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Predictors of Outcome During Sub-Acute Recovery From Mild Traumatic Brain Injury

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Title:
Predictors of Outcome During Sub-Acute Recovery From Mild Traumatic Brain Injury
Creator:
Greif, Sarah M
Place of Publication:
[Gainesville, Fla.]
Florida
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University of Florida
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english
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1 online resource (57 p.)

Thesis/Dissertation Information

Degree:
Master's ( M.S.)
Degree Grantor:
University of Florida
Degree Disciplines:
Psychology
Clinical and Health Psychology
Committee Chair:
BAUER,RUSSELL M
Committee Co-Chair:
JOHNSON,CYNTHIA R
Committee Members:
MARSISKE,MICHAEL
WHITEHEAD,NICOLE ENNIS

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Subjects / Keywords:
concussion -- predictors -- risk-assessment -- tbi
Clinical and Health Psychology -- Dissertations, Academic -- UF
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bibliography ( marcgt )
theses ( marcgt )
government publication (state, provincial, terriorial, dependent) ( marcgt )
born-digital ( sobekcm )
Electronic Thesis or Dissertation
Psychology thesis, M.S.

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Abstract:
Researchers have successfully identified several predictors of patient outcome following mild traumatic brain injury (mTBI). However, the lack of evidence-based post-injury interventions and assessment of recovery for mTBI leaves many health care providers unsure of how to most effectively manage their patients care. The current study evaluates relationships between demographic, injury characteristics, and prior medical history variables that have been identified as risk factors for protracted recovery following mTBI. Data of 25 participants diagnosed with concussion/mTBI within the prior 14-25 days were retrospectively withdrawn form a larger clinical trial. Symptoms were quantified using the Sport Concussion Assessment Tool, 3rd Edition Symptom Evaluation, Beck Depression Inventory, Second Edition, and the State-Trait Anxiety Inventory which were administered at two time points; approximately M=15.2 (SD=3.5) days post-injury and again M=29 (SD=5.3) days post-injury. Hierarchical regression modeling bootstrapped for 1,000 95% bias-corrected and accelerated bootstrapped parameter estimates were used to assess the relationship between the predictors and symptom outcome. Results indicate that sex and injury characteristics such as loss of consciousness weakly predict symptom outcome. However, pre-existing medical history may serve as a promising prediction tool for symptoms of depression and anxiety during sub-acute recovery from mTBI in a non-elite athlete, university population. Such findings suggest the need to further investigate predictors of recovery, to establish extended models of support for post-mTBI patient care, to evaluate mood symptoms in the aftermath of concussion, and to implement risk-assessment to identify patients who might develop more protracted post-injury symptoms and thus benefit from intervention. ( en )
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In the series University of Florida Digital Collections.
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Includes vita.
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Includes bibliographical references.
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Description based on online resource; title from PDF title page.
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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.
Thesis:
Thesis (M.S.)--University of Florida, 2017.
Local:
Adviser: BAUER,RUSSELL M.
Local:
Co-adviser: JOHNSON,CYNTHIA R.
Statement of Responsibility:
by Sarah M Greif.

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Applicable rights reserved.
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PREDICTORS OF OUTCOME DURING SUB ACUTE RECOVERY FROM MILD TRAUMATIC BRAIN INJURY By SARAH M. GREIF A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2017

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2017 Sarah M. Greif

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To my grandfather who taught me the keys to success : com passion, humor & garlic

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4 ACKNOWLEDGMENTS I thank my mentors, Dr s Russell Bauer and Shelley Heaton for their support and patience as well as the members of my thesis committee, Drs. Michael Marsiske, Nicole Whitehead, and Cynthia Johnson I am grateful to Aliyah Snyder for her continued guidance and generosity in allowing me to use portions of her dissertation data for this manuscript and to the NIH/NCATS Clinical and Translational Science Award that supported the clin ical trial in part through the University of Florida TL1 TR000066 and UL1 TR000064 grants secured by Aliyah and Dr. Bauer. I would be remiss to not mention the backbone of this study; our exceptional team of undergraduate research assistants, CTSI nurses a nd staff, and of course the participants ; without whom this study would not exist

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ........................................................................................................... 4 LIST OF TABLES ...................................................................................................................... 7 LIST OF FIGURES .................................................................................................................... 8 LIST OF ABBREVIATIONS ...................................................................................................... 9 ABSTRACT ............................................................................................................................. 10 CHAPTER 1 INTRODUCTION ............................................................................................................. 12 Predictors of Outcome ........................................................................................................ 13 Sex .............................................................................................................................. 14 Prior Medical History .................................................................................................. 15 Pre existing history of concussion ........................................................................ 15 Pre existing history of mood disorder ................................................................... 16 Pre existing history of Attention deficit/hyperactivity disorder ............................. 16 Pre existing history of migraine ............................................................................ 17 Injury Characteristics .................................................................................................. 17 The Current Study .............................................................................................................. 18 Specific Aims .............................................................................................................. 19 Aim 1 ................................................................................................................... 19 Hypothesis 2 ......................................................................................................... 19 Aim 2 ................................................................................................................... 19 Hypothesis 2 ......................................................................................................... 19 2 METHODS ........................................................................................................................ 21 Procedure ........................................................................................................................... 21 Participant Recrui tment ............................................................................................... 21 Participant Eligibility .................................................................................................. 21 Study Design ............................................................................................................... 22 Measures ............................................................................................................................ 23 Demographic Questionnaire ........................................................................................ 23 Sport Concussion Assessment Tool, 3rd Edition Symptom Evaluation (S3SE) ............. 23 Beck Depression Inventory, Second Edition (BDI II) .................................................. 23 State Trait Anxiety Inventory (STAI) .......................................................................... 24 An alysis Plan ..................................................................................................................... 25 Descriptive Statistics .......................................................................................................... 25 Analyses ............................................................................................................................ 25

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6 3 RESULTS .......................................................................................................................... 30 Aim 1: To Determine Demographic, Injury, and Pre existing Medical History Characteristics of Participants Post mTBI and Their Relations hip with S3SE, STAI, and BDI II Symptom Report at Pre Intervention ............................................................. 30 Pre Intervention Characteristics ................................................................................... 30 Predicting PreIntervention symptom report ......................................................... 31 Post intervention characteristics ........................................................................... 34 Aim 2: Investigate the Relationship Between Injury Characteristics, Pre existing Medical History, and Sex and Symptom Change in Measures of Depression, Anxiety, and Concussionrelated Symptoms ................................................................................. 35 4 DISCUSSION .................................................................................................................... 45 Predictors of Symptom Report in Sub acute Recovery From mTBI .................................... 45 Clinical Application .................................................................................................... 48 Study Li mitations and Future Directions ..................................................................... 49 Conclusion ......................................................................................................................... 50 REFERENCES ......................................................................................................................... 51 BIOGRAPHICAL SKETCH ..................................................................................................... 57

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7 LIST OF TABLES Table page 11 American Congress of Rehabilitation Medicine definition of mild traumatic brain injury ............................................................................................................................. 20 21 Participant eligibility criteria .......................................................................................... 28 22 Sample characteristics ................................................................................................... 29 31 Linear logistic model of predictors of symptomatic classification at PreIntervention, with standard errors based on 1000 bootstrap samples ................................................... 38 32 Linear model of predictors of STAI State symptom report at Pre Intervention, with standard errors based on 1000 bootstrap samples ........................................................... 39 33 Linear model of predictors of STAI Trait symptom report at Pre Intervention, with standard errors based on 1000 bootstrap samples ........................................................... 40 34 Linear model of predictors of BDI II symptom report at Pre Intervention, with standard errors based on 1000 bootst rap samples ........................................................... 41 35 Linear model of predictors of S3SE Symptom Score report at Pre Intervention, with standard errors based on 100 0 bootstrap samples ........................................................... 42 36 Linear model of predictors of S3SE Symptom Severity report at Pre Intervention, with standard errors based on 1000 bootstrap samples ................................................... 43 37 Residualized change scores between PreIntervention and Post Intervention symptom report, with standard errors based on 1000 bootstrap samples ......................... 44

