 61% of the variance in fast myosin heavy chain isoform.87 2. 1.3.3 Card io -respiratory system post -stroke Stroke leads to profound cardiorespiratory deconditioning. Chronic stroke patients peak oxygen consumption is approximately half that of age -matched controls.64 91 Post -stroke decreased cardio respiratory fitness is commonly attributed to reduced central neural drive,

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26 secondary biological abnormalities in skeletal muscle, and impairments in paretic leg peripheral blood flow.64 2. 2 Unde rlying Dimensions of Habitual Ambulation and Habitual Ambulation in the Post -Stroke Population A return to home and community-based ambulatory activity is a major rehabilitation goal for many stroke survivors.3 Following is a review of the concept of community ambulation, the major determinants of community am bulation post -stroke, and of post -stroke community ambulation status. 2. 2.1 Community Ambulation -Definition The definition of community ambulation varies in the literature.28 Lord and colleagues61 defined community ambulation as locomotion outdoors to encompass activities such as visits to the supermarket, shopping malls, and back; social outings; vacations; and pursuit of leisure activities. More recently, Lord et al.28 described community ambulation as the ability to integrate walking with other tasks in a complex environment. According to the Patla and Shumway Cook operational definition92 community ambulation consists of 8 environmental dimensions: ambient conditions (e.g., average temperature ), terrain characteristics (e.g., stairs and curbs), external physical load (e.g., manual doors and weight of packages), attentional demands (e.g., distractions in the environment), postural transitions (e.g., number of times that a subject backed up and t urned his/her head), traffic level (e.g., number of people within arms range and unexpected collisions), time constraints (e.g., subjects ability to maintain the same speed as those around them), and walking distance (mean distance walked). These 8 dimen sions do not simply catalogue the environmental features that one encounters in the community. Rather, they capture the interaction between the individual and the environment during the performance of activities of

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27 daily living93 In a follow up study, Patla and Shumway-Cook94 examined the influence of these 8 dimensions on mobility in older adults with and without mobility disabilities. The investigators concluded that temporal factors, postural transitions, physical load, and terrain, are all important dimensio ns. For the purpose of this study, community ambulation was defined as steps activity volume, intensity, and pattern taken at home (e.g., walking from the kitchen to the bedroom) and outdoors (e.g., visits to shopping malls and social outings) with or wit hout assistive devices. 2. 2.2 Major Determinants of Community Ambulation Function Post -Stroke The ability to ambulate at home and in the community may be influenced by several underlying factors. Below is a review of factors which may be important for safe and effective community ambulation. 2. 2.2.1 Motor control According to Lord and Rochester95 motor control is important for the performance of automatic movements such as walking in predictable environments (e.g., walking in a well lit room without obstructions).The ability to perform automat ic movements is an important aspect of movement control. However, higher levels of the nervous system are not essential for the generation of such rhythmic limb movements, as the generation of rhythmical patterns of motor activity is mediated by central pa ttern generators in the spinal cord in the absence of sensory input from peripheral receptors.96 In contrast, higher levels of motor control, which require cortical input and afferent feedback are required in order to adjust gait in less predictable environments, to initiate and terminate gait, and to perform complex tasks.97 2. 2.2.2 Balance Michael et al.23 quantified household and community ambulatory activity profile s of persons with chronic mild to moderate stroke, and examined the relative contributions of

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28 mobility and cardiovascular fitness toward ambulatory activity levels. Ambulatory activity was quantified by stride counts obtained from SAMs. The authors reporte d that balance as established via the Berg Balance Scale (BBS), was the only predictor of ambulatory outcomes. The data suggested that persons with the poorest balance will have the lowest ambulatory activity levels. Balance may be important for habitual ambulation because ambulating in the community is characterized by initiating gait from various postures, terminating gait, changing direction and speed, adapting the step cycle to meet unpredictable terrain and environmental conditions, avoiding expected and unexpected obstacles, and postural shifts such as turning to look at something. These transitions stress the balance control system.98 For example, gait initiation requires going from a stable standing posture to essentially falling (where the center of mass is moved outside the base of support), followe d by recovery with the positioning of the swing limb. Gait termination requires the control of forward linear momentum, which, if not done properly, can be destabilizing.92 2. 2.2.3 Endurance Michael et al.23 reported that in persons with chronic mild to moderate stroke, neither gait economy nor peak oxygen consumption were significantly related to stride counts obtained from SAMs. The authors hypothesized that the lack of a demonstrable relation betwee n fitness outcomes and ambulatory activity may reflect a floor effect, attributable to the universally low peak oxygen consumption values and limited variability to reveal an association. Other researchers also suggested that walking endurance is less impo rtant for community ambulation in older adults.94 In contrast, van De Port and associates99 found that walking endurance was an important factor for outdoor mobility. Numerous additional studies have demonstrated that walking endurance is crucial for community ambulation,23, 60, 61 and related to community

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29 reintegration post -stroke,100, 101 especially in persons with gait speed > 0.48 m/s.102 In addition, walking distance of 300 to 500 continues meters is suggested for community ambulation.60, 103 2. 2.2.4 Gait speed Michael et al.23 concluded that post -stroke self -selected floor walking speed did not predict ambulatory outcomes (total daily step activity). In contrast, Lord et al.61 reported that post -stroke (mean time from str oke 16 weeks), individuals with different self reported community ambulation levels had significantly different gait speeds. In addition, in an effort to assess requirements associated with community ambulation, Lerner et al.103 examined the mobility requirements associated with instrumental activities of daily living in the Los Angeles, California area. The authors recommended that for safe community ambulation, persons should be able t o walk 1.16 m/s in order to cross a street in the time provided by a traffic light. Cohen and associates104 suggested a similar gait speed requirement of 1.21 m/s. Perry et al.9 reported that gait speed significantly predict community ambulation post -stroke. The authors recommended a threshold of 0.8 m/s for community ambulation. However, other investigators have suggested that this threshold may be too high.105 For instance, van de Port et al.99 reported that a cut -off point of 0.66 m/s was optimal to distinguish between community and noncommunity walkers. Whereas, Hill and colleagues60 recommended a minimum gait speed of 0.48 m/s. Differences between studies recommendations for minimal gait speed required for com munity ambulation may be due to several factors: (1) methods used to assess community ambulation, (2) methods used to assess gait speed, (3) time from stroke (chronic patients may use more compensatory strategies, which they have learned over the years), a nd (4) underlying factors which may confound the relationship between gait speed and community ambulation.

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30 Factors which confound the relationship between gait -speed and community ambulation : Numerous factors have the potential to confound the relationship between gait speed and community ambulation. According to van de Port and colleagues,99 the association between gait speed and community ambulation post -stroke, as determined by a self a dministered questionnaire, was confounded by control of standing balance. Van de Ports results99 are in agreement with those of previous studies.23, 60, 61 Accordingly, it has been reported that the ability to contro l posture while performing everyday functional tasks accounted for approximately 30% of ambulatory activity variance as measured by the number of steps performed per day.23 Balance control may confound the r elationship between gait speed and community ambulation, as it enables individuals to ambulate in the community despite slower gait speeds.99 van de Port and associates,99 also reported that motor function, as determined by the Motricity Index, changed the regression coefficient of gait speed by more than 15%. Therefore, the authors concluded that motor function distorts the association between gait speed and community ambulation. Additionally, van de Port et al.99 concluded that walking endurance and the use of walking devices also confounded the relationship between gait speed and community ambulation. The authors hypothesized that the use of an assistive device may confound the relationship between gait speed and community ambulation, probably because it enables individuals to walk in the community despi te lower gait speeds.99 Similarly, Logan et al.106 reported that providing walking aids helps individuals increase mobility outdoors. Moreover, it is reasonable to believe that balance self -ef ficacy might also confound the relationship between gait speed classification system and community ambulation, as fear of falling may lead to the avoidance of activities that the individual remains capable of performing.107 Finally, van de Port and associates99 reported that age, hemisph ere, living alone, history of falls, executive function,

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31 fatigue or depression did not confound the association between gait speed and community ambulation. In summary, it appears that gait speed is an important factor related to community ambulation. Nev ertheless, the ability to walk in the community is determined by numerous underlying factors such as endurance, assistive walking devices, motor control, and balance.99 In addition, there are clinical grounds to believe that balance self -efficacy will also confound this relationship. However, the participation level measures of home and community ambulation used in the reviewed studies mainly consisted of self administered questionnaires. Such instruments do not measure directly individuals mobility level and rely on factors related to recall and cognitive ability which contribute to the inaccuracy of these approaches.108 Therefore, further investigation which involves the direct measurement of habitual ambulation is warranted. 2. 2.2.5 Stairs management Safe community ambulation requires the ability to manage stairs and curbs.60, 104, 109 For instance, Lernier et al.103 state that safe community ambulation entails negotiating a 17.8 to 20.3cm (7 to 8 in) curb, and climbing 3 steps and a ramp without a handrail.103 In addition, Hill and colleagues60 recommended that for safe community ambulation, individuals should have a Functional Independence Measure (FIM) locomotion domain of at least 5, and at least 6 on the Functional Ambulation Classification (FAC). 2. 2.2.6 Cognitive flexibility Theoretically, cognitive flexibility may also be important for community ambulation, as walking in the real world is highly complex, and requires to address motor requirements while attending to a wide range of concurrent ta sks and environmental stimuli.110 Executive function is an important aspect of cognition. The executive function system is a theorized cognitive system tha t controls and manages cognitive processes. This system comprises a broad class of mental

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32 processes involved in initiating and maintaining smooth information processing and coordinated actions in the central nervous system.111 The mental processes involved in the executive function system include attention allocation, goal representation and maintenance, restraining and delaying responses, attending selectively, and planning.112, 113 Therefore, executive function involved in defining the problem at ha nd, which requires that one stop to plan and analyze rather than act.114 In addition, these functions are fundamental to inhibiting irrelevant actions and in setting future goals such as performing complex motor acts.115 The executive function system may be important for independent and safe home and community ambulation because unlike am bulating in the clinic, ambulating outdoors is unpredictable, complex, requires planning of path selection, navigation, speed of information processing to enable adaptations to meet environmental demands, and the ability to divide attention between mobilit y and other tasks (e.g. conversing with a friend).92 2. 2.2.7 Balance self -efficacy Balance self -efficacy could be another potential covariate that could confound the relationship between gait speed and home and community ambulation. According to Band uras theory of self -efficacy116 persons with low self -efficacy for a particular activity will tend to avoid that activity. In contrast, individuals with high self -efficacy for a particular activity will engage in the activity with enhanced confidence. Thus, according to Bandura s theory, individuals who have low confidence in their ability to ambulate at home and in the community without sustaining a fall might tend to avoid ambulation at home and in the community. 2. 2.3 Post -Stroke Community Ambulation Status Approximately 75 85% of stroke survivors ultimately are discharged home.117, 118 However, the majority suffer from mobility impairments59 that hamper their ability to independently walk at home and in the community. Several researchers assessed post -stroke

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33 community ambulation status. Lord et al.61 assessed community ambulation ability via a self report questionnaire. The sample consisted of 130 post -stroke participants that lived at home. Participants were placed into one of the following four ambulation categories: (1) not ambulating outside of the home, (2) ambulating as far as the letterbox, (3) ambulating within the immediate environment, and (4) ambulating in a shopping center and/or other places of interest. The self report questionnaire indicated that 14.6% of the participants were unable to leave their home unsupervised, 16.9% were able to walk to the letterbox, 7.6% were limited to ambulating in their immediate environment, and 60.7% were able to ambulate within a shopping center and/or other places of interest. The study demonstrated that nearly one third of the sample was not able to ambulate independently within the community after stroke. In a more recent study, van de Port et al.99 assessed community ambulation of a relatively young group (mean age 59 years) of stroke survivors with moderate stroke severity. Community ambulation was determined according to the previously described Lord et al.61 self administered community ambulation questionnaire. The authors reported that 26% of participants were non community or limited community walkers. Perry and associates9 reported that only 17% of stroke survivors could independently ambulate in the community post -stroke. The patients' level of functional walking ability at home and in the community was assigned by expert clinicians to one of the six categories of a modified Hoffer Functional Ambulation scale at least 3 m onths after discharge (see table 2 2). Other researchers reported that approximately 70% to 80% of stroke survivors will recover the ability to walk short distances on flat surfaces. However, only 50% achieve limited community ambulation119 and less than 20% have unlimited ambulation in the community.120

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34 In summary, the available data suggest that approximately 20 70% of stroke survivors manage to walk independently in the community again.9, 61, 99 Differences between studies in the reported percentage of stroke survivors who can independently ambulate in the community may be due to different methods used to establish community ambulation and sample characteristics (e.g., age, time from stroke, and stroke severity). 2. 3 The International Classification of Functioning, Disability and Health as a Conceptual Framework for Studying Home and Community Ambulation A two part conceptual model adapted from the ICF model was specifically proposed to guide these research study questions. Part A of the conceptual model describes the variables that might influence ambulation in the community, and is based on the ICF model.121 Part B of the model addresses community ambulation descriptors: walking volume, intensity, and patterns. This part is an elaboration of the ICF participation domain (see figure 2 1). 2. 3.1 Conceptual Model Part A -Community Ambulation Barriers and Facilitators The ICF framework is the most current and comprehensive disability model available. The ICF system organizes health related information into two broad categories --functioning and disability, and contextual factors. The category of functi oning and disability encompasses the components of Body Functions and Structures (impairment) and Activities and Participation. The category of contextual factors includes the components of Environmental Factors and Personal Factors. 2. 3.1.1 Health conditi on In this study, the general health condition is: stroke. The World Health Organization have defined stroke as a rapid onset event of vascular origin reflecting a focal disturbance of cerebral function, excluding isolated impairments of higher function and persisting longer than

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35 24 hours.35 The type of stroke and severity were included in the model as they are important aspects that may impact post -stroke community ambulation. 2. 3.1.2 Body functions and structures Body structures are the a natomical parts of the body and include organs, limbs, and their components. Body functions are the physiological or psychological functions of body systems.121 In this study, stroke related body functions and structures include stroke related sensory, cogn itive, and motor impairments, as they may have detrimental effects on ambulation. Depression and fear of falling were also included in the model. Depression after stroke is common,122 has been linked with great er impairments in activities of daily living,123127 and increased mortality after stroke. Fear of falling is a disabling symptom of impaired mobility among frail older people that is significantly associated with depression, diminished perfo rmance in gait, and restricted instrumental activities of daily living.128 Moreover, fear of falling may lead to the avoidance of activities that the individual remains capabl e of performing.107 2. 3.1.3 Activities Activities can be defined as the execution of a task or act ion by an individual.121 Mobility is a component of activity domain, and has been defined as the ability to move from one place to another without assistance.129 Mobility disability is defined b y the inability of individuals to move effectively in their surroundings.130 Gait speed was included in this studys activity domain, as mobility and mobility disability are commonly demonstrated by changes in gait velocities.131 Impaired ability to ambulate short distances and to maintain balance were also included in the model, as the y are important mobility dimensions which might reduce the ability to move effectively and safely in the community.

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36 2. 3.1.4 Participation Participation is the involvement in a life situation, relationships, community, social, and civic life, and it may in clude domestic life, and interpersonal interactions.121 Community ambulation may be considered a participation domain component as it refers to persons ability to ambulate in the community in order to achieve behavioral goals and to fulfill community, soci al, and civic life roles. This dissertation study focuses on post -stroke community ambulation characteristics, its relationship to the different components of the ICF model, and evaluates the utility of the SAM as a community ambulation outcome measure pos t -stroke. 2. 3.1.5 Personal factors Personal factors are the particular background of an individuals life and living, and comprise features of the individual that are not part of a health condition or health states. These factors may include gender, race age, other health conditionsand other characteristics.121 Age and co -morbidities were included in the model. Numerous studies have reported that age is an important outcome of mobility within the stroke population.132134 Van de Port132 developed a prognostic model to predict mobility outcomes one year post -stroke. The authors reported significant association between mobility and co-morbidity (the presence of cardiovascular and/or respiratory disease, diabetes mellitus and co -morbidities of the locomotor system), but not between mobility and gender. 2. 3.1.6 Environmental factors Environmental factors make up the physical, social and attitudinal environment i n which people live and conduct their lives.121 In this study, environmental factors will not be assessed, however, they are likely to affect step activity (e.g., the availability of public transportation is likely to influence the number of steps a person takes in 24 hours).

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37 2. 3.2 Conceptual Model Part B -Community Ambulation Descriptors Community ambulation via monitoring of step activity can be described in terms of ambulation volume, intensity, and pattern. 2. 3.2.1 Community ambulation volume Ambulatio n volume refers to activity quantity and/or duration. Community ambulation volume can be described in terms of mean activity level (e.g., the number of minutes of walking each day), and total activity conducted (e.g., total number of steps taken, distance walked, and total kilocalories expended). However, ambulation volume can be an incomplete descriptor of function because two subjects with nearly the same community ambulation volume (e.g., step count) can accumulate their steps very differently14 in terms of ambulation intensity and pattern. 2. 3.2.2 Community ambulation intensity Ambulation intensity refers to the overload on the cardiovascular system. The intensity can be described in terms of percent of max oxygen consumption.135 Because oxygen requirement is a function of walking speed,136 step vigor (steps/min) can be used to infer ambulation intensity (e.g., light, moder ate, vigorous).26, 27 2. 3.2.3 Community ambulation pattern Like other physiological signals, such as heart rate, gait measures are not constants but fluctuate with time.137 Community ambulation pattern include the following two concepts: (A) variability magnitude, which reflects the degree to which scores in a distribution are spread out or clustered together. Community ambulation variability magnitude variables may consist of the dispersion of 1 -minute stride count values greater than zero, and the number of 1 -minute intervals for which the subject switched from inactivity (str ide count=0) to activity (stride count > 0);14 and (B) variability structure, assessment of whether the activity fluctuates in an orderly, repetitive, or predictable manner.

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38 2. 4 Post -Stroke Commonly Used Activity, Participation, and Quali ty of Life Community Ambulation Outcomes The World Health Organization ICF model8 can serve as the underlying framework for community ambulation assessment. Community ambulation outcomes can represent the ICF activity and/or participation domains. Below is a review of commonly used post -stroke activity and participation mobility outcomes. 2. 4.1 Post -Stroke Activity Level Mobility Outcomes Post -stroke activity level mobility outcomes consist of tasks that researchers and clinicians consider best representing community ambulation.28 Below is a description of eight commonly used post -stroke activity level mobility outcomes. Special attention will be given to gait speed. 2. 4.1.1 Gait speed Gait speed, measured using the 10-meter timed walk, is used in both research and clinical situations,28 as it is simple to measure, reliable, valid, and sensitive to post-s troke recovery stage.9, 138 Furthermore, gait speed is related to rehabilitation prognosis and discharge destination in stroke patients during inpatient rehabilitation,29 and the elderly.139, 140 Studenski et al.67 established gait speed of less than 0.6 m/s as a predictor of declines in health status and future health care utilization in old er adults (65 and older). Decreased gait speed has also been reported to be related to fear of falling, falls in elderly community dwellers,141 and to the ability to complete activities of daily living and instrumental activities of daily living in the community dwelling elderly population.67, 142 Moreover, gait speed is associated with motor recovery143 and kinetic variables duri ng gait.144 Perry and associates9 examined the relationship betw een gait speed and mobility in persons three months post -stroke. Six ambulation categories related to post -stroke mobility and

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39 ambulation inside and outside of the house were developed. Five clinical outcome measures were used to place the participants int o one ambulation category. The measures included: a walking ability questionnaire, motor control, proprioception, gait speed, and other stride characteristics. Gait speed was the only clinical measure to significantly predict placement into the ambulation categories. Due to similarities in gait speed between household walkers, the classification system was condensed to depict four distinct gait speed classifications (see table 2 3). Recently, Schmid et al.32 evaluated whether changes in Perry et al.9 speed -based community ambulation classification were related to clinically meaningful changes in stroke related function and quality of life. Study population consisted of subacute stroke survivor s with mild to moderate deficits. The researchers assessed the effect of success versus failure to achieve a transition to the next class of function and quality of life according to domains of the SIS. After treatment, household ambulators who transition ed to limited or full community ambulation had significantly better SIS mobility and participation scores. Limited community ambulators who achieved the transition to full community ambulatory status had significantly better scores in SIS participation. Thus, the authors concluded that gait speed classification acts as a potentially meaningful indicator of clinical benefit. Lord et al.61 examined the relationships between post -stroke gait speed and mob ility among 130 post -stroke participants who lived at home. Mobility outcome measures consisted of gait endurance, gait speed, walking ability (indoor and outdoor), functional mobility, and community ambulation, according to an unsupervised mobility self r eport questionnaire. Participants were placed into one of four of the following ambulation categories: not ambulating outside of the home, ambulating as far as the letterbox, ambulating within the immediate environment, and ambulating in shopping center and/or other places of interest. The authors

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40 concluded that gait speed was important to community ambulation as gait speed differed between the four categories (see table 2 4). The association of gait speed with other clinical measures of impairment and acti vity : According to Patterson et al.102 and Pang and associates,62 balance was the stronge st predictor of gait speed in stroke survivors with severe impairments. However, gait speed has also been reported to correlate significantly with cardiovascular fitness,102 especially in pe ople with mild to moderate gait deficits.89 In addition, numerous studies show that paretic leg lower extremity muscle strength, especially that of the ankle pla ntar flexors, hip flexors, knee flexors, and knee extensors, is moderately to highly correlated (r=0.50.8) with self -paced or maximal gait speed.102, 145, 146 Lower ex tremity muscle strength of the non -paretic leg, especially of the knee flexors and ankle plantar flexors, was also found to moderately -highly correlate with both habitual and maximal gait speed.102, 145, 146 Accordingly, it has been reported that percentage of paretic leg lean mass and body fat significantly correlates with gait speed.102 Paretic lower extr emity motor control, as measured by the Chedoke -McMaster Stroke Assessment or Fugl Meyer Assessment (FMA), was also found to moderately correlate (r= 0.5 0.75) with self or fast gait speed.147, 148 However, the av ailable literature suggests that sensory impairment of the paretic lower extremity has low correlations with gait speed.147 F inally, there is conflicting evidence on how spasticity influences gait speed. Hsu et al.147 showed a negative correlation betw een spasticity and gait speed. Other studies found low or non -significant correlations between gait speed, spasticity, and passive joint stiffness of the ankle plantar flexors or knee extensors.147, 149, 150 Overall, gait speed has been described as the almost -perfect mobility outcome measure in neurological populations.151 However, there are a few drawbacks: (1) early during

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41 rehabilitation phase, patients may not be able to walk 10 meters and are therefore not testa ble; (2) as patients improve, 10 meters walking speed becomes a less credible measure of walking speed over more functionally relevant distances outside the home;152 (3) a ceiling effect is apparen t at normal walking speeds (1.2 1.4 m/s);153 (4) measured indoors in a pre dictable, controlled, and uncluttered environment without consideration of environmental factors. Thus, clinical -based fast walk tests (e.g., 10 meter walk test) physical, psychological, and cognitive demands may not be similar to the demands of walking in the community;28 (5) it has been reported that the clinic -based 10 meter walk test is able to predict walking speed in a community setting of patients with chronic stroke with gait speeds of 0.8 m/s or greater. However, for those with gait speeds of less than 0.8 m/s in the clinic, gait speed in the community may be overestimated; and105 (6) there is evidence to suggest that although gait speed is an important determinant of community ambulation, the ability to walk in the community is determined by several underlying factors such as endurance, balance, and motor function.99 2. 4.1.2 Six -minute walk test The six -minute walk test (6MWT) is commonly used in clinical trials as it is simple,152 well tolerated, reflects activities of daily living,154 is a continuous variable without floor or ceiling eff ects,152 is quick and easy to implement, and can be completed by many patients.155 The test measures the distance that a patient can quickly walk on a flat, hard surface in a period of 6 minutes It evaluates the global and integrated responses of all the systems involved during exercise, including the pulmonary and cardiovascular systems, systemic circulation, peripheral circulation, neuromuscular units, and muscle metabolism.156 The association of 6MWT with other clinical measures of impairment and activity : Several investigators indicate that self -selected gait speed used for short distances is similar to gait speed in the 6MWT.31, 157, 158 However, contradicting results exist.159 Dobkin31 reported that the

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42 comfortable walking speed over 10 meters overestimated the distance walked in the 6MWT of individuals with gait speed < 0.5 m/s. Differences between studies may be explained by differences in study participants in terms of time from stroke and stroke severity. There is conflicting evidence regarding the association between the 6MWT and cardiovascular fitness in stroke patients. Pan g et al.62 examined the relationship between the maximal oxygen consumption during cycle ergometry and the distance covered in the 6MWT post -stroke. The 6 MWT distance had a low correlation with the maximal oxygen consumption. Therefore, it was concluded that cardiorespiratory status does not influence the ambulatory capacity as measured by the 6MWT. The low correlation between the 6MWT distance and cardiova scular fitness may be explained by the presence of stroke specific impairments. According to Kelly et al.158 and Tang and associates157 the contribution of cardiovascular fitness to distance walked in the 6MWT has been found to be moderate to high (r=0.560.84) in subacute stroke. During the chronic phase of stroke recovery, low to moderate (r=0. 4 0.57) correlations have been reported.62, 160, 161 Howev er Patterson and associates102 reported that variance in the 6MWT was largely explained by cardiovascular fitness, especially for those with gait velocities > 0.48 m/s for short distances. D ifferences between studies in regard to contribution of cardiovascular fitness to 6MWT may be due to differences in sample characteristics (e.g., stroke severity, time from stroke, and co -morbidities) as well as the method used to determine cardiovascular fitness. For example, either cycle ergometry or treadmill walking protocols can be used to determine peak oxygen consumption. However, cycle ergometry underestimates cardiovascular fitness in healthy individuals162 and particularly in deconditioned populations,163 mainly because it recruits less muscl e mass than treadmill walking

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43 protocols. In addition, cycle ergometry and treadmill walking protocols yield different oxygen consumption values as they entail different muscle activation patterns.164 The available published data also suggest that 6MWT is related to lower extremity impairment and balance control. For instance, Pohl and associates165 concluded that the lower limb FMA and BBS scores explained 45% of the distance variance of the 6MWT. Pang e t al.62 reported that balance was the major contributor for 6MWT distance, accounting for 66.5% of its variance. Patterson et al.102 established that the variance in 6MWT was largely explained by balance, especially for those with gait velocities of < 0.48 m/s for short distances. Accordingly Patterson and colleagues102 and Eng and associates148 reported moderate to high correlations (r=0.66 0.78) between the 6MWT and the BBS. 6MWT also correlates with strength and lean mass. For example, Patterson and colleagues102 reported that the 6MWT correlated significantly with paretic leg strength, non paretic leg strength, and paretic lean mass, but not with percentage of body fat or non -paretic lean mass. Moreover, non -paretic leg strength was independently associated wi th the 6MWT. On the other hand, Pang et al.62 reported that leg strength and spasticity explained to a smaller extent 6MWT distance. Finally, there is evi dence that walking endurance, as assessed by the 6MWT, significantly correlates with community reintegration in people post -stroke.100, 101 6MWT advantages and clinical uses are evident. However, there are a few challenges associated with this measure: (1) the 6MWT cannot assess other important aspects of gait such as quality of movement;152 (2) it has been suggested that the 6MWT provides insight into endurance level of persons with stroke, as heart ra te and systolic blood pressure increase significantly with the test.165 However, there are a number of stroke specific impairments that could potentially

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44 alter the outcome of the test (e.g., muscle weakness and balance impairment);148 and (3) the test is measured indoors rather than in the participants natural environments.28 2. 4.1.3 Timed Up and Go The Timed Up and Go test measures the overall time in seconds to complete the following functionally important tasks: standing up from a standard armchair, walking a distance of three meters, turning, walking back to the chair, and sitting down again.166 The Timed Up and Go test is reliable,167 quick, objective, easy to administer, can be accomplished in the community, and requires n o specialized equipment or training.168 In addition, the test corre lates well with strength and 6MWT, and is able to differentiate the patients from healthy elderly subjects.167 In healthy community dwellin g, persons 65 85 years of age performed the Timed Up and Go in less than 20 seconds, without an assistive device.169 In addition, in community-dwelling frail elders, a cut -off score of > 13.5 seconds predicts falling.170 Shumway Cook171 reported that adults without neurological impairments, who are independent with mobility and balance, are capable of completing the test in less than 10 seconds. However, the test may not be suitable for use among indivi duals exhibiting cognitive impairment.172 In addition, the test attempts to derive mobility fro m brief observation, dependent on instructions given by the tester, and does not directly measure actual activity in the home environment. 2. 4.1.4 Functional Ambulation Classification The FAC scale is a common gait assessment distinguishing 6 levels of wa lking ability, on the basis of level of physical support needed by persons to ambulate safely.173 This instrument is a quick and simple measurement of walking, and is cost effective (requires only stairs and 15 meters of indoor floor).151 Furthermore, re search indicates that the test has excellent reliability,

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45 good concurrent and predictive validity, and is responsive to change over time in patients with hemiparesis after stroke.173 FAC score correlates well with walking speed, step length,174 176 the distance walked in the 6MWT,173 cadence, and temporal -distance measures.174, 175 Furthermore, FAC score is associated with standing balance control gene rated from the non-paretic side.177 In addition, Mehrholz et al.173 suggest that a dichotomized FAC (FAC > 4 and FAC < 4) may be use ful in predicting level of community ambulation with high sensitivity and specificity. However, the use of FAC to predict function has been limited to validation with questionnaires and clinical examinations and has not been validated with a quantitative m easure of walking at home and in the community. 2. 4.1.5 Rivermead Mobility Index The Rivermead Mobility Index is a dichotomous scale consisting of 15 items assessing persons ability to perform common daily movements.178 This outcome measure has 7 items that assess locomotion, spanning a range of disabilities: walking inside with a device if needed, managing stairs without help, walking outside on even ground, walking inside with no aid, walking outside on uneven ground, going up and down four steps, and running.179 This measure is a widely used rehabilitation medicine outcome measure,179 as it is a simple quick test, requires no special equipment or training, and can be conducted in a vari ety of settings.179 In addition, the t est has good validity within the stroke population,178, 180 is sensitive to change over time,180 and highly correlates with BBS and Barthel Index (BI) scores.180 Despite Rivermead Mobility Index popularity, there is no published data regarding the psychometric properties of the items that assess locomotion. In addition, the test does not take into consideration increases in mobility achieved through assistive devices or environmental

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46 modifi cations,179 is subjective as it is bas ed on patients reported ability to ambulate, and does not provide direct measurement of ambulation in the ecological environment. 2. 4.1.6 Barthel Index BI is a commonly used stroke outcome measure assessing the extent to which persons can independently fu nction. This measure was developed for use in persons post -stroke, and with other neuromuscular or musculoskeletal disorders.181 The measure consists of 10 common activities of daily living items (e.g., feeding, bathing, and dressing), including ambulation on level surface s and stair climbing. The index yields a total score out of 100-higher scores, representing a greater degree of functional independence.182 A score of 0 20 suggests total, 21 60 severe, 61 90 moderate, and 9199 slight dependence.183 The measure has excellent reliability and validity,184 predicts length of inpatient hospitalization,185 and is widely used for stroke.3, 186 However, the psychometric properties of the locomotion items were not investigated. In addition, the measure has reported la rge ceiling and floor effects,187 and is established via self -report from the patient/friends/relatives/nurses,188 rather than through direct measurement of what is completed within the community. 2. 4.1.7 Functional Independence Measure The FIM is a widely used outcome measure post -stroke, assessing disability and burden of care.189 The FIM is comprised of 18 items (many of the same items of the BI) evaluating the following areas of function: self -care, sphincter control, mobility, locomotion ( distance walked and ability to climb stairs), communication, and social cognition.190 The items fall into two domains: motor (13 items) and cognitive (5 items). Each item on the FIM is scored on a seven point Likert -scale ranging from 1 (patient is dependent and able to perform le ss than 25% of the activity) to 7 (patient is independent without the use of assistive devices).191 The final summed score ranges from 18 (indicates the lowest level of function) to 126 (denotes the highest level of

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47 function). In the literature different FIM values were used to denote mild, moderate and severe post -stroke impairments.189 Patients had the following FIM score ranges: 70192 to 97 for mil d stroke,193 37192 to 54 for severe stroke,193 and moderate stroke ranging between these extremes.192, 193 Subscales score of the motor and cognitive domains can also be calculated.194 The use of a 7 -point scale increases FIMs sensitivity.3 Moreover, the absolute admission FIM score is a strong predictor of outcome disability and discharge destination.189 However, the FIM suffers from a few shortcomings. First, there is no information regarding the psychometric properties of FIM locomotion items. Second, it has ceiling and floor effects at the upper and lower ends of function.3 Third, FIM reliability is dependent on tester.195 Finally, FIM ratings can be acquired by patient or telephone interview or medical records,196 rather than via objective observation of performance within the community. 2. 4.1.8 Frenchay Activities Index Frenchay Activities Index is a str oke specific outcome184 which measures the ability to conduct a broad range of everyday life activities in the recent past.28 The measure contains 15 items from three domains: domestic chores (e.g., preparing meals), leisure/work (e.g., social outings) and outdoor activities (e.g., walking outdoors for over 15 minutes). The measure is based on patient interview, with or without family, of what the patient did retrospectively at three and six months. It uses a four point scale ranging from 1 (never) to 4 (frequent).197 Within the stroke population, Frenchay Activities Index correlates well with BI198, 199 and the FIM motor subscale.200 In addition, the outdoor activity domain is strongly related to visuospatial ability.199 The Frenchay Activities Index is simple, quick, and suitable to use with proxy administration.184 Moreover, within the stroke population, Frenchay Activities Index has excellent validity201 203 and reliability.184, 198, 201, 203, 204 However, the scale has several problems associated with it. First, there is no information regarding psychometric properties of the locomotion items. Additionally,

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48 Frenchay Activities Index does not provide standard guidelines for administration184 and has a ceiling effect at six months post -stroke.205 Finally, the measure relies on recall, perception, and estimation of physical activity rather than direct measurement of performance in the community.10 In summary, although quick, inexpensive, and psychometrically sound, post -stroke activity level mobility outcomes evaluate the individual at a specific point in time, in a nonfamiliar environment, attempt to derive mobilit y from brief observations, and overall have limited accuracy in the assessment of actual daily activity of the population of interest in their natural environment. For a summary of the strengths and weaknesses of the aforementioned commonly used post -stro ke activity level mobility outcomes, see table 2 5. 2. 4.2 Post -Stroke Participation and Quality of Life Mobility Outcomes Measures of participation and quality of life often contain community ambulation items.28 Below is a description of three commonly used post -stroke participation level and quality of life mobility outcomes. 2. 4.2.1 Stroke Impact Scale The SIS is a comprehensive an d psychometrically robust stroke specific outcome measure,208, 209 which was developed from the patient, caregiver, and stroke expert perspective.208 210 It was designed to assess multidimensional health related quality of life stroke outcomes, i ncluding strength, memory and thinking, emotions, communication, activities of daily living, hand function, participation, and ability to be mobile at home and in the community.13 Duncan et al.13 evaluated the concurrent validity of SIS version 2.0 with the BI, FIM, FMA, Mini Mental State Examination (MMSE), National Institutes of Health Stroke Scale (NIHSS), Medical Outcomes Study Short Form (MOS SF36), Duke Mobility Scale, and

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49 Geriatric Depression Scale. The authors reported high correlations between SIS Mobility domain and FIM Motor (r=0 .83), BI (r=0.82), Duke Mobility Scale (r=0.83), and MOS SF36 Physical Functioning (r=0.84). In addition, all domains of the SIS 2.0, with the exception of the memory and thinking and emotion, were able to differentiate between people based on stroke sever ity, as determined by the four Rankin levels of stroke severity. Duncan and associates13 also reported that SIS version 2.0 sensitivity is affect ed by stroke severity and time since stroke. For patients with mild stroke, all SIS domains show statistically significant change from 1 to 3 and 1 to 6 months post -stroke. However, statistically significant change was not observed between 3 and 6 months post -stroke for the following domains: hand function, mobility, activities and instrumental activities of daily living, and participation. For persons with moderate stroke, statistically significant change was also observed for all domains at both 1 to 3 and 1 to 6 months post -stroke. From 3 to 6 months post stroke, statistically significant change was observed for the domains of mobility, activities and instrumental activities of daily living, and participation. More recently, Duncan and associates211 examined the concurrent validity of the SIS version 3.0 and SIS 16 using Pearson correlations. Various SIS domains were correlated with the Mini -Mental State Exam, BI, Lawton Instrumental Activities of D aily Living, and the Motricity Index. The authors reported adequate correlation between the MMSE and SIS memory domain (r=0.42), and excellent correlation between both BI and SIS activities of daily living/instrumental activities of daily living (r=0.72) a nd mobility domains (r=0.69). 2. 4 .2.2 Medical Outcomes Study Short Form -36 The MOS -SF36 questionnaire is a widely used3 multi item scale that assesses eight health related quality of life domains: physical functioning, limitations in social activities because of physical or emotional problems, limitations in us ual role activities because of physical health

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50 problems, bodily pain, mental health, limitations in usual role activities because of emotional problems, vitality (energy and fatigue), and health perceptions. The physical functioning domain consists of nume rous mobility items, such as the ability to run, walking more than a mile, and walking several blocks.212 Correlation coefficients of the MOS SF36 physical functioning subscale and the BI in first -time stroke patients were reported to be excellent (r=0.81).213 The measure has excellent validity,214 is brief, can be self administered or administered by phone or interview, is simple to administer, and215 standardized norms are available.214 Furthermore, the measure is capable of discriminating those with first time stroke from their age and gender matched controls.216 However, patients with tran sient ischemic attack and stroke had similar scores, thus it is questionable whether MOS SF36 is able to discriminate and respond to clinical changes in patients with stroke.187 In addition, the measure has possible floor3, 186 and ceiling effects,217 and low rated of agreement between proxy respondent and patient respondent ratings.218 Finally, the psychometric properties of MOS SF36 within the first year post stroke are not well known, as most of the current data are from patients one year or more post -stroke.187 2. 4.2.3 EuroQol Quality of Life Scale EuroQol Quality of Life Scale (EQ 5D) is a self administered questionnaire developed to assess self -perceived current health relate d quality of life. The EQ 5D has two parts. The first section evaluates the level of difficulty (some, moderate, or extreme) individuals encounter at the time of assessment of task performance within the following five dimensions: mobility, self -care, usua l activities, pain/discomfort, and anxiety/depression.219 The second part of the EQ 5D consists of a visual analogue scale. Respondents rate their present state of health from 0 (worst imaginable) to 100 (best possible).214 The measure is short and requires no special training to administer. In addition, within the stroke population the EQ 5D has a high response rate220 and has been evaluated for proxy use.221

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51 Within the stroke population, EQ 5D significantly correlates with BI (r=0.7) and Frenchay Activities Index (r=0.65).222 Johnson et al.223 reported that in adults (mean age 52 years) with considerable number of chronic medical conditions (e.g., diabetics, hypertension, and heart diseases), increa sing age relates to all EQ 5 dimensions except for anxiety/depression. Moreover, employment status, education, household income, marital status, and presence of chronic medical conditions are all significantly related to all EQ 5D dimensions scores in the expected direction. It was also demonstrated that mobility, self -care and usual activities correlate with MOS SF36 physical functioning (r=0.57, 0.65, and 0.63, respectively). Nevertheless, the ability of elderly acute care patients (65 years and over) to self -complete the questionnaire is directly related to age and cognitive function,224 the measure has low reliability with a proxy respondent post -stroke, 221 and is based on subjective assessment rather than direct observation of mobility level. In summary, par ticipation and quality of life self report approaches are especially useful in large population studies.14, 15 However, such instruments are subjective in nature and rely on factors related to recall and cognitive ability which contribute to the inaccuracy of these approaches.16 Nevertheless, outcomes such as the SIS, provide information on how stroke has impacted the participants ability to conduct specific activities.13 Therefore, it will be useful to evaluate the association between the SIS and other ambulation outcomes such as gait speed and SAM. This examination will assist in establishing how gait speed and SAM relate to what a person can actually do in the community. SAM is likely to have stronger association with the SIS because it reflects what a person is actually doing at home and in the community, rather than in the clinic. For a summary of th e strengths and weaknesses of the aforementioned commonly used post -stroke participation and quality of life outcomes, see table 2 6.

