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Predicting Physical Impairment and Recovery Following an Exercise-Induced Shoulder Injury

Permanent Link: http://ufdc.ufl.edu/UFE0042086/00001

Material Information

Title: Predicting Physical Impairment and Recovery Following an Exercise-Induced Shoulder Injury
Physical Description: 1 online resource (105 p.)
Language: english
Creator: Parr, Jeffrey
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: exercise, genetics, physical, predicting, shoulder
Applied Physiology and Kinesiology -- Dissertations, Academic -- UF
Genre: Health and Human Performance thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: PREDICTING PHYSICAL IMPAIRMENT AND RECOVERY FOLLOWING AN EXERCISE-INDUCED SHOULDER INJURY By Jeffrey J. Parr August 2010 Chair: Paul A. Borsa Major: Health and Human Performance The shoulder is the most mobile, but least stable joint in the entire body. This causes the shoulder joint to be predisposed to a variety of injuries, ranging from acute to chronic pathologies. Shoulder injuries account for about 20% of all injuries in sports. Research has shown that the prevalence rates for recurrent shoulder pain are significant. Previous studies have attempted to predict outcomes of shoulder pain at several time points. However, determining outcomes for physical impairment and disability are less common. The purpose of this study was to see what variables of a biopsychosocial model of pain influences the outcomes of muscle torque, self-report disability, shoulder internal rotation and level of distress at 48 and 96 hours following induction of a shoulder fatigue protocol. Patient demographics for this study were otherwise healthy men and women of any racial/ethnic background (n = 99, 24.1 + 6.4 y.o.). All subjects underwent five testing sessions that were held over five consecutive days (Day 0 to Day 4). Psychological questionnaires examined fear of pain, anxiety, catastrophizing and kinesiophobia. Participates also filled out a self-report disability questionnaire for the upper extremity, had range of motion and muscle strength measured and underwent several tests to determine pain sensitivity. Participates were then put through a shoulder fatigue protocol that was intended to induce delayed onset muscle soreness (DOMS). Follow-up measurements were taken throughout the week to determine the influence of an induced muscle injury on their outcomes. Several buccal swabs were also collected during the week to assess genetic markers from DNA. Results from our primary and secondary outcome measures showed that the shoulder fatigue protocol did a good job of inducing delayed onset muscle soreness. We found that muscle torque was significantly influenced by gender and fear of pain at both 48 and 96 hours. At 48 hours age, baseline torque measure and thermal threshold also contributed to a significant amount of variance in muscle torque. In addition, current level of pain was introduced into our model at 96 hours. Upper extremity disability, measured by the QuickDASH, was unable to be explained at 48 hours. The only significant predictor at 96 hours was the QuickDASH score (? = 0.594, P < 0.001) reported at 48 hours. While the measure of kinesiophobia over the course of the study did not change significantly, several factors were found to have a potential association at both time points. Age and gender were not found to have any significant influence on kinesiophobia at either 48 or 96 hours. However, both baseline TSK scores (? = 0.485, P < 0.001) and 48 hour TSK scores (? = 0.679, P < 0.001) were significant predictors of kinesiophobia at 48 and 96 hours respectively. The only other unique predictor of kinesiophobia at both time points was pain catastrophizing. Shoulder internal rotation also had several unique contributors that were found in models at both time points. Both the baseline internal rotation measurement (? = 0.655, P < 0.001) and the 48 hour internal rotation measurement (? = 0.857, P < 0.001) were significant predictors of shoulder internal rotation at 48 and 96 hours respectively. In addition, current pain level was a significant predictor of shoulder internal rotation at both 48 (? = -0.140, P = 0.030) and 96 (? = 0.257, P = 0.003) hours. Several models were designed that explained a large amount of variance in our primary and secondary outcomes. However, these models were not similar across outcome measures suggesting that multiple factors may influence physical impairment, upper extremity disability and levels of distress over the course of an injury. While genetic factors have been shown to play a key role in the development of chronic shoulder pain, further research is needed to determine what role these may play on other outcomes.
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 Jeffrey Parr.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Borsa, Paul A.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042086:00001

Permanent Link: http://ufdc.ufl.edu/UFE0042086/00001

Material Information

Title: Predicting Physical Impairment and Recovery Following an Exercise-Induced Shoulder Injury
Physical Description: 1 online resource (105 p.)
Language: english
Creator: Parr, Jeffrey
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: exercise, genetics, physical, predicting, shoulder
Applied Physiology and Kinesiology -- Dissertations, Academic -- UF
Genre: Health and Human Performance thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: PREDICTING PHYSICAL IMPAIRMENT AND RECOVERY FOLLOWING AN EXERCISE-INDUCED SHOULDER INJURY By Jeffrey J. Parr August 2010 Chair: Paul A. Borsa Major: Health and Human Performance The shoulder is the most mobile, but least stable joint in the entire body. This causes the shoulder joint to be predisposed to a variety of injuries, ranging from acute to chronic pathologies. Shoulder injuries account for about 20% of all injuries in sports. Research has shown that the prevalence rates for recurrent shoulder pain are significant. Previous studies have attempted to predict outcomes of shoulder pain at several time points. However, determining outcomes for physical impairment and disability are less common. The purpose of this study was to see what variables of a biopsychosocial model of pain influences the outcomes of muscle torque, self-report disability, shoulder internal rotation and level of distress at 48 and 96 hours following induction of a shoulder fatigue protocol. Patient demographics for this study were otherwise healthy men and women of any racial/ethnic background (n = 99, 24.1 + 6.4 y.o.). All subjects underwent five testing sessions that were held over five consecutive days (Day 0 to Day 4). Psychological questionnaires examined fear of pain, anxiety, catastrophizing and kinesiophobia. Participates also filled out a self-report disability questionnaire for the upper extremity, had range of motion and muscle strength measured and underwent several tests to determine pain sensitivity. Participates were then put through a shoulder fatigue protocol that was intended to induce delayed onset muscle soreness (DOMS). Follow-up measurements were taken throughout the week to determine the influence of an induced muscle injury on their outcomes. Several buccal swabs were also collected during the week to assess genetic markers from DNA. Results from our primary and secondary outcome measures showed that the shoulder fatigue protocol did a good job of inducing delayed onset muscle soreness. We found that muscle torque was significantly influenced by gender and fear of pain at both 48 and 96 hours. At 48 hours age, baseline torque measure and thermal threshold also contributed to a significant amount of variance in muscle torque. In addition, current level of pain was introduced into our model at 96 hours. Upper extremity disability, measured by the QuickDASH, was unable to be explained at 48 hours. The only significant predictor at 96 hours was the QuickDASH score (? = 0.594, P < 0.001) reported at 48 hours. While the measure of kinesiophobia over the course of the study did not change significantly, several factors were found to have a potential association at both time points. Age and gender were not found to have any significant influence on kinesiophobia at either 48 or 96 hours. However, both baseline TSK scores (? = 0.485, P < 0.001) and 48 hour TSK scores (? = 0.679, P < 0.001) were significant predictors of kinesiophobia at 48 and 96 hours respectively. The only other unique predictor of kinesiophobia at both time points was pain catastrophizing. Shoulder internal rotation also had several unique contributors that were found in models at both time points. Both the baseline internal rotation measurement (? = 0.655, P < 0.001) and the 48 hour internal rotation measurement (? = 0.857, P < 0.001) were significant predictors of shoulder internal rotation at 48 and 96 hours respectively. In addition, current pain level was a significant predictor of shoulder internal rotation at both 48 (? = -0.140, P = 0.030) and 96 (? = 0.257, P = 0.003) hours. Several models were designed that explained a large amount of variance in our primary and secondary outcomes. However, these models were not similar across outcome measures suggesting that multiple factors may influence physical impairment, upper extremity disability and levels of distress over the course of an injury. While genetic factors have been shown to play a key role in the development of chronic shoulder pain, further research is needed to determine what role these may play on other outcomes.
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 Jeffrey Parr.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Borsa, Paul A.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042086:00001


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1 PREDICTING PHYSICAL IMPAIRMENT AND RECOVERY FOLLOWING AN EXERCISE INDUCED SHOULDER INJURY By JEFFREY J. PARR A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUI REMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2010

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2 2010 Jeffrey J. Parr

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3 This dissertation is dedicated to my parents, Mr. Thomas R. Parr and Mrs. Darlene J. Parr, who have given me invaluable educational opportunities and have always encouraged me to do my best.

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4 ACKNOWLEDGMENTS I would like to acknowledge and thank my committee for all the hard work they have put in towards completing this dissertation. I appreciate Dr. Paul Borsa for taking me on as a doctoral stu dent and giving me guidance during times when my path seemed unclear. I thank Dr. Mark Tillman, Dr. Chris Gregory, and Dr. Todd Manini for filling gaps on my committee and always being patience during major changes in my research path. Most importantly, I would like to thank Dr Steven George for allowing me to use a portion of his research data for this current project. In addition to my committee, several other people were very helpful during my time at the University of Florida. Kelly A. Larkin, a fello w doctoral student and friend, who helped in collecting data, editing papers, and analyzing data. Dr. Keith Naugle who would always encourage d me and len t an ear when thinks did not turn out as planned. I would also like to thank the many interns and volun teers who worked in the Sports Medicine Research Laboratory during the time of this project.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 8 LIST OF FIGURES ................................ ................................ ................................ .......... 9 ABSTRACT ................................ ................................ ................................ ................... 10 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 13 Background and Significance ................................ ................................ ................. 13 Specific Aims ................................ ................................ ................................ .......... 18 Specific Aim 1 ................................ ................................ ................................ ... 18 Specific Aim 2 ................................ ................................ ................................ ... 18 Specific Aim 3 ................................ ................................ ................................ ... 19 2 REVIEW OF LITERATURE ................................ ................................ .................... 20 Inflammatory Cascade ................................ ................................ ............................ 20 Acute Pain and Dysfunction ................................ ................................ .................... 21 Chronic Pain and Dysfun ction ................................ ................................ ................. 22 Fear Avoidance Model ................................ ................................ ............................ 23 Biopsychosocial Model ................................ ................................ ........................... 24 Geneti cs ................................ ................................ ................................ ........... 27 Biopsychosocial Influence on Pain ................................ ................................ ......... 31 Exercise Induced Injury Model ................................ ................................ ................ 33 Rotator Cuff Injury ................................ ................................ ................................ ... 34 3 MATERIALS AND ANALYTICAL PLAN ................................ ................................ .. 37 Subject Identification and Selection ................................ ................................ ........ 37 Inclusion and Exclusion Criteria ................................ ................................ .............. 37 Experimental Design ................................ ................................ ............................... 37 Psychosocial Risk Facto rs ................................ ................................ ...................... 38 State Trait Anxiety Questionnaire (STAI) ................................ ......................... 38 Pain Catastrophizing Scale (PCS) ................................ ................................ .... 39 Fear of Pain Questionnaire (FPQ) ................................ ................................ .... 39 Tampa Scale of Kinesiophobia (TSK) ................................ .............................. 39 Impairment Measures ................................ ................................ ............................. 39 Brief Pain Inventory (BPI) ................................ ................................ ................. 39 Range of Motion (ROM) ................................ ................................ ................... 40 Contractile Functi on ................................ ................................ ......................... 40

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6 QuickDASH ................................ ................................ ................................ ...... 40 Pain Sensitivity Measures ................................ ................................ ....................... 41 Mechanical Pain Threshold (MPT) ................................ ................................ ... 41 Experimental Pain Sensitivity ................................ ................................ ........... 41 Genetic Profile ................................ ................................ ................................ ........ 42 Shoulder Injury Protocol ................................ ................................ .......................... 43 Fatigue Protocol ................................ ................................ ............................... 43 Analytic Plan ................................ ................................ ................................ ........... 44 Statistical Procedures ................................ ................................ ....................... 44 4 RESULTS ................................ ................................ ................................ ............... 49 Demographics ................................ ................................ ................................ ......... 49 E ffect of Fatigue Protocol ................................ ................................ ........................ 49 Biopsychosocial Influence on Muscle Torque at 48h ................................ .............. 49 Biopsychosocial Influence on Disability at 48h ................................ ....................... 50 Biopsychosocial Influence on Kinesiophobia at 48h ................................ ............... 50 Biopsychosocial Influence on Shoulder Internal Rotation at 48h ............................ 5 1 Biopsychosocial Influence on Muscle Torque at 96h ................................ .............. 52 Biopsychosocial Influence on Disability at 96h ................................ ....................... 52 Biopsychosocial Influence on Kinesiophobia at 96h ................................ ............... 53 Biopsychosocial Influence on Shoulder Internal Rotation at 96h ............................ 53 Genetic Influences on Outcome Measures ................................ ............................. 54 5 DISCUSSION ................................ ................................ ................................ ......... 68 Demographics ................................ ................................ ................................ ......... 69 Primary Outcome Measures ................................ ................................ ................... 69 Self Report Disability ................................ ................................ ........................ 69 Muscle Torque ................................ ................................ ................................ .. 70 Secondary Outcome Measures ................................ ................................ .............. 74 Kinesiophobia ................................ ................................ ................................ ... 74 Shoulder Internal Rotation ................................ ................................ ................ 76 Genetic Influence on Outcomes ................................ ................................ .............. 77 Conclusions ................................ ................................ ................................ ............ 80 Limitations ................................ ................................ ................................ ............... 82 Implications and Future Research ................................ ................................ .......... 83 APPENDIX A STATE TRAIT ANXIETY INVENT ORY ................................ ................................ ... 85 B PAIN CATASTROPHIZING SCALE ................................ ................................ ........ 86 C FEAR OF PAIN QUESTIO NNAIRE ................................ ................................ ........ 87 D TAMPA SCALE OF KINES IOPHOBIA ................................ ................................ .... 88

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7 E QUICKDASH ................................ ................................ ................................ .......... 89 F BRIEF PAIN INVENTORY ................................ ................................ ...................... 91 LIST OF REFERENCES ................................ ................................ ............................... 92 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 105