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8 LIST OF FIGURES Figure page 21 Eligibility flow diagram ................................................................................................. 26 22 Overview of study design and measures administered .................................................... 27 31 S3SE symptom report at Pre Intervention and Post Intervention .................................... 37

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9 LIST OF ABBREVIATIONS ACRM American Congress of Rehabilitation Medicine ADHD Attention deficit/hyperactivity disorder BDI II Beck Depression Inventory, 2nd E dition CT Computed tomography DMN Default mode network Dx Diagnosis DSM IV Diagnostic and Statistical Manual of Me ntal Disorders, 4 th Edition EEG Electroencephalogram fMRI Functional magnetic resonance imaging Hx History LD Learning disorder LOC Loss of consciousness MRI Magnetic resonance imaging mTBI Mild traumatic brain injury NCATS National Center for Advancing Translational Sciences NIH National Institute of Health PCS Post concussion syndrome PTA Post traumatic a mnesia REDCap Research Electronic Data Capture S3SE SCAT 3 Symptom Evaluation SCAT 3 Sport Concussion Assessment Tool, 3 rd Edition STAI State Trait Anxiety Inventory TBI Traumatic brain injury UFSHCC University of Florida Student Health Care Center WHO World Health Organization

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10 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science PREDICTORS OF OUTCOME DURING SUB ACUTE RECOVERY FROM MILD TRAUMATIC BRAIN INJURY By Sarah M. Gre if May 2017 Chair: Russell M. Bauer Major: Psychology Researchers have successfully identified several predictors of patient outcome following mild traumatic brain injury (mTBI). However, the lack of evidencebased post injury interventions and assessment of recovery for mTBI leaves many health care providers unsure of how to most effectively manage their patients care. The current s tudy evaluates relationships between demographic, injury characteristics, and prior medical history variables that have been identified as risk factors for protracted recovery following mTBI. Data of 25 participants diagnosed with concussion/mTBI within t he prior 1425 days were retrospectively withdrawn form a larger clinical trial. Symptoms were quantified using the Sport Concussion Assessment Tool, 3rd Edition Symptom Evaluation, Beck Depression Inventory, Second Edition, and the State Trait Anxiety Inventory which were administered at two time points; approximately M=15.2 (SD=3.5) days post injury and again M=29 (SD=5.3) days post injury. Hierarchical regression modeling bootstrapped for 1,000 95% bias corrected and accelerated bootstrapped parameter estimates were used to assess the relationship between the predictors and symptom outcome. Results indicate that sex and injury characteristics such as loss of consciousness weakly predict symptom outcome. However, preexisting medical history may serve as a

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11 promising prediction tool for symptoms of depression and anxiety during sub acute recovery from mTBI in a nonelite athlete, university population. Such findings suggest the need to further inve s tigate predictors of recovery, to establish extended model s of support for post mTBI patient care, to evaluate mood symptoms in the aftermath of concussion, and to implement risk assessment to identify patients who might develop more protracted post injury symptoms and thus benefit from intervention.

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12 CHAPTER 1 INTRODUCTION Once an expected consequence of any contact sport getting your bell rung has assumed a new status in the modern American consciousness : epidemic ( Langlois, Rutland Brown, & Wald, 2006; Prevention & Control, 2003 ) Concussions have risen to the top of news headlines prompting nationwide legislation, securing self titled dramatization in theatres and increasingly becoming a point of discussion at both the laymans dinner table and internat ional scientific conferences The increasing exposure to, and awareness of, concussions in the public sphere has resulted in an increase in the demand for concussion related medical care in both primary and specialty care settings ( Bakhos, Lockhart, Myers, & L inakis, 2010; Daneshvar, Nowinski, McKee, & Cantu, 2011; Gibson, Herring, Kutcher, & Broglio, 2015) In response, medical communities have created and disseminated evidencebased di agnostic an d treatment methods to improve care and fill the concussion knowledge gap ( Bires, Leonard, & Thurber, 2017; Stoller et al., 2014) Th is gap is made more complicated by the complex, multifaceted nature of mild traumatic brain injury ( mTBI ) which contributes to the widespread inconsistency of concussion definitions, diagnostic criteria, and research outcomes ( Carroll et al., 2004 ; Chan, Thurairajah, & Colantonio, 2015; Frmont, 2016) In an effort to develop consensus within the clinical and scientific communities, and enhance patient care, task forces such as the International Conference on Concussion in Sport and The World Health Organization ( WHO ) Task Force have formed ( Carroll et al., 2004 ; McCrory et al., 2012 ; McKay & Velikonja, 2016) The Zurich Conference created one of the most highly recognized and referenced definitions of concussion ( McKay & Velikonja, 2016) Conceptualized as a traumatically induced complex pathophysiological process affecting the

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13 brain, ( McCrory et al., 2013 ) concussions produce heterogeneou s symptoms and recovery trajectories in multiple domains such as cognition, somatic symptoms, sleep functioning, mood, behavior, vestibular function, and physical signs ( McCrory et al., 2013 ) The WHO Task Force further defined the boundaries of mTBI favoring the definition developed by the Mild Traumatic Brain Injury Committee of the Head Injury Interdisciplinary Special Interest Group of the American Congress of Rehabilitation Medicine ( Carroll et al., 2004 ) Though t he majority of patients who sustain a single concussive event are expected to recover preconcussion neurocognitive and symptomati c function in approximately 7 14 days ( Belanger & Vanderploeg, 2005; McCrory et al., 2013 ) some individuals display persisting problems, and predicting individual outcome is often difficult. A n estimated 10 15% of individuals experience protracted recoveries (> 10 days) the majority of which will eventually recover within three months after injury ( Alexander, 1995; Binder, Rohling, & Larrabee, 1997; Ponsford et al., 2000) However, a smaller but sizeable percentage o f individuals who researchers have described as the miserable minority remain symptomatic for several weeks, months, and sometimes years following their injur y ( Bigler, 2008; McCrory et al., 2013 ) Predictors of Outcome The development of tools that can predict clinical recovery from mTBI is considered a high priority for future research as no validated practical tool yet exists ( Kristman et al., 2014; Zemek et al., 2016) This has become particularly relevant as medical providers seek methods that will enable the identification of those most in need of intervention. Without prognosticators or clear guidance, many medical p roviders often prescribe highly conservative management methods to avoid symptom exacerbation. However, such methods are largely based on clinical folklore or custom ary practice preferences For example, the overuse of such strategies as

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14 cocooning or t otal rest may actually hinder recovery and produce new problems of their own ( Thomas, Apps, Hoffmann, McCrea, & Hammeke, 2015) Because of this researchers have sought to i dentify variables that predict recovery following mTBI. Several factors have been i dentified through prospect ive studies, as described below: Sex Though several animal studies have demonstrated that females show better recovery from TBI than their male counterparts ( Bazarian, Blyth, Mookerjee, He, & McDermott, 2010 ) the data in human females has been less convincing. Wit hin both adult and pediat ric populations, female sex has demonstrated an increased risk for protracted recovery and increased symptom reporting. ( Babcock et al., 2013 ; McNally et al., 2013; Meares et al., 2011 ; Sharp & Jenkins, 2015; Tator et al., 2016) with the minority of results suggesting otherwise in NCAA athle tes ( Zuc kerman et al., 2016) There are several explanations for longer recovery times in females than in males. Regarding psychological factors, Harmon and colleagues suggest that females are simply more likely to report symptoms ( Harmon et al., 2013 ) and thus continue to report problems long after the males have ceased to do so. Biologically, differences in endogenous levels of progesterone and estrogen have been posited to predict recovery, with better outcomes presenting in post menopausal and pre menarche females ( Bazarian et al., 2010 ) as well as during the luteal phase of menstruation when progesterone levels are increased ( Wunderle, Hoeger, Wasserman, & Bazarian, 2014 ) Others have hypothesized that the reduced size and musculature of the neck in females decreases the ability to stabilize the head, increasing the acce leration deceleration component of injury head neck segment peak, angular acceleration, and displacement and risk of rotational damage ( Dvorak, McCrory, & Kirkendall, 2007; Tierney, 2005)