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52 2. 5 Direct Objective Measurement of Home and Community Ambulation The gold standard for walking activity and community am bulation should measure performance, the total ambulatory activity conducted by an individual in their environment while performing usual daily activities. The aforementioned activity and participation commonly used outcome measures were found to be psychometrically sound within the stroke population. However, none of these measures can meet the gold-standard requirements for community ambulation as they rely on patient report or observer -rated standardized scales, or use laboratory -based measures to chara cterize locomotor performance. These conventional instruments do not measure free living ambulatory behavior or mobility -driven social participation.12 Below is a review of outcomes which enable an objective and d irect measurement of physical activity at home and within the community. 2. 5. 1 Doubly Labeled Water Method Doubly labeled water method is regarded as the gold standard for validation of other instruments measuring physical activity because it provides a highly accurate estimate of energy expenditure in free living situations. It involves the ingestion of an isotopic form of water that is eliminated from the body through metabolism.225 With use of the doubly labeled water method, physical activity can be measured over a 2 -week period, without interfering with the partic ipants natural activity behavior.226 It is an easily applicable technique, which requires the collection of urine samples only. Unfortunately, it is high in cos t and can be used to measure average physical activity level only.227 2. 5 .2 Pedometers Pedometers can also be used to provide an objective estimate of community ambulation. Pedometers are movement monitors designed to measure number of the steps an individual takes during ambulatory activity, such as walking or running.24, 228, 229 These devices are relatively

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53 inexpensive, capable of capturing intermittent or continuous activity, and accurate in counting steps of individuals without impairments or gait speeds of at least 0.9 m/s.230 H owever, pedometers are unable to provide information on the pattern or duration of specific activities (i.e., the number of steps a person accumulated at 4:00 PM) because they do not have an internal clock, and they may not take activity intensity into acc ount. Moreover, pedometers may underestimate steps taken at gait speeds of less than 0.9 m/s20 22 or unsteady and irregular gait patterns.1921, 231 Accordingly, within the stroke population, Macko et al.19 reported that the SAM, not the pedometer, provides accurate and reliable measurements. Pedometers inaccuracy within the stroke population may be due to stroke survivors slow gait speeds and irregular and unsteady gait patterns. Pedometers are typically worn at the waist. Therefore, they tend to function best during vigorous walking (gait speed > 0.9 m/s), when the motion of the hips is more pronounced.20 22 In addition, pedometers respond to vertical (up and -down) displacement only and are not capable to detect movements in the sagittal (forward/backward) and frontal (right/left) planes. Therefore, pedometers tend to underesti mate steps taken at irregular gait patterns.20, 21 2. 5.3 Accelerometers Accelerometers measure body motions in terms of acceleration.232 Acceleration is defined as the change in speed with respect to time. Speed is the change in position with respect to time.233 Numerous accelerometers are available in the market. The Minisun -Intelligent D evice for Energy Expenditure and Activity (IDEEA) monitor and the SAM are often used in research. Minisun IDEEA monitor is a system of lower extremity accelerometers that collects spatiotemporal data during walking. One accelerometer is attached to the che st, one to each thigh, and one to each medial foot. The system is capable to measure walking speed, cadence, stride length, single limb support, double limb support, stance, and swing time.232 Despite the

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54 wide range of information that the MinisunIDEEA provides, this review will mainly focus on a particular accelerometer, the SAM, for various reasons: (1) SAM is a highly accurate accelero meter that directly and continuously counts and records individuals number of steps taken while ambulating in their natural environment,14, 18 (2) SAM has a feature of adjustable calibration which enables it to accommodate numerous gait styles in both humans and animals,232 (3) SAM was designed to overcome the limitations of previously available devices by recording a detailed minute by minute activity profile for extended periods,18 and (4) a variety of techniques are available to analyze SAM data.14 Below is a review of the SAM as a home and community ambulation outcome measure post -stroke. 2. 6 Step Activity Monitor as a Home and Community Ambulation Outcome Measure Post -Stroke 2. 6.1 Psychometric Properties in Persons Pos t-Stroke Numerous studies found that accelerometers are reliable indicators of habitual physical activity in healthy adults,234 238 elderly,239, 240 patients with chronic obstructive pulmonary disease,241, 242 peripheral arterial disease,243245 neurological disorde rs (e.g., brain injury, multiple sclerosis, and Parkinson)246249 and lower extremity amputation.250 Within the stroke population, Macko et al.19 investigated the accuracy and reliability of SAM against visually counted strides. Participants conducted two 6MWT (measure d on two different days) at their usual pace, and two 1 -minute walks at their self -selected and fastest comfortable paces. The studys sample consisted of 16 gait impaired chronic hemiparetic stroke patients. SAM was found to be highly accurate during the 1 -minute walk at self -selected speeds (98.7%), fastest comfortable walking paces (97.8%), and during the 6MWT (98.7%). The test retest reliability for total number of strides measured during the 6MWT on separate days was high (r=0.975). Accordingly, the au thors

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55 concluded that the SAM was a valid and reliable method for measuring ambulatory activity in hemiparetic stroke patients with a broad range of gait deficits. Haeuber and colleagues6 have investigated the reliability and validity of the SAM to quantify home and community ambulation post -stroke. Study participants were 17 chronic (> 6 month post -stroke) ischemic stroke survivors with mild to s evere gait impairments (gait speed ranged from 0.08 1.28 m/s). Within 3 weeks, each participant repeated the 48 hour monitoring period wearing both SAM and Caltrac (a device that estimates total energy expenditure). Intraclass correlation coefficients (ICC) were calculated to analyze the test retest reliability of the SAM derived from the two 48 hour monitoring periods. Pearson correlation coefficients were used to analyze the strength of the relationship between Caltrac estimates of energy expenditure and total ambulatory activity, as derived by SAM. SAM total stride count per 24 hours across each 48 hour monitoring period showed excellent test retest reliability (r=0.96). Simple regression analysis revealed a positive relationship between the Caltrac and S AM derived total stride counts over the two 48 hour monitoring periods (r=0.77). Thus, the investigators concluded that the SAM is a reliable and valid measure of free living physical activity in this population. Shaughnessy and associates12 compared SAM with established instruments (FIM mobility, SIS mobility and self selected gait speed over 30 foot) to assess ambulatory recovery in sub acute stroke patients with severe post -stroke hemiparetic gait deficits (mea n gait speed of 0.37 m/s). Participants were assessed in the aforementioned outcome measures two weeks post rehabilitation discharge and three months later. Significant improvement was measured in SAM derived daily steps but not with self selected gait spe ed, FIM, or SIS mobility scales. The authors

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56 concluded that SAM is a sensitive index of ambulation in subacute stroke patients that measures improvements not captured by other conventional outcome instruments.12 In summary, SAM has been found to provide reliable and valid information regarding habitual physical activity in the stroke population. SAM also has proven to be sensitive to changes in physical activity post -stroke. Although SAM has sound psychometric pr operties within the stroke population, in order for the SAM to be a valuable tool in clinical trials, determining the inferred rate of compliance with SAM protocol post -stroke is essential, as participant compliance in wearing the monitor is critical for o btaining accurate physical activity measurement.15 2. 6.2 Protocol Compli ance To date, no study has evaluated factors associated with lower extremity accelerometer monitoring protocol compliance within the stroke population. However, there is limited published data on factors associated with upper -extremity accelerometer monito ring compliance post -stroke and on lower extremity accelerometer monitoring compliance in non-stroke populations. Below is a review of methods to identify inferred accelerometer wearing period, identify minimal wear requirement for a valid day, potential f actors which may have an impact on level of compliance, and reported pedometers and accelerometers levels of compliance. 2. 6.2.1 Identification of inferred accelerometer wearing period Identifying when a participant is not wearing the accelerometer is dif ficult because long continuous bouts of accelerometer inactivity (continuous zero readings) may mean either that the participant removed the accelerometer or was completely inactive (e.g., sleeping).251 Therefore, researchers need to infer participants compliance with the accelerometer protocol. A number of investigators have addressed the issue of compliance by quantitatively defining what constitutes a complete monitoring day. However, there is no general consensus on how to

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57 estimate interruption in wearing time during the day, or the minimum number of hours the accelerometers should be worn each day.251 Masse et al.251 reviewed decision rules researchers have used in the past to process and summarize accelerometer data. Sixty four studies were identified. Only eight studies reported how they determined the wearing period. These studies used 10 min,252258 15 min,255 20 min,259, 260 and 30 min261 of continuous zero counts to estimate interrup tions in accelerometer wearing time. Masse et al.,251 provided three additional definitions for non -wearing time: 1 Method 1: One unit of non-wearing time was defined as 60 minutes of consecutive zero counts when counting began each time a zero count was encountere d.251 2 Me thod 2: One unit of non-wearing time was defined as 20 minutes of consecutive zero counts in a given day when the data was scanned in 20-min blocks.251 3 Method 3: One unit of non-wearing time was defined as 20 minutes of consecutive zero counts in a given day when counting began each time a zero count was encountered.251 No evidence in the literature supports an age -specific cut point for interruption in wearing time.251 However, stroke patients are typically older individuals with secondary motor deficits who are likely to remain still for long periods. Therefore, using a compliance definition which permits long periods of interruption in wearing time may be appropriate in this population. In addition, on account of the lack of standardized methods on how to estimate compliance wit h the accelerometer monitoring protocol, a pilot study was conducted to test several different methods to estimate inferred compliance with SAM protocol post -stroke (see Appendix B). Findings from this pilot were used to direct the methodology for chapter 3, inferred compliance with a SAM protocol to measure free physical activity in moderate to severe subacute stroke survivors. 2. 6.2.2 Identification of minimal wear requirement for a valid day What is the minimum of hours or proportion of the target monit oring period the accelerometers should be worn each day in order for a day to be included in the analysis?251A

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58 number of investigators have addressed this issue of compliance by quantitatively defining what constitutes a complete monitoring day. However, there is no general consensus on the minimum number of hours the accelerometers should be worn each day. Masse et al.251 investigated the criteria that researchers use to determine minimal wear requirement for a valid day. The review revealed that out of the 64 studies id entified only 21 studies reported the criteria they used to determine whether the accelerometer was worn a significant proportion of the day. The identified studies used cut points of 1,262 4,263 5,37 6,264 8,265267 9,261 10,252256, 258 11.20,268 12,269, 270 15.60,271 and 16.67259 hours per day. In post -stroke arm activity accelerometer monitoring studies, Uswatte et al.272, 273 deemed recordings invalid if they were of insufficient length to yield reliable data (< 8h of data on a single day or 16 hours of data total). The minimal we ar requirement can also be based on percent of awake time in which the accelerometer was worn (e.g., 80% of waking hours). This definition of minimal wear requirement takes into account intraindividual and intraday variability in waking -sleeping patterns.251 Other investigators used the 70/80 rule. According to that rule, a day can be defined as the period during which at least 70% of the study population has recorded accelerometer data, and 80% of that observed period constitutes a minimal day for inclusion in dat a analysis.15, 251, 274 Patient compliance can also be categorized as poor (0 25%), low (2650%), moderate (51 75%), or high (76 100%).275 2. 6.2.3 Factors associated with accelerometer monitoring protocol compliance Personal factors : Uswatte and colleagues276 examined the p sychometric properties of accelerometry for monitoring real -world arm activity in patients during the sub acute phase of stroke recovery (3 to 9 months post -stroke), with mild to moderate hemiparetic arm motor impairment. The authors reported that compliance with accelerometer monitoring of real world arm activity post -stroke did not differ for older and younger individuals. However, the authors

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59 did not report samples age variability. Therefore, the null findings may be attributable to limited variability in age. Despite Uswatte and colleagues276 results, there are theoretical grounds to hypothesis that advanced age will be associated with lower accelerometer monitoring non compliance. Impairment of motor skills, decreased vision, and cognitive decline are more common among the elderly and may influence the ability to handle the accelerometer.277 In addition, Uswatte and colleagues276 concluded that both gender and literacy were not related to com pliance with upper extremity accelerometer monitoring either. The authors reasoned that highest level of education may not be related to accelerometer monitoring compliance per se, but rather may have an impact on the methods that should be used to promote compliance with the monitoring protocol and to describe appropriate use of the accelerometer to the study population.278 Cognition : No study examined cognition effects on accelerometer monitoring compli ance. Cognition may be associated with post -stroke accelerometer monitoring compliance for numerous reasons. First, cognitive declines may have an impact on prospective memory, which refers to the ability of an individual to remember to carry out a task in the future.279 Therefore, declines in prospective memory could affect an individuals ability to remember to wear an accelerometer and could thus directly affect compliance to an accelerometer protocol.278 Second, impaired cognition may hamper the individuals ability to understand the accelerometer protocol which may also result in decreased compliance. Depression : The effect of depression on compliance wi th medical treatment has been examined in numerous populations. DiMatteo et al.280 in their meta analysis of the effects of depression on pa tient adherence with medical treatment reported greater noncompliance with medical treatment recommendations among individuals with symptoms of depression. Pappa and colleagues,281 also

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60 conclude d that higher rates of depression were a significant predictor of noncompliance with glaucoma treatment. Depression was also associated with poor treatment compliance in 102 patients with asthma, ages 18 70.282 Depression may also be associated with decreased compliance with accelerometer monitorin g protocol as common depression symptoms consisted of difficulty concentrating and remembering details.283 Therefore, an individual who is depressed m ay be more likely to forget to wear an accelerometer as instructed or forget how to wear it properly. In addition, other common symptoms of depression such as pessimism, persistent sad feelings, loss of interest in activities, hopelessness feelings, and fa tigue, may also have a negative effect on the individual with depression motivation to wear the monitor.283 Mobility impairment (gait, balance, and b alance self -efficacy) : Individuals with mild gait impairments, balance control impairment, and self -efficacy are more likely to comply with accelerometer protocol because they may place more importance on wearing the accelerometer than individuals with sev ere gait impairments, balance control impairment, and self -efficacy simply because they are performing the behavior being measured more frequently.278 Therefore, increased gait impairment, balance contro l, and balance self -efficacy may be associated with decreased accelerometer monitoring compliance. Upper extremity impairment : Though limitations in upper extremity motor control and dexterity due to stroke may make it difficult for the patient to use an a ccelerometer properly, it has been reported by Uswatte et al.276 that post -stroke level of arm function was not related to compliance wi th upper extremity accelerometer monitoring. One possible explanation for the luck of impact of upper extremity function on accelerometer compliance is that the study participants might have received support from their caregivers in applying the accelerom eter.

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61 Stroke related characteristics : Uswatte and associates276 reported that affected side and stroke etiology were not related to com pliance with upper extremity accelerometer monitoring in patients with sub acute stroke. In summary, increased cognition, younger age, lower depression levels, decreased gait impairment, better balance control, and higher balance self -efficacy may be ass ociated with increased compliance with accelerometer monitoring protocol post -stroke. Finally, gender, literacy, upper extremity impairment, and stroke related characteristics are likely to not be associated with post -stroke compliance with lower extremity accelerometer monitoring. 2. 6.2.4 Levels of compliance rates There is limited data on pedometer and accelerometer measurement levels of compliance. In a feasibility study of the use of accelerometers to measure physical activity in young adolescents, the percentage of students with complete accelerometer data for > 3 days was 92%.284 Cooper et al.285 reported a 77.7% compliance rate for a 7 -day accelerometer measurement in normal, overweight, and obese adults. According to Stel and colleagues,286 the feasibility of obtaining complete 7 -day pedometer data from men and women age 65 years and older was 84%. Silver et al.287 reported an 89% pedometer 10 -day compliance rate among community dwelling Hispanic older Americans. Within the stroke popul ation, Uswatte et al.276 reported that the participants wore accelerometers on both upper and lower extremity for approximately 86% of t heir waking hours. In a more recent study, Uswatte et al.272 reported that sub acute stroke patients wore upper ext remity accelerometers for approximately 76% of their waking hours. In summary, studies reported compliance rate varied according to the measured population, device used, number of recording days, and definition of non-compliance. It is reasonable to assume that stroke patients, due to possible post -stroke depression, mental

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62 processing deficits, and impaired gait, will have lower compliance rates than previously reported populations. Participants compliance in wearing the monitor is critical for obtaining a ccurate physical activity measurement,15 as non -compliance is usually co nsidered a source of bias. Therefore, in order to assess whether the SAM could be a valuable tool in clinical trials, it is important to establish post -stroke compliance rate with accelerometer monitoring. Based on the importance of compliance with acceler ometer monitoring protocol and on the available literature on accelerometer monitoring compliance, primary aim number 1 was established. 2. 6.3 Primary Aim Number One 2. 6.3.1 Specific Aim 1 To assess the rate of and predictors for inferred compliance with an accelerometer monitoring protocol two months post -stroke. 2. 6.3.2 Hypothesis 1 Increased cognition younger age, lower depression scores, decreased gait impairment, and higher levels of balance control and balance self -efficacy will predict increased i nferred protocol compliance. Gender, education, upper extremity motor control, stroke location, and stroke type will not predict inferred protocol compliance. Knowledge of compliance rate with the SAM protocol will enable researchers to make decisions reg arding who accelerometer monitoring is appropriate for and how to increase participant compliance in wearing the monitor. For example, if severe depression will be significantly associated with diminished protocol compliance, it will indicate that it may n ot be appropriate using the SAM as an outcome measure in persons post -stroke with severe depression until methods to increase compliance in this population were established.

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63 2. 6.4 Identifying Spurious Data Care should be taken in cleaning accelerometer dat a from data points that are outside the range of plausibility.15 Acceler ometer data can be corrupted by brief periods of device malfunction,15 e rrors in initializing or downloading stored records,251 and possible tampering by research participants.251 Nevertheless, studies utilizing accelerometers in field settings to monitor activity patterns commonly do not report the manner in which spurious data was identifie d. Masse et al.251 reviewed decision rules researchers used in the past to analyze accelerometer data. The authors reported that identifying spurious data was mentioned only in one out of 64 studies evaluated. In this study, data with distinct nonphysiological pa tterns (i.e. greater than 9 standard deviations away from the median) should be regarded as spurious data.252 On the other hand, Uswatte et al.,272 in an investigation of upper extremity accelerometer monitoring within the stroke population, d efined spurious data as values exceeding 3 standard deviations (SD) above the mean. It may be appropriate to define spurious data within the stroke population with a smaller SD from the mean or median, as the range of physical abilities of persons with str oke is smaller than in healthy individuals. Other methods to identify spurious data have been suggested. Masse et al.251 evaluated the impact of spurious data on outcome variables. Four conditions were evaluated, in three of them spurious data was defined as (1) > 20,000 counts, (2) > 16,000 counts, (3) no counts for 10 min, and (4) Spurious data were included in the analysis. Comparisons of the 4 different approaches and sensitivity analysis failed to demonstrate the impact of spurious data on the outcome variable s (wearing time, average activity, time spent in moderate and vigorous physical activity).

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64 2. 6.5 Data Outcomes SAM data provides information on individuals community ambulation volume, intensity and pattern. 2. 6.5.1 Ambulation volume outcomes Ambulation v olume refers to activity quantity and/or duration. The following ambulation volume outcomes can be derived from SAM: 1 Number of steps taken over 24 hours. Number of steps per day is defined as twice the total stride count in 24 hours.285Number of steps taken over 24 hours is the most commonly reported SAM index. Unfortunately, this outcome can be an incomplete descriptor of function because two subjects with nearly the same step count could accumulate their steps in very different manners (see figure 2 2). 2 Total activity level. Total activity le vel is defined as the tally of the number of one -minute intervals with stride counts of > 1. Can be presented as the percentage of the total time in a 24hour period where steps are recorded.25 3 Number of steps in one min intervals. The mean, median, minimum, and mode of number of s teps taken in active 1 min intervals. 4 Number of steps in five minutes intervals. Number of steps in five minutes intervals is defined as the mean, median, minimum, and mode of number of steps taken in 5 -minute intervals in which activity was recorded. 2. 6. 5.2 Ambulation intensity outcomes Ambulation intensity refers to the overload on the cardio respiratory system. The following ambulation intensity outcomes can be derived from SAMs: 1 Peak activity index. Peak activity index represents the maximal number of steps a person took in a predetermined period of time (e.g., 1 and 5 min intervals). 2 Time spent in low, medium, and high absolute activity intensity. Absolute step activity intensity can be stratified as low (less than 10 steps per minute), medium (1020 steps per minute) and high (more than 20 steps per minute). 3 Percentage of the day spent in low, medium, and high relative activity intensity. Training on less than 60% of heart rate reserve is considered low intensity training, 60%85% is moderate in tensity and > 85% is high intensity.135 Similarly, low relative intensity can be defined as walking at less than 60% of peak activity index, moderate intensity 6085% of peak activity index, and high intensity > 85% of peak activity index.

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65 2. 6.5.3 Ambulation pattern outcomes Like other physiological signals, such as heart rat e, gait measures are not constants but fluctuate with time.137 The following activity pattern outcomes can be evaluated: 1 Number of activity bouts. Number of activity bouts is defined as the number of 1 -min intervals for which the subject switched from inactivity (stride count=0) to activity (stride count > 0).14 2 Number of steps in an activity bout. Number of steps in an activity is defined as activity bout mean, median, minimum, maximum, and mode number of steps. 3 One minute variability of activity. One minute variability of activity is defined as the coefficient of variation (CV) of 1 -min of stride count values, excluding those 1 min intervals with zero steps.14 CV is a measure of variability calculated as the SD of the measures expressed as a percentage of the mean (CV=SD/mean X 100) One of the CV advantages is that it accounts for differences i n the magnitude of the mean (a measure of relative variation).288 4 Five minute variability of activity. F ive minute variability of activity is defined as the stride counts CV of 5 -min intervals in which activity was recorded. 5 Activity bouts variability. Activity bouts variability is defined as the CV of number of steps in activity bouts. 6 Activity randomness. Activity randomness is defined as the amount of randomness of minute to -minute activity fluctuations in each day of recorded ambulatory activity. Randomness of activity can be quantified by analyzing 1 -minute stride counts using approximate entropy.289 Approximate entropy can be used to determine the probability that sequences of consecut ive 1 -minute stride counts repeated throughout the longer temporal sequence of 1,440 daily 1 -minute intervals. Approximate entropy generates a unitless number between 0 and 2. Zero values correspond to a time series in which short sequences of data points are perfectly repeatable. Values of 2 correspond to time series for which any repeating sequences of points occur by chance alone.290 In summary, SAM data can be analyzed in a variety of ways. However, certain SAM derived outcomes may be potentially more useful than others for detecting differences in ambulatory activity according to age and health. Therefore, researchers face the deci sion of which SAM derived outcomes to use in their studies. Understanding steps activity daily values, between day patterns of activity, and SAM indices relationships to other outcome measures, may aid researchers in making an informative decision on which SAM indices to analyze.

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66 2. 6.6 Daily Values 2. 6.6 .1 Ambulation volume Mean number of steps taken per day : A value of 10,000 steps per day is recommended for health promotion.291, 292 A value of 10,000 steps/day is approximately equivalent to an energy expenditure of 300 to 400 kcal/day (depending on body size and walking speed).293 This specific value originated from Japanese walking clubs and a Japanese manufacturer who introduced the pedometer to the market in 1965, under the name of manpo-kei which translates to ten thousand steps meter.293 Nevertheless, there is evidence that 10,000 steps/day is associated with indicators of good health including decreased body fat and blood pressure.293295 According to Tudor Locke,294 healthy adults take between 7000 to 13,000 steps per day. However, these values may reflect individual p articipation in exercise and sports as part of habitual activity. The impact of these activities must be considered separately if we are to identify a value more typical of uninterrupted daily activity.295 Welk and associates296 reported that on days without physical activity, young adults (mean age of 29) took approximately 7400 steps. Bassett and colleagues10 reported that healthy adults (average age 40 years), took approximately 6000 steps/day, not including number of steps taken during sports and recreation. Similar values were reported by Tudor -Locke et al.297 (i.e. 6000 steps/day) in a non-exercise day sample with an average age of 69 years. Thus, current evidence supports that healthy adult usual daily activity is approximately 6000 7000 steps/day (excluding volitional sport or exercise ).295 A recent study by Cavanaugh et al.14 examined the number of daily steps taken by younger (aged 36.6 + 2.6) and older heal thy adults (aged 83.7 + 2.3), and older adults with functional limitations (aged 79.3 + 4.5). The authors reported no significant differences in the number of daily steps between healthy younger and older adults (healthy younger adults: 11,074 steps; older adults: 9,981 steps). However, older adults with functional limitations accumulated significantly

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67 fewer daily steps than healthy younger adults (healthy older adults with functional limitations: 7,681 steps, healthy older adults: healthy younger adults: 11,074). Within the stroke population it has been reported that at discharge from inpatient rehabilitation (mean days post -stroke at discharge was not reported), the average number of steps taken per day was 1536, and at three months post discharge 2765 st eps/day.12 Four studies used accelerometer measurement with mild to severe stroke survivors during the chronic phase of stroke rehabilitation (between 10 to 41 months post -stroke). The average number of steps parti cipants took in these studies was 2520 steps/day (between 1380 to 3035 steps per day).6, 23, 34, 298 As expected, the aforementioned data suggest that stroke patients take considerably less steps/day than healthy adults. Moreover, the average number of steps participants took in these studies is far below the recommended 10,000 steps per day for health promotion.291, 292 Mean activity levels : Healthy older and younger adults are active approximately 60% of the day.14, 299 In contrast, Cavanaugh et al.14 reported that healthy younger adults were active approximately 30% of the day, healthy older adults 25% of the day, and older adults with functional limitations 20% of the day. Persons with neurological disorders and/or functional limitations/illness are also active approximately 20% of the day.14, 190, 300 Knowledge of mean activity levels is important as it is a good indicator of progress in rehabilitation programs and of ambulatory activity associated wit h age.14 2. 6.6 .2 Ambulation intensity Meijer et al.299 reported that healthy elderly spend 82% of the day at low, 15% at moderate, and 4% at high intensity. Michael et al.34 quantified community ambulatory activity profiles in terms of step counts and i ntensity (low intensity < 16 steps per minute; medium greater than or equal to 16 and < 30 steps per minute; and high greater than or equal to 30 steps per minute), along with cardiovascular fitness in terms of peak oxygen consumption, in a

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68 convenience sam ple of communitydwelling volunteers with mild to moderate (NIHSS mean=3.57 points) chronic (mean time post -stroke=10 month) hemiparetic stroke. Participants spend 45% of their activity at low, 46% at medium, and less than 3 minutes a day at high intensities. However, consideration needs to be taken when comparing the aforementioned study results, as each study used different definitions to determine activity intensity. 2. 6.6 .3 Ambulation pattern Number of activity bouts and activity randomness : Only one s tudy reported number of activity bouts and randomness.14 The authors reported that healthy older adults accumulated a similar number of steps (and minutes of activity) as did healthy younger adults, but did so in fewer bouts (healthy old er adults: 67 activity bouts; healthy younger adults: 77 activity bouts).14 This finding may indicate that initiating ambulatory activity becomes more difficult with age. Whether this phenomenon relates to physiological, psychological, or other mechanisms is not yet known14 Older adults reporting functional limitations accumulated fewer bouts of activity and less random minute to minute activity fluctuations than healthy younger adults (older adults with functional limita tions: 62 activity bouts and 0.38 approximate entropy; healthy younger adults: 77 activity bouts and 0.492 approximate entropy). Older adults reporting functional limitations displayed no significant differences from healthy older adults in both activity b outs and activity randomness. Activity variability : Busse et al.25 reported 30% daily activity variability in neurological patients with mobility restrictions. According to Cavanaugh and associates,14 healthy and older adults with functional limitations display le ss variable minute to -minute activity than healthy younger adults (older adults with functional limitations CV=85.2%; healthy younger adult CV=100%). Thus, variability of minute to -minute ambulatory activity may represent a potentially useful indicator of age associated walking.

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69 In summary, SAM values are sensitive to age, functional limitations, and illness. Appendix A provides information on SAM values of healthy younger and older adults, persons post -stroke, and persons with functional limitations due t o illnesses other than stroke. 2. 6.7 Analysis of Between Day Patterns of Activity Differences To estimate consistency of physical activity behaviors across days, ICC have been used. A multiple day ICC of .80 is the usually desired value.301 For example, the daily physical activity levels of individuals with peripheral arterial disease were examined over two day periods, separated by one week. The authors reported ICC of .86 for st eps per day. These results suggest that 2 days of monitor was sufficient to capture the activity level of the study participants, and that a shorter sampling period may be sufficient for the sedentary population.302 2. 6.8 Relationships of Step Activity Monitor Volume, Intensity, and Pattern Indices with Impairment and Activity Measures 2. 6.8 .1 Ambulation volume Petrella et al.303 examined differences in daily pedometer measured ambulation activity between communit y -dwelling older adults above and below an empirically derived physical threshold that has been linked to independence. Twenty community-dwelling older adults were categorized into groups based on functional performance. The high functioning group took si gnificantly more steps/day (9503 + 4623) compared to the low functioning group (5048 + 2917). Cooper et al.285 determined the levels and patterns of daily activity in groups of normal -weight, overweight, and obese adults using accelerometry. Hour by-hour physical activity patterns demonstrated that obese participants have less step counts than the non -obese for nearly every hour of everyday. Similarly, numerous investigators reported that physical activity measured with a pedometer is inversely correlated with body mass index,294, 304 waist circumference,304 and percentage body fat.294

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70 The data from Cavanaugh et al.14 indicated the number of daily minutes of ambulatory activity did not distinguish between healthy younger and older adults, but did distinguish between healthy younger adults and older adults with functional limitations (healthy younger adults: 399.9 + 17.4; healthy older adults: 355.7 + 18.1; older adults with functional limitations: 298.0 + 27.6). Busse and colleagues25 investigated the reliability and validity of SAM in mobility restricted neurological patients (motor neuron disease, hereditary spastic paraparesis, peripheral neuropathy, myasthenia gravis, myositis, muscular dystrophy, thyroid myopathy, and Parkinsons disease) and healthy subjects. They reported a moderate correlation between 7 days me an step count and gait speed (as measured at a self -selected walking pace over 10 meters) in the group of neurological patients, but not the Rivermead Mobility Index. In a more recent study, Busse et al.300 aimed to determine walking mobility in the community in individuals with lower limb weakness due to neurological conditions. Five groups of patients with neurological conditions were recruited, namely extra -pyramidal, pyramidal (upper mot or neurons lesions), lower motor neuron disorders with no or minor sensory loss, peripheral nerve disorder with sensory loss, and primary muscle or neuromuscular junction disorders. The investigators reported that daily mean step counts were significantly correlated with the score of Rivermead Mobility Index, time required to stand upright, and self -selected gait speed. However, self selected gait speed was the only significant predictor in the regression analysis. Quadricep and hamstring muscle strength wa s not significantly correlated with daily mean steps. Pearson et al., 193 in a review article on walking mobility in neurological disorders, reported that compared with healthy individuals, patients with multiple sclerosis have reduced mean steps/24 hours over 7 days and increased percent inactivity over 24 hours.

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71 Shaughnessy and colleagues12 compared SAM -derived daily steps with established instruments to assess ambulatory recovery in community -dwelling men and women with mild to -moderate post -stroke hemiparetic gait deficits at discharge from inpatient rehabilitation. The authors reported that SAM -derived daily steps were strongly related to FIM mobility scores at discharge from inpatient rehabilitation and three months later, but not to SIS mobility and self selected gait speed. Michael et al.23 evaluated ambulatory activity in a sample of community dwelling individuals with mild to moderate chronic stroke. Ambulator y activity was quantified by stride counts obtained from SAM. The authors reported that the participants mean ambulatory activity profiles were extremely low, and were strongly associated with BBS scores and self -selected gait speed. In a more recent stud y by Michael and associates,298 fatigue level showed no r elationship to total daily step activity in a sample of community -dwelling subjects with chronic hemiparetic stroke. Finally, symptoms of depression such as pessimism, persistent sad feelings, loss of interest in activities, hopelessness feelings, and fati gue,283 may have a negative effect on the individual with depression motivation to ambulate at home and in the community. Therefore, it is reasonable to hypothesis that depression may be associated with activity volume as well. 2. 6.8 .2 Ambulation intensity Michael et al.34 quantified community ambulatory activity profiles in terms of step counts and intensity in a convenience sample of com munity -dwelling volunteers with mild to moderate (NIHSS mean=3.57 points) chronic (mean time post -stroke=10 month) hemiparetic stroke. Total low and high intensity ambulatory activity were associated with peak oxygen consumption. Therefore, the authors con cluded that step activity intensity is strongly associated with cardiovascular fitness.

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72 Pearson et al.190 and Busse et al.25 reported that healthy subjects have higher sustained activity and mean peak levels than individuals with decreased physical functioning. Thus, it was concluded that fatigue and relate d impairments may limit neurological patients by restricting the actual number of steps they are able to maintain for an extended period of time.25 Similarly, numerous studies have reported that activity intensity varies between groups with different health status.285, 299, 300 Hoos and associates227 reported that the amount of time spent on low intensity activities was smaller in children than in young adults and elderly pe rsons, while the amount of time spent on highintensity activities was larger. In addition, physical activity level showed an inverse relation with the percentage of time spent on low -intensity activities, and a positive relation with the percentage of tim e spent on highintensity activities. Finally, Cooper et al.285 reported that obese participants spent significantly less time in activity of at least moderate intensity than the non -obese. Activity intensity is a measure of the maximal number of steps a person takes in a predetermined period of time. Thus, it is influenced by how fast a person can walk. Therefore, it is reasonable to speculate that maximal gait speed will be associated with activity intensity outcomes. In addition, because balance is a strong predictor of gait speed,62, 102 balance is likely to be associated with activity intensity as well. Finally, as with activity volume, based on clinical grounds, depression may also be associated with activity intensity. 2. 6.8 .3 Ambulation pattern Healthy younger adults have higher minute to -minute activity variability than older adults. Thus, older individuals have narrower ranges of ambulatory behavior, probabl y due to inability to perform a range of ambulatory tasks under various environmental conditions.14 These results

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73 suggest that this metric is a potentially useful indicator of age and health associated walking function. Cavanaugh and assoc iates14 reported that healthy older adults accumulated a similar number of steps and minutes of activity as healthy younger adults, but did so in fewer bouts (healthy older adults: 67.8 + 2.7 bouts; healthy younger adults: 77.3 + 2.6 bouts ). These findings indicate that initiating ambulatory activity becomes more difficult with age. Thus, this measure is particularly important because it may be more sensitive to differences between groups with varying functional statuses.14 In addition, number of activity bouts metric represents the frequency in which an in individual initiated and terminated gait. As high levels of motor control via cortical input and afferent feedback are important to initiate and terminate gait,95 number of activity bouts might be associated with measures of motor control such as the FMA. Moreover, initiating and terminating gait stresses the balance control system.98 For example, gait initiation requires going from a stable standing posture to essentially falling. Gait termination requires the control of forward linear momentum, which, if not d one correctly, may be destabilizing.92 Therefore, balance control is likely to have an impact on activity bouts measure. Number of steps in a bout may be related to both muscular endurance and cardiovascular fitness. Muscular endurance may be related to these indices because they assess the ability of the lower extremity muscle groups to maintain a specific percentage of the maximum voluntary contraction for a prolonged period of time. Cardiovascular fitness might be associated with these indices as well because they reflect the ability to perform large muscle, dynamic exercise (walking) for prolonged periods. Accordingly, it has been reported that fatigue may limit

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74 individuals with decreased physical functioning by restricting the actual number of ste ps they are able to maintain for an extended period of time.25 Community ambulation is also characterized by adapting and modifying gait (e.g., changing walking speed) to both expected and unexpected challenges to locmotion.305 SAM activity variability and randomness measures assess the extent in which walking fluctuates minute t o -minute and whether these fluctuations occur in orderly, repetitive and predictable pattern. Such measures reflect ones ambulatory activity range and hypothetically may reflect ones capability to adjust gait to unpredictable changes in task demands or en vironmental conditions. Cortical input and afferent feedback act to adjust gait when the environment is less predicted.95 Therefore, SAM indexes of activity variability and randomness may also be related to measures of motor control. Moreover, these indexes may also be affected by the persons cognitive flexibility to alter gait pattern. Executive function is an important aspect of cognition flexability.111 The Trail Making Test Part B (TMT B) can be used to measure executive function. The test requires the patient to draw lines alternating between numbers and letters in consecutive order. Performance is assessed by the time taken to complete the trial correctly.306 It has been reported that performance in Part B is indicative of executive function, executive control, and the ability to flexibly shift the course of an ongoing activity.307 Activity pattern represents the degree to which number of steps fluctuates minute to minute. Individuals with mi ld gait impairments are likely to have greater ability than individuals with severe gait impairment to fluctuate there minute to -minute number of steps, simply because their gait speed range is bigger. Therefore, maximal gait speed is likely to be associat ed with activity pattern. Finally, as with activity volume and intensity, based on clinical grounds it is reasonable to hypothesis that depression may also influence activity pattern performance.

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75 Several variables may not associate with SAM activity volum e, intensity or pattern. Side of stroke is an important factor which has an impact on post -stroke outcomes as left -hemispheric and right -hemispheric strokes have different characteristics and symptoms. Nevertheless, side of stroke may not have an impact on SAM activity as side of stroke does not appear to be associated with independence in activities of daily living outcome43 47 or gait.48 In addition, because walking chiefly involves rhythmic movements of the lower extremity, upper extremity impairment is also likely not to affect SAM activity. Finally, gender may not have an impact on SAM activity, as no significant correlation between gender and functional outcomes following stroke were reported.308, 309 In summary, within the stroke population there is evidence that SAM derived walking activity volume correlates with FIM and BBS. There is conflicting information regarding whether SAM volume correlates well with self -selected gait speed. Moreover, it is reasonable to hypothesis that activity volume is also associated with depression. There is evidence that SAM derived activity intensity correlates with cardiovascular fitness. In addition, activity intensity may also be associated with balance, gait speed, and depression. Within the stroke population there is no information on the association between SAM patterns and clinical based outcomes. In other neurological pop ulations, older adults with or without functional limitation, and in healthy younger adults, there is evidence that SAM volume correlates with functional limitation, body composition, and self -selected gait speed. SAM intensity is associated with functiona l limitations, age, and body composition. SAM pattern is associated with health and age. In addition, there are clinical grounds to hypothesize that SAM pattern will be associated with gait speed, depression, motor control, balance, muscle endurance, cardi ovascular fitness, and cognition as well. Overall, short distance gait speed test reflects the persons ability to maintain

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76 gait speed for short distances. It does not reflect persons ability to initiate, terminate, and alter gait. The ability to initiate terminate, and alter gait is crucial for successful performance in SAM pattern outcomes. Therefore, gait speed is more likely to reflect successful performance in SAM volume and intensity outcomes rather than in SAM pattern outcomes. SAM temporal series of data can be analyzed in a variety of ways. Therefore, a pilot study was conducted to examine processes for accelerometer data reduction, including selection of outcomes, correcting outcome variables for days with no activity, analyzing the first and sec ond measurement days separately or both days together, and defining low, moderate, and vigorous physical activity (see Appendix C). In addition, this dissertation will focus on the following outcomes only: (1) activity volume outcomes number of steps take n over 24 hours and total activity level;(2) ambulation intensity outcome time spent in low, medium, and high absolute activity intensity; and (3) activity pattern outcomes number of activity bouts, mean number of steps in an activity bout, one minute va riability of activity, and activity randomness. These outcomes were selected mainly because the literature supports the utility of these outcomes to detect differences in ambulatory activity associated with age, functional limitation, or health.6, 12, 14, 23, 25, 34, 285, 299, 300, 302 303, 310 Although number of steps in an activity bout has not been previously used in research, it will also be included in this dissertation as based on physiological grounds it could be potentially a good indicator of both muscular endurance and car diovascular fitness. In addition, it has previously been suggested that the minimum walking distance required for community ambulation is 300 500 continuous meters.60, 103 Although SAM does not provide information on walking distance, it is reasonable to assume that number of step s taken in an activity bout (continues walking) is related to continues walking distance. Therefore, knowing number of steps in an activity bout might be a good indicator of the ability to ambulate

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77 functionally in the community (e.g., covering the distance from the car to the restaurant). Other variables which will not be included in this project, such as number of steps in one minute intervals, may also be useful indicators of health and age associated walking function. Therefore, tertiary analysis of these variables will be conducted in the future. Based on the available literature on the association between gait speed and community ambulation, factors which confound the association between gait and community ambulation, and the relationship between SAM a nd other clinical measures, the dissertation primary aims number 2 and 3 were developed. 2. 6.9 Primary Aim Number Two 2. 6.9 .1 Specific Aim 2a To explore the relationship between speed-based classification system and SAM derived home and community ambulati on, and whether lower extremity sensory and motor impairment, balance, walking endurance, and balance self -efficacy, significantly confound this relationship in persons with moderate to severe subacute stroke. 2. 6.9 .2 Specific Aim 2b To explore the assoc iation of speed -based classification system and SAM derived ambulation outcomes to mobility and social participation domains of the SIS. 2. 6.9 .3 Hypothesis 2a The speed -based classification system will have significantly stronger correlations with SAM der ived ambulation volume and intensity than with ambulation pattern. In the case of which the speed based classification system will have significantly stronger correlations with SAM derived ambulation volume and intensity outcomes, it may indicate that th e performance in the speed -based classification system reflects better the human -body systems that contribute to habitual ambulation volume and intensity rather than to ambulation pattern.

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78 Therefore, the speed -based classification ability to reflect habitu al ambulation volume and intensity is better than its ability to reflect ambulation pattern. 2. 6.9 .4 Hypothesis 2b Potential variables that could significantly distort the relationship between speed -based classification system and home/community ambulati on are balance, lower extremity motor control, walking endurance, and balance self -efficacy. Variables which will be found to distort the relationship between speed based classification system and SAM -derived home and community ambulation outcomes will represent underlying factors which determine the ability to walk at home and in the community. 2. 6.9 .5 Hypothesis 2c SAM derived outcomes and gait speed will have moderately strong to very strong (r > 0.60) positive correlations with SIS. However, SAM derived ambulation outcomes will have significantly stronger associations with SIS than gait speed. In the state of which SAM derived outcomes will have stronger associations than the speed based classification system with the SIS, it will indicate that SA M reflects better activities that a person partakes in at home and in the community (e.g., shopping and visiting friends). 2. 6.10 Primary Aim Number Three 2. 6.10.1 Specific aim 3 To determine whether patients impairment level, activity limitations, and p ersonal factors can predict SAM derived community ambulation volume, intensity, and pattern during the subacute phase of stroke rehabilitation.

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79 2. 6.10.2 Hypothesis 3a In a community dwelling individuals two month post -stroke balance, lower extremity motor control, body composition, depression and walking endurance will predict activity volume (number of steps taken over 24 hours and total activity level). 2. 6.10.3 Hypothesis 3b Balance, self -selected gait speed, walking endurance, lower extremity motor c ontrol, body composition, age, and depression will predict step activity intensity (absolute activity intensity, relative activity intensity, and peak activity index). 2. 6.10.4 Hypothesis 3c Balance, executive function, walking endurance, lower extremity m otor control, age, depression, and body composition will predict step activity pattern (number of activity bouts, number of steps in an activity bout, one min variability, and activ ity randomness). 2. 6 .10 .5 Hypothesis 3d Gender, stroke location, and upper limb motor control will not predict step activity volume, intensity, and pattern. Variables which will be found to predict SAM volume, intensity, or pattern will represent constructs which are essential for home and community ambulation volume, intensity or pattern. For instance, if the 6 -min walk test (a measure of walking endurance), TMT B (a measure of executive function), and the BBS (a measure of balance impairment) will be found to predict activity pattern, it will indicate that walking endurance, executive function, and balance are important for attaining high levels of activity pattern at home and in the community.