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8 LIST O F TABLES Table page 4 1 Descriptive Statistics for Induced Shoulder Pain ................................ ................ 56 4 2 Effect of Fatigue Protocol on Primary and Secondary Outcome Measures ........ 56 4 3 Predictor Variables at Baseline ................................ ................................ ........... 57 4 4 Factors Influencing Muscle Torque at 48hrs ................................ ....................... 57 4 5 95% Confidence Interval for Muscle Torque at 48 hours ................................ .... 57 4 6 Factors Influencing Disability at 48hrs ................................ ................................ 58 4 7 Factors Influencing Kinesiophobia at 48hrs ................................ ........................ 58 4 8 Factors Influencing Shoulder Internal Rotation at 48hrs ................................ .... 59 4 9 Predictor Variables at 48 hours post fatigue induction ................................ ....... 59 4 10 Factors Influencing Muscle Torque at 96hrs ................................ ....................... 60 4 11 95% Confidence Interval for Muscle Torque at 96 hours ................................ .... 60 4 12 Fa ctors Influencing Disability at 96hrs ................................ ................................ 60 4 13 95% Confidence Interval for Disability at 96 hours ................................ ............. 61 4 14 Factors Influencing K inesiophobia at 96hrs ................................ ........................ 61 4 15 Factors Influencing Shoulder Internal Rotation at 96hrs ................................ ..... 62 4 16 Descriptive Statistics Genetic M arkers ................................ ............................... 63 4 17 Mean and SD for Muscle Torque at 48 hours for Genetic Markers ..................... 64 4 18 Mean and SD for QuickDASH at 48 hours for Genetic Markers ......................... 65 4 19 Mean and SD for Muscle Torque at 96 hours for Genetic Markers ..................... 66 4 20 Mean and SD for QuickDASH at 96 hours for Genetic Markers ......................... 67

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9 LIST OF FIGURES Figure page 2 1 Fear Avoidance Model of Pain. ................................ ................................ ........... 36 2 2 Biopsychosocial Model. ................................ ................................ ...................... 36

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10 A bstract 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 PREDICTING PHYSICAL IMPAIRMENT AND RECOVERY FOLLOWING AN EXERCISE INDUCED SHOULDER INJURY By Jeffre y J. Parr August 2010 Chair: Paul A. Borsa Major: Health and Human Performance The shoulder is the most mobile, but least stable joint in the entire b ody. This causes the shoulder joint to be predisposed to a variety of injuries, ranging from acute to chronic pathologies. Shoulder injuries account for about 20% of all injuries in sports. Research has shown that the prevalence rates for recurrent shoulde r pain are significant. Previous studies have attempted to predict outcomes of shoulder pain at several time points. However, determining outcomes for physical impairment and disability are less common. The purpose of this study was to see what variables o f a biopsychosocial model of pain influences the outcomes of muscle torque, self report disability, shoulder internal rotation and level of distress at 48 and 96 hours following induction of a shoulder fatigue protocol. Patient demographics for this study were otherwise healthy men and women of any racial/ethnic background (n = 99 24.1 + 6.4 y.o. ). All subjects underwent five testing sessions that were held over five consecutive days (Day 0 to Day 4). Psychological questionnaires examined fear of pain, anx iety, catastrophizing and kinesiophobia. Participates also filled out a self report disability questionnaire for the upper extremity, had range of motion and muscle strength

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11 measured and underwent several test s to determine pain sensitivity. Participates w ere then put through a shoulder fatigue protocol that was intended to induce delay ed onset muscle soreness (DOMS) Follow up measurements were taken throughout the week to determine the influence of an induced muscle injury on their outcomes. Several bucca l swabs were also collect ed during the week to assess genetic markers from DNA. Results from our primary and secondary outcome measures showed that the shoulder fatigue protocol did a good job of inducing delayed onset muscle soreness. We found that muscle torque was significantly influence d by gender and fear of pain at both 48 and 96 hours. At 48 hours age, baseline torque measure and thermal threshold also contributed to a significant amount of variance in muscle torque. In addition, current level of pai n was introduced into our model at 96 hours. Upper extremity disability, measured by the QuickDASH, was unable to be explained at 48 hours. The only significant predictor at 96 hours was the QuickDASH score ( = 0.594, P < 0.001) reported at 48 hours. While the measure of kinesiophobia over the course of the study did not change significantly, several factors were found to have a potential association at both time points. Age and gender were not found to have any significant influence on kinesiophobia at either 48 or 96 hours. However, both baseline TSK scores ( = 0.485, P < 0.001) and 48 hour TSK scores ( = 0.679, P < 0.001) were significant predictors of kinesiophobia at 48 and 96 hours respectively. The on ly other unique predictor of kinesiophobia at both time points was pain catastrophizing. Shoulder internal rotation also had several unique contributors that were found in models at both time points. Both the baseline internal rotation measurement ( = 0.6 55, P < 0.001) and the 48 hour internal rotation measurement ( = 0.857, P < 0.001) were significant predictors of

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12 shoulder internal rotation at 48 and 96 hours respectively. In addition, current pain level was a significant predictor of shoulder internal rotation at both 48 ( = 0.140, P = 0.030) and 96 ( = 0.257, P = 0.003) hours. Several models were designed that explained a large amount of variance in our primary and secondary outcomes. However, these models were not similar across outcome measures su ggesting that multiple factors may influence physical impairment, upper extremity disability and levels of distress over the course of an injury. While genetic factors have been shown to play a key role in the development of chronic shoulder pain, further research is needed to determine what role these may play on other outcomes.

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13 CHAPTER 1 INTRODUCTION Background and Significance The shoulder joint is the most mobile, and least stable, joint in the human body and is therefore vulnerable to injuries from spo rt, falls, and other accidents, as well as injuries from repetitive loading as seen in some types of overhead work or sport activities. 1 In sports that are classified as upper extremity intensive (i.e. baseball, volleyball) shoulder injuries have been found to account for up to 20% of the total number of injuries. 2, 3 A study examining collegiate baseball injuries found that shoulder injuries accounted for 24% of the total injuries and 32% of the time lost from competition. 2 They also found that pitchers accounted for greatest number of shoulder injuries with 69%. 2 Injury rates are similar in sports that are not strictly conside red overhead. A review of golf related injuries found that 20% of individuals had sustained a shoulder injury involving the rotator cuff. 4 Another small study of 29 recreational golfers who required sur gical repair of the rotator cuff found that 50% of these golfers required additional surgery at a three year follow up. 5 Rotator cuff pathology is among the most common cause of pain and physical impairment in the shoulder. 6 Rotator cuff pathology can range from mild edema to massive tears of one or more muscles of the rotator cuff. Anatomically, the rotator cuff is responsible for maintaining integrity of the humeral head within the glenoid fossa and is the primary stabilizer of the glenohumeral joint. Injury to the rotator cuff can cause wide spanning problems within the shoulder joint, typically resulting in physical impairment and pain. 7, 8 When the injury involves a rotator cuff tear, surgery is usually required to regain pain free mobility. However, surgical repair does not guarantee individuals will

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14 become asymptomatic post operatively 7, 8 Thus, shoulder pathologies involving the rotator cuff may result in individuals hav ing chronic pain and prolonged physical impairments. The econom ic impact of musculoskeletal disorders in the United States continues to rise. 9 A cross sectional survey found that 35% of individuals reported persistent musculoskeletal pain that had lasted at least 3 months. 10 Prevalence rates for musculoskeletal pain in the shoulder have been found to range from 5% to 47% in the general adult population. 11, 12 While pain intensity is not a direct indicator of level of dysfunction, musculoskeletal pain has been shown to limit functional ability and prolong recovery following injury. 13, 14 Van Der Windt et al. found a correlation between severity of injury and odds of having an unfavorable outcome. 15 Van Der Windt et al. also found that 41% of individuals reporting new episodes in the shoulder were still having symptoms at a 12 month follow up. 15 A prospective cohort found that only 20% of individuals reporting new shoulder episodes completely resolved their symptoms following six months, and 50% had still not resolved after 18 months. 13 Persistent shoulder symptoms may represent a significant source of chronic pain and disability. Biological symptomatic response and disability following musculoskeletal injury. 16 23 Studie s have determined that females present with greater sensitivity to several types of stimuli following exercise induced muscle pain. 23 While the exact mechanisms for this enhanced sensitivity are not known, cultural, psychological and physiological factors have been implicated. Studies repo rting on muscular strength indicate that males have greater absolute strength than females. 17 These strength differences have been found

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15 both before and after induction of exercise induced muscle pain and dysfunction. 17 Aging in men and women has been shown to have an effect on strength measures and has also been linked to changes in pain perception. 18 Even though age and gender related pain has been extensively researched, knowledge is still limited on how the aging process influences pain perception and changes in the central nervous system functioning. For this reason geneti c factors, and their influence on shoulder pain, are receiving greater attention in the scientific literature. Most recently, a group of pain candidate genes has been gaining momentum in identifying individuals who may be at risk for experiencing increased pain intensity following injury However, these pain candidate genes have not been able to identify variations in physical impairment. Therefore, a select group of pro inflammatory genes (IL ha ve been identified as possible genetic fact or s that may play a role in the development of physical impairments following injury. 24, 25 Specifically, if these pro inflammatory genes have a role in the up regulation of pro inflammatory mediators, a greater magnitude of inflammatory response may be expected. These genes are identified by examining genetic variations for each genotype. These genetic variations determine whether an individual may have an increased or decreased expression of inflammatory mediators following injury. Psychological factors are also known pain. 26 29 The fear avoidance model, which accounts for psychological factors, was designed as a way to understand the link between injury and the chronic pain state. Several studies have suggested that catastrophizing increas es pain related fear, which

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16 in turn increases the attention and focus to the injury stimulus. 27 29 The inclination of an individual to catastrophize pain can lead to a prolonged and sometimes incomplete recovery following musculos keletal injury and has been associated with an increased rate of chronic (lingering) pain and muscle soreness. 27, 28 Individuals who catastrophize their pain tend to avoid activities they see as potentially painful. This avoidance then leads to disuse of the injured area, causing greater disability. 30 Fear avoidance then becomes a continuous cycle of catastrophizing and disuse. Another psychological factor much like fear is a nxiety. However, anxiety, unlike fear, does not need to have a present threat. Anxiety focuses on the long term before a painful threat is even present. 31 Several self anxiety (STAI), fear of pain (FPQ) and pain catastrophizing (PCS). These questionnaires can evaluate which individua ls may be at a higher risk of developing chronic pain due to the avoidance cycle. The exercise induced injury model is commonly used for answering questions related to clinical populations. 17, 26, 27, 32 35 The exercise induced injury model has a controlled and predictable inflammatory response. 17, 19, 36, 37 Therefore, using the exercise induced injury model allows one to perform measurements on healthy volunteers in a pain free state before injury is induced. Following injury induction, the changes in measurements can then be tracked during the pre clinical phase (up to 48 hrs) and through to the recovery period (48 96 hrs +). Within the exercise induced injury period, the pain response has been found to peak between 24 48 hrs, with full recovery usually occurring between five to fifteen days. 37 39 However, this time course does not hold true for all individuals. The exercise induced injury model is considered clinically

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17 relevant because participants experience inflammatory pain and related symptoms, altered proprioceiption and neuromuscular control, and difficulty in self care behaviors. 17, 19, 26, 32, 35 Previous research has acknowledged that exercise induced shoulder pain, in their negative orientation toward pain (pain catastrophizing). 26 29 In addition, exerci se induced shoulder pain has been found to be a significant factor in the variance of muscle function during recovery within 24 hrs of inducing exercise pain and soreness in otherwise healthy subjects. 26 However, additional factors need to be examined and for a longer time period Our purpose was to determine which biological and psychological risk factors, as well as which other assessment measures best explain the variance in physical impairment, self report disability and level of distress in healthy indivi duals recovering from exercise induced injury Regression models were designed to examine these factors at two time points, 48 hours and 96 hours Physical impairments were measured using maximum voluntary isometric contraction (MVIC) and range of motion ( ROM). The disability of the arm, shoulder and hand (QuickDASH) questionnaire was used to determine self report disability, while the Tampa Scale of Kinesiophobia (TSK) examine d built prediction equations t o calculate expected results for our primary outcomes (MVIC and QuickDASH) on both regression models. These regression models can then eventually be applied to the clinical setting to develop individualized rehabilitation programs based on patient risk

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18 fac tors. The ultimate goal is to identify individuals who are at an increased risk for experiencing delayed recovery of symptoms and physical impairments. Specific Aims Specific Aim 1 We design ed regression models that explain ed variance in physical impairme nt (MVIC and ROM), self report disability (QuickDASH) and level of distress (TSK) during the pre clinical phase of an exercise induced muscle injury protocol for the shoulder. Psychosocial risk factors (STAI, PCS, FPQ), pain sensitivity measures (MPT, ther mal pain threshold and tolerance, col d pain rating), time to fatigue and current level of pain were used as predictors. Hypothesis Individuals who possess psychosocial risk factors and have low levels of pain sensitivity will be associated with a greater m agnitude of physical impairment, self report disability and higher levels of distress at 48 hours. Specific Aim 2 Regression models were designed to determine if psychosocial risk factors (STAI, PCS, FPQ) pain sensitivity measures (MPT, thermal pain thres hold and tolerance, cold pain rating), current level of pain and time to fatigue were able to explain the variance in physical impairment (MVIC and ROM), self report disability (QuickDASH) and level of distress (TSK) at 96 hours

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19 Hypothesis Individuals who possess psychosocial risk factors and have low levels of pain sensitivity will be associated with delayed recovery of symptoms and ph ysical impairments at 96 hours. Specific Aim 3 A subset of our total population for genetic factors was added to all re gression models (Pre clinical Phase and Recovery Period) to determine if variation in pro inflammatory genotypes (IL ed to the predictive ability of each model. Hypothesis Variations in the selected pro inflammatory genes that results in a potentially greater magnitude of the inflammatory response will aid in the predictive ability of the pre clinical phase and recovery period.

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20 CHAPTER 2 REVIEW OF LITERATURE Acute and chronic pain are an enormous problem in the United States, resultin g in 650 million lost workdays and a financial loss of $65 billion a year. 40 The development of how pain turns from acute to chronic is still now fully understood. Research is now pointing towards chronic pain and dysfunction being related to psychosocial aspects for each individual. More recently, genetic prof iling has been examined as having a possible link to why some individuals develop chronic pain, and others do not. Understanding how pain and dysfunction becomes chronic starts with understanding the process of how it begins Acute pain is usually coincide nt with the inflammatory cascade that occurs following musculoskeletal trauma. Inflammatory Cascade Musculoskeletal trauma has two distinct segments: primary and secondary injury. Primary injury is caused by the initial tissue destruction directly associat ed with the traumatic force that occurs with ligament sprains, muscular strains and soft tissue contusions. 41 Secondary injury accompanies the acute inflammatory cascade and is characterized by ischemia and cell hypoxia, oxidative and enzymatic stress, and tissue necrosis (cell death ). 41 The damage produced dur ing the primary injury segment is irreversible, and the treatments used after the initial trauma attempt to contain the deleterious effects of inflammation, ischemia and oxidative stress, thereby limiting the extent of secondary injury. The inflammatory ca scade, although necessary, should be controlled so that healing may occur in a more timely and orderly fashion. The longer the acute inflammatory stage last, the greater the incident of secondary trauma that can occur.