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15 Prior Medi cal History Pre existing history of concussion Concern about t he potential long term consequences of r ecurrent concussion exposure is one of the most potent driving forces in the rise of concussion awareness. Abnormal accumulation of p tau in the depth of the cortical sulci suggests a harmful neuropathological consequence for individuals with a history of repeated brain trauma ( McKee et al., 2016 ) Howe ver, very little is known about the pathological processes that give rise to neurodegeneration after repetitive concussions, and caution should be exercised in attributing mechanism solely to repetitive head injury ( Asken et al., 2016) There is a lack of unanimous agreement regarding whether prior history of concussion predicts protracted recovery Wh ile some researchers have argued that pre existing concussion is associated with protracted recovery ( Zemek, Farion, Sampson, & McGahern, 2013; Harmon et al., 2013, Zuckerman et al., 2016; Morgan et al., 2015 ), others h ave not found this effect ( McCrea et al., 2013 ) One reason for this disagreement is that some studies have attempted to predict recovery usi ng the number of prior concussions, while others have used dichotomous measures (i.e., presence v. absence of prior history ). One question that remains unanswered is whether recovery is different after two closely spaced concussion s (e.g. two concussions s eparated by days weeks) than it is when the second concussion is separated from the first by a longer time duration (months years). Due to the complex cerebral pathophysiological process that follows concussion (described below) and the window of vulnerability that follows the time interval between successive concussion events may affect recovery trajectory ( Prins, Alexander, Giza, & Hovda, 2013; Vagnozzi et al., 2008 ) Despite this, such data are rarely reported within these studies.

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16 Following mTBI, a cascade of neurometabolic and physiological changes of the brain occurs (referred to as th e neurometabolic cascade of concussion ) lower ing the threshold for subsequent injury ( Giza & Hovda, 2001) If a second subconcussive or concussiv e blow is sustained during this vulnerable metabolic window, the brain may be unable to accommodate the additional demands leading to increased neu ronal death, risk of seizure activity and neurobehavioral deficits ( Giza & Hovda, 2001; MacFarlane & Glenn, 2015) Revealed by specialized neurodiagnostic testing, t his vulnerable window for insult generally extends beyond the point at which symptoms resolve and may persist for periods up to a year ; creating a false sense of recovery as the individual returns to activity ( MacFarlane & Glenn, 2015) Pre e xisting h istory of m ood d isorder P re existing histo ry of anxiety and/or depression has been found in the majority of studies to predict protracted recovery in both pediatric and adult populations ( Ponsford et al., 2012; Morgan et al., 2015; Emery et al., 2016 ; Meares et al., 2011 ) though some s tudies have not found this effect ( Eisenberg, Andrea, Meehan, & Mannix, 2013) In those individuals with pre existing mood disorder, it might be argued that post injury mood symptoms represent simple continuation of the disorder. However, Gould and colleagues found that development of novel mood disorder post TBI occurs frequently ( Gould, Ponsford, Johnston, & Schnberger, 2011) P re existing history of Attention d eficit/ h yperactivity d isorder Attention deficit/hyperactivity disorder (ADHD) is a relatively commo n neurodevelopmental disorder that is hypothesized to reduce the brains tolerance to neurological insult ( Mautner, Sussman, Axtman, Al Farsi, & AlAdawi, 2015) C hildren with ADHD have a higher probability of sustaining head injuries than their neurotypical counterparts and they experience increased disability post mTB I ( Bonfield, Lam, Lin, & Greene, 201 3; Owens, Goldfine, Evangelista, Hoza, & Kaiser, 2007 ) Because of this, many studies have explicitly

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17 excluded ADHD participants, and as a result, we know less than we could about their post mTBI present ation and recovery trajectory ( Yeates, 2010 ) However many studies that have included injured participants with a prior history of ADHD have re ported that a positive history increases the risk of protracted recovery after mTBI ( Gessel, Collins, & Dick, 2007; Kimberly G Harmon et al., 2013; Mautner et al., 2015; Zemek, 2013) as have not ( Asplund et al., 2004; Eisenberg et al., 2013; Lau, Collins, & Lovell, 2011) Pre existing histo ry of m igraine Prior medical history of migraine is recognized by the 2012 Zurich Convention as a modifier of concussion ( McCrory et al., 2012) Study results are mixed regarding whether history of migraine predicts prolonged recovery from concussion. Several studies of pediatric and adult samples have confirmed its significan ce ( Kuczynski, Crawford, Bodell, Dewey, & Barlow, 2013; Sharp & Jenkins, 2015; Hoffman et al., 2011; K. G. Harmon et al., 2013 ) while some s tudies of pediatric samples have not ( Eisenberg et al., 2013) ; ( Morgan et al., 2015) However a family history of migraine has been found to predict prolonged post concussive symptoms in the absence of personal history which may suggest some degree of heritability ( Morgan et al. 2015, Kuczynski, 2013) Injury Characteristics Loss of consciousness ( Bakhos et al. ,2010) after concussion represents acute brain dysfunction attributed to changes in ionic concentrations, metabolic activity, and cerebral blood flow termed spreading depression ( Giza & Hovda, 200 1; McKay & Velikonja, 2016 ) Importantly, LOC is not required for a diagnosis of concussion ( Harmon et al., 2013 ). Within military and athletic populatio ns, the presence of LOC has been associated with a protracted course of symptom expression ( Asplund, McKeag, & Olsen, 2004; Wilk, Herrell, Wynn, Riviere, & Hoge, 2012 ) and when combined with blast injury, is associated with a greater likelihood of

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18 white matter abnormalities on MRI ( Hayes, Miller, Lafleche, Salat, & Verfaellie, 2015 ) LOC lasting longer than 60 seconds is considered by neurologic evaluation standards as a more serious injury; its presence triggers the use of neuroimaging protocols ( McKay & Velikonja, 2016) In contrast, some studies of athletic and non athletic populations suggest that the presence of LOC does not necessarily signal a more significant injury and does not strongly predict outcome after concussion ( Silverberg et al., 2015; Standaert, Herring, & Cantu, 2007; Zuckerman et al., 2016) It remains unclear whether the presence, versus the duration, o f LOC is the most important predictive variable. In prevailing definitions of concussion, LOC (if present) is by definition less than 30 minutes and is frequently much shorter ( Harmon et al., 2013 ) The Current Study The current study seeks to determine demographic, injury, and medical history characteristics of university students during sub acute recovery from mTBI and their relationship with self reported symptoms of concussion related sequelae, anxiety, and depression at approximately 14 days and 30 days after injury. Individuals who report three or more symptoms at these time points are thought to reflect protracted and persistent postconcussional syndrome, respectively. We selected predictor s based on the foregoing review and chose clinically validated questionnaires to evaluate self reported symptoms The population of interest, college students who are not elite athletes, is underrepresented in a literature dominated b y mostly pediatric and elite athletic populations. As the data suggests that non sport related mTBI populations recover more slowly than do their athletic counterparts, and that t he collegiate lifestyle has unique health implications, investigation of this sub population is warranted. T he current study seeks to identify predictors of outcome in this sub population to better inform patient care and ultimately decrease recovery time via early identification and intervention of at risk patients

