Concussion Severity and Recovery in Collegiate Female Athletes Using Synthetic
Progestin

A DISSERTATION
Submitted to the Faculty of the School of Graduate Studies and Research of California
University of Pennsylvania in partial fulfillment of the requirements of the degree of
Doctor of Health Science (DHSc) in Health Science and Exercise Leadership

Catherine A. Holt

Research Adviser, Dr. Carol Biddington
California, Pennsylvania
2019
CALIFORNIA UNIVERSITY of PENNSYLVANIA
CALIFORNIA, PA

DISSERTATION APPROVAL

Health Science and Exercise Leadership

We hereby approve the Dissertation of

Catherine A. Holt
Candidate for the degree of Doctor of Health Science (DHSc)

Date

Faculty

12-4-19

Digitally signed by Carol
Biddington
Date: 2019.12.09 12:16:20 -05'00'
____________________________________________

_____________

Carol Biddington

Carol Biddington, EdD. (Chairperson)

12-4-19
_____________

12-4-19
_____________

Joni Roh
2019.12.07 12:59:44 -05'00'

____________________________________________
Joni Roh, EdD.

Jamie Weary
____________________________________________
Digitally signed by Jamie Weary
Date: 2019.12.11 13:15:18 -05'00'

Jamie Weary, D.P.T.

Acknowledgements

I would like to extend my most heartfelt gratitude to a number of people without
whom this dissertation would likely never have seen its completion. First and foremost,
to my advisor, Dr. Carol Biddington, your attention to detail, unwavering support, and
guidance throughout this process was immeasurable. To my committee members, Drs.
Joni Roh and Jamie Weary, your expertise and input from inception through research
completion were invaluable. To Dr. Melissa Sovak, you likely have no idea how much
your patience and willingness to metaphorically “hold my hand” throughout the data
analysis was appreciated. To all of you and to California University of Pennsylvania,
thank you for granting me the opportunity to research a topic that I have been passionate
about for years.
This research would not have been possible without the dedication of the CARE
researchers who collected all of the data used in this analysis. Additionally, the hard work
and oversight of those at FITBIR allowed for the analysis of CARE study data. The
dedication of everyone involved in this process is a testament to your collective drive to
further the research centered around TBI, mTBI, and SRC.
To Nana, Grandpop, Granny, and Grandpa, thank you for sharing the stories of
your life with me. Unbeknownst to you, they taught me the true meaning of grit,
resilience, and hard work. To Mom and Dad, thank you for supporting me throughout all
of my successes, as well as blunders, and for teaching me that the most cherished
accomplishments in life are often the most difficult to achieve. In doing so, you gave me
the drive to push my own limits.

To Nate, Cole, Brayden, and Peyton, thank you for giving Mommy a reason to
continue on the days it seemed much easier to quit. You reminded me every day why I
embarked on this journey and your love carried me through some of the most challenging
moments of the last three years. Thank you for helping me smile through them.
Finally, to Scott, there are no words to express how much your love and support
meant to me throughout this process. Your confidence in me far exceeded that which I
had in myself and gave me the courage to approach a goal that had long seemed
insurmountable. You have been my partner throughout this process, and I am in awe of
the sacrifices you have made for our family throughout these last three years. Thank you
for taking on more so that I could achieve a dream, for always being my biggest
cheerleader and most ardent supporter, and for continuing to “grow” with me through this
amazing adventure that is our life together.

Table of Contents
List of Figures

i

List of Tables

ii

Abstract

iii

Introduction

1

Methods

5

Research Design

5

Subjects

6

Instruments

8

Procedures

12

Data Analysis

18

Results

21
CARE Consortium Study Data

21

FITBIR

21

Study Sample Characteristics

23

Hypothesis Testing

30

Discussion

38

Conclusion

45

Future Research

46

References

48

Appendices

58

Appendix A: Review of the Literature
Appendix B: Problem Statement

58
100

Appendix C: Additional Methodology

102

Appendix C1: CARE Consortium Informed Consent

103

Appendix C2: IRB Approval

114

Appendix C3: FITBIR Data Access Request

116

Appendix C4: FITBIR Access Approval

126

Appendix C5: Example ImPACT Clinical Test Report

129

Appendix C6: ImPACT Baseline Demographic Worksheet

135

Appendix C7: Example ImPACT Database

138

Appendix C8: Data Extraction Sheet

155

List of Figures
Figure 1: CARE Consortium collection sites

14

Figure 2: Sport participation by group assignment

24

Figure 3: Prior concussion history by group assignment

25

Figure 4: Mean somatization across symptoms

29

Figure 5: Symptoms included in the symptom total score

30

Figure 6: Distribution of differences in box plots

36

Figure 7: Distribution of differences in histograms

37

Figure 8: Differences in mean somatization

40

Figure 9: Standard Symptom Index

80

Figure 10: Endocrine, histological, and body temperature changes

91

i

List of Tables
Table 1: Covariates stratified by group: Adjusted data set following matching

26

Table 2: Self-reported symptoms and total days to symptom resolution

28

Table 3: Adjusted covariates following matching

32

Table 4: Results: Paired t-test Analysis

34

ii

Abstract
This research aimed to determine if there was a difference in concussion severity and/or
recovery among collegiate female athletes following sports related concussion (SRC)
between two groups: those athletes who used synthetic progestins and those who did not
use synthetic progestins. A causal comparative design utilized data recorded through the
CARE (CARE Consortium, 2018a) study and available through FITBIR (FITBIR, n.d.a)
for the analysis of 536 concussion incidents; allowing for subsequent data extraction of
symptom scores, total days to symptom resolution, and assignment to groups: (1)
currently using synthetic progestins (n = 219) and (2) not currently using synthetic
progestins (n = 317). Statistical matching was used to control for the co-variables of sport
participation and prior concussion history. Following this process, paired t-tests were
conducted across 378 concussion incidents (189 per group) to examine differences in
somatization and total days to recovery. The use of synthetic progestins was associated
with significantly less severe somatization across seven symptom scores (p < .001) and
total symptom scores (p < .05). However, no significance was found in total recovery
differences between groups (p = 0.865). These results indicate that use of synthetic
progestins may result in decreased somatization severity following SRC. Further, these
results contribute to existing evidence in support of a possible link between hormonal
fluctuations associated with menstruation and somatization severity following SRC.
Keywords: Concussion, brain injury, CARE Consortium, synthetic progestin,
menstruation

iii

1
Sports related concussion (SRC) is a specific type of injury that falls under the
umbrella of both traumatic brain injury (TBI) and mild traumatic brain injury (mTBI).
SRC has been specifically defined by the Concussion in Sport Group as “a traumatic
brain injury induced by biomechanical forces” (McCrory et al., 2017, p.2). The number
of SRCs in the United States has been of concern for years and research indicates that
reported rates of SRC continue to rise (Bakhos, Lockhart, Myers, & Linakis, 2010; Faul,
Xu, Wald, & Coronado, 2010; Gilchrist, Thomas, Xu, McGuire, & Coronado, 2011;
O’Connor et al., 2017), possibly as a direct result of noted increases in athletic
participation (National Collegiate Athletic Association [NCAA], 2018; National
Federation of High School Sports [NFHS], 2017). For example, the NFHS (2017)
reported that 7.9 million individuals participated in high school sports during the 20162017 academic year and an additional 460,000 student-athletes competed at the collegiate
level in 2018 according to the NCAA (2018). Students who sustain SRCs miss school
days, struggle academically, and refrain from physical activities during recovery which
create challenges for academic institutions as they work to implement return to learn and
return to play protocols aimed to foster the health and well-being of their student-athletes.
Research has been consistently clear about factors associated with higher risks of
SRC and often points to football as the sport that has been consistently found to carry the
highest risk of SRC in the U.S. (Gessell, Fields, Collins, Dick, & Comstock, 2007;
Macpherson, Fridman, Scolnik, & Corallo, 2014; Marar, McIlvain, Fields, & Comstock,
2012; Sheu, Chen, & Hedegaard, 2016). However, it is important to note that
participation in football is primarily limited to males and at present there is not a
comparable high contact/collision sport among females that would allow for appropriate

2
gender comparisons focused on ascertaining differences in risk across genders. This is
notable because one study focused on risk of SRC in gender comparable sports (i.e.,
soccer and basketball) found higher rates of SRC among females (1.7) as opposed to
males (1.0) (Marar et al., 2012). Additional differences have been noted between both
gender and age groups in relationship to somatization and recovery following SRC as
well. These noted differences have, in part, prompted researchers to hypothesize that
hormonal fluctuations associated with the menstrual cycle may play a role in causing
these gender differences (Wang et al., 2016; Wunderle, Hoeger, Wasserman, & Bazarian,
2014).
Substantial somatization differences have been documented in relationship to
type, severity, and duration between genders across multiple studies (Baker et al., 2015;
Frommer et al., 2011; Preiss-Farzanegan, Chapman, Wong, Wu, & Bazarian, 2009; Root
et al., 2016; Sufrinko et al., 2017) with adolescent females reporting both a greater
number of symptoms and higher symptom severity as compared to males following SRC
(Baker et al., 2015). Standard symptom indexes (SSIs) used throughout both the
diagnosis and recovery of SRC (McCrory et al., 2017) have helped researchers highlight
some of these differences. The data collected via SSIs not only provide health care
practitioners with a valuable tool to determine treatment following SRC, but have also
been linked to predicting length of symptom resolution (Resch et al., 2015). Disparities in
SSI scores have been demonstrated to be consistent throughout recovery, with females
reporting higher scores than males on SSI when evaluated at both ≤7 days and >7 days
post-concussion (Berz et al., 2013). These findings coupled with a growing concern that
those who experience prolonged somatization may suffer from decreased quality of life

3
(Neidecker, Gealt, Luksch, & Weaver, 2017; Nelson et al., 2016) have, in part, lead to the
realization that females may suffer from decreases in quality of life far more frequently
than their male counterparts.
Additional research has aimed to determine whether age is an important variable
in recovery following concussion. Nelson and colleagues (2016) found college students
tend to recover one to two days sooner when compared to their high school counterparts,
but noted that there were no drastic differences in recovery time between these groups.
Similarly, additional research focused on cognitive scores found that college athletes took
five days to recover as opposed to the seven-day time frame associated with high school
athletes (Williams, Puetz, Giza, & Broglio, 2015). While these findings certainly
contribute to a greater understanding of concussion recovery, it is critical to note that
research has yet to specifically identify the root causes of discrepancies across these
variables as they relate to both age and gender.
Hypotheses surrounding the potential cause(s) of gender differences as they are
related to SRC have considered neck strength (Tierney et al., 2005) and a bevy of
biological differences (Wunderle et al., 2014). To date, there has not been a consensus
regarding the root cause of these gender differences (Neidecker et al., 2017). Therefore,
researchers continue to work towards identifying the variable(s) that ultimately lead to
these demonstrable differences in severity and recovery between genders. Specific
attention has focused on the role of progesterone during the luteal phase of the menstrual
cycle (Wunderle et al., 2014). Studies highlighting the potential neuroprotective
capability of progesterone (Wang et al., 2016) indicate that disruption of pituitary
hormones at the time of SRC may deleteriously affect and/or interrupt the normal

4
progesterone surge of the menstrual cycle. While it has been determined that there is no
efficacy for the use of progesterone as a treatment agent following TBI (Wang et al.,
2016), it has not yet been determined if the long-term prescription use of synthetic
progestin may have a neuroprotective effect following SRC.
Although, to date, much research has focused on the collegiate athletic population
in relationship to SRC (Williams et al., 2015), there are still large gaps in the body of
literature. As a result, McCrory and colleagues (2017) highlighted the need for further
research, in part, focused on the reduction of the effects of concussion. The prevalence of
both menstrual dysfunction (Beals & Manore, 2002; Matzkin, Curry, & Whitlock, 2015;
Prather et al., 2016) and oral contraceptive use among the college demographic (Daniels,
Daugherty, & Jones, 2014) may point to a new variable for consideration throughout the
diagnosis and recovery of SRC among collegiate athletes.
Therefore, the purpose of this research was to determine if there was a difference
in concussion severity and/or recovery among collegiate female athletes following sports
related concussion (SRC) between two groups: those athletes who used synthetic
progestin and those who did not use synthetic progestin. Hypotheses included: (1)
collegiate female athletes who were not using synthetic progestin would report greater
symptom severity (measured by total symptom score) as a group than those who did use
synthetic progestin following SRC and (2) collegiate female athletes who were not using
synthetic progestin would take longer to recover from SRC than those who did use
synthetic progestin.

5
Methods

This study utilized a causal comparative design to analyze outcomes between two
distinct groups. This section reviews the research design, subjects, instruments,
procedures, and data analysis utilized throughout this study.

Research Design
Causal comparative research is a type of quasi-experimental design frequently
used for comparing outcomes for distinct groups when experimental manipulation is not
possible or unethical and when an event has already occurred (Creswell, 2015; Salkind,
2010). This research design was advantageous to this study because it allowed for the
analysis of two different conditions (independent variable) following diagnosis of and
recovery from SRC: (1) use of synthetic progestin and (2) non-use of synthetic progestin.
However, it was also prudent to acknowledge that this research design did not allow for a
control group and, therefore, probable cause and effect could not be established using this
design. Rather, the outcomes of this research design only allowed for the identification of
an association between the two identified groups and the dependent variables (Creswell,
2015; Salkind, 2010). As to the researcher’s knowledge synthetic progestin use and SRC
among collegiate female athletes has never been studied before, a causal comparative
design allowed for the determination of a possible relationship between variables and
acted as a precursor to more substantial experimental studies related to this topic.
A causal comparative design was utilized to determine any differences between
severity of self-reported symptoms, length of recovery following SRC as the dependent

6
variables, due to the use or non-use of synthetic progestins as the independent variable.
This methodology was ideal for this study because it allowed for a retrospective review
of differences associated with an uncontrollable independent variable: the use or non-use
of synthetic progestins. Use of synthetic progestins was identified as a result of selfreporting in conjunction with the Immediate Post-Concussion Assessment and Cognitive
Testing (ImPACT) demographic prompt instructing athletes to list all the medications
which they were currently taking. The dependent variables of symptom severity were
self-reported on the most current iteration of the standard symptom index (SSI) as
administered within the ImPACT test, and length of time to full recovery in days
following SRC as defined by the date the subject was cleared to begin the return-to-play
protocol. Concerns associated with this methodology included the potential that subjects
would not be matched equivalently as a result of unaccounted for co-variables such as
prior history of concussion and sport participation. The ideal approach to mitigating this
threat to internal validity was to match subjects from each group across co-variables that
would potentially affect outcomes. Statistical matching was, thus, employed to control for
variations in two co-variables: sport and prior concussion history.