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21 Acute Pain and Dysfunction The pain or algesic response following musculoskeletal trauma is initiated by stimulation of nociceptors, specialized nerve ending that respond to noxious stimuli such as thermal, mechanical and biochemical stress. 42, 43 After trauma, chemical mediators such as histamine, bradykinin, prostaglandins, and substance P are released in and around the injured tissues. 41 Chemical mediators are responsible for activating nociceptors in the zone of injury. At the site of injury primary hyperalgesia is caused by lowering the nociceptors threshold response to noxious stimuli and as a consequence magnifies the pain response. 42, 43 Secondary hyperalgesia occurs within hours a fter the initial trauma and increases the algesic zone as the noxious stimuli diffuses throughout the surrounding tissues. 44 Following trauma, two specific types of nociceptors, A delta ( ) and C fibers, transmit noxious stimuli from the periphery via afferent pathways to the dorsal horn of the spinal cord. 42, 43 Triggered by strong mechanical pressure from local swelling, chemical stimuli or intense heat, A fibers produce a fast, sharp, localized pain sensation. 42, 43 C fibers on the other hand are slow, polymodal nociceptors that are triggered by similar stimuli and generate a more diffuse, dull aching and longer lasting sensation. 43 A transmission of an impulse through the nerve fiber, but decrease their discharge rate quickly after activation. C fibers on the other hand have a longer discharge rate, and are smaller and unmyelinated, thus having a slower impulse transmission compared to their A

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22 Chronic Pain and Dysfunction Chronic pain persists longer than the temporal course of natural healing. Clinically, chronic pain is defined as pain that has persist ed for at least three months and is primarily C fiber mediated. 45 Chronic pain impairs the ability to direct attention and may significantly reduce the ability of individuals to perform attention demanding tasks. 46 Chronic pain is highly complex and not completely understood phenomenon, which is thought to be initially driven by tissue injury. 47 The most common form of chronic pain is that rs after tissue damage. 47 This has also been termed peripheral sensitization. Under persistent activation, nociceptive transmission to the dorsal horn may cause pathological changes that lower the threshold for pain signals to be transmitted. Specifically the inflammator y mediators, PGE2, bradykinin and nerve growth factor (NFG) which are released following injury effect local receptors expressed on nociceptors terminals. 48 This activates i ntracellular signaling pathways that change the threshold levels and increase the sensitivity and excitability of the nociceptors terminal. 48 Peripheral sensitization produc es increases in pain sensitivity that is restricted to the site of inflammation. 48, 49 damage to or dysfunction of the peripheral or central nervous system. 47 Central sensitization occurs in the dorsal horn of the spinal cord and is a result of the summation of stimuli caused at the peripheral. 48 that causes a cumulative depolarization that leads to a reduct ion in threshold and increases in the responsiveness of dorsal horn neurons. 48 Additionally, it has been found that levels of neurotransmitters and brain electrical signals change as neurons 48 This results in a permanent

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23 lowering of the pain threshold level within the dorsal horn and has been found to have a more diffuse effect then peripheral sensitization. 48 Fear Avoidance Model acute i njury has baffled researchers and clinicians for decades 30 Researchers have not been able to determine the mechanisms that turn acute pain and dysfunction into chronic pathologies. The fear avoidance model was developed as a way to understand this transformation from acute to chronic pain (Figure 2 1) Fear avoidance, which refers to the avoidance of movements or activities based on fear, has been put forth as a central mechanism in the development of chronic pain pathologies. In the past several decades, ther e has been an increasing number of experimental and clinical studies that have been able to show that fear and anxiety influence the experience of pain and in particular, support a link to chronic pain. 30 The fear avoidance model examines the emotional behavior of an individual and then separates them into confronters or avoiders. 30 Confronters display several adaptive traits: views pain as a temporary nuisance, strongly motivated to return to normal activities, and is prepared to confront personal pain barriers. Avoiders on the other hand tend to steer clear of activities that they see as potentially painful. This avoidance then leads to physical consequences of inactivity. Depending on the nature of the injury, the y may then develop adhesions resulting in loss of motion, loss of muscular strength, and the inability to perform normal activities of daily living. Prolonged avoidance of movements and activities is assumed to cause detrimental changes in the musculoskele 50, 51 This disuse is described as a physical deconditioning as a consequence of reduced use of the

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24 musculoskeletal system 52 and impairments in muscle coordination, leading to guarded moveme nts. 53 The hardest part of the fear avoidance model is being able to identify and measure beliefs of pain related fear. Many questionnaires have been developed over the years to measures attitudes and beliefs about pain; however, one of the more recent questionnaires has gained the most attention. The Tampa Scale of Kinesiophobia (TSK) is a questionnaire that is aimed at assessing fear of (re)injury due to movement. 54 Research has shown that levels of disability were best predicted by the TSK. 55 57 In addition, the TSK has been shown to discriminate well between avoiders and confronters during a behavioral performance task. 56 The only downside to these questionnaires is that they do not identify what causes the individual to be fearful However, the TSK is a relatively short questionnaire that is appropriate for use in a clinical setting. Biopsychosocial Model The biopsychosocial model of fear related pain was developed to fill in the gaps that the fear avoidance model left out (Figure 2 2) Specifically, what causes one individual to develop chronic pain while another d oes not? The biopsychosocial model focuses on pathology as a complex interaction of biological, psychological, and social factors. The biological component of the biopsychosocial model seeks to understand how the cause of the pathology stems from the funct 58, 59 The psychological component of the biopsychosocial model looks for potential psychological causes for lacking of healing, such as negative thinking. 58, 59 The social part of the biopsychosocial model i nvestigates how different social factors such as

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25 socioeconomic status, culture, poverty, technology, and religion can influence health. 58, 59 Negative mood plays a significant role in individuals who develop chronic pain pathologies. An individua negative mood is likely to influence treatment motivation and compliance with treatment recommendations. A negative orientation toward actual or anticipated painful experiences has been termed pain catastrophizing. Pain catastrophizing is cons idered a maladaptive cognitive coping style comprised of symptom magnification, rumination, and feelings of helplessness. 26 29, 60 Pain catastrophizing, or responses to pain that characterize it as being awful, horrible, and unbearable, are increasingly recognized as extremely important contributors to the experien ce of pain. Individuals who catastrophize have difficulty shifting their focus of attention away from painful or threatening stimuli and attach more threat or harm to a non painful stimuli. 61 63 Several studies have suggested that catastrophizing incre ases pain related fear, which in turn increases the attention to the stimulus. 26 28 The impulse of an individual to catastrophize pain c an lead to diminished recovery following musculoskeletal injury and has been associated with an increased rate of chronic pain and dysfunction. 26, 27, 29 The Pain Catastrophizing Scale (PCS) was developed to assess the three components of catastrophizin g: rumination, magnification, and helplessness. 64 The PCS has been shown to be a valid tool across gender and in clinical and non clinical populations. 64 66 The PCS asks subjects to rate the extent to which they believe an experience would be painful by recalling former painful experiences. Pain catastrophizing has also been associated with a number of indices of pain sensitivity in

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26 experimental pain testing, both among healthy pain free participants and ind ividuals with various chronic pain conditions. 67, 68 Most importantly, pain catastrophizing is related to adverse pain related outcomes. 68 70 Pain catastrophizing assessed pre surgery has been shown to account for significant variance in postsurgical pain ratings, drug use, depression, and disability levels. 71 75 Once catastrophizing is i dentified, the next question becomes what to do with individuals who are considered at high risk for developing chronic pain. Most research has focused on patient education, however additional steps may be necessary. 76, 77 In addition to education, graded exposure to fearful activities can b e incorporated into patients rehabilitation programs. 78 Graded exposure involves determining which activities are deemed the most fearful by the patient. Then activities that are deemed minimally fearful are performed until patients realize they can perform the task without pain. Patients then proceed to the next fear ful task until they are able to perform even the most feared activity. Research has shown that graded exposure has been very effective in altering fear avoidance beliefs and catastrophizing in patients experiencing chronic pain. 79, 80 The main addition to the biopsychosocial model was the examination of biological processes within the human body. With the completion of the human genome project, researches became more interested in the link between DNA and chronic pain pathologies. Early studies indicated that when monozygotic (MZ) twins were compared to dizygotic ( DZ) twins, where both twins shared a similar family environment, MZ twins had similar pain responses while DZ twins were different. This led researchers to believe that pai n may be genetically regulated. 81

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27 Genetics Genetics is the study o f biologically inherited traits including traits that are influenced in pa rt by the environment. 82 Each species of living organism has a unique set of inherited characteristics that makes it different from other species. Each species has its own developmental plan, often called molecules present in its cells. 82 However, human beings are by no means identical to each other. DNA consist of two long chains of subunits, each twisted around the other to form a double stranded helix. 82 The subunits of each strand are nucleotides, each of which contains any one of four chemical element s called a base. Each strand is encoded with a specific se quence of bases that makes up its functional characteristics. When the bases of a specific DNA sequence are altered, added, or deleted the gene is said to have a mutation. 82 This mutation, in some cases, causes a different functioning of the gene. When a single gene coded for a specific function can act in two or more ways, it is said to have a polymorphism. Recent studies have begun to look at genetics and there relationship in the development of chronic pain pathologies. Studies have identified several high priority pain candidate genes due to their physiological processes involved in pain sensitivity. Some recent studies have identified catechol O methyltranserase (COMT) as a specific gene responsible f 27, 28 COMT is an enzyme with wid e ranging biological functions and is potentially involved in a number of pathophysiological processes. 27, 28 One of the primary functions of COMT is metabolizing involved in pain modulation via endogenous opioid function. 27, 28 George et al. were able to show that individuals possessing genetic (low COMT activity) and psychological (high pain catastrophizing) risk factors had a

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28 tendency to report higher pain perception rating s following musculoskeletal injury. 27, 28 This was later confirmed by Nackley et al. when they f ound that low COMT activity leads to increased pain sensitivity via a beta2 adrenergic mechanisms. 83 On the contrary, a recent study by Hocking et al. reported the COMT had no association with pain status. 84 However, they did find that functional variants in the beta2 adrenergic receptor may predispose individuals to chronic pain development. 84 Several other neurotransmitters, receptors, transporters, and metabolic enzymes have also been identified as possible key contri butors in the development of chronic pain. Inflammatory cytokines have also gained recent popularity for their possible role in pain sensitivity and their ability to regulate peripheral and central nervous system mediated responses to inflammation. Pain is associated with inflammation in that nerve endings sense the swelling of local tissues and this mechanical pressure causes painful impulses triggered in the brain. Inflammation can also cause loss of mobility and decreases in muscular strength due to the mechanical pressure it exerts on the injured area. Several cytokines have been cited for their pro inflammatory role during injury. Interleukin 1 Interleukin 1 (IL 1) exists in two forms, IL 1 and IL 1 which are controlled in separate sections of DNA. 85, 86 Even thought two form s of IL 1 exist both bind to IL 1 receptor subtypes and share biologic activities. 86 IL 1 and its related family members are primarily pro inflammatory cytokines and stimulate the expression of genes associated with inflammation. 87 The main property of IL 1 in inflammation is the initiation of cyclooxygenase type 2, type 2 phosp holipase A and inducible nitric oxide synthase. 87 IL 1

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29 dendritic cells. 85, 87 IL 1 fulids. 85 Circulating IL 1 has also been know to mediate the release of other cytokines, mainly prostaglandin E2 (PGE2). 85, 87 PGE2 is thought to be the most potent inducer of fever in humans. 85, 88 Hyperalgesia, or increased pain sensitivity, is closely associated with fever following systemic and peripheral administration of IL 86, 88, 89 Several studies in rat models have identified that IL 1 injected into the rat induces fever and affects pain sensitivity. 86, 88, 89 The exact mechanism(s) that cause this are still controversia l. It is believed that IL 1 released peripherally causes activation in the peripheral nerves that transmit signals back to the brain. This causes release of pro inflammatory cytokines in the glia in the central nervous system. The release of pro inflammato ry cytokines in the central nervous system triggers an illness response that signals the hypothalamus to increase core body temperature. It has also been found that IL 1 produced in the glial cells may mediate chronic neuropathological changes associated w ith some neurodegenerative diseases. 85 The primary focus of IL 1 has been on IL 1 because of its know interaction with IL 6 and tumor necrosis factor (TNF ). 90 Interleukin 6 Interleukin 6 (IL 6) is a cy tokine much like IL 1. IL 6 is produced in various cells throughout the body including T cells, B cells, monocytes, fibroblasts, osteoblasts, and endothelial cells. It is thought to play a key role in the systemic inflammatory response during illness or fo llowing injury. Il 6 binds to the IL 6 receptor in humans, which in turns binds to gp130, a membrane based protein. Once IL 6 and its receptor bind to gp130 it activ ates and its functions include promotion of T cell proliferation and the differentiation

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30 of B cells and macrophages. Unlike IL 1, IL 6 is considered both a pro inflammatory and anti inflammatory cytokine. It is considered anti inflammatory because of its inhibitory effects on IL 1 IL 6 pro inflammatory functions are the stimulation of acute phase proteins and the induction of leukocytosis, fever and angiogenesis. IL 6 has the ability to cross the blood brain barrier and initiate synthesis of PGE2 in the hypothalamus. IL 6 is also thought to contribute to the translation from acute to chronic inflammation by its mononuclear cell accumulation at the site of injury. Edwards et al. found that healthy individuals who reported high pain catastrophizing scores had elevated pain in tensity rating during noxious heat and cold. 91 Additionally, administration of the noxious stimuli resulted in higher serum levels of IL 6, which were related to greater pain sensit ivity in those with high catastrophizing scores. 91 IL 6 has also been linked to pain severity in individuals experiencing chronic inflammatory conditions. Elevated levels of IL 6 ha s also been found to play a key role in individuals with rheumatoid arthritis, 92 osteoarthritis 93, 94 back pain, 95, 96 and in cardiac 97 and fibromyalgia patients. 98, 99 Studies have found that IL 6 levels are associated with postoperative pain 96, 100, 101 and reduced functioning. 102 Variations in the IL 6 gene have been linked to a wide variety of diseases and are known to play a role in pain sensitivty. 90 Tumor Necrosis Factor Tumor Necrosis Factor (TNF ) is another pro inflammatory cytokine much like IL 1 and IL 6. TNF 2. There biological functions are similar, but there locations vary. TNF receptors can be found on virtually all cell types except for red blood cells. 103 Specifically, TNF receptor 1 can be