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19 Specific Aims Aim 1 To determine demograp hic, injury, and pre existing medical history characteristics of participants post mTBI and their r elationship with concussion related, anxiety, and depressive symptom r eport as measured by the Sport Concussion Assessment Tool, Third Edition Symptom Evaluation (S3SE), State Trait Anxiety Inventory (STAI), and Beck Depression Inventory, Second Edition (BDI II) at 14 25days post injury. Hypothesis 2 In line with the literature, demographic factors such as sex, and existence of preexisting medical history in the form of ADHD, migraine, mood disorder, and/or history of concussion will predict increased symptom reporting upon the S3SE, BDI II, and STAI at Pre Intervention. However, LOC will fail to predict symptom reporting across any of the self report measures. Aim 2 Investigate the r elationship b etween demographic factors such as sex, and positive existence of pre existing medical history (i.e., ADHD, migraine, mood disord er, history of concussion) in predict ing symptom change in self report measures of concussion related s ymptoms (S3SE), depression (BDI II) and anxie ty (STAI) from approximately 14days post injury to 30days post injury. Hypothesis 2 In line with Hypothesis 2, demographic factors such as sex and pre existing medical history in the form of ADHD, migraine, mood disorder, and/or history of concussion will predict c hange scores in S3SE, BDI II, and STAI between the two time points. However, LOC will not predict symptom change across the two time points

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20 Table 1 1. American Congress of Rehabilitation Medicine definition of mild traumatic brain injury A patient wit h mild traumatic brain injury is a person who has had a traumatically induced physiological disruption of b rain function as manifested by At least one of the following: Where severity does not exceed: Any period of loss of consciousness Any peri injury amnesia Any injury acute change in mental state Focal neurological deficits that may or may not be transient Loss of consciousness <30 minutes 30 minutes post injury, Glasgow Coma Scale =13 15 P ost traumatic amnesia < 24 hours *Note. CT, MRI, EEG, or routine neurological evaluation may be normal.

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21 CHAPTER 2 METHODS Procedure Part icip ant data were extracted from the University of Florida prospective controlled clinical trial The Effect of E xercise on Neurorecovery Following Mild Traumatic Brain Injur y Participant Recruitment Prospective p articipants diagnosed with mTBI per the American Congress of Rehabilitation Medicine Guidelines ( Mild Traumatic Brain Injury Committee, 1993 ; see Table 1 1. ) were recruited from the University of Florida Student Hea lth Care Center (UFSHCC) UFSHCC Sports Conc ussion Center and UFHealth Emergency Department in Gainesville, Florida between 2015 and 2016. Ninety two p atients interested in the study were referred by their diagnosing physician to an on site research study staff member The study staff obtained the prospective participants consent to be contacted using an IRB approved form, which collected their name, telephone number, and/or email address. Participant Eligibility Study staff contacted prospective participants via phone and/or email using the patient provided consent to be contacted information. Of the 92 candidates for whom consent to be contacted was obtained 31 were lost to follow up as they were unable to be reached Those contacted (N=61) were administer ed a brief eligibility screening via telephone (see Table 2 1. for eligibility criteria) Of the sample, four individuals failed to meet inclusion criteria; three stating that they had not sustained a concussion, one having sustained a severe TB I. Three individuals did not meet the study s exclusion criteria; two due to pre existing medical conditions and one due to lacerations sustained during their injury that prevented their participation in aerobic activity. Fifty three individuals underwent eligibility screening and met inclusion and exclusion

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22 criteria. However, due to the fact that the study requir ed weekend on campus participation, 23 individuals declined to participate due to reported scheduling conflicts (e.g., football game conflict, social events, etc.) One additional individual declined to participate as they did not have regular transportation available to them. Thirty participants met study inclusion and exclusion criteria, agreed verbally to participate and were scheduled for an initial appointment in which final eligibility and consent to participate would be administered by one of the studys research coordinators Of these 20, one prospective participant failed to show to their first appointment citing schedul ing conflicts, two participants were removed after failing to attend their second and third appointments, and one participant was discontinued by the study Safety Monitor due to exacerbation of symptoms because of injury related soft tissue damage. The complete eligibility flow diagram is presented at the end of the chapter within Figure 21. Study Design The first (Pre Intervention) appointment was scheduled 14 25 days post injury ( M =15.2, SD =3.5) in line with the literature which estimates that the majority of patients wil l recover within 7 14 days post concussion ( Belanger & Vanderploeg, 2005; McCrory et al., 2013 ) F ollowing completion of the informed consent protocol participants were administered measures assessing demographic information, concussion related symptoms, symptoms of depression, and symptoms of anxiety via REDCap administered questionnaire Participants were then scheduled to attend daily exercise sessions f or a period of one week at the Clinical Research C enter with trained study staff. Following this seven da y exercise interventio n, participants were scheduled for their Post Intervention appointment approximately 30 days after injury ( M =29.0 days post injury, SD =5.3), in which they were again assessed using the same measures of concussion related symptoms, symptoms of depression, and symptoms of anxiety which were administered

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23 at th e Pre Intervention appointment. An overview of the studys measurement timeline and des ign may be viewed in Figure 2 2. Measures Demographic Questionnaire A demographic questionnaire was ad ministered via REDCap assessing the participants age, sex, date of injury, education, and Race/Ethnicity. P resence of LOC in addition to pre existing medical history of AD HD, concussion, mood disorder (i.e ., depression or anxiety), and migraine were also obtained. Sport Concussion Assessment Tool, 3rd Edition Symptom Evaluation (S3SE) T he Sport Concussion Assessment Tool, 3rd Edition Symptom Evaluation (S3SE) is a 22item ( Guskiewicz et al., 2013 ) self report questionnaire measuring the presence (i.e., Symptom Score) and severity of symptoms (i.e., Symptom Severity) following a concussion. The S3SE assesses multiple domains of common pos t concussion symptoms including somatic, sleep, mood, and physical signs which it measures using a six point Likert scale. Psychometrics suggest high specificity (0.911.0), moderate to high sensitivity (0.640.89), a nd moderately high to high reliability (0.880.94) ( Guskiewicz et al., 2013 ) The S3SE is ap proved for use in individuals above the age of 12 and is available for free use by medical providers via the British Journal of Sports Medicine ( McCrory et al., 2013 ) Beck Depression Inventory, Second Edition (BDI II) The Beck Depression Inventory, second edition (BDI II) is a psychometr ically validated self report measure of depressive symptomology that is frequently used in clinical populations. Developed to map onto the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition ( DSM IV ) diagnostic criteria for depressive disorders, the BDI II surveys several symptoms of depression (i.e., sadness, pessimism, past failure, loss of pleasure, guilty feelings,

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24 punishment feelings, self dislike, self criticalness, suicidal thoughts or wishes, crying, agitation, loss of interest, indecisiveness, worthlessness, loss of energy, changes in sleeping pattern, irritability, changes in appetite, concentration difficulty, tiredness or fatigue, and loss of interest in sex) over the span of a two week period and is appropriate for use in individuals over the age of 13 Higher scores indicate greater depression severity ( Beck, Steer, & Brown, 1996 ) Nonclinical sample coefficient alpha estimates of reliability is 0.93 and it demonstrates moderately high concurrent validity with other validated depression rating scales ( Beck et al., 1996 ) The BDI II utilizes four cut scores of minimal (0 13), mild (1419), moderate (20 28), and severe (29 63) to demonstrate depression severity. However, their use in nonclini cal populations in which the rate of major depressive disorder is lower, is not advised ( Meehl & Rosen, 1955 ) State Trait Anxiety Inventory (STAI) The State Trait Anxiety Inventory (STAI) is a psychometrically validated self report questionnaire of current and enduring anxious symptomology frequently used in clinical settings with higher scores representing greater anxiety. Comprised of two scales, t he State Anxiety Scale assesses feelings of anxiety which increase with temporal danger and stress. The Trait Anxiety scale evaluates long standing anxiety and is effective in nonclinical populations to screen for anxiety problems. The STAI is written at a sixthgrade reading level and is appropriate for use in individuals between the ages of 14 69. The STAI demonstrates moderately high to high internal consistency, moderate to moderately high test retest reliability coefficients, as well as adequate const ruct and concurrent validity ( Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983)