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80 2. 7 Locomotor Experience Applied Post -Stroke (LEAPS) Trial Data in this study will be derived from a parent study; an ongoing 5 year, phase III, single blind, 5 -site, randomized controlled trial, the LEAPS trial.311 The parent study pre randomization data from 2 month post -stroke base line assessments will be used. 2. 7.1 Objective The primary purposes of the LEAPS trial are to determine if a specialized locomotor training program that incorporates a body weight support system and treadmill as a treatment modality can produce a functionally significant improvement in walking of persons post -stroke compared to a control group (non -specific, low intensity exercise program), and whether timing of therapy (early or late delivery), severity of locomotor impairment, and the number of treatment s affects locomotor outcome. All research procedures conducted during the LEAPS trial were approved by an Institutional Review Board at each participating site.311 2. 7.2 Inclusion and Exclusion Criteria 2. 7.2.1 Stroke definition Stroke was defined as, a rapid onset event of vascular origin reflecting a focal disturbance of cerebral function, excluding isolated impairments of higher function and persist ing longer than 24 hours.35 Stroke diagnosis is confirmed by Magnetic Resonance Imaging or Computed Tomography or, if scan is not available, by clinical criteria. 311 2. 7.2.2 Inclusion criteria Those eligible for the LEAPS study had an ischemic or hemorrhagic stroke within 30 days. Additional LEAPS trial inclusion criteria consisted of > 18 years, residual paresis in the lower extremity (lower extremity FMA score < 34), able to walk at least 10 feet with maximum one person assist, able to follow a three step command, and provide informed consent.311

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81 2. 7.2.3 Exclusion criteria LEAPS trial exclusion criteria for participation include: (1) lived in nursing home prior to stroke, (2) unable to ambulate at least 150 feet prior to stroke, (3) serious cardiac conditions (e.g., New York Heart Associati on criteria for Class 3 or Class 4 heart disease), (4) history of serious chronic obstructive pulmonary disease, (5) severe weight bearing pain, (6) preexisting neurological disorders such as Parkinsons disease, (7) history of major head trauma, (8) lower extremity amputation, (9) legal blindness, (10) severe arthritis that limit passive ranges of motion of lower extremity, (11) major post -stroke depression, (12) uncontrolled diabetes, (13) severe hypertension, (14) lives more than 50 miles from the traini ng sites, (15) unable to travel three times per week for outpatient training programs, (16) and intracranial hemorrhage related to aneurismal rapture or an arteriovenous malformation.311 2. 7.3 Assessments All measures selected have established reliability and validity and are captured according to standardized protocols by blinded per diem therapists.311 A manual of procedures has been developed during the LEAPS planning phase. The manual of procedures includes the study procedures for data collection, blinded evaluator procedures, and standardization. For a list of LEAPS enrollment, screening, and baseline assessments, see table 2 7. For further details about the LEAPS design, outcomes, and inclusion and exclusion criteria, please refer to Duncan et al.311

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82 Table 2 1. Trial of Org 10172 in Acute Stroke Treatment Classification of subtypes of ischemic stroke Ischemic stroke subtype Definition Large artery atherosclerosis (embolus/thrombosis) Cortica l or cerebellar lesions and brain stem or subcortical hemispheric infarcts greater than 1.5 cm in diameter are considered to be of potential large artery atherosclerotic origin. These patients will have clinical and brain imaging findings of either significant stenosis or occlusion of a major brain artery or branch cortical artery, presumably due to atherosclerosis. Clinical findings include those of cerebral cortical impairment (aphasia, neglect, restricted motor involvement, etc.). Cardioembolism This ca tegory includes patients with arterial occlusions presumably due to an embolus arising in the heart. Clinical and brain imaging findings are similar to those described for large artery atherosclerosis. Small artery occlusion (lacune) Lacunar strokes are c aused by occlusion of a single small nonbranching end artery, which arise directly from larger arteries (e.g., the anterior and middle cerebral arteries). The patients should have a brain stem or subcortical hemispheric lesion with a diameter of less than 1.5 cm. Stroke of other determined etiology This category includes patients with nonatherosclerotic, hypercoagulable, or hematologic causes of stroke. Stroke of undetermined etiology Cases in which the cause of stroke cannot be determined with any degre e of confidence. Note: Adapted from Adams HP, Jr., Bendixen BH, Kappelle LJ, Biller J, Love BB, Gordon DL, Marsh EE, 3rd. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TAST. Trial of org 10172 in acute stroke treatment. Stroke. 1993;24:3541.41

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83 Table 2 2. Level of functional walking ability at home and in the community Ambulation level Ambulation ability Percentage of participants Physiological walker Walks o nly for exercise or in therapy 10.2% Limited household walker Requires assist for some walking activities, does walk during some home activities 18.3% Unlimited household walker Able to walk for all household activities, has difficulty with stairs and un even terrain, not able to enter and leave the house independently 18.3% Most limited community walker Able to enter and leave their home independently, able to ascend and descend curbs, able to manage stairs with assistance, independent in at least one co mmunity activity, but need assistance with others 17.6% Least limited community walker Independent in stairs, all moderate community activities, and in local stores and un-crowded shopping centers 17.6% Community walker Independent in all home and commun ity activities, can walk with crowds and on uneven terrain, and able to be independent in shopping centers 17.6% Note: Adapted from Perry J, Garrett M, Gronley JK, Mulroy SJ. Classification of walking handicap in the stroke population. Stroke. 1995;26:982989.9 Table 2 3. Gait speed classification Gait speed, m/s Gait impairment Ambulation ability < 0.4 Severe Household ambulation 0.4 0.8 Moderate Limited community ambulation > 0.8 Mild Community ambulation 1.33 None Normal, healthy elderly popul ation Note: Adapted from Perry J, Garrett M, Gronley JK, Mulroy SJ. Classification of walking handicap in the stroke population. Stroke. 1995;26:982989.9 Table 2 4. Community ambulation classification Community ambulation level Gait speed (m/s) Una ble to leave home 0.52 Able to walk to the letter box 0.66 Limited to immediate environment 0.82 Ambulate in shopping center 1.14 Note: Adapted from Lord SE, McPherson K, McNaughton HK, Rochester L, Weatherall M. Community ambulation after stroke: how important and obtainable is it and what measures appear predictive? Arch Phys Med Rehabil. 2004;85:234239.61

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84 Table 2 5. Activity level post -stroke outcomes summary table Test Name Strengths Weakne sses Ten meters gait speed206 Simple; fast; rel ated to impairment severity;9 less likely to have a ceiling effect;30 high validity and reliability;207 sensitive measure of mobility recovery;9 and a gait speed gain that results in a transition to a higher class of ambulation results in better function and quality of life.32 Early during rehabilitation phase, patients may not be able to walk 10 meters and are therefore not testable. As patients improve, 10 meters walking speed becomes a less credible measure of walking speed over more functionally relevant distances outsid e the home;152 and measured in a controlled environment.28 Six minute wa lk test148 Simple; 152 well tolerated; reflects activities of daily living;154 a continuous variable without floor or ceiling effect;152 quick and easy to implement; and can be completed by many patients. 155 Cannot assess other important aspects of gait such as quality of movement;152 usually described as a measure of endurance and cardiovascular fitness,31 but there are a number of stroke specific impairments that could potentially alter the outcome of the test (e.g., muscle weakness and balance impairment); 148 and measured indoors. 28 Timed Up and Go184 Quick; easy to administer; can be accomplished in the community; requires no specialized equipment and training;168and has excellent reliability.184 May not be suitable for use among individuals exhibiting cognitive impairment;172 attempts to derive mobility from brief observation; dependent on instructions given; and does not measure directly actual activity in the home environment. Functional Ambulation Classification174 Useful in predicting level of community ambulation with high sensitivity and specificity.173 Does not take into consideration environmental factors and does not measure actual mobility ability in the community. Rivermead Mobility Index184 Short; simple; requires no special equipment or training; and can be conducted in a variety of settings.179 Does not take into consideration increases in mobility achieved through assistive devices or environmental modifications,179 no information on the psychometric properties of locomotion items, and does not provide direct measurement of persons community ambulation. Barthel Index 184 Excellent reliability and validity,184 widely used for stroke,3, 186 and predicts length of stay in hospital.185 Large reported ceiling and floor effects; 187 the psychometric properties of the locomotion items are unknown; and does not provide direct and objective measurement of locomotion ability in the community.

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85 Table 2 5. Continued Test Name Strengths Weaknesses Functional Independence M easure184 Excellent reliability; 184 7 point scale increases sensitivity;3 and a strong predictor of outcome disability and discharge destination.189 Ceil ing and floor effects at the upper and lower ends of function;3 reliability is dependent on tester;195 there is no informati on regarding the psychometric properties of the locomotion items; and ratings are not necessarily acquired via objective observation of persons performance in the community. Frenchay Activities Index197 Excellent validity; stroke specific; simple to administer; and suitable to use with proxy administration.184 Relies on perception and estimation of physical activity;10 lacking standard guidelines for administration;184 ceiling effect has been reported at 6 months post stroke; 121 there is no information on the psychometric properties of the l ocomotion items; and does not directly measure ambulation in the community. Table 2 6. Participation and quality of life post -stroke outcomes summary table Measure Strengths Weaknesses Stroke Impact Scale 186 Excellent validity; 214 does not have significant ceiling or floor effects; and validated with telephone and proxy administration.211 Mobility level assessment is not based on direct measurement of what the person can do in the community. Medical Outcomes Study Short Form 36214 Excellent validity; 214 widely used;3 brief; can be self administered or administered by phone or interview; simple to administer;215 and standardized norms are available.214 Possible floor effect in seriously ill patients;3, 186 low rated of agreement between proxy respondent and patient respondent ratings;218 and mobility level assessment is subjective and not based on direct measurement of what the person can or cannot do. EuroQol Quality of Life Scale214 Short and simple; hi gh response rate;221and has been evaluated for proxy use.214 The ability to self complete is directly related to age and cognitive function;224 low reliability with a proxy respondent;221 and mobility level assessment is subjective.

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86 Table 2 7. Locomotor Experience Applied Post -Stroke enrollment, screening, and baseline assessments Assessment Screening and enrollment (5 30 days post stroke) Screening and enrollment (45 60 days post stroke) Baseline (2 month post stroke) Primary Outcome Measures Ten meter walk (Functional Ambulation Classification) X Six minute walk test X Step activity monitor X Secondary Outcome Measure s Exercise tolerance test X Mini Mental State Examination X Patient Health Questionnaire Nine item Depression Scale X Fugl Meyer Assessment X Berg Balance Scale X Activities specific Balance Confidence Scale X Stroke Impact Scale (ve rsion 3.0) X Trail Making Test (Part A and Part B) X Height, Weight, Waist Circumference X Orpington Prognostic Scale X National Institutes of Health Stroke Scale X Co morbidity Index X

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87 PART A: COMMUNITY AMBULATION BARRIERS AND FACILITAT ORS PART B: COMMUNITY AMBULATION DESCRIPTORS (The International Classification of Functioning, Disability and Health) Figure 2 1. The International Classification of Functioning, Disability and Health as a conceptual framework for studying community Ambulation [Adapted from WHO. Introduction. World Health Organization: International Classification of Functioning, Disability and Health: ICF. Geneva: WHO Library Cataloguing in -Publication Data;2001:325.]121 Health condition (disorder or disease ) Stroke: type and severity Body functions & structures Stroke related impairments (sensory, cognitive, and motor), depression, and fear of falling Activity Mobilit y: gait speed, walking and exercise endurance, and balance. Participation Domestic life; community, social, and civic life; community ambulation Environmental factors Adaptation to physical, social and attitudinal constraints in the environment Personal factors Age, co morbidities, and gender. Ambulation intensity Time spent in low, medium, and high activity intensity; sustained activity measures; and peak activity index Ambulation pattern Number of activity bouts, number of steps in activity bouts, activity variability, and activity randomness Ambulation volume Number of steps taken per hour and day, mean activity levels (percentage of the total time within a 24 hour period steps were taken)

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88 0 1 2 3 4 5 6 7 8 9 10 12:00:10 AM 2:06:40 AM 4:13:10 AM 6:19:40 AM 8:26:10 AM 10:32:40 AM 12:39:10 PM 2:45:40 PM 4:52:10 PM 6:58:40 PM 9:05:10 PM 11:11:40 PM Time (hour of day) Steps/ 10 seconds Subject one: 2134 steps Subject two: 2168 steps Figure 2 2. One day of step activity data. This figure illustrates one day of step activity data from two subjects who took similar number of steps but demonstrated varying activity patterns.

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89 CHAPTER 3 INFERRED COMPLIANCE WITH A STEP ACTIVITY MONITOR PROTOCOL T O MEASURE FREE PHYSICA L ACTIVITY IN MODERA TE TO SEVERE SUB -ACUTE STROKE SURVIVORS 3. 1 Introduction Stroke is frequently a significant contributor to long -term adult disability and burden of care,1, 2 as following stroke, individuals commonl y have residual neurologic deficits, which impair mobility.3 An inability to walk safely and functionally at home and within the community is a common mobility problem post -stroke.4 Consequently, a return to home and communitybased ambulatory activity is a major rehabilitation goal for many stroke survivors.3 Due to the importance of ambulation in people who have survived a stroke, quantifying mobility at home and in the communi ty is an important outcome measure in treatment and rehabilitation.6 Stroke research relies heavily on activity measures such as gait speed and w alking distance to predict community ambulation ability. These measures are simple, fast, and related to the severity of impairment in the home and the community.9 However, such measures evaluate the individual in a non -familiar environment,10 attempt to derive mobility from brief snapshots of walking performance,11 are not sensitive to subtle changes in function,11, 12 and do not directly assess persons free living ambulation or mobility -driven social participation.12 Self reported questionnaires which have community ambulation and mobility items, such as the SIS are also commonly used to assess community ambulation levels. Self report approaches are especially useful in large population studies.14, 15 However, such instruments rely on factors related to recall and cognitive ability which contribute to the inaccuracy of these approaches.16 An alternative approach to participation level home and community ambulation measurement involves direct recording of ambulatory activity14 with microprocessor -based accelerometers, such as the SAM In stroke clinical trials SAM is being used with increasing

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90 regular ity.6, 12, 23, 34, 298, 311 However, participants compliance in wearing the monitor is critical for obtaining accurate physical activity measurement,15 as non -compliance is usually considered a source of bias. Therefore, in order to assess whether the SAM could be a valuable tool in clinical trials, it is important to establish post -stroke inferred compliance with accelerometer monitoring. Compliance with the accelerometer monitoring protocol is inferred because identifying when a participant is not we aring the accelerometer is difficult, as long continuous bouts of accelerometer inactivity (zero counts) may mean either that the participant was completely inactive (e.g., sleeping or napping) or removed the accelerometer.251 Therefore, researchers develop decis ion rules to evaluate inferred compliance with the accelerometer monitoring protocol. Understanding factors that predict post -stroke inferred compliance with accelerometer measurement protocol may aid researchers in determining the profiles of stroke survi vors for whom accelerometer measurement is appropriate and how to increase accelerometer measurement compliance in this population. Numerous factors may be associated with inferred accelerometer monitoring compliance post -stroke. Increased cognition, young er ag e, lower depression scores, reduced gait impairment and higher levels of balance control and balance self -efficacy may predict increase in inferred protocol compliance. Cognitive declines may have an impact on individuals ability to remember to carr y out a task in the future.279 Therefore, declines in cognition could affect an individuals ability to remember to wear an accelerometer and could thus directly affect compliance with an accelerometer proto col.278 In addition, impaired cognition may hamper the individuals ability to understand the accelerometer protocol which may also result in decreased compliance. Age may also be an important compliance predictor. Uswatte and colleagues276 reported that compliance with accelerometer monitoring of real world arm activity did not differ f or older and younger

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91 individuals. However, the authors did not report the samples age range. The studys null results may be attributable to limited variability in age. Age related changes such as motor skills impairments, decreased vision, and cognitive decline are common among the elderly and may influence the ability to handle the accelerometer.277 Therefore, despite Uswatte and colleagues276 results, younger age has the potential to predict post -stroke lower extremity accelerometer monitoring compliance. Lower depression scores may be associated with increas ed inferred compliance in accelerometer monitoring protocol as well. Common depression symptoms consisted of difficulty concentrating and remembering details,283 pessimism, persistent sad feelings, loss of interest in activities, hopelessness feelings, and fatigue.283 Consequently, an individual wh o is depressed may be more likely to forget and not to be motivated to wear an accelerometer as instructed. Reduced gait and balance control impairment, as well as higher balance self -efficacy, may also predict increased inferred post -stroke accelerometer measurement protocol compliance. Individuals who have r educed gait and balance control impairment, and higher balance self -efficacy, are more likely to walk on a regular basis than individuals w ith more severe gait9 and balance control impairment, and low balance self -efficacy. Therefore, these individuals may place more importance on w earing the accelerometer simply because they are performing the behavior being measured more frequently.278 Finally, the literature suggests that within the stroke population, gender, literacy, stroke location, stroke type, and upper extremity motor control are not associated with inferred upper extremity accelerometer protocol compliance.276 The purpose of this study was to assess the rate of and predictors for inferred compliance with lower extremity accelerometer monitoring protocol two months post -strok e. We hypothesized that increased cognition, lower depr ession scores, younger age, mild (or less) gait

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92 impairment, and higher levels of balance control and balance self -efficacy, will predict increased inferred protocol compliance. Finally, gender, educati on, stroke location, stroke type, and upper extremity motor control impairment will not predict inferred protocol compliance. Knowledge of compliance rate with the SAM protocol will enable researchers to make decisions regarding for who the SAM use will no t be appropriate. For example, if severe depression will be found to predict diminished protocol compliance, it will indicate a need to implement compliance strategies in persons post -stroke with severe depression. 3. 2 Methods 3. 2.1 Design Data in this cr oss -sectional study was derived fro m a parent study, the LEAPS pre randomization data from two -month post -stroke baseline assessments. The LEAPS is an ongoing 5 year, phase III, single -blind, 5 -site, randomized controlled trial. Details about design, outc omes, and inclusion and exclusion criteria of this study are published elsewhere.311 Briefly, the LEAPS trial recruits individuals post -stroke over the age of 18, five to thirty days post stroke. Individuals with uncontrolled health conditions for which exercise is contraindicated (e.g., serious cardiac conditions, severe arthritis or orthopedic problems, severe hypertension, and uncontrollable diabetes) are excluded. The primary purposes of the LEAPS trial are to determine if a specialized locomotor training program that incorporates a body weight support system and treadmill as a treatment modality can produce a functionally significant improvement in wa lking of persons post -stroke compared to a control group (non -specific, low intensity exercise program), and whether timing of therapy (two or six months post -stroke), severity of locomotor impairment (moderate or severe), and the number of treatments affects locomotor outcome. The LEAPS trial primary

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93 outcome is the proportion of participants who successfully recover walking one year post -stroke, based on the gait speed classification system.311 3. 2.2 Testing Procedures and Measurements 3. 2.2.1 Ambulatory activity Habitual ambulatory activity was assessed with the SAM. Within the stroke population, SAM was found to be valid, reliable,19, 23 and sensitive to change.12 SAMs were programmed with an initial generic calibration.19 Participants performed a one -minute walk test. SAM counts of strides were compared with visual counts. Adjustments were made to the calibration setting and the timed walks were repeated to ensure greater than 90% accuracy against visual counts. To achieve optimal sensitivity for step detection, the SAM was programmed and applied above the malleolus of the less involved limb with two adjustable elastic straps. Participants wore the SAM for a period of two consecutive days, throughout the day, removing the SAM only to bathe, shower, swim, or sleep. A demonstration and written instructions regarding appropriate use of the device were provided to subjects. In an effort to enhance compliance, the writte n directions included what days the subjects were to wear the SAM. In addition, there was an attempt to conduct follow up phone calls to remind the subjects to wear the SAM. However, the reminder calls were not fully successful due to scheduling conflic ts to making calls, LEAPS team member and patient/family did not speak the same language, or no one answered the phone and there was not an opportunity to leave a message. Data reduction : On completion of a 48-hour monitoring period, SAM data were downloaded using an infrared docking port. SAMs raw data from the manufacturer software were imported into Microsoft Excel. Data were expressed as stride counts every 60 seconds during the 48 hours. Identification of inferred accelerometer compliance rate : Inferred compliance in 24 and 48 hours were established in three time periods (windows) of six hours: (1) 6:00AM 12:00PM, (2)

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94 12:01PM 6:00PM, and (3) 6:01PM 12:00AM. It is reasonable to assume that most individuals sleep between 12:00AM to 6:00AM or are engaged in sedentary activities such as reading or watching television. Therefore, compliance in wearing the accelerometer was inferred only for 18 waking hours of the day, between 6:00A M to 12:00AM. If activity was recorded in only one portion of a window it was in ferred that the person wore the accelerometer throughout the window as participants are not likely to remove accelerometers multiple times per day. In addition, windows with long recording epochs were selected because stroke patients are typically older individuals with secondary motor deficits. Thus, they are more likely to be inactive compared to younger and healthy individuals. According to this method, a compliant person in 24 and 48 hours was defined as a participant who has activity recorded in each o f the three or six recording windows, respectively. The samples inferred compliance with the accelerometer monitoring protocol was categorized as poor (0 20%), low (2140%), moderate (41 60%), high (61 80%), or very high (81 100%) based on the percentage of the sample with activity in all the three or six windows. Figure 3 1 illustrates the use of the six hours windows method to identify inferred accelerometer wearing activity. 3. 2.2.2 Accelerometer monitoring infer red compliance predictors The potential of cognition, depression, age, gait impairment balance control, and balance self -efficacy to predict compliance were investigated. These predictors were included based on the available literature284 and clinical grounds. In addit ion, the association of the following variables with compliance were investigated: gender, education, stroke location, stroke type, and upper extremity motor control impairment. These variables associations with compliance were examined as there is limite d data suggesting these variables have no impact on post -stroke upper extremity accelerometer monitoring compliance.272

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95 All measures selected to assess the potential predictors have established reliability and validity and were captured according to standardized protocols by blinded per diem therapists.311 Demographic : The age, gender, and highest level of education achieved (< high school, high school or GED, some college, Associates degree, Bachelors degree, and advanced degree) were obtained from patient and/or f amily members interviews, and were confirmed via medical records review. Cognition : The MMSE and the TMT B were used to evaluate cognitive function two months post -stroke. The MMSE assesses orientation to time and place, immediate recall, short -term verba l memory, calculation, language, and constructs ability.312 Folstein, Folstein, McHugh, and Fanjiang 313 recommended the following cutoff scores: scores > 27 represent no cognitive impairment; 21 26, mild cognitive impairment; 11 20, moderate impairment; and < 10 severe impairment.313 The TMT B requires the patient to draw lines alterna ting between numbers and letters in consecutive order. Performance was assessed by the time taken to complete the trial correctly.306 Performance in TMT B reflects executive function, executive control, and the ability to flexibly shift the course of an ongoing activity.307 Depression : The Patient Health Questionnaire nine item depression scale (PHQ 9) was used to measure depression two month post -stroke. The PHQ 9 usefulness as a depression screening and diagnostic instrument has been established recently for stroke survivors.314 The PH Q 9 scores range from 0 to 27. Cutoff points of 5, 10, 15, 20 represent the thresholds for mild, moderate, moderately severe, and severe depression.13 Stroke location and type : Stroke location (right/left hemisphere, brainstem, or bilateral with no bilateral paresis) were confirmed by Computed Tomography or Magnetic Resonance Imaging

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96 scans. Stroke type (ischemic, hemorrhagic, or uncertain) was determi ned from reviewing the participants medical records. Upper extremity motor control impairment : The upper extremity motor function portion of the Fugl -Meyer Assessment (FM -UE) was used to determine upper extremity motor control impairment two months post -s troke. The FMA is one of the most commonly used and comprehensive scales of motor and sensory recovery following stroke. Standardized protocols for administration were followed.315 Gait impairment : Gait impairment was assessed via gait speed measured during a 10 -meter walk. The time that it took to traverse the 10 meters at the subjects comfortable pace using their usual assistive devices or orthoses was recorded. Gait speed has been described as the almost perfect mobility outcome measure in neurological populations151 as it is simple to measure and is reliable, valid, and sensitive to post -stroke recovery stage.9, 138 Furthermore, gait speed is related to rehabilitation prognosis29, 139 and discharge destination.140 Balance control : Balance was measur ed by the BBS .324 The BBS consists of 14 items which assess subjects ability to maintain movements or positions of increasing difficulty via decreasing the base of suppor t.324 Each item is graded 0 to 4. The global score of the BBS ranges is from 0 to 56 points. A score of 56 indicates functional balance, scores < 45 indicate that a person may be at greater risk of falling.325 Therefore, the B BS score was dichotomized into more severe balance deficits (scores < 45) and less severe balance deficits (scores > 45).325 The test has been used in a stroke population and has well -established validity and reliability.324 A standardized protocol for administration was followed.324 Balance self -efficacy : Activities Specific Balance Scale (ABC) was used to assess perceived confidence in maintaining balance while performing common community related activities such

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97 as bendin g, reaching, and walking both inside and outside the house. This measure has good reliability and internal consistency.327 Myers et al.328 found that in a group of communitydwelling older adults, ABC sc ores of <50 indicated a low level of physical functioning (home care clients) ; ABC of more than 50 and less than 80 indicated a moderate level of functioning (older adults in a retirement home setting) ; and ABC scores of > 80 indicated high functioning (ph ysically active older adults). Accordingly, in this study ABC scores were dichotomized into low level of physical functioning (ABC < 49 points) and moderate to high level of physical functioning (ABC > 50 points). 3. 2.3 Data Analysis The initial step was to determine the percentage of participants who complied with the accelerometer measurement protocol in the first day, second day, and in both days (participants who wore the device on the first and the second measurement days). In a second step, demographic and clinical characteristics of the participants who comply and did not comply with the SAM protocol during the first day, second day, and both days was described by descriptive statistics. Demographic and clinical characteristics of subjects who compli ed with the SAM protocol were compared to those who did not comply with the SAM protocol, with independent t tests for normally distributed interval variables Wilcoxon rank -sum test for not normally distributed interval variables, and by Chi -square test o r Fishers exact test for categorical variables. In a third step, backward logistic regression based on maximum likelihood estimates was used to identify predictors for compliance in the first day, second day, and both days. Data were analyzed with SAS ver sion 9.1 Statistical software. A priori power analysis was conducted. Within the stroke population, in individuals with sub acute to chronic stroke (3 166 months post -stroke) correlations between SAM outcomes and othe r clinical outcomes range from 0.316 (w ith peak oxygen consumption) 23 to 0.62 (with FIM

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98 mobility).12 Based on the lowest correlation reported (r=0.316), with 10 predictors, and an adjusted alpha level of 0.005, a sample size of 62 will be needed to get a power of 0.80. 3. 3 Results 3. 3.1 Study Population The sample consisted of 228 individuals two months post -stroke. The average a ge of the participants was 63.92 + 12.87 (SD) years. The sample represents 51% women. The majority of study participants had ischemic stroke (82%) and right hemisphere stroke (47%). Eighteen percent of the sample had less than high school education. The average self -selected walking speed was 0.39 + 0.22. 3. 3.2 Compliance Rates Co mpliance rates in the first and second measurement days were high (72 and 66%, respectively). Ho wever, only 55% of the participants wore the SAM in both the first and the second measurement days (moderate both days compliance rate) (Figure 3 2). Table 3 1 presents the demographic and clinical characteristics of the participants by compliance in day one, day two, and in both days. Statistical tests of mean difference at p < 0.05 indicated that only the differences in gait speed (in day 2 and in both days), a ge (in day two and in both days) balance control (in day 2), and balance self -efficacy (in day 1 and in both days), were found to be significant between compliers and noncompliers. Participants who complied with SAM protocol were no different from those who did not comply with the measurement protocol with respect to cogniti on, depression, gender, education, stroke type, stroke location and upper extrem ity motor control (Table 3 1). Out of the 228 participants, five were primarily wheelchair users. Eight y percent of the primarily wheelchair ambulators complied with the first measurement day, as opposed to 60% in the second and in both measurement days.

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99 3. 3.3 Compliance Predictors It was hypothesized that increased cognition, lower depression scores, young er age, decreased gait impairment, and higher balance control scores and balance self -efficacy will predict increased inferred protocol compliance, whereas gender, education, stroke location, stroke type, and upper extremity motor control will not be assoc iated with inferred protocol compliance. Logistic regression analysis indicated that none of the considered independent variables predicted compliance in the first day However, t he model indicated that compared to younger individuals, older patients have higher probability to comply with SAM protocol in the second measurement day and in both days (odds ratio=1.046 and 1.043, respectively) (Tables 3 2 and 3 3). In addition, as hypothesized, individuals with more severe balance control deficits were less likely than individuals with less severe balance control deficits to comply with SAM protocol on day two(odds ratio of individuals with more severe balance control deficits=0.380) (Table 3 2).Similarly, individuals with low balance self -efficacy were less l ikely to comply with SAM protocol than individuals with moderate to high levels of self -efficacy on both measurement days (odds ratio of persons with low level of balance self -efficacy=0.520) (Table 3 3). 3. 4 Discussion 3. 4.1 Compliance Rates Inferred comp liance with the monitoring protocol is of critical importance in any study using accelerometers (e.g., SAM) to quantify physical activity at home and in the community. The results of the present study revealed high compliance rates with the SAM protocol in the first and second measurement days. However, compliance rate dropped from 72% in the first measurement day to 66% in the second measurement day. Furthermore, only 55 % of the

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100 participants were in compliance for two consecutive days. To the best of our knowledge, the present study is the first to report inferred compliance rates with lower extremity accelerometer monitoring post -stroke. The topic of compliance has received relatively little attention in other populations as well.317 In a feasibility study of the use of accelerometers to measure physical activity in young adolescents, the percentage of students w ith complete accelerometer data for > three days was 92%.284 Cooper et al.285 reported a 78 % compliance rate for a 7 -day accelerometer measurement in normal, overweight, and obese adults. These reported compliance rates are considerably higher than the compliance rate s of the participants in our study. However, caution should be taken when comparing compliance rates across studies as differences in study population and methods to infer compliance may be accountable for the observed differences. On the account of the o bserved decrease in compliance rate from the first measurement day to the second it may be appropriate to implement strategies to improve compliance if data is being collected for more than one day. Different strategies can be used to promote compliance wi th SAMs. These strategies may be categorized as participant or investigator -based. Participant based approaches require the persons wearing the accelerometers to complete specific tasks that may promote greater compliance (e.g., participant records the time the accelerometer is on and off and the activities performed without the monitor). Investigator -based compliance strategies consisted of activities that performed by the research staff that assist study participants remember to wear the monitor (i.e., ma king reminder calls, providing participants with written materials to display on refrigerators to prompt wearing the monitor, providing incentives contingent on compliance, and educating family members and caregivers about the wearing protocol).317 3. 4.2 Compliance Predictors Interestingly, the parameter estimates for age were greater than one. This means that the older an individual is the more likely he or she is to comply with wearing the SAM device

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101 Within other populations, it has also been reported that older age is associated with greater likelihood of compliance with various medical treatment regimens.318 320 Within the stroke population, Usawate et al.272 examined factors associated with upper extremity accelerometry monitoring in individuals three to nine months post -stroke with mild to moderate hem iparetic arm motor impairment. The authors reported that compliance with accelerometer monitoring of real world arm activity post -stroke did not differ for older and younger individuals. However, the authors did not report samples age variability. Therefore, the null findings may be attributable to limited variability in age. In contra ry to our hypothesis, cognition, depression and gait speed did not predict compliance with SAM monitoring. Cognition may not have been an important predictor for compliance with SAM in this study because individuals with severe cognitive impairment, who could not perform a thr ee-step command, were excluded from the study. In addition, the accelerometer is user friendly. Therefore, individuals with mild and moderate cognitive deficits may not encounter difficulties understanding the accelerometer measurement protocol. Depression was found to be a significant predictor of noncompliance with medical treatment in numerous populations.280282 However, in this study decreased depression was not a significant predictor of compliance. One explanation to this lack of association between depression and compliance may be accountable to the study inclusion and exclusion criteria. Individuals with major post -stroke depression as indicated by a PHQ 9 score of greater than 10 (moderate to severe depression) were includ ed in the study only if there was documented management of the depression by a health care provider. Therefore, the study depression variability was limited. Finally, gait impairment was not a significant predictor of comp liance either. The null results

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102 may be accountable to the reduced variability in the samples gait speed as individuals with very mild gait impairment ( > 0.8 m/s) were excluded. As hypothesized, individuals with less severe balance control impairment and higher levels of balance self -efficacy were more likely to comply with SAM protocol. It is reasonable to hypothesize that individuals with better balance control and balance self -efficacy are more likely to comply with SAM because they are more likely to wa lk on a regular basis than individuals w ith more severe impairments. Therefore, individuals with less severe balance and self -efficacy deficits may place more importance on w earing the accelerometer simply because they are performing the behavior being mea sured more frequently. As expected, the logistic regression analysis also indicated that stroke location, stroke type, upper extremity motor control impairment, gender, and education did not predict compliance with SAM protocol. Accordingly, Uswatte and c olleagues272 concluded that stroke location, stroke type, upper extremity motor control impairment, gender, and edu cation did not predict compliance with upper extremity accelerometry protocol post -stroke. The authors reasoned that highest level of education may not be related to accelerometer monitoring compliance per se, but rather may have an impact on the methods t hat should be used to promote compliance with the monitoring protocol and to describe appropriate use of the accelerometer to the study population. 3. 4.3 Study Limitations Our study has a number of limitations. First, our study excluded individuals with severe untreated depression, severe cognitive impairment, and mild gait impairment. Therefore, the effect of these variables on compliance with SAM monitoring can not be fully established. Second, despite the widespread use of accelerometers, there is no s tandardized way to assess compliance with the monitoring protocol. Therefore, the definition developed to assess

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103 compliance in this study is likely to have an impact on compliance rates and predictors. Third, study participants consisted of individuals dur ing the sub acute phase of stroke recovery (two month post -stroke). Therefore, it is unclear how generalizable our findings are to individuals during the acute and chronic phase of stroke recovery. Finally, the study was not designed to evaluate how differ ent factors might improve compliance. Testing the differences of effectiveness of the different strategies to promote compliance would be a contribution for others using activity monitors 3. 5 Conclusion Understanding the factors that influence SAM protoc ol compliance is an important aspect of clinical trials which use accelerometery as mobility outcome measure. This study demonstrates high compliance rates with SAM monitoring protocol in the first and second measurement days (72 and 66%, respectively). However, compliance rate dropped from the first to the second measurement day by six percent. In addition, compliance ra te in both days was as low as 55 % (moderate compliance).Therefore, it is recommended to implement strategies to improve compliance, especi ally in studies which attempt to collect data for more than one day, and in samples that are mostly consisted of younger individuals, persons with relatively severe balance control deficits (BBS < 45), and low levels of balance self -efficacy (ABC <50). The se strategies may consist of making reminder calls, providing participants with written materials to display on refrigerators to prompt wearing the monitor, providing incentives contingent on compliance, educating family members and caregivers about the we aring protocol, and etc.317 Participants compliance in wearing the monitor is critical for obtaining accurate physical activity measurement,15 as non -compliance is usually considere d a source of bias. Therefore, having compliance rate of at least 80% may be an appropriate goal.

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104 No variable predicted compliance in the first measurement day. Age and balance control predicted compliance in the secon d measurement day. However, age and ba lance self -efficacy predicted compliance in both measurement days. Cognition, depression, gait impairment, education, stroke location, stroke type, and upper extremity motor control impairment did not predict SAM protocol inferred compliance. Finally, this study consisted of approximately 50% of the LEAPS participants. Due to the drop in inferred compliance rate from the first to the second measurement days and the moderate compliance rate in both measurement days, we are currently using additional strategi es to enhance LEAPS participants SAM compliance over the last phase of the trial (e.g., better reinforcement of follow up phone calls). Within the stroke population, Uswatte et al.272 reported that the participants wore accelerometers on both upper and lower extremity for approximately 86% of their waking hours. Therefore, it is reasonable to aim to raise LEAPS participants inferred compliance to at least 80%.

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105 Table 3 1 Demographics and clinical characteristics of the participants by compliance status Characteristics of patients Compliance in first day Compliance in second day Compliance in both day Yes N=165 (7 2.4 %) No N=63 (27.6%) Yes N=151 (66.2 %) No N=77 (33.8%) Yes N=125 (54.8 %) No N=103 (45.2 %) Gait speed (SD) (m/s) 0.41(0.21) 0.33(0.22) 0.41(0.21)* 0.33(0.22) 0.41(0.22)* 0.35(0.22) Age (SD) (years) 64.6(12.9) 62.2(12.7) 65.4(12.3)* 61.1(13.5) 66.6(11.7)* 60.7(13.6) Mini Mental State Examination (SD) 26.2(3.31) 26.0(3.2) 26.2(3.1) 25.9(3.6) 26.3(3.1) 25.9(3.5) Trail Making Test Part B (SD) (sec) 217.2(94.6) 225.9(105.9) 213.3(94.3) 232.0(103.3) 212.9(93.8) 227.8(101.9) Patient Health Questio nnaire (SD) 4.6(5.0) 4.6(5.5) 4.5(5.1) 4.9(5.1) 4.4(4.9) 4.8(5.4) Fugl Meyer Assessment upper extremity (SD) 37.6(21.1) 33.0(19.9) 37.6(20.6) 33.9(21.0) 38.3(20.8) 33.9(20.6) Berg Balance Scale Less impaired 33.3% 27.0% 36.4%* 22.1% 36.0% 26.2% More im paired 66.7% 73.0% 63.6%* 77.9% 64.0% 73.8% Activities Specific Balance Scale Moderate to high level 49.4%* 33.9% 49.0% 37.3% 51.2%* 37.6% Low level 50.6%* 66.1% 51.0% 62.7% 48.8%* 62.4% Gender Male 50.3% 47.6% 53.0% 42.9% 51.2% 47.6% Female 49.7% 52.4% 47.0% 57.1% 48.8% 52.4% Education < high school 20.00% 15.9% 19.9% 16.9% 20.0% 17.5% High school or GED 28.5% 28.6% 26.5% 32.5% 27.2% 30.1% Some college 20.0% 30.2% 20.5% 27.3% 18.4% 28.2% Associates degree 9.7% 3.2% 10.6% 2.6% 12.0% 2.9% Bachelors degree 15.2% 9.5% 13.2% 14.3% 14.4% 12.6% Advanced degree 6.7% 12.7% 9.3% 6.5% 8.0% 8.7% Stroke type Ischemic 83.0% 81.0% 84.1% 79.2% 84.8% 79.6% Hemorrhagic 15.8% 15.9% 14.6% 18.2% 13.6% 18.4% Uncertain 1.2% 3.2% 1.3% 2.6% 1.6% 1.9% Stroke location Right hemisphere 44.8% 54.0% 44.4% 53.2% 43.2% 52.4%

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106 Table 3 1 Continued Characteristics of patients Compliance in first day Compliance in second day Compliance in both day Yes N=165 (72.4 %) No N=63 (27.6%) Yes N=151 (66.2 %) No N=77 (33.8%) Yes N=125 (54.8 %) No N=103 (45.2 %) Left hemisphere 38.2% 34.9% 39.7% 32.5% 40.0% 34.0% Brainstem 15.2% 11.1% 15.2% 11.7% 16.0% 11.6% Bilateral 1.8% -0.7% 2.6% 0.8% 1.9% SD=standard deviation; *d ifferences between compliers and non -co mpliers are significant at the < 0.05 level (2 tailed).