Subjects
This study utilized an existing database housed by the Federal Interagency
Traumatic Brain Injury Research (FITBIR, n.d.b) Informatics System consisting of the
post-injury ImPACT tests (as compared to baseline scores) of NCAA athletes originally
collected by the Concussion, Awareness, Research, and Education (CARE) Consortium
(FITBIR, n.d.b) (see Procedures). Prior to data collection at CARE sites, informed

7
consent (Appendix C1) was obtained for each enrolled individual (Broglio et al., 2018).
The database was available following IRB approval (Appendix C2) and the completion of
the FITBIR data access request (FITBIR, n.d.a) (Appendix C3). Following FITBIR data
access approval (personal communication, The FITBIR Operations team, November 6,
2018) (Appendix C4), data was extracted from this database for analysis of independent
(use or non-use of synthetic progestins) and dependent (severity of symptoms and length
of recovery following SRC) variables while considering the potential effect of covariables.
This study focused on determining the role of synthetic progestin use or non-use
on symptom severity and length of recovery following SRC among collegiate female
athletes. As a result, data extracted for inclusion in this study needed to meet several
inclusion criteria. All extracted data was female, enrolled in courses at a college,
university, or military academy within the U.S., between the ages of 18 and 23, and had
suffered a concussion as identified, assessed, and diagnosed “by the research and medical
staff at each site” (Broglio et al., 2017, p.1442). Given that this study used existing data
from a public database, consent for use, in this case, was granted through the official
request process outlined by FITBIR (n.d.a).
Consistent with similar studies focused on concussion recovery and symptom
severity, exclusion criteria for this study included the following: (1) previous history of
seizures or known neurological disorder, (2) history of attention deficit disorder requiring
medication, and (3) history of psychiatric disorder requiring medication. All of this
information was available as a result of self-reported answers to standard demographic
questions available within FITBIR’s (n.d.b) public database.

8
Determination of use or non-use of synthetic progestins was the result of selfreporting within the database in conjunction with the ImPACT demographic prompt
instructing athletes to list all the medications they are currently taking. At this point, data
sets associated with individual ImPACT post-injury (as compared to baseline) tests were
assigned to the respective group: (1) currently using synthetic progestin and (2) not
currently using synthetic progestin. This methodology assumed that all athletes
accurately reported all current medications, including the use of synthetic progestin
medication. However, the potential for inaccurate reporting of birth control/synthetic
progestin use was possible and as such was considered a limitation to this methodology.

Instruments
The use of neuropsychological testing has long been considered to play a vital
role in the management of SRC (McCrory et al., 2017). However, the use of
computerized neuropsychological testing is not just limited to management of SRC.
Rather, computerized neuropsychological testing, specifically, ImPACT is often used in
the literature as an instrument for data collection examining both recovery and reported
symptom severity following SRC (Colvin et al., 2009; Covassin, Elbin, Larson, &
Konstos, 2012; Schatz, Pardini, Lovell, Collings, & Podell, 2006).
This study utilized ImPACT datasets housed within the FITBIR database (n.d.b)
for re-analysis of data to determine if there was a difference in concussion severity and/or
recovery among collegiate female athletes following sports related concussion (SRC)
between two groups: those athletes who use synthetic progestin and those who do not use
synthetic progestin. FITBIR is a joint venture between the Department of Defense (DoD)

9
and National Institutes of Health (NIH) with the goal of sharing data across the entire
research field of traumatic brain injury (TBI) (FITBIR, n.d.c). As a result, datasets related
to TBI are housed within the FITBIR database and are available for the re-analysis of
existing data (FITBIR, n.d.c).
The ImPACT test is a brief (25-30 mins.) computerized test that encompasses
three sections: (1) demographic data, (2) neuropsychological tests, and (3) PostConcussion Symptom Scale (PCSS) (Colvin et al., 2009; Schatz et al., 2006). At the
conclusion of the test, results are automatically scored and a six-page report is generated
(Appendix C5). The demographic section provides researchers with data surrounding
pertinent sport, medical, and concussion history information. For the purpose of this
study, the demographic section of ImPACT data provided the criteria necessary for
determining inclusion/exclusion from the study. Inclusion in this study required data sets
to meet several inclusion criteria. All extracted data were female, enrolled in courses at a
college, university, or military academy within the U.S., between the ages of 18 and 23,
and had suffered a concussion as diagnosed “by the research and medical staff at each
site” (Broglio et al., 2017, p.1442) associated with the CARE Consortium. In order to
standardize the definition of concussion for the purpose of the CARE Consortium data
collection, the Consortium adopted the following definition of concussion: “a change in
brain function following a force to the head, which may be accompanied by temporary
loss of consciousness, but is identified in awake individuals with measurements of
neurologic and cognitive dysfunction” (Broglio et al., 2017, p. 1442). Consistent with
similar studies focused on concussion recovery and symptom severity, exclusion criteria
for this study included the following: (1) previous history of seizures or known

10
neurological disorder, (2) history of attention deficit disorder requiring medication, and
(3) history of psychiatric disorder requiring medication. These criteria were assessed as a
result of the responses to the demographic question prompts (Appendix C6) as they
appear in the data sets (Appendix C7). ImPACT data also provided the criteria necessary
for the identification of the independent variable (use or non-use of synthetic progestins)
and the ultimate assigning of subjects to groups: (1) use of synthetic progestin and (2)
non-use of synthetic progestin. Group assignment of data sets was based off of the selfreported answer to the prompt instructing athletes to list all the medications they are
currently taking. Finally, demographic data also allowed for the determination of the
possible effect of co-variables such as prior history of concussion and sport participation.
The neuropsychological test section consists of six neuropsychological tests.
These tests have been designed to assess four different aspects of cognitive function: (1)
attention, (2) memory, (3) processing speed, and (4) reaction time (Schatz et al., 2006).
Four composite scores are then generated as a result of performance on the six
neuropsychological tests: (1) verbal memory, (2) visual memory, (3) visuomotor speed,
and (4) reaction time (Fonseca, Reynolds, & Almquist, 2014; Schatz et al., 2006).
Baseline ImPACT tests are typically administered prior to the start of a new athletic
season and are valid for two years (Fonseca et al., 2014). Following the diagnosis of
concussion, a post-injury test is administered and the results are compared to the
established baseline values for an athlete. For the purpose of this study, the dependent
variable of recovery length was determined in days based upon the identification of both
the date of SRC and the date the athlete was cleared to begin the return-to-play protocol.

11
Finally, the Post-Concussion Symptom Scale (PCSS) section of the ImPACT test
allows for the documentation and recording of concussion symptoms using a 22symptom checklist. Each symptom is listed and athletes are asked to rank each symptom
on a seven-point scale, zero to six, for which zero indicates that the athlete is not
experiencing the symptom and six indicates that the athlete is experiencing a severe
symptom. For the purpose of this study, the results of the PCSS were used to measure the
second dependent variable, symptom severity, across seven of the 22 symptoms: (1)
headache, (2) drowsiness, (3) feeling slowed down, (4) difficulty concentrating, (5)
feeling more emotional, (6) irritability, and (7) sadness. These symptoms were
highlighted as a result of previous research outlining them to be the symptoms that
females are more likely to suffer than males (Baker et al., 2015; Frommer et al., 2011).
Originally, the methodology for this study had also intended to include individual
symptom scores for sensitivity to noise and pressure in the head as they were also
outlined in the literature as symptoms that females reported more frequently than males.
However, the fields designated in the FITBIR data set for individual sensitivity to noise
and pressure in the head symptom scores were incomplete and, as a result, needed to be
excluded from data analysis. Finally, self-reported scores across these seven symptoms
were added together to determine a total symptom score for each subject. The total
symptom scores (total of all 22 symptoms in the ImPACT test battery) were also included
within the data analysis.
The ImPACT test battery has been found to be both reliable and valid (Iverson,
Gaetz, Lovell, & Collins, 2004; Iverson, Gaetz, Lovell, & Collins, 2005; Iverson, Brooks,
Lovell, & Collins, 2006). Research by Iverson and colleagues (2006) determined that

12
there were no significant practice effects in non-injured high school athletes who were
tested twice within two days, thereby ensuring the reliability of the ImPACT test. More
recent research examining the test-retest reliability of symptom indices associated with
ImPACT testing among undergraduate college students, reported r-values ranging from
.44 to .80 (Merritt, Bradson, Meyer, & Arnett, 2018). Further research from Broglio and
colleagues (2018) determined that among Level A concussion assessment tools, ImPACT
visual motor speed (0.72) and King-Devick test (0.74) were the only evaluations to near
0.75 reliability, indicating that these are the two best tools for concussion assessment at
present. An additional study also found that post-concussive symptoms were
significantly related to decreased performance on ImPACT verbal memory, processing
speed, and reaction time (Iverson et al., 2004). Further research identified the sensitivity
and specificity of ImPACT to be 81.9% and 89.4% respectively (Schatz et al., 2006).
Finally, previous validity studies have correlated ImPACT processing speed and reaction
time to performance on the Symbol Digit Modalities test, a test that is often used to
assess cognitive speed in research with athletes (Iverson et al., 2005).

Procedures
Following the approval of this study by the California University of Pennsylvania
Institutional Review Board (IRB) (Appendix C2), data collection and analysis began. The
data extracted and then analyzed for the purpose of this study was from a pre-existing
dataset that was available through FITBIR (n.d.b.). Specifically, this study accessed the
data associated with the CARE Consortium study and deposited within FITBIR (n.d.b).
The CARE Consortium was created as a partnership between the NCAA and the

13
Department of Defense (DoD) in a joint effort to continue to further advance the science
of SRC and contribute to the development of guidelines for best practice related to SRC
(CARE Consortium, 2018a; CARE Consortium, 2018b). Beginning in the fall of 2014,
23,533 student athletes and military cadets from CARE Consortium institutions were
enrolled as subjects within the CARE Consortium database prospectively. Among those
enrolled, 1174 concussions were diagnosed by medical professionals trained in
accordance with CARE protocols and further data was collected on those diagnosed with
concussion (Broglio et al., 2017).
Project CARE. Data submitted to the CARE Consortium comes from 30
universities and four military service academies in the United States (Asken et al., 2018).
Prior to data collection from these sites, all personnel were trained on collection protocol,
school level IRB approval from each collection site and the DoD Human Research
Protections Office (HRPO), and participant consent were all obtained (Broglio et al.,
2017). The resultant dataset included clinical, biomechanical, neuroimaging, genetic, and
biomarker variables including, but not limited to baseline and post-injury ImPACT test
scores (Broglio et al., 2017). The complete methodology and driving rationale for the
CARE Consortium were described in depth by Broglio and colleagues (2017) in previous
publications. As a result of this partnership between the DoD and NCAA, data associated
with the CARE Consortium project was uploaded into FITBIR (personal communication,
Heather Rodney, March 22, 2019). Figure 1 depicts the institutions participating in the
NCAA-DoD Grand Alliance CARE Consortium.

14

Figure 1. NCAA-DoD Grand Alliance CARE Consortium data collection sites. Graphic
available from http://www.careconsortium.net/about/
FITBIR. A joint venture between the DoD and the National Institutes of Health
(NIH), FITBIR is a joint effort to help accelerate the research related to TBI (FITBIR,
n.d.c). To date, FITBIR houses over 1.5 million data records resulting from studies
funded by either the DoD or the NIH (FITBIR, n.d.d). These data include demographics,
outcome assessments, imaging, and biomarkers and the most recent versions of the
Common Data Elements (CDEs) developed by the National Institute of Neurological
Disorders and Stroke (NINDS) (FITBIR, n.d.d). NINDS recommends ImPACT for
sports-related studies outcomes measures (National Institute of Neurological Disorders
and Stroke [NINDS], 2018) and as a result, the FITBIR (n.d.b) database includes an
estimated 2500 curated ImPACT tests. Furthermore, all of the data collected by the
contributing members of the CARE Consortium (see Figure 1) was scheduled to be

15
uploaded to FITBIR by March of 2019 (personal communication, Dr. Kaminski, October
19, 2018).
Procuring the database. FITBIR (n.d.a) makes data available to qualified
researchers via an application process concluding in the granting of data access
privileges. Obtaining access privileges requires the applicant to complete an application
and provides step-by-step instructions for completing the process (FITBIR, n.d.a). The
first step in the process requires the applicant to complete the Data Access Request
(Appendix C3). This document is then submitted to FITBIR’s account request portal,
reviewed, and then typically approved within two days. Following account approval
(Appendix C4), the applicant will complete “Query Tool and Study” permission within
their account privileges tab. At this point, the applicant will upload the required signed
documents, including the data use certification, to complete the data access request. After
data request approval, the investigator is notified via e-mail and explained the specific
conditions for which the data approval is granted.
Data extraction. Following approval and data access being granted through
FITBIR (n.d.a), the researcher received the requested data scrubbed of any and all
identifiable information from the original data collected by the CARE Consortium and
uploaded to FITBIR (personal communication, Dr. Kaminski, October 19, 2018).
Determination was then made as to which datasets met the inclusion or exclusion criteria.
Inclusion criteria consisted of the following: (1) be female, (2) be enrolled in courses at a
college, university, or military academy within the U.S., (3) be between the ages of 18
and 23, and (4) have suffered a concussion as diagnosed “by the research and medical
staff at each site” (Broglio et al., 2017, p.1442) associated with the CARE Consortium.