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25 Analys is Plan Descriptive Statistics Descriptive statistics for p articipant sample characteristics are presented in Table 22. Twenty five participants were ultimately included in the current analyses. Participants were 48% female and ranged in age from 18 32 (M=21, SD=3.3) with educational attainment ranging from 1220 years (M=14.5, SD=2.1). Regarding Race/Ethnicity, 68% o f the participants identified as White or Caucasian, 20% as Hispanic or Latino, 8% as African American or Black, and 4% as Pacific Islander or Native Hawaiian. Regarding prior medial history, 8% reported migraines 4% reported ADHD, 76% concussion, and 12% mood disorder. Injury characteristic of LOC following their most r ecent concussion was present in 36% of the sample. Analyses Data were analyzed using the IBM SPSS Statistics 24.0 statistical software. Effect size indices of 0.1, 0.3, and 0.5 are define d as small, medium, and large effects, respectively ( Cohen, 1992) Individuals were considered symptomatic if they reported three or more symptoms upon the S3SE Symptom Evaluation at Pre Intervention in line with ICD 10 diagnostic c riteria for postconcussional syndrome (World Health Organization, 1992). Those who were symptomatic were assigned a dummy coded value of 1, while those who were not symptomatic were coded as 0 . Injury characteristics, and preexisting injury charac teri stics were dichotomized in a similar fashion i n which individuals who reported the presence of a characteristic were assigned a value of 1, and those that denied the presence of a specific characteristic were assigned a value of 0 for that specific var iable. Symptom report as measured by the BDI II and STAI remained continuous, refle cting their raw and standard scores, respectively.

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26 Figure 21. Eligibility flow diagram Referred to study ( n=92) Pre Intervention Scheduled ( n= 30) Post Intervention n= (25) Analyzed ( n=25) Removed from study ( n=5) No contact or schedule failure ( n=31) Declined to participate ( n=24) Inclusion criteria failure (no mTBI; n=4) Did not meet exclusion criteria ( n=3)

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27 mTBI incurred Eligibility assessed Informed consent obtained Pre intervention assessment One week exercise intervention Post intervention assessment M =15.2 ( SD =3.5) days post injury M =29.0 ( SD =5.3) days post injury Demographics X BDI II BDI II S3SE S3SE STAI STAI Figure 22. Overview of study design and measures administered

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28 Table 2 1 Participant eligibility criteria Inclusion criteria Exclusion criteria Diagnosed mTBI per A merican C ongress of R ehabilitative M edicine mTBI sustained in past 1425 days Aged 1840 Speak English Comorbid orthopedic injury that inhibits movement History of psychiatric hospitalization Diabetes History of moderatesevere TBI Neurological disorder unrelated to TBI Physician recommends against exercise Non neurological explanation for symptoms

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29 Table 2 2. Sample characteristics Age in years M ( SD ) Education 21 (3.3) 14.5 (2.1) Gender n (%) Male Female 13 (52) 12 (48) Race /Ethnicity n (%) White/Caucasian Hispanic/Latin American Black/African American Pacific Islander/ Native Hawaiian Predictor n (%) Female History of migraine History of ADHD History of concussion History of mood disorder L oss of consciousness 16 (68) 5(20) 3 (8) 1(4) 12(48) 2 (8) 1(4) 19 (76) 3(12) 9 (36) *Note. ( N =25)

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30 CHAPTER 3 RESULTS Aim 1: To Determine D emograp hic, Injury, and Pre existing Medical History Characteristics of P artici pants Post mTBI and Their R elationsh ip with S3SE, STAI, and BDI II Symptom R eport at Pre Intervention Pre I ntervention C har acteristics One quarter of the population was symptomatic at PreIntervention ( N =5 ) The average age of the symptomatic sample was within one standard deviation of the entire group ( M =20, SD =1.41) and was composed of mostly White/Caucasian individuals ( N =4). Regarding injury characteristics, two of the participants reported LOC at the time of injury. In regards to preinjury characteristics, 100% of the sample reported a prior history of concussion, 20% a prior history of mood disorder, 20% a prior histor y of migraines, and none of the symptomatic population reported a history of ADHD. Sixty percent of the sample was male ( N =3). The n umber of symptoms reported upon the S3SE ranged from four to twenty ( M =11.40, SD =6.47) with symptom severity ranging from f our to forty eight ( M =21 .40, SD = 16.99) Regarding symptoms of depression, symptomatic participants reported symptom scores of 7 16 ( M =10, SD =3.54) upon the BDI II, which in a clinical population would range from minimal to mild depressiv e symptomology Regarding State and Trait anxiety, participants reported anxiety within normal limits ranging from a T score of 41 60 ( M =52.4, SD =7.83) and 4568 ( M =57.6, SD =8.47) respectively. In contrast, 75% of the sample were not symptomatic (i.e., individuals who reported less than three symptoms). The nonsymptomatic sample at Pre Intervention ranged in age from 18 32 ( M =21.65, SD =3.61), and wer e 65% White/Caucasian, 20% Hispanic /Latin American 10% African American /Black and 5% Native Hawaiian/ Pacific Islander. Regarding injury characteristics, seven of the participants reported LOC at the time of injury. In regards to pre-

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31 injury characteristics, 70% of the sample reported a prior history of concussion, 20% a prior history of mood disorder, 10% a prior history o f migraines, and 5% a history of ADHD. Fifty percent of the sample was male. Number of symptoms reported upon the S3SE ranged from zero to two ( M =0.65 SD =0.88) with 60% percent of the nonsymptomatic participants reporting zero symptoms. Symptom severity for the nonsymptomatic sample also ranged from zero to two with identical mean and standard deviation ( M =0.65 SD =0.88) Regarding symptoms of depression, nonsymptomatic participants reported symptom scores of 0 47 ( M =9, SD =11.64) upon the BDI II, which in a clinical population would range from minimal to severe depressive symptomology. Regarding State and Trait anxiety, participants reported anx iety ranging from a T score of 3476 ( M =45.85, SD =11.32) and 3482 ( M =47.45, SD =10.04) respectively sugge sting what should be interpreted cautiously as reaching into the clinically elevated range. Predicting Pre I ntervention symptom r eport A three stage hierarchical multiple regression with 1,000 95% bias corrected and accelerated bootstrapped parameter es timates were used to assess the predictive power of sex pre existing medical history, and the injury characteristic LOC to predict the dependent variables: symptomatic classification, concussion related symptoms, depressive symptoms, and anxiety symptoms as measured by the S3 SE Symptom Severity and Symptom Score BDI II, and STAI State and Trait at Pre Intervention for all participants The robust method of bootstrapping was utilized due to the nonnormal distribution of the current dataset. The variables were assembled within each block by order of support within the literature ; sex within the first block, pre existing medical history in the second block, and the injury characteristic LOC in the third block. Assumption of non collinear independent variabl es was met; VIF values were well below