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107 Table 3 2 Second day p redictors for compliance with step activity monitor Covariate Odds Ratio 95% Confidence Interval P value Age 1.046 (1.011, 1.067) 0.005 More severe balance control i mpairment 0.380 (0.181, 0.796) 0.010 Table 3 3 First and second days p redictors for compliance with step activity monitor Covariate Odds Ratio 95% Confidence Interval P value Age 1.043 (1.017, 1.070) 0.001 Low balance self efficacy 0.520 (0.282,0.9 60) 0.036 Window 1 Window 2 Window 3 (6:00AM -12:00PM) (12:01PM -6:00PM) (6:01PM 12:00AM) Figure 3 1. Inferred wearing activity based on the windows method. The Figure illustrates activity in all three windows. Therefore, the person in this figure has compl ete compliance with the monitoring protocol [adapted from Franklin PD, McLaughlin J, Boisvert CB, Li W, Ayers DC. Pilot study of methods to document quantity and variation of independent patient exercise and activity after total knee arthroplasty. J Arthro plasty. 2006;21:157163.].109

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108 Figure 3 2 Compliance with the step activity monitor by day. This figure illustrates the drop in inferred compliance rate from the first measurement day to the second measurement day (a six percent drop). However, the percentage of individuals who wore the step activity monitor in the first and the second measurement da ys (both days compliance) was the lowest. 0 10 20 30 40 50 60 70 80 First day Second day Both days Inferred protocol compliance by day Percent Yes No 66% 55% 72%

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109 CHAPTER 4 CLINICAL BASED GAIT SPEED IN PERSONS WIT H SUB -ACUTE STROKE: RELATIONSHIP TO HOME AND COMMUNITY AMBULA TION ACTIVITY 4. 1 Introduction Stroke is the leading cause of longterm disability among adults in the United States.37 An inability to independently ambulate at home and in the community is one of the most disabling aspects for stroke survivors.5 Gait restoration is therefore one of the most important components of post -stroke rehabilitation.321 Consequently, m easuring the ability to ambulate safely and effectively at home and in the community is crucial. Gait speed is an activity level outcome measure which has often been used as a proxy measure for home and community ambulation28 in post -stroke clinical trials.322 However, gait speed may not be a perfect measure of home and community ambulation as it evaluates the individual in a non -familiar environment,10 derive s mobility from brief snapshots of walking performance,11 is not sensitive to subtle changes in function,11, 12 and overall, do es not directly assess persons free -living ambulation or mobility -driven social participation.12 Thus, the skills necessary to perform gait speed tests likely do not automatically transfer to mobility at home and in the community.28 Therefore, it is important to understand the relationship between gait speed and participation level measures of home/community ambulation and whether this relationship is confounded by other variables. Participation level measures of home/co mmunity ambulation may be ascertained with self report mobility questionnaires or via direct measures of step activity in the community. The SIS is an example of a psychometrically robust post -stroke self -report which includes items that assess the ability to be mobile at home and in the community. Recently, Schmid et al.32 evaluated whether changes in speed-based community ambulation classification were related to meaningful changes in stroke -related function and quality of life as measured by the SIS. The s tudy

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110 population consisted of subacute stroke survivors with mild to moderate deficits. The authors concluded that the gait -speed classification system is associated with mobility in the com munity as a gait speed gain that results in a transition to a higher class of ambulation also results in better SIS mobility and participation scores. Although Schmid et al. s32 stud y supports the use of the speed-based classification as a proxy measure of community ambulation, numerous factors might confound this relationship. According to van de Port et al.,99 association bet ween post -stroke gait speed and community ambulation was confounded by control of standing balance, motor function, the use of walking devices, and walking endurance. Van de Ports and associates results99 are in agreement with those of previous studies.23, 60, 61 Moreover, balance self -efficacy might also confound the relationship between gait speed classification system and community ambulation, as fear of falling may lead to the avoidance of activities that the indi vidual remains capable of performing.107 Finally, van de Port and associates99 reported that age, lesion location by hemisphere, living alone, history of falls, executive function, fatigue or depression did not confound the association between gait speed and community ambulation. The a forementioned studies provide valuable information on the association between gait -speed and community ambulation and on confounders to this relationship. However, these studies used self administered questionnaires to assess community ambulation. Self adm inistered questionnaires rely on factors related to recall and cognitive ability which contribute to the inaccuracy of these approaches.108 Therefore, it is important to validate the speed -based classification system against a direct measure of community ambulation as well. Pedometers and microprocessor based accelerometers, suc h as the SAM enable direct recording of ambulatory activity. Pedometers count t he number of steps taken during ambulatory

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111 activity. However, pedometers are unable to breakdown activity minute -by-minute, therefore, they do not provide information on activity intensity and pattern. Furthermore, within the stroke population, pedometers do not provide accurate and reliable measure of stride counts.19 Pedometers inaccuracy within the stroke population may be due to stroke survivors slow gait speeds and irregular gait patterns. Unlike the pedometers, the SAM was found to be valid, reliable,19, 23 and sensitive to change12 when used with individuals post -stroke. In addition, SAM provides minute by-minute measurements. The resulting temporal series of data can be analyzed in a variety of ways:24 (1) Activity volume, refers to activity quantity and/or duration (e.g., the number of steps an individual took in 24 hour period),25 (2) Activity intensity, as determined via step vigor (steps/min) ,26, 27 and (3) Activity pattern, represents the degree to which step activity fluctuates minute -to -minute, and whether these mi nute -to -minute fluctuations occur in an orderly, repetitive, or predictable manner. Michael et al.23 used the SAM to evaluate ambulatory activity (stride counts) i n a sample of community -dwelling individuals with mild to -moderate chronic stroke. The authors reported that the participants mean ambulatory activity profiles were strongly associated with self selected gait speed. More recently, Bowden et al.33 demonstrated that classifying individuals with chronic stroke (greater than six -months post -stroke) by self -selected gait speed is associated with walking behavior in the community as qua ntified by daily steps counts. Howev er, c ontrasting results exist. Shaughnessy and colleagues12 reported that SAM -derived daily steps were not related to self -selected gait speed in persons with mild to -moderate post -stroke hemiparetic gait deficits at di scharge from inpatient rehabilitation and three months later. Similarly, Mudge and Stott343 concluded that SAM -derived number of steps/day and percentage of the day with no steps did not significantly correlate with habitual gait speed in persons with

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112 chro nic stroke (greater than six months post -stroke) and moderate post -stroke hemiparetic gait deficits (mean gait speed =0.67 m/s). However, Mudge and Stott343 reported that habitual gait speed significantly correlated with numerous activity intensity outcome s (e.g., step rate of the fastest 30 minutes and the highest step rate in one minute). The association between gait speed and step activity pattern is unknown. Knowledge on the association between gait speed and step activity pattern is also important as two subjects who took similar number of steps might demonstrate vary ing step activity pattern s Therefore, gait speed -classification system might be associated with step activity volume and intensity but not w ith step activity pattern. There is also a need to establish factors which confound the association between gait speed and community ambulation with quantitative measures of walking performance in the community. This knowledge will help in determining when it is appropriate to use the gait speed classif ication system as a proxy measure of community ambulation. For instance, if severe balance control deficits distort the relationship between gait speed and step activity in the community, gait speed may not be a good proxy measure of community ambulation f or persons with severe balance control deficits Finally, examining the association of gait speed and SAM with a measure of participation such as the SIS will assist in determining what type of community -based activities people with certain gait speeds and step activity profiles are capable of doing. For instance, is a pers on with gait speed of 0.4 m/s capable of climbing one flight of stairs? Does a perso n with an average of a 1000 steps per day find it difficult to go shopping? The purpose of this study is to assess the relationship between gait speed and SAM derived home and community ambulation, and whether lower extremity motor control impairment, balance control walking endurance, and balance self -efficacy, significantly

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113 confound this relati onship in persons with moderate -to -severe sub acute stroke. In addition, we explored the association of gait speed and SAM step activity to mobility and social par ticipation domains of the SIS. It was hypothesized that habitual gait speed will mainly correlate with SAM volume and intensity outcomes but not with SAM pattern indices. In addition, it was hypothesized that potential variables that could significantly distort the relationship between gait speed and home/community ambulation are balance control lower ext remity motor control impairment, walking endurance, and balance self -efficacy. Finally, it was hypothesized that SAM derived outcomes and gait speed will have moderately strong to very strong (r > 0.60) positive correlations with SIS. However, SAM derived ambulation outcomes will have significantly stronger associations with SIS than gait speed. 4. 2 Methods 4. 2.1 Design Data in this cross -sectiona l study were derived fro m a parent study, the LEAPS pre randomization data from two -month post -stroke baseline assessments. The LEAPS is an ongoing 5 year, phase III, single -blind, 5 -site, randomized controlled trial. Details about design, outcomes, and inclusion and exclusion criteria of this study are published elsewhere.311 Briefly, the LEAPS trial recruits individuals post -stroke over the age of 18, five to thirty days post stroke. Individuals with uncontrolled health conditions for which exercise is contraind icated (e.g., serious cardiac conditions, severe arthritis or orthopedic problems, severe hypertension, and uncontrollable diabetes) are excluded. The primary purposes of the LEAPS trial are to determine if a specialized locomotor training program that inc orporates a body weight support system and treadmill as a treatment modality can produce a functionally significant improvement in walking of persons post -stroke compared to a control group (non -specific, low intensity exercise program), and whether timing

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114 of therapy (two or six months post -stroke), severity of locomotor impairment (moderate or severe), and the number of treatments affects locomotor outcome. The LEAPS trial primary outcome is the proportion of participants who successfully recover walking o ne year post -stroke, based on the gait speed classification system.311 4. 2.2 Testing Procedures and Measurements 4. 2.2.1 Ambulatory activity Habitual a mbulatory activity was assessed with the SAM. SAMs were programmed with an initial generic calibration.19 Participants performed a one -minute walk test. SAM counts of strides were compared with visual counts. Adjustments were made to the calibration setting and the timed walks were repeat ed to ensure greater than 90% accuracy against visual counts. To achieve optimal sensitivity for step detection, the SAM was programmed and applied above the malleolus of the less involved limb with two adjustable elastic straps. Participants wore the SAM for a period of two days, throughout the day, removing the SAM only to bathe, shower, swim, or sleep. A demonstration and written instructions regarding appropriate use of the device were provided to subjects. Data reduction : On completion of a 48-hour m onitoring period, SAM data were downloaded using an infrared docking port. SAMs raw data fr om the manufacturer software were imported into Microsoft Excel. Data were expressed as stride counts every 60 seconds during the 48 hours. There were no s ignifica nt differences in SAM indices between the first and the second measurement days (Table 4 1). Furthermore, inferred compliance rate was highest in the first measurement day (72.4%), followed by the second measurement day (66.2%) and in both days (54.8%). I nferred compliance in 24 and 48 hours was established in three time periods (windows) of six hours: (1) 6:00AM 12:00PM, (2)12:01PM 6:00PM, and (3) 6:01PM 12:00AM. According to this method, a

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115 compliant person in 24 and 48 hours was defined as a participant who has activity recorded in each of the three or six recording windows, respectively. P articipants compliance in wearing the monitor is critical for obtaining accurate physical activity measurement,15 as non -compliance is usually considered a source of bias. Therefore, on account of the highest compliance rate in the first m easurement day and the lack of differences in SAM indices between the first and the second measurement days, the analyses in this study are based on the data obtained in day one only. Moreover, all analyses were conducted for all participants (individuals with and without activity in each of the first day three windows) and for compliant patients only (individuals with activity in each of the first measurement day three windows). Identifying when a participant is not wearing the accelerometer is difficult as long continuous bouts of accelerometer inactivity (continuous zero readings) may mean either that the participant removed the accelerometer, was completely inactive (e.g., sleeping),251 or the monitor was malfunctioning. Therefore, in an effort to ensure that t he SAM data included in the analyse s represent the construct to which they refer (home a nd community ambulation), FAC of individuals with non active days (less than 20 steps per day ) was determined. Comparing the FAC and SAM step activity enabled to discov er inconsistencies between functional ambulation level and step activity due to incompliance or SAM malfunctioning. Mehrholz et al.173 suggest that a dichotomized FAC (FAC > 4 and FAC < 4) may be useful in predicting level of community ambulation with high sensitivity and specificity. Therefore, it is likely that individuals with FAC > 4 with non active days either did not wear the monitor or the monitor was malfunctioning. Consequently, it will be appropriate to remove these individuals files from the dataset in order to prevent false low SAM activity counts. In contrast, individuals with FAC < 4, nonactive days are probably not due to being non-compliant with the monitoring protocol

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116 but simply because they did not walk. Therefore, it will be appropriate to include these individuals files in the a nalysis in order to prevent false high SAM step activity counts. Figure 4 1 illu strates data analyses decision making flow chart. Ambulation volume outcomes: 1 Number of steps taken over 24 hours. Number of steps per day was defined as twice the total stride count in 24 hours.285 2 Total activity level. Total activity level was defined as the tally of the number of onemin ute intervals with stride counts of > 1. Presented as the percentage of the total time in a 24hour period where steps are recorded.25 A mbulation intensity o utcomes : 1 Peak activity index. Peak activity index represents the maximal number of steps a person took in a one minute int erval. 2 Percentage of the day spent in low, medium, and high absolute activity intensity. Absolute step activity intensity were stratified as low (less than 16 steps per minute), medium (16 30 steps per minute) and high (more than 30 steps per minute). Pe rcentage of time spent at each level was calculated. Minutes in which activity was not recorded were excluded from the analysis. 3 Percentage of the day spent in low, medium, and high relative activity intensity. Low relative activity intensity was defined a s walking at less than 60% of peak acti vity index, moderate intensity 60 85% of peak activity index, and high intensity > 85% of peak activity index. Ambulation pattern outcomes : 1 Number of activity bouts. Number of activity bouts was defined as the number of 1 -min intervals for which the subject switched from inactivity (stride count=0) to activity (stride count > 0).14 2 Number of steps in an activity bout. Number of steps in an activity bout was defined as activity bout mean number of ste ps. 3 One minute variability of activity. One minute variability of activity was defined as the CV of 1 -min of stride count values, excluding those one min intervals with zero steps.14 CV is a measure of variability calculated as the stand ard deviation of the measures expressed as a percentage of the mean (CV=standard deviation/mean X 100). One of the

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117 CV advantages is that it accounts for differences in the magnitude of the mean (a measure of relative variation).288 4 Activity randomness. Activity randomness was defined as the amount of randomness of minute to -minute activity fluctuations in each day of recorded ambulatory activity. Randomness of activity was quantified by analyzing 1 -minute stride counts using approximate entropy.289 Approximate entropy was used to determine the probability that sequences of consecutive 1 -minute stride counts repeated throughout the longer temporal sequence of 1,440 daily 1 -minute intervals. Approximate entropy generates a unitless number between zero and two. Zero values correspond to a time series in which short sequences of data points are perfectly repeatable. Values of two correspond to time series for which any repeating sequences of poi nts occur by chance alone.290 4. 2.2.2 Mobility impairment Mobility deficit severity was determined by gait speed. Gait speed was m easured during a 10meter walk. The time that it took to traverse 10 meters at the subjects usual pace, using their usual assistive devices or orthoses, was recorded. Participants were stratified into two functional groups by self -selected gait speed per the Perry9 classification system: the household ambulators walked < 0.4 m/s, and the limited community ambulators walked from 0.4 to 0.8 m/s. 4. 2.2.3 Mobility and social participation The SIS was used to measure mobility and social participation. The SIS is a comprehensive and psychometrically robust stroke specific outcome measure,13, 208 which was developed from the patient, caregiver, and stroke expert perspective.13, 2 08, 210 It was designed to assess multidimensional health related quality of life stroke outcomes, including strength, memory and thinking, emotions, communication, activities of daily living, hand function, social participation, and ability to be mobile at home and the community.13 The social participation and mobility domains include items which represent community ambulation and mobility aspec ts (e.g., the ability to climb flights of stairs and to participate in active recreation).13

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118 4. 2.2.4 Potential covariates Four potential covariat es were investigated: (1) lower extremity motor control impairment, (2) balance control (3) walking endurance, and (4) balance self -efficacy. These variables were chosen on the basis of the results of previous studies12, 14, 23, 34, 294 a nd on clinical grou nds. The lower extremity motor function portion of the Fugl Meyer Assessment (FM -LE) was used to determine lower extremity motor control impairment.315, 323 The Fugl -Meyer Assessment is one of the most commonly used and comprehensive scales of motor recovery following stroke. The FM -LE includes items that assess reflex activity, synergies, and coordination. Each item is graded on a three point scale (0 cannot perform, 1 partly performs, and 2 performs fully).315, 323 The maximum lower extremity motor performanc e score is 34 points. A > 10 points change in Fugl -Meyer motor scores may represent clinically meaningful improvement based on stroke neurologists and physical therapists.315 T he LE FM was dichotomized into mild lower extremity motor control impairment (scores=24 34) and moderate -to -severe lower extremity motor control impairment (scores= 0 23) Standardized protocols for administration were followed.315 Balance control was meas ured by the BBS .324 The BBS consists of 14 items which assess subjects ability to main tain movements or positions of increasing difficulty via decreasing the base of support.324 Each item is graded 0 to 4. The global score of the BBS ranges is from 0 to 56 points. A score of 56 indicates functional balance, whereas, scores < 45 indicate that a person may be at greater risk of falling.325 Therefore, the BBS score was dichotomized into more severe balance deficits (scores < 45) and less severe balance deficits (scores > 45).325 The test h as been used in a stroke population and has well -established validity and reliability.324 A standardized protocol for administration was followed.324

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119 Walking endur ance was determined via th e 6MWT The 6MWT was originally developed to assess cardiopulmonary function in cardiac patients.326 However, it is commonly used as an endurance measure in the elderly and in individuals who suffered a stroke.165 Participants performed the 6MWT using a previously standardized protocol.165 According to the protocol, during the 6MWT, the participants were allocated six minutes to walk as far as they can at their comfortable pace. The distance they covered in six minutes was recorded. During the test, participants used their customary orthotics and assistive devices. Walking distance of 300 to 500 continues meters is suggested for community ambulation.60, 103 However, in this study walking distance variability was small (zero to 315 meters). Walking distance mean and median was 128 and 126 meters, respectively. Thus, overall the sample had poor walking endurance fun ction. Therefore, walking distance was dichotomized on the basis of the samples median walking distance (126 meters). ABC was used to assess perceived confidence in maintaining balance while performing common community related activities such as bending, reaching, and walking both inside and outside the house. This measure has good reliability and internal consistency.327 Myers et al.328 found that in a group of community -dwelling older adults, ABC sco res of < 50 indicated a low level of physical functioning (home care clients) ; ABC of more than 50 and less than 80 indicated a moderate level of functioning (older adults in a retirement home setting) ; and ABC scores of > 80 indicated high functioning (physically active older adults). Accordingly, in this study ABC scores were dichotomized into low level of physical functioning (ABC < 49 points) and moderate to high level of physical functioning (ABC > 50 points). 4. 2.3 Data Analysis All SAM indices but for peak activity index were not normally distributed. Therefore, group differences were analyzed using the Wilcoxon rank -sum test for non -normally distributed

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120 variables. Relationships between gait speed and SAM derived parameters were determined via Spear mans Rank Order Correlation coefficients. Association of SAM outcomes and gait -speed to SIS were determined via Spearmans Rank Order Correlation as well Fishers Z transformation of the correlation test was used to test the differences between SAM and g ait speed associations with SIS. Spearmans Rank Order Correlation and Fishers Z transformation were used to establish confounders to the association between gait speed and SAM indices. All statistics but for Fishers Z transformation were run using SAS v ersion 9.1. R version 2.81 software was used to conduct Fishers Z transformation. All hypotheses were tested in a 2 -tailed fashion with a P < 0.05 level of significance. A priori power analysis was conducted. Within the stroke population, in individuals w ith sub acute to chronic stroke (3 166 months post -stroke) correlations between SAM derived outcomes and other clinical outcomes range from 0.316 (with peak oxygen consumption)23 to 0.62 (with Functional Independence Measure mobility).12 Thus, with a sample size of 228, an alpha level of 0.05, and power of 0.80, there was sufficient power to detect correlations as low as 0.184. 4. 3 Results 4. 3.1 Sample Characteristic s Data were collected from 228 individuals two months post -stroke. When all the participants were included in the analyses, inc ongruence between FAC scores and step activity level in the first measurement day was detected for three subjects only. However, when only the compliers were analyzed, no incongruences between FAC scores and step activity level were detected. Due to the small number of subjects with incongruent data, theses subjects were included in the analyses. The samples mean age (all patients) was 63.9 + 12.9 years. The sample represents 50% women. The majority of study population had ischemic stroke (82%) a nd right hemisphere

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121 stroke (47%). The subjects who complied with SAM protocol in the first day mean age was 64.6 + 12.9 years. Fifty percent of the compliers were women. The majority of the compliers had ischemic stroke (83%) and right hemisphere stroke (45%). 4. 3.2 Daily Ambulatory Activity of Household and Limited Community Ambulators 4. 3.2.1 First measurement day -all patients The results show significant differences between the two ambulation functional groups in all SAM outcomes but for percentage of the day in relative low activity intensity (an activity intensity outcome) and activity variability (an activity pattern outcome) (Table 4 2 ). 4. 3.2.2 First measurement day -compliers only Household ambulators differed from the limited community ambulators in all SAM outcomes but for one activity intensity outcome (percentage of the day in relative high activity intensity) and three activity pattern outcomes (number of activity bouts, activity variability, and activity randomness) (Table 4 3). 4. 3.3 Correlations between Habitual Gait Speed and Step Activity Monitor Indices It was hypothesized that gait speed will significantly correlate with both SAM activity volume and intensity outcomes, but not with SAM activity pattern outcomes. A summary of the results for all the patients and for the compliers group only follows 4. 3.3.1 First measurement day -all patients As hypothesized, s elf -selected w alking speed was significantly positively correlated with both number o f steps/day (r= 0.531 ) and percen tage of the day active (r= 0.327). In addition, self -selected gait speed significantly correlated in the expected direction with all activity intensity outcomes but for percentage of the day in relative high activity intensity. Finally, gait speed significantly correlated with all activity pattern outcomes but for activity variability (Table 4 4).

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122 4. 3.3.2 First measurement day -c ompliers only As expected, self -selected walking speed was significantly positively correlated with both number of steps taken in a day (r= 0.599) and percen tage of the day active (r= 0.295). In addition, similar to all patients analysis, self -selected gait speed significantly cor related with all activity intensity outcomes but for percentage of the day in relative high activity intensity. However, self -selected gait speed significantly correlated with only one out of the four activity pattern outcomes (number of steps in an activi ty bout, r= 0.528) (Table 4 4). 4. 3.4 Confounders to the Asso ciation between Gait Speed and Step Activity Monitor Outcomes In order to establish the confounders to the association between gait speed and SAM indices, we compared the correlation s bet ween gait speed and SAM indices based on the various levels of the hypothesized confounders (e.g., gait speed of individuals with less severe balance deficits correlation with percentage of the day active vs. gait speed of individuals with more severe bala nce control deficits correlation with percentage of the day active). A variable was considered a confounder to the association between gait speed and SAM when gait speed of persons with different impairment levels (e.g., more and less severe balance contro l deficits) correlation with SAM was significantly different. Following, are the results for all the patients and for the compliers only. 4. 3.4.1 First measurement day -a ll patients Balance control confounded the relationship between gait speed and step act ivity volume (steps/day and percentage of the day active). Balance control also confounded the relationship between gait speed and four out of the seven activity intensity outcomes (peak activity index and percentage of the day in absolute low, moderate, a nd high activity intensity). However, balance control did not confound the relationship between gait speed and relative activity intensity

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123 indices (percentage of the day in relative mild, moderate, and high activity intensity). Finally, balance control con founded the relationship between gait speed and one of the activity pattern outcomes (number of steps in an activity bout) (Table 45). Lower extremity motor control confounded the relationship between gait speed and percentage of the day in absolute mediu m activity intensity solely (an activity intensity outcome). Finally, walking endurance and balance self -efficacy did not confound the relationship between gait speed and any of the 13 SAM indices (Table 4 5). 4. 3.4.2 Fist measurement day -compliers only Balance control confounded the relationship between gait speed and activity volume (steps/day and percentage of the day active), three out of the seven activity intensity outcomes (peak activity index, percentage of the day in absolute low, and percentage of the day in absolute high), and one out of the four activity pattern outcomes (number of steps/activity bout).Walking endurance confounded the relationship of one activity volume index (steps/day), three activity intensity indices (percentage of the day i n absolute low, medium, and high activity intensity), and one activity pattern outcome (number of steps/activity bout). Lower extremity motor control confounded the relationship between gait speed and activity volume indices (steps/day and percentage of th e day active), two activity intensity indices (peak activity index and percentage of the day in medium absolute activity intensity), and one activity pattern outcome (number of steps/activity bout). Finally, balance self -efficacy confounded the relationshi p between gait speed and activity volume (percentage of the day active) and activity pattern (activity randomness) (Table 4 6). Figure 4 2 illustrates the possible confounding effect s of individuals clinical characteristics on the relationship between ga it speed and SAM indices of two subjects who complied with the SAM protocol during the first SAM measurement day. The gait velocity of the

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124 person illustrated i n blue and red is similar (0.45 m/s and 0.43 m/s, respectively). However, these two individuals d iffered in terms of their step activity (F igure 4 2 and Table 4 7 ) and cli nical characteristics (Table 4 8 ) For instance, the individual with data reported in pink accumulated greater number of steps/day than the individual with data reported in blue (1802 and 575 steps/day, respectively). Fur thermore, the individual in pink percentage of the day active and peak activity index are clearly greater than that of the individual in blue. The individual depicted in pink also spent less time than the person in blu e in relative high activity intensity (0.3% vs. 4.8%, respectively). In addition, the two individuals have remarkably different activity patterns (number of activity bouts, activity variability, and activity randomness) (Table 4 7 ). For example, the person in pink took 81 activity bouts whereas the person in blue took only a total of 24 activity bouts. The two individuals have similar levels of balance control (Berg Balance Score=45). However, their 6MWT, FM LE, and ABC scores appear to be different (Table 4 8). Therefore, walking endurance, lower extremity motor control, and balance self -efficacy may be responsible for the observed differences between the two individuals in SAM indices despite their very similar gait speeds (0.45 m/s and 0.43 m/s). 4. 3.5 Co rrelations of S tep Activity Monitor Outcomes and Habitual Gait S peed with Stroke Impact Scale Mobility and P articipation Domains Table 4 9 presents results of the associations of SAM outcomes and habitual gait -speed with SIS mobility and participation usi ng Spearman Rank correlation coefficients for all the patients and the compliers only. 4. 3.5.1 First measurement day -a ll patients The results showed that gait speed significantly corr elated with both SIS mobility (r=0.546) and SIS participation (r=0.212). Similarly, most SAM indices significantly correlated

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125 with SIS mobility The only SAM outcomes that did not significantly correlate with SIS mobility are: percentage of the day in relative low activity intensity, percentage of the day in relative high acti vity intensity, and activity variability. However, only four out of the 13 SAM indices significantly correlated with SIS participation (steps/day, peak activity index, percentage of the day in absolute high, and number of steps in an activity bout) (Table 4 9). 4. 3.5.2 First measurement day -compliers only The compliers only analysis yielded similar results to all patients analysis. Following, is a description of the differences between the two sets of analyses: 1 Unlike, all patients analysis, when the co mpliers were analyzed separately, percentage of the day in absolute high activity intensity no longer significantly correlated with SIS participation (table 4 9). 2 In the all patients analysis percentage of the day in relative low activity intensity did no t correlate with SIS mobility or SIS participation. However in the compliers only analysis percentage of the day in relative low activity intensity significantly correlate with SIS mobility (Table 4 9). 3 In all patients analysis, number of steps in an activity bout significantly correlated with both SIS mobility and participation. However, in the compliers only analysis number of steps in activity bout significantly correlated with SIS mobility only (Table 4 9). 4. 3.6 Differences between step activity moni tor and gait speed strength of associations with Stroke Impact Scale When all patients were included in the analysis, the association of gait speed with SIS mobility was stronger than all SAM indices association with SIS mobility. However, when only the compliers were included in the analysis the association of gait speed with SIS mobility was stronger than all SAM indices association with SIS mobility, but for number of steps/day. When all patients or compliers only were included in the analysis, the ass ociation of gait speed with SIS participation was stronger than the association of four SAM indices with SIS participation

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126 (percentage of the day in absolute low, percentage of the day in relative medium, percentage of the day in relative high, and activit y variability) (Table 4 10). 4. 4 Discussion 4. 4.1 Differences between All Patients and Compliers Only Analyses In this study we analyzed the data set in two different ways. In the first approach all patients data from the first measurement day were includ ed in the analyses. However, in the second approach, only the data of individuals who complied with SAM protocol on the first measurement day were included in the analyses. A nalyzing all patients data and compliers data only yielded different results in te rms of differences between the two ambulation functional groups in SAM indices (Tables 4 2 and 4 3); correlation coefficients between habitual gait speed and SAM indices (Table 4 4); confounders to the association between gait speed and SAM indices (Tables 4 5 and 4 6); correlation coefficients between habitual gait speed, SAM indices and SIS (Table 4 9); and differences between SAM and gait speed strength of associations with SIS (Table 4 10). The observed differences in the studys results in the two diff erent analyses approaches (all patients day one SAM data vs. compliers only day one SAM data) clearly imply that compliance with the SAM protocol is of critical importance.317 Consequently, because the data obtained from the compliers only is more likely t o accurately represent the subjects true home and community ambulation level, the following discussion pertains to the compliers results only. 4. 4.2 Daily Ambulatory Activity of Household and Limited Community Ambulators Stratifying individuals post -str oke based on self -selected walking speed is associated with percentage of the day active and number of steps/day. The latter finding is in agreement with those of Bowden et al.33 who found that steps/day were significant ly different between household, limited community, and community ambulators. Gait -speed classification system is

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127 also associated with all activity intensity indices, but for percentage of the day in relative high activity intensity. Accordingly, Mudge and Stott343 reported that in individuals with moderate gait impairment (mean gait speed=0.67 m/s) during the chronic phase of stoke recovery (more than six months post -stroke), habitual gait speed significantly correlated with numerous absolute activity inten sity outcomes, namely, number of steps at low rate (< 30 steps/min), average step rate of the fastest 30 minutes, and highest step rate in one minute. However, in our study, the two functional groups differed only in one out of the four activity pattern ou tcomes (number of steps in an activity bout). Thus, this study demonstrated that classifying individuals two months post stroke by self -select ed walking speed is mainly associated with actual home and community ambulation vol ume. However, the gait -speed cl assification system only partly represents actual habitual ambulation intensity and pattern. Assessing ambulation intensity and pattern is especially important as ambulation volume can be an incomplete descriptor of function because two subjects with nearl y the same community ambulation volume (e.g., step count) can accumulate their steps very differently14 in terms of ambulation intensity and pattern. Moreover, activity pattern outcomes may potentially be useful in determining the individuals ability to ambulate in complex environments. Community ambulation is characterized by adapting and modifying gait (e.g., changing walking speed) to both expected and unexpected challenges to locmotion.305 SAM activity pattern measures (i.e. activity variability and activity randomness) assess the exte nt in which walking fluctuates minute -to -minute and whether these fluctuations occur in orderly, repetitive and predictable pattern. Such measures reflect ones ambulatory activity range and hypothetically may reflect ones capability to adjust gait to unpr edictable changes in task demands or environmental conditions. Because household ambulators do not significantly differ from limited

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128 community ambulators in terms of activity variability and randomness the gait speed classification system may not well rep resent ones ability to adapt gait and to ambulate in complex and unpredictable environments two month s post -stroke. The household and limited community ambulators also did not differ in terms of number of activity bouts. Number of activity bouts metric r epresents the frequency in which an individual initiated and terminated gait. Initiating and terminating gait stresses the balance control system.98 For example, gait initiation requires going from a stable standing posture to essentially falling. Gait termination requires the control of forward linear moment um, which, if not done correctly, may be destabilizing.92 The ability to initiate and terminate gait is crucial for adjusting gait in an unpredictable environment (e.g., crowded street) or when performing complex tasks.95 4. 4.3 Confounders to the Asso ciation between Gait Speed and Step A ctivit y Monitor Outcomes Although walking speed significantly correlated with numerous home and community ambulation indices (Table 4 4) the present study also shows that the correlation between walking speed and h ome and community ambulation was distorted by balance control, walking endurance, lower extremity motor control, and balance self -efficacy (Table 4 6). This latter finding is in agreement with those of previous studies.23, 60, 61, 99 However, t he current study examined these confounders using quantitative measures of self -selected walking behavior. Balance control may confound the relationship between gait speed and community ambulation, as it enables individuals to ambulate in the community despite slower gait speeds.99 Balance self efficacy may also be a significant confounder as fear of falling may lead to the avoidance of activities that the individu al remains capable of performing.107 These results suggest that at two months post -stroke, gait speed may not be the best predictor of home and community ambulation

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129 volume, intensity, and pattern in individuals with more severe balance control deficits ( BBS < 45), more severe walking endurance impairment (less than 126 meters in the 6MWT), moderate to -sev ere lower extremity motor control impairment (FM LE= 0 23), and low level of balance self -efficacy (ABC < 49). 4. 4.4 Correlations between Step Activity Monitor Outcomes and Habitual Gait S peed to Stroke Impact Scale Mobility and Participation Domains G ait speed significantly correlated with both SIS mobility and participation. Our results are in agreement with those of Schmid et al.32 which reported that in subacute stroke survivors (stroke within 30 to 150 days), among household ambulators (gait speed < 0.4 m/s), those who transitioned to limited or full community ambulation (gait speed 0.4 to 0.8 m/s and > 0.8 m/s, respectively) had significantly better mobility SIS scores. Schmid and asso ciates32 also reported that household ambulators who transitioned to limited o r full community ambulation had significant better participation SIS scores. Moreover, limited community ambulator s who achieved the transition to full community ambulatory status had significant better SIS participation scores as well. The results also indicated that all SAM indices but for percentage of the day in relative high and activity variability significantl y correlated with SIS mobility. However, only two SAM indices significantly correlated with SIS participation, namely, number of steps per day and peak activity index. Our results are in disagreement with those of Shaughnessy et al.12 that reported that in a sample of community -dwelling men and women during the subacute phase of stroke recovery (at discharge from inpatient rehabilitation) number of steps/ day did not significantly correlate with SIS mobility. Diffe rences in participants gait impairment may explain the discrepancies between the two studies. Shaughnessy and associates12 recruited people with mild-

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130 to -moderate post -stroke hemiparetic gait deficit, whereas the current study recruited individuals with moderate to -severe gait impairment. Both gait speed and SAM outcomes correlations with SIS mobility and participation were weaker than expected (r > 0.60). The lower associations may be due to certain SIS items whic h may not be influenced by gait speed and steps activity in the community. For instance, SIS mobility item get in and out of a car may be more effected by what car type a person has rather than by their gait speed and activity variability. Similarly, the participation items ability to engage in quiet recreation and participation in spiritual or religious activities may be more effected by factors such as the individuals attention span and transportation rather than by their gait speed and activity ra ndomness. 4. 4.5 Differences between Step Activity Monitor and Gait Speed Strength of Associations with Stroke Impact Scale Mobility and Participation T he degree of association between gait speed and SIS mobility was significantly stronger than the assoc iation of SAM with SIS mobility in all but for one of the 13 comparisons (steps/day ) (Table 4 10) In addition, the degree of association between gait speed and SIS participation was significantly stronger than the association of four SAM indices with SIS participation (percentage of the day in absolute low activity intensity, percentage of the day in relative medium and high activity intensity, and activity variability). The nature of SIS mobility and participation items, gait speed test, and SAM indices m ay explain these unexpected results. Gait speed tests and SIS mobility and participation items may not represent mobility in a complex environment. Gait speed tests measured indoors in a predictable, controlled, and uncluttered environment without consider ation of environmental factors. Similarly, SIS mobility items such as walk fast reflect a person ability to simply initiate gait and maintain gait speed. In both gait speed tests and items such as walk fast the individual is not being tested or asked

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131 o n their ability to alter their walking speed, adapt to unpredicted environmental demands, or to initiate/terminate gait. Therefore, gait speed and SIS items may not reflect the persons ability to walk in more complex environments such as crowded areas, uneven or slippery surfaces, and conducting activities that require often changes in walking directions an d gait speed (e.g., shopping). Unlike, gait speed and SIS, SAM outcomes (namely pattern outcomes) reflect the persons ability to alter their gait to bo th expected and unexpected environmental demands and their ability to initiate and terminate gait. 4. 4.6 Study Limitations The study consisted of individuals two month s post -stroke with moderate -to -severe hemiparetic gait impairment. Therefore, the results may not be generalizable to people with mild gait impairment and individuals during the chronic phase of stroke recovery. In addition, we may have not included in the model other factors which may affect the association between gait speed and home and com munity ambulation (e.g., the use of assistive devices). Finally, the activities that the person can perform in the community (e.g. walk one block) were evaluated with a self report instrument (the SIS). Self-report approaches are especially useful in large population studies.14, 15 However, such instruments do not take direct measures of individua ls mobility level and rely on factors related to recall and cognitive ability which contribute to the inaccuracy of these approaches.16 4. 5 Conclusion Gait speed is strongly related to numerous SAM derived home and community ambulation indices. However balance control, walking endurance, lower extremity motor control impairment and balance self -efficacy, significantly distorted the relationship between walking speed and home and community ambulation. Moreover, gait speed did not significantly correlate with SAM indices which may reflect ones ability to ambulate in complex and

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132 unpredictable environments (i. e., one minu te variability of activity activity randomness and number of activity bouts ). Taken together, the results suggest that gait speed may not be an optimal indicator of actual step activity at home and in the community two months post -stroke. Nevertheless, s ignificant correlations of gait speed with SIS mobility and participation suggest that gait speed may be a good indicator of the ability to be mobile in non -complex environments (e.g., move from bed to chair and walk fast). Finally, this is the first stud y to assess the relationship between gait -speed and home and community ambulation two months post -stroke The results suggest tha t simply improving stroke survivors gait speed may not be sufficient for them to regain home and community ambulation.99 Other factors such as balance control, walking endurance, lower extremity motor control, and balance self -efficacy may also be important to consider. Additional factors which were not included in this studys analyses may be important for home and community ambulation as well (e.g., caregiver support and use of walking aids ). However, this study consisted of individuals who had experienced a stroke two months earlier (sub acute stroke). Thus, th e relationship between gait speed and SAM derived home and community ambulation may be different in individuals with chronic stroke.

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133 Table 4 1 First and second days step activity monitor characteristics Step activity monitor variables First me asurement day (n=228) Second measurement day (n=228) Steps activity volume Number of steps/day 2300.5 (2518.8) 2278.772 (2671.99) Percentage of the day active 9.3 (7.1) 9.5 (7.7) Steps activity intensity Peak intensity index 52.8 (32.3) 49.5 (31.1) P ercentage of the day in absolute low 68 (21) 71 (21) Percentage of the data in absolute medium 15 (10) 15 (10) Percentage of the day in absolute high 15 (14) 14 (13) Percentage of the day in relative low 86 (13) 83 (22) Percentage of the day in relative medium 7.3 (6) 7.4 (6) Percentage of the day in relative high 6 (13) 9 (22) Steps activity pattern Number of activity bouts 99.72 (98.22) 103.1 (103.47) Number of steps in an activity bout 26.10 (27.58) 24.29 (28.68) Activity variability (coefficient of variation %) 84.68 (32.72) 81.16 (37.54) Activity randomness (approximate entropy) 0.21 (0.13) 0.21 (0.95) Note : values are mean (standard deviation); based on Wilcoxon tests at the < 0.05 level (2 tailed), there were no s ignificant d ifferences in step activity monitor indices between t he first and second measurement days

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134 Table 4 2 Summary statistics of step activ ity monitor indices by functional groups (all patients) SAM indices Household ambulators (gait speed < 0.4 m/s) Limited community ambulators (gait speed 0.4 0.8 m/s) P value Activity volume Number of steps/day 1143.1(1339.4) 3457.8(2872.1) <0.00 1* Percentage of the day active 0.07(0.06) 0.11(0.07) <0.00 1* Activity intensity Peak activity index 36. 93 (24.96) 68.58 (31.20) <0.00 1* Percentage of the day in absolute low 0.79(0.18) 0.57(0.18) <0.00 1* Percentage of the day in absolute medium 0.12(0.10) 0.18(0.08) <0.00 1* Percentage of the day in absolute high 0.07(0.10) 0.23(0.13) <0.001* Percen tage of the day in relative low 0.87(0.13) 0.85(0.14) 0.033* Percentage of the day in relative medium 0.06(0.06) 0.08(0.05) 0.005* Percentage of the day in relative high 0.06(0.12) 0.06(0.13) 0.313 Activity pattern Number of activity bouts 80.11(83.64 ) 119.34(107.74) 0.002* Number of steps in an activity bout 17.86(23.72) 34.34(28.79) <0.00 1* Activity variability (c oefficient of variation ) 85.77(27.58) 83.59(37.23) 0.825 Activity randomness (approximate entropy) 0.19(0.14) 0.23(0.12) 0.004* Val ues are mean (standard deviation); significant differences between the two functional groups at the < 0.05 level (2 tailed)

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135 Table 4 3 Summary statistics of step activ ity monitor indices by functional groups (compliers only) SAM indices Household amb ulators (gait speed < 0.4 m/s) Limited community ambulators (gait speed 0.4 0.8 m/s) P value Activity volume Number of steps/24 hours 1491.05(1419.56) 3834.72(2858.88) <0.00 1* Percentage of the day active 0.09(0.06) 0.12(0.06) 0.002* Activity intens ity Peak activity index 43.81(20.16) 72.72(24.67) <0.00 1* Percentage of the day in absolute low 0.77(0.16) 0.54(0.15) <0.00 1* Percentage of the day in absolute medium 0.14(0.09) 0.20(0.07) <0 .00 1* Percentage of the day in absolute high 0.08(0.09) 0.2 5(0.12) <0.00 1* Percentage of the day in relative low 0.89(0.08) 0.86(0.06) 0.009* Percentage of the day in relative medium 0.07(0.06) 0.08(0.04) 0.011* Percentage of the day in relative high 0.03(0.03) 0.04(0.04) 0.254 Activity pattern Number of ac tivity bouts 108.90(86.75) 131.63(105.18) 0.186 Number of steps in an activity bout 18.93(21.72) 35.96(26.34) <0.001* Activity variability (c oefficient of variation ) 98.00(23.51) 92.23(16.93) 0.127 Activity randomness (approximate entropy) 0.25(0.13) 0.26(0.10) 0.342 Values are mean (standard deviation); significant differences between the two functional groups at the < 0.05 level (2 tailed).

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136 Table 4 4 Spearman correlation coefficients between habitual gait speed and step activity monitor in dices Step activity monitor indices All patients Compliers only R P value R P value Activity volume Number of steps/ 24 hours 0.531 <0.0001* 0.599 <0.0001* Percentage of the day active 0.327 <0.0001* 0.295 <0.0001* Activity intensity Peak activity index 0.552 <0.0001* 0.620 <0.0001* Percentage of the day in absolute low 0.574 <0.0001* 0.674 <0.0001* Percentage of the day in absolute medium 0.400 <0.0001* 0.452 <0.0001* Percentage of the day in absolute high 0.583 <0.0001* 0.659 < 0.0001* Percentage of the day in relative low 0.131 0.048* 0.233 0.002* Percentage of the day in relative medium 0.199 0.003* 0.241 0.002* Percentage of the day in relative high 0.018 0.785 0.090 0.250 Activity pattern Number of acti vity bouts 0.205 0.002* 0.127 0.103 Number of steps in an activity bout 0.453 <0.0001* 0.528 <0.0001* Coefficient of variation for one minute steps 0.0518 0.436 0.147 0.058 Activity randomness 0.192 0.004* 0.100 0.201 *Significa nt correlations between step activity indices and habitual gait speed at the < 0.05 level (2 tailed).

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137 Table 4 5 Comparisons of the correlation s between gait speed and step activity monitor indices based on categories of balance, walking endurance, lowe r extremity motor control impairment, and balance self -efficacy (all patients). Step activity monitor indices Balance (Berg Balance Scale) Walking endurance (six minute walk test) Lower extremity motor control (Fugl -Meyer Assessment) Balance self ef ficacy (Activities -specific Balance Confidence Scale) Less impaired (n=72) More impaired (n=156) Less impaired (n=113) More impaired (n=114) Mild impairment (n=153) Moderate to severe impairment (n=75) Moderate to high confidence (n=102) Low confiden ce (n=124) Steps/ day 0.047 0.402* 0.220 0.280 0.416 0.584 0.431 0.448 % of the day active 0.110 0.200* 0.140 0.138 0.214 0.406 0.124 0.293 Peak activity index 0.209 0.440* 0.208 0.257 0.428 0.570 0.556 0.452 % of the day in absolute low 0.192 0.45 8* 0.280 0.332 0.523 0.551 0.585 0.484 % of the day in absolute medium 0.090 0.340* 0.163 0.327 0.292 0.499* 0.392 0.334 % of the day in absolute high 0.176 0.489* 0.246 0.352 0.507 0.574 0.579 0.499 % of the day in relative low 0.004 0.149 0 .065 0.121 0.080 0.082 0.146 0.101 % of the day in relative medium 0.042 0.173 0.120 0.219 0.188 0.186 0.158 0.167 % of the day in relative high 0.104 0.042 0.081 0.094 0.003 0.113 0.109 0.039 Number of activity bouts 0.000 0.050 0.159 0.018 0. 170 0.248 0.010 0.192 Steps/ activity bout 0.008 0.387* 0.100 0.271 0.325 0.490 0.501 0.354 Coefficient of variation 0.074 0.003 0.081 0.029 0.085 0.074 0.044 0.128 Activity randomness 0.126 0.097 0.096 0.050 0.113 0.269 0.017 0.185 *The stre ngth of association between gait speed of individuals with different levels of balance control, walking endurance, lower extremity motor control, and balance self -efficacy with step activity indices is significant at the < 0.05 level (2 tailed).