16
Inclusionary criteria was determined based upon the methodology outlined by Broglio
and colleagues (2017) in reference to the diagnosis of concussion and enrollment of
student athletes at one of the institutions associated with the CARE Consortium.
Additional inclusion criteria was established as the result of CARE program participants’
answers to the ImPACT demographic section (Appendix C6) as they appeared within the
datasets (Appendix C7). Specifically, inclusionary criteria was determined in reference to
the prompts asking subjects to provide their date of birth, gender, and years of education
completed. These data appeared in a spreadsheet in which each column directly correlates
to specific questions and/or test results identified during the completion of the ImPACT
test battery.
Additionally, datasets were excluded if they met the following criteria: (1)
previous history of seizures or known neurological disorder, (2) history of attention
deficit disorder requiring medication, and (3) history of psychiatric disorder requiring
medication. Like inclusion, exclusion was also determined based upon the result of
answers of the participants of the CARE Consortium study to the ImPACT demographic
section (Appendix C6) as they appeared within the datasets (Appendix C7). Specifically,
exclusion was determined based upon the participants’ responses to the prompts within
the ImPACT test battery which ask participants if they have received treatment for
epilepsy/seizures, ADD/ADHD, and/or a psychiatric condition (depression, anxiety).
Inclusion or exclusion from this research study was documented in detail prior to
the assignment of included datasets to groups. Following the determination of inclusion
or exclusion from the study, included datasets were assigned to one of two groups: (1)
use of synthetic progestin and (2) non-use of synthetic progestin. Group assignment was

17
determined based upon answers of the participants of the CARE Consortium study to the
ImPACT demographic section (Appendix C6) as they appear within the datasets
(Appendix C7). Specifically, group assignment was determined based upon participants’
responses to the prompt asking for participants to list any prescription medication(s) they
are currently taking. The researcher identified the listed prescription drug as either
synthetic progestin or not by using the FDA Approved Drug Products database available
at https://www.accessdata.fda.gov/scripts/cder/daf/index.cfm. This database allows users
to look up prescription drugs by name and determine the active ingredient. Once the
active ingredient was identified, the researcher continued to use the database to determine
the purpose of the active ingredient, ultimately determining the drug to be a synthetic
progestin or not and assigning the dataset to the appropriate group.
Following group assignment, potential co-variables including prior history of
concussion and sport participation were identified. Once co-variables were determined
and coded, statistical matching was employed to match datasets across these co-variables.
Further data extraction and coding was completed for the identified dependent variables:
severity of self-reported symptoms and length of recovery following SRC.
Originally, severity of self-reported symptoms was expected to be determined
across nine symptoms previously identified in the literature as those that females are
more likely to suffer than males (Baker et al., 2015; Frommer et al., 2011): (1) headache,
(2) sensitivity to noise, (3) drowsiness, (4) pressure in the head, (5) feeling slowed down,
(6) difficulty concentrating, (7) feeling more emotional, (8) irritability, and (9) sadness.
Unfortunately, the data associated with symptom scores related to sensitivity to noise and
pressure in the head were incomplete and, therefore, needed to be excluded from the data

18
analysis. Ultimately, severity of self-reported symptoms was determined across the
remaining seven symptoms previously identified in the literature as those that females are
more likely to suffer than males (Baker et al., 2015; Frommer et al., 2011): (1) headache,
(2) drowsiness, (3) feeling slowed down, (4) difficulty concentrating, (5) feeling more
emotional, (6) irritability, and (7) sadness. The values assigned to each of these
symptoms as recorded within the datasets (Appendix C7) at 24-48 hours post-injury
(Broglio et al., 2017) were extracted and prepared for data analysis. Selected symptoms
were analyzed both individually and added together to create a “seven symptom total
score” that was analyzed. Additionally, all 22 symptoms assessed in the PCSS of the
ImPACT test battery were added together to create a “total symptom score” that was
analyzed as well.
Length of recovery following SRC was determined in days. Date of concussion
was determined as a result of the documented date of last concussion within the
demographic section of the ImPACT test battery as it appeared within FITBIR (n.d.b).
Date of recovery was determined based upon the date at which the ImPACT scores
returned to baseline values indicating that student-athletes could begin a return-to-play
progression (Broglio et al., 2017). These dates were extracted from the datasets
(Appendix C7) and prepared for data analysis.

Data Analysis
As previously mentioned, the purpose of this research was to determine if there
was a difference in concussion severity and/or recovery among collegiate female athletes
following sports related concussion (SRC) between two groups: those athletes who use

19
synthetic progestin and those who do not use synthetic progestin. Hypotheses to be tested
included:
1. Collegiate female athletes who were not using synthetic progestin would report
greater symptom severity, as measured by symptom scores, as a group than those
who did use synthetic progestin following SRC.
2. Collegiate female athletes who were not using synthetic progestin would take
longer to recover from SRC (measured in days) than those who did use synthetic
progestin.
These hypotheses were tested against the null hypothesis: use/nonuse of synthetic
progestin would not cause a difference in either symptom severity or recovery time.
Following datasets meeting the inclusion criteria, the assignment of individual
sets to groups, and the extraction of pertinent data (Appendix C8), analysis ensued. Data
analysis first focused on using standard graphical techniques. Given that the data was
quantitative, standard summary techniques such as mean and standard deviation of both
duration of recovery following SRC and symptom scores following SRC across the two
designated groups: (1) currently using synthetic progestin and (2) not currently using
synthetic progestin were determined.
Data analysis was completed using paired t-tests to assess whether both duration
of recovery following SRC and total symptom score following SRC were affected by the
use or non-use of synthetic progestins. The significance level was set at 0.05 and p-values
were used to determine if the differences between means was statistically significant. All
analysis of data was completed using SAS University Edition statistical software. This

20
software allowed for an interface with Microsoft Excel (SAS, 2016) which allowed for a
more seamless transition of extracted data for data analysis.

21
Results

CARE Consortium Study Data
The complete methodology of the CARE Consortium study has been described in
length by Broglio and colleagues (2017) in previous publications. At the time of
publication, the CARE study had collected data on 23,533 individuals with a total of
1174 reported concussion incidents, over the course of 24 months (Broglio et al., 2017).
Methodology for the CARE study outlined specific data capture time points following
injury: (1) within 6 hours of injury, (2) 24 to 48 hours post-injury, (3) at the beginning of
return-to-play protocol (symptom resolution), (4) at return-to-play, and (5) at six months
post-injury (Broglio et al., 2017). For the purpose of this study, the 24 to 48 hours postinjury and beginning of return-to-play protocol time points were selected for analysis.
According to the CARE Consortium methodology, these time points had the highest rate
of data capture at publication; reported to be 81.6% and 84.1% respectively (Broglio et
al., 2017).

FITBIR
Data from the CARE Consortium study was uploaded to FITBIR (personal
communication, Heather Rodney, March 22, 2019) and available to FITBIR users for
analysis on July 1, 2019. A query was performed for the CARE Consortium study, with
Steven P. Broglio listed as the primary author, in the FITBIR database and the
appropriate data set (CARE Consortium study) was then selected for further analysis.
The data associated with the CARE Consortium study was then broken into forms which

22
could be analyzed individually or combined depending upon the preferences of the
researcher. The demographics form, for example, housed the ages and genders of all
participants [identified by Globally Unique Identifier (GUID)] in the CARE Consortium
study and encompassed 34,561 rows of data. For the purpose of this study, five forms
were initially combined to allow for the determination of inclusion or exclusion: (1)
FITBIR Medical History Form, (2) FITBIR Medical History Form Appendix, (3) FITBIR
Demographics Form, (4) FITBIR Demographics Form Appendix, and (5) the Post-Injury
Form. Following this process, 627 observations were included, assigned to groups [(1)
use of synthetic progestin or (2) non-use of synthetic progestin], and prepped for further
data extraction.
The completion of data extraction required the use of three forms: (1) Immediate
Post-Concussion Assessment Testing (ImPACT), (2) Post-Injury Form, and (3) Sport
Concussion Assessment Tool 3rd edition (SCAT). At the completion of the data
extraction process, an additional 91 observations were excluded as a result of incomplete
data fields, conflicting dates, or conflicting answers throughout the demographics section.
For example, an individual who answered “no” to having a history of attention deficit
disorder but listed Ritalin under current medications was excluded. Additionally,
observations were excluded when the 24 to 48 hour data collection date clearly exceeded
the required time frame based upon the date of concussion incident listed within the data
point.

23
Study Sample Characteristics
The final subset of CARE Consortium study data analyzed in this study included
536 observations. Group 1 (use of synthetic progestins) accounted for 219 observations
while Group 2 (non-use of synthetic progestins) accounted for 317 observations. Between
groups one and two (Figure 2), 25 sport selections were represented including: soccer,
basketball, lacrosse, volleyball, swimming, softball, intramurals, club, cross-country,
gymnastics, diving, golf, cheerleading, ice hockey, rugby, fencing, non-sport cadet, field
hockey, rifle, water polo, beach volleyball, field event, rowing, sailing, and tennis.
Additionally, prior concussion history was noted across groups (Figure 3) in a range from
zero reported prior concussions to six reported prior concussions. Sample characteristics
by group are detailed thoroughly in Table 1.

24
Sunday, September 15, 2019 02:08:39 PM 1

Group 1 by Sport
Basketball
13
Volleyball
19

Club
22
Intramurals
27

Soccer
32

Swimming
12

Softball
14

XC
12

Other
58

Gymnastics
10

Figure 2. Sport participation by group assignment.
Note. Group 1=Synthetic progestin users; Group 2=Non-users of synthetic progestin

25

Figure 3. Prior concussion history by group assignment.
Note. Group 1=Synthetic progestin users; Group 2=Non-users of synthetic progestin

26
Table 1
Covariates Stratified by Group

Note. Group 1=Synthetic progestin users; Group 2=Non-users of synthetic progestin

27
Standard summary techniques were used to determine mean and standard
deviation between groups across each of the seven symptoms highlighted at the 24-48
hour concussion timeline (Table 2) within this study: (1) headache, (2) drowsiness, (3)
feeling slowed down, (4) difficulty concentrating, (5) feeling more emotional, (6)
irritability, and (7) sadness (Figure 4). Similarly, the same summary techniques were also
used to determine mean and standard deviation between groups for the seven symptom
total, total symptom score, and total days to symptom resolution (Table 2). The 22
symptoms included in the ImPACT test and scored on a scale of zero (symptom not
present) to six (severe) are represented in Figure 5 below.

28
Table 2
Self-Reported Symptoms and Total Days to Symptom Resolution (as Collected in the
CARE Study and Housed in FITBIR) by Individual Symptoms and Total Symptom Scores
Across Groups

Note. Group 1=Synthetic progestin users; Group 2=Non-users of synthetic progestin.
CARE = Concussion, Awareness, Research, and Education Consortium. FITBIR =
Federal Interagency Traumatic Brain Injury Research Informatics System. BMI = Body
Mass Index. M = Mean. SD = Standard Deviation. SSI = Standard Symptom Index.
Two observations did not include the height and/or weight measurements under Group 1.

29

Mean Somatization Across Symptoms
Sadness
Irritability
Feeling More Emotional
Difficulty Concentrating
Feeling Slowed Down
Drowiness
Headache
0

0.5

1
Group 2

1.5

2

2.5

3

Group 1

Figure 4. Mean self-reported somatization levels across symptoms included in the sevensymptom total score. Group 1=Synthetic progestin users; Group 2=Non-users of synthetic
progestin

30

Figure 5. Symptoms included in the symptom total score.

Hypothesis testing
Following the determination of mean and standard deviation across groups using
the raw data, control matching was used to account for the identified co-variables of sport
and prior concussion history. Both of these co-variables had been identified in the
literature previously as known risk factors for sustaining concussion (Gessell et al., 2007;
Macpherson et al., 2014; Marrar et al., 2012; Teel, Marshall, Shankar, McCrea, &
Guskiewicz, 2017). After control matching, there were 189 matched data points
representing 378 observations included for hypotheses testing. As a result, group one was
reduced by 30 data points and group two was reduced by an additional 128 data points

31
prior to additional statistical analysis. The characteristics of the adjusted data set are
listed in Table 3.
The purpose of this research was to determine if there was a difference in
concussion severity and/or recovery among collegiate female athletes following sports
related concussion (SRC) between two groups: those athletes who used synthetic
progestin and those who did not use synthetic progestin. Two hypotheses were tested:
1. Collegiate female athletes who are not using synthetic progestin will report
greater symptom severity, as measured by symptom scores, as a group than
those who do use synthetic progestin following SRC.
2. Collegiate female athletes who are not using synthetic progestin will take
longer to recover from SRC (measured in days) than those who do use
synthetic progestin.
These hypotheses were tested against the null hypothesis: use/nonuse of synthetic
progestin will not cause a difference in either symptom severity or recovery time. The
significance level was set at 0.05.

32
Table 3
Covariates Stratified by Group: Adjusted Data Set Following Matching

Note. Group 1=Synthetic progestin users; Group 2=Non-users of synthetic progestin.
BMI = Body Mass Index. M = Mean. SD = Standard Deviation.
One observation did not include the height and/or weight measurements under Group 1.

33
Paired t-tests were used to complete hypothesis testing across both groups and
three variables: seven symptom total score, total symptom score, and total days to
symptom resolution. The paired t-test completed across groups one and two for the
analysis of seven symptom scores resulted in a p-value of 0.0012 (Table 4). The
differences between the means was statistically significant (t = -3.3, df = 188, p < .01).
Additionally, the paired t-test completed across groups one and two for the analysis of
total symptom score resulted in a p-value of 0.0202 (Table 4). The differences between
the means was statistically significant (t = -2.34, df = 188, p < .05). Together, these
findings indicated that female collegiate athletes who were using synthetic progestins
(Group 1) at the time of concussion incident suffered fewer cumulative symptoms (seven
symptom and total symptom scores) than their counterparts who were not using synthetic
progestins (Group 2) at the time of concussion incident. Conversely, the paired t-test
completed across groups one and two for the analysis of total days to symptom resolution
resulted in a p-value of 0.8650 (Table 4). The differences between the means were not
statistically significant (t = 0.17, df = 188, p > .05) and, therefore, the null hypotheses
could not be rejected. Complete t-test analysis results were presented in Table 4.

34
Table 4
Results: Paired t-test Analysis.

Note. Sx = Symptom
*p<0.05; **p<0.01

The distribution of differences between groups across (a) seven-symptom total
score, (b) total symptoms, and (c) total days to symptom resolution were depicted
visually in Figure 6 utilizing box plots and in Figure 7 utilizing histograms with a density
plot overlay. The density plot overlay illustrates both a normal and kernel curve of the
data points in the histogram. The kernel curve creates a smooth density curve that is
representative of every data point captured (Koehrsen, 2018) for each paired t-test
analysis.

35
The box plot for the (a) seven-symptom total shows the mean of -2.3651 (+ 9.8591) and
the corresponding histogram (with density plot overlay) shows a unimodal, symmetrical
distribution with a range of -29 to 28 in which most of the data are within a few points of

the mean. However, it is important to note that the maximum range is an outlier (28).
Similarly, the box plot for (b) total symptoms shows the mean of -3.7884 (+ 22.23335)
with the corresponding histogram (with density plot overlay) showing a unimodal,
symmetrical distribution with a range of -79 to 54. There were also outliers at both ends
of the data set, but as the histogram indicates, those accounted for less than five percent
of the overall sample. Finally, the box plot for (c) total days to symptom resolution shows
the mean of 0.2222 (+ 17.9445) and the corresponding histogram (with density plot
overlay) shows a unimodal, yet asymmetric distribution skewed to the right. However, it
is important to note that data ranged from -101 to 52 with multiple outliers at both the
positive and negative end of the distribution. While the overall percentage of data outside
the normal distribution of data is small, it still further underscores the high variability of
recovery following SRC and the need for individualized approaches to recovery and
eventual return to play.

36

(a) 7 Symptom Total

(b) Total Symptoms

(c) Total Days to Symptom Resolution
Figure 6. Distribution of Difference Between Groups Depicted in Box Plots for Seven
Symptom Total (a), Total Symptom Score (b), and Total Days to Symptom Resolution
(c).