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32 10 and tolerance statistics were greater than 0.2 ( Field, 2013) Tabl es 31, 3 2, 33, 3 4, 35, and 36 depict the predictors, their associated steps, and the resulting regression statistics. Symptomatic classification hierarchical logistic regression model. Results suggested that having a prior history of concussion or migraine pred icted whether a participant would be symptomatic at pre intervention with medium effect sizes. Prior history of mood predicted symptomatic c lassification whereas absence of ADHD predicted symptomatic classification with medium effect size. Sex and presence of LOC appeared to explain minimal variance in whether an individual was classified as symptomatic (Table 3 1) STA I State hierarchical regression model Results suggested that having a prior medical history of ADHD was associated with heightened self report of current anxiety with a predicted heightened current self report of anxiety, with a medi um positive magnitude of effect Being male history of m igraine, and history mood disorder also all showed relationships suggestiv e of increased symptom report, however with small medium magnitudes of effect. LOC appeared to contribute very little to the model and did not predict incr eased State anxiety self re port Of note, sex as a predictor fails to explain much variance in the model until the addition of the medical history variables in Block 2. Together, all 6 predictors explained 63% of the variance in STAI State symptom report. Results are presented at th e end of the chapter in Table 32. STAI Trait h ierarchical regression model Regarding self reported Trait or enduring anxiety, r esults suggested that having a prior medical history of migraine was associated with lower self report of enduring anxiety with a large negative magnitude of effect ( 83). Having a prior history of a mood disorder had the opposite impact resulting in over a one

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33 standard deviation (15.88 point) higher STAI Trait score with a large effect size of ( .51). H ist ory of concussion, history of ADHD, and being male predicted heightened self report of trait anxiety, with medium large, small medium, and small medium positive magnitude s of effect, respectively Again, LOC appear ed to contribute little to the model with an effect size of 0. Of note, similar to Trait anxiety report, sex as a predictor fail ed to explain much variance in the model until the addition of the medical history variables in Block 2. Likewise, having a prior history of migraine appears to expla in l ittle of the model until LOC was added in Block 3. Together, all 6 predictors explained 65 % of the va riance in STAI Trait symptom report (Table 33). BDI I I hierarchical regression model Results revealed (Table 3 4.) that having a prior medical ADHD exerted a large positive magnitude of effect, predicting heightened self report of depressive symptoms. Both having a prior history of concussion and experiencing LOC following mTBI suggested a small medium magnitude of effect, predicting higher sel f report of depressive symptoms as well. History of migraine, history of mood disorder, and sex appeared to explain minimal variance in self report depression symptoms. Together, all 6 predictors explained 65.4% of the variance in BDI II symptom report. S3 SE Symptom Score hierarchical regression model Results revealed (Tables 3 5) weaker effect sizes, with a prior history of ADHD predicting lower S3SE symptom scores ( medium effect size, .31) magnitude decreased symptoms reported. History of concussion and history of mood disorder demonstrated small medium positive magnitudes of effect, and LOC represented a small positive magnitude of effect. Neither sex nor history of migraine appeared to contribute much variance to the model. Overall, the model expla ined 17.5% of the variance in Symptom Score.

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34 S3SE Symptom Severity hierarchical regression model. Results revealed (Table 3 6) smallmedium effect sizes for being female, having a prior history of concussion, history of mood disorder, history of ADHD, and LOC. With the exception of history of ADHD, all predicted higher symptom severity scores. A prior history of ADHD was associated with lower reported Symptom Severity scores Prior histo ry of migraine appeared to contribute little to the model. Overall, the model explained 16% of the variance in Symptom Severity. Post intervention characteristics Sixteen percent of the population ( N =4) were symptomatic at Post Intervention. However, of th e five participants that were symptomatic at Pre intervention, onl y three remained symptomatic at Post Intervention. The average age of the Post intervention symptomatic sample was also within one standard deviation of the entire group ( M =19.75, SD =1.50), and was 50% White/Caucasian, 25% African American/Black, and 25% Hispanic. Regarding pre existing medical history, 100% of the sample reported a prior history of concussion but denied a prior history of mood disorder. One individual reported a prior histor y of migraine, and again none of the symptomatic population reported a history of ADHD. Seventy five percent of the sample was male. Number of symptoms reported upon the S3SE ranged from three to five ( M =3.50 SD =1.00) with symptom severity ranging from thr ee to seven ( M =4.50 SD =1.91). Regarding symptoms of depression, symptomatic participants reported symptom scores of 2 27 ( M = 12, SD = 10.68) upon the BDI II, which in a clinical population would range from minimal to moderate depressive symptomology. Rega rding State and Trait anxiety, participants reported anxiety within normal limits ranging from a T score of 37 60( M = 51.00, SD = 10.80) and 3856 ( M = 48.25 SD = 8.73 ), respectively. In contrast, 84% of the sample was asymptomatic (i.e., reported less than three symptoms) at Post Intervention. The nonsymptomatic sample ranged in age from 18 32

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35 ( M =21.62 SD =3.53), and was 71.4% White/Caucasian, 19% Hispanic, 4.8% Afric an American, and 4.8% Native Hawaiian/ Pacific Islander. Reg ardi ng injury characteristics, eight of the participants reported LOC at the time of injury. In regards to pre injury characteristics, 15 of the participants reported a prior history of concussion, three a pri or history of mood disorder, one a pr ior history of migraines, and one a history of ADHD. Fifty two percent of the sample was fe male. Number of symptoms reported upon the S3SE ranged from zero to two ( M =0.24 SD =0.62) with 85.7% percent of the non symptomatic participants reporting no symptoms. Symptom sev erity for the nonsymptomatic sample also ranged from zero to two ( M =0.29 SD =0.72). Figure 31 depicts S3SE Symptom Score report from each participant from Pre Intervention to Post Intervention. Regarding symptoms of depression, non symptomatic participant s reported symptom scores of 028 ( M = 6.42, SD = 8.38) on the BDI II, which in a clinical population would range from minimal to moderate depressive symptomology. Regarding State and Trait anxiety, participants reported anxiety ranging from a T score of 3469 ( M = 43.67 SD = 9.80 ) and 3471 ( M = 45.86 SD = 11.47), respectively Aim 2 : Investigate the Relationship Between Injury Characteristics, Pre existing Medical History, and Sex and Symptom Change i n Measures of Depression, Anxiety, and Concussion related S ymptoms To address Aim Two Analysis of Covariance (ANCOVA) was utilized to create residual gain scores (change from Pre Intervention to Post Intervention, corrected for Pre Intervention values) with 1,000 95% Bias corrected and accelerated bootstrapped est imates to provide robust methods to counter violations to normality Covariate by outcome interaction s were not significant, suggesting that the assumption of homogeneity of regression slopes ha d been met. Levels of the covariate d id not appear to be significantly different across time points. Levenes test was nonsignificant, suggesting homogeneity of variance. To predict the role of injury characteristics (i.e., LOC ) pre existing medical history of concussion, mood disorder, ADHD,

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36 and /or migraine, sex and symptomatic classification at Pre Intervention upon the change in depression, anxiety, and concussion related symptom report Post Intervention s ymptom reports were treated as the dependent variable with Pre Intervention symptom report treated as a covariate. Regarding change in outcome between Pre Intervention and Post Intervention, being classified as symptomatic at 14 25days post injury did not differentially predict change in reported depressi on or anxiety. P rior history of migraine differentially predicted pre post change in STAI Trait anxiety symptoms with a small medium magnitude of effect Prior history of migraine predicted less prepost change in anxiety than seen in those without prior history. Smallmedium effect sizes were noted for the predictive role of prior concussion on prepost change in state anxiety and S3SE Symptom Score. Similarly, history of ADHD predicted less pre post change in Symptom Severity score with a small medium effect si ze. Neither LOC nor sex appeared to explain a notable amount of variance across symptom chan ge in of the described domains.