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138 Table 4 6 C ompari sons of the correlation s between gait speed and step activity monitor indices based on categories of balance, walking endurance, lower extremity motor control impairment, and balance self -efficacy (compliers only). Step activity monitor indices B alance (Berg Balance Scale) Walking endurance (six minute walk test) Lower extremity motor control (Fugl -Meyer Assessment) Balance self efficacy (Activities -specific Balance Confidence scale) Less impaired (n=72) More Impaired (n=156) Less impaired (n= 113) More impaired (n=114) Mild impairment (n=153) Moderate to severe impairment (n=75) Moderate to high confidence (n=102) Low confidence (n=124) Steps/day 0.018 0.488* 0.136 0.409* 0.404 0.655* 0.443 0.616 % of the day active 0.226 0.167* 0.047 0.1 68 0.096 0.388* 0.065 0.362* Peak activity index 0.165 0.546* 0.114 0.342 0.413 0.643* 0.568 0.619 % of the day in absolute low 0.201 0.592 0.259 0.517* 0.571 0.704 0.636 0.660 % of the day in absolute medium 0.189 0.392 0.207 0.449* 0.305 0.647 0.422 0.453 % of the day in absolute high 0.122 0.611* 0.184 0.485* 0.526 0.656 0.612 0.651 % of the day in relative low 0.097 0.264 0.163 0.402 0.223 0.246 0.250 0.243 % of the day in relative medium 0.049 0.229 0.151 0.373 0.228 0.280 0.284 0.211 % of the day in relative high 0.156 0.125 0.153 0.111 0.098 0.025 0.164 0.083 Number of activity bouts 0.043 0.049 0.057 0.040 0.036 0.184 0.082 0.171 Steps/Activity bout 0.007 0.503* 0.109 0.422* 0.374 0.602* 0.571 0.495 Coefficient of vari ation 0.066 0.031 0.181 0.032 0.221 0.021 0.078 0.190 Activity randomness 0.236 0.010 0.013 0.042 0.044 0.222 0.119 0.191* *The strength of association between gait speed of individuals with different levels of balance control, walking endurance, lower extremity motor control, and balance self -efficacy with step activity indices is significant at the < 0.05 level (2 tailed)

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139 Table 4 7 Step activity of two individuals with a similar gait speed Step activity variables The person depicted by the blue color The person depicted by the pink color Number of steps/day 575 1802 Percentage of the day active 5% 20% Peak activity index (steps/min) 46 72 Percentage of the day in relative high activity intensity 4.8% 0.3% Number of activity bouts 24 81 Activity variability (coefficient of variation) 81 91 Activity randomness (approximate entropy) 0.10 0.42 Table 4 8 Clinical characteristics of two study participants with a similar gait speed Clinical characteristics The person depicted by the blue color The person depicted by the pink color Berg Balance Score 45 45 Six minutes walk test (meters) 164 147 Fugl Meyer Lower extremity score 29 25 Activities Specific Balance Confidence Scale 36 57

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140 Table 4 9 Spearman correlation coefficients between habitual gait speed, step activity monitor indices, and Stroke Impact Scale mobility and participation domains All patients Compliers only Mobility Participation Mobility Participation Gait Speed 0.546* 0.212* 0.535* 0.226* Activity volume St eps/day 0.372* 0.153* 0.421* 0.212* Percentage of the day active 0.299* 0.119 0.292* 0.145 Activity intensity Peak activity index 0.307* 0.177* 0.336* 0.247* Percentage of the day in absolute low 0.323* 0.122 0.391* 0.140 Percentage of the day i n absolute medium 0.299* 0.113 0.376* 0.076 Percentage of the day in absolute high 0.314* 0.137* 0.337* 0.161 Percentage of the day in relative low 0.120 0.086 0.173* 0.067 Percentage of the day in relative medium 0.202* 0.036 0.248* 0.084 Perc entage of the day in relative high 0.009 0.069 0.014 0.043 Activity pattern Number of activity bouts 0.239* 0.061 0.223* 0.082 Steps/activity bout 0.260* 0.136* 0.281* 0.141 Activity variability (coefficient of variation) 0.004 0.031 0.091 0.043 Activity randomness (approximate entropy) 0.238* 0.087 0.196 0.116 *The correlation between gait speed or step activity monitor indices with Stroke Impact Scale is significant at the < 0.05 level (2 tailed).

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141 Table 4 10. Differences between step act ivity monitor and gait speed strength of associations with Stroke Impact Scale mobility and participation. *Correlation is significantly different at the < 0.05 level (2 tailed). Correlation differences with Stroke Impact Scale mobility All Patients Compliers only Z P value Z P value R(Gait speed) R( steps/24 hours )=0 2 .358 0.009* 1.327 0.092 R(Gait speed) R( % active )=0 3.231 0.001* 2.662 0.004* R(Gait speed) R( Peak index )=0 3.130 0.001* 2.226 0.013* R(Gait speed) R( % at absolute low intensity )=0 10.052 <0.001* 9.089 <0.001* R(Gait speed) R(% at absolute medi um intensity)=0 3.228 0.001* 1.809 0.035* R(Gait speed) R(% at absolute high intensity)=0 3.049 0.001* 2.209 0.014* R(Gait speed) R(% at relative l ow intensity )=0 7.777 <0.001* 6.940 0.000* R(Gait speed) R(% at relative medium intensity)=0 4.320 < 0.001* 3.091 0.001* R(Gait speed) R(% at relative high intensity)=0 6.595 <0.001* 5.495 <0.001* R(Gait speed) R(activity bouts)=0 3.909 <0.001* 3.332 <0.001* R(Gait speed) R( steps in an activity bout )=0 3.680 0.001* 2.768 0.003* R(Gait speed) R (activity variability)=0 6.452 <0.001* 6.192 <0.001* R(Gait speed) R(one minute activity randomness)=0 3.929 <0.001* 3.584 <0.001* Correlation differences with Stroke Impact Scale participation Z P value Z P value R(Gait speed) R( steps/24 hours )=0 0 .642 0.260 0.131 0.448 R(Gait speed) R( % active )=0 1.010 0.156 0.757 0.224 R(Gait speed) R( Peak index )=0 0.387 0.350 0.201 0.420 R(Gait speed) R( % at absolute low intensity )=0 3.576 0.001* 3.341 <0.001* R(Gait speed) R(% at absolute medium int ensity)=0 1.079 0.140 1.378 0.084 R(Gait speed) R(% at absolute high intensity)=0 0.820 0.206 0.604 0.273 R(Gait speed) R(% at relative l ow intensity )=0 1.365 0.086 1.464 0.072 R(Gait speed) R(% at relative medium intensity)=0 2.658 0.004* 2.825 0 .002* R(Gait speed) R(% at relative high intensity)=0 3.011 0.001* 2.454 0.007* R(Gait speed) R(activity bouts)=0 1.633 0.051 1.329 0.092 R(Gait speed) R( steps in an activity bout )=0 0.829 0.204 0.792 0.214 R(Gait speed) R(activity variability) =0 1.952 0.025* 1.679 0.047* R(Gait speed) R(one minute activity randomness)=0 1.353 0.088 1.017 0.155

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142 Second measurement day First measurement day Sample size: N=228 Inferred compliance rate in day one: 72% ( N=165 ) Sample size: N=228 Inferred compliance rate in day two: 66% (N=151) Subjects with incongruent data : N=3 (1%) On account of the small p ercentage of individuals with incongruent data, these individuals were not excluded from the analysis Subjects with incongruent data : N=0 Participants compliance in wearing the monitor is critical for obtaining accurate physical activity measurement, as non compliance is usually considered a source of bias. Therefore, on account of the lowe r compliance rate in the second measurement day and o n both days day two was excluded from the analyses First analysis (all subjects) : N=228 Second anal ysis (compliers only): N=165 Data a vailable Data analyzed First and second days Sample size: N=228 Inferred c ompliance rate: 55% (N=125) Figure 4 1. Data analyses decision making flow chart. The analyses in this stu dy are based on the first measurement day step activity monitor data. The first analysis consisted of all subjects day one step activity monitor files. Whereas, the second analysis consisted of day one step activity monitor files of individuals w ho were in complete compliance with step activity monitor protocol. Figure 4 2. Step activity of two individuals with a similar gait speed

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143 CHAPTER 5 AMBULATORY ACTIVITY DURING THE SUB -ACUTE PHASE OF STROK E RECOVERY: RELATIONSH IP TO IMPAIRMENT LEVEL, ACTIVITY LIMI TATIONS, AND PERSONAL FACTORS 5. 1 Introduction A return to home and community-based ambulatory activity is a major rehabilitation goal for many individuals with stroke.3 Therefore, understanding factors which are associated or predict ambulatory activity (e.g., impairment level, activity limitation, and personal factors) is important, as they may reveal underlying mechanisms contributing to community ambulation. Surprisingly, there is limited published data on factors which are associated with or predict ambulatory activity. Lai et al.329 showed that in a sample of mostly mild and moderate s troke patients, baseline National Institutes of Health Stroke Scale scores predicted one, three, and six month s post -stroke Medical Outcome s Study Short Form 36 physical functioning. The Medical Outcome s Study Short Form 36 physical functioning includes it ems which represent home and community ambulation such as running, climbing one flight of stair s, and walking several blocks. Other studies propose that gait speed is significantly related to community ambulation.9, 61, 99 However, the ability to walk in the community is determined by several other underlying factors such as balance, motor function, endurance, and assistive walking device.99 Although these studies provide valuable information on post -stroke community ambulation, self -report questionnaires were used to determine community ambulation. Though self -report approaches are especially useful in large population studies,14, 15 they rely on factors related to recall and cognitive ability, which contributes to the inaccuracy of these approaches.16 An alternative approach to community ambulation measurement involves direct recording of ambul atory activity.14 Microprocessor -based a ccelerometers, such as the SAM enables direct and long -term monitoring of individuals normal activities conducted in their own

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144 environments.17 18 In addition, SAM provides minute -by-minute measurements. The resulting temporal series provides an array of informatio n.24 For instance, the number of s teps a person took in a 24 hour period provides insight into the persons activity volume, step vigor (steps/min) sheds light on an individuals activity intensity, whereas fluctuations in number of steps a person takes per minute provides information on a ctivity pattern. Measures of activity intensity and pattern are especially valuable for numerous reasons: (1) they illustrate the manner in which a person accumulated their steps (two subjects with nearly the same step count could accumulate their steps in very different ways in terms of activity intensity and pattern), and (2) activity pattern is an especially useful mean of quantifying physiological improvement/decline resulting from rehabilitation intervention,14 as it reflects capability to respond quickly and effectively to sudden and unpredictable changes in task demands or environmental conditions.14 Within the stroke population, activity volume correlates well with the Functional Independence Measure mobility subscal e ,12 BBS ,23 and age.343 However, there are conflicting data regarding whether activity volume significantly correlates with self -selected gait speed.12, 23,343 Moreover, during the chronic phase of stroke recovery (at least six months post -stroke), activity intensity significantly correlates with cardiovascular fitness (peak oxygen consumption),34 6MWT habitual gait speed, age, and Rivermead Mobility Index.343 To date, there are no data on the association between demographic and clinical outcomes association with activity pattern post -stroke. Considering the limited data to guide clinicians and resea rchers on the association of demographic and clinical outcomes with step activity volume and intensity, and the absence of data on the association between demographic and cl inical outcomes with step activity pattern, it may be valuable to establish these r elationships. This knowledge may be beneficial in

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145 development of interventions resulting in better carryover of everyday ambulatory activity. In addition, it will shed light on which clinically -based outcome measures best predict home and community ambulat ion level, and thus would be beneficial clinically and in research. The objective of this study was to determine whether patients impairment level, activity limitations, and personal factors can predict SAM derived home and community ambulation volume, in tensity, and pattern during the sub acute phase of stroke rehabilitation. On the basis of the results of previous studies12, 14, 23, 34, 294,343 a nd on clinical grounds four hypothesizes were developed: 1 In a community dwelling individuals two month s post -str oke balance control balance self efficacy, lower extremity motor control impairment body composition, depression, and walking endurance will predict activity volume (number of steps taken over 24 hours and total activity level). 2 Balance control balance self -efficacy, self -selected gait speed, walking endurance, lower extremity motor control impairment body composition, age, and depression will predict step activity intensity (absolute activity intensity, relative activity intensity, and peak activity i ndex). 3 Balance control balance self -efficacy, executive function, walking endurance, lower extremity motor control impairment age, body composition, and depression will predict step activity pattern (number of activity bouts, number of steps in an activi ty bout, activity variability and activity randomness). 4 Gender, stroke location, and upper limb motor control impairment will not predict step activity volume, intensity, and pattern. 5. 2 Methods 5. 2.1 Design Data i n this cross -sectional study were deri ved from a parent study, the LEAPS pre randomization data from two -month post -stroke baseline assessments. The LEAPS is an ongoing 5 year, phase III, single -blind, 5 -site, randomized controlled trial. Details about design, outcomes, and inclusion and excl usion criteria of this study are published elsewhere.311 Briefly,

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146 the LEAPS trial recruits individuals post -stroke over the age of 18, five to thirty da ys post stroke. Individuals with uncontrolled health conditions for which exercise is contraindicated (e.g., serious cardiac conditions, severe arthritis or orthopedic problems, severe hypertension, and uncontrollable diabetes) are excluded. The primary pu rposes of the LEAPS trial are to determine if a specialized locomotor training program that incorporates a body weight support system and treadmill as a treatment modality can produce a functionally significant improvement in walking of persons post -stroke compared to a control group (non -specific, low intensity exercise program), and whether timing of therapy (two or six months post -stroke), severity of locomotor impairment (moderate or severe), and the number of treatments affects locomotor outcome. The L EAPS trial primary outcome is the proportion of participants who successfully recover walking one year post -stroke, based on the gait speed classification system.311 5. 2.2 Testing Procedures and Measurements All measures selected have established reliability and validity and are captured according to standardized protocols at two months post -stroke by blinded per diem therapists. 311 5. 2.2.1 Dependent variables (habitual ambulatory activity) Habitual ambulatory activity was assessed with the SAM. Within the stroke population, SAM was found to be valid, reliable,19, 23 and s ensitive to change.12 SAMs were programmed with an initial generic calibration.19 Participants performed a one -minute walk test. SAM counts of strides were compared with visual counts. Adjustments were made to the calibration setting and the timed walks were repeated to ensure greater than 90% accuracy against visual counts. To achieve optimal sensitivity for step detection, the SAM was programmed and applied above the malleolus of the less involved limb with two adjustable elastic straps. Particip ants wore the SAM for a period of two consecutive days, throughout the day, removing the SAM only to bathe,

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147 shower, swim, or sleep. A demonstration and written instructions regarding appropriate use of the device were provided to subjects. Data reduction : On completion of a 48-hour monitoring period, SAM data were downloaded using an infrared docking port. SAMs raw data fr om the manufacturer software were imported into Microsoft Excel. Data were expressed as stride counts every 60 seconds during the 48 ho urs. There were no s ignificant differences in SAM indices between the first and the second measurement days (Table 4 1). Furthermore, inferred compliance rate was highest in the first measurement day (72.4%), followed by the second measurement day (66.2% ) and in both days (54.8%). Inferred compliance in 24 and 48 hours was established in three time periods (windows) of six hours: (1) 6:00AM 12:00PM, (2)12:01PM 6:00PM, and (3) 6:01PM 12:00AM. According to this method, a compliant person in 24 and 48 hours was defined as a participant who has activity recorded in each of the three or six recording windows, respectively. P articipants compliance in wearing the monitor is critical for obtaining accurate physical activity measurement,15 as non -compliance is usually considered a source of bias. Therefore, on account of the highest c ompliance rate in the first measurement day and the lack of differences in SAM indices between the first and the second measurement days, the analyses in this study are based on the data obtained in day one only. Moreover, all analyses were conducted for a ll participants (individuals with and without activity in each of the first day three windows) and for compliant patients only (individuals with activity in each of the first measurement day three windows). Identifying when a participant is not wearing th e accelerometer is difficult as long continuous bouts of accelerometer inactivity (continuous zero readings) may mean either that the participant removed the accelerometer, was completely inactive (e.g., sleeping),251 or the monitor

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148 was malfunctioning. Therefore, in an effort to ensure that the SAM data included in the analyse s represent the construct to which they refer (home a nd community ambulation), FAC of individuals with non active days (less than 20 steps per day ) was determined. Comparing the FAC and SAM st ep activity enabled to discover inconsistencies between functional ambulation level and step activity due to incompliance or SAM malfunctioning. Mehrholz et al.173 suggest that a dichotomized FAC (FAC > 4 and FAC < 4) may be useful in predicting level of community ambulation with high sensitivity and specificity. Therefore, it is likely that individuals with FAC > 4 with non ac tive days either did not wear the monitor or the monitor was malfunctioning. Consequently, it will be appropriate to remove these individuals files from the dataset in order to prevent false low SAM activity counts. In contrast, individuals with FAC < 4, non active days are probably not due to being non-compliant with the monitoring protocol but simply because they did not walk. Therefore, it will be appropriate to include these individuals files in the a nalysis in order to prevent false high SAM step act ivity counts. Figure 4 1 illustrates data analyses decision making flow chart. Ambulation volume outcomes : 1 Number of steps taken over 24 hours. Number of steps per day was defined as twice the total stride count in 24 hours.285 2 Total activity level. Total activity level was defined as the t ally of the number of one minute intervals with stride counts of > 1. Presented as the percentage of the total time in a 24hour period where steps are recorded.25 Ambulation intensity o utcomes : 1 Peak activity index. Peak activity index represents the maximal number of steps a person took in a one minute interval. 2 Percentage of the day spent in low, medium, and high absolute activity intensity. Absolute step activity intensity was stratified as low (less than 16 steps per minute), medium (16 30 steps per minute) and high (more than 30 steps per minute). Percentage of time spent at

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149 each level was calculated. Minutes in which activity was not recorded were excluded from the analysis. 3 Percentage of the day spent in low, medium, and high relative activity intensity. Low relative inte nsity was defined as walking at less than 60% of peak activity index, moderate intensity 60 85% of peak activity index, and high intensity > 85% of peak activity index. Ambulation pattern outcomes : 1 Number of activity bouts. Number of activity bouts was de fined as the number of 1 -min intervals for which the subject switched from inactivity (stride count=0) to activity (stride count > 0).14 2 Number of steps in an activity bout. Number of steps in an activity bout was defined as activity bout mean number of steps. 3 One minute variability of activity. One minute variability of activity was defined as the CV of 1 -min of stride count values, excluding those 1 min intervals with zero steps.14 CV is a measure of variability calcu lated as the standard deviation of the measures expressed as a percentage of the mean (CV=standard deviation/mean X 100). One of the CV advantages is that it accounts for differences in the magnitude of the mean (a measure of relative variation).288 4 Activity randomness. Activity randomness was defined as the amount of randomness of minute to -minute activit y fluctuations in each day of recorded ambulatory activity. Randomness of activity was quantified by analyzing 1 -minute stride counts using approximate entropy.289 Approximate entropy was used to determine the probability that sequences of consecutive 1 -minute stride counts repeated throughout the longer temporal sequence of 1,440 daily 1 -minute intervals. Approximate entropy generates a unitless number between 0 and 2. Zero values correspond to a time series in which short sequences of data points are perfectly repeatable. Values of 2 correspond to time series for which any repeating seque nces of points occur by chance alone.290 5. 2.2.2 Independent variables Gait impairment : Gait impairment was assessed via gait speed measured during a 10-meter walk. The time that it takes to traverse the 10 meters at the subjects usual pace using their usual assistive devices or orthoses was recorded. Gait speed has been described as the almost -perfect mobility outcome measure in neurological populations151 as it is simple to measure, reliable,

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150 valid, and sensitive to post -stroke recovery stage.9, 138 Furthermore, self -selected gait speed is related to rehabilitation prognosis29, 67 and discharge destination.140 Balance control : Balance was measured by the BBS.324 The BBS consists of 14 items which assess subjects ability to maintain movements or positions of increasing difficulty via decreasing the base of support.324 A score of 56 indicates functional balance, scores < 45 indicate that a person may be at greater risk of falling.325 Therefore, the BBS score was dichotomized into more severe balance deficits (scores < 45) and less severe balance deficits (scores > 45).325 The test has been used in a stroke population and has well -established validity and reliability.324 A standardized protocol for administration was followed.324 Balance self -e fficacy: ABC was used to assess perceived confidence in maintaining balance while performing common community related activities such as bending, reaching, and walking both inside and outside the house. This measure has good reliability and internal consis tency.327 Myers et al.328 found that in a group of communitydwelling older adults, ABC scores of < 50 indicated a low level of physical functioning (home care clients) ; ABC of more than 50 and less than 80 indicated a moderate level of functioning (older adult s in a retirement home setting); and ABC scores of > 80 indicated high functioning (physically active older adults). Accordingly, in this study ABC scores were dichotomized into low level of physic al functioning (ABC < 49 points) and moderate to high level of physical functioning (ABC > 50 points). Walking endurance : Walking endurance was determined via the 6MWT The 6MWT was originally developed to assess cardiopulmonary function in cardiac patient s.326 However, it is commonly used as an endurance measure in the elderly and in individuals who suffered a stroke.165 Participants performed the 6MWT using a previously standardized protocol.165 According to the protocol, during the 6MWT, the participants were allocated six minu tes to walk

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151 as far as they can at their comfortable pace. The distance they covered in six minutes was recorded. During the test, participants used their customary orthotics and assistive devices. Walking distance of 300 to 500 continues meters is suggeste d for community ambulation.60, 103 However, in this study walking distance variability was small ( 0 to 315 meters) Walking distance mean and median was 128 and 126 meters, respectively. Thus, overall the sample had poor walking endurance function. Therefore, walking distance was dichotomized on the basis of the samples median walking distance (126 meters). Upper and lower limb motor control impairment : The upper and lower extremity motor function portions of the Fugl -Meyer Assessment ( FM-UE and FM-LE, respectively) were used to determine both upper and lower extremity motor control impairment. The Fugl -Meyer Assessment is one of the most commonly used and comprehensive scales of motor and sensory recovery following stroke. The minimal clinically important difference on the Fugl -Meyer is not yet known.315 However a > 10 points change in Fugl -Meyer motor scores may represent clinically meaningful improvement based on stroke neurologists and physical therapists.315 Therefore, the FM -LE scores were grouped in > 10 points increments: 0 11 points= severe lower extremity motor impairment, 12 23=moderat e lower extremity motor impairment, and 24 34= mild lower extremity motor impairment. The FM -UE scores were also grouped into three categories in >10 increments: scores of 022=severe upper extremity motor impairment, 2344= moderate upper extremity motor i mpairment, and 45 66=mild upper extremity motor impairment. Standardized protocols for administration were followed.315 Body composition: Body composition was assessed via the waist circumference measure. Waist circumference is an effective way to measure abdominal obesity in adults330 and is an important predictor for metabolic complications and mortality in both adult men and women.331333 In

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152 addition, waist circumference may be better predictor of cardiovascular disease risk than body mass index in adults.334 Waist circumference was measured at the narro west part of the waist above the umbilicus and below xiphoid processes. Waist circumference was dichotomized into normal (< 102 cm and 88 cm for men and women, respectively) and increased risk (> 102 cm and 88 cm for men and women, respectively).335 Depression: The PHQ 9 was used to measure depression. The PHQ 9 usefulness as a depression screening and diagnostic instrument has been established recently for stroke survi vors.314 The PHQ 9 scores range from 0 to 27. Cutoff scores of 5, 10, 15, 20 were used to represent the thresholds for mild, moderate, moderately severe, and se vere depression.13 Executive function: The TMT B was used to assess executive function. The TMT B requires the patient to draw lines alternating between numbers and letters in consecutive order. Performance is assessed by the time taken to complete the trial correctly.306 It has been reported that performance in TMT B is indicative of executive function, executive control, and the ability to flexibly shift the course of an ongoing activity.307 According to Lezak et al.,336completion of the TMT -B in > 273 seconds indicates a deficiency in the executive function. Accordingly, in this study a cut -off score of 273 seconds was used (>273 seconds=deficient).336 Side of Lesion: Side of lesion was confirmed by Computed Tomography or Magnetic Resonance Imaging scans. 5. 2.3 Data Analysis Data were analyzed by using SAS version 9.1. All variables were examined by descriptive statistics. Data departure from normality was verified visually with probability plots, and statistically with the skewness and kurtosis test for normality. Differences in s tep activity indices based on subjects characteristics (e.g., differences between individuals with normal an d abnormal executive function in percentage of the day active) were analyzed using Wilcoxon

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153 rank -sum test. Differences in step activity indices in variables with more than two levels (FM UE, FM -LE, and PHQ 9) were analyzed using Kruskal -Wallis Test. Regres sion analysis with backward method was used to identify SAM indices predictors. Natural logarithm of steps/day, number of activity bouts, and mean number of steps in an activity bout were used in the regression analysis. No transformation was applied for t he other variables. A difference was regarded as statistically significant at P < 0.05 (two -tailed) Adjusted R2 values were used to show the extent (percentage) which variability of ambulatory activity variables can be explained by the independent variabl es. Presence of multicollinearity among independent variables was assessed via the variance of inflation factor.309, 316 Multicollinearity was defined as variance of inflation factor exceeding 10. Based on this definition there was no multicollinearity between variables in the model. A priori power analysis was conducted. Within the stroke population, in individuals with sub acute to chronic stroke (3 166 months post -stroke) correlations between SAM derived outcomes and other cli nical outcomes range from 0.316 (with peak oxygen consumption)23 to 0.62 (with Functional Independence Measure mobility) .12 Thus, with a sample size of 228 an alpha level of 0.05, and power of 0.80, there was sufficient power to detect correlations as low as 0.184. 5. 3 Results 5. 3.1 Sample Characteristics Data were collected from 228 individuals two months post -stroke. When all the participants were included in the a nalyses, inc ongruence between FAC scores and step activity level in the first measurement day was detected for three subjects only. On account of the small number of individuals with incongruent data, these individuals data were included in the all patients analyses. However, when only the day one compliers were analyzed (n=165), no incongruences

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154 between FAC scores and step activity level were detected. All patients mean age was 63.9 + 12.9. The sample represents 50% women. The majority of study populatio n had right hemisphere stroke (47%). The subjects who complied with SAM protocol in the first day mean age was 64.6 + 12.9 years. Fifty percent of the compliers were women. The majority of the compliers had right hemisphere stroke (45%). For additional dem ographic and clinical characteristics of all patients a nd compliers only, see Table 5 1 Table 5 2 summarizes all patients and compliers only SAM indices. Because the SAM indices of all patients and compliers appear to be different, in order to increase th e accuracy of the results in this study, the data of day one compliers only (n=165) were included in the regression analyses. 5. 3.2 Step Activity Monitor Volume, Intensity, and Pattern Predictors The backward multiple linear regression analysis constructed a model with age, gender, body composition (waist circumference), stroke location, lower and upper extremity motor control, balance control, balance self -efficacy, self -selected gait speed, walking endurance, depression, and executive function. 5. 3.2.1 Ac tivity volume predictors Balance control, walking endurance, and executive function were identified as significant activ ity volume predictors (Table 5 3 ). For individuals with more severe balance control deficits, average number of steps/day is 0.440 times as large as that of individuals with less severe balance control deficits (1792 and 4765 steps/day respectively) (Table 5 4 ). In addition, f or persons with more severe walking endurance disability average number of steps/day is 0.494 times as large as t hat of persons with less severe walking endurance disability (1423 and 3983 steps/day respectively). Moreover, s ubjects with deficient executive function, average number of steps/day is 0.707 times as large as that of subjects with normal executive functi on (2497 and

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155 3076 steps/day respectively) Similarly, individuals with more severe balance deficits and deficient executive function spend smaller percentage of the day active (parameter estimates = 5.016 and 2.930, respectively) (Tables 5 4 and 5 5 ). As expected, SAM activity volume indices were independent of self -elected gait speed age, gender, stroke location, and upper extremity motor control impairment However, in contrary to the hypothesis, balance self -efficacy, lower extremity motor control, body composition, and depression score did not predict any of the activity volume indices. The model adjusted R2 show s that the variables in the model were able to explain 38% of the variation in the number of steps taken in a day and 23% of the variati on in the percent age of the day active (Table 5 3 ). 5. 3.2.2 Activity intensity predictors Activity intensity was affected significantly by balance control, walking endurance, gait speed, executive function and l eft hemisphere stroke (Table 5 5 ). A shift from less to more severe balance deficits resulted in an 11.454% decrea se in peak activity index (77 and 50 steps/min, respectively). Similarly, a shift from less to more severe walking endurance impairment resulted in a 26.259% decrea se in peak activity i ndex (74 and 42 step s/min, respectively) (Tables 5 6 and 5 7 ). Both gait speed and balance control predicted percentage of the day in absolute low and medium activity intensity. Faster gait speed resulted in smaller percentage of the day in absolute low ( effect estimate= 4.567) and greater percentage of the day in absolute medium activity intensity (effect estimate= 1.351). A shift from less to more severe balance control deficits resulted in a 9.463% increase in percentage of the day in absolute low (50. 33 and 70.02%, respectively) and a 4.332% decrease in percentage of the day in absolute medium activity intensity (22.71 and 14.76%, respectively) (Tables 5 6 and 5 7 ).

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156 Gait speed and walking endurance predicted percentage of the day in relative low and me dium activity intensity. An increase of gait speed by 0.1 m/s resulted in a 1.968% decrease in percentage of the day in relative lo w and a 1.037% increase in percentage of the day in relative medium activity intensity A shift from less to more severe walk ing endurance disability resulted in a decrease in percentage of the day in relative low activity intensity (effect estimate= 5.308) and an increase in percentage of the day in relative medium activity intensity (effect estimate=3.262). E xecutive function was an important predictor of percentage of the day in relative low activity intensity A shift from normal to deficient executive function resulted in a decrease in percentage of the day in relative low activity intensity (effect estimate= 3.211) (Table s 5 6 and 5 7 ). In addition, having a left hemisphere stroke predicted greater percentage of the day in relative medium activity intensity (effect estimate=1.86). As hypothesized, gender and upper limb motor control impairment did not predict any of the SA M intensity outcomes. However, in contrary to the hypothesis, balance self -efficacy, lower extremity motor control, body composition, age, and depression did not predict SAM intensity outcomes either. The model adjusted R2 show that the variables in the mo del were able to explain 38% of the variation in peak activity index up to 50 % of the variation in absolute intensity activities, and up to 25% of the variation in relative intensity activities (Table 5 5 ). However, assumptions of normality and homoscedas ticity were not held for percentage of the day in absolute and relative high activity intensities. Therefore, further analyses are needed in order to establish high absolute a nd relative activity intensity predictors. 5. 3.2.3 Activity pattern predictors A ge, balance control, balance self -efficacy, executive function, gait speed, and lower extremity motor control predicted SAM pattern outcomes (Table 5 7 ). More specifically, age, balance control, balance self -efficacy, and executive function were all import ant predictors of

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157 number of activity bouts. As patients age increases in one unit the mean number of activity bouts decreased by one percent (effects estimate= 0.990). Individuals with more severe balance control deficits average number of activity bouts is 0.724 times as large as that of individuals with less severe balance control deficits (101.58 and 161.16 activity bouts, respectively). Similarly, persons with low balance self -efficacy average number of activity bouts is 0.740 times as large as that of persons with moderate to high balance self efficacy (98.7 and 143.63 activity bouts, respectively). Moreover, patients with deficient executive function mean number of activity bouts is 0.749 times as large as that of patients with normal executive functi on (98.53 and 136.62 activity bouts, respectively) (Tables 5 8 and 59). Age and balance control were also important predictors of number of steps in an activity bout. Surprisingly, one unit increase in patients age resulted in a 1.1% increase in the mean number of steps in an activity bout. In addition, individuals with more severe balance impairment mean number of steps in an activity bout is 0.645 times as large as that of individuals with less severe balance impairment (22.8 and 39.37 steps/activity bo ut, respectively) (Tables 5 8 and 5 9 ). Moreover, a gait speed increase of 0.1 m/s results in a 0.18 times increase in mean steps in an activity (e ffect estimate=1.180) (Table 5 7 ). Balance and lower extremity motor control impairment predicted one minute variability of activity. Unexpectedly, a shift from less to more severe balance control impairment resulted in an 8.058% increase in one minute variability of activity (89.38 and 97.54% coeffici ent of variation, respectively). Similarly, a shift from one FM-LE category to the other (base category=mild impairment) results in a 4.721% increase in one minute variability of activity. Balance, balance confidence, and executive function were significant predictors of activity randomness. As expected, a shift fro m less to more severe balance control deficits resulted in a

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158 5.852% decrease in activity randomness (0.3 and 0.24 approximate entropy, respectively). A shift from moderate/high balance confidence to low balance confidence resulted in a 4.413% decrease in a ctivity randomness (0.29 and 0.23 approximate entropy, respectively). Accordingly, a shift from normal to deficient executive function resulted in a 7.168% decrease in activity randomness (0.3 and 0.21 approximate en tropy, respectively) (Tables 5 8 and 5 9 ) As hypothesized, gender, stroke location, and upper extremity motor control did not predict any of the activity pattern indices. However, i n contrary to the hypothesis, walking endurance, body composition, and depression did not predict any of the activi ty pattern indices either Overall, the regression model explains approximately 21% of the variation in number of activity bouts, 32% in number of steps in an activity bout, 6% in one minute variability of activity, and 17% i n activity randomness (Table 5 7 ). 5. 4 Discussion 5. 4 .1 Post -Stroke Step Activity Profiles This study quantified household and communit y ambulatory activity profiles i n a sample of patients with sub acute stroke, and it examined the relative contributions of demographic and clinical ch aracteristics toward ambulatory activity level. We found that individuals with sub acute stroke had extremely low ambulatory activity profiles in terms of activity volume, intensity, and pattern. The ambulatory activity volume and pattern were well below t he range of healthy younger adults, healthy older adults, and older adults with functional limitations.14, 38 Moreover, 65 to 68 % of total ambulatory activity fell into the low absolute intensity category. Whereas Michael et al.34 found that the average percentage of the day s troke patients spend in low absolute activity intensity was about 45%. Differences in sample characteristics are likely to be responsible for the discrepancy in absolute activity intensity levels between our study and Michael and associates.34 Our study recruited individuals with moderate to -severe gait

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159 impairment two month s post -stroke, whereas Michael and colleagues34 sample consisted of individuals with milder gait deficits during the chronic phase of stroke recovery. It is expe cted that individuals during the chronic phase of stroke recovery with milder gait impairments will spend smaller percentage of the day in low absolute intensity activities. 5. 4 .2 Step Activity Predictors This study confirms our hypotheses that lower extr emity motor control impairment, balance control, balance self -efficacy, walking endurance, gait speed, executive function, and age are all important predictors of home and community ambulation In addition, as expected, upper limb motor control impairment and gender did not significantly predict any of the SAM volume, intensity, or pattern indices. However, surprisingly, body composition and depression also were not important home and community ambulation predictors, whereas stroke location predicted activi ty intensity. 5. 4.2.1 Lower extremity motor control as a ho me and community ambulation predictor In contrast to our hypothesis, lower extremity motor control predicted one minute variability of activity only (activity pattern). Theses results suggest tha t overall changes in lower extremity motor control impairment do not relate to changes in home and community ambulation volume and intensity. Furthermore, the association between lower extremity motor control impairment and one minute variability of activity was in an unexpected direction. An increase in lower extremity motor control impairment resulted in an increase in one minute variability of activity. Theses unexpected results may be due to the limited variability of lower extremity motor control impai rment. Sixty -seven percent of the sample had mild lower extremity motor control impairment (FM LE=24 34), whereas only 3% of the sample had severe lower extremity motor control impairment (FM LE=0 11).

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160 5. 4.2.2 Balance control as a ho me and community ambul ation predictor Our results indicated that balance control is an important predictor of SAM volume, intensity, and patter n indices Consistent with our study results, Michael et al.23 in a sample of community -dwelling individuals with mild to -moderate chronic stroke reported that the participants mean ambulatory activity in term of steps/day (activity volume) were strongly associated with balance as measured by the BBS. To date, no study explored the associat ion of activity intensity and pattern outcomes with balance. However, based on clinical grounds it is expected that balance will be associated with both activity intensity and pattern for numerous reasons. For instance, the activity intensity outcome peak activity index, measures the maximal number of steps a person takes in a minute. Thus, it is likely to be influenced by how fast a person walks (gait speed). Therefore, it is reasonable to speculate that gait speed is associated with activity intensity out comes. Moreove r, according to Patterson et al.102 and Pang and associates,62 balance was the strongest predictor of gait speed in stroke survivors with severe impairments. Because SAM intensity indices are likely to be influenced by gait speed, and on acco unt of the strong association between balance and gait speed, it is not surprising that balance is an important predictor of activity intensity. Considering the nature of activity pattern outcomes, i t was also not surprising that balance control was found to be an important predictor of activity pattern. For instance, the measure of number of activity bouts represents the frequency in which a person initiated and terminated gait. Initiating and terminating gait stresses the balance control system98 as gait initiation requires moving from a stable standing pos ture to essentially falling and gait termination requires the control of forward linear momentum, which, if not performed correctly or well may be destabilizing.92

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161 5. 4.2.3 Balance self -efficacy as a ho me and community ambulation predictor It was hy pothesized that balance self -efficacy will predict SAM volume, intensity, and pattern. However, the results show that balance self -efficacy was an important predictor of activity pattern only (number of activity bouts and activity randomness). These result s indicate that balance self -efficacy may effect mainly the degree to which an individual feels confident to initiate and terminate gait often (number of activity bouts) which requires high levels of balance control, and to fluctuate unpredictably their st epping pattern (activity randomness). 5. 4.2.4 Walking endurance as a ho me and community ambulation predictor In our study walking endurance predicted both activity volume and intensity. Similarly, Michael et al.34 quantified community ambulat ory activity profiles in terms of step counts and intensity in a convenience sample of community -dw elling volunteers with mild -to -moderate (National Institutes of Health Stroke Scale mean=3.57 points) chronic (mean time post stroke=10 months ) hemiparetic s troke. Total number of steps/ day and total low and high absolute intensity ambulatory activity were associated with peak oxygen consumption. Therefore, the authors concluded that step activity volume and intensity are strongly associated with cardiova scula r fitness. Moreover, Mudge and Stott343 multiple regression showed that in individuals during the chronic phase of stroke recovery (at least six months post -stroke) with moderate gait impairment (mean gait speed=0.67 m/s), the 6MWT predicted number of step s/day, percentage of the day active, low absolute activity intensity (<30 steps/min), high absolute activity intensity (>60 steps/min), the average step rate of the fastest 30 minutes over 24 hours, highest step rate in 60 minutes, and the highest step rat e in one minute. 5. 4.2.5 Habitual gait s peed as a home and community ambulation predictor Our study shows that gait speed is not an activity volume predictor. This is consistent with Shaughnessy and colleagues 12 study which compared SAM -derived daily steps (activity

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162 volume) with established instruments to assess ambulatory recovery in community -dwelling men and women with mild to -moderate post -stroke hemiparetic gait deficits at discharge from inpatient rehabili tation. The authors reported that SAM -derived daily steps were not strongly related to self -selected gait speed. Similarly, Mudge and Stott343 reported that in a sample of individuals with chronic stroke (at least six months post -stroke) and mean gait spee d of 0.67 m/s (moderate gait impairment) gait speed was not a significant predictor of number of steps/day and percentage of the day with no steps. The findings of the present study, however, are in contrast to those of Michael et al.23 and Bowden and associates33 who reported that the participants mean ambulatory activity profiles in terms of steps/day were strongly associated with self -selected gait speed. Discrepancies in the results may be due to differences between studies in sample stroke characteristics. People with chronic stroke participated in their studies, whereas the participants in the present study had sub acute stroke. In addition, the lack of relationship between gait speed and SAM outcomes in our study may reflect a floor effect, attributable to the samples slow gait speeds (mean gait speed = 0.40 + 0.23 m/s) and limited variability to reveal a relationship. Our findings also show that habitual gait spee d is an important predictor of activity intensity (percentage of the day in absolute low/medium, and percentage of the day in relative low/medium activity intensity). These findings are in contrast to those of Mudge and Stott343 who reported that gait spee d did not predict SAM absolute activity intensity outcomes. Discrepancies in the results may be due to differences between studies in sample stroke characteristics. People with chronic stroke participated in Mudge and Stott343 study, whereas the participan ts in the present study had sub acute stroke. Moreover, Mudge and Stotts343 study used different absolute low and high activity intensity definitions. We defined low and high step

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163 activit y intensity as taking less than 16 steps/min and greater than 30 ste ps /min respectively. Whereas, Mudge and Stott343 defined low and high step activity intensity as taking less than 30 steps/min and greater than 60 steps/ minute, respectively. Finally, in our study, habitual gait speed was found to be an important predic tor of number of steps in an activity bout (activity pattern) only. The lack of association in our study between gait speed and more complex activity pattern outcomes (i.e. one minute variability of activity and activity randomness) suggests that gait spee d does not affect ones ability to alter gait to unexpected environmental demands. 5. 4.2.6 Executive function as a ho me and community ambulation predictor Consistent with the study hypothesis, executive function predicted activity pattern (number of acti vity bouts and activity randomness). SAM activity pattern variables such as activity randomness assess whether minute to -minute fluctuation in gait occurs in orderly, repetitive and predictable pattern. Such measure reflects ones ambulatory activity range and hypothetically may reflect ones capability to adjust gait to unpredictable changes in task demands or environmental conditions. The executive function system may be important for activity pattern outcomes such as activity randomness because unlike amb ulating in the clinic, ambulating outdoors is unpredictable and complex. Therefore, executive function may be important for determining the persons ability to process information and adapting gait to meet environmental demands.92 Unexpectedly, execu tive function also predicted activity volume (number of steps/day and percentage of the day active ). T he executive function system may be important for independent and safe community ambulation volume because ambulating outdoors requires planning of path s election, navigation, speed of information processing to enable adaptations to meet environmental demands, and the ability to divide attention between mobility and other tasks