37

(a) 7 Symptom Total

(b) Total Symptoms

(c) Total Days to Symptom Resolution
Figure 7. Distribution of Difference Between Groups Depicted in Histograms for Seven
Symptom Total (a), Total Symptom Score (b), and Total Days to Symptom Resolution
(c). Note. Y-axis = percent of data points; X-axis = range of difference in means between
groups

38
Discussion

Preliminary research aimed at investigating whether phase of menstruation
effected changes in factors linked to the somatization and recovery of TBIs have focused
on healthy females in the general population (Mihalik, Ondrak, Guskiewicz, &
McMurray, 2009; Wunderle et al., 2014). Mihalik and colleagues (2009) aimed to
investigate whether menstrual phase (follicular or luteal) affected change in
neurocognitive function, postural stability, and self-reported symptoms by studying a
healthy (non-injured) college-aged female population (n=36). While their findings
showed no significant differences related to phase of menstruation across these variables,
the researchers did report that participants using an oral contraceptive self-reported lower
levels of somatization than their counterparts throughout the study (Mihalik et al., 2009).
Wunderle and colleagues (2014) also studied phase of menstruation as it related to
quality of life and neurological outcomes but focused on women aged 16 to 60 following
mTBI (n=144). Their results showed significant decreases in QOL and neurologic
outcomes at one month post-mTBI in those women who sustained mTBI during the luteal
phase of their menstrual cycle as opposed to those in the follicular phase or using
synthetic progestins. While these findings drew attention to the role that synthetic
progestins may play in recovery following mTBI, the researchers cautioned that their
findings were preliminary and more research on the topic was warranted.
As previously demonstrated, the hypothesis that menstrual phase may play a role
in both somatization and recovery following mTBI is not new. However, previous
research has looked at the topic more broadly by recruiting non-injured participants

39
(Mihalik et al., 2009) or studying a population with a wide range in age thereby making it
more difficult to control for a variety of co-variables that may have influenced outcomes.
In contrast, this study specifically addressed an athletic population who sustained SRC
and narrowed the age range of participants to a six-year (18-23 y/o) span with a mean age
of 19.37 y/o among participants in Group 1 and 19.22 y/o among those in Group 2.
Additionally, the sample population included within this study is much larger (n=536)
than the aforementioned studies, thereby increasing the power of the statistical analysis.
Furthermore, as a result of the large sample size included within this study, we were able
to use statistical matching to control for prior concussion history and sport; two covariables consistently linked to increased risk of SRC, with prior concussion history also
qualifying as a predictor for somatization severity and length of recovery (Colvin et al.,
2009; Covassin, Stearne, & Elbin, 2008; Lax et al., 2015; Marar et al., 2012; Teel et al.,
2017).
To date, this is the first study to examine SRC somatization and recovery in
female collegiate athletes across two groups: (1) those who used synthetic progestins and
(2) those who did not use synthetic progestins at the time of SRC. The retrospective
statistical analysis of this large, multi-site sample showed that those female athletes who
used synthetic progestins reported significantly less somatization across both seven
symptom and total symptom scores. Self-reported somatization scores at 24 to 48 hours
post SRC indicated that female athletes who were not using synthetic progestins reported
2.3651 points higher somatization across the seven-symptom total score than their
counterparts who were using synthetic progestins. Additionally, self-reported
somatization scores at 24 to 48 hours post SRC indicated that female athletes who were

40
not using synthetic progestins reported 3.7884 points higher somatization across the total
symptom score than their counterparts who were using synthetic progestins. A visual
depiction of these differences is presented in Figure 8. Despite this research’s focus on a
collegiate athletic population rather than the female population at large, these findings
were still consistent with those of Wunderle and colleagues (2014).

Somatization Differences Across Groups

Total Symptoms

7 Symptom Total

0

5

10

15

20

25

30

Points of Somatization

Group 2

Group 1

Figure 8. Representation of differences in mean somatization across groups.
Note. Group 1=Synthetic progestin users; Group 2=Non-users or synthetic progestin

It is necessary to point out that there were relatively large standard deviations in
reported symptoms throughout both individual SSI scores, seven symptom total score,
and total symptom score. This indicates high variability in relationship to self-reported
scores and further supports the importance of an individualized approach to recovery

41
following SRC. While increased somatization following SRC among eumenorrheic
women (not using synthetic progestins) is significant, it is also important to consider the
role these findings may play in overall female wellness following SRC. This is especially
true when considering a collegiate athletic population as these athletes are expected to
take on dual roles in both the classroom and on the playing field. Current research
supports the need for individualized care following SRC that includes accommodations
both in the classroom and on the playing field (Majerske et al., 2008; McCrory et al.,
2017), thereby altering the cognitive and cardiovascular loads on injured athletes to
reduce somatization and promote recovery. Given that current best practice treatments
following SRC involve altering normal day to day activities, it is prudent to be concerned
with more broad measures of health outcome following SRC, such as quality of life
(QOL).
QOL considers multiple factors of wellness and has long been used as a measure
of health/health outcomes. Multiple studies have linked increased somatization following
SRC to decreases in QOL (Neidecker et al., 2017; Nelson et al., 2016; Wunderle et al.,
2014) and to prolonged recovery following SRC (Resch et al., 2015; Root et al., 2016).
Most recently, Iverson and colleagues’ (2017) systematic review determined that the
most accurate predictor of recovery following SRC was acute and subacute symptom
severity as measured within the first 24 to 48 hours following injury. Unfortunately, the
results of this study showed no significant difference in recovery time from SRC between
groups one and two and therefore did not correlate with Iverson’s findings.
Despite the hypothesis that subjects in Group 2 (those who did not use synthetic
progestins) would take longer to recover following SRC than their counterparts in Group

42
1 (those who used synthetic progestins), the findings of this study were not significant.
When the data is depicted visually (Figures 6c and 7c), the variability of recovery
following SRC is evident from the large number of data points that lie outside the
interquartile range; further underscoring the rationale for individualized concussion
management following SRC. Two possible explanations of these findings were of
important consideration: the decision to use a retrospective data set and the resultant
methodology. The CARE Consortium’s data collection from 30 universities and four
military academies (Broglio et al., 2017; CARE Consortium, 2018a) allowed for a
relatively large sample size to be analyzed in this study despite strict inclusion and
exclusion criteria. This larger sample size lent to greater statistical power, however, the
retrospective analysis of an existing data set also created a confounding challenge: an
inability to adjust the methodology of the original data collection. As a result, this study
was limited to those data points collected by the CARE Cosortium study which did not
include specific details regarding participants’ menstrual cycles.
As previously mentioned, existing literature has pointed to the possibility that
sustaining SRC during the luteal phase of the menstrual cycle when the progesterone
surge is occurring may be associated with poorer outcomes in women who do not use
synthetic progestins (Wang et al., 2016; Wunderle et al., 2014). Research has previously
demonstrated that hypopituitarism is a common, yet under diagnosed, side effect of TBI
(Agha & Thompason, 2006), which leads to the dysfunction of the pituitary gland and has
the potential to deleteriously affect normal concentrations of sex hormones (luteinizing
hormone, follicle stimulating hormone, and progesterone). However, in those females
using synthetic progestins, these hormones would continue to flow in normal

43
concentrations following TBI, lending credence to the hypothesis presented by Wunderle
and colleagues (2014). Referred to as the “withdrawal hypothesis,” (p. 7) it suggests that
a hit to the head during the luteal phase may result in a sudden and drastic decrease in
progesterone thus leading to more severe somatization among women as compared to
their male counterparts. (Wunderle et al., 2014).
Given that CARE Consortium study methodology did not include the collection
of data specific to menstruation, there was no way to examine menstrual phase at the time
of SRC among those subjects assigned to Group 2 (non-use of synthetic progestin).
Current research suggests that menstrual phase at time of SRC may be a prognostic
indicator of both QOL and somatization one-month post-injury (Wunderle et al., 2014).
Additionally, the work of La Fountaine and colleagues (2019) found that 50% of SRCs
sustained in female athletes aged 18 to 22 occurred during a seven-day period
characterized as the late luteal phase of menstruation. When accounting for the first two
days of menstruation as well, which are also associated with declining levels of estrogen
and progesterone, that percentage rose to 66.7%, indicating that eumenorrheic women
may be at higher risk of sustaining a concussion during the luteal phase of their menstrual
cycle (La Fountaine et al., 2019).
The inability to include information related to phase of menstruation and potential
for inaccurate self-reporting were certainly limitations to this study; both of which need
to be addressed in order to ensure transparency. While missing data related to the phase
of menstruation was not available for analysis in this study, it can easily be addressed in
future research. Wunderle and colleagues (2014) accomplished this by using blood draws
thereby utilizing the gold standard and ensuring accuracy in determining menstrual phase.

44
However, La Fountaine and colleagues (2019) were able to establish menstrual phase
through charting of menstruation which was a far less invasive technique and one that
may increase participation overall in future research by circumventing the need for blood
draws.
Reliance upon the self-reporting of health history as well as somatization of
student athletes to determine inclusion and exclusion criteria, group assignment, and
dependent variable (seven total symptom score and total symptom score) measurements
was necessary. As such, it is possible that student-athletes submitted inaccurate responses
to prompts for answers related to these topics. Attempts to mitigate this limitation as it
pertained to inclusion/exclusion and group assignment hinged on excluding studentathletes who provided contradictory answers to demographic questions. No precautions
were taken to mitigate inaccuracies in reporting of somatization given that symptom
severity is subjective and universally used as a determining factor in both diagnosis and
recovery following SRC (McCrory et al., 2017).

45
Conclusion

This study was the first of its kind to examine the relationship between use and
non-use of synthetic progestin and SRC among female collegiate athletes. The resultant
findings support those of previous studies as well as the theory that use of synthetic
progestins may help reduce somatization following SRC. Retrospectively reviewing the
CARE Consortium study’s large, multi-site data set lent both power and relevance to this
study. The resultant findings suggest that the regular use of synthetic progestins may have
a role in decreasing somatization among users following SRC, however, caution in this
area is still warranted. It is important to acknowledge that this study utilized a causal
comparative design which does not determine a cause and effect relationship; rather it
allows for the comparison of outcomes between groups (Creswell, 2015; Salkind, 2010).
Additionally, this study was unable to identify any significant differences with regard to
recovery time following SRC between groups. Despite these findings, it is prudent to
mention that the methodology failed to identify menstrual phase among participants in
Group 2 (non-users); a variable that may have impacted outcomes but could not be
controlled for in this study. Therefore, the findings of this study do indicate that use of
synthetic progestins is associated with decreases in severity of reported acute
somatization following SRC. Furthermore, this study contributes to the literature focused
on identification of the root cause of gender differences (La Fountain et al., 2019; Wang
et al., 2016; Wunderle et al., 2014) associated with SRC and further highlights a need for
continued research focused on establishing cause and effect relationships across these
variables.

46
Future Research

This study was intended to inform the direction of future research and as a
precursor to more substantive studies aimed at establishing a true cause and effect
relationship between the use of synthetic progestins and reduction in reported
somatization following SRC, not to inform changes to clinical practice. As this area of
research continues to expand, ethical challenges will need to be carefully considered in
the development of future studies related to synthetic progestins as well as in the potential
resultant changes to clinical practice. Future research should continue to develop
prospective studies to work towards the goal of determining the root cause of gender
differences across SRC. As research continues to suggest a linkage between the
menstrual cycle and somatization and quality of life outcomes, there is a clear need for
more studies aimed at determining if there is a true cause and effect relationship between
these variables while taking into consideration the impact that synthetic progestins may
have across somatization and recovery following SRC. A special note of consideration
should also be given to the fact that, in this study, headache was the symptom that
received the highest severity score across both groups at two times that of the symptom
ranked second in severity. Future researchers should take this into consideration and
make appropriate alterations to methodology considering that one of the side effects
associated with multiple forms of synthetic progestin use is headache (Food and Drug
Administration [FDA], 2019). As such, it may be prudent to consider analyzing the
difference between baseline and post-injury in reported headache severity as opposed to
just a post-injury severity score. Furthermore, QOL following SRC should be considered

47
as an additional dependent variable in future research as it provides a more holistic view
of wellness than somatization and length of recovery alone. Finally, the relationship
between the menstrual cycle, synthetic progestin use, and length of recovery following
SRC certainly warrants follow up research considering that, to date, findings have been,
at best, inconclusive.

48
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Appendix A
Review of the Literature

59
The term concussion is frequently used interchangeably with mild traumatic brain
injury (mTBI) and has been “defined as representing the immediate and transient
symptoms of traumatic brain injury (TBI)” by the Concussion in Sport Group (McCrory
et al., 2017, p.2). Those who sustain a concussion can experience a variety of mild to
severe signs or symptoms (McCrory et al., 2017). As the rate of sports related
concussions (SRC) continues to rise, especially among children between 10 and 19 years
of age (Gilchrist, Thomas, Xu, McGuire, & Coronado, 2011), students who sustain
concussions miss school days, struggle academically, and refrain from physical activities
while they recover. Ensuring safe return to learn and return to play strategies for these
students is a priority. However, research indicates that a variety of mitigating factors
ranging from previous concussion history (Colvin et al., 2009; Covassin, Stearne, &
Elbin, 2008; Lax et al., 2015; Teel, Marshall, Shankar, McCrea, & Guskiewicz, 2017) to
symptom severity (Resch et al., 2015; Root et al., 2016) may influence recovery length
following SRC. Injury rate data also indicates gender may play a role in prevalence and
recovery length (Marar et al., 2012; Resch et al., 2017). Additionally, research has
suggested that contraceptive use among women may need to be considered and further
addressed within the future body of research dedicated to concussion among females
(Wunderle, Hoeger, Wasserman, & Bazarian, 2014). This review aims to address
multiple factors associated with concussions including injury rates/risk, prior history,
neurocognitive testing, and symptoms with the goal of highlighting gender differences
and the potential role of the menstrual cycle across these variables. Specifically, this
review aims to identify the importance of hormonal fluctuations present throughout the
menstrual cycle and the use of synthetic progestins (birth control) as important variables

60
for consideration throughout the treatment of and recovery from SRC in collegiate female
athletes.

Traumatic Brain Injury Rates

In the United States, the number of reported traumatic brain injuries from multiple
causes has continued to rise (Faul, Xu, Wald, & Coronado, 2010; Taylor, Bell, Breiding,
& Xu, 2017). Sports related concussions (SRC) among athletes have long been a public
health concern (Taylor et al., 2017) leading to a copious amount of research on the topic.
Among the research, there is data indicating that SRC rates are higher in some sports as
compared to others, most notably American football (Marar et al., 2012). This may be
one reason that the majority of SRC research has focused on male athletes (Resch, Rach,
Walton, & Broshek, 2017). However, multiple studies point to differential injury rates
dependent upon both gender (Marar et al., 2012; Resch et al., 2017) and age (Bakhos,
Lockhart, Myers, & Linakis, 2010; Gilchrist et al., 2011; Macpherson, Fridman, Scolnik,
Corallo, & Guttmann, 2014; Sheu, Chen, & Hedegaard, 2016). These findings are
indicative that the impact of SRC may vary depending upon several different variables.