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37 *Note: Red dotted line represent s symptomatic cut point of three or more reported symptoms upon the S3SE. Figure 31. S3SE symptom report at Pre Intervention and Post Intervention 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 221 2 Pre Intervention Post Intervention

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38 Table 3 1. Linear logistic model of predictors of symptomatic classification at Pre Intervention, with standard errors based on 1000 bootstrap samples Variable b SE p R 2 Step 1 Sex Step 2 Sex History of concussion History of mood disorder History of migraine History of ADHD Step 6 Sex History of concussion History of mood disorder History of migraine History of ADHD Loss of Consciousness 0.06 0.04 0.30 0.25 0.42 0.05 0.04 0.30 0.26 0.42 0.5 0.02 0.16 0.18 0.17 0.41 0.34 0.39 0.20 0.18 0.43 0.35 0.39 0.22 .08 .05 .32 .21 .29 .25 .05 .32 .21 .28 .25 .02 .690 .837 .103 .679 .240 .456 .844 .120 .670 .278 .471 .959 .01 .20 .00

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39 Table 3 2. Linear model of predictors of STAI State symptom report at Pre Intervention, with standard errors based on 1000 bootstrap samples Variable b p R 2 Step 1 Sex Step 2 Sex History of concussion History of mood disorder History of migraine History of ADHD Step 6 Sex History of concussion History of mood disorder History of migraine History of ADHD L oss of Consciousness 1.30 5.40 7.33 6.94 8.40 31.5 5.43 7.28 6.67 8.52 31.50 0.43 4.39 2.95 2.68 7.87 3.43 7.54 3. 28 3.01 8.12 3.88 7.54 4.07 .06 .25 .29 .21 .21 .58 .25 .29 .20 .22 .58 .02 .768 094 .015 .543 .010 .002 107 .023 .561 .034 .002 .924 .01 .63 .00

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40 Table 3 3. Linear model of predictors of STAI Trait symptom report at Pre Intervention, with standard errors based on 1000 bootstrap samples Variable b p R 2 Step 1 Sex Step 2 Sex History of concussion History of mood disorder History of migraine History of ADHD Step 6 Sex History of concussion History of mood disorder History of migraine History of ADHD L oss of Consciousness 1.08 3.72 8.84 15.84 3.01 12.5 3.72 8.85 15.88 3.12 12.5 0.07 4.1 2. 92 3.01 4.07 2.59 3.50 3. 12 3.18 5.17 2.88 7.54 4.00 .05 .18 .37 .50 .08 .24 .18 .37 .51 .83 .24 .00 .790 212 .0 06 .004 .208 .002 .239 .008 .010 .265 .002 .986 0.00 0. 65 0.00

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41 Table 3 4. Lin ear model of predictors of BDI II symptom report at Pre Intervention, with standard errors based on 1000 bootstrap samples Variable b p R 2 Step 1 Sex Step 2 Sex History of concussion History of mood disorder History of migraine History of ADHD Step 6 Sex History of concussion History of mood disorder History of migraine History of ADHD L oss of Consciousness 2.15 1.13 5.82 1.27 2.11 37.5 1.38 5.32 1.36 3.24 37.50 4.13 3.49 3.21 2.76 2.97 3.97 1.19 3.10 2.56 4.92 3.76 1.19 4.97 .11 06 24 04 .06 72 07 22 04 .09 .72 .19 .636 683 .0 52 708 545 .002 .639 .0 61 805 357 002 450 0.0 1 0.62 0.03

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42 Table 3 5. Linear mod el of predictors of S3SE S ymptom Score report at Pre Intervention, with standard errors based on 1000 bootstrap samples Variable b p R 2 Step 1 Sex Step 2 Sex History of concussion History of mood disorder History of migraine History of ADHD Step 6 Sex History of concussion History of mood disorder History of migraine History of ADHD Loss of Consciousness 0.1 0.52 2.97 5.09 0.82 8.00 1.38 5.32 1.36 3.24 37.50 4.1 3 2.12 2.01 1.32 6.30 1.20 6.22 2.22 1.35 7.26 1.33 6.22 3.75 .01 .05 .25 .33 .04 .31 .07 .22 .04 .09 .72 .19 .962 .813 .113 .586 504 .444 .840 .121 .695 .358 .469 .759 0.00 0. 16 0.01

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43 Table 3 6. Lin ear model of predictors of S3SE Symptom Severity report at PreIntervention, with standard errors based on 1000 bootstrap samples Variable b p R 2 Step 1 Sex Step 2 Sex History of concussion History of mood disorder History of migraine History of ADHD Step 6 Sex History of concussion History of mood disorder History of migraine History of ADHD L oss of Consciousness 2.47 3.62 5.20 8.69 0.52 12.5 3.93 4.67 5.37 0.90 12.5 5.21 4.52 4.25 3.07 9.69 2.52 9.54 4.63 2.86 12.53 2.81 9.54 8.76 .12 .17 .21 .26 .01 .23 .18 .18 .16 .02 .23 .23 .632 474 .223 .518 .789 .357 .491 .200 .768 .689 .384 .626 0.01 0.11 0.04

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44 Table 3 7. Residualized change scores between Pre Intervention and Post Intervention symptom report with standard errors based on 1000 bootstrap samples BDI II STAI State STAI Trait S3SE SxSc S3SE SxS Variable p b p b p b p b p b Symptomatic at PreInt. Sex .360 .211 3.58 3.36 .265 .306 4.83 3.63 .410 .882 1.49 0.175 .037 .219 3.67 0.65 .001 .418 4.15 0.57 History of Concussion .158 2.85 .056 7.09 .929 0.206 .102 0.68 .129 0.93 History of Mood .428 8.58 .181 14.38 .388 2.31 .430 0.85 .684 0.61 History of Migraine .851 0.65 .003 12.08 .081 2.54 .553 0.84 .634 0.76 History of ADHD .462 7.56 .625 3.16 .301 2.72 .141 0.60 .073 1.43 L oss of consciousness .198 5.42 .242 5.77 .778 0.40 .526 0.43 .469 0.69 *Not e Pre Int = Pre Intervention

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45 CHAPTER 4 DISCUSSION Predictors of Symptom Report in Sub acute Recovery From mTBI The current study investigated the role of predictors of protracted recovery that had been identified in the literature on subacute recovery after mTBI. Overall, results suggest that having a prior medical history of ADHD increases risk of heightened reporting of anxiety and depress ion 15 days after injury, and is more likely to be seen in individuals who remain symptomatic at that point in time. The rela tionship between prior history of ADHD and report of anxiety and depression after concussion is likely multifactorial. Even in the absence of mTBI, ADHD shows strong comorbidity with mood disorders, and there is strong data to suggest some degree of develo pmental frontal/executive dysfunction in this population ( A merican P sychiatric Association, 2013) When a patient with pre existing ADHD is further exposed to neurological insult ( Carroll et al., 2004 ) it is not surprising that positive signs of anxiety and depressive symptoms would be found. However, this vulnerability hypothesis would also predict that prior history of ADHD would also predict the kinds of post concussion symptoms measured by the S3SE. It is unclear why the magnitude of effect in S3SE was not larger ; one factor may be the relatively mild injuries suffered by study participants. It may be that the anxiety and depre ssion symptoms are being primarily driven by psychosocial factors related to injury related lifestyle disruption, while the S3SE symptoms are driven primarily by injury severity. Since most prior studies have excluded participants with prior ADHD, more re search is needed to better understand these findings. Pre existing history of mood disorders also emerged as significant predictors of both State and Trait anxiety, explaining an impressive 34.4% of the variance in Trait anxiety. One unanticipated finding was that a self reported pre existing history of mood disorders did not