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164 (e.g. conversing with a friend).92 Therefore, individuals with better execu tive function may be more inclined to leave the house and to ambulate in the community more frequent ly than individuals with low levels of executive function. 5. 4.2.7 Age as a ho me and community ambulation predictor Our study showed that age is not a sign ificant predictor of activity volume (number of steps/day and percentage of the day active). Accordingly, Mudge and Stott343 in a recent study reported that age did not predict mean steps/day and percentage of the day active of individuals with a mean gait speed of 0.67 m/s during the chronic phase of stroke recovery. In addition, our findings show that older individuals have a smaller number of activity bouts. Similarly, Cavanaugh and associates14 reported that healthy older adults accumu lated a similar number of steps and minutes of activity as healthy younger adults, but did so in fewer bouts (healthy older adults: 67.8 + 2.7 bouts; healthy younger adults: 77.3 + 2.6 bouts).14 Cavanaugh and associates14 suggest that the decreased number of activity bouts in the older individuals may indicate that initiating gait becomes more difficult with age. In contrast to Cavanaugh and colleagues,14 our study failed to show that age is a predictor of activity variability Cavanaugh et al.14 showed that younger adults have higher minute to -minute activity variability than older adults. Thus, older individuals have narrower ranges of ambulatory behavior, probably due to inability to per form a range of ambulatory tasks under various environmental conditions.14 These results suggest that this metric is a potentially useful indicator of age associated walking function. Discrepancies in the results between our study and Cava naugh and associates14 study are likely due to demographic differences between the two studies. Cavanaugh et al.14 compared the activity patterns of healthy younger adults with a mean age of 36.6 years to these of healthy older adults with a mean age of 83 years. However, our

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165 study participants were mainly older adults (mean age=63.84 + 12.93) with limited variability in age. 5. 4.2.8 Body composition as a ho me and community ambulation predictor We hypothesized that body c omposition will predict SAM volume, intensity, and pattern. Surprisingly, body composition did not predict any of the 13 SAM indices. The lack of association between body composition and activity volume and intensity is in contrast to Cooper et al. 285 study which determined the levels and pat terns of daily activity in groups of normal weight, overweight, and obese adults using accelerometry. Hour -by -hour physical activity patterns demonstrated that obese participants have less step counts than the non-obese for nearly every hour of everyday. S imilarly, numerous investigators reported that physical activity measured with a pedometer is inversely correlated with body mass index,294, 304 and percentage body fat.294 Cooper et al.285 also reported that obese participants spent significantly less time in activity of at least moderate int ensity than the non-obese. Differences between our study and the previously reported studies may be due to the different methods used to assess body composition. We used the waist circumference measure to assess body composition whereas other studies, such as Cooper et al.285 used the body mas s index method or percentage of body fat.294 In addition, previous studies recruited healthy adults, whereas our study recruited individuals post -stroke who suffer from stroke -specific impairments (e.g., muscle weakness and spast icity) that could potentially alter the relationship of body composition with home and community ambulation. 5. 4.2.9 Depression as a ho me and community ambulation predictor The PHQ 9 scores enable to classify individuals into five levels of depression: ( 1) no depression (0 4 points), (2) mild depression (59 points), (3) moderate depression (1014 points), (4) moderately severe depression (15 19 points), and (5) severe depression (2027 points). We

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166 hypothesized that higher depression levels will be assoc iated with lower levels of activity volume, intensity, and pattern as common depression symptoms consisted of persistent sad feelings, loss of interest in activities, hopelessness feelings, and fatigue.283 However, our results failed to show a relationship between depression and SAM volume, intensity, or pattern. T he literature indicates that the association between functional impairment and post -strok e depression has not been fully established as previous studies on post -stroke depression and functional impairment reported conflicting results.126 A number of studies have shown a significant association between depression and functional impairment during the initial period post stroke,337 339 whereas other researchers did not.340 Therefore, it appears that the association between depression and functional impairment is complex.126 Similarly, the association between depression and home and community ambu lation may be complex as well. Moreover, our null results may be accountable to the study inclusion and exclusion criteria. Individuals with major post -stroke depression as indicated by a PHQ 9 score > 10 (moderate to -severe depression) were included in the study only if there is a documented management of the depression by a health c are provider. In addition, the majority of the sample did not suffer from depression (68% of the sample), whereas only three and 2% of the sample had moderately severe and severe depression, respectively. The exclusion of individuals with untreated major d epression and the limited number of individuals with high levels of depression may be responsible for the null results. 5. 4.2.10 Stroke location as a ho me and community ambulation predictor In contrary to the hypothesis, left hemisphere stroke predicted greater percentage of the day in relative medium activity intensity. This indicates that individuals with left hemispheric stroke were more likely to engage in relative higher activity intensities than individuals with right hemispheric stroke bilateral s troke, or brainstem stroke Relative intensity is the effort required to do an activity. Less fit people generally require a higher level of effort than fitter people to do

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167 the same activity (e.g., walking to the kitchen). However, do individuals with left right, bilateral, and brainstem strokes engage in similar activities? In our study we did not examine the type of activities individuals with various stroke etiologies partake in. However, considering the common symp toms and characteristics of left, righ t, bilateral, and brain stem strokes it is likely that each group engages in different types of activities. For instance, i ndividuals with left hemispheric stroke may suffer from an inability to solve problems, have impaired retention of information, are o ften more easily frustrated and angered, and may have apraxia or language difficulties.42 Persons with right -hemispheric stroke often experience difficu lty with spa tial perceptual tasks, develop impulsive style, tend to have greater balance problems, and may experience left -side neglect.42 Whereas, brai n stem strokes are especially devastating as they may result in paralysis in one or both sides of the body, loss of sensation, trouble with visual perception, impaired cognitive function, and difficulty coordinating daily tasks such a walking.342 Consideri ng each stroke type unique characteristics, individuals with right stroke which may suffer from l eft side neglect, greater balance problems, and difficulties with spatial perceptual tasks and individuals with brain stem stroke are less likely than individuals with left stroke to engaging in absolute moderate and vigorous intensity activities (e.g., walking briskly and gardening) which require higher levels of efforts as well. 5. 4.3 Study Limitations The present study has several limitations. First, the re gression models that were used accounted for less than 50% of the variance of the different SAM outcomes. That is, more than 50% of the variance in SAM home and community ambulation volume, intensity, and pattern remained unexplained. One explanation is that home and community ambulation is complex and may involve multiple determinants, some of which were not included in the model (e.g., use of assistive device, co -morbidities, social network, caregiver support). Nevertheless, this is the first

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168 study to rep ort the amount of variance in SAM derived home and community ambulation at two months post -stroke. Second, all participants in the present study were two month s post -stroke with moderate to -severe hemiparetic gait impairment. Therefore, the results may not be generalizable to people with mild gait impairment and individuals during the chroni c phase of stroke recovery. Home and community ambulation determinants at two months post -stroke may certainly differ than those at six or 12 months post -stroke. Future comparisons across these time periods will be important in assessing the factors that contribute to community -based step activity post -stroke. 5. 5 Conclusion The results of this study suggest that at two months post -stroke, walking in the real world may i nvolve multiple determinants Balance control, walking endurance, executive function, age, balance confidence, lower extremity motor control impairment, and gait speed are all important factors involved in determining a persons home and community ambulati on function. However, usual gait -speed did not predict activity volume and it predicted only four out of the seven activity intensity outcomes. Moreover, gait speed was not an important predictor of complex activity pattern outcomes which represent activit y variability and randomness. Activity variability and randomness are important as they may reflect ones ambulation range, ability to ambulate in nonpredictable environments (e.g., a crowded street), and ability to successfully adapt to environmental dem ands. An in -depth understanding of the predictors of SAM derived home and community ambulation is beneficial in development of interventions resulting in better carryover of everyday ambulatory activity.34 In addition, understanding ambulatio n predictors will shed light on which clinically -based outcome measures best predict home and community ambulation level, and thus would be beneficial clinically and in research. Finally, in the LEAPS study SAM derived ste p

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169 activity is also measured at six and 12 month post -stroke. In future work, the effect of time since stroke, gait training with a treadmill and a body weight support system and home -based exercise program on SAM derived step activity at home and the community will be examined

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170 Table 5 1. All subjects and compliers only demographic and clinical characteristics Characteristic All Patients Compliers only Age a (SD) (years) 63.9 (12.9) 64.6 (12.9) Gender (male) 49.6% 50.3% Stroke location Right hemisphere Left hemisphere Brainstem Bilateral 47% 37% 14% 1% 45% 38% 15% 2% Berg Balance Scale ( SD) 36.37 (13.70) 36.8 (13.7) Habitual gait speed (SD) (m/s) 0.39 (0.22) 0.40 (0.23) Six minute walk test (SD) (meters) 128.05 (7 7.75) 133.0 (77.7) Upper extremity motor function portion of the Fugl Meyer Assessment (SD) 36.34 (20.81) 37.6 (21.1) Lower extremity motor function portion of the Fugl Meyer Assessment (SD) 25.28 (6.39) 25.5 (6.4) Waist circumference (SD) (cm) 94.45 (17.89) 93.8 (14.3) Patient Health Questionnaire Nine item Depression Scale (SD) 4.63 (5.11) 4.63 (4.98) Trail Making Test Part B (SD) (seconds) 219.48 (97.51) 217.2 (94.6) Activities Specific Balance Scale (SD) 44.46 (23.26) 46.2 (23.1) aSD= standar d deviation

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171 Table 5 2. Sample step activity monitor characteristics Step activity monitor variables All Patients (n=228) Compliers only (n=165) Steps activity volume Number of steps taken over 24 hours 2300.5 (2518.8) 3783.6 (2597.9) Percentage of t he day active 9 (7) 11 (7) Steps activity intensity Peak intensity index 52.8 (32.3) 59.8 (26.9) Percentage of the day in absolute low 68 (21) 65 (19) Percentage of the data in absolute medium 15 (10) 17 (9) Percentage of the day in absolute high 15 (14) 18 (14) Percentage of the day in relative low 86 (13) 88 (7) Percentage of the day in relative medium 7 (6) 8 (6) Percentage of the day in relative high 6 (13) 4 (4) Steps activity pattern Number of activity bouts 99.72 (98.22) 121.4 (97.7 ) Number of steps in an activity bout 26.10 (27.58) 28.3 (25.8 One minute variability of activity (coefficient of variation %) 84.68 (32.72) 94.8 (20.3) Activity randomness (approximate entropy) 0.21 (0.13) 0.26 (0.12) Note : values are mean (standar d deviation). Table 5 3. Step activity volume predictors Step activity volume outcomes Predictor Estimate P value Adjusted R square Number of s teps /24 hours (natural logarithm) Balance (more severe deficits ) 0.440 0.000* 0.379 Walking endurance (more severe deficits ) 0.494 0.000* Executive function (deficient) 0.707 0.016* Percentage of the day active Balance (more severe deficits ) 5.016 0.000* 0.234 Balance confidence (low) 1.786 0.071 Executive function (deficient) 2. 930 0.003* *Significant predictor at the < 0.05 level (2 tailed).

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172 Table 5 4 Step activity volume summary statistics based on subjects characteristics Demographics and clinical characteristics (percentage of the population) Number of steps/day Percen tage of the day active Gender: Male (50%) Female (50%) 2617.18 (2606.44) 2952.10 (2594.33) 10.77 (5.93) 11.99 (7.33) Stroke location: Right hemisphere (45%) Left hemisphere (38%) Brainstem (15%) Bilateral (2%) 2673.35 (2399.08) 2678.25 (2699.96) 3519.76 (2957.15) 1582 (1641.88) 11.64 (6.21) 10.80 (7.09) 12.43 (7.25) 8.43 (3.81) Balance: Less severe deficits (33%) More severe deficits (67%) 4765.16 (2956.64)* 1792.85 (1688.83) 15.34 (6.53)* 9.40 (5.83) Walking enduranc e: Less severe deficits (54%) More severe deficits (46%) 3983.55 (2853.74)* 1423.92 (1296.46) 13.04 (6.53)* 9.5 (6.4) Upper extremity motor control impairment: Mild (46%) Moderate (22%) Severe (32%) 3424.85 (2612.48)a 3106 (3172.40) a 1651.17 (1622. 30) 12.44 (6.35) a 12.19 (7.6) a 9.32 (6.04) Lower extremity motor control impairment: Mild (67%) Moderate (30%) Severe (3%) 3348.85 (2822.7)a 1711.47 (1557.38) 742.8 (590.93) 12.31 (6.87) 9.42 (5.83) 9.83 (6.57) Waist circumference: Normal (57%) In creased risk (43%) 2813.63 (2645.9) 2803.83 (2575.63) 11.51 (6.76) 11.36 (6.62) Depression: No depression (68%) Mild (21%) Moderate (6%) Moderately severe (3%) Severe (2%) 2858.88 (2720.31) 2629.88 (2084.86) 2102.60 (1646.83) 1322.40 (813.89) 2386 (2762.68) 11.76 (6.8) 10.6 (6.5) 10.43 (5.52) 7.66 (4.18) 8.08 (5.25) Executive function: Normal (56%) Abnormal (44%) 3076.05 (2614.93)* 2497.85 (2608.2) 13.04 (6.48)* 9.28 (6.27) Balance confidence: Moderate high level (51%) Low level (49%) 3294.74 ( 2570.14)* 2299.59 (2557.42) 12.98 (6.53)* 9.8 (6.51) Values are mean (standard deviation); the two levels of the variable are significantly different at the < 0.05 level (2 tailed); astatistically significant differences (P < 0.05) relative to severe im pairment.

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173 Table 5 5. Step activity intensity predictors Step activity monitor intensity outcomes Predictor Estimate P value Adjusted R -square Peak activity index Balance (more severe deficits) 11.454 0.006* 0.384 Walking endurance (more severe defici ts) 26.259 0.000* Percentage of the day in absolute low Gait Speed (0.1m/sec) 4.567 0.000* 0.497 Balance (more severe deficits) 9.463 0.001* Stroke location (Left hemisphere) 4.035 0.085 Stroke location (Brainstem) 5.716 0.075 Percenta ge of the day in absolute medium Gait Speed (0.1m/sec) 1.351 0.000* 0.494 Balance (more severe deficits) 4.332 0.006* Percentage of the day in relative low Gait Speed (0.1m/sec) 1.968 0.000* 0.247 Walking endurance (more severe deficits) 5.308 0 .019* Upper extremity motor control impairment 1.491 0.075 Executive function (deficient) 3.211 0.007* Percentage of the day in relative medium Gait speed (0.1m/sec) 1.037 0.004* 0.068 Walking endurance (more severe deficits) 3.262 0 .043* Stroke location (Left hemisphere) 1.860 0.032* *Significant predictor at the < 0.05 level (2 tailed).

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174 Table 5 6. Step activity intensity summary statistics based on subjects characteristics Demographics and clinical characteristics (percentage of The population) Peak activity index (steps/min) Percentage of the day in absolute low Percentage of the day in absolute medium Percentage of the day in absolute high Percentage of the day in relative low Percentage of the day in relative medium Percentage of the day in relative high Gender: Male (50%) Female (50%) 57.11 (27.61) 62.44 (26.04) 66.19 (20.1) 63.38 (18.43) 16.52 (9.17) 18.31 (8.68) 17.29 (14.24) 18.31 (13.22) 87.54 (7.26) 88.23 (7.67) 8.09 (5.18) 8.01 (5.85) 4.38 (3.79) 3.76 (4.06) Stroke location: Right hemisphere (45%) Left hemisphere (38%) Brainstem (15%) Bilateral (2%) 58.11 (27.7) 58.54 (25.57) 68.96 (28.08) 49.33 (13.61) 67.93 (18.8) 63.45 (19.11) 57.78 (19.58) 73.88 (22.58) 16.61 (9.13) 17.53 (8.73) 19.82 (8.69) 14.56 (11.81) 15.46 (13.75) 19.02 (13.05) 22.4 (14.56) 11.57 (10.91) 88.69 (7.42) 86.07 (8.02) 89.88 (4.92) 89.24 (8.76) 7.35 (5.18) 9.33 (6.2) 7.17 (4) 5.88 (6.08) 3.96 (4.65) 4.6 (3.5) 2.95 (2.15) 4.88 (3.7) Balanc e: Less severe deficits (33%) More severe deficits (67%) 77.31 (22.75)* 50.98 (24.46) 50.33 (12.13)* 72.02 (18.12) 22.71 (5.72)* 14.76 (9.11) 26.97 (11.82)* 13.22 (12.25) 87.27 (6.33) 88.19 (7.96) 8.96 (4.08)* 7.6 (6.06) 3.78 (3.65) 4.21 (4.07) Wal king endurance: Less severe disability (54%) More severe disability (46%) 74.75 (23.31)* 42.74 (19.61) 53.75 (14.8)* 77.19 (15.77) 20.55 (6.86)* 13.94 (9.73) 25.7 (12.07)* 8.87 (9.18) 87.59 (6.58) 88.1 (8.36) 8.36 (4.59) 7.77 (6.42) 4.05 (3.84) 4.13 (4.06) Upper extremity motor control impairment: Mild (46%) Moderate (22%) Severe (32%) 68.45 (24.93)a 58.59 (25.21)a 48.26 (26.67) 57.61 (16.71)a 64.79 (18.96)a 74.96 (18.62) 20.43 (8.11)a,b 16.2 (7.38) 13.98 (9.74) 21.96 (12.66) 19.01 ( 14.66) 11.07 (12) 88.29 (6.18) 86.01 (7.84) 88.6 (8.65) 8.05 (4.64) 9.24 (5.08) 7.22 (6.74) 3.66 (3.36) 4.75 (5.33) 4.17 (3.51) Lower extremity motor control impairment: Mild (67%) Moderate (30%) Severe (3%) 66.74 (26.23)a,b 46.86 (22.13) 31. 2 (20.96) 59.76 (17.89)a 73.08 (17.62)a 95.26 (3.81) 18.86 (8.36)a 15.48 (9.24)a 4.05 (2.83) 21.38 (13.59)a,b 11.44 (10.85)a 0.69 (1.18) 87.57 (6.65) 87.87 (8.98) 94.92 (5.16) 8.2 (4.74) 8.23 (6.9) 2.91 (4.31) 4.23 (4.12) 3.89 (3.63) 2. 17 (1.56) Waist circumference: Normal (57%) Increased risk (43%) 60.41 (27.34) 59.8 (26.27) 65.44 (19.8) 63.21 (18.43) 16.88 (9.02) 18.46 (8.7) 17.67 (13.89) 18.33 (13.64) 87.93 (7.71) 87.86 (7.28) 8.23 (6.03) 7.85 (4.87) 3.84 (3.18) 4.29 (4.76) D epression: No depression (68%) Mild (21%) Moderate (6%) Moderately severe (3%) Severe (2%) 59.71 (27.31) 60.35 (26.98) 55.4 (27.65) 53.6 (14.31) 62 (34.7) 65.43 (19.38) 62.32 (18.97) 68.63 (19.94) 70.99 (5.32) 65.07 (30.49) 16.86 (8.57) 19.48 (10.12) 1 5.06 (8.06) 16.48 (4.19) 17.85 (16.16) 17.71 (14.26) 18.2 (11.82) 16.31 (14.24) 12.53 (4.21) 17.09 (21.16) 87.71 (7.84) 88.19 (6.72) 86.79 (7.38) 88.79 (6.65) 93.64 (5.23) 8.03 (5.54) 8.3 (5.52) 9.33 (6.26) 6.41 (4.28) 2.4 (2.42) 4.26 (4.45) 3.51 (2.42) 3.89 (2.6) 4.8 (3.45) 3.96 (3.15)

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175 Table 5 6 C ontinued Demographics and clinical characteristics (percentage of the population) Peak activity index (steps/min) Percentage of the day in absolute low Percentage of the day in absolute medium Percentage of the day in absolute high Percentage of the day in relative low Percentage of the day in relative medium Percentage of the day in relative high Executive function: Normal (56%) Abnormal (44%) 62.88 (26.07) 58.18 (28.26) 64.48 (17.92) 64.09 (21.24) 18.18 (8.22) 16.49 (9.52) 17.33 (13.41) 19.42 (14.55) 89.28 (6.46)* 86.13 (8.54) 7.62 (4.91) 8.47 (6.32) 3.1 1 (3.08)* 5.41 (4.68) Balance confidence: Moderatehigh level (49%) Low level (51%) 64.74 (26.42)* 54.65 (26.63) 61.1 (17.59)* 68.13 (2 0.28) 18.9 (7.93)* 16.07 (9.67) 20 (13.36)* 15.8 (13.82) 88.15 (6.42) 87.5 (8.33) 8.05 (4.3) 8.15 (6.46) 3.8 (3.69) 4.36 (4.17) Values are mean (standard devia tion); *the two levels of the va riable are significantly different at the < 0.05 level (2-t ailed); astatistically significant differences (P < 0.05) relative to severe impairment; bstatistically significant differences (P < 0.05) relative to moderate impairment.

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176 Table 5 7 Step activity pattern predictors Step activity monitor pattern outcomes Predictor Estimate P value Adjusted R square Number of activity bouts (natural logarithm) Age 0.990 0.027* 0.213 Balance (more severe deficits) 0.724 0.011* Balance confidence (low) 0.740 0.013* Depression (base level=no depression) 0.876 0.071 Executive function (deficient) 0.749 0.014* Mean steps in an activity bout (natural logarithm) Age 1.011 0.030* 0.319 Gender (female) 1.203 0.135 Gait speed (0.1m/sec) 1.180 0.000* Balance (more severe deficits) 0.645 0.008* Dep ression (base level=no depression) 1.114 0.124 Activity variability (one minute variability of activity) Balance (more severe deficits) 8.058 0.021* 0.058 Lower extremity motor control impairment (base level=mild impairment) 4.721 0.028* Upper ex tremity motor control impairment (base level=mild impairment) 6.070 0.082 Waist circumference (increased risk) 4.846 0.130 Activity randomness (approximate entropy) Gait speed (0.1 m/sec) 0.836 0.086 0.170 Balance (more severe deficits) 5.852 0.012* Balance confidence (low) 4.413 0.021* Executive function (deficient) 7.168 0.000* *Significant predictor at the < 0.05 level (2 tailed).

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177 Table 5 8. Step activity pattern summary statistics based on subjects characteristics Demographic s and clinical characteristics (percentage of the population) Number of activity bouts Number of steps in an activity bout Activity variability (coefficient of variation) Activity randomness (approximate entropy) Gender: Male (50%) Female (50%) 119.47 (83.97) 123.44 (110.41) 25.69 (26.05) 31 (25.33) 95.8 (19.27) 93.84 (21.35) 0.25 (0.11) 0.27 (0.13) Stroke location: Right hemisphere (45%) Left hemisphere (38%) Brainstem (15%) Bilateral (2%) 117.49 (89.91) 115.25 (104.53) 149.44 (106.33) 115.67 (44.55) 28.83 (26.6) 28.43 (25.52) 28.53 (25.63) 11.96 (8.63) 96.36 (21.53) 94.55 (19.26) 89.17 (18.09) 109.62 (24.89) 0.27 (0.12) 0.25 (0.13) 0.27 (0.13) 0.22 (0.05) Balance: Less severe deficits (33%) More severe deficits (67%) 161.16 (117.95)* 101.58 (79.26) 39.37 (28.1)* 22.8 (22.68) 89.38 (13.88)* 97.54 (22.4) 0.3 (0.11)* 0.24 (0.12) Walking endurance: Less severe disability (54%) More severe (46%) 134.27 (105.13) 106.24 (87.32) 37.74 (27.79)* 17.75 (18.14) 92.63 (15.66) 96.99 (24.39) 0.27 (0.1) 0.25 (0.14) Upper extremity motor control impairment: Mild (46%) Moderate (22%) Severe (32%) 124.13 (97.9) 142.16 (106.16) 103.17 (89.56) 35.57 (27.43)a,b 25.03 (24.2) 20.38 (21.63) 92.07 (16.97)a 91.78 (20.62)a 1 00.84 (23.25) 0.27 (0.11) 0.27 (0.14) 0.24 (0.13) Lower extremity motor control impairment: Mild (67%) Moderate (30%) Severe (3%) 128.04 (104.7) 109.04 (84.35) 96.6 (29.64) 33.45 (28.07)a 18.88 (16.3) 7.22 (4.93) 94.29 (20.64) 96.55 (19.53) 89.8 5 (22.73) 0.27 (0.12) 0.24 (0.11) 0.31 (0.16) Waist circumference: Normal (57%) Increased risk (43%) 123.25 119.64 (97.74) 27.75 (25.68) 29.89 (26.23) 96.61 (19.24) 92.29 (21.51) 0.26 (0.13) 0.26 (0.12) Depression: No depression (68%) Mild (21%) Mo derate (6%) Moderately severe (3%) Severe (2%) 135.03 (106.55) 81.76 (62.66) 110.7 (65.39) 77.4 (37.83) 76 (54.62) 25.44 (23.21) 39.58 (31.51) 21.78 (12.3) 18.26 (8.99) 43.56 (63.16) 95.4 (19.57) 91.66 (22.84) 90.85 (16.33) 112.7 (15.49) 105.06 (28.79) 0.27 (0.13) 0.23 (0.1) 0.27 (0.13) 0.21 (0.08) 0.19 (0.07) Executive function: Normal (56%) Abnormal (44%) 136.62 (101.09)* 98.53 (88.13) 29.28 (25.81) 29.4 (27.08) 96.11 (17.44) 93.7 (23.29) 0.3 (0.12)* 0.21 (0.1) Balance confidence: Moderate high level (49%) Low level (51%) 143.63 (107.51)* 98.7 (81.94) 29.96 (25.88) 26.99 (25.75) 93.94 (16.17) 95.08 (23.17) 0.29 (0.l2)* 0.23 (0.12) Values are mean (standard deviation);*The two levels of the variable are significantly different at the < 0.05 level (2 tailed); astatistically significant differences (P < 0.05) relative to severe impairment; bstatistically significant differences (P < 0.05) relative to moderate impairment.

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178 CHAPTER 6 CONCLUSION The present study aimed to establish the compliance rate for use of an accelerometer protocol by individuals post -stroke, the relationship of gait speed to actual step activity at home and the community, and to examine factors that may influence step activity at home and the community in individuals who had experienced a stroke two months earlier. In Chapter Two, we presented a two part conceptual framework as the foundation for this research (Figure 2 1). The framework indicates that post -stroke body functions and structures (e.g., sensory, cognitive, and m otor impairments), activity limitations (e.g., reduced walking endurance and balance), and personal factors (e.g. age and gender) may have an impact on the utility of gait -speed as a proxy measure of home and community ambulation. In a ddition, post -stroke impairments severity, activity limitations, and personal factors might also influence home and community ambulation level in terms of steps activity volume, intensity and pattern. Our findings show promise for use of SAM in studies of physical functioning post -stroke. The first study indicates that the samples inferred compliance rate was moderate to -high. However, inferred compliance rate dropped in 6% from the first to the second measurement day (72 vs. 66%, respectively) and in 18% from the first meas urement day to both days (72 vs. 54%, respectively). In addition, younger age, more severe balance control deficits, and low balance self -efficacy were associated with non-compliance. Stroke type, stroke location, gait impairm ent, cognition, depression, u pper extremity motor control function, gender, and education were not associated with SAM inferred compliance. Therefore, researchers are encouraged to implement strategies to increase compliance with SAM post -stroke (e.g., making reminder calls and providing incentives contingent on compliance), especially in studies which attempt to collect SAM data for more than one day, and when the sample is mainly consisted of younger

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179 individuals, participants with more severe balance control deficits (BBS < 45) and l ow levels of balance self -efficacy (ABC < 49). However, further studies examining the effectiveness of the various available strategies to improve compliance are warranted. Moreover, future research is necessary to establish SAM compliance rates during the chronic phase of stroke recovery. The second study also supports the use of SAM as a post -stroke home and community ambulation outcome measure. Certain SAM indices significantly correlated with both SIS mobility and participation. SIS mobility assesses ones ability to be mobile at home and in the community (e.g., walk without losing balance, walk one block, and walk fast), whereas SIS participation evaluates how stroke affected the stroke survivor ability to participate in activities which are meaningfu l to him or her (e.g., social activities, active recreation, and quiet recreation). However, SAM outcomes correlations with SIS were weaker than expected (r > 0.60). The lower associations may be due to certain SIS items which may not be influenced by step s activity (e.g., engage in quiet recreation and participation in spiritual or religious activities). When the compliers data were included in the analysts, t he second study indicated that the gait -speed classification system validity as a home and co mmunity ambulation is limited at two months post -stroke as the two gait -speed functional groups (household ambulators and limited community ambulators) did not differ in numerous important activity pattern indices (number of activity bouts, activity variab ility, and activity randomness). The lack of differences between the two functional groups in SAM pattern indices, may indicate that the speed based classification system does not well represent ones ability to adapt and modify gait (e.g., changing walkin g speed) which are important in order to walk in complex environments (e.g. in a crowded street). When gait -speed was treated as a continues variable similar results were

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180 obtained The compliers group gait -speed significantly correlated with the steps acti vity volume outcomes, most of activity intensity indices, but only with one out of the four activity pattern outcomes These results also confirm that gait speed association with activity pattern is limited. Moreover, when only the compliers were included in the analyses, the association of gait speed with the SAM outcomes was significantly distorted by balance control, walking endurance, lower extremity motor control, and balance self -efficacy. Accordingly, study number three indicated that gait -speed was a significant predictor of five out of the 13 SAM indices only (four activity intensity outcomes and one activity pattern outcome). T aken together, these results suggest that gait -speed may not always reflect ones ability to walk at home and in the commu nity two month post -stroke. In addition, improving gait speed may not yield improvements in home and community ambulation level, especially in less predicted environments which require high levels of activity variability and randomness. The results indicat ed that balance control, walking endurance, executive function, gait speed, stroke location, age, balance self -efficacy, and lower extremity motor control impairment are all important factors involved in determining a persons home and community ambulation function. This is important as it may assist researchers in developing outcome measures which will better predict steps activity at home and in the community and in developing interventions resulting in better carryover of everyday ambulatory activity. For instance, programs that are designed to improve ambulation typically focus on reducing physical activity limitations (e.g., improving balance control and walking endurance). However, our results indicate that factors such as balance self -efficacy and exe cutive function are also important for home and community ambulation. Therefore, in order to improve home and community ambulation at two -month post -

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181 stroke, it may be appropriate to integrate in to the rehabilitation program interventions which target balan ce self -efficacy and executive function In conclusion, the present studies provide support that walking in the real world is highly complex and may involve multiple determinants. However, our regression model was able to explain only zero to 50 % of the variation in steps activity at home and in the community. Therefore, in order to better understand home and community ambulation determinants, there is a need to investigate additional factors that may be important for home and community ambulation such a s social support and the use of assistive device The studies also demonstrate that usual gait -speed, although commonly used as a proxy measure for home and community amb ulation does not reflect all the various aspects of home and community ambulation The refore, continued reliance on gait -speed as a proxy measure for home and community ambulation at two -months post -stroke may be misplaced.28 Collectively, this research promotes our understanding of home and community ambulation prerequisites two-months post -stroke. This knowledge is important as it may assist in establishing a theoretical framework for home and community ambulation. Su ch a framework can be used to develop interventions which will yield greater improvements in home and community ambulation function and a battery of measurements which will better predict ones ability to ambulate in his or her ecological environment.

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182 A PPENDIX A STEP ACTIVITY MONITOR VALUES IN DIFFERENT POPULATIONS

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183 Population Ambulation volume Ambulation intensity Ambulation patterns Type Characteristics Number of steps in a day Activity or inactivity time Activity intensity Peak activity index (steps/ Minute) Number of activity bouts Activity variability (coefficient of variation) Randomness (approximate entropy) Stroke Mild severe chronic stroke.6 3035 + 1944; Range: 4006472 Not reported Not reported Not reported Not reported Not reported Not reported Mild moderate subacute stroke12 At discharge from inpatient rehab: 1536+ 106; Three months post discharge: 2765 + 1677 Not reported Not reported Not reported Not reported Not reported Not reported Mild moderate chronic stroke 23 2837 + 1503 Not reported Not reported Not reported Not reported Not repo rted Not reported Mild moderate chronic stroke 34 1389 + 797 Not reported Low: 45% of activity (624 + 289 steps). Range: 1201,545 steps; medium: 46% of activity (640 + 536 steps). Range: 12 3,217 steps; high: less than 3 min a day (83 + 168 st eps). Range: 0 846 steps Not reported Not reported Not reported Not reported Mild to moderate chronic stroke 298 2821 + 1527 Not reported Not reported Not reported Not reported Not reported Not reported

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184 Population Ambulation volume Ambulation intensity Ambulation patterns Type Characteristics Number of ste ps in a day Activity or inactivity time Activity intensity Peak activity index (steps/ Minute) Number of activity bouts Activity variability (coefficient of variation) Randomness (approximate entropy) Neurological patients Neurological disorders wi th leg weakness300 3090 + 1902 19.1 hours of inactivity Low 3.7 + 1.42 hours, medium 0.96+ 0.62 hours, high 0.15+ 0.24 hours. Not reported Not reported Not reported Not reported Mobility restr icted Neurological patients 25 3268 (range: 6895391) Not reported Not reported 36 Not reported Day to day: 30; week to week:12 Not reported Multiple sclerosis25 21179 (range: 20003876) 79.5% inactivity Not reported 29.6 (range: 5.7 44.5) Not reported Not reported Not reported

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185 Population Ambulation volume Ambulation intensity Ambulation patterns Type Characteristics Number of steps in a day Activity or inactivity time Activity intensity Peak activity index (steps/ Minute) Number of activity bouts Activity variability (coefficient of variatio n) Randomness (approximate entropy) Persons with functional limitations and illnesses Low functioning community dwelling elderly303 5048 + 2917 Not reported Not reported Not reported Not reported Not reported Not reported People with disabilities and chronic illnesses 3500 5500 Not reported Not reported Not reported Not report ed Not reported Not reported Patients with diabetic foot ulceration (mean age 65.0+ 7.6) .310 1219.1 + 821 Not reported Not repor ted Not reported Not reported Not reported Not reported Patients with peripheral arterial disease (mean age 65.0+ 7.0).302 4556.5 + 2803.5 N ot reported Not reported Not reported Not reported Not reported Not reported Obese (mean age 40.2+ 7.9)285 Not reported Not reported M oderate activity : 31.3+ 14.0, hard0.8+ 1.7, very hard 0.2 + 0.5 Not reported Not reported Not reported Not reported Persons aged 79.3+ 4.514 7,681.5 + 844 298 + 27.6 minutes of activity Not reported Not reported 62.5 + 4.1 85.2 + 2.9 0.383 + 0.03

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186 Population Ambulation volume Ambulation intensity Ambulation patterns Type Characteristics Number of steps in a day Activity or inactivity time Acti vity intensity Peak activity index (steps/ Minute) Number of activity bouts Activity variability (coefficient of variation) Randomness (approximate entropy) Healthy adults and older adults Healthy older adults (aged 83.7+2.3) 14 9,981 .7 + 552.8 355.5.7 + 18.1 min of activity Not reported Not reported 67.8 + 2.7 92.4 + 1.9 0.454 + 0.02 Healthy older adults.38 6000 8500 Not reported Not reported Not reported Not reported Not reported Not reported Sedentary older adults who are free of disabilities.295 < 5000 steps /day for sedentary lifestyle, 5,0007,499 steps /day for low activity, 7,500 to 9,999 steps /day for somewhat active, 10,000 to 12,499 st eps /day for active, and > 12,500 steps /day for highly active. Not reported Not reported Not reported Not reported Not reported Not reported High functioning elderly303 9503 + 4623 Not reported Not reported Not reported Not reported Not reported Not reported Elderly. 299 Not reported Active 14.4 hours/day (range: 11.717.2) Low 82% of the day; moderate 1 5% of the day; high 4% of the day Not reported Not reported Not reported Not reported

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187 Healthy adults (mean age 51.3 + 17.9) 25 6214 (range: 288610002) Not reported Not reported 59 Not reported Day to day: 28%, week to week: 8.8% Not reported Healthy adults190 38391 (range: 1967268464) 74.6% inactivity Not reported 49.2 (range: 37.3 62.7) Not reported Not reported Not reported

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188 Population Ambulation volume Ambulation intensity Ambulation patterns Type Characteristics Number of steps in a day Activity or inactivity time Activity intensity Peak activity index (steps/ Minute) Number of activity bouts Activity variability (coefficient of variation) Randomness (approximate entropy) Healthy younger adults Healthy, younger adults.38 7000 13,000 Not reported Not reported Not reported Not reported Not reported Not reported Young adults.299 Not reported Active 13.7 hours/day (range 10.916.1 Low 65 (range 5282); moderate 25 (range 11 36), high 9 (range 315) Not reported Not reported Not reported Not reported Healthy younger adults (aged 36.6+ 2.6) 14 11,074.7 + 534.1; 399 .9 + 17.4 minutes of activity Not reported Not reported 77.3 + 2.6 100.0 + 1.8 0.492 + 0.02 Non obese (aged 39.7+ 9.2)285 Not reported Not reported During the week activity level (minutes/day): moderate activity: 39.8+ 19.5, hard3.9+ 5.8, very hard 1.5+ 4.5 Not reported Not reported Not reported Not reported Healthy (gait speed 1.2 m/s) 300 6374 + 1819 Healthy: 17.4 hours of inactivity low 4.2 + 0.8, medium 1.5+ 0.5, high 0.8+ 0.5 Not reported Not reported Not reported Not reported Note: values are mean (standard deviation).

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189 APPENDIX B PILOT WORK: INFERRED COMPLIANCE WITH ACCE LEROMETER MONITORING PROTOCOL The purpose of this pilot was to test several different methods to estimate inferred compliance with SAM protocol post -stroke. Findings from this pilot were used to direct the methodology for chapter three, inferred compliance with a SAM protocol to measure free physical activity in moderate to severe subacute stroke survivors. B. 1 Methods B. 1.1 Design Data in this cross -sectional stud y was derived from a parent study, the LEAPS.311 B. 1.2 Testing Procedures and Measurements B. 1.2.1 Ambulatory activity Habitual ambulatory step activi ty was assessed with the SAM. SAMs were programmed with an initial generic calibration.19 Participants performed a one -minute walk test. SAM counts of strides were compared with visual counts. Adjustments were made to the calibration setting and the timed walks were repeated to ensure gre ater than 90% accuracy against visual counts. To achieve optimal sensitivity for step detection, the SAM were programmed and applied above the malleolus of the less involved limb with two adjustable elastic straps. Participants wear the SAM for a period o f two consecutive days, throughout the day, removing the SAM only to bathe, shower, swim, or sleep. A demonstration and written instructions regarding appropriate use of the device were provided to subjects. On completion of a 48 -hour monitoring period, S AM data were downloaded using an infrared docking port. SAMs raw data from the manufacturer software were imported into Microsoft Excel. Data were expressed as stride counts every 60 seconds during the 48 hours.

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190 B. 1.2.2 Quantification of inferred accelerometer compliance rate Three methods to establish compliance rate were developed. The first method (six hours windows method) established inferred compliance in three time periods (windows) of six hours: (1) 6:00AM 12:00PM, (2) 12:01PM 6:00PM, and (3) 6:01PM12:00AM. It is reasonable to assume that most individuals sleep between 12:00AM to 6:00AM or are engaged in sedentary activities such as reading or watching television. Therefore, compliance in wearing the accelerometer is inferred only for 18 waking ho urs of the day, between 6:00AM to 12:00AM. If activity was recorded in only one portion of a window it was inferred that the person wore the accelerometer throughout the window as participants are not likely to remove accelerometers multiple times per day. According to this method, a compliant person in 24 hours period is defined as a participant who has activity recorded in each of the 3 windows. The samples inferred compliance with the accelerometer monitoring protocol was categorized as poor (0 25%), l ow (26 50%), moderate (5175%), or high (76100%),275 based on the percentage of the sample with activity in all the three windows. Figure 3 1 illustrates the use of the six hours windows method to identify inferred accelerometer wearing time. Accord ing to the second method (3 hours windows method), periods in which no activity (zero counts) was recorded for over three hours were considered periods in which the monitor was not worn. Therefore, these periods were subtracted from the 24 hours of the day This method compliance rate categories are similar to method number one. For example, it is inferred that a person with four periods of no activity for over three hours, wore the accelerometer for 12 hours only (24 hours minus 12 hours with no activity) Therefore, it is inferred that the person wore the accelerometer for 50% of the day (moderate compliance rate). The third method (activity method) is based on the percent of the 24 hours in which activity was recorded (number of 1 minute intervals with s tep counts > 1). This method

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191 underlying assumption is that when ever activity was rerecorded, the person wore the accelerometer. As with the previous methods, 24 hours inferred compliance rate was categorized as poor (0 25%), low (2650%), moderate (51 75% ), or high (76 100%).275 For example, a person who has 600 minutes with step counts > 1 wore the accelerometer for 10 out of the 24 hours of the day. This will be equivalent to 41.6% compliance rate (low compliance). B. 1.3 Data Analysis Inferred com pliance rate in the first and second days was calculated for each of the three inferred compliance methods. Compliance in each window of the six hours windows method was also reported. Within group differences in compliance in the first and second measurem ent days were analyzed using the students dependent t test. All analyses were conducted in a 2 tailed fashion with a 0.05 alpha level. Statistical analyses were conducted using SAS version 9.1. B. 2 Results Two -hundred and thirty subacute stroke patient s (two -month post -stroke) with moderate to severe gait impairments (gait speed < 0.8 m/s) participated in this study. An examination of the 230 SAM files revealed deviations from the LEAPS study manual of procedures. According to the LEAPS manual of proced ures, SAMs should be programmed to take 60 seconds recording intervals. Nevertheless, only 85 SAM files were in 60 seconds -intervals, whereas 127 and 18 data files were in ten and six seconds intervals, respectively. Six 10 seconds intervals can be analyze d as one 60 seconds -interval. Similarly, ten 6 seconds -intervals can be analyzed as one 60seconds interval. However, 60 and 10 seconds intervals cannot be analyzed in shorter intervals (e.g., a 60 seconds -interval cannot be treated as a 10 or 6 seconds in terval). Therefore, in order to be able to use all the SAM data files, SAM data will be analyzed in 60 seconds intervals.