61
General Traumatic Brain Injury Rates
Faul et al. (2010) found that between 2002-2006 there were 1.7 million TBIs that
resulted in either a trip to the emergency room, hospitalization, or death in the United
States. More recent research however, indicates that these rates increased dramatically by
2013 with a reported 2.8 million TBIs resulting in a trip to the emergency room,
hospitalization, or death (Taylor et al., 2017). While these studies do address motorvehicle crashes as a categorical mechanism of TBI, they also offer two vague categorical
mechanisms: (1) a fall and (2) being struck by or striking something. Given that this data
is limited to only those individuals who sought treatment at hospitals, it is likely skewed
as a result of not taking into account patient preference for treatment from other health
care providers. One data set compiled in Ontario showed that from 2003-2010, slightly
more pediatric concussions were treated in physician offices (46,406) than in emergency
rooms (42,292) (Macpherson et al., 2014). This data is indicative that concussion injury
rates may be even higher than those reported using previous methods.

Sports Related Concussion Injury Rates
SRC is a specific type of injury that falls under the umbrella of traumatic brain
injury (TBI) and mild traumatic brain injury (mTBI). The Concussion in Sport Group has
defined SRC as “a traumatic brain injury induced by biomechanical forces” (McCrory et
al., 2017, p.2). Data has suggested that incidence of SRC has been rising dramatically
(Bakhos et al., 2010; Gilchrist, et al., 2011; Gilchrist, Thomas, Wald, & Langlois, 2007;
Sheu et al., 2016), but flaws in data collection and population may indicate that SRC
injury rates are even higher than expected.

62
The Centers for Disease Control and Prevention (CDC) reported in children aged
5-18, 65% of TBIs were the result of sports or recreation activities (Gilchrist et al., 2011).
Estimates indicate that approximately 6 (ages 14-19) in 1000 US children visited the
emergency room for a SRC from 2001 to 2005 (Bakhos et al., 2010). However, as
previously noted, MacPherson, et al. (2014) reported that children aged 3 to 18 years-old
may also seek treatment from private physicians as opposed to emergency rooms,
indicating that these numbers may be even higher than previously thought.
As a result of somewhat ambiguous data collection, there has been a need to
further clarify risk of SRC. Current research has looked at the rate of concussion during
sports in terms of athlete-exposure (AE), with one AE defined as either one practice or
one game. This differential methodology indicates that among high school athletes 2.5
concussions occur per every 10,000 AEs (one practice or game), with higher incidence
rates reported during competition (6.4) than practice (1.1) (Marar et al., 2012). Marar and
colleagues’ (2012) findings that SRCs makeup 13.2% of reported injuries among high
school athletes supports the notion that there is likely a higher rate of SRC than what has
been previously reported in the literature. However, these numbers are in contrast to
those presented by Gessell, Fields, Collins, Dick, and Comstock (2007) who focused on
both high school and collegiate athletes, finding that concussions represented 8.9% of all
high school and 5.8% of all collegiate athletic injuries. It is important to note that the rate
of concussions has been shown to be higher in a collegiate athletic population as opposed
to a high school demographic despite the aforementioned percentage of concussions
among total injuries measured across both groups (Gessell et al., 2007). Moreover, there
is clear evidence supporting that athletes who participate in specific sports have a higher

63
risk of concussion than those who participate in other sports (Bakhos et al., 2010; Cohen,
Gioia, Atabaki, & Teach, 2009; Gessell et al., 2007; MacPherson et al., 2014; Marar et
al., 2012; Sheu et al., 2016), thereby adding an additional critical variable to establishing
accurate rates and risk of concussion among athletes.

Effect of Sport Participation on Concussion Rates
The National Federation of High School Sports (NFHS) (2017) reported that
during the 2016-2017 academic year, 7.9 million individuals participated in high school
athletics. Although the numbers are not as profound, the National Collegiate Athletic
Association (NCAA) (2018) reports an additional 460,000 student-athletes compete
across 24 sports each year. Given that recovery from concussion impacts academics and
such large numbers of high school and college students are participating in athletics, SRC
should certainly be a concern, not only to athletic departments, but academic institutions
as well. As expected, the risk of sustaining a concussion is higher in collision sports as
opposed to sports that tend to involve less opportunity for contact (Gessell et al., 2007;
Macpherson et al., 2014; Marrar et al., 2012). The sport that has been consistently
demonstrated to carry the highest risk of SRC in the United States has been identified as
football (Gessell et al., 2007; Macpherson et al., 2014; Marrar et al., 2012; Sheu et al.,
2016). Research has indicated that up to 47% of SRCs among high school athletes are the
result of football (Marrar et al., 2012) and account for approximately 14% of SRCs in
those <19 years old in the emergency room (Gilchrist et al., 2011). Further research
indicates that the risk of SRC among collegiate football players may be even higher with
one study finding that 80.6% of collegiate football players suffered from at least one

64
reported concussion within one season of play (Kerr et al., 2017). Despite this alarming
trend among football players, it is important to note that in Canada, ice hockey and iceskating were the most frequently reported causes of concussions treated in the emergency
room (Macpherson et al., 2014). This difference indicates that depending upon cultural
norms and sport popularity, sports that carry the highest risk of concussion may vary
depending upon geography and demographics.
Although there has been a push to promote better education about concussions
among middle and high schools, inconsistencies in state mandates has created concern
that parents/guardians may not understand the risk of SRC associated with sports that do
not involve tackling or checking (Harvey, 2013). The research does show that football,
boys ice hockey, and boys lacrosse have the highest rates of concussion in the U.S.
(Marar et al., 2012). However, it is important to note that some sports not frequently
considered to be high contact (like those previously mentioned) also carry a formidable
risk of SRC.
In examining a broader age demographic, Sheu et al. (2016) pointed to both
cycling and basketball as demonstrating higher incidence in specific age groups. Similar
research indicated that football, wrestling, men’s and women’s basketball, men’s and
women’s lacrosse, men’s and women’s soccer, women’s volleyball and men’s and
women’s ice hockey have been responsible for the greatest number of SRCs among
NCAA athetes (Kerr et al., 2017). Acknowledging the need for more specific sportrelated risk of SRC, Marar and colleagues (2012) found that the majority of concussions
occur as a result of football, followed by girls’ soccer, wrestling, and girls’ basketball
when examining the epidemiology of concussions across 20 high school sports in the US.

65
Not only does this study quantify both risk and concussion percentage according to sport,
but it lends credence to the argument that gender may play a role in concussion incidence
as well.

Gender Differences
Evidence supports that males account for more than half of SRC incidents in the
U.S. (Sheu et al., 2016), which suggests that perhaps SRC is more prevalent among male
athletes. However, it is important to note that football, a sport almost entirely played by
males, is responsible for a high rate of SRCs occurring among collegiate athletes (Gessell
et al., 2007; Kerr et al., 2017). Without a comparable high contact/collision sport among
females to study, it is difficult to determine with certainty that male athletes are at higher
risk of sustaining SRC than females.
Evidence suggests that activities at the time of concussion vary dependent upon
both gender and age (Gilchrist et al., 2011; Sheu et al., 2016). In addressing the risk of
SRC associated with gender-comparable sports, research indicates that females had
higher rates of concussion (1.7) than their male (1.0) counterparts (Marar et al., 2012).
Noteworthy research has suggested that females might actually be predisposed to
sustaining SRCs; citing neck strength and hormone levels as potential reasons (Resch et
al., 2017). Similarly, research has indicated that a prior history of concussion also
increases the risk of sustaining an additional SRC (Teel et al., 2017).

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Previous Concussion History

At present, prior history of SRC is considered a known risk factor for sustaining
another SRC (McCrory et al., 2017). Multiple studies have focused on prior history of
SRC and its potential effect on a variety of factors associated with overall risk and brain
health. The focus of some research has been on identifying the risk of sustaining a
subsequent SRC following a history of one and/or multiple SRCs (Teel et al., 2017) thus
supporting the need for attaining accurate patient histories during both pre-participation
exams and concussion evaluation following injury (McCrory et al., 2017). However,
there has also been a more specific focus on the effect that prior history of SRC has on
recovery time (Covassin et al., 2008) and the potential long-term effects of concussions
(Daneshvar et al., 2011; McCrory, 2011). There is also evidence to suggest that
neurocognitive scores are decreased in those individuals who have a prior history of SRC
supporting the argument that full recovery may take longer than previously thought
(Colvin et al., 2009; Covassin et al., 2008; Thériault, Beaumont, Tremblay, Lassonde, &
Jolicoeur, 2011). These studies establish that there is evidence to suggest that a prior
history of SRC may lead to both short and long-term effects.

Increased Risk of New Concussion
As has been previously noted by Marar and colleagues (2012), concussions
account for 13.2% of injuries reported among high school athletes. However, this study
did not identify how many of those concussions were the result of first time SRCs as
opposed to second, third, or more SRCs in a given athlete. The lack of attention to this

67
important detail highlights, in part, highlights the need for more specific reporting
methods among studies that aim to quantify risk of SRC among athletic populations. This
distinction is of importance because research has identified a link between prior
concussion history and risk of SRC. Given the high numbers of student athletes
participating in high school athletics (NFHS, 2017) that could potentially be joining
NCAA athletic programs in the future, the effect of prior concussion history on the risk of
sustaining an additional SRC should be of concern to both high school and college
athletics programs.
Earlier research has identified an increased risk of SRC associated with prior
history of head impact (Collins et al., 2002; Delaney, Lacroix, Gagne, & Antoniou,
2001). Collins et al. (2002) found that those with a prior history of concussions were 9.3
times more likely to present with loss of consciousness, anterograde amnesia, and
confusion. Similarly, Delaney and colleagues (2001) use of questionnaires found that in
soccer players, a prior SRC increased the risk of another SRC by 11.1 times while risk
was only increased 4.2 times for football players who had a prior history of concussion.
More recently, Teel et al. (2017) was also able to quantify an increased risk of
SRC in athletes who reported a prior history of concussion. Focusing on both high school
and collegiate athletes, Teel et al. (2017) studied the impact of concussion history (as
well as amnesia and loss of consciousness) on acute recovery. They found that those
athletes who self-reported one prior concussion within the previous two years were 2.2
times more likely to sustain a concussion than those athletes who did not report a prior
history. Further, their data showed that those athletes who had sustained two or more
prior concussions (as self-reported) within the previous two years were significantly more

68
likely (4.2 times) to sustain a concussion than those who had no prior history.
Additionally, this study indicated that athletes with a prior history of SRC experienced
more profound balance deficits than their peers.

Potential Long-Term Effects and Increased Recovery
Despite the reported increase in balance deficits among athletes with a prior
history of SRC, Teel et al. (2017) were careful to point out that these deficits resolved
within three days. Well within the documented 10-14 day timeframe during which most
individuals recover from concussion (McCrory et al., 2017). However, there is concern
that cumulative concussions may affect brain health in both the short and long term.
Studies examining changes in recovery from SRC resulting from prior history ranges
from broad to specific to speculative. These studies suggest that prior history of
concussion may either decrease long term brain health or increase length of acute
recovery time, however differences in subject selection and procedures has led to mixed
results (Corwin et al., 2014; Covassin et al., 2008; Daneshvar et al., 2011; Iverson,
Brooks, Lovell, & Collins, 2006b; McCrory, 2011; McCrory et al., 2017). As a result,
MCrory et al. (2017) highlighted the continued need for more research aimed at
determining the long-term effects of concussion.
Attention has been drawn to cases in which there is anecdotal evidence suggesting
a link between (retired) football players and the development of chronic traumatic
encephalopathy (CTE) (McCrory, 2011). Given the high incidence of reported SRCs
among football players (Sheu et al., 2016), there is a clear need to further study the
association between concussion history and long-term cognitive and neurological

69
impairments. McCrory and colleagues (2017) support this need when they suggest the
continued examination of the potential relationships between repetitive head trauma and
the development of CTE. Additional research has highlighted evidence suggesting that
cumulative head trauma may play a role in the development of CTE, Alzheimer’s
Disease, and Parkinson’s Disease (Daneshvar et al., 2011). However, these findings are
based upon a broad definition of head trauma that does not directly correlate to incidence
of SRC. Ultimately, further studies of SRC and these degenerative diseases will be
necessary to determine whether there is a clear cause and effect relationship between
SRC and these disease states. While research regarding the long-term effects of SRC is
unclear, evidence indicating increased length of recovery related to increased
neurocognitive deficits among athletes with a prior history of SRC is much more
profound.
Prolonged length of recovery in association with prior concussion history has
been found in a pediatric population aged 5 to 18 years of age with full clearance ranging
from 64 days in those with ≤1 prior concussion to 243 days in those with a history of ≥3
prior concussions (Corwin et al., 2014). Supporting the theory that prior history of SRC
results in increased recovery time, Covassin et al. (2008) specifically identified
neurocognitive deficits associated with prior history of concussion. They found that
recovery of both verbal memory and reaction time were negatively influenced among
collegiate athletes with a prior history of (≥2) concussion. There is additional research to
indicate that athletes with a prior history of SRC perform significantly worse in these
areas of standard neurocognitive testing following concussion (Colvin et al., 2009).

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Decrease in Neurocognitive Scores
Multiple studies have indicated that a prior history of concussion is indicative of
significantly worse neurocognitive results following SRC (Colvin et al., 2009; Covassin
et al., 2008). Covassin and colleagues (2008) specifically outline deficits in both verbal
memory and reaction time following SRC in those who report a prior concussion.
Similarly, Colvin and colleagues (2009) found that soccer players without prior history of
concussion performed better on standardized neurocognitive testing following concussion
than their peers with a prior history of concussion. Specifically, decreased scores in both
memory and visual processing were noted in those with prior history of concussion
(Colvin et al., 2009).
Despite research finding that history of prior concussion does not impact
subsequent baseline symptom or cognitive scores (Iverson et al., 2006b), there is also
research indicating that sustained posterior contralateral negativity (SPCN) amplitude is
increased among athletes who have a history of ≥3 prior concussions (Thériault et al.,
2011). These findings are indicative of worse outcomes in individuals with a more
profound history of prior concussions (Thériault et al., 2011) and are notably different
than studies focusing primarily on neurocognitive scores. Given the role of
neurocognitive and neuropsychological testing in SRC management, these studies
highlight the importance of considering and accounting for all variables that may play a
role in these tests; especially as they relate to recovery and determination of both
academic and athletic restrictions.

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Neurocognitive and Neuropsychological Testing

The use of neuropsychological testing has long been considered to play a vital
role in the management of SRC (McCrory et al., 2017). Computerized neurocognitive
testing is completed at multiple levels of play including professional, collegiate, and high
school athletics (Fonseca, Reynolds, & Almquist, 2014). These tests help assist clinicians
in treating SRCS and ultimately making both return to learn and return to play decisions
(Fonseca et al., 2014). The generation of data associated with one such test, immediate
post-concussion assessment and cognitive testing (ImPACT), gives clinicians tangible
information regarding composite verbal memory, visual memory, visual motor speed,
reaction time, and impulse control (Fonseca et al., 2014). However, there is evidence to
suggest that despite return to baseline or normative values on these standardized
neurocognitive tests, individuals may still suffer from poor executive function and/or
decreased academic success. Also notable is the indication that there are gender
differences associated with performance following the analysis of these
neuropsychological testing data sets (Colvin et al., 2009; Lax et al., 2015). Together these
factors highlight the importance of individualized care following SRC, especially as
clinicians work with athletes to reintegrate individuals back into the classroom and field
of play.