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31 found on nociceptors, which is believe to influence pain sensitivity during periods of illness or injury. 103 TNF receptor 2 is found mainly on immune cells and is primarily responsible for cell apoptosis. 103 TNF produced by a variety of other immune related cells. 104 Its release is stimulated by circulating IL 1 in the plasma. It functions much like IL 1 and IL 6 and usually acts in conjunction with these other cytokines. TNF 6, that it influences severe chronic conditions. However, TNF disease and dementia. Previous studies suggest TNF or component in the development of sciatica, cervical radiculopathy, and other forms of disc related pain. 105 107 The mechanism(s) through which TNF understood, however it is thought to occur because of increased glial activity, over activation of nociceptors, and /or irritation of the nucleus pulposus of the spinal intervertebral discs. 105, 106, 108 Biopsychosocial Influence on Pain Musculoskeletal pain sensitivity is a complex phenotype associated with large inter individual variation. 81 While biological, psychological and social factors are s perception of pain and their overall pain experience, little is still known on how all these factors are connected. Several studies have begun to examine the influence of fear avoidance and biopsychosocial risk factors on the development of chronic pain pathologies. The fear avoidance model was initially developed as a cognitive behavioral model for the treatment of chronic low back pain patients. 30, 56, 109 At its simplest form, it saw individuals as either confronters or avoiders. 31 Several studies found that when patients were p resented with a potential

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32 pain experience, those who were currently experience chronic low back pain would either avoid the activity all together or only perform sub maximally and with safety behaviors. 110, 111 Picavet et al. realized that pa in catastrophizing also played a role in the development of future chronic low back pain and disability. 112 Individuals with anxiety and a negative mood toward physical therapy were less likely to perform the prescribed exercises. Karjalainen et al. found that when a multidisciplinary biopsychosocial approach was used in the rehabilitation programs of patients experiencing sub acute lo w back pain, the occurr ence of chronic disability, were significantly decreased and patients had a faster return to work. 113 Criticisms of chronic low back pain studies were that individuals presented with different levels of dysfunction and pain, which were attributed to differences in mechani sm of injury, pain duration, and variations in treatment levels. Researchers began looking at the biopsychosocial model of pain in an exercise induced injury model. This allowed researches to control many of the factors that influenced chronic low back pai n studies in an environment that is both controlled and predictable. More recently, these studies have been performed by inducing delayed onset muscle soreness in the shoulder. The shoulder region represents an area that is a common source of musculoskelet al pain and has an accessible protocol for inducing injury. George et al. found that fear of pain was a primary predictor in clinical and evoked pain reports as well as disability and kinesiophobia in the shoulder 26 However, they later found that pain catast rophizing was the primary outcome predictor in individuals who were currently experiencing clinical pain in their shoulder. 114 This was consistent from other studies that looked at the association between pain catastrophizing and pain

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33 intensity. 112, 115 118 George et al. went on to find that the relationship between pain catastrophizing and the gen e COMT was a key interaction in determining potential for the development of chronic pain syndromes in both exercise induced injury 27 and individuals experiencing clinical shoulder pain. 28 Exercise Induced Injury Model During eccentric exercise the resistance against an applied force is greater than the muscular f orce being produced, and the muscle lengthens while activated, hence acting as a brake to control motion of the body. 119 A consequence of eccentric exercise is structural damage of the sarcomere, protein degradation and leakage from injured muscle fiber, an acute inflammatory reaction, loss of muscle force and delayed onset muscle soreness (DOMS). 14, 37, 39, 120, 121 DOMS is a sensation of dull, aching pain, usua lly felt during movement or palpation of the affected muscle, combined with tenderness and stiffness, which deve lops from several hours to 24 48 hours after eccentric exercise. 37 39 DOMS has been found to peak between one to three days post exercise and disappears within five to fifteen days. 37 39 Another response to eccentric exercise is an activation of the inflammatory cascade, which starts within several hours of t he muscle becoming injured and is characterized by neutrophils migrating to the site of injury. 37 The acute inflammatory process may last up to 48 to 72 hours. The inflammatory and symptomatic responses to eccentric exercise may differ between muscle injury protocols of the leg (e.g. quadriceps) and arm (e.g. rotator cuff) muscles. Jamurtas et al. showed that arm eccentric exercise induced greater decreases and slower recovery of strength, and greater increases in blood biomarker concentrations then leg exercises. 14 Theories behind the mechanisms for these differences include variations on the daily use of the muscle groups, as well as muscle

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34 structure and fiber type. Since lower extremity muscles have shown a greater resistance to producing DOMS, more researchers are choosing the upper extremity exercise induced injury model. This model has a controlled and predictable inflammatory response, which allows research to studies the functional and symptomatic responses to injury. 17, 19, 20, 36, 38, 122 Rotator Cuff Injury The rotator cuff is a group of muscles that originates on the scapula and attaches to the humerus. This group of muscles consists of the supraspinatus, infraspinatus, teres minor, and subscapularis. It s functional role is as the primary stabilizer of the glenohumeral joint, although it plays a role in the internal and external rotation of the shoulder. The tendons of the rotator cuff pass through the coracoacromial arch as they att ach to the head of the humerus. It is at the site under the acromion process that a number of pathological conditions for the rotator cuff can occur. During elevation above shoulder level, the tendons of the rotator cuff become impinged on the undersurface of the acromion. Impingement syndrome and tendonitis of the rotator cuff are the most common intrinsic causes of shoulder pain and disability. 123, 124 Furthermore, w hen the rotator cuff muscles become injured, it can also lead to glenohumeral joint instability. 125 Most impingement syndromes and tendonitis of the rotator cuff occur due to repetitive overhead movements. Glenohumeral joint instability can occur because o f a weaken of the stabilizing muscles with repetitive movement or due to traumatic injury. 126 Traumatic glenohumeral joint instability is more common in contact sports (i.e. football, wrest ling, ice hockey). 126 Many overhead athletes move through the extremes of motion and become predisposed to the development of instability through a mechanism of plastic capsular deformation 127 Studies have found that collegiate athletes with

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35 glenohumeral joint instability fail conservative treatment about 75% of the time and end up having recurre nt instability episodes. Surgical treatment minimizes recurrent instability and is unsuccessful in restoring normal function in 70% of patients. 128, 129 Impingement of the rotator cuff between the acromion process and the greater humeral tuberosity occurs during overhead activities. Shoulder impingement is characterized by pain that is greater when the arm is elevated above shoulder level. A survey examining competitive and recreational athletes in Hong Kong found that 44% of these athletes response with symptoms related to shoulder impingement. 130 Of this 44%, 52% reported pain as there major complaint, whil e 36% reported functional impairments as their greatest complaint. This injury rate is comparable to another study performed in Australia that evaluated the injuries seen by 192 different chiropractors. 131

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36 Figure 2 1 Fear Avoidance Model of Pain. Figur e 2 2 Biopsychosocial Model Waddell G. (1998) The back pain revolution.

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37 CHAPTER 3 M ATERIALS AND ANALYTI CAL PLAN Subject Identification and Selection Patient demographics for this study were otherwise healthy men and women of any racial/ethnic background between the age of 18 and 85 years old (n = 9 9 ) Research assistants conduct ed the recruitment and initial screening of subjects and were responsible for study retention and adherence to the protocol. Recruitment strategies include d the use of posted advertisements in bu ildings located at various sites around campus and student classrooms. Volunteers who respond to the advertisement were initially screened for eligibility. Inclusion and Exclusion Criteria Each subject was screened either over the phone or through email. Individuals were excluded if they have had a recent (previous six months) injury to their upper extremity that prevent ed them from performing the exercise. We exclude d subjects if they regularly participate in upper extremity weight training or regularly t ook pain medication. Regular upper extremity weight training refer red to any weight (resistance) training more than once per week in the previous six weeks. Individuals were also excluded if they ha d any type of neurological impairments to the upper extrem ity or were currently experiencing pain. Experimental Design All subjects underwent five testing sessions that were held over five consecutive days (Day 0 to Day 4). During the first session (Day 0), subjects (1) read and sign ed the informed consent, (2) complete d a series of brief questionnaire asking for demographic data including age, height, and weight, (3) fill ed out four self validated questionnaires

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38 examining psychosocial risk factors, (4) ha d pre injury (baseline) impairment measurements taken (5) ha d three buccal swabs for DNA performed, and (6) perform ed an eccentric exercise protocol on their dominant shoulder to induce pain and dysfunction. Subjects were asked to return to our lab on days 1, 2, 3, and 4 post injury. All follow up visits were sch edule within a plus or minus three hour window of their recorded time of fatigue. Follow up measurements were identical to baseline measurements. However, DNA was only collected again on their final visit. Psychosocial Risk Factors Evidence shows that psyc perception of pain as well as their overall pain experience and the likelihood that they will take longer to recover from musculoskeletal injury. Kinesiophobia, negative mood (anxiety) and pain catastrophizin g are maladaptive cognitive coping behaviors involved in the development and maintenance of lingering pain and disability following musculoskeletal injury. We include d four validated self report questionnaires in our study in order to determine if a subjec t possesses the selected psychological risk factors. The four questionnaires are described below. State Trait Anxiety Questionnaire (STAI) The STAI is a 20 item, 4 point rating scale that was used to assess dispositional and situational anxiety symptoms. Subjects complete d this questionnaire only on day 0 before the experimentally induced injury model was performed. The STAI was only performed on Day 0 because previous research has showed the STAI to be very consistent over short intervals.

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39 Pain Catastr ophizing Scale (PCS) The PCS consists of 13 items and is rated on a 5 point scale. Subjects were instructed to rate the degree to which they have specified thoughts and feelings when experiencing pain. Subjects were asked to fill out the PCS on each visit to the lab. The PCS is validated for clinical and nonclinical populations. 64, 66 Fear of Pain Quest ionnaire (FPQ) The FPQ is an abridged version of the FPQ III. The FPQ III is a commonly used and well validated instrument that is appropriate for use in non clinical and clinical populations. 132 The shortened version contains 9 items and uses a 5 point rating scale that me asures fear about specific situations that would normally produce pain. Subjects were asked to complete this questionnaire only on the first visit. Tampa Scale of Kinesiophobia (TSK) The TSK consists of 11 items and is rated on a 4 point scale. The TSK is used to measure the fear of movement or re injury. Subject s were asked to fill out the TSK on each visit to the lab. The TSK has been deemed a valid and reliable method for determining fear of re injury in both clinical and nonclinical populations. 133 Impairment Measures Brief Pain Inventory (B PI) The BPI was used to measure pain intensity. The BPI is an abridged version used in non clinical populations, consists of 4 questions, and is rated on an 11 point scale (0 10). The BPI asks subjects to rate their pain at worst, best and average over the past 24hrs and includes a rating for current pain. The BPI has been found to have good test retest reliability, especially over shorter intervals. 134

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40 Range of Motion (ROM) Passive internal rotation of the gleno humeral joint was measured using a standard plastic goniomete r. Subjects were placed in a supine position on a padded table with their shoulder abducted to 90 and their elbow slightly off the table. The stationary arm of the goniometer was held perpendicular to the floor and the moment arm was aligned with the medi al styloid of the ulna. The fulcrum of the goniometer was aligned with the olecranon process of the ulna. The subject was instructed to relax as they were pushed into internal rotation. The end point for internal rotation was shoulder beg an to lift off the table. Contractile Function Muscle strength was measured on a Kin Com dynamometer (Chattecx Corp., Chattanooga, TN). Subjects were strapped into the Kin Com per recommended standards by the manufacture. The dominant arm was p ositioned at 45 o of abduction and 45 o of external rotation. This position has been found to limit impingement of the rotator cuff under the acromion process. 34, 135 The arm remain ed stationary while subjects push against the pad with a force sensor embedded. T he subject perform ed three trials and the highest strength measurement was recorded in pounds of force This measurement was then converted to torque (F x D). Distance was recorded as the length of the level arm, which remained constant throughout the week QuickDASH The QuickDASH (Disability of the Arm, Shoulder, and Hand) was completed and focus ed living. The QuickDASH is an abridged version of the full DASH and has been f ound to be a valid and reliable tool for determining functional status. 136 138

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41 Pain Sensitivity Measures Mechanical Pain Threshold (MPT) Mechanical pain threshold was measured on the acromion process u sing an instrumented device (Force Ten FDX, Wagner Instruments, Greenwich, CT) that applie d focal pressure to the targeted area. 139, 140 Pressure was applied to th e specified point at a rate of 1 kg/sec until the pressure turn ed to pain. The subject was instructed to indicate by ed to pain. Each measurement was performed four times and the average of the four scores was recorded in kg of force. 141 Experimental Pain Sensitivity Subjects und erwent psychophysical sensory testing to determine experimental pain sensitivity to thermal stimuli. Contact heat stimulation was performed using the Pain & Sensory Evaluation System (Medoc Ltd., Ramat Yishai, Israel), which uses t he Advanced Thermal Stimu lator (ATS) thermode to increase temperature over the forearm. The thermode heating rate is up to 70 o C/s, the cooling rate is up to 40 o C/s, and the baseline temperature is 32 o C. All thermal stimuli were delivered bilaterally to the upper extremity. Stimula tion sites were varied to prevent carryover effects due to local sensitization. Throughout the testing subjects were asked to rate their pain intensity on a visual analog scale ( VAS ) Subjects were Thermal Pain Initially, thermal pain threshold and tolerance were measured using the ATS thermode. The ATS thermode was forearm. Pain threshold was performed by applying the thermode and instructing the

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42 tolerance was became intolerable. Following each measurement, subjects were asked to rate t heir pain intensity on a VAS. Threshold and tolerance measures were recorded twice each on both arms. Cold Press o r Test Subjects were asked to submerge their dominant hand in a circulating cold tank. The water temperature was set at 8C. Subjects were inst ructed that they need ed to submerge their hand for a minimum of 30 s econds At the 30 second mark, they were ask ed for their pain rating in their dominant hand. After a pain rating was recorded, subjects were told they may remove the ir hand if necessary, b ut try to leave it in for up to a full minute. Subjects were then instructed to remove the ir hand from the cold water bath at the 1 minute mark if they had not already done so Finally, tim e spend in the cold water bath was recorded. Subjects were not give n any indicators of time remaining. Genetic Profile All genotyping was previously implemented. DNA was extracted from subject buccal swabs using the Gentra PureGene system (Minneapolis, MN), with whic extensive experience. DNA quality and quantity was verified with spectrophotometry and samples w ere diluted to 10 ng/ul. The DNA samples were genotyped in 96 well plate formats using the ABI TaqMan single nucleotide polymorphism ( SN P ) genotyping system at the UF Pharmacogenetics Core building. The plates include d some blanks and duplicates for quality control. These samples were genotyped in batches since the samples were arriving throughout th e project. In addition, the SNP s were ge notyped by