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192 Overall, the six hours windows method yielded the highest inferred compliance rate (moderate compliance in both the first and second days), whereas the three windows and activity methods yielded the lowest inferred compliance rates (three hours windows methodlow compliance on both days, activity method -poor compliance on both days). In addition, in the six windows method, the highest compliance rate with a specific window was observed between 12:00PM to 6:00PM whereas the lowest compliance between 12:00AM to 6:00AM. Finally, statistically significant differences between the first and the second days were found in the six windows method between 12:00AM to 6:00AM. Despite the observed statistically significant differences, compliance in these windows fall in the same inferred compliance category (low compliance). Table B 1 reports each compliance method rates of inferred compliance with t he accelerometer monitoring protocol. B. 3 Discussion Three methods to establish inferred compliance with accelerometer monitoring protocol post -stroke were examined. Overall, it appears that using different compliance methods affected the inferred complian ce rate with the monitoring protocol. From the six hours windows method it was concluded that the sample had moderate inferred compliance rates with the accelerometer monitoring protocol. The three hours windows and activity methods yielded low and poor in ferred compliance rates, respectively. The six hours windows method yielded the highest compliance rate because in order to conclude that a person did not wear the accelerometer, a long period of interruption in wearing time (accelerometer counts of zero) is required (six hours). No evidence in the literature supports an age -specific cut point for interruption in wearing time.251 However, stroke patients are typically older individuals with secondary motor deficits who are likely to remain still for long periods. T herefore, using a compliance definition which permits long periods of interruption in wearing time may be appropriate in this population. In addition,

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193 participants are not likely to remove accelerometers multiple times per day.251 Thus, it is reasonable to assume that an individual with steps activity in each six hours wore the accelerometer throughout these windows or removed it for legetimic reasons (e.g., showering, swimming, or sleeping). Therefore, the three hours windows and activity methods are likely to und erestimate the participants true compliance rate with the accelerometer monitoring protocol. Finally, the data confirms that most individuals were not active between 12:00AM to 6:00AM. It is likely that most participants were sleeping or engaging in seden tary activities during this period of time. Therefore, it is appropriate to omit this period of time from the compliance analysis. Table B 1. Inferred compliance rate with the accelerometer monitoring protocol Step activity outcomes All data Day 1 (sta ndard deviation) Day 2 (standard deviation) Day 1 Day 2 (standard deviation) Six hours windows method Percent of sample with activity between 12:00AM 6:00AM 0.1 (0.300) 0.165 (0.372) 0.065 (0.407)* Percent of sample with activity between 6:00AM 12:00P M 0.839 (0.368) 0.813 (0.390) 0.026 (0.457) Percent of sample with activity between 12:00PM 6:00PM 0.895 (0.306) 0.856 (0.351) 0.039 (0.431) Percent of sample with activity between 6:00 PM12:00AM 0.795 (0.404) 0.752 (0.432) 0.043 (0.483) Overall per cent compliance (6:00AM 12:00AM) 0.713 Moderate compliance 0.657 Moderate compliance 0.056 Three hours windows method Overall percent compliance 0.45 (5.1574) Low compliance 0.437 (6.0286) Low compliance 2.7 (6.2117) Activity method Overall percen t compliance 0.092 (0.071) Poor compliance 0.094 (0.077) Poor compliance 0.002 (0.001) *Statistically significant differences (P < 0.05) between the first and the second days.

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194 APPENDIX C PILOT WORK: COMPUTIN G ACCELEROMETER OUTC OME MEASURES The purpo se of this pilot was to examine processes for accelerometer data reduction, including selection of outcomes, correcting outcome variables for days with no activity, analyzing the first and second measurement days separately or both days together, and defin ing low, moderate, and vigorous physical activity. C. 1 Methods C. 1.1 Design Data in this cross -sectional study was derived from a parent study, the LEAPS.311 C. 1.2 Testing Procedures and Measurements C. 1.2.1 Ambulatory activity Habitual ambulatory activity was assessed with the SAM. SAM is a highly accurate microprocessor -based instrument that directly and continuously counts and records individuals number of steps taken while ambulating in their natural environment for extended periods.14, 18 SAMs were programmed with an initial generic calibration.19 Participants performed a one minute walk test. SAM counts of strides were compared with visual counts. Adjustments were made to the calibration setting and the timed walks were repeated to ensur e greater than 90% accuracy against visual counts. To achieve optimal sensitivity for step detection, the SAM were programmed and applied above the malleolus of the less involved limb with two adjustable elastic straps. Participants wore the SAM for a per iod of two consecutive days, throughout the day, removing the SAM only to bathe, shower, swim, or sleep. A demonstration and written instructions regarding appropriate use of the device were provided to subjects. On completion of a 48 hour monitoring peri od, SAM data were downloaded using an infrared docking port.

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195 SAMs raw data from the manufacturer software were imported into Microsoft Excel. Data were expressed as stride counts every 60 seconds during the 48 hours. C. 1.2.2 Outcome measures Ambulation volume outcomes : 1 Number of steps taken over 24 hours. Number of steps per day is defined as twice the total stride count in 24 hours.285 2 Total activity level. Total activity level is defined as the tally of the number of one-minute intervals with stride counts of > 1. Will be presented as the percentage of the total time in a 24hour period where steps are recorded.25 Ambulation i ntensity o utcomes : Percentage of the day spent in low, medium, and high absolute activity intensity. Absolute step activity intensity will be stratified as low (less than 10 steps per mi nute), medium (10 20 steps per minute) and high (more than 20 steps per minute). Percentage of time spent at each level will be calculated. Minutes in which activity was not recorded will be excluded from the analysis. Ambulation pattern outcomes : 1 Number of activity bouts. Number of activity bouts is defined as the number of 1 -min intervals for which the subject switched from inactivity (stride count=0) to activity (stride count > 0).14 2 Number of steps in an activity bout. Number of steps in an activity bout is defined as the mean number of steps in an activity bout. 3 One minute variability of activity. One minute variability of activity is defined as the CV of 1 -min of stride count values, excluding those 1 min intervals with zero steps .14 CV is a measure of variability calculated as the SD of the measures expressed as a percentage of the mean (CV=SD/mean X 100). One of the CV advantages is that it accounts for differences in the magnitude of the mean (a measure of relat ive variation).288 4 Activity randomness. Activity randomness is defined as the amount of randomness of minute to -minute activity fluctuations in each day of recorded ambulatory activity. Randomness of activity will be quantified by analyzing 1 -minute stride counts using approximate entropy.289 Approximate entropy will be used to determine the probability that sequences of consecutive 1 -minute stride counts repeated throughout the longer tem poral sequence of 1,440 daily 1-minute intervals. Approximate entropy generates a unitless number between 0 and 2. Zero values correspond to a time series in which short

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196 sequences of data points are perfectly repeatable. Values of 2 correspond to time seri es for which any repeating sequences of points occur by chance alone.290 C. 1.3 Data Analysis All variables were examined by descript ive statistics (mean and SD). Statistical procedures were conducted before and after excluding days in which no activity was recorded (zero 24 hours strides count). Within group differences in the first and second measurement days were analyzed using the s tudents dependent t test. All analyses were conducted in a 2 tailed fashion with a 0.05 alpha level. Statistical analyses were conducted using SAS version 9.1. C. 2 Results Two -hundred and thirty subacute stroke patients (two -month post -stroke) with mod erate to severe gait impairments (gait speed < 0.8 m/s) participated in this study. In the first and second measurement days 18 and 19 participants had no step activity, respectively. However, only five participants had no step activity in both the first a nd the second days. Figure C 1 illustrates 24 hours step activity collected from two people who took considerably different number of steps per day (575 vs.1684 steps per day). Visual inspection of the two activity records provides a gross indication that the person that accumulated fewer steps was also active for fewer minutes, had lower peak activity index, spent greater percentage of the day in absolute low activity intensity, and apparently engaged in fewer bouts of activity. Figure C 2 illustrates 24 hours step activity collected from two subjects with nearly the same steps count (1067 vs.1084 steps per day). Visual inspection of the two activity records demonstrates that the two subjects accumulated their steps in very different manners. Compares to the person who accumulated 1067 steps, the person with 1084 steps per day spent greater percentage of the day in lower activity intensities, was active for more minutes, had lower peak activity index, and engaged in greater number of activity bouts.

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197 Tabl e C 1 summarizes the results of step activity volume, intensity, and pattern outcomes. Significant differences between the first and the second measurement days were found for percentage of time spent in absolute low and high activity intensity, and number of steps in an activity bout. In addition, excluding non active days from the analysis resulted in higher values in all variables, but for the percentage of time spent in absolute low intensity. Finally, between 54 60% of total ambulatory activity fell into absolute low intensity category. The absolute medium activity made up 15 18%, whereas absolute high intensity activity 23 to 29%. C. 3 Discussion Number of steps taken over 24 hours is the most commonly reported SAM index. However, from a visual inspect ion of the data it appears that this outcome can be an incomplete descriptor of function because two subjects with nearly the same step count can accumulate their steps in very different manners in terms of activity intensity and pattern (see Figure C 2). Other investigators also reported that number of steps taken over 24 hours is not a sufficient descriptor of activity. For example, Cavanaugh et al.14 reported that healthy older adults accumulated a similar number of steps and minutes of activity as did healthy younger adults (healthy older adults: 9,981 steps; healthy younger adults 11,074 steps), but did it in fewer activity bouts (healthy older adults: 67; healthy younger adults: 77) and variability (healthy older adults: CV=92.4; healt hy younger adults: CV=100). These findings demonstrate that measures of activity intensity and pattern are especially valuable as they illustrate the manner in which activity unfolds over the course of the day. All statistical procedures were conducted be fore and after excluding non active days as non active days may indicate that the monitor was not worn throughout the day. As expected, analysis after excluding nonactive days yielded higher SAM values. However, for most variables, the differences between the mean values before and after excluding non active days

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198 are negligible and probably not clinically significant. For instance, day one number of steps in an activity bout before excluding non active days is 25 steps, whereas after excluding nonactive d ays is 28 steps (three steps difference). LEAPS participants consisted of individuals with moderate to severe gait impairment. Participants with severe gait impairments might be non functional ambulators and wheelchair users. These individuals might have non active days not because they did not wear the accelerometer but simply because they are predominantly wheelchair users for mobility. Therefore, excluding these individuals from the analysis might falsely increase SAM values. However, individuals with moderate gait impairment which are functional ambulators, nonactive days are likely to be accountable for not wearing the monitor or monitor malfunctioning rather than to not ambulating throughout the day. Consequently, including their nonactive days in th e analysis might falsely lower SAM values. Thus, in an effort to ensure that the SAM data included in the analysis represents the construct to which they refer (home and community ambulation), it may be beneficial to establish the FAC of individuals with n onactive days. This will enable to discover inconsistencies between functional ambulation level and SAM step activity. The FAC scale is a common gait assessment distinguishing six levels of walking ability, on the basis of level of physical support needed by persons to ambulate safely (see table C 2).173 Mehrholz et al.173 suggest that a dichotomized FAC (FAC > 4 and FAC < 4) may be useful in predicting level of community ambulation with high sensitivity and specificity. Therefore, it is likely th at individuals with FAC > 4 with zero step counts did not wear the monitor rather than not walked throughput the day. Consequently, it may be appropriate to remove these individuals files from future analyses. In contrast, individuals with FAC < 4 with a 24 hours zero steps count might not have step activity not because they did not wear the monitor

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199 but simply because they did not walk. Therefore, it will be appropriate to include theses individuals files in future analyses. Results of within -group comparisons established that for the most part, SAM values in the first and second measurement days were not significantly different. Significant differences between the first and the second measurement days were observed for one variable (absolute high intens ity) when all data was included in the analysis and for three variables (absolute low intensity, absolute high intensity, and number of steps in an activity bout) after excluding nonactive days. However, the magnitude of mean difference between the first and second days is probably not clinically meaningful. For instance, when all data was included in the analysis, the mean difference between the first and the second measurement days in percentage of time spent in absolute high intensity was merely 0.023 percent. Statistical significant results for small mean differences may be attributable to the large sample size.341 Therefore, for future data analysis purposes it will be appropriate t o aggregate SAM data in the first and second days measurement days. Finally, higher intensity activities place greater physiological load on the human systems. Accordingly, high intensity activities are hard to maintain for long period of times.135 Therefore, people are more likely to spend the majority of the day in low or m oderate intensity activities rather than in intense activities. However, according to the absolute intensity definitions of this pilot study, participants spend greater percentage of the day in high intensity activities rather than in moderate activity int ensity (2329% vs. 1517%, respectively). Therefore, it will be appropriate to modify our definition of absolute activity intensity. Michael et al.34 reported that in a convenience sample of community -dwelling volunteers with mild to moderate (NIHSS mean=3.57 points) chronic (mean time post -stroke=10 month) hemiparetic stroke, participants

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200 spend 45% of their activity at low (< 16 steps per minute), 46% at medium (16 to 30 steps per minute), and less than 3 minutes a day at high intensities (>3 0 steps per minute). Because Michaels et al.34 study results adhere to basic science, this studys absolute intensity activity level definition will be used in future post -stroke SAM studies. In summary, the pilot study findings show that m easures of activity intensity and pattern are especially valuable as they have the potential to differentiate between individuals who accumulated similar number of steps per day. Moreover, non active days of individuals with FAC < 4 will be included in the analysis, whereas non active days of persons with FAC > 4 will be excluded. In addition, for future data analysis purposes it will be appropriate to aggregate the mean values of the first and second measurement days. Finally, Michael and associates34 definition of absolute activity intensity will be used in future analysis of the LEAPS SAM data.

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201 Table C 1. Step activity volume, intensity, and pattern outcomes Step activity monitor outcomes All After excluding non active days Day 1 (standa rd deviation) Day 2 (standard deviation) Day 1 day 2 (standard deviation) Day 1 (standard deviation) Day 2 (standard deviation) Day 1 Day 2 Steps activity volume Number of steps taken over 24 hours 2281.844 (2515.783) 2258.965 (2668.726) 22.8783 (1953. 335) 2547.505 (2527.940) 2663.436 (2706.726) 115.931 Total activity level (min) 133.065 (102.267) 135.569 (111.102) 2.504 (94.326) 148.519 (96.865) 159.712 (103.536) 11.193 Steps activity intensity Absolute low intensity (%) 0.588 (0.249) 0.603 (0. 255) 0.015 (0.2106) 0.542 (0.220) 0.541 (0.212) 0.001** Absolute medium intensity (%) 0.152 (0.092) 0.160 (0.114) 0.008 (0.112) 0.167 (0.078) 0.179 (0.080) 0.012 Absolute high intensity (%) 0.259 (0.195) 0.236 (0.188) 0.023 (0.150)* 0.290 (0.183) 0.278 (0.1737) 0.012** Steps activity pattern Number of activity bouts 99.165 (98.037) 102.165 (103.464) 3.000 (74.260) 110.674 (97.266) 120.323 (102.263) 9.648 Number of steps in an activity bout 25.898 (27.559) 24.094 (28.645) 1.804 (23.965) 2 8.794 (27.698) 28.084 (29.350) 0.709** 1 min variability (coefficient of variation) 84.298 (33.048) 80.458 (38.139) 3.840 (41.919) 93.057 (20.149) 94.005 (21.159) 0.947 Activity randomness 0.2135 (0.1389) 0.2139 (0.1485) 0.0004 (0.1383) 0.2381 (0.1 254) 0.2515 (0.1292) 0.0134 *Statistically significant differences (P < 0.05) relative to day one when all data was included in the analysis; **Statistically significant differences (P < 0.05) relative to day one after excluding nonactive days from t he analysis.

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202 Table C 2. Functional Ambulation Classification scale Functional Ambulation Classification level Description 0 (nonfunctional ambulator) Indicates a patient who is not able to walk at all or needs the help of 2 therapists. 1 ( ambulator, dependent on physical Assistance level II) Indicates a patient who requires continuous manual contact to support body weight as well as to maintain balance or to assist coordination. 2 ( ambulator, dependent on physical Assistance level I) Ind icates a patient who requires intermittent or continuous light touch to assist balance or coordination. 3 (ambulatory, dependent on supervision) Indicates a patient who can ambulate on level surface without manual contact of another person but requires st andby guarding of one person either for safety or for verbal cueing. 4 (ambulator, independent, level surface Only) Indicates a patient who can ambulate independently on level surface but requires supervision to negotiate (e.g., stairs, inclines, no nlevel surfaces). 5 (ambulatory, independent) Indicates a patient who can walk everywhere independently, including stairs. Note: adapted from Holden MK, Gill KM, Magliozzi MR, Nathan J, Piehl Baker L. Clinical gait assessment in the neurologically impair ed. Reliability and meaningfulness. Phys Ther 1984;64:3540.174 0 5 10 15 20 25 30 35 12:01:00 AM 1:51:00 AM 3:41:00 AM 5:31:00 AM 7:21:00 AM 9:11:00 AM 11:01:00 AM 12:51:00 PM 2:41:00 PM 4:31:00 PM 6:21:00 PM 8:11:00 PM 10:01:00 PM 11:51:00 PM Time Number of steps in 60 sec 575 steps 1684 steps Figure C 1. Twenty -four hours ambulatory activity data of two subjects who took different number of steps.

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203 0 1 2 3 4 5 6 7 8 9 10 12:00:10 AM 1:09:50 AM 2:19:30 AM 3:29:10 AM 4:38:50 AM 5:48:30 AM 6:58:10 AM 8:07:50 AM 9:17:30 AM 10:27:10 AM 11:36:50 AM 12:46:30 PM 1:56:10 PM 3:05:50 PM 4:15:30 PM 5:25:10 PM 6:34:50 PM 7:44:30 PM 8:54:10 PM 10:03:50 PM 11:13:30 PM Time Number of steps in 10 seconds 1067 steps 1084 steps Figure C 2. Twenty -four hours ambulatory activity data of two subjects who took similar number of steps.

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204 LIST OF REFERENCES 1. Wade DT, Wood VA, Heller A, Maggs J, Langton Hewer R. Walking after stroke. Measurement and recovery over the first 3 months. Scand J Rehabil Med. 1987;19:2530. 2. Wade DT, Hewer RL. Functional abilities after stroke : Measurement, natural history and prognosis. J Neurol Neurosurg Psychiatry 1987;50:177182. 3. Gresham GE, Duncan PW, Stason WB, et al. Post -stroke rehabilitation: Assessment, referral, and patient management. Clinical practice guideline. Quick referenc e guide for clinician, No. 16. U.S. Department of health and human services, public health service, agency for health care policy and research. Rockville, MD: AHCPR Pub. No. 95 0663.; 1995. 4. Granger CV, Hamilton BB, Gresham GE. The stroke rehabilitation outcome study-part I: General description. Arch Phys Med Rehabil 1988;69:506509. 5. Pound P, Gompertz P, Ebrahim S. A patient -centered study of the consequences of stroke. Clin Rehabil. 1998;12:338347. 6. Haeuber E, Shaughnessy M, Forrester LW, Cole man KL, Macko RF. Accelerometer monitoring of home and community-based ambulatory activity after stroke. Arch Phys Med Rehabil 2004;85:19972001. 7. Duncan PW. Measuring recovery of function after stroke: Clinical and measurement issues in selecting str oke outcome measures in clinical trials. In: Goldstein LB, ed. Restorative neurology: Advances in pharmacotherapy for recovery after stroke New York, NY: Futura Publishing; 1998:225240. 8. World Health Organization. International Classification of Funct ioning, Disability and Health: ICF Geneva: WHO Library Cataloguing in -Publication Data; 2001. 9. Perry J, Garrett M, Gronley JK, Mulroy SJ. Classification of walking handicap in the stroke population. Stroke 1995;26:982989. 10. Bassett DR, Jr., Cureto n AL, Ainsworth BE. Measurement of daily walking distance questionnaire versus pedometer. Med Sci Sports Exerc 2000;32:10181023. 11. Nelson ME, Layne JE, Bernstein MJ, Nuernberger A, Castaneda C, Kaliton D, Hausdorff J, Judge JO, Buchner DM, Roubenoff R Fiatarone Singh MA. The effects of multidimensional home based exercise on functional performance in elderly people. J Gerontol A Biol Sci Med Sci. 2004;59:154160. 12. Shaughnessy M, Michael KM, Sorkin JD, Macko RF. Steps after stroke: Capturing ambula tory recovery. Stroke 2005;36:13051307.

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205 13. Duncan PW, Wallace D, Lai SM, Johnson D, Embretson S, Laster LJ. The Stroke Impact Scale version 2.0. Evaluation of reliability, validity, and sensitivity to change. Stroke 1999;30:21312140. 14. Cavanaugh JT Coleman KL, Gaines JM, Laing L, Morey MC. Using step activity monitoring to characterize ambulatory activity in community -dwelling older adults. J Am Geriatr Soc 2007;55:120124. 15. Ward DS, Evenson KR, Vaughn A, Rodgers AB, Troiano RP. Accelerometer use in physical activity: Best practices and research recommendations. Med Sci Sports Exerc 2005;37:S582588. 16. Welk GJ. Physical activity assessments for health -related research Champaign, IL: Human Kinetics; 2002. 17. Schutz Y, Weinsier S, Terrier P, Durrer D. A new accelerometric method to assess the daily walking practice. Int J Obes Relat Metab Disord 2002;26:111118. 18. Boone DA, Coleman KL. Use of a step activity monitor in determining outcomes. JPO 2006;18:8692. 19. Macko RF, Haeuber E, Shaughnessy M, Coleman KL, Boone DA, Smith GV, Silver KH. Microprocessor -based ambulatory activity monitoring in stroke patients. Med Sci Sports Exerc 2002;34:394399. 20. Bassett DR, Jr., Ainsworth BE, Leggett SR, Mathien CA, Main JA, Hunter DC, Duncan GE. Accuracy of five electronic pedometers for measuring distance walked. Med Sci Sports Exerc 1996;28:10711077. 21. Cyarto EV, Myers AM, Tudor -Locke C. Pedometer accuracy in nursing home and community -dwelling older adults. Med Sci Sports Exerc 2004;36:205209. 22. Motl RW, McAuley E, Snook EM, Scott JA. Accuracy of two electronic pedometers for measuring steps taken under controlled conditions among ambulatory individuals with multiple sclerosis. Mult Scler 2005;11:343345. 23. Michael KM, Allen JK Macko RF. Reduced ambulatory activity after stroke: The role of balance, gait, and cardiovascular fitness. Arch Phys Med Rehabil 2005;86:15521556. 24. LaPorte RE, Montoye HJ, Caspersen CJ. Assessment of physical activity in epidemiologic research: Pro blems and prospects. Public Health Rep 1985;100:131146. 25. Busse ME, Pearson OR, Van Deursen R, Wiles CM. Quantified measurement of activity provides insight into motor function and recovery in neurological disease. J Neurol Neurosurg Psychiatry 2004; 75:884888.

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206 26. Bassett DR, Jr., Ainsworth BE, Swartz AM, Strath SJ, O'Brien WL, King GA. Validity of four motion sensors in measuring moderate intensity physical activity. Med Sci Sports Exerc 2000;32:S471480. 27. Freedson PS, Miller K. Objective monit oring of physical activity using motion sensors and heart rate. Res Q Exerc Sport 2000;71:S2129. 28. Lord SE, Rochester L. Measurement of community ambulation after stroke: Current status and future developments. Stroke 2005;36:14571461. 29. Goldie P A, Matyas TA, Evans OM. Deficit and change in gait velocity during rehabilitation after stroke. Arch Phys Med Rehabil 1996;77:10741082. 30. Salbach NM, Mayo NE, Higgins J, Ahmed S, Finch LE, Richards CL. Responsiveness and predictability of gait speed a nd other disability measures in acute stroke. Arch Phys Med Rehabil 2001;82:12041212. 31. Dobkin BH. Short distance walking speed and timed walking distance: Redundant measures for clinical trials? Neurology 2006;66:584586. 32. Schmid A, Duncan PW, S tudenski S, Lai SM, Richards L, Perera S, Wu SS. Improvements in speed-based gait classifications are meaningful. Stroke 2007;38:20962100. 33. Bowden MG, Balasubramanian CK, Behrman AL, Kautz SA. Validation of a speed based classification system using quantitative measures of walking performance poststroke. Neurorehabilitation and Neural repair In press 34. Michael K, Macko RF. Ambulatory activity intensity profiles, fitness, and fatigue in chronic stroke. Top Stroke Rehabil 2007;14:512. 35. Stroke -1989. Recommendations on stroke prevention, diagnosis, and therapy. Report of the WHO task force on stroke and other cerebrovascular disorders. Stroke 1989;20:14071431. 36. Brandstater ME. Stroke rehabilitation. In: Gans BM, deLisa JA, eds. Rehabilitation medicine: Principal and practice Lippincott -raven.; 1998. 37. Steele BG, Belza B, Hunziker J, Holt L, Legro M, Coppersmith J, Buchner D, Lakshminaryan S. Monitoring daily activity during pulmonary rehabilitation using a triaxial accelerometer. J Car diopulm Rehabil 2003;23:139142. 38. Tudor Locke CE, Myers AM. Methodological considerations for researchers and practitioners using pedometers to measure physical (ambulatory) activity. Res Q Exerc Sport 2001;72:112.

PAGE 207

207 39. The Internet Stroke C enter Sit e [Internet]. About stroke: Types of stroke; [updated 2008 August 12; cited 2008 August 12]. Available from: http://www.s t rokecenter.org/pat/stroke_types.h tm 40. Kolominsky Rabas PL, Weber M, Gefeller O, Neundoerfer B, Heuschmann PU. Epidemiology of ischemic stroke subtypes according to TOAST criteria: Incidence, recurrence, and longterm survival in ischemic stroke subtypes: A populationbased study. Stroke 2001;32:27352740. 41. Ad ams HP, Jr., Bendixen BH, Kappelle LJ, Biller J, Love BB, Gordon DL, Marsh EE, 3rd. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of org 10172 in acute stroke treatment. Stroke 1993;2 4:3541. 42. Umphred DA. Neurological rehabilitation. St. Louis, Mo: Mosby; 1995. 43. Fullerton KJ, Mackenzie G, Stout RW. Prognostic indices in stroke. Q J Med 1988;66:147162. 44. Heinemann AW, Roth EJ, Cichowski K, Betts HB. Multivariate analysis of improvement and outcome following stroke rehabilitation. Arch Neurol 1987;44:11671172. 45. Kinsella G, Ford B. Acute recovery from patterns in stroke patients: Neuropsychological factors. Med J Aust 1980;2:663666. 46. Kinsella G, Ford B. Hemi -inatte ntion and the recovery patterns of stroke patients. Int Rehabil Med 1985;7:102106. 47. Mills VM, DiGenio M. Functional differences in patients with left or right cerebrovascular accidents. Phys Ther 1983;63:481488. 48. Gowland C. Recovery of motor fu nction following stroke: Profile and predictors. Physiother Can. 1982;34:7784. 49. Denes G, Semenza C, Stoppa E, Lis A. Unilateral spatial neglect and recovery from hemiplegia: A follow up study. Brain 1982;105 (Pt 3):543552. 50. Friedman PJ. Gait recovery after hemiplegic stroke. Int Disabil Stud. 1990;12:119122. 51. Andrews AW, Bohannon RW. Distribution of muscle strength impairments following stroke. Clin Rehabil. 2000;14:7987. 52. Canning CG, Ada L, O'Dwyer N. Slowness to develop force contribu tes to weakness after stroke. Arch Phys Med Rehabil 1999;80:6670.

PAGE 208

208 53. Wade DT, Wood VA, Hewer RL. Recovery after stroke -the first 3 months. J Neurol Neurosurg Psychiatry 1985;48:713. 54. Sandin KJ, Smith BS. The measure of balance in sitting in strok e rehabilitation prognosis. Stroke 1990;21:8286. 55. Nakamura R, Hosokawa T, Tsuji I. Relationship of muscle strength for knee extension to walking capacity in patients with spastic hemiparesis. Tohoku J Exp Med. 1985;145:335340. 56. Pollock A, Baer G Pomeroy V, Langhorne P. Physiotherapy treatment approaches for the recovery of postural control and lower limb function following stroke. Cochrane Database Syst Rev .CD001920. 2003. 57. Bohannon RW. Gait performance of hemiparetic stroke patients: Selec ted variables. Arch Phys Med Rehabil 1987;68:777781. 58. Hocherman S, Dickstein R, Pillar T. Platform training and postural stability in hemiplegia. Arch Phys Med Rehabil 1984;65:588592. 59. Gresham GE, Fitzpatrick TE, Wolf PA, McNamara PM, Kannel WB Dawber TR. Residual disability in survivors of stroke -the Framingham study. N Engl J Med 1975;293:954956. 60. Hill K, Ellis P, Bernhardt J, Maggs P, Hull S. Balance and mobility outcomes for stroke patients: A comprehensive audit. Aust J Physiother 1997;43:173180. 61. Lord SE, McPherson K, McNaughton HK, Rochester L, Weatherall M. Community ambulation after stroke: How important and obtainable is it and what measures appear predictive? Arch Phys Med Rehabil 2004;85:234239. 62. Pang MY, Eng JJ, D awson AS. Relationship between ambulatory capacity and cardiorespiratory fitness in chronic stroke: Influence of stroke -specific impairments. Chest 2005;127:495501. 63. Ryan AS, Dobrovolny CL, Smith GV, Silver KH, Macko RF. Hemiparetic muscle atrophy and increased intramuscular fat in stroke patients. Arch Phys Med Rehabil 2002;83:17031707. 64. Ivey FM, Macko RF, Ryan AS, Hafer -Macko CE. Cardiovascular health and fitness after stroke. Top Stroke Rehabil 2005;12:116. 65. Loewen SC, Anderson BA. Pred ictors of stroke outcome using objective measurement scales. Stroke 1990;21:7881.

PAGE 209

209 66. Kwakkel G, Wagenaar RC, Kollen BJ, Lankhorst GJ. Predicting disability in stroke -a critical review of the literature. Age Ageing 1996;25:479489. 67. Duncan P, Stude nski S, Richards L, Gollub S, Lai SM, Reker D, Perera S, Yates J, Koch V, Rigler S, Johnson D. Randomized clinical trial of therapeutic exercise in subacute stroke. Stroke 2003;34:21732180. 68. Lofgren B, Nyberg L, Osterlind PO, Gustafson Y. In-patient rehabilitation after stroke: Outcome and factors associated with improvement. Disabil Rehabil. 1998;20:5561. 69. Tyson SF, Hanley M, Chillala J, Selley A, Tallis RC. Balance disability after stroke. Phys Ther 2006;86:3038. 70. Corriveau H, Hebert R, Raiche M, Prince F. Evaluation of postural stability in the elderly with stroke. Arch Phys Med Rehabil 2004;85:10951101. 71. de Haart M, Geurts AC, Huidekoper SC, Fasotti L, van Limbeek J. Recovery of standing balance in postacute stroke patients: A reha bilitation cohort study. Arch Phys Med Rehabil 2004;85:886895. 72. Lamontagne A, Paquet N, Fung J. Postural adjustments to voluntary head motions during standing are modified following stroke. Clin Biomech (Bristol, Avon) 2003;18:832842. 73. Ikai T, Kamikubo T, Takehara I, Nishi M, Miyano S. Dynamic postural control in patients with hemiparesis. Am J Phys Med Rehabil 2003;82:463469. 74. Mizrahi J, Solzi P, Ring H, Nisell R. Postural stability in stroke patients: Vectorial expression of asymmetry, s way activity and relative sequence of reactive forces. Med Biol Eng Comput 1989;27:181190. 75. Eng JJ, Chu KS. Reliability and comparison of weight -bearing ability during standing tasks for individuals with chronic stroke. Arch Phys Med Rehabil 2002;83:11381144. 76. Goldie PA, Matyas TA, Evans OM, Galea M, Bach TM. Maximum voluntary weight bearing by the affected and unaffected legs in standing following stroke. Clin Biomech (Bristol, Avon) 1996;11:333342. 77. Turnbull GI, Charteris J, Wall JC. Def iciencies in standing weight shifts by ambulant hemiplegic subjects. Arch Phys Med Rehabil 1996;77:356362. 78. Sackley CM. Falls, sway, and symmetry of weight -bearing after stroke. Int Disabil Stud 1991;13:14. 79. Di Fabio RP, Badke MB. Extraneous mo vement associated with hemiplegic postural sway during dynamic goal directed weight redistribution. Arch Phys Med Rehabil 1990;71:365371.

PAGE 210

210 80. Rogers MW, Hedman LD, Pai YC. Kinetic analysis of dynamic transitions in stance support accompanying voluntary leg flexion movements in hemiparetic adults. Arch Phys Med Rehabil 1993;74:1925. 81. Pai YC, Rogers MW, Hedman LD, Hanke TA. Alterations in weight transfer capabilities in adults with hemiparesis. Phys Ther 1994;74:647657. 82. Di Fabio RP, Badke MB. Relationship of sensory organization to balance function in patients with hemiplegia. Phys Ther 1990;70:542548. 83. Au Yeung SS, Ng JT, Lo SK. Does balance or motor impairment of limbs discriminate the ambulatory status of stroke survivors? Am J Phys Me d Rehabil 2003;82:279283. 84. Garland SJ, Willems DA, Ivanova TD, Miller KJ. Recovery of standing balance and functional mobility after stroke. Arch Phys Med Rehabil 2003;84:17531759. 85. Niam S, Cheung W, Sullivan PE, Kent S, Gu X. Balance and physi cal impairments after stroke. Arch Phys Med Rehabil 1999;80:12271233. 86. Keenan MA, Perry J, Jordan C. Factors affecting balance and ambulation following stroke. Clin Orthop Relat Res 1984:165171. 87. DeDeyne PG, Hafer -Macko CE, Ivey FM, Ryan A, Ma cko RF. Muscle molecular phenotype after stroke is associated with gait deficit severity. Muscle Nerve 2004;30:209215. 88. Kernan WN, Inzucchi SE, Viscoli CM, et al. Impaired insulin sensitivity among nondiabetic patients with a recent TIA or ischemic s troke. Neurology 2003;60:14471451. 89. Ryan A, Dobrovolny C, Silver KH, Smith GG, Macko RF. Cardiovascular fitness after stroke: Role of muscle mass and gait deficit severity. J Stroke Cerebrovasc Dis 2000;9:185191. 90. Daugaard JR, Richter EA. Relat ionship between muscle fiber composition, glucose transporter protein 4 and exercise training: Possible consequences in noninsulin dependent diabetes mellitus. Acta Physiol Scand. 2001;171:267276. 91. Bruce RA, Kusumi F, Hosmer D. Maximal oxygen intake and nomographic assessment of functional aerobic impairment in cardiovascular disease. Am Heart J 1973;85:546562. 92. Patla A, Shumway Cook. Dimensions of mobility: Defining the complexity and difficulty associated with community mobility. J Aging Phys Act 1999;7:719.

PAGE 211

211 93. Aftab PE. Mobility in complex environments: Implications for clinical assessment and rehabilitation. 2001;Neurology Report: September. 94. Shumway -Cook A, Patla AE, Stewart A, Ferrucci L, Ciol MA, Guralnik JM. Environmental demands associated with community mobility in older adults with and without mobility disabilities. Phys Ther 2002;82:670681. 95. Lord SE, Rochester L. Walking in the real world: Concepts related to functional gait. New Zealand Journal of Physiotherapy 2007;35: 126 130. 96. Armstrong DM. The supraspinal control of mammalian locomotion. J Physiol 1988;405:137. 97. Morris ME, Iansek R, Matyas TA, Summers JJ. Stride length regulation in Parkinsons disease. Normalization strategies and underlying mechanisms. Bra in 1996;119 (Pt 2):551568. 98. Winter DA. Human balance and posture control during standing and walking. Gait and posture 1995;3:193214. 99. van de Port IG, Kwakkel G, Lindeman E. Community ambulation in patients with chronic stroke: How is it relate d to gait speed? J Rehabil Med 2008;40:2327. 100. Mayo NE, WoodDauphinee S, Ahmed S, Gordon C, Higgins J, McEwen S, Salbach N. Disablement following stroke. Disabil Rehabil. 1999;21:258268. 101. Pang MY, Eng JJ, Miller WC. Determinants of satisfactio n with community reintegration in older adults with chronic stroke: Role of balance self -efficacy. Phys Ther 2007;87:282291. 102. Patterson SL, Forrester LW, Rodgers MM, Ryan AS, Ivey FM, Sorkin JD, Macko RF. Determinants of walking function after strok e: Differences by deficit severity. Arch Phys Med Rehabil 2007;88:115119. 103. Lernier F, M., Vargas S, Brown M, Krusell L, Schoneberger W. Functional community ambulation: What are your criteria? Clin Manag Phys Ther 1986;6:1215. 104. Cohen JJ, Svee n JD, Walker JM, Brummel -Smith K. Establishing criteria for community ambulation. Top Ger Rehabil 1983;1:7177. 105. Taylor D, Stretton CM, Mudge S, Garrett N. Does clinic -measured gait speed differ from gait speed measured in the community in people wit h stroke? Clin Rehabil. 2006;20:438444.

PAGE 212

212 106. Logan PA, Gladman JR, Avery A, Walker MF, Dyas J, Groom L. Randomized controlled trial of an occupational therapy intervention to increase outdoor mobility after stroke. Bmj 2004;329:13721375. 107. Tinetti ME, Powell L. Fear of falling and low self -efficacy: A case of dependence in elderly persons. J Gerontol 1993;48 Spec:35 38. 108. Bassett DR, Strath SJ. Use of pedometers to assess physical activity. In: Welk GJ, ed. Physical activity assessments for hea lth -related research Champaign, IL: Human Kinetics; 2002:163177. 109. Franklin PD, McLaughlin J, Boisvert CB, Li W, Ayers DC. Pilot study of methods to document quantity and variation of independent patient exercise and activity after total knee arthrop lasty. J Arthroplasty 2006;21:157163. 110. Woollacott M, Shumway Cook A. Attention and the control of posture and gait: A review of an emerging area of research. Gait Posture 2002;16:114. 111. Tamminga CA. Images in neuroscience. Cognition: Executive function. Am J Psychiatry 2000;157:326. 112. Barkley RA. Behavioral inhibition, sustained attention, and executive functions: Constructing a unifying theory of ADHD. Psychol Bull. 1997;121:6594. 113. Barkley RA. Linkages between attention and executiv e functions. In: Lyon GR, Krasnegor NA, eds. Attention memory, and executive function Baltimore, MD: Paul Brookes; 1996:307325. 114. Borkowski JG, Burke JE. Theories, models, and measurements of executive functioning: An information processing perspecti ve. In: Lyon GR, Krasnegoor NA, eds. Attention, memory, and executive function Baltimore, MD: Paul Brookes; 1996:235261. 115. Denckla MB. Understanding the role of executive function in language, academics, and daily life. American international college conference Springfield, MA; 1998. 116. Bandura A. Social foundations of thought and action: A social cognitive theory. N.J: Prentice Hall; 1986. 117. Kelly Hayes M, Beiser A, Kase CS, Scaramucci A, D'Agostino RB, Wolf PA. The influence of gender and age on disability following ischemic stroke: The Framingham study. J Stroke Cerebrovasc Dis 2003;12:119126. 118. Portelli R, Lowe D, Irwin P, Pearson M, Rudd AG. Intercollegiate stroke working party. Institutionalization after stroke. Clin Rehabil. 2005;19:97 108.

PAGE 213

213 119. Sullivan KJ, Brown DA, Klassen T, Mulroy S, Ge T, Azen SP, Winstein CJ. Effects of task -specific locomotor and strength training in adults who were ambulatory after stroke: Results of the steps randomized clinical trial. Phys Ther 2007;87:1580 1602. 120. Hachisuka K, Umezu Y, Ogata H. Disuse muscle atrophy of lower limbs in hemiplegic patients. Arch Phys Med Rehabil 1997;78:1318. 121. WHO. Introduction. World Health Organization: International Classification of Functioning, Disability and Health: ICF Geneva: WHO Library Cataloguing in Publication Data; 2001:325. 122. Williams LS. Depression and stroke: Cause or consequence? Semin Neurol 2005;25:396409. 123. Parikh RM, Robinson RG, Lipsey JR, Starkstein SE, Fedoroff JP, Price TR. The impact of poststroke depression on recovery in activities of daily living over a 2 -year follow up. Arch Neurol 1990;47:785789. 124. Morris PL, Raphael B, Robinson RG. Clinical depression is associated with impaired recovery from stroke. Med J Aust 1992;157:239242. 125. Schubert DS, Taylor C, Lee S, Mentari A, Tamaklo W. Physical consequences of depression in the stroke patient. Gen Hosp Psychiatry 1992;14:6976. 126. Ramasubbu R, Robinson RG, Flint AJ, Kosier T, Price TR. Functional impairment asso ciated with acute poststroke depression: The stroke data bank study. J Neuropsychiatry Clin Neurosci 1998;10:2633. 127. Kauhanen M, Korpelainen JT, Hiltunen P, Brusin E, Mononen H, Maatta R, Nieminen P, Sotaniemi KA, Myllyla VV. Poststroke depression co rrelates with cognitive impairment and neurological deficits. Stroke 1999;30:18751880. 128. Chandler J, Duncan PW, Studenski LS. The fear of falling syndrome: Relationship to falls, physical performance and activities of daily living in frail older pers ons. Top Geriatr Rehabil. 1996;11:110. 129. Chang JT, Morton SC, Rubenstein LZ, Mojica WA, Maglione M, Suttorp MJ, Roth EA, Shekelle PG. Interventions for the prevention of falls in older adults: Systematic review and meta analysis of randomized clinical trials. Bmj. 2004;328:680687. 130. Fried LP, Bandeen Roche K, Chaves PH, Johnson BA. Preclinical mobility disability predicts incident mobility disability in older women. J Gerontol A Biol Sci Med Sci 2000;55:M4352.