Return to Learn and Return to Play
Neurocognitive testing continues to be an important tool in determining the
diagnosis, acute care, and recovery of athletes following SRC (McCrory et al., 2017).

72
Current best practice guidelines outline the need for athletes to rest following SRC until
symptoms resolve (McCrory et al., 2017). However, determining how much rest is
necessary following SRC is still debatable with evidence being insufficient to outline an
absolute template. Further it is important to note that, at present, protocols dictate that
athletes may participate in limited cognitive and cardiovascular activity during the
recovery process as long as symptoms are not exacerbated by either (McCrory et al.,
2017).
Research supports the need for rest following SRC to ensure recovery from both
persistence of symptoms and cognitive deficits associated with SRC (Majerske et al.,
2008). It has been found that adolescents engaging in higher levels of exertion following
SRC experience both increased somatization and decreased neurocognitive function
(Majerske et al., 2008). Therefore, it is evident that exertion is indicative of both
cardiovascular and cognitive load. As student-athletes maintain dual roles in the
classroom and on the playing field it is imperative that consideration be made for
accommodations on both fronts (Majerske et al., 2008).
Support for individualized care and attention to cognitive stress was highlighted
in research focused on amateur athletes. The findings of which showed that one day after
sustaining SRC, athletes performed significantly worse on neuropsychological testing and
complained of substantial symptoms. Reported symptoms then resolved and
neuropsychological testing returned to normal between five and ten days post-concussion
(Iverson et al., 2006a), consistent with the 10 to 14 day window previously identified
(McCrory et al., 2017). However, Iverson et al. (2006a) were quick to point out that the
normative data in this instance masked outliers whose symptom scores continued to

73
increase and neurocognitive scores decreased over the course of two separate testing
intervals. Thus, providing further evidence supporting the need for individualized care as
well as prompting questions about the reliability of these brief neurocognitive tests to
determine full recovery.

Executive Function
Despite neuropsychological testing’s regular use as a clinical tool, there is
evidence to suggest that these standard tests may not adequately represent full recovery
following SRC (Boutin, Lassonde, Robert, Banassing, & Ellemberg, 2008; Howell,
Osternigh, Van Donkelaar, Mayr, & Choue, 2013; Ozen, Itier, Preston, & Fernandes,
2013). Studies have outlined decreased executive function (Hartikainen et al., 2010;
Howell et al., 2013), long term working memory deficits (Ozen et al., 2013), and
decreases in academic performance (Boutin et al., 2008) beyond the 10 to 14 day
recovery window previously identified (McCrory et al., 2017). Individualized treatment
protocols following SRC often rely on both return to baseline following
neuropsychological testing and resolution of concussion like symptoms. However, it has
been suggested that baseline testing may not be a reliable snapshot of an athlete’s
cognitive capabilities and self-reported symptom scores can be easily manipulated.
In examining adults following MTBI, it was found that reaction time (RT) was
significantly affected by the presence of persistent symptoms (Hartikainen et al., 2010).
As such, performance on executive RT tests performed at six months post-injury were
found to be indicative of whether a subject was suffering from persistent somatization or
asymptomatic. Conversely, neuropsychological testing performance and composite

74
scores were incapable of clearly differentiating between groups (symptomatic and
asymptomatic) (Hartikainen et al., 2010). These findings support the theory that basic
neuropsychological testing is too broad to determine complete recovery following SRC.
Additional research has yielded similar results in a younger athletic population. In
following concussed adolescents over two months post impact with a focus on examining
both attention and executive function, results showed concussed athletes performed
significantly worse across both variables than their healthy peers (Howell et al., 2013).
This finding is notable because disruptions in these variables were documented for up to
two months post-concussion (Howell et al., 2013), far exceeding what is frequently
considered to be a normal recovery time frame following SRC. Additional concerns have
been raised with regard to potential deficits in long-term working memory following
SRC. Research has indicated that at six-months post concussion, there is still a subtle
change in processing during working memory tasks when compared to individuals with
no prior history of concussion (Ozen et al., 2013).
Given that executive function, attention, and memory are all called upon during
cognitive stress and academic load, it is not surprising that academic success has been
shown to decline following SRC (Boutin et al., 2008). In one such documented case, an
eight-year-old girl’s neuropsychological testing scores had returned to normative values
at 22 weeks following her first concussion. However, for one year following SRC, her
teachers noted that she needed help and interventions where she had not required either
prior to SRC (Boutin et al., 2013). Although these findings were documented in the form
of a case study, similar results were documented in female university soccer players
(Ellemberg, Leclerc, Couture, & Daigle, 2007). More specifically, it was determined that

75
both cognitive function and processing speed were still vulnerable to impairment for up
to six months following a first concussion in female university soccer players.

Notable Gender and Age Differences in Relation to Testing
Suggestions of decreases in executive function, attention, memory, and academic
success should be of particular concern given the research indicating significant
differences in neuropsychological testing between genders. The presence of gender
differences in relationship to neuropsychological testing has been documented both at
baseline (Covassin, Elbin, Larson, & Kontos, 2012; Lax et al., 2015) and throughout
concussion recovery (Colvin et al., 2009; Covassin, Schatz, & Swanik, 2007). Additional
differences have also been noted across age groups (Covassin et al., 2012; Lax et al.,
2015).
Specific differences between genders and age groups have been documented
during baseline neurocognitive testing (Covassin et al., 2012; Lax et al., 2015). Youth
female hockey players have been shown to perform significantly better in assessments of
visual motor speed, sustained attention, and visual short-term memory as opposed to their
male counterparts (Lax et al., 2015). Similarly, among high school and collegiate aged
athletes, females outperformed their male counterparts with better scores related to verbal
memory at baseline (Covassin et al., 2012). Unsurprisingly, age has also been determined
to play a role in baseline test scores. Research has illustrated that collegiate athletes
perform better in terms of processing speed than their high school counterparts (Covassin
et al., 2012). In line with these findings, older youth hockey players were also found to
perform better than their younger counterparts on tests that examine information

76
processing, visual motor speed, attention, and working memory (Lax et al., 2015). These
differences are not just limited to normative baseline scores, but extend to recovery as
well.
Poorer reaction time (as measured by ImPACT) following SRC has been noted in
athletes with a prior history of concussion, but significantly poorer reaction time has also
been documented in females aged eight to twenty four suffering from SRC as opposed to
their male counterparts (Covlin et al., 2009). In addition, demonstrable deficits have also
been documented in females (as compared to males) in assessments of inhibition and
cognitive flexibility (Lax et al., 2015). Although not of statistical significance, an
important trend in which females with acute SRC performed poorer than their male
counterparts in both memory and visual-motor processing speed has also been identified
(Colvin et al., 2009). Additionally, collegiate female athletes performed significantly
worse on visual-memory than their male-counterparts when tested between two and eight
days post-injury (Covassin et al., 2007). Conversely, a similar study conducted within 21
days post-concussion found no notable differences in neurocognitive scores between
genders (Sufrinko et al., 2017). However, it is important to note the extended period
between concussion and neurocognitive testing in this instance given Iverson et al.’s
(2006a) identification of return to baseline neuropsychological scores at five to ten days
post-concussion. These neuropsychological tests also address symptom resolution by
including a section in which patients provide self-reported symptom scores. As a result,
there has been ample data recovered and analyzed in relation to these self-reported
symptom scores.

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The Role of Symptoms Throughout Recovery

As expected, statistically significant differences in neurocognitive function has
been easily documented in athletes who are symptomatic following SRC as opposed to
those who are asymptomatic (Hartikainen et al., 2010). Neurocognitive testing is
completed in conjunction with self-reported symptom scores, both of which are used to
determine treatment plans and recovery following SRC (McCrory et al., 2017). Standard
symptom indexes (SSI) have also commonly been used in the literature to determine
length, severity, and resolution of symptoms associated with concussion over time (Baker
et al., 2015; Berz et al., 2013). These symptom scores play an important role in the
diagnosis and treatment of concussion, but for those suffering from a concussion, these
symptoms can also play a role in overall quality of life (Matsuveciene, Eriksson, &
DeBoussard, 2016). Further, it has been documented that males and females do not seem
to experience the same somatization types, severity, or duration (Baker et al., 2015;
Frommer et al., 2011). The utilization of SSIs in both clinical and research settings has
led to better understanding of somatization following SRC.

Standard Symptom Index
A standard symptom index (SSI) is routinely used to determine the type and
severity of symptoms an athlete is experiencing following SRC (Baker et al., 2015; Berz
et al., 2013). Utilization of SSI is recommended for research as well as clinical use as a
tool to determine recovery from SRC (Randolph et al., 2009). The symptoms in a SSI
may include, but are not limited to: headache, nausea, dizziness, blurred vision, and

78
sensitivity to noise or light (Echemendia et al., 2017). These symptoms are listed and
following suspicion of concussion, an athlete is asked to rank the severity of each
symptom according to a Likert scale (Echemendia et al., 2017, Fonseca et al., 2014).
A standardized assessment known as the Sport Concussion Assessment Tool
(SCAT) was updated at the 5th International Conference on Concussion in Sport in 2016
(McCrory et al., 2017). Figure 1 depicts the latest iteration of the standard symptom
index as presented in the SCAT5 (Echemendia et al., 2017). As anticipated, research has
demonstrated that self-reported symptom scores are generally worse on day one of
concussion recovery than on consecutive days of recovery (Iverson et al., 2006a). Also
notable, the most commonly reported symptom has been identified as headache
(Frommer et al., 2011).
Interestingly, there is research to suggest that the presence of certain symptoms
following SRC may predict length to symptom resolution among collegiate athletes
(Resch et al., 2015). A formula based upon five variables including ImPACT total
symptom score, duration of neck pain, drowsiness, nervousness, and tingling as
determined by the Revised Head Injury Scale has found preliminary success in
determining length to overall symptom resolution. Although more research is certainly
necessary, the formula has been utilized with the appropriate variables at 24 hours
following injury and predicted those athletes that may take >10 days to become
asymptomatic with 64% accuracy (Resch et al., 2015). Still other research indicates that
pre-injury baseline somatization scores may be indicative of acute symptom duration
post-concussion (Nelson et al., 2016).

79
Following SRC among high school athletes, total symptom resolution as selfreported was documented to be within three to fifteen days of initial injury, with the most
commonly reported symptom being identified as headache (Frommer et al., 2011;
Williams, Puetz, Giza, & Broglio, 2015). Conversely, documentation of self-reported
symptom resolution among collegiate athletes indicates that recovery occurs at day six
post-concussion (Williams et al., 2015). However, it is important to point out that in some
cases, self-reported symptoms can linger for a time frame well beyond normal resolution
of symptoms, this is routinely referred to as post-concussion syndrome. Research aimed
at determining the prevalence of post-concussion syndrome among adults following
concussion found that 10.3% of patients still experience somatization at one-month postconcussion, with 6% and 0.9% still reporting somatization at three and six months
respectively (Spinos et al., 2010). It is important to consider that individuals who
experience symptoms for greater lengths of time as opposed to those who fall into
normative recovery patterns may experience a decrease in quality of life. Figure 9, the
Standard Symptom Index, provides the self-rating system form for athletes.

80

Figure 9. Standard Symptom Index (Halstead & Walter, 2010, p. 3).

81
Quality of Life
Quality of life is a commonly used measure of health/health outcomes that
addresses five domains: mobility, self-care, usual activity, pain, and emotional health
(Wunderle et al., 2014). Documentation surrounding both length and type of somatization
is enough to suggest that quality of life may be affected following SRC (Neidecker,
Gealt, Luksch, & Weaver, 2017; Nelson et al., 2016; Ozen et al., 2015; Root et al., 2016;
Spinos et al., 2010; Sufrinko et al., 2017). Specific considerations related to quality of life
following SRC include pre-injury somatization (Nelson et al., 2016; Root et al., 2016),
length of somatization following concussion (Neidecker et al., 2017; Preiss-Farzanegan,
Chapman, Wong, Wu, & Bazarian, 2009; Spinos et al., 2010), and the identification of
prolonged deficits associated with SRC (Ozen et al., 2013; Sufrinko et al., 2017).
Research focusing on quality of life has highlighted specific deficits in multiple areas
measured among patients following SRC (Matuseviciene et al., 2016).
Histories of somatization pre-injury have been highlighted as both an indicator of
and factor related to increased length of time to symptom resolution (Nelson et al., 2016;
Root et al., 2016). This relationship appears most evident in 10 to 18 year old females
and has been directly correlated with high self-reported symptom scores at four weeks as
well as a prolonged recovery following SRC (Root et al., 2016). Research indicates that
pre-injury somatization influences acute symptoms following SRC thereby affecting both
symptom severity and duration (Nelson et al., 2016).
Additional research indicates that pre-injury somatization history may increase
the risk of post-concussion syndrome (Spinos et al., 2010). Special consideration should
be given to adult females who have been found more likely than adult males or minor

82
females to suffer from post-concussion syndrome at three months post injury (PreissFrazanegan et al., 2009). Specifically, patients report experiencing prolonged headache,
fatigue, irritability, and concentration difficulties at three months post SRC (PreissFarzanegan et al., 2016). There is concern that prolonged continuation of symptoms
associated with post-concussion syndrome may impact physical, mental, and social
health, leading to decreases in quality of life (Spinos et al., 2010). However, continuation
of symptoms is not the only factor indicating that quality of life may be affected by SRC.
Notable deficits have also been identified despite self-reported symptom resolution (Ozen
et al., 2013; Sufrinko et al., 2017).
Most notably, long-term working memory deficits have been found at six months
post SRC despite self-reported symptom resolution and return to neuropsychological
baseline levels (Ozen et al., 2013). Working memory affects the execution of cognitive
tasks (Repovs & Baddeley, 2006), thus it is an integral component associated with
meeting academic and corporate expectations. Similarly, vestibular/ocular function
affects many activities of daily life including, but not limited to reading, computer usage
(Broglio, Collins, Williams, Mucha, & Kontos, 2015), and discomfort in busy
environments (Furman, Raz, & Whitney, 2010). Therefore, noted increases in
vestibular/ocular somatization following SRC (Sufrinko et al., 2017) may be an
additional variable influencing quality of life. This leads to concern that prolonged
continuation of symptoms associated with post-concussion syndrome may impact
physical, mental, and social health, leading to decreases in quality of life (Spinos et al.,
2010).