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43 as a further quality control. Three establi shed SNP s for each genetic marker (IL 6 were genot yped. The three established SNP s for IL were rs3917368, rs1143627, and hCV138866, which distinguish 88% of the Caucasian haplotypes. 90 The three established SNPs for IL 6 were rs4719714, rs1800795, and hCV283721, which distinguish 91% of the Caucasian haplotypes. 90 The three established SNP were rs1800683, rs2857713, and rs1800629, which distinguish 84% of the Caucasian haplotypes. 90 Shoulder Injury P rotocol Fatigue Protocol Shoulder injury was induced using a Kin Com isokinetic dynamometer. Subjects were placed in a seated position, with shoulder straps applied to support the torso as was place d in the scapular plane because this position has been associated with high test retest reliability and is believed to have decreased impingement of the greater tuberosity under the acromion. Maximum voluntary isometric contractions (MVIC) w ere determined by having the participants perform three repetitions of isometric shoulder external rotation. Participants were asked to perform the contraction with maximal effort and were given verbal encouragement during the contractions. The highest value was recorded as the ir MVIC. After initial MVIC was determined, subjects complete d isokinetic concentric/eccentric external rotation repetitions to induce an experimental muscle injury. The first set of repetitions was completed at 100 o /s to familiarize the subjects w ith the testing apparatus and protocol. Then, the speed was lowered to 60 o /s for 3 sets of

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44 10 repetitions that constitute d the injury protocol. Following the isokinetic repetitions, MVIC was measured and if subjects c ould still generate greater than 50% of their initial MVIC, they perform ed an additional 1 to 8 sets of 10 repetitions at 60 o /s until their peak force was lower than 50% of the initial MVIC. Previous research has indicated the inability to achieve 50% of initial peak MVIC is a consistent indica tor of muscle fatigue. Subjects were given 30 seconds rest between sets. Time to fatigue was recorded as total number of repetitions performed at a speed of 60 o /s. Analytic Plan Statistical Procedures All data analyses were performed using PASW for Window s 18.0 (SPSS, Inc., Chicago, IL). Standard significance for non regression statistics were set a prior at an alpha of < 0.05. Summary statistics are provided for all demographic, psychological, pain sensitivity and impairment measures. Genetic data was ana lyzed by calculating allele frequencies and testing for Hardy Weinberg equilibrium, as well as for differences in allele frequencies across race and gender categories. In addition, a series of analyses was needed t o properly examine the data. B ivariate cor relation s were run for all predictors to determine if any of our predictor variables were highly correlated. Any correlation found to be above 0.75 was considered to have a significant correlation. 142 Outliers were examined by running stem & leaf plots along with box plots Any dat um that fell + 3 SD outside the mean was evaluate d further to determine if th e datum was a true random outlier versus being consistent with the rest of the subjects data 143 All data was excluded in a pair wise fashion, meaning only that cell of dat um that was an outlier was eliminated. To determine normality of our data, scatter plots and histograms were examined All multiple regres sions were run using forward selection A significance level

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45 criteria of p < 0.05 for entry and p < 0.10 for removal was used to determine which variables were added or excluded from the equation. 144 147 Predictor variables were added i n order from lowest to highest p value u ntil all remaining variables me t the selection criteria. Specific Aim 1 Descriptive statistics and Pearson correlations were run for psychosocial risk factors, impairment measures, and pain sensitivity measures. A mu ltiple regression analysis was run to determine which predictors explain ed the most variance for MVIC, ROM, QuickDASH and the TSK. Each multiple regression was run using a block design. For MVIC, the first block contain ed the independent variables age, gen der and basel ine MVIC. The second block include d psychosocial risk factors (STAI, PCS, FPQ), pain sensitivity measures (MPT, thermal pain threshold and tolerance, cold pain rating), time to fatigue and current pain intensity. Independent variables from the first block were automatically used in the final model. Independent variables from the second block were only kept in the final model if they were found to be statistically reliable predictors of MVIC at 48 hrs. These results were then used to create a pr ediction equation, with a 95% confidence interval on each variable containing age, gender, baseline MVIC and any independent variable from the second block found to be statistically reliable for predicting MVIC outcomes at 48 hrs. For QuickDASH, the first block contain ed the independent variables age, gender and baseline QuickDASH score. The second block include d psychosocial risk factors (STAI, PCS, FPQ), pain sensitivity measures (MPT, thermal pain threshold and tolerance, cold pain rating), time to fati gue and pain intensity. Independent variables

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46 from the first block were automatically used in the final model. Independent variables from the second block were only kept in the final model if they were found to be statistically reliable predictors of Quick DASH scores at 48 hrs. For ROM, the first block contain ed the independent variables age, gender and baseline ROM. The second block include d psychosocial risk factors (STAI, PCS, FPQ), pain sensitivity measures (MPT, thermal pain threshold and tolerance, co ld pain rating), time to fatigue and pain intensity. Independent variables from the first block were automatically used in the final model. Independent variables from the second block were only kept in the final model if they were found to be statistically reliable predictors of ROM at 48 hrs. For TSK, the first block contain ed the independent variables age, gender and baseline TSK scores. The second block include d psychosocial risk factors (STAI, PCS, FPQ), pain sensitivity measures (MPT, thermal pain thre shold and tolerance, cold pain rating), time to fatigue and pain intensity. Independent variables from the first block were automatically used in the final model. Independent variables from the second block were only kept in the final model if they were fo und to be statistically reliable predictors of TSK scores at 48 hrs. Specific Aim 2 Descriptive statistics and Pearson correlations were run for psychosocial risk factors, impairment measures, and pain sensitivity measures. A multiple regression analysis w as run to determine which predictors explain ed the most variance for MVIC, ROM, QuickDASH and the TSK. Each multiple regression was run using a block design.

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47 For MVIC, the first block contain ed the independent variables age, gender and MVIC at 48 hrs. The second block include d psychosocial risk factors (STAI, PCS, FPQ), pain sensitivity measures (MPT, thermal pain threshold and tolerance, cold pain rating), and time to fatigue as measured at 48 hrs. Independent variables from the first block were automatica lly used in the final model. Independent variables from the second block were only kept in the final model if they were found to be statistically reliable predictors of MVIC at 96 hrs. These results were then used to create a prediction equation, with a 95 % confidence interval on each variable containing age, gender, MVIC at 48 hrs and any independent variable from the second block found to be statistically reliable for predicting MVIC outcomes at 96hrs. For QuickDASH, the first block contain ed the indepen dent variables age, gender and QuickDASH scores at 48 hrs. The second block include d psychosocial risk factors (STAI, PCS, FPQ), pain sensitivity measures (MPT, thermal pain threshold and tolerance, cold pain rating), and time to fatigue as measured at 48 hrs. Independent variables from the first block were automatically used in the final model. Independent variables from the second block were only kept in the final model if they were found to be statistically reliable predictors of QuickDASH scores at 96 h rs. These results were then used to create a prediction equation, with a 95% confidence interval on each variable containing age, gender, baseline QuickDASH scores and any independent variable from the second block found to be statistically reliable for p redicting QuickDASH scores at 96 hrs. For ROM, the first block contain ed the independent variables age, gender and ROM at 48 hrs. The second block include d psychosocial risk factors (STAI, PCS, FPQ),

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48 pain sensitivity measures (MPT, thermal pain threshold a nd tolerance, cold pain rating), and time to fatigue as measured at 48 hrs. Independent variables from the first block were automatically used in the final model. Independent variables from the second block were only kept in the final model if they were fo und to be statistically reliable predictors of ROM at 96 hrs. For TSK, the first block contain ed the independent variables age, gender and TSK scores at 48 hrs. The second block include d psychosocial risk factors (STAI, PCS, FPQ), pain sensitivity measures (MPT, thermal pain threshold and tolerance, cold pain rating), and time to fatigue as measured at 48 hrs. Independent variables from the first block were automatically used in the final model. Independent variables from the second block were only kept in the final model if they were found to be statistically reliable predictors of TSK scores at 96 hrs. Specific Aim 3 A subset of genetic samples ( n = 88 ) was used to identify if variations in genetic markers of pro inflammatory genes aid in the predictive ab ility of MVIC, ROM, QuickDASH and TSK. Descriptive statistics and Pearson correlations were run for genetic markers, psychosocial risk factors, impairment measures, and pain sensitivity measures. Multiple regression analysis based on the final models from specific aims 1 and 2 w ere run, to determine which predictors explain the most variance for MVIC, ROM, QuickDASH and the TSK. Variables found to be statistically reliable predictors of our outcomes in specific aims 1 and 2 were entered first. Our eight gen etic SNPs were then added in a second block to determine if they added to the predictive ability of each model.

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49 CHAPTER 4 R ESULTS Demographics All subjects read and signed the university approved informed consent prior to participation in this investigati on. A total of 100 subjects were collected. One subject s data w ere removed completely due to experiencing back pain during participation (n = 99) Preliminary analysis of our data showed none of our predictor variables were significantly correlat ed One da tum point was removed from the baseline score of MPT due to it being an extreme outlier. Another datum point was removed from session five of the muscle torque due to an unusually high output that was greater th a n the initial baseline score. Only one minor adverse event was report ed due to a participa nt becoming lightheaded and dizzy during the cold press o r test. However, this participant agreed to continue with all testing. Demographic data for all subjects are summarized in Table 4 1. Effect of Fatigue Pr otocol A preliminary analysis was run to verify that the fatigue protocol induced DOMS in our primary and secondary outcome measures (Table 4 2) In our two primary outcomes (MVIC and QuickDASH) and internal rotation of the shoulder mean scores were signi ficantly different from baseline during each session and still had not return ed to normal by 96 hours. There was no effect on the TSK over the time course of the fatigue protocol. Biopsychosocial Influence on Muscle Torque at 48h Age, gender and the baseli ne torque score were entered first into our regression model. These variables explained 57% of the variance in muscle torque at 48 hours

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50 after induction of muscle fatigue. Next, we entered our second group of predictor variables (Table 4 3). The first vari able entered into the model was thermal threshold ( = 0. 20 P = 0.005) and explained an additional 3.6% of the variance in muscle torque. Next, fear of pain was added to the model ( = 0.1 4 P = 0.036) and explained 1.9% of the variance in muscle torque. The final model, which included age, gender baseli ne torque score, thermal pain threshold and fear of pain explained 62.6 % of the variance in muscle torque at 48 hour post fatigue induction (Table 4 4) Using the final predictor variables, a prediction equation was created to estimate an peak torque production at 48 hours post fatigue The equation formed from these variables is as follows: 30.325 0.192(X 1 ) + 6.020(X 2 ) + 0.346(X 3 ) + 0.651(X 4 ) + 0.175(X 5 ) Confidence intervals for each variable included in our final model can be found in Table 4 5. Biopsychosocial Influence on Disability at 48h Age, gender and the baseline QuickDASH score were entered first into our regression model. These variables explained 5.9% of the variance in upper extremity disability at 48 hours after induction of muscle fatigue (Table 4 6 ) No other variables were entered into the final model (Ps > 0.05). A prediction equation for upper extremity disability at 48 hours was not created due to low explanation of the variance and a lack of significance Biopsychoso cial Influence on Kinesiophobia at 48h Age, gender and the baseline TSK score were entered first into our regression model. These variables explaine d 49% of the variances in level of distress at 48 hours after induction of muscle fatigue. Next, we entered our second group of predictor

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51 variables (Table 4 3). The first variable entered was the pain catastrophizing ( = 0. 344 P < 0.001) and explaine d 6.9% of the variance in level of distress. The second variable entered was work to fatigue ( = 0.246, P < 0.001) and explained 5.5% of the variance in level of distress. Next, anxiety was introduced into the model ( = 0.1 8 3, P = 0.0 08 ) and explained 2. 9 % of the variance in level of distress. Finally, thermal tolerance was added to the model ( = 0. 143 P = 0.0 45 ) and explained 1.6% of the variance in level of distress. The final model, which included age, gender, the b aseline TSK score, pain catastrophizing, work to fatigue, anxiety and thermal tolerance explained a total variance of 65.7 % post fatigue induction (Table 4 7 ) Biopsychosocial Influence on Shoulder Internal Rotation at 48h Age, gender and the baseline int ernal rotation measure were entered first into our regression model. These variables explained 46.7% of the variances in shoulder internal rotation at 48 hours after induction of muscle fatigue. Next, we entered our second group of predictor variables (Tab le 4 3). The first variable entered was work to fatigue ( = 0. 226 P = 0.00 4 ) and explained 4.6% of the variance in shoulder internal rotation at 48 hours after induction of muscle fatigue. Next, fear of pain ( = 0. 201 P = 0.00 6 ) was entered into our mo del and explained 3.9 % of the variance in shoulder internal rotation Finally, current pain level ( = 0.204 P = 0.0 5 ) was added to the model and explained 3.9 % of the variance in shoulder internal rotation. The final model, which included age, gender, b aseline shoulder internal rotation, work to fatigue, fear of pain and current pain level explained 5 9.1 % of the variance in shoulder internal rotation at 48 hours (Table 4 8 )

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52 Biopsychosocial Influence on Muscle Torque at 96h Age, gender and the torque mea surement from 48 hours were entered first into our regression model. These variables explained 86% of the variance in muscle torque at 96 hours after induction of muscle fatigue. Next, we entered our second group of predictor variables (Table 4 9 ). The fir st variable entered into the model was current pain level ( = 1.02 P = 0.0 13 ) and explained an additional 0 .6% of the variance in muscle torque. Next, fear of pain was added to the model ( = 0. 08, P = 0.032 ) and explained 0.6 % of the variance in muscle torque. The final model, which included age, gender torque at 48 hours current pain level and fear of pain explained 87.8 % of the variance in muscle torque at 96 hour p ost fatigue induction (Table 4 10 ). Using the final predictor variables, a prediction equation can be created to estimate an peak torque production at 96 hours post fatigue. The equation formed from these variables are as follows: 1 ) + 2.67(X 2 ) + 0.926(X 3 ) + 0.376(X 4 ) 0.114(X 5 ) Confidence intervals for ea ch variable included in our final model can be found in Table 4 11 Biopsychosocial Influence on Disability at 96h Age, gender and the QuickDASH score at 48 hours were entered first into our regression model. These variables explained 42% of the variance i n upper extremity disability at 96 hours after induction of muscle fatigue (Table 4 1 2 ). No other variables were entered into the final model (P > 0.05). Using the final predictor variables, a prediction equation can be created to estimate an upper extremity disability at 96 hours post fatigue. The equation formed from these variables is as follows: 11.060 0.1 46 (X 1 ) 3.215 (X 2 ) + 0. 433 (X 3 )

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53 Confidence intervals for each variable included in our final model can be found in Table 4 13. Biop sychosocial Influence on Kinesiophobia at 96h Age, gender and the TSK score at 48 hours were entered first into our regression model. These variables explained 66% of the variances in level of distress at 96 hours after induction of muscle fatigue. Next, w e entered our second group of predictor variables (Table 4 9 ). The first variable entered was the pain catastrophizing ( = 0.194, P = 0.015) and explained 2.1 % of the variance in level of distress. The second variable entered was current pain level ( = 0. 149 P = 0.0 15 ) and explained 1.4 % of the variance in level of distress. Finally, cold pain rating was added to the model ( = 0. 150 P = 0.0 17 ) and explained 1.9 % of the variance in level of distress. The final model, which included age, gender, the TSK score at 48 hours pain catastrophizing, current pain level and cold pain rating explained a total variance of 71 % at 96 hours post fatigue induction (Table 4 1 4 ). Biopsychosocial Influence on Shoulder Internal Rotation at 96h Age, gender and internal ro tation measured at 48 hours were entered first into our regression model. These variables explained 54.4% of the variances in shoulder internal rotation at 96 hours after induction of muscle fatigue. Next, we entered our second group of predictor variables (Table 4 9 ). The first variable entered was current pain level ( = 0.257, P = 0.001) and explained 3.2% of the variance in shoulder internal rotation at 96 hours after induction of muscle fatigue. Finally, cold pain rating ( = 0.240, P = 0.001) was add ed to the model and explained 5% of the variance in shoulder internal rotation. The final model, which included age, gender, shoulder internal rotation