PAGE 214

214 131. Jylha M, Guralnik JM, Balfour J, Fried LP. Walking difficulty, walking speed, and age as predictors of self rated health: The women's health and aging study. J Gerontol A Biol Sci Med Sci 2001;56:M609617. 132. van de Port IG, Kwakkel G, Schepers VP, Lindeman E. Predicting mobility outcome one year after stroke: A prospective cohort study. J Rehabil Med 2006;38:218223. 133. Sanchez Blanco I, Ochoa Sangrador C, Lopez -Munain L, Izquierdo -Sanchez M, Fermoso Garcia J. Predictive model of functional independence in stroke patients admi tted to a rehabilitation programme. Clin Rehabil. 1999;13:464475. 134. Hellstrom K, Lindmark B, Wahlberg B, Fugl Meyer AR. Self -efficacy in relation to impairments and activities of daily living disability in elderly patients with stroke: A prospective i nvestigation. J Rehabil Med 2003;35:202207. 135. Powers SK, Howley ET. Exercise prescriptions for health and fitness. In: Kane KT, Malinee V, eds. Exercise physiology: Theory and application to fitness and performance New York, NY: McGrawHill; 2001:293 307. 136. Powers SK, Howley ET. Measurement of work, power, and energy expenditure. In: Kane KT, Malinee V, eds. Exercise physiology: Theory and application to fitness and performance New York, NY: McGrawHill; 2001:98110. 137. Hausdorff JM. Gait var iability: Methods, modeling and meaning. J Neuroengineering Rehabil 2005;2:1927. 138. Richards C, Olney SJ. Hemiparetic gait following stroke. Part iii: Recovery and physical therapy. Gait Posture 1996;4:149162. 139. Studenski S, Perera S, Wallace D, Chandler JM, Duncan PW, Rooney E, Fox M, Guralnik JM. Physical performance measures in the clinical setting. J Am Geriatr Soc 2003;51:314322. 140. Friedman PJ, Richmond DE, Baskett JJ. A prospective trial of serial gait speed as a measure of rehabilita tion in the elderly. Age Ageing 1988;17:227235. 141. Maki BE. Gait changes in older adults: Predictors of falls or indicators of fear. J Am Geriatr Soc 1997;45:313320. 142. Potter JM, Evans AL, Duncan G. Gait speed and activities of daily living func tion in geriatric patients. Arch Phys Med Rehabil 1995;76:997999. 143. Brandstater ME, de Bruin H, Gowland C, Clark BM. Hemiplegic gait: Analysis of temporal variables. Arch Phys Med Rehabil 1983;64:583587.

PAGE 215

215 144. Olney SJ, Griffin MP, Monga TN, McBrid e ID. Work and power in gait of stroke patients. Arch Phys Med Rehabil 1991;72:309314. 145. Bohannon RW, Walsh S. Nature, reliability, and predictive value of muscle performance measures in patients with hemiparesis following stroke. Arch Phys Med Rehabil. 1992;73:721725. 146. Ostchega Y, Dillon CF, Lindle R, Carroll M, Hurley BF. Isokinetic leg muscle strength in older Americans and its relationship to a standardized walk test: Data from the national health and nutrition examination survey 19992000. J Am Geriatr Soc 2004;52:977982. 147. Hsu AL, Tang PF, Jan MH. Analysis of impairments influencing gait velocity and asymmetry of hemiplegic patients after mild to moderate stroke. Arch Phys Med Rehabil 2003;84:11851193. 148. Eng JJ, Chu KS, Dawson AS, Kim CM, Hepburn KE. Functional walk tests in individuals with stroke: Relation to perceived exertion and myocardial exertion. Stroke 2002;33:756761. 149. Lamontage A, Malouin F, Richards CL. Contribution of passive stiffness to ankle plantarflexor mo ment during gait after stroke. Arch Phys Med Rehabil 2000;81:351358. 150. Lin PY, Yang YR, Cheng SJ, Wang RY. The relation between ankle impairments and gait velocity and symmetry in people with stroke. Arch Phys Med Rehabil 2006;87:562568. 151. Wade D. Measurement in neurological rehabilitation. Oxford, UK: Oxford University Press; 1992. 152. Kosak M, Smith T. Comparison of the 2, 6, and 12minute walk tests in patients with stroke. J Rehabil Res Dev 2005;42:103107. 153. Bohannon RW, Andrews AW, T homas MW. Walking speed: Reference values and correlates for older adults. J Orthop Sports Phys Ther 1996;24:8690. 154. Solway S, Brooks D, Lacasse Y, Thomas S. A qualitative systematic overview of the measurement properties of functional walk tests use d in the cardiorespiratory domain. Chest 2001;119:256270. 155. Berry MJ, Rejeski WJ, Adair NE, Zaccaro D. Exercise rehabilitation and chronic obstructive pulmonary disease stage. Am J Respir Crit Care Med 1999;160:12481253. 156. ATS statement: Guidel ines for the six -minute walk test. Am J Respir Crit Care Med 2002;166:111117.

PAGE 216

216 157. Tang A, Sibley KM, Bayley MT, McIlroy WE, Brooks D. Do functional walk tests reflect cardiorespiratory fitness in sub acute stroke? J Neuroengineering Rehabil 2006;3:233 0. 158. Kelly JO, Kilbreath SL, Davis GM, Zeman B, Raymond J. Cardiorespiratory fitness and walking ability in subacute stroke patients. Arch Phys Med Rehabil 2003;84:17801785. 159. Dean CM, Richards CL, Malouin F. Walking speed over 10 meters overesti mates locomotor capacity after stroke. Clin Rehabil. 2001;15:415421. 160. Eng JJ, Dawson AS, Chu KS. Submaximal exercise in persons with stroke: Test -retest reliability and concurrent validity with maximal oxygen consumption. Arch Phys Med Rehabil 2004; 85:113118. 161. Werner C, Von Frankenberg S, Treig T, Konrad M, Hesse S. Treadmill training with partial body weight support and an electromechanical gait trainer for restoration of gait in subacute stroke patients: A randomized crossover study. Stroke 2002;33:28952901. 162. Hermansen L, Ekblom B, Saltin B. Cardiac output during submaximal and maximal treadmill and bicycle exercise. J Appl Physiol 1970;29:8286. 163. Rowell LB. Human cardiovascular adjustments to exercise and thermal stress. Physiol Rev 1974;54:75159. 164. MacKay Lyons MJ, Howlett J. Exercise capacity and cardiovascular adaptations to aerobic training early after stroke. Top Stroke Rehabil 2005;12:3144. 165. Pohl PS, Duncan PW, Perera S, Liu W, Min Lai S, Studenski S, Long J. Influence of stroke -related impairments on performance in 6 -minute walk test. Journal of Rehabilitation Research and Development 2002;39:439444. 166. Mathias S, Nayak US, Isaacs B. Balance in elderly patients: The "get up and go" test. Arch Phys Med Rehabil. 1986;67:387389. 167. Ng SS, Hui Chan CW. The timed up & go test: Its reliability and association with lower limb impairments and locomotor capacities in people with chronic stroke. Arch Phys Med Rehabil 2005;86:16411647. 168. Whitney SL, Poole JL, Cass SP. A review of balance instruments for older adults. Am J Occup Ther 1998;52:666671. 169. Medley A, Thompson M. The effect of assistive devices on the performance of community dwelling elderly on the timed up and go test. Issues Aging 1997;20:37.

PAGE 217

217 170. Shumway -Cook A, Brauer S, Woollacott M. Predicting the probability for falls in community -dwelling older adults using the timed up & go test. Phys Ther 2000;80:896903. 171. Shumway -Cook A, Woollacott MH. Motor control: Theory and practical appl ications. Baltimore, Md: Williams & Wilkins; 1995. 172. Rockwood K, Awalt E, Carver D, MacKnight C. Feasibility and measurement properties of the functional reach and the timed up and go tests in the Canadian study of health and aging. J Gerontol A Biol S ci Med Sci 2000;55:M7073. 173. Mehrholz J, Wagner K, Rutte K, Meissner D, Pohl M. Predictive validity and responsiveness of the functional ambulation category in hemiparetic patients after stroke. Arch Phys Med Rehabil 2007;88:13141319. 174. Holden M K, Gill KM, Magliozzi MR, Nathan J, PiehlBaker L. Clinical gait assessment in the neurologically impaired. Reliability and meaningfulness. Phys Ther 1984;64:3540. 175. Holden MK, Gill KM, Magliozzi MR. Gait assessment for neurologically impaired patien ts. Standards for outcome assessment. Phys Ther 1986;66:15301539. 176. Kollen B, Kwakkel G, Lindeman E. Time dependency of walking classification in stroke. Phys Ther 2006;86:618625. 177. Kollen B, van de Port I, Lindeman E, Twisk J, Kwakkel G. Predi cting improvement in gait after stroke: A longitudinal prospective study. Stroke 2005;36:26762680. 178. Antonucci G, Aprile T, Paolucci S. Rasch analysis of the Rivermead Mobility Index: A study using mobility measures of first -stroke inpatients. Arch P hys Med Rehabil 2002;83:14421449. 179. Collen FM, Wade DT, Robb GF, Bradshaw CM. The Rivermead Mobility Index: A further development of the Rivermead Motor Assessment. Int Disabil Stud 1991;13:5054. 180. Hsieh CL, Hsueh IP, Mao HF. Validity and respo nsiveness of the Rivermead Mobility Index in stroke patients. Scand J Rehabil Med. 2000;32:140142. 181. Richardson MT, Leon AS, Jacobs DR, Jr., Ainsworth BE, Serfass R. Comprehensive evaluation of the Minnesota leisure time physical activity questionnair e. J Clin Epidemiol. 1994;47:271281. 182. McDowell I, Newell C. Measuring health: A guide to rating scales and questionnaires. New York: Oxford University Press; 1996:6367.

PAGE 218

218 183. Shah S, Vanclay F, Cooper B. Improving the sensitivity of the Barthel Index for stroke rehabilitation. J Clin Epidemiol. 1989;42:703709. 184. Salter K, Jutai JW, Teasell R, Foley NC, Bitensky J, Bayley M. Issues for selection of outcome measures in stroke rehabilitation: ICF activity. Disabil Rehabil. 2005;27:315340. 185. Gra nger CV, Albrecht GL, Hamilton BB. Outcome of comprehensive medical rehabilitation: Measurement by pulses profile and the Barthel Index. Arch Phys Med Rehabil 1979;60:145154. 186. Duncan PW, Zorowitz R, Bates B, Choi JY, Glasberg JJ, Graham GD, Katz RC, Lamberty K, Reker D. Management of adult stroke rehabilitation care: A clinical practice guideline. Stroke 2005;36:e100143. 187. Duncan PW, Samsa GP, Weinberger M, Goldstein LB, Bonito A, Witter DM, Enarson C, Matchar D. Health status of individuals wi th mild stroke. Stroke 1997;28:740745. 188. McGinnis GE, Seward ML, DeJong G, Osberg JS. Program evaluation of physical medicine and rehabilitation departments using self report Barthel. Arch Phys Med Rehabil 1986;67:123125. 189. Timbeck RJ, Spaulding SJ. Ability of the Functional Independence Measure to predict rehabilitation outcomes after stroke: A review of the literature. Physical and Occupational Therapy in Geriatrics 2003;22:6376. 190. Pearson OR, Busse ME, van Deursen RW, Wiles CM. Quantifi cation of walking mobility in neurological disorders. Qjm 2004;97:463475. 191. Black TM, Soltis T, Bartlett C. Using the Functional Independence Measure instrument to predict stroke rehabilitation outcomes. Rehabil Nurs 1999;24:109114. 192. Oczkowski WJ, Barreca S. The Functional Independence Measure: Its use to identify rehabilitation needs in stroke survivors. Arch Phys Med Rehabil 1993;74:12911294. 193. Ween JE, Mernoff ST, Alexander MP. Recovery rates after stroke and their impact on outcome pr ediction. Neurorehabil Neural Repair 2000;14:229235. 194. Linacre JM, Heinemann AW, Wright BD, Granger CV, Hamilton BB. The structure and stability of the Functional Independence Measure. Arch Phys Med Rehabil 1994;75:127132. 195. Cavanagh SJ, Hogan K, Gordon V, Fairfax J. Stroke -specific FIM models in an urban population. J Neurosci Nurs. 2000;32:1721.

PAGE 219

219 196. Robinett CS, Vondran MA. Functional ambulation velocity and distance requirements in rural and urban communities. A clinical report. Phys Ther 1988;68:13711373. 197. Holbrook M, Skilbeck CE. An activities index for use with stroke patients. Age Ageing 1983;12:166170. 198. Wade DT, Legh Smith J, Langton Hewer R. Social activities after stroke: Measurement and natural history using the Frenchay Activities Index. Int Rehabil Med 1985;7:176181. 199. Mahoney FI, Barthel DW. Functional evaluation: The Barthel Index. Md State Med J 1965;14:6165. 200. Keith RA, Granger CV, Hamilton BB, Sherwin FS. The Functional Independence Measure: A new too l for rehabilitation. Adv Clin Rehabil. 1987;1:618. 201. Schuling J, de Haan R, Limburg M, Groenier KH. The Frenchay Activities Index. Assessment of functional status in stroke patients. Stroke 1993;24:11731177. 202. Sveen U, Bautz Holter E, Sodring K M, Wyller TB, Laake K. Association between impairments, self -care ability and social activities 1 year after stroke. Disabil Rehabil 1999;21:372377. 203. Tooth LR, McKenna KT, Smith M, O'Rourke P. Further evidence for the agreement between patients with stroke and their proxies on the Frenchay Activities Index. Clin Rehabil 2003;17:656665. 204. Miller WC, Deathe AB, Harris J. Measurement properties of the Frenchay Activities Index among individuals with a lower limb amputation. Clin Rehabil. 2004;18:414422. 205. Pedersen PM, Jorgensen HS, Nakayama H, Raaschou HO, Olsen TS. Comprehensive assessment of activities of daily living in stroke. The copenhagen stroke study. Arch Phys Med Rehabil 1997;78:161165. 206. Frytak J. Measurement. Journal of Rehabilitation Outcomes Measurement 2000;4:1531. 207. Collen FM, Wade DT, Bradshaw CM. Mobility after stroke: Reliability of measures of impairment and disability. Int Disabil Stud 1990;12:69. 208. Duncan PW, Bode RK, Min Lai S, Perera S. Rasch analysis o f a new stroke-specific outcome scale: The Stroke Impact scale. Arch Phys Med Rehabil 2003;84:950963. 209. Duncan PW, Lai SM, Bode RK, Perera S, DeRosa J. Stroke Impact Scale 16: A brief assessment of physical function. Neurology 2003;60:291296.

PAGE 220

220 210. Lai SM, Studenski S, Duncan PW, Perera S. Persisting consequences of stroke measured by the stroke impact scale. Stroke 2002;33:18401844. 211. Duncan PW, Lai SM, Tyler D, Perera S, Reker DM, Studenski S. Evaluation of proxy responses to the Stroke Impa ct Scale. Stroke 2002;33:25932599. 212. Ware JE, Jr., Sherbourne CD. The MOS 36 item short -form health survey (SF 36). I. Conceptual framework and item selection. Med Care 1992;30:473483. 213. Wilkinson PR, Wolfe CD, Warburton FG, Rudd AG, Howard RS, Ross -Russell RW, Beech R. Longer term quality of life and outcome in stroke patients: Is the barthel index alone an adequate measure of outcome? Qual Health Care 1997;6:125130. 214. Salter K, Jutai JW, Teasell R, Foley NC, Bitensky J, Bayley M. Issues for selection of outcome measures in stroke rehabilitation: ICF participation. Disabil Rehabil 2005;27:507528. 215. Walters SJ, Munro JF, Brazier JE. Using the SF 36 with older adults: A cross -sectional community -based survey. Age Ageing 2001;30:337343. 216. Mayo NE, WoodDauphinee S, Cote R, Durcan L, Carlton J. Activity, participation, and quality of life 6 months poststroke. Arch Phys Med Rehabil 2002;83:10351042. 217. O'Mahony PG, Rodgers H, Thomson RG, Dobson R, James OF. Is the SF 36 suitable for assessing health status of older stroke patients? Age Ageing 1998;27:1922. 218. Segal ME, Schall RR. Determining functional/health status and its relation to disability in stroke survivors. Stroke 1994;25:23912397. 219. Finch E, Brooks D, Stratf ord PW, Mayo NE. Physical rehabilitation outcome measures. A guide to enhanced clinical decision-making. Toronto, Ontario: Canadian Physiotherapy Association; 2002. 220. Dorman PJ, Waddell F, Slattery J, Dennis M, Sandercock P. Are proxy assessments of he alth status after stroke with the Euroqol questionnaire feasible, accurate, and unbiased? Stroke 1997;28:18831887. 221. Dorman P, Slattery J, Farrell B, Dennis M, Sandercock P. Qualitative comparison of the reliability of health status assessments with the euroqol and sf 36 questionnaires after stroke. United kingdom collaborators in the international stroke trial. Stroke 1998;29:6368. 222. Cup EH, Scholte op Reimer WJ, Thijssen MC, van Kuyk -Minis MA. Reliability and validity of the Canadian occupatio nal performance measure in stroke patients. Clin Rehabil 2003;17:402409.

PAGE 221

221 223. Johnson JA, Coons SJ. Comparison of the EQ 5d and SF 12 in an adult us sample. Qual Life Res 1998;7:155166. 224. Coast J, Peters TJ, Richards SH, Gunnell DJ. Use of the Euro qol among elderly acute care patients. Qual Life Res 1998;7:110. 225. Schulz S, Westerterp KR, Bruck K. Comparison of energy expenditure by the doubly labeled water technique with energy intake, heart rate, and activity recording in man. Am J Clin Nutr 1989;49:11461154. 226. Westerterp KR, Wouters L, van Marken Lichtenbelt WD. The maastricht protocol for the measurement of body composition and energy expenditure with labeled water. Obes Res. 1995;3 Suppl 1:4957. 227. Hoos MB, Kuipers H, Gerver WJ, W esterterp KR. Physical activity pattern of children assessed by triaxial accelerometry. Eur J Clin Nutr 2004;58:14251428. 228. Saris WH, Binkhorst RA. The use of pedometer and actometer in studying daily physical activity in man. Part ii: Validity of pe dometer and actometer measuring the daily physical activity. Eur J Appl Physiol Occup Physiol 1977;37:229235. 229. Saris WH, Binkhorst RA. The use of pedometer and actometer in studying daily physical activity in man. Part i: Reliability of pedometer an d actometer. Eur J Appl Physiol Occup Physiol 1977;37:219228. 230. Shepherd EF, Toloza E, McClung CD, Schmalzried TP. Step activity monitor: Increased accuracy in quantifying ambulatory activity. J Orthop Res 1999;17:703708. 231. Swartz AM, Bassett DR, Jr., Moore JB, Thompson DL, Strath SJ. Effects of body mass index on the accuracy of an electronic pedometer. Int J Sports Med. 2003;24:588592. 232. Green LB. Assessment of habitual physical activity and paretic arm mobility among stroke survivors by accelerometry. Top Stroke Rehabil 2007;14:921. 233. Chen KY, Bassett DR, Jr. The technology of accelerometry-based activity monitors: Current and future. Med Sci Sports Exerc 2005;37:S490500. 234. Levine JA, Baukol PA, Westerterp KR. Validation of th e tracmor triaxial accelerometer system for walking. Med Sci Sports Exerc 2001;33:15931597. 235. Chen KY, Sun M. Improving energy expenditure estimation by using a triaxial accelerometer. J Appl Physiol 1997;83:21122122. 236. Freedson PS, Melanson E, Sirard J. Calibration of the computer science and applications, inc. Accelerometer. Med Sci Sports Exerc 1998;30:777781.

PAGE 222

222 237. Ekelund U, Sjostrom M, Yngve A, Poortvliet E, Nilsson A, Froberg K, Wedderkopp N, Westerterp K. Physical activity assessed by a ctivity monitor and doubly labeled water in children. Med Sci Sports Exerc 2001;33:275281. 238. Leenders NY, Sherman WM, Nagaraja HN, Kien CL. Evaluation of methods to assess physical activity in free living conditions. Med Sci Sports Exerc 2001;33:1233 1240. 239. Ichinoseki Sekine N, Kuwae Y, Higashi Y, Fujimoto T, Sekine M, Tamura T. Improving the accuracy of pedometer used by the elderly with the fft algorithm. Med Sci Sports Exerc 2006;38:16741681. 240. Kochersberger G, McConnell E, Kuchibhatla MN, Pieper C. The reliability, validity, and stability of a measure of physical activity in the elderly. Arch Phys Med Rehabil 1996;77:793795. 241. Singh S, Morgan MD. Activity monitors can detect brisk walking in patients with chronic obstructive pulmo nary disease. J Cardiopulm Rehabil 2001;21:143148. 242. Sewell L, Singh SJ, Williams JE, Collier R, Morgan MD. Can individualized rehabilitation improve functional independence in elderly patients with COPD? Chest 2005;128:11941200. 243. McDermott MM Ohlmiller SM, Liu K, Guralnik JM, Martin GJ, Pearce WH, Greenland P. Gait alterations associated with walking impairment in people with peripheral arterial disease with and without intermittent claudication. J Am Geriatr Soc 2001;49:747754. 244. McDer mott MM, Liu K, O'Brien E, Guralnik JM, Criqui MH, Martin GJ, Greenland P. Measuring physical activity in peripheral arterial disease: A comparison of two physical activity questionnaires with an accelerometer. Angiology 2000;51:91100. 245. Garg PK, Tia n L, Criqui MH, Liu K, Ferrucci L, Guralnik JM, Tan J, McDermott MM. Physical activity during daily life and mortality in patients with peripheral arterial disease. Circulation 2006;114:242248. 246. Tweedy SM, Trost SG. Validity of accelerometry for mea surement of activity in people with brain injury. Med Sci Sports Exerc 2005;37:14741480. 247. Motl RW, Snook EM, McAuley E, Scott JA, Douglass ML. Correlates of physical activity among individuals with multiple sclerosis. Ann Behav Med. 2006;32:154161. 248. Koller WC. Pharmacologic treatment of parkinsonian tremor. Arch Neurol 1986;43:126127. 249. Schrag A, Schelosky L, Scholz U, Poewe W. Reduction of parkinsonian signs in patients with Parkinsons disease by dopaminergic versus anticholinergic single -dose challenges. Mov Disord 1999;14:252255.

PAGE 223

223 250. Kiani K, Snijders CJ, Gelsema ES. Recognition of daily life motor activity classes using an artificial neural network. Arch Phys Med Rehabil 1998;79:147154. 251. Masse LC, Fuemmeler BF, Anderson CB, Matthews CE, Trost SG, Catellier DJ, Treuth M. Accelerometer data reduction: A comparison of four reduction algorithms on select outcome variables. Med Sci Sports Exerc 2005;37:S544554. 252. Brage S, Wedderkopp N, Ekelund U, Franks PW, Wareham NJ, Ander sen LB, Froberg K. Objectively measured physical activity correlates with indices of insulin resistance in Danish children. The European youth heart study (EYHS). Int J Obes Relat Metab Disord 2004;28:15031508. 253. Brage S, Wedderkopp N, Ekelund U, Fra nks PW, Wareham NJ, Andersen LB, Froberg K. Features of the metabolic syndrome are associated with objectively measured physical activity and fitness in Danish children: The European youth heart study (EYHS). Diabetes Care 2004;27:21412148. 254. Ekelund U, Yngve A, Brage S, Westerterp K, Sjostrom M. Body movement and physical activity energy expenditure in children and adolescents: How to adjust for differences in body size and age. Am J Clin Nutr 2004;79:851856. 255. Schmidt MD, Freedson PS, Chasan T aber L. Estimating physical activity using the CSA accelerometer and a physical activity log. Med Sci Sports Exerc 2003;35:16051611. 256. Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, Ainsworth BE, Pratt M, Ekelund U, Yngve A, Sallis JF, Oja P International physical activity questionnaire: 12 country reliability and validity. Med Sci Sports Exerc 2003;35:13811395. 257. Ekelund U, Aman J, Westerterp K. Is the arteacc index a valid indicator of free living physical activity in adolescents? Ob es Res 2003;11:793801. 258. Riddoch CJ, Bo Andersen L, Wedderkopp N, Harro M, Klasson-Heggebo L, Sardinha LB, Cooper AR, Ekelund U. Physical activity levels and patterns of 9 and 15 yr old European children. Med Sci Sports Exerc 2004;36:8692. 259. T reuth MS, Sherwood NE, Baranowski T, Butte NF, Jacobs DR, Jr., McClanahan B, Gao S, Rochon J, Zhou A, Robinson TN, Pruitt L, Haskell W, Obarzanek E. Physical activity self report and accelerometry measures from the girls health enrichment multi -site studie s. Prev Med 2004;38:S4349. 260. Treuth MS, Sherwood NE, Butte NF, McClanahan B, Obarzanek E, Zhou A, Ayers C, Adolph A, Jordan J, Jacobs DR, Rochon J. Validity and reliability of activity measures in African -American girls for gems. Med Sci Sports Exerc 2003;35:532539.

PAGE 224

224 261. Cradock AL, Wiecha JL, Peterson KE, Sobol AM, Colditz GA, Gortmaker SL. Youth recall and tritrac accelerometer estimates of physical activity levels. Med Sci Sports Exerc 2004;36:525532. 262. Pate RR, Pfeiffer KA, Trost SG, Zieg ler P, Dowda M. Physical activity among children attending preschools. Pediatrics 2004;114:12581263. 263. Samuel Hodge CD, Fernandez LM, Henriquez Roldan CF, Johnston LF, Keyserling TC. A comparison of self -reported energy intake with total energy expenditure estimated by accelerometer and basal metabolic rate in african american women with type 2 diabetes. Diabetes Care 2004;27:663669. 264. Jackson DM, Reilly JJ, Kelly LA, Montgomery C, Grant S, Paton JY. Objectively measured physical activity in a r epresentative sample of 3 to 4 -year old children. Obes Res 2003;11:420425. 265. Burdette HL, Whitaker RC, Daniels SR. Parental report of outdoor playtime as a measure of physical activity in preschool aged children. Arch Pediatr Adolesc Med 2004;158:3 53357. 266. Dubbert PM, Vander Weg MW, Kirchner KA, Shaw B. Evaluation of the 7-day physical activity recall in urban and rural men. Med Sci Sports Exerc 2004;36:1646-1654. 267. Prochaska JJ, Sallis JF. A randomized controlled trial of single versus multiple health behavior change: Promoting physical activity and nutrition among adolescents. Health Psychol 2004;23:314318. 268. McMurray RG, Ring KB, Treuth MS, Welk GJ, Pate RR, Schmitz KH, Pickrel JL, Gonzalez V, Almedia MJ, Young DR, Sallis JF. Compa rison of two approaches to structured physical activity surveys for adolescents. Med Sci Sports Exerc 2004;36:21352143. 269. Cooper AR, Page AS, Foster LJ, Qahwaji D. Commuting to school: Are children who walk more physically active? Am J Prev Med 2003;25:273276. 270. DuVall C, Dinger MK, Taylor EL, Bemben D. Minimal -contact physical activity interventions in women: A pilot study. Am J Health Behav 2004;28:280286. 271. Saunders DH, Greig CA, Young A, Mead GE. Physical fitness training for stroke pa tients. Cochrane Database Syst Rev .CD003316. 2004. 272. Uswatte G, Giuliani C, Winstein C, Zeringue A, Hobbs L, Wolf SL. Validity of accelerometry for monitoring real -world arm activity in patients with subacute stroke: Evidence from the extremity constr aint induced therapy evaluation trial. Arch Phys Med Rehabil 2006;87:13401345.

PAGE 225

225 273. Uswatte G, Taub E, Morris D, Vignolo M, McCulloch K. Reliability and validity of the upper -extremity motor activity log 14 for measuring real -world arm use. Stroke 2005; 36:24932496. 274. Catellier DJ, Hannan PJ, Murray DM, Addy CL, Conway TL, Yang S, Rice JC. Imputation of missing data when measuring physical activity by accelerometry. Med Sci Sports Exerc 2005;37:S555562. 275. Arif H, Aijaz B, Islam M, Aftab U, Kuma r S, Shafqat S. Drug compliance after stroke and myocardial infarction: A comparative study. Neurol India. 2007;55:130 135. 276. Uswatte G, Foo WL, Olmstead H, Lopez K, Holand A, Simms LB. Ambulatory monitoring of arm movement using accelerometry: An obje ctive measure of upper extremity rehabilitation in persons with chronic stroke. Arch Phys Med Rehabil 2005;86:14981501. 277. Burkhardt H, Gladisch R. Pharmacotherapy in the elderly. In: Karasek M, ed. Aging and age -related diseases: The basics Hauppaug e, NY: Nova Publishers; 2006:421459. 278. Gemmil EL. Compliance to an accelerometer protocol in older adults. Department of Epidemiology 2008:104. 279. Grady CL, Craik FI. Changes in memory processing with age. Curr Opin Neurobiol 2000;10:224231. 280. DiMatteo MR, Lepper HS, Croghan TW. Depression is a risk factor for noncompliance with medical treatment: Meta analysis of the effects of anxiety and depression on patient adherence. Arch Intern Med 2000;160:21012107. 281. Pappa C, Hyphantis T, Pappa S, Aspiotis M, Stefaniotou M, Kitsos G, Psilas K, Mavreas V. Psychiatric manifestations and personality traits associated with compliance with glaucoma treatment. J Psychosom Res. 2006;61:609617. 282. Bosley CM, Fosbury JA, Cochrane GM. The psychologica l factors associated with poor compliance with treatment in asthma. Eur Respir J 1995;8:899904. 283. National Institute of Mental H ealth Site [Internet]. Depression; [updated 2008 December 6; cited 2008 December 13]. Available from: http://www.nimh.nih.gov/health/topics/depression/index.shtml 284. Van Coevering P, Harnack L, Schmitz K, Fulton JE, Galuska DA, Gao S. Feasibility of using accelerometers to measure physical a ctivity in young adolescents. Med Sci Sports Exerc 2005;37:867871.

PAGE 226

226 285. Cooper AR, Page A, Fox KR, Misson J. Physical activity patterns in normal, overweight and obese individuals using minute -by-minute accelerometry. Eur J Clin Nutr 2000;54:887894. 286. Stel VS, Smit JH, Pluijm SM, Visser M, Deeg DJ, Lips P. Comparison of the LASA physical activity questionnaire with a 7 -day diary and pedometer. J Clin Epidemiol. 2004;57:252258. 287. Silver HJ, Wellman NS, Cuervo L. Using pedometers to promote walking activity in community dwelling Hispanic older Americans: A steps to healthy aging pilot project. J Am Diet Assoc 2003;103:A 61. 288. Portney LG, Watkins MP. Descriptive statistics and measures of variability. In: Mehalik C, ed. Foudations of clinical research: Applications to practice Upper Saddle River, New Jersey: Prentice -Hall; 2000:367387. 289. Pincus SM, Gladstone IM, Ehrenkranz RA. A regularity statistic for medical data analysis. J Clin Monit. 1991;7:335345. 290. Stergiou N, Buzzi UH, Kurz MJ, al. e. Nonlinear tools in human movement. In: Stergiou N, ed. Innovative analyses of human movement Champaign, IL: Human Kinetics; 2004:6390. 291. Iwane M, Arita M, Tomimoto S, Satani O, Matsumoto M, Miyashita K, Nishio I. Walking 10,000 steps/day or more reduces blood pressure and sympathetic nerve activity in mild essential hypertension. Hypertens Res 2000;23:573580. 292. Yamanouchi K, Shinozaki T, Chikada K, Nishikawa T, Ito K, Shimizu S, Ozawa N, Suzuki Y, Maeno H, Kato K, et al. Daily walkin g combined with diet therapy is a useful means for obese NIDDM patients not only to reduce body weight but also to improve insulin sensitivity. Diabetes Care 1995;18:775778. 293. Hatano Y. Use of pedometer for promoting daily walking exercise. ICHPER 1 993;29:48. 294. Tudor Locke C, Ainsworth BE, Whitt MC, Thompson RW, Addy CL, Jones DA. The relationship between pedometer determined ambulatory activity and body composition variables. Int J Obes Relat Metab Disord 2001;25:15711578. 295. Tudor Locke C Bassett DR, Jr. How many steps/day are enough? Preliminary pedometer indices for public health. Sports Med. 2004;34:18. 296. Welk GJ, Differding JA, Thompson RW, Blair SN, Dziura J, Hart P. The utility of the digi -walker step counter to assess daily physical activity patterns. Med Sci Sports Exerc 2000;32:S481488.

PAGE 227

227 297. Tudor Locke C, Jones R, Myers AM, Paterson DH, Ecclestone NA. Contribution of structured exercise class participation and informal walking for exercise to daily physical activity in com munity -dwelling older adults. Res Q Exerc Sport 2002;73:350356. 298. Michael KM, Allen JK, Macko RF. Fatigue after stroke: Relationship to mobility, fitness, ambulatory activity, social support, and falls efficacy. Rehabil Nurs 2006;31:210217. 299. M eijer EP, Goris AH, Wouters L, Westerterp KR. Physical inactivity as a determinant of the physical activity level in the elderly. Int J Obes Relat Metab Disord. 2001;25:935939. 300. Busse ME, Wiles CM, van Deursen RW. Community walking activity in neurol ogical disorders with leg weakness. J Neurol Neurosurg Psychiatry 2006;77:359362. 301. Baranowski T, de Moor C. How many days was that? Intra individual variability and physical activity assessment. Res Q Exerc Sport 2000;71:S7478. 302. Sieminski DJ, Cowell LL, Montgomery PS, Pillai SB, Gardner AW. Physical activity monitoring in patients with peripheral arterial occlusive disease. J Cardiopulm Rehabil 1997;17:4347. 303. Petrella JK, Cress ME. Daily ambulation activity and task performance in commu nity dwelling older adults aged 63 71 years with preclinical disability. J Gerontol A Biol Sci Med Sci 2004;59:264267. 304. Chan CB, Spangler E, Valcour J, Tudor -Locke C. Cross -sectional relationship of pedometer determined ambulatory activity to indica tors of health. Obes Res. 2003;11:15631570. 305. Patla AE. Understanding the roles of vision in the control of human locomotion. Gait and posture 1997;5:5469. 306. Reitan R. The halstead-reitan neuropsychological test battery: Theory and clinical inte rpretation Tucson, AZ: Neuropsychology Press; 1993. 307. Arbuthnott K, Frank J. Trail Making Test, part B as a measure of executive control: Validation using a set -switching paradigm. J Clin Exp Neuropsychol 2000;22:518528. 308. Twigg A, Cifu DX, Keys er -Marcus L, Swartz L. The association between gender, race and marital status on functional outcome at rehabilitation discharge after thromboembolic stroke: A prospective analysis. Neurorehabilitation. 1988;11:249254. 309. Fritz SL, Light KE, Clifford S N, Patterson TS, Behrman AL, Davis SB. Descriptive characteristics as potential predictors of outcomes following constraint induced movement therapy for people after stroke. Phys Ther 2006;86:825832.

PAGE 228

228 310. Armstrong DG, Lavery LA, Kimbriel HR, Nixon BP, B oulton AJ. Activity patterns of patients with diabetic foot ulceration: Patients with active ulceration may not adhere to a standard pressure off -loading regimen. Diabetes Care 2003;26:25952597. 311. Duncan PW, Sullivan KJ, Behrman AL, Azen SP, Wu SS, N adeau SE, Dobkin BH, Rose DK, Tilson JK. Protocol for the locomotor experience applied post -stroke (LEAPS) trial: A randomized controlled trial. BMC Neurol 2007;7:3962. 312. Folstein MF, Folstein SE, McHugh PR. "Mini Mental State". A practical method fo r grading the cognitive state of patients for the clinician. J Psychiatr Res 1975;12:189198. 313. Folstein MF, Folstein SE, McHugh PR, Fanjiang G. Mini Mental State Examination user's guide Odessa, FL: Psychological Assessment Resources; 2001. 314. Wi lliams JI. Performance of the PHQ 9 as a screening tool for post -stroke depression. Submitted 2004. 315. Gladstone DJ, Danells CJ, Black SE. The Fugl -Meyer Assessment of motor recovery after stroke: A critical review of its measurement properties. Neurore habil Neural Repair 2002;16:232240. 316. Fritz SL, Light KE, Patterson TS, Behrman AL, Davis SB. Active finger extension predicts outcomes after constraint induced movement therapy for individuals with hemiparesis after stroke. Stroke 2005;36:11721177. 317. Trost SG, McIver KL, Pate RR. Conducting accelerometer -based activity assessments in field -based research. Med Sci Sports Exerc 2005;37:S531543. 318. Tuncay R, Eksioglu E, Cakir B, Gurcay E, Cakci A. Factors affecting drug treatment compliance i n patients with rheumatoid arthritis. Rheumatol Int 2007;27:743746. 319. Viller F, Guillemin F, Briancon S, Moum T, Suurmeijer T, van den Heuvel W. Compliance to drug treatment of patients with rheumatoid arthritis: A 3 year longitudinal study. J Rheuma tol. 1999;26:21142122. 320. Kauppi M, Sokka T, Hannonen P. Survey nonresponse is associated with increased mortality in patients with rheumatoid arthritis and in a community population. J Rheumatol 2005;32:807810. 321. Suzuki K, Imada G, Iwaya T, Hand a T, Kurogo H. Determinants and predictors of the maximum walking speed during computer assisted gait training in hemiparetic stroke patients. Arch Phys Med Rehabil 1999;80:179182. 322. Richards C, Malouin F, Wood Dauphinee S. Gait velocity as an outcom e measure of locomotor recovery after stroke. In: Craik R, Oatis C, eds. Gait analysis: Theory and applications St Louis: Mosby; 1995:355364.

PAGE 229

229 323. Fugl -Meyer AR, Jaasko L, Leyman I, Olsson S, Steglind S. The post -stroke hemiplegic patient. 1. A method for evaluation of physical performance. Scand J Rehabil Med. 1975;7:1331. 324. Berg K, Wood Dauphinee S, Williams JI. The balance scale: Reliability assessment with elderly residents and patients with an acute stroke. Scand J Rehabil Med. 1995;27:2736. 3 25. Berg KO, Wood -Dauphinee SL, Williams JI, Maki B. Measuring balance in the elderly: Validation of an instrument. Can J Public Health. 1992;83:S711. 326. Enright PL, McBurnie MA, Bittner V, Tracy RP, McNamara R, Arnold A, Newman AB. The 6 -min walk tes t: A quick measure of functional status in elderly adults. Chest 2003;123:387398. 327. Powell LE, Myers AM. The Activities -Specific Balance Confidence (ABC) scale. J Gerontol A Biol Sci Med Sci. 1995;50A:M28 34. 328. Myers AM, Fletcher PC, Myers AH, Sh erk W. Discriminative and evaluative properties of the Activities Specific Balance Confidence scale. Journals of Gerontology Series A: Biological Sciences and Medical Sciences 1998;53:M287M294. 329. Lai SM, Duncan PW, Keighley J. Prediction of functiona l outcome after stroke: Comparison of the Orpington Prognostic scale and the NIH Stroke scale. Stroke 1998;29:18381842. 330. Pouliot MC, Despres JP, Lemieux S, Moorjani S, Bouchard C, Tremblay A, Nadeau A, Lupien PJ. Waist circumference and abdominal sa gittal diameter: Best simple anthropometric indexes of abdominal visceral adipose tissue accumulation and related cardiovascular risk in men and women. Am J Cardiol 1994;73:460468. 331. Bjorntorp P. Abdominal obesity and the metabolic syndrome. Ann Med. 1992;24:465468. 332. Carey VJ, Walters EE, Colditz GA, Solomon CG, Willett WC, Rosner BA, Speizer FE, Manson JE. Body fat distribution and risk of noninsulin -dependent diabetes mellitus in women. The nurses' health study. Am J Epidemiol. 1997;145:6146 19. 333. Despres JP, Moorjani S, Lupien PJ, Tremblay A, Nadeau A, Bouchard C. Regional distribution of body fat, plasma lipoproteins, and cardiovascular disease. Arteriosclerosis. 1990;10:497511. 334. Zhu S, Wang Z, Heshka S, Heo M, Faith MS, Heymsfield SB. Waist circumference and obesity associated risk factors among whites in the third national health and nutrition examination survey: Clinical action thresholds. Am J Clin Nutr 2002;76:743749.

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230 335. Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, Gordon DJ, Krauss RM, Savage PJ, Smith Jr SC, Spertus JA, Costa F. Diagnosis and management of the metabolic syndrome. An American Heart Association/national heart, lung, and blood institute scientific statement. Executive summary. Cardiol Rev 2005;13:322 327. 336. Lezak MD, Howieson DB, Loring DW. Neuropsychological assessment New York: Oxford University Press; 2004. 337. Wade DT, Legh Smith J, Hewer RA. Depressed mood after stroke. A community study of its frequency. Br J Psychiatry 1987;151:200205. 338. Sinyor D, Amato P, Kaloupek DG, Becker R, Goldenberg M, Coopersmith H. Post -stroke depression: Relationships to functional impairment, coping strategies, and rehabilitation outcome. Stroke 1986;17:11021107. 339. Robinson RG, Star r LB, Kubos KL, Price TR. A two -year longitudinal study of post stroke mood disorders: Findings during the initial evaluation. Stroke 1983;14:736741. 340. Robinson RG, Price TR. Post -stroke depressive disorders: A follow up study of 103 patients. Stroke 1982;13:635641. 341. Stommel M, Wills C. The logic of statistical inference after probability sampling. Clinical research" concepts and principles for advanced practice nurses Philadelphia, PA: Lippincott Williams & Wilkins; 2003:310 328. 342. Nation al Stroke Association Site [Internet] Effects of Stroke; [updated 2009 Feb 13; cited 2009 Feb 13]. Available from: http://www.stroke.org/site/PageServer?pagename=EFFECT 343. Mud ge S, Stott S. Times walking tests correlate with daily step activity in persons with stroke. Arch Phys Med Rehabil 2009; 90: 296301.

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231 BIOGRAPHICAL SKETCH Sharon Ba rak was born in Haifa, Israel. Upon high school graduation, Sharon entere d the Israeli Defense Forces active duty. In the military Sharon served in numerous commanding and instruction duties. In 1996 Sharon was discharged from the Israeli Defense Forces as a Lieutenant. Upon discharge from the Israeli Defense Forces, Sharon sta red her college career. Ms. Barak earned her Bachelor of Education in adapted physical e ducation a nd exercise t herapy at the Zinman College of Physical Education & Sport Sciences at the Wingate Institute, Netanya, Israel. As an undergraduate student, Sha ron worked as an exercise therapy instructor, and as a teaching assistant in the nervous system and disorders class and anatomy class. In May 2004 Sharon earned a M aster of Science at the University of Florida in adapted physical education and exercise t he rapy. While in the program Sharon served as an adapted aquatics and hydrotherapy instructor, trainer and supervisor of therapeutic exercise program for persons with peripheral arterial disease, and a trainer of individuals who have experienced stroke in th e Extremity Constraint Induced Therapy Evaluation national clinical research trial. Upon graduating in May 2004 with her Master of Science, Sharon entered the University of Florida rehabilitation science doctoral program. While in the rehabilitation scien ce d octoral program, Sharon served as a graduate research assistant at the North Florida/South Georgia Veterans Health System, Rehabilitation Outcomes Research Center. In addition, Sharon served as a teaching assistant in the nervous system and disorders c lass and Musculoskeletal Anatomy class. After receiving her Ph.D. from the University of Florida, Sharon returned to Israel to work as a researcher and an educator.