83
Individuals are considered to be at high risk for experiencing quality of life
deficits when they present complaining of ≥3 symptoms consistent with concussion
following mTBI (Matuseviciene et al., 2016). Conversely those who present experiencing
fewer symptoms are at less risk for deficits related to activity and participation. The most
commonly cited quality of life deficit following concussion among high-risk individuals
has been identified as fatigue related to work; however, other deficits include gaps in
leisure and social activity (Matsuveciene et al., 2016). Therefore, it is evident that history
of somatization prior to injury, symptom duration, and prolonged deficits associated with
concussion may have dramatic effect on activity and participation levels associated with
quality of life. Gender differences in somatization should be of particular concern in
relationship to quality of life outcomes especially given evidence that duration of
symptoms following first-time SRC is longer in females (28 days) as opposed to males
(11 days) (Neidecker et al., 2017).

Notable Gender Differences in Somatization
Substantial somatization differences have been documented between genders
across multiple studies (Baker et al., 2015; Frommer et al., 2011; Preiss-Farzanegan et
al., 2009; Root et al., 2016; Sufrinko et al., 2017). Research has highlighted differences in
type (Baker et al., 2015; Frommer et al., 2011), severity (Baker et al., 2015; Berz et al.,
2013), and duration (Baker et al., 2015; Frommer et al., 2011; Preisss-Farzanegan et al.,
2009) of symptoms between genders following SRC.
Among adolescents, females seem to experience greater somatization than their
male counterparts (Baker et al., 2015; Frommer et al., 2011; Sufrinko et al., 2017).

84
Regardless of gender, headache continues to be the most frequently reported symptom
associated with SRC (Baker et al., 2015). However, male high school athletes tend to
report amnesia and confusion/disorientation more frequently than females (Frommer et
al., 2011). Comparatively, females have a tendency to suffer more frequently from
sensitivity to noise and drowsiness than males (Frommer et al., 2011). This is consistent
with the noted increased vestibular ocular reflex in females compared to males (Sufrinko
et al., 2017), perhaps explaining the differential somatization experiences between
genders.
Differences in severity of reported symptoms have also been documented between
genders (Baker et al., 2015; Berz et al., 2013). Adolescent females reported both a greater
number of symptoms and higher symptom severity as compared to males following SRC
(Baker et al., 2015). Statistically significant disparities between genders have been
documented showing that females report increased severity with relationship to headache,
pressure in the head, feeling slowed down, difficulty concentrating, feeling more
emotional, irritability, and sadness (Baker et al., 2015). This disparity in SSI scores has
been demonstrated to be consistent throughout recovery, with females reporting higher
scores than males on SSI when evaluated at both ≤7 days and >7 days post-concussion
(Berz et al., 2013). These are significant findings and likely contribute to the prolonged
recovery from somatization documented among females.
It has been previously demonstrated that females take longer to return to baseline
neuropsychological scores following SRC (Preiss-Farzanegan et al., 2009) and prolonged
symptom resolution has also been highlighted among females (Baker et al., 2015;
Frommer et al., 2011; Preiss-Farzanegan et al., 2009). Although it is important to note

85
research demonstrating no significant differences in length to symptom resolution or
return-to-play following SRC between genders (Frommer et al., 2011), there is ample
research to indicate the contrary. Notably, among an adolescent population suffering
from SRC, 65% of females took longer than 10 days to recover as opposed to only 53%
of males (Baker et al., 2015). This is consistent with Neidecker and colleagues’ (2017)
findings that females took 28 days to achieve recovery as opposed to 11 days for males.
In an adult population, females were found to be at greater risk for experiencing
symptoms at three months post-concussion when compared to adult males (PreissFarzanegan et al., 2009). These findings clearly indicate that gender specific variables
need to be considered throughout diagnosis and recovery of SRC.

Current Hypotheses for Gender Differences Related to Concussions

This review has thus far identified differences between genders across multiple
variables related to SRC, with females seemingly experiencing poorer outcomes than
their male counterparts. Although identifying these differences is important and adds
value to clinical practice, it is also imperative to address the potential cause of these
differences. Current research is limited and to date has yet to unequivocally identify the
root cause of gender differences in relation to SRC (Neidecker et al., 2017). However,
there is research to suggest that neck strength (Tierney et al., 2005) and biological
(Wunderle et al., 2014) differences may be linked to gender differences related to SRC.

86
Neck Strength and Mass
One study concluded that females were able to recruit significantly less isometric
neck strength than males. This perhaps contributed to the findings that females
demonstrated 50% greater head-neck acceleration and 39% greater displacement than
males. This is of particular interest because results also showed that females began neck
acceleration sooner than males (Tierney et al., 2005). It is likely that decreased neck girth
and head mass along with limited isometric neck strength may have contributed to these
differences in this dynamic movement associated with the sport specific function of
heading a soccer ball.
These results led researchers to hypothesize that strengthening exercises may
improve head-neck dynamic stabilization. Specific research focused on an eight-week
resistance program consisting of exercises in both cervical flexion and extension.
Although isometric strength was found to increase as a result of the resistance training
protocol, there was no indication that the program was able to positively influence headneck dynamic stabilization (Mansell, Tierney, Sitler, Swanik, & Stearne, 2005). Future
resistance training programs should aim to include lateral and diagonal exercises as well
as flexion and extension in order to address the entire head-neck segment. In addition to
gender differences in the head-neck segment, there are also other theories suggesting that
biological differences may play a role in the aforementioned gender differences related to
SRC.

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Biological Differences
Differences in brain glucose metabolism and hormonal status have also been
identified as possible contributors to the differences noted in multiple variables
surrounding SRC (Broshek et al., 2005; Stein, 2007). Research has found that females
experience an increased rate of brain glucose metabolism as compared to males (Hu et
al., 2013). Glucose is the main source of energy for the brain and as such its metabolism
has a demonstrable effect on both brain physiology and function (Mergenthaler,
Lindauer, Dienel, & Meisel, 2013). Broshek and colleagues (2005) suggest that the
increases in glycemic demands following SRC may result in longer somatization among
females due to their already increased rate of glucose metabolism. However, this is not
the only potential biological factor considered to be pertinent to gender differences in
relation to SRC.
Although there is a plethora of research related to sex differences in hormonal
status as it relates to a number of physiological factors (Stein, 2007), studies focusing on
hormone differences in humans as they relate to concussion are not as prevalent. Rather,
much of the research in this area has been in rodents and has yet to be validated among
human subjects. The results of studies in animals led to the belief that progesterone,
specifically, may act as a neuroprotective agent following brain injury (Wang et al.,
2016). However, these findings make it difficult to explain why women with naturally
occurring higher levels of progesterone experience more severe sequalae following SRC.
These findings and unanswered questions have led researchers to examine the role of the
menstrual cycle in severity and recovery following SRC (Wunderle et al., 2014).

88
Potential Role of the Menstrual Cycle and Synthetic Progestins

The menstrual cycle is a hormonal process that occurs monthly (National Institute
of Health [NIH], 2018) and may play a role in many of the gender differences that have
been previously discussed in relation to SRC. The aforementioned research surrounding
progesterone and the surges that occur during the menstrual cycle indicate that
identification of menstrual phase at the time of injury may be an important variable in
recovery following SRC (Wang et al., 2016). If the natural hormonal surges during the
menstrual cycle play a role in recovery following SRC, then the use of oral
contraceptives also needs to be considered. Furthermore, when addressing gender
differences related to SRC, age needs to be an important consideration. Not only have
there been noted differences identified in SRC between genders, but there have also been
notable differences documented between high school and collegiate athletes (Nelson et
al., 2016). Further exploration of these variables and their potential role in SRC is
necessary.

Menstrual Phase
Typically, the menstrual cycle lasts between 25 to 30 days, with 28 days being the
norm. The menstrual cycle is broken into two phases: follicular (or proliferative) and
luteal (or secretory). Each phase is associated with endocrine, histological, endometrial,
and body temperature changes (Reed & Carr, 2015). Figure 2 depicts these changes in
the endocrine system throughout the menstrual cycle. A complete analysis of the
menstrual cycle is beyond the scope of this paper. Therefore, consideration of the

89
potential role of the menstrual cycle in relationship to gender differences associated with
SRC will focus on endocrine differences throughout the follicular and luteal phases.
The follicular phase of the menstrual cycle is characterized most importantly by
the development of the ovarian follicles. Although development of the follicles begins in
the last few days of the previous menstrual cycle, the follicular phase is typically defined
as day one of the current menstrual cycle through the release of the mature follicle during
ovulation. Important changes in the endocrine system during this phase foster the
development of the mature follicle (Reed & Carr, 2015).
Steroid production declines during the follicular phase allowing follicle
stimulating hormone (FSH) to increase at the onset of this phase. However, FSH will then
decline until ovulation occurs at the end of the follicular phase. Conversely, luteinizing
hormone (LH) is present in low amounts at the beginning of the follicular phase, but
begins to rise during the mid-follicular phase. Additionally, serum estradiol levels
increase in parallel with FSH during the follicular phase. Together, the presence of
estradiol and FSH begin to cause the secretion of progesterone. As the follicular phase
continues to progress, gonadotropin is required to allow for the continued growth of the
follicle until ovulation. At the time of ovulation, LH peaks with the surge typically
occurring between 34 and 36 hours prior to ovulation. The surge in LH leads to the
production of progesterone, the presence of which puts into motion the reactions that
ultimately lead to ovulation. LH then falls dramatically just prior to ovulation (Reed &
Carr, 2015).
Following ovulation, the female body prepares for a potential pregnancy should
fertilization have occurred. This phase of the menstrual cycle is known as the luteal phase

90
and its primary purpose it to prepare the endometrium for implantation. During this
phase, progesterone and estradiol surge in preparation for implantation at approximately
eight to nine days following ovulation. If pregnancy does not occur, hormone levels
begin to fall leading to decreased levels of progesterone that ultimately lead to the
sloughing off of the endometrial lining thus leading to menstruation (Reed & Carr, 2015).
It is important to note that not all of the hormones involved in the menstrual cycle
experience drastic fluctuation in levels. Also, of importance is the relationship that both
LH and FSH have with the pituitary gland. Both hormones are classified as pituitary
hormones (Reed & Carr, 2015) and as such may be influenced by SRC. Research
indicates that hypopituitarism is a common side effect of TBI that is under diagnosed
(Agha & Thompson, 2006). This leads to dysfunction of the pituitary gland, which could
potentially affect both LH and FH, which in turn could deleteriously affect the normal
progesterone surge present in the luteal phase of the menstrual cycle. Furthermore, there
has been a distinct relationship documented between TBI and amenorrhea. Research
shows that there is a significant increase in amenorrhea following TBI (Ripley et al.,
2008). These findings in conjunction with those demonstrating that progesterone may be
a neuroprotector has led to the consideration of progesterone therapy as a treatment
following TBI. Figure 10 explains the follicular and luteal phases of the menstrual cycle.

91

Figure 10. Endocrine, histological, and body temperature changes throughout the
follicular and luteal phases of the menstrual cycle (Reed & Carr, 2015).
Progesterone Therapy and Birth Control
As previously discussed, progesterone was found to behave as a neuroprotector in
animal studies and phase II trials of progesterone therapy in humans showed promising
results (Wang et al., 2016). However, progression to stage III trials showed negative
results and a meta-analysis of randomized controlled trials concluded that there is no
efficacy for the use of progesterone as a treatment agent following TBI (Wang et al.,
2016). However, one study found that the use of oral contraceptives among females at the

92
time of mTBI had a protective effect (Wunderle et al., 2014). This is likely because
synthetic progesterone would not be impacted by hypopituitarism in the same way that
naturally occurring progesterone is impacted. Thus, further study of the hormonal
fluctuations throughout the menstrual cycle and their impact on concussion recovery and
symptom severity are certainly warranted. Continued research on this topic should also
aim to identify the protective effect identified by Wunderle et al. (2014).
Oral contraceptives and intrauterine devices (IUD) are prescribed and used to
protect against pregnancy. The term oral contraceptives are frequently used
interchangeably with the pill and birth control. However, in the literature and relation to
research, oral contraceptives may be referred to under the umbrella term synthetic
progestins. Oral contraceptives use a combination of synthetic estrogen and progesterone
in order to stop the egg from fully developing (Mayo Clinic, 2015). Estimates indicate
that 15.9% of women in the United States between the ages of 15 and 44 use oral
contraceptives with an additional 8% estimated to use IUDs for protection against
pregnancy (Centers for Disease Control and Prevention [CDC], 2018).
A study by Wunderle and colleagues (2014) utilized a cohort study to determine if
there was a relationship between menstrual phase and outcomes at one month following
mTBI. Participants were recruited from a group of 144 females between the ages of 16
and 60 years old who reported to any of six emergency departments within four hours of
injury. Glasgow Coma Scale scores were recorded and all subjects had blood drawn
within six hours of injury. Progesterone serum levels were then used to determine
menstrual cycle phase at which point postmenopausal subjects were excluded. However,
those subjects who reported using synthetic progestins in the form of oral contraceptives

93
or IUDs were not included in the measurement of progesterone serum levels. The results
found that women in the luteal phase at the time of impact reported more severe postconcussion symptoms and a decrease in quality of life as compared to those in the
follicular phase of their menstrual cycle. Additionally, of importance was the finding that
there were no clear outcomes between the subjects who were on synthetic progestin and
those in the follicular phase. This research highlights multiple areas of study that should
be addressed as continued efforts are made to further understand the complexity of SRC
outcomes. One such variable among collegiate females is the use of synthetic progestins
(birth control).