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54 at 48 hours, current pain level and cold pain rating explained 62.6% of the variance in shoulder interna l rotation at 96 hours (Table 4 1 5 ). Genetic Influences on Outcome Measures Genetic markers were analyzed separately from other predictor variables due to having a smaller subset (n = 8 8 ) of our total population. Some individual SNPs in subjects could not be identified, so the N for each SNP was different. Initially, each of our eight SNPs was analyzed individually. Descriptive statistics for allele frequency among our SNPs, along with Hardy Weinberg Equilibrium test for each SNP are presented in Table 4 16 Previous prediction variables that were found to be statistically reliable predictors of each outcome were force entered into the regression model Genetic markers were then put in a second block and entered into the model with forward selection. For ea ch of our outcomes at 48 hours post fatigue induction, SNPs were found to add no predictive ability to our models. This was also the case from 48 hours to 96 hours with muscle torque and upper extremity disability. However, we found that SNP TNF 308/rs180 00629 (n = 84, = 0.151, P = 0.024) explained an addition 2% of the variance in kinesiophobia at 96hrs. This aided the explanation of variance in kinesiophobia and increased the total amount of variance explained to 73%. The predictive ability of shoulde r internal rotation was aided with the addition of three SNPs to the final model. SNP IL6rs1800/rs1800797 (n = 85, = 0.193, P = 0.00 6 ) was entered first and explained an additional 3.2% of the variance in shoulder internal rotation. Next, SNP IL1 31/rs1 143627 (n = 87, = 0.662, P = 0.009 ) was added to the model and explained an additional 2.6% of the variance in shoulder internal rotation. Finally, SNP IL1 511/rs16944 (n = 86, = 0.518, P = 0.039) was added to the model and explained an additional 1 .9% of the variance in shoulder internal rotation. With the

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55 addition of these three SNPs, a total of 70.3% of the variance was explained for shoulder internal rotation. Next, an ANOVA w as run for each SNP to find if any association existed between allele f requencies for each outcome (Tables 4 17 to 4 20) Of our eight outcomes and eight SNPs, only one outcome showed an association with a SNP. This SNP was then examined to determine groupings between allele codes that appear to cause risk versus no risk. Thi s new variable was then entered into the second block of a regression analysis to determine its effect on muscle torque at 96 hours. As previously stated, the final model for muscle torque was force entered into block one. The new variable of TNF 208/rs18 000629 risk type (n = 84, = 0.113, P = 0.006) was found to explain an extra 1.2% of the variance in muscle torque at 96 hours. The total variance explained for muscle torque at 96 hours in this model was 89%.

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56 Table 4 1 Descriptive Statistics for Induc ed Shoulder Pain Variable Values % or SD Sex (no. females, %) 60 60 .6 % Age 24.1 6.4 Height ( cm ) 170.2 9.9 Weight (kg ) 67.9 15.0 Hand dominance (no. ri ght handed, %) 88 88.8 % Race No. White, % 80 80.08 % No. African American, % 6 6 .1 % Other, % 13 13 .1 % All values are means and SDs, unless otherwise indicated Table 4 2 Effect of Fatigue Protocol on Primary and Second ary Outcome Measures Variable Baseline 24 Hours 48 Hours 72 Hours 96 Hours Muscle torque (Nm) 19.94 1 2.81 12.51 13.42 14.43 QuickDASH 2.59 1 3.20 18.99 17.56 1 1.30 TSK 18.14 18.51 19.05 17.97 16.89 ROM ( o ) 61.72 51.43 50.02 52.77 57.40 *Indicates statistically significant difference (P < 0.001) from baseline. Indicates statistically significant difference (P < 0.05) from baseline DASH indicates Disability of Arm, Shoulder and Hand; ROM indicates Shoulder Internal Rotation Range of Motion

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57 Table 4 3 Predictor Variables at Baseline Variable Values SD Fear of Pain Questi onnaire 22.7 5.9 State Trait Anxiety Inventory 31.1 7.1 Pain Catastrophizing Scale 10.2 8.3 MPT (kgf) 5.4 2.1 Thermal Threshold ( o C) 43.3 2.2 Thermal Tolerance ( o C) 47.4 1.9 Cold Pain Rating (VAS) 53.8 28.1 Current Pain Level (BPI) 0.16 0.42 Work to Fatigue (# of reps) 59.9 26.0 All scores are mean and SD. MPT indicates mechanical pain threshold VAS indicates visual analog scale from 0 100 BPI indicates brief pain inventory scale from 0 10 Table 4 4 Factors Influencing Muscle Torque at 48hrs Final Model R square = 0.626 Adjusted R square = 0.605 F 5,91 = 30.453 P < 0.001 Variables B Std. t P Age 0.192 0.169 2.462 0.016 Gender* 6.020 0.405 3.799 0.000 Baseline Torque 0.346 0.430 3.935 0.000 Thermal Threshold 0.651 0.200 2.978 0.004 Fear of Pain 0.175 0.140 2.124 0.036 *Coded as 1 = female, 2 = male Table 4 5 95% Confidence Interval for Muscle Torque at 48 hours Variable Lower Bound Upper Bound (Constant) 49.464 11.186 Age 0.345 0.038 Gender 2.890 9.150 Baseline Torque 0.172 0.520 Thermal Threshold 0.219 1.083 Fear of Pain 0.012 0.337

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58 Table 4 6 Factors Influencing Disability at 48hrs Final Model R square = 0. 059 Adjusted R square = 0. 029 F 3,94 = 1.953 P = 0. 126 Variables B Std. t P Age 0.028 0.012 2.462 0.912 Ge nder* 4.061 0.129 1.229 0.222 Baseline QuickDASH score 0.696 0.181 1.741 0.085 *Coded as 1 = female, 2 = male DASH indicates disability of the arm, shoulder and hand Table 4 7 Factors Influencing Kinesiophobia at 48hrs Final Model R square = 0.657 Adjusted R square = 0.630 F 7,90 = 27.097 P < 0.001 Variables B Std. t P Age 0.068 0.083 1.209 0.230 Gender* 0.372 0.035 0.500 0.618 Baseline TSK 0.585 0.485 6.024 0.000 Pain catastrophizing 0.212 0.336 4.107 0.000 Work to Fatigue 0.051 0.255 3.942 0.000 Anxiety 0.147 0.199 2.981 0.004 Thermal Tolerance 0.386 0.143 2.034 0.045 *Coded as 1 = female, 2 = male TSK indicates Tampa Scale of Kinesiophobia Pain catastrophizing measured with the pain catastrophizing scale Anxiety measured with the State Trait Anxiety Inventory

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59 Table 4 8 Factors Influencing Shoulder Internal Rotation at 48hrs Final Model R square = 0.536 Adjusted R square = 0.511 F 5,91 = 21.047 P < 0.001 Variables B Std. t P Age 0.044 0.024 0.321 0.749 Gender* 3.192 0.131 1.708 0.091 Baseline Internal Rotation 0.817 0.655 8.781 0.000 Fear of Pain 0.449 0.219 3.047 0.003 Current Pain Level 4.579 0.140 2.207 0. 030 *Coded as 1 = female, 2 = male Current pain level measured using brief pain inventory Table 4 9 Predictor Variables at 48 hours post fatigue induction Variable Values SD Fear of Pain Questionnaire 22.7 5.9 State Trait Anxiety Inventory 31.1 7.1 Pain Catastrophizing Scale 8.6 8.6 MPT (kgf) 4.8 1.8 Thermal Threshold ( o C) 44.2 1.9 Thermal Tolerance ( o C) 48.3 1.6 Cold Pain Rating (VAS) 5 7.7 26.7 Current Pain Level (BPI) 2.23 2.2 Work to Fatigue (# of reps) 59.9 26.0 All scores are mean and SD. MPT indicates mechanical pain threshold VAS indicates visual analog scale from 0 100 BPI indicates brief pain inventory scale from 0 10

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60 Table 4 10 Factors Influencing Muscle Torque at 96hrs Final Model R square = 0.877 Adjusted R square = 0.870 F 5,90 = 128.195 P < 0.001 Variables B Std. t P Age 0.045 0.036 0.929 0.356 Gender* 2.670 0.163 3.058 0.003 48hr Torque 0.926 0.842 15.387 0.000 Current Pain Level 0.376 0.102 2.534 0.013 Fear of Pain 0.114 0.083 2.179 0.032 *Coded as 1 = female, 2 = male Current pain level measured using brief pain inventory Table 4 11. 95% Confidence Interval for Muscle Torque at 96 hours Variable Lower Bound Upper Bound ( Constant) 3.585 3.640 Age 0.050 0.139 Gender 0.946 4.395 Torque at 48 hours 0.807 1.045 Current Pain Level 0.083 0.670 Fear of Pa in 0.218 0.011 Table 4 1 2 Factors Influencing Disability at 96hrs Final Mod el R square = 0.417 Adjusted R square = 0.398 F 3,92 = 21.942 P < 0.001 Variables B Std. t P Age 0.146 0.083 1.017 0.312 Gender* 3.215 0.140 1.688 0.095 48hr QuickDASH score 0.433 0.594 7.357 0.000 *Coded as 1 = female, 2 = male DASH indicates disability of the arm, shoulder and hand

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61 Table 4 13 95% Confidence Interval for Disability at 96 hours Variable Lower Bound Upper Bound (Constant) 2.759 19.360 Age 0.427 0.136 Gender 6.955 0.526 QuickDASH at 48 hours 0.318 0.549 DASH indicates disability of the arm, shoulder and hand Table 4 1 4 Factors Influencing Kinesiophobia at 96hrs Final Model R square = 0.710 Adjusted R square = 0.691 F 6,89 = 36.343 P < 0.001 Variables B Std. t P Age 0.035 0.045 0.739 0.462 Gender* 0.386 0.038 0.625 0.533 TSK at 48 hours 0.652 0.679 8.847 0.000 Pain catastrophizing 0.114 0.194 2.492 0.015 Current Pain Level 0.345 0.255 2.477 0.015 Cold Pain Rating 0.028 0.150 2.437 0.017 *Coded as 1 = female, 2 = male TSK indicates Tampa Scale of Kinesiophobia Pain catastrophizin g measured with the pain catastrophizing scale Current pain level measured using brief pain inventory

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62 Table 4 1 5 Factors Influencing Shoulder Internal Rotation at 96hrs Final Model R square = 0.626 Adjusted R square = 0.605 F 5, 90 = 30.122 P < 0.001 Variables B Std. t P Age 0.225 0.139 2.048 0.043 Gender* 2.314 0.109 1.490 0.140 48hr Internal Rotation 0.746 0.857 11.643 0.000 Current Pain Level 1.237 0.257 3.525 0.003 Cold Pain Rating 0.094 0.240 3.454 0.030 *Coded as 1 = female, 2 = male Current pain level measured using brief pain inventory

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63 Table 4 16. Descriptive Statistics Genetic Markers Variable N Hardy Weinberg P value TNF 308/rs18000629 0.821 GG 61 AG 21 AA 2 Total 84 TNF/LTA/rs2229094 0.088 TT 45 CT 30 CC 7 Total 82 TNF/LTA/rs1800683 0.857 GG 34 AG 43 AA 7 Total 84 IL6rs1800/rs1800797 0.777 GG 36 AG 38 AA 11 Total 85 IL6rs206/rs2069840 0.633 CC 44 CG 33 GG 10 Total 87 IL1B 31/rs1143627 0. 775 GG 12 AG 45 AA 30 Total 87 IL1B 511/rs16944 0.868 GG 32 AG 43 AA 11 Total 86 IL1B 105F/rs1143634 0.082 GG 53 AG 25 AA 6 Total 84

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64 Table 4 17. Mean and SD for Muscle Torque at 48 hours for Genetic Markers Torque Variable N M SD P Value TNF 308/rs18000629 0.152 GG 61 13.42 7.66 AG 21 10.01 3.97 AA 2 13.87 6.54 Total 84 12.57 6.99 TNF/LTA/rs2229094 0.933 TT 45 12.58 7.02 CT 30 12.02 7.30 CC 7 11.98 2.58 Total 82 12.32 6.82 TNF/LTA/rs1800683 0.213 GG 34 14.00 6.88 AG 43 11.7 5 7.21 AA 7 9.64 6.36 Total 84 12.47 7.07 IL6rs1800/rs1800797 0.849 GG 36 12.83 7.63 AG 38 12.10 7.24 AA 11 11. 61 4.26 Total 85 12.35 7.05 IL6rs206/rs2069840 0.837 CC 44 12.09 7.25 CG 33 12.32 6.51 GG 10 13.57 7.83 Total 87 12.35 6.98 IL1B 31/rs1143627 0.701 GG 12 12.37 6.20 AG 45 11.78 6.38 AA 30 13.18 8.15 Total 87 12.35 6.98 IL1B 511/rs16944 0.499 GG 32 13.49 8.01 AG 43 11.55 6.31 AA 11 12.6 8 6.41 Total 86 12.42 6.99 IL1B 105F/rs1143634 0.366 GG 53 12.78 6.24 AG 25 10.69 7.80 AA 6 14.31 10.02 Total 84 12.26 7.03 All scores are mean and SD.

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65 Table 4 18. Mean and SD for QuickDASH at 48 hours for Genetic Markers QuickDASH Variable N M SD P Value TNF 308/rs18000629 0.702 GG 61 18.67 15.42 AG 21 15.49 14.79 AA 2 15.90 12.87 Total 84 17.81 15.12 TNF/LTA/rs2229094 0.818 TT 45 19.55 16.43 CT 30 18.33 16.36 CC 7 15.59 11.40 Total 82 18.77 15.92 TNF/LTA/rs1800683 0.705 GG 34 20.39 15.85 AG 43 17.60 16.55 AA 7 21.11 13.30 Total 84 19.02 15.92 IL6rs1800/rs1800797 0.150 GG 36 14.83 12.10 AG 38 21.83 16.47 AA 11 18.81 19.92 Tot al 85 18.48 15.45 IL6rs206/rs2069840 0.607 CC 44 20.20 17.64 CG 33 16.60 13.79 GG 10 19.54 13.13 Total 87 18.76 15.73 IL1B 31/rs1143627 0.384 GG 12 1 6.85 7.53 AG 45 21.02 17.23 AA 30 16.14 15.64 Total 87 18.76 15.73 IL1B 511/rs16944 0.285 GG 32 15.70 15.23 AG 43 21.15 17.12 AA 11 16.11 7.42 Total 86 18.47 15.59 IL1B 105F/rs1143634 0.498 GG 53 19. 17 15.36 AG 25 21.01 17.30 AA 6 12.48 13.08 Total 84 19.24 15.78 All scores are mean and SD.