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46 predict post injury report of depressive symptoms. Limitations in study design prevent us from further deconstructing this predictive relationship in terms of the severity or chronicity of preexisting mood disturbance or with regard to whether the participants mood symptoms would meet diagnostic criteria for a current depressive episode. Furthermore, use of anxiolytic or antidepressant medication was not assessed, so it is not known whether, in some participants, current features of m ood disturbance were attenuated pharmacologically. Clinically, the same medications typically used for the treatment of anxiety and depression in a non TBI population are identical to those used for treatment of post concussive syndrome so this a potential confound of treatment. Future studies should address these issues more directly. Finally, a prior history of concussion was a positive predictor of post injury Trait anxiety, which reflects likely mood dysregulation that stands as one of the strongest predictor s of postconcussional syndrome at 1425 days. H aving a prior history of concussion positively predicted the frequency and severity of symptoms reported though this effect was not statistically significant with the small sample size of this stud y. However, as mentioned in the introduction, there are multiple methodological differences that might account for this inconsistency. Notably, our sample was dummy coded 0 and 1 to represent a lack of prior concussion or a history of prior concussio n, respectively. T here is evidence to suggest that using a continuous measure may better predict outcome and impairment (Guskiewicz, 2003). Furthermore, our study does not account for the interval between prior and current concussions in those with a posit ive concussion history. As the literature suggests that temporally contiguous concussions may increase neuropathological and neurobehavioral consequences, recent presence

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47 of a prior concussion could complicate their presentation ( Vagnozzi et al., 2008) A final possibility is that our small sample size lacked power to detect a significant effect. Regarding change, only prior history of migraines differentially predicted change in STAI Trait anxiety symptoms between pre and post intervention measurement points. Contrary to expectations, results showed that individuals with a prior history of migraine reported significantly less anxiety than their counterparts who did not report a history of migraines. Smallmedium effect sizes were noted for tests that evaluated the ability of prior history of concussion to predict changes in state anxiety change and S3SE Symptom Score. Similarly small effect sizes were seen for the ability of ADHD history to predict prepost change in Symptom Severity Score. One limiting factor is that this study addressed only the first month of recovery. Though ICD 10 criteria suggests that a specific constellation of symptoms past 30 days is considered abnormal, many studies suggest that symptom resolution, especially in a nonathletic population, may have longer recovery times. As such, future research with this population would likely benefit from evaluating symptomatic recovery over a longer time range. Previous literature has provided a significant amount of support f or female sex as being a predictive factor of protracted outcome in women of child bearing age. Our results were not in line with this. Also of note, many studies fail to use standardized measures of symptom report ( Zemek et al., 2016) Based on our results, it appears that the standardized measures used for assessment of depressive and anxious symptomology (BDI II and STA I) were more sensitive at detecting subclinical mood related symptomology. Though the S3SE does screen for mood related symptoms, it does not do so with as much specificity as the BDI II and STAI as represented in the current study. The S3SE is used widely in practice and has demonstrated its validity and reliability in detecting concussive symptoms (Guskiewicz, 2013). However, medical

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48 providers may be encouraged on the basis of this data to supplement their mTBI evaluation with a standardized measure of mo od, particularly for those at risk for experiencing protracted outcome or mood symptomology beyond those individuals whom are considered asymptomatic following mTBI. Clinical A pplication This study attempted to provide initial data on ability of validated practical, empirically based tools to predict post injury mood, cognitive, and symptom status in an mTBI population Because broad awareness of concussion has emerged only recently, there is a general need not only to develop validated risk assessment t ools, but also to develop individualized intervention pathways based on assessment results. In practice, many un validated, pseudoscientific, and potentially harmful approaches (e.g., long term cocooning) obscure the picture with respect to expected recov ery trajectories ( Zemek et al., 2016; Zemek, 2013) Because of this, there is a need for objective, sensitive, and specific metrics and markers of concussion diagnosis, prognosis, and recovery ( Collins et al., 2016) The data fr om the current study adds to the body of literature in the underrepresented non athletic collegiate population. Research indicates that athletic, non athletic, and military populations recover differentially from mTBI, prompting the need for more specific data collection ( Youth, 2013 ) Though collegiate populations make up a large proportion of res earch samples due to their proximity and availability to University based researchers, data assessing non athletic mTBI in a collegiate population is rather scant. The collegiate population itself is regarded as an at risk group not only for mTBI, but also for the development of depression with estimates rising yearly ( Collins et al., 2016) Of those university students diagnosed with depression, less than 25% of them were receiving treat ment for their condition ( American College Health Association, 2009 ; Buchanan, 2012) As such, students presenting with mTBI with untreated mood disorder may particularly benefit from early

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49 identification and extended follow up so as to increase their awareness of, and enrollment in, appropriate interventions for mTBI and mood related symptoms. By recognizing the importance of preexisting health factors, medical providers viewing the patien t at both acute and subacute point of recovery may be more inclined to recommend early intervention, repeat follow up, and make referrals for specialized care (e.g., psychotherapeutic therapy, neurology, etc.) for individuals at risk for persistent sympto ms. If this became standard practice, it could shorten recovery and could prevent some patients from beginning on a complicated, protracted recovery trajectory ( A merican C ollege H ealth Association, 2009) Study Limitations and Future D irections Ther e are several limitations to this study. The current analysis of recovery predictors would benefit from a larger and more diverse sample. Since the data were taken from an small clinical trial investigating the safety and tolerability of aerobic exercise during sub acute recovery from mTBI, selection bias may further limit generalizability as individuals within the study had to receive physician clearance to participate, were capable of attending and scheduling several appointments (e.g., access to transportation, planning, motivation) resulting in an arguably less severely injured and possibly higher SES population. The study also collected data from only the Gainesville, Florida area and was comprised of largely university students. However, this also serves as strength of the study, since the majority of research performed in this segment of the population involves student athletes who, due to their elite status and support syste ms, are likely to have different recovery patterns when compared to non athletes. Regarding future directions, further analyses are ongoing as the study has collected follow up data or each participant three months after their initial date of injury. Thi s third Time period will allow for further assessment of participant outcome at a point which has been

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50 recognized as distinguishing chronic sequelae ( Bigler, 2008) Additionally, all participants underwent repeated high resolution MRI, and future investigation of the predictive value of these variables on brain based functionality and structural integrity via analyses of associated regions is unde rway. Conclusion The current study serves to add to the standing literature of predictors of mTBI recovery. As the existing body of literature has variable results, future research utilizing consistent methodological technique in diverse populations is ne eded. Though standardization in a clinical setting is necessary before overall clinical utility can be determined, preliminary data may aid medical providers in utilizing a more tailored bio psycho social model in their assessment and treatment of mTBI ( Collins et al., 2016) Such recommendations might include more in depth assessment of depression and anxiety in mTBI populations, particularly of those with a prior history of ADHD, mood dis order, or concussion. Though current concussion check lists do assess mood briefly, they may lack the ability to detect clinically relevant anxiety and depression in this population.

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57 BIOGRAPHICAL SKETCH Sarah M. Greif is a second year graduate student in the neuropsychology track of the Department of Clinical and Health Psychology at the University of Florida. She received her M.S. in Psychology from the University of Florida under the mentorship of Dr. R ussell M. Bauer and Dr Shelley C. Heaton in the spring of 2017. In addition to her appointment as a graduate research assistant, Sarah serves as a Neuropsychology Clinical and Research Coordinator for the UFHealth Interdisciplinary Concussion Clinic and U F Student Health Care Center Sports Concussion Center, and as the Graduate Advisor of Athlete Brain. Sarah completed her Bachelor of Science degree in p sychology at the University of Florida where she worked as a Clinic & Research Coordinator in the UF Ped iatric Neuropsychology Clinic, Program Assistant within The University of Florida Multidisciplinary Diagnostic and Training Program, and as a Research Assistant in The UF Sports Concussion Neuropsychology Research Lab where she assisted in the CTSIs Healt h IMPACTS for Florida networks Concussion Surveillance and Monitoring Program.