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66 Table 4 19. Mean and SD for Muscle Torque at 96 hours for Genetic Markers Torque Variable N M SD P Value TNF 308/rs18000629 0.016 GG 61 15.64 8.06 AG 21 11.11 4.52 AA 2 22.94 1.90 Total 84 14.68 7.58 TNF/LTA/rs2229094 0.905 TT 45 14.28 8.17 CT 30 15.05 6.93 CC 7 14.38 2.27 Total 82 14 .57 7.34 TNF/LTA/rs1800683 0.248 GG 34 16.52 7.95 AG 43 13.65 7.20 AA 7 13.71 8.79 Total 84 14.82 7.68 IL6rs1800/rs1800797 0.977 GG 36 14.71 7.41 AG 38 14.33 8.47 AA 11 14.44 5.05 Total 85 14.51 7.59 IL 6rs206/rs2069840 0.721 CC 44 14.45 8.27 CG 33 14.38 6.60 GG 10 16.50 8.03 Total 87 14.66 7.59 IL1B 31/rs1143627 0.728 GG 12 15.09 7.34 AG 45 14.04 7.08 AA 30 15.43 8.54 Total 87 14.66 7.59 IL1B 511/rs16 944 0.620 GG 32 15.67 8.33 AG 43 13.93 7.13 AA 11 14.98 7.69 Total 86 14.71 7.62 IL1B 105F/rs1143634 0.566 GG 53 15.06 7.09 AG 25 13.31 8.90 AA 6 16.21 7.69 Total 84 14.62 7.66 All scores are mean and SD.

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67 Table 4 20 Mean and SD for QuickDASH at 96 hours for Genetic Markers QuickDASH Variable N M SD P Value TNF 308/rs18000629 0.864 GG 61 11.70 11.19 AG 21 11.14 14.08 AA 2 15.90 0.00 Total 84 11.66 11.79 TNF/LTA/rs2229094 0.768 TT 45 12.73 11.22 CT 30 11.97 13.71 CC 7 8.76 6.63 Total 82 11.86 11.83 TNF/LTA/rs1800683 0.738 GG 34 12.63 11.89 AG 43 11.21 12.44 AA 7 9.09 7.43 Total 84 11.60 11.82 IL6rs1800/rs1800797 0.594 GG 36 10.35 11.61 AG 38 13.10 12.20 AA 11 10.95 10.74 Tot al 85 11.66 11.71 IL6rs206/rs2069840 0.691 CC 44 11.88 12.64 CG 33 10.26 8.91 GG 10 13.63 15.54 Total 87 11.47 11.66 IL1B 31/rs1143627 0.542 GG 12 11.55 9.38 AG 45 12.68 13.42 AA 30 9.61 9.52 Total 87 11.47 11.66 IL1B 511/rs16944 0.260 GG 32 9.01 9.51 AG 43 13.48 13.48 AA 11 10.74 9.38 Total 86 11.47 11.72 IL1B 105F/rs1143634 0.792 GG 53 11.31 11.53 AG 25 13.19 12.26 AA 6 10.97 12.13 Total 84 11.85 11.68 All scores are mean and SD.

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68 CHAPTER 5 DISCUSSION The purpose of this study was to inves tigate what parts of a biopsychosocial model aid prediction of physical impairment, disability and levels of distress at 48 and 96 hours post shoulder fatigue induction. Additionally, equations were buil t from regression models that can be used in a clinic al setting to better predict patient outcomes. The primary hypotheses were that Individuals who possess psychosocial risk factors and have low levels of pain sensitivity will be associated with a greater magnitude of physical impairment, self report disabi lity and higher levels of distress at 48 and 96 hours following induction of shoulder fatigue. In addition, we also examine d the effect of several genetic markers on physical impairment, self report disability and levels of distress. These genetic markers were examined by identifying single nucleotide polymorphisms (SNPs) among eight different preselected inflammatory genes. This study is considered novel for several reasons First, only limited research exists regarding how a biopsychosocial model influenc es outcomes in the shoulder. Second, very limited data exist to suppo rt outcomes other then pain past 24 hours. Finally, to our knowledge, this is the only study that has used pro inflammatory cytokines to determine the predictive ability of shoulder outco mes. Induction of shoulder fatigue was completed by having subjects perform concentric/eccentric isokinetic contractions until their maximal voluntary isometric contraction (MVIC) had decrease d by 50% of the ir initial MVIC. This method of injury induction has been p er formed previously and has been shown to be a valid method of producing injury. 17, 19, 20, 26, 32, 35 Our results concur as we showe d significant decreases in MVIC and shoulder internal rotation with significant increases in self report disability

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69 of the upper extremity In addition, a significant increase in current level of pain over time was found However, one of our secondary outcomes, kinesiophobia, showed not to be affected by shoulder fatigue and remained stable over the course of injury and recovery. To our knowledge this is the first time kinesiophobia has been used a s an outcome measure, thus we had no previous information on how it would be affected by inducing injury. Nevertheless, we did expect kinesiophobia to increase in response to our fatigue protocol similarly to self report disability. Demographics Our baseli ne demographic data w ere found to be consist ent with other studies reporting in a group of young healthy subjects. 26, 28, 29, 148 As would be expected, our results showed that young healthy individuals had relatively low levels of anxiety, fear of pain and pain catas trophizing. Other predictor measures were also found to be within normal ranges found in other studies Primary Outcome Measures Previous researchers have used pain as a primary measure when trying to predict outcomes. 27 29 To the best of our knowledge this is the first study that has used muscle torque and self report disability as primary outcome measures. A previous study by George et al 26 did examine similar outcomes to the present study, however, they only observed at 24 hours p ost injury induction Self Report Disability George et al. examine d the predictive ability of upper extremity disability 24 hours after induction of delayed onset muscle soreness (DOMS) and determined that pain intensity and fear of pain, measured through the fear of pain questionnaire III were significant predictors of self report upper extremity disability. 26 However, we were

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70 unable to discover any significant predictors of self report disability at 48 hours post fatigue induction. We were able to find that an score on the QuickDASH at 48 hours w as a significant predictor of self report disability score s at 96 hours. Th is result better supports our hypothesis, since in a healthy population baseline disability scores as measured through the Quick DASH tend to be very low prior to injury but following induction of shoulder fatigue have been found to be a valid and reliable method of assessing self report upper extremity disability. 136 138 This positive association between scores verifies that individuals who report higher levels of upper extremity disability at 48 hours tend to have higher levels of upper ex tremity disability at 96 hours suggesting a longer recovery time This is an important finding as most injuries are reported at a time of immense disability, so understanding an individual s current disability level is extremely important in the recovery process. Muscle Torque Several of our predictor s were able to explain variance within mu scle torque at 48 and 96 hours post shoulder fatigue induction. When examining muscle torque we found that all of our variables in the final model including age, gender and baseline torque measure, were significant predictors of muscle torque at 48 hours. Of these variables, age was the only variable that had a negative relationship with muscle torque at 48 hours. This is consistent with res earch that has shown muscle strength begins to decrease with increasing age 18 Also seen in previous research is the effect of gender on muscle torque. 17, 19, 20 We confirmed the influence of gender and found that females had signif icantly lower torque production during each session. B aseline levels of torque played a significant role in the production of torque at 48 hours. Baseline levels of torque had the highest standard beta score (Table 4 4), proving that baseline scores are

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71 the most predictive me asure that can be assessed following injury. F ear of pain was identified as a significant contributor to the prediction of muscle torque at 48 hours. However, it had the lowest standard beta score (Table 4 4), demonstrating it was the least predictive vari able fit into our model. It was also found to have a positive association with muscle torque, which would mean those with lower scores on the fear of pain questionnaire would have lower scores for muscular torque. This does not support the hypothesis of pa in influencing muscular strength. In a previous study, we demonstrated that muscular strength and pain do not always mirror each other, 38 thus variables that predict pain may not be strong predictors for muscular torque. How ever, George et al. identified pai n intensity, not fear of pain, as the only unique predictor of muscle force production at 24 hours after induction of DOMS. 26 Conversely they found fear of pain to be a significant predictor of clinical pain and evoked press ure pain at 24 hours in the same study. 26 This c ould suggest that an individual s ability to produce torque is not related to current pain levels further supporting our previous findings Conversely Sullivan et al found a relationship between strength outp ut during activity and catastrophizing. 149 However, George et al. 26 and our current study did not come up with any association with pain catastrophizing at any time point in relation to muscle strength. Additionally, thermal threshold was found to be a significant predictor in our study To our know ledge, thermal threshold has not been examined as a predictive tool. What e ffect thermal threshold has on the prediction of muscle torque is still largely unknown. However, we can surmise that if an individual has a lower threshold to thermal stimuli, thei r ability or willingness to create torque following DOMS induction

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72 may be lower. However, this is purely speculative and would need to be examined in more detail. Using the equation designed to predict muscle torque at 48 hours, a female who has a baseline torque of 13.0 Nm and mean scores found in table 4 2 on other variable s would be predicted to have a muscle torque score of 7.75 Nm at 48 hours post fatigue induction. This same person would then score a 12.52 Nm if their fear of pain questionnaire were t o increase to a score of 50, which all other variables remain constant. This is contrary to what would be expected, as fear of pain should cause muscle torque to decrease. However, if this same individual only had a lower thermal threshold of 40 degrees Ce lsius, the ir muscle torque score at 48 hours would be 5.60 Nm. This would support the hypothesis that individuals with lower thermal threshold have a decreased ability or willingness to produce torque at 48 hours post fatigue induction. The muscle torque m odel that was built for 48 hours post fatigue induction was similar to the model for muscle torque at 96 hours. However, thermal threshold level was not entered in the 96 hour model and was replaced by current level of pain. Current level of pain is more i n agreement with George et al. and their result s for predicting muscle force at 24 hours. 26 However, we found that current pain level and muscle torque had a positive association, whereas George et al. found a negative association. negative as sociation would better support the hypothesis that as pain increases, muscular strength decreases. The positive association seen in the present study would need further consideration, but again may indicate strength and pain have little association with ea ch other. In addition, age was not found to have a significant

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73 relationship with muscle torq ue production at 96 hours. This may indicate that as you get older, even though strength loss may be greater, the recovery process will be unchanged It is also int eresting to note that fear of pain was found to have a negative relationship with muscle torque production at 96 hours. Since fear of pain was only measured at baseline (pre fatigue induction), this negative relationship could indicate that individuals who initially had lower scores on the fear of pain questionnaire not only tend to lose a smaller amount of muscle torque at 48 hours, but may also recover faster at 96 hours. Additionally gender was seen to be a s trong predictor in both models, however, if mu scle torque was normalized to body weight, this could potentially eliminate the affect of gender in our models. Both models proved, based on standard beta scores, that the previous score of muscle torque is the single most important predictor of an individ outcome 48 hours post fatigue induction While age was found to be negatively associated with m uscle torque at 48 hours, this association was weak in the model. This may be related to the small age range of the participants. Using the equation design ed for predicting muscle torque from variables at 48 hours, you can predict outcomes at 96 hours. Variables included in this equation are age, gender, 48 hour muscle torque score, current pain level and fear of pain score. For example, a female with a musc le torque score of 8.0 Nm at 48 hours and mean scores found on other predictor variables in Table 4 9 would be expected to have a muscle torque of 9.44 Nm at 96 hours. This same female with a score of 50 on her fear of pain questionnaire would be predicted to have a score of 6.32 in muscle torque at 96 hours.

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74 This individual, according to our equation, would still be in the injury phase and is predicted to still show a decreasing trend in muscle torque. Secondary Outcome Measures Kinesiophobia Kinesiophobia is a measure of fear of re injury or fear of movement. Th is assessment was performed using the Tampa Scale of Kinesiophobia (TSK). Our hypothesis stated that scores would get worse (increasing) for kinesioph obia following shoulder fatigue induction. Howev er, this was not the case for our results and scores remained relatively stable. Kinesiophobia has not been used as an outcome measur e following the induction of muscle fatigue but as a predictive tool in analyzing fear avoidance models. When we analyzed kinesiophobia at 48 and 96 hours post fatigue induction, we identified that age and gender did not play a significant role in the models predictive ability. Specifically, for kinesiophobia at 48 hours post fatigue induction, we found that not only was the baseline score of the TSK significant, but so was the measure of pain catastrophizing and anxiety. This may suggest that some correlation among these three measurements exist when examining a biopsychosocial model of pain. However, no significant correlati on was seen between these psychological measures during our preliminary analysis. In contrast, George et al. found that fear of pain, not pain catastrophizing or anxiety was a significant predictor of Kinesiophobia at 24 hours. 26 Additionally in our model wo rk to fatigue, measured as the number of repetitions to induce fatigue and thermal tolerance were also entered as significant predictors of kinesiophobia at 48 hours. Work to fatigue showed a negative relationship with kinesiophobia at 48 hours suggesting that people who may be more fatigue resistan t report lower levels of distress. This is an interesting finding and needs to be

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75 evaluated further since fatigue resistance can be trained and this would potentially decrease levels of distress More specifica lly individuals may be able to perform preventative exercise that would increase their level of fatigue resistance and potentially lower their levels of distress in response to training. We found that subjects with higher thermal tolerances would report h igher levels of distress. This is contradictory to what one would postulate as those individuals who can tolerate greater amounts of pain would be expected to report lower levels of distress. However, this may indicate a difference between thermal pain an d other types of pain ful stimuli In addition the association between kinesiophobia and thermal tolerance was small and this needs further examination. In the model designed at 96 hours, pain catastrophizing continue d to be a significant predictor of leve l of distress. However, this association was not as strong in the 96 hour model as compared to the 48 hour model. Other variables identified in the 48 hour model were no longer significant, but the 96 hour model now included current pain level and cold pai n rating as significant predictors Current pain level showed a negative association with kinesiophobia at 96 hours which is contrary to what would be expected during recovery from DOMS As pain levels decrease, it would be expected that levels of distress would decrease as well. This was verified by Sullivan et al. who found an association with catastrophizing and emotional distress. 68 Therefore, our results need to be viewed with caution and future studies should examine if the negative association identified in our study can be repeated In contrast, it would be expected that cold pain rating would be negatively associat ed with kinesiophobia, h owever, this was not the case I ndividuals who have a

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76 greater tolerance to noxious (painful) stimuli such as extremely cold water would be expected to report lower levels of distress following injury. This may be due to the measure of kinesiophobia remaining stable following shoulder fatigue and its inability to measure changes in distress over a short period in healthy young individuals. A nother pos sible explanation is th at levels of distress as measured for two separate stimuli may be different. If an individual perceives pain differently when experiencing heat versus cold, this may potentially influence their rating on the TSK. This seems to be pl ausible as this occurred in both our 48 and 96 hour models with various types of stimuli Shoulder Internal Rotation Our final outcome measure was passive shoulder internal rotation. We choose internal rotation as our sole range of motion measure as it has been found to be one of the m ovements that are most limited following injury to the external rotators 150 We confirmed that the external rotators were impaired following fatigue induction as range of motion was significantly decreased over all four follow up sessions. Age and gender provided no significant predictive ability to our model of shoulder internal rotation at 48 hour s. However, the baseline measurement of shoulder internal rotation proved to be the largest predictor of variance at 48 hours with a standard beta score of 0.655 Fear of pain was entered as a significant predictor of internal rotation at 48 hours with a s tandard beta score of 0.219 Since range of motion is typically limited by an pain, it is not surprising that fear of pain is included in the model. This was confirmed by having a negative association between current pain level and shoulder in ternal rotation model at 48 hours. The negative association suggest that as pain levels increase range of motion measurements for internal rotation will decrease. Pain intensity has been a

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77 primary outcome measure i n several other studies and has been foun d to be a significant factor i n determining outcomes following shoulder fatigue induction. 26, 114, 148 Fear of pain was removed in the shoulder internal rotation model at 96 hours and replaced by cold pain rating. Cold pain rating had a negative associati on with shoulde r internal rotation at 96 hours suggesting that as cold pain ratings increased shoulder internal rotation decreased. T his would support our hypothesis in that as pain ratings for a noxious stimuli increase, an individual s measurements of in ternal rotation become worse. In addition current pain level was still in cluded in the model at 96 hours, however it was positively associated with shoulder internal rotation. This would not support the hypothesis that pain should be decreasing and should er internal rotation would be increasing at 96 hours post fatigue induction. The removal of fear of pain in the 96 hour mode l may suggest that fear of pain, as measured through the fear of pain questionnaire is not as important in the early phase after i njury as d uring the recovery period As previously seen in muscle torque, disability and kinesiophobia models, we found that the previous measurement of shoulder internal rotation is the single most important predictor for determining follow up scores at 4 8 and 96 hours post fatigue induction Genetic Influence on Outcomes Many inflammatory SNPs ( IL 1 6 and TNF do not have functional data that implicate their behavior in relation to their variation. S peculation of their function al implications exist with most evidence revolv ing around transcriptional regulation or protein expression within these genes. Specifically, many of these genes have only been examined in relation to chronic inflammatory diseases or cardiovascular disease. It is well documented that the inflammatory response in exercise induced muscle injury

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78 and the contribution of gene va riation to the inflammatory response in other systems led to the hypothesize by Yamin et al. that the degree of creatine kinase (CK) response would be associated with specific cytokine gene SNPs. 151 Yamin et al. used SNPs of IL 6 and TNF because they are both variations of gene promoters, implying transcriptional regulation and suggesting possible association with chronic inflammation. 151, 152 Yamin et al. believed that mutations positioned in the promoter regions could affect the b inding of transcription factors that resulted in altered mRNA e xpression o f protein levels. 151 However, the direct mechanism by which IL 6 and TNF s CK levels response to eccentric exercise is unknown. 151 These changes are hypothesized to be either directly related to CK synthesis or by influencing the magni tude of the reactive inflammatory process. 151 Dennis et a l. also found that polymorphisms in IL 1 were associated with the inflammatory response in humans following inju r y induction, 153 h owever, they concluded that the strongest association occurred when the five IL 1 SNPs they choose were haplotyped. 153 In addition, they offered no explanation on what mechanisms may be affect ed by the IL 6 variations. S ince little i s known about what influence these specific genetic markers play in physical impairment and disability, our aims with genetic markers were largely exploratory within this study. No ne of our genetic markers were found to influence outcomes at the 48 hour time point. This was a surprising outcome since it is suspected that variations in these genes may increase the expression of corresponding pro inflammatory cytokines. Specifically, pr evious studies have found that IL 1 injected into rats induced an inflammatory reaction and affected pain sensitivity. 86, 88, 89 Similar

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79 results were found with IL 6 and TNF in human studies that examined pain sensitivity. Edwards et al. found that IL 6 had a strong association with pain catastrophizing, which results in greater pain sensitivity. 67, 91 One explanation for our findings may be due to the specific SNPs we examined may not have a strong enough influence on the inflammatory process. W e were able to identify some influence with genetic markers at the 96 hour time point. Kinesiophobia was influence d by one of our selected TNF S NPs, h owever, w hen evaluating t he means for kinesiophobia at 96 hours between allele codes, there appears to be no differences that would sug gest a protective or active cod ing sequence. In addition we found that three SNPs were included in our final regression model for shoulder intern al rotation at 96 hours. These included two IL 1 SNPs and one IL 6 SNP. Both IL 1 SNPs had a negative association with shoulder internal rotation. The IL 6rs1800/rs1800797 SNP indicates that the minor allele code G may have a protective effect on loss of range of motion, as both the AG and GG coding h ad higher mean measurements at this time point This was also similar to the IL 1 511/rs16944 SNP, which showed a potential protective effect from the minor A allele code. However, when further analyzed the means were not found to be statistically signifi cant and further exam ination needs to be conducted. T he role of IL 6 can be questioned as it displays properties of both a pro and anti inflammatory cytokine. What role variations play in the inflammatory process is still largely unknown caution needs to be taken when analyzing IL 6 until further functional data can be confirmed We found that certain allele frequencies had small sample representations, therefore we attempted to determine if allele codes could be combined into those that

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80 presented risk an d those that did not. A one way ANOVA was run with each of our outcome measures as the dependent variable and the individual SNP as a factor. We then examined each SNP for associations with the specific outcome by looking for significant main effects. Only one SNP was found to have any significant association, TNF 208/rs18000629. Post hoc analyses were then examined and it was found that the AA allele code might have a protective role in muscle torque at 96 hours. However, it is of interest to note that the AG code was not found to be significantly different from the GG code signifying that for the protective effect to be present, you may need to possess the AA code and not just a minor allele A. In addition, the allele frequency for this specific SNP showed that the AA code (n = 2) is rare, so these findings need to be taken with caution and a larger sample size should be evaluated Conclusion s Previous studies using a biopsychosocial model have primarily focused on the influence of predictive variables and the ir relationship to pain. Only more recently were outcom es of pain examined in the shoulder. Fear of pain and pain catastrophizing have been two of the primary psychological questionnaires that have been found to influence a person s perception and rating of pain intensity. While we did find that fear of pain a nd pain catastrophizing influence d our models of muscle torque, kinesiophobia and shoulder internal rotation we were unable to consistently produce a model that could be viewed as similar across all of our primary and secondary outcomes The content and o rder of the models even changed in most cases from 48 to 96 hours suggesting that different factors may influence physical impairment and disability over the time course of recovery However, we did find some variables that proved to be reliable predictors in our study that were consistent with previous studies. These predict ors included current

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81 level of pain gender, pain catastrophizing and fear of pain. Gender was found to only affect muscle torque in our study, whereas pain catastrophizing and/or fear o f pain were found to be reliable predictors of muscle torque at 48 and 96 hours, kinesiophobia at 48 and 96 hours and shoulder internal rotation at 48 hours post fatigue induction. Current level of pain was found in both muscle torque and shoulder internal rotation at 96 hours, suggesting that pain may be a greater factor in later stages of the injury process. Future studies need to examine these variables in greater detail to verify their usefulness in predicting outcomes of physical impairment and disabil ity at several time points throughout the injury/recovery process This should be done on a larger scale without force entry of variables that may not be significantly reliable predictors for each outcome. We also included some novel predictors that may be difficult to explain once they are fit into a model. One would expect individuals who have higher thermal threshold and tolerance levels for pain to be less affected by pain and soreness following injury. However, this was not consistent with our findings and these ratings may only hold true for a clinical outcome like pain, which was not one of our primary or secondary outcome measures. In addition, new genetic markers, including pro inflammatory cytokines, may play a significant role in the development o f physical impairment and disability, however these markers of inflammation need to be examined on a larger scale to ensure quantity distribution across allele codes. While the specific SNPs were chosen on a hypothetical basis for the ir interaction with th e inflammatory process, a better functional understanding of the variations in these pro inflammatory cytokines need s to be identified Functional characteristics of these pro inflammatory cytokines could have

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82 been evaluated by measuring blood cytokine lev els on a daily basis however, this requires a more invasive study Nevertheless, t his would have allowed for a comparison between coding sequences and plasma levels of each cytokine. These comparisons between plasma cytokine levels and allele coding seque nces can aid in understanding what affect genetics play in physical impairment and disability following injury. Limitations Study limitations preclude the generalization of these finding s to other populatio ns. This study was performed on young healthy ind ividuals who for the most part had low baseline levels of anxiety, fear of pain and catastrophizing. We also used a delayed onset muscle injury model to reproduce injury, so findings generalized to clinical populations need to be done with caution Also, our model only examined outcomes up to 96 hours, so generalization past this point may not be applicable. The fear of pain questionnaire used in this study is a smaller version of the fear of pain III questionnaire and has not been validated in any popula tions. In addition questionnaires are semi quantitative in nature and their interpretation is somewhat subjective Specifically, the QuickDASH ask s several questions on different task s and then ask s you to rate the extent to which you are having trouble p erforming them. If a person has not performed the task within the past 24 hours, they may not be able to give an accurate rating for that particular question. All models had twelve predictor variables run as independent variables. With only 99 total subjec ts, there could be a saturation problem within our models. Additionally, since previous research showed that age and gender were significant factors in develop ing a biopsychosocial model of pain, these factors were forced into each of our models. However age and gender were not always found to have unique contributions to the model and in some cases may not belong. Finally our

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83 work to fatigue measures was calculated as total rep etition s performed before reaching 50% of initial MVIC. Since repetitions were performed in sets of ten, there could be error in this variable. Future research should consider measuring total work performed in joules, which can easily be recorded on most isokinetic dynamometers. Implications and Future Research The knowledge of what risk factors contribute to a biopsychosocial model of shoulder pain can potentially lead to better healthcare practices that result in better patient outcome s We found that several of our models included psychological questions which can easily be assesse d prior to surgery or in pre season screenings for athletes. These questionnaires can then be scored and athletes can be categorized so that following injury, the athletic trainer can have a better understanding of an e expected length of recovery. Since previous scores were found to consistently be the best predictor of follow up measurements on each outcome you could predict that an individual with greater disability following injury would take longer to recover. In a ddition to the psychological questionnaires, t his information can then be useful in determining a time table for when an athlete may return to activity. Also, individuals who are more likely to report greater disability following injury due to psychologica l risk factors can potentially have these issues addressed by a sports psychologist prior to competition. If genetics markers are found to be an important part of injury response and recovery, a genetic profile can then be created so that more a complete u can be taken into account. However, the potential influence of genetics would have t o consider the cost to benefit ratio, as this may not be practical in all settings.

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84 Future research needs to duplicate these findings in both a larger healthy population, as well as in a clinical population currently experiencing shoulder pain and other pathological conditions In addition, if genetic markers are going to be identified, a functional basis needs to be deter mined for their potential influence in these types of models. This can be done by assessing SNPs and then tracking cytokine levels in the plasma following induction of an injury model. Allele codes would need to be verified so that each coding sequences is at minimal meeting population representation with in the study sample Once valid and reliable models are found, prediction equations can be designed that can easily be used in a clinical or field based setting. These equations should be designed with not only high predictive power, but accessibility to the proper equipment to perform test. While measures like thermal threshold and tolerance may show good predictive ability, they may not be cost effective or practical to do on a large scale.

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85 APPENDIX A ST ATE TRAIT ANXIETY I NVENTORY

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86 APPENDIX B PAIN CATASTROPHIZING S CALE

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87 APPENDIX C FEAR OF PAIN QUESTIO NNAIRE Pain Assessment Information The items below describe painf ul experiences. Please look at each item and think about how fearful you are of experiencing the pain associated with each item. If you have never experienced the pain of a particular item, please answer on the basis of how fearful you expect you would be if you had such an experience. 1. Breaking your arm. Not at all A little A fair amount Very much Extreme 2. Having someone slam a heavy car door on your hand. Not at all A little A fair amount Very much Extreme 3. Falling down a flight of concrete stairs. Not at all A little A fa ir amount Very much Extreme 4. Receiving an injection in your hip/buttocks. Not at all A little A fair amount Very much Extreme 5. Receiving an injection in your mouth. Not at all A little A fair amount Very much Extreme 6. Getting a paper cut on your finger. Not at all A little A fair amount Very much Extreme 7. Having a foot doctor remove a wart from your foot with a sharp instru ment. Not at all A little A fair amount Very much Extreme 8. Gulping a hot drink before it has cooled. Not at all A little A fair amount Very much Extreme 9. Getting strong soap in your eyes while bathing or showering. Not at all A little A fair amount Very much Extreme

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88 APPENDIX D TAMPA SCALE OF K INESIOPHOBIA

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89 APPENDIX E QUICK DASH

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90

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91 APPENDIX F BRIEF PAIN INVENTORY

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10 5 BIOGRAPHICAL SKETCH Jeffrey J. Pa rr was born in Trenton, NJ. Jeffrey attended school in the public system of Hamilton Township, NJ, where he graduated from Nottingham High School (Hamilton North) in June 1994. He entered Mercer County Community College in September 1994 as a b iology major After working for two years while attending community college, he transferred to East Stroudsburg University in September 1996, entering into t he Movement Studies and Exercise Science, Pre Athletic Training Major. In May 1997, he was accepting into the A thletic Training Education Program. He graduated with a Bachelor of Science degree in Movement Studies and Exercise Science with an emphasis in A thletic Training/Sports M edicine in May 1999. In April 1999, he sat for and passed the American College of Spor t Medicine Health and Fitness Certification exam in June 1999 and became a nationally certified athletic trainer. In July 1999, he officially accepted a graduate assistantship at The University of Tennessee at Chattanooga. He successfully defended this thesis on July 18, 2000 and officially on August 14, 2000. Jeffrey then spent three years working as a certified athletic trainer at the collegiate level. In April 2003, he officially accepted a position in the doctor of philosophy program in Health and Human Performance at the University of Florida. During this time, he held various assistantships: working in a physical ther apy clinic, as the athletic trainer for the University of Florida cheerleading and spirit squad, a teaching assist for anatomy and physiology labs, and as a research assistant in the Sports Medicine Research Laboratory.