Birth Control Use Among Collegiate Athletes
The CDC and NIH report that 62% of women in the United States were using
contraception from 2011 through 2013, with the most commonly used contraceptive
method identified as “the pill,” which is the conventionally used term for oral
contraceptives (Daniels, Daugherty, & Jones, 2014). Research indicates that oral
contraceptives qualify as the most popular contraceptive in the U.S. with a reported 10.6
million women reporting the use of oral contraceptives (Jones, Mosher, & Daniels, 2012).
Additional statistics indicate that the use of oral contraceptives are highest among women
aged 15 to 24 at 22.4% as compared to 16.9% of women aged 25-34 and only 8.7% of
women aged 35-44 (Daniels et al., 2014). This statistic is of particular interest
considering that the 15 to 24-year-old demographic is the age group most closely
associated with the age range of the traditional college student, and as such examination

94
of the health benefits associated with the use of oral contraceptives among collegiate
athletes requires further attention.
Although the most widely accepted use of oral contraceptives is the prevention of
unwanted pregnancy among sexually active women, other benefits associated with the
use of oral contraceptives are also notable. Other areas of research focused on athletic
injuries have taken an interest in the use of oral contraceptives as well with one study by
Herzberg and colleagues (2017) determining that the use of oral contraceptives offered up
to a 20% reduction in anterior cruciate ligament (ACL) injury. Profound gender
differences have also been noted in relationship to ACL injuries and as such, the role of
the menstrual cycle in these injuries has been of interest. Additional research has
indicated that women attending colleges and universities suffered from iron deficiency in
numbers that were disproportionate to the rates associated with women of reproductive
age at large (Le, 2016; Parks, Hetzel, & Brooks, 2017). Women of reproductive age have
been found to be particularly susceptible to iron deficiency as a result of menstruation
(Miller, 2014). Ultimately, this leads to particular concern given the findings of Scott and
Murray-Kolb (2016) which indicated that mild iron deficiency in women of reproductive
age resulted in worse performance on attention, inhibitory control, and planning ability as
compared to those women who were not experiencing mild iron deficiency. It is
important to note that the use of oral contraceptives has been found to reduce the risk of
anemia among women of reproductive age (Bellizzi & Ali, 2017) and as such would be a
likely treatment option among college females suffering from iron deficiency. Further,
the variables studied by Scott and Murray-Kolb (2016) have not only been linked to
quality of life assessments but have also frequently been addressed within research that

95
utilized ImPACT testing and focused on highlighting gender differences throughout the
SRC recovery process.
In addition to the use of oral contraceptives to combat anemia, research has found
that other benefits associated with the use of oral contraceptives among female athletes
have included the previous treatment of female athlete triad syndrome (Matzkin, Curry,
& Whitlock, 2015) and the manipulation of menstruation (Schaumberg et al., 2018).
Female athlete triad syndrome has been defined by three criteria: decreases in body mass
index (BMI), decreases in bone density, and most important to the purpose of this
literature review, amenorrhea or the absence of menstruation (Matzkin et al., 2015).
Matzkin and colleagues (2015) note that previous treatment of female athlete triad
syndrome frequently consisted of oral contraceptive use as a strategy for resumption of
menstruation. Despite, gold standard treatment for female athlete triad syndrome now
consisting of strategies designed to increase energy availability, it is important to note
that menstrual dysfunction among collegiate athletes has not only been associated with
female athlete triad syndrome. Beals and Manore (2002) found that approximately 31%
of collegiate athletes suffered from menstrual irregularity. Additional research by Prather
and colleagues (2016) found that 17.9% of collegiate female soccer players and 19.4% of
professional soccer players reported menstrual dysfunction. These studies not only
underscore the pervasive problem of menstrual dysfunction among female collegiate
athletes but also may provide some context as to why manipulation of menstruation may
be beneficial to collegiate athletes.
Schaumberg and colleagues (2018) note that among oral contraceptive users, 74%
report having deliberately manipulated menstruation. While most participants reported

96
manipulating one to three consecutive cycles, this practice was highest (63.5%) among
competitive athletes (Schaumberg et al., 2018). Menstruation has often been perceived to
negatively influence athletic prowess and physical performance leading to the belief that
the use of oral contraceptives may provide a competitive edge. Research has been shown
to support this belief in studies focused on female military personnel (Schneider, Fisher,
Friedman, Bijur, & Toffler, 1999). As researchers continue to work towards clarifying the
root causes behind gender differences associated with specific injuries, natural hormonal
fluctuations will undoubtedly continue to be at the forefront of research. However, as the
body of literature continues to expand, research focused on the effect of the menstrual
cycle will also need to take into consideration the use of synthetic progestins.

Conclusion

SRCs in the U.S. are prevalent and research indicates injury rates are continuing
to rise (Bakhos et al., 2010; Faul et al., 2010; Gilchrist et al., 2011). Given the flaws in
data collection among studies that aimed to quantify SRC injury rates, it is likely that
these rates are higher than previously thought. However, research has been consistent in
identifying specific variables that may increase risk associated with SRC. Current
research found that there are higher rates of concussion among females as opposed to
males in gender comparable sports (Marar et al., 2012). Additional research has
demonstrated that a prior history of concussion greatly increases the risk of sustaining an
additional SRC (Delaney et al., 2011; Teel et al., 2017).

97
These increased risks are of importance to both the short and long term effects of
SRC. While research surrounding the long-term consequences of SRC is in its infancy
and tenuous at best, studies focused on the short-term outcomes are much clearer.
Prolonged recovery in both a pediatric and collegiate population has been well
documented (Corwin et al., 2014; Covassin et al., 2008). Increased recovery length is
frequently documented in reference to neurocognitive scores, most specifically declines
in memory and visual processing (Colvin et al., 2009), both of which have the potential to
deleteriously affect academic success. At this time, standard of care following SRC
addresses return to learn (cognitive stress) and return to play (cardiovascular stress)
(McCrory et al., 2017). Brief neurocognitive tests are used to distinguish when acute
cognitive deficits associated with SRC have returned to baseline values (Funesca et al.,
2014). However, there is evidence that suggests clinical use of these tests may not
accurately determine full recovery of executive function (Hartikainen et al., 2013).
Further gender differences have also been highlighted within neurocognitive and
neuropsychological testing as well as in somatization.
SSIs are frequently used throughout the diagnosis and recovery of SRC (McCrory
et al., 2017). The findings of which provide an important role in the treatment of SRC
and provides information that may predict length to symptom resolution (Resch et al.,
2015). Concern has been raised that those who experience prolonged somatization may
suffer from decreased quality of life (Neidecker et al., 2017; Nelson et al., 2016). This is
of particular concern among females who experience symptoms more than twice as long
as male counterparts following their first SRC (Neidecker et al., 2017).

98
Hypotheses outlining the cause(s) behind these differences highlight neck strength
(Tierney et al., 2005) and biological differences (Wunderle et al., 2014) as potential
important areas for future research. However, at this point there is not consensus as to the
root cause of these gender differences (Neidecker et al., 2017). As a result, research
continues to focus on these variables to positively identify factors that may be at the root
cause of gender differences associated with SRC. One important variable is the role of
menstruation. Specifically, research has drawn attention to the role of progesterone
during the luteal phase of the menstrual cycle (Wunderle et al., 2014). Studies
highlighting the potential neuroprotective capability of progesterone (Wang et al., 2016)
indicate that disruption of pituitary hormones at the time of SRC may deleteriously affect
and/or interrupt the normal progesterone surge of the menstrual cycle.
Finally, the use of synthetic progestins (birth control) may play an important role
in predicting both severity and length of recovery following SRC among the demographic
of collegiate female athletes. National statistics indicate that the use of oral
contraceptives among women aged 15 to 24 are the highest among all women of
reproductive age (Daniels et al., 2014), thereby supporting the theory that birth control
use among college aged women in the United States is common. In addition, there is a
compelling amount of evidence highlighting both menstrual dysfunction among
collegiate athletes (Beals & Manore, 2002; Matzkin et al., 2015; Prather et al., 2016) and
a multitude of potential alternative purposes for the use of oral contraceptives among
collegiate women (Bellizzi & Ali, 2017; Herzberg et al., 2017; Le, 2016; Parks et al.,
2017). Furthermore, it has been documented that menstruation has been perceived to
negatively affect physical performance outcomes (Schneider et al., 1999) which, in part,

99
led to the prevalence of manipulation of menstruation for the purpose of achieving a
competitive edge among athletic populations (Schaumberg et al., 2018). Taken together,
these findings lend support to the theory that the number of collegiate female athletes
using oral contraceptives is likely higher than that associated with the broader female
demographic. In addition, Wunderle et al.’s findings that women who utilized synthetic
progestins (associated with the use of birth control or an IUD) at the time of impact
demonstrated symptom severity and quality of life measures that were more consistent
with those women in the follicular phase of their cycle as opposed to those in the luteal
phase Lend further support to the theory that the use of synthetic progestins may have a
profound effect on both symptom severity and recovery following SRC. Future research
should aim to focus not only on identifying the root cause of gender differences
associated with SRC, but also the effect of synthetic progestin use on both symptom
severity and length of recovery following SRC.

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Appendix B
Problem Statement

101
Research has continually recognized gender differences across multiple
parameters related to SRC (Marar et al., 2012). Hypotheses regarding the potential cause
of these gender differences include noted body mass and neck strength deficits among
females (Tierney et al., 2005), increased rate of brain glucose metabolism in females (Hu
et al., 2013), and hormonal surges associated with the menstrual cycle (Wunderle et al.,
2014). However, to date researchers have still been unable to identify the variables that
cause these noted gender differences. One notable study indicated that women in the
luteal phase of their menstrual cycle reported more severe symptoms related to
concussion and a decrease in quality of life as compared to those in the follicular phase of
their cycle (Wunderle et al., 2014). Additionally, the study found that those women who
utilized synthetic progestins (associated with the use of birth control or an IUD) at the
time of impact demonstrated symptom severity and quality of life measures that were
more consistent with those women in the follicular phase of their cycle. Given that 10.6
million women in the United States (U.S.) have reported using oral contraceptives (Jones,
Mosher, & Daniels, 2012) with additional statistics indicating that the use of oral
contraceptives are highest among women aged 15 to 24 at 22.4% (Daniels et al., 2014)
and the prevalence of manipulation of menstruation for the purpose of achieving a
competitive edge among athletic populations (Schaumberg et al., 2018). Future research
should consider the use of synthetic progestin as an important variable for consideration
following SRC. Therefore, the purpose of this research is to determine if there is a
difference in concussion severity and/or recovery among collegiate female athletes
following sports related concussion (SRC) between two groups: those athletes who use
synthetic progestins and those who do not use synthetic progestins.

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Appendix C
Additional Methods

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Appendix C1
CARE Consortium Informed Consent
Obtained from the University of Delaware
(personal communication, Dr. Thomas Kaminski, October 30, 2018)

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Appendix C2
IRB Approval

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Appendix C3
Federal Interagency Traumatic Brain Injury Research Informatics System (FITBIR)
Data Access Request
(FITBIR, n.d.e)

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Appendix C4
FITBIR Account/Access Approval
(personal communication, The FITBIR Operations team, November 6, 2018; personal
communication, Heather Rodney, November 16, 2018)

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Your FITBIR website account has been approved.
FITBIR-ops@mail.nih.gov

F

Reply all|
Tue 11/6, 4:15 PM
HOL1178 - HOLT, CATHERINE A

Dear Holt, Catherine, You recently requested an account to access the FITBIR website.
The FITBIR Operations team has approved your request and you may log in to the
website at your convenience.
If you have any questions, please contact us at FITBIR-ops@mail.nih.gov.
Sincerely,
The FITBIR Operations team
301-594-3532

128

Rodney, Heather (NIH/CIT) [C] <heather.rodney@nih.gov>
Reply all|
Fri 11/16, 2:55 PM
HOL1178 - HOLT, CATHERINE A;
FITBIR OPS <fitbirops@mail.nih.gov>;
+1 more

Hi Catherine,
Yes, there is a data share deadline of March 2019 for CARE. When the data is shared,
you will be able to access and download the data. You mentioned that you are putting
together a research proposal, so I'm assuming you intend on using some of the CARE
data. Just as a reminder, as per the agreement for your DAR Access, under Terms and
Conditions, you have to acknowledge FITBIR and the Submitters:
9. Acknowledgments. Recipient agrees to acknowledge the contribution of the FITBIR
bioinformatics platform, the relevant FITBIR dataset identifier(s) (a serial number), and
the Submitter(s) in any and all oral and written presentations, disclosures, and
publications resulting from any and all analyses of data using the FITBIR tools, whether
or not Recipient is collaborating with Submitter(s). The manuscript should include the
following acknowledgement or other similar language: Data and/or research tools used
in the preparation of this manuscript were obtained and analyzed from the controlled
access datasets distributed from the DOD- and NIH-supported Federal Interagency
Traumatic Brain Injury Research (FITBIR) Informatics Systems. FITBIR is a collaborative
biomedical informatics system created by the Department of Defense and the National
Institutes of Health to provide a national resource to support and accelerate research in
TBI. Dataset identifier(s): [provide]. This manuscript reflects the views of the authors and
may not reflect the opinions or views of the DOD, NIH, or of the Submitters submitting
original data to FITBIR Informatics System. If the Research Project involves collaboration
with Submitters or FITBIR staff then Recipient will acknowledge Submitters as coauthors, if appropriate, on any publication. In addition, Recipients agree to include a
reference to FITBIR Informatics System.
If you have any other questions please feel free to reach out to me or FITBIR Ops.
Regards,
Heather Rodney, M.S.

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FITBIR Operations

Appendix C5
Example of an ImPACT Baseline Clinical Test Report

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Appendix C6
ImPACT Baseline Demographic Worksheet
(Frommer et al., 2011)

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Baseline Worksheet
I. Demographic and Background Information
School / Organization: ________________________________________
Date of Birth: _________ month ______ date _______year
First Name: ______________________
Last Name: _________________________
Height: _____ft _____in

Weight: _______

Gender: _____ male _____ female

Handedness: _____ right _____ left _____ ambidextrous (both right and left)
Native Country / Region: ______________________________
Native Language: ____________________________________
Second Language: __________________________ (only if fluent in speaking and writing)
Years of education completed excluding kindergarten: __________
(e.g., high school senior is 11 years)
Check any of the following that apply:
_____ Received speech therapy
_____ Attended special education classes
_____ Repeated one or more years of school
_____ Diagnosed attention deficit disorder or hyperactivity
_____ Diagnosed learning disability
While in school, what type of student were / are you?
_____ Below Average ______Average
______Above Average
Current Sport: _________________________________________
Current position / event / class: ___________________________
(e.g., quarterback, forward, 1st base, etc.)
Current level of participation: _____________________________(e.g., junior high, high school)
Years of experience at this level: _______ (0 - 4)
(e.g., number of years in high school, high school senior = 3)
Please list your 5 most recent concussions: __________ month __________ year
__________ month __________ year
__________ month __________ year

137
__________ month __________ year
__________ month __________ year
Concussion History
_____ Number of times diagnosed with a concussion (excluding current injury)
_____ Total number of concussions
_____ Total number of concussions that resulted in confusion
_____ Total number of concussions that resulted in difficulty with memory for events
that occurred
immediately after injury
_____ Total number of concussions that resulted in difficulty with memory for events
that occurred
immediately before injury
_____ Total number a games that were missed as a direct result of all concussions
combined

I. Demographic and Background Information (cont.)

Baseline Worksheet
Indicate if you have had any of the following:
_____ yes _____ no Treatment for headaches by physician
_____ yes _____ no Treatment for migraine headaches by physician
_____ yes _____ no Treatment for epilepsy / seizures
_____ yes _____ no Treatment for brain surgery
_____ yes _____ no Treatment for meningitis
_____ yes _____ no Treatment for substance abuse / alcohol abuse
_____ yes _____ no Treatment for psychiatric condition (depression, anxiety)
Have you been diagnosed with any of the following?
_____ yes _____ no ADD/ ADHD
_____ yes _____ no Dyslexia
_____ yes _____ no Autism
Have you participated in any strenuous exercise and/or exertion in the last 3 hrs?
_____ yes _____ no
Date of your last concussion: __________ month _____ date _____ year
Number of hours slept last night: _____ (approximate if uncertain)
Please list any PRESCRIPTION medication (s) you are currently taking:
___________________________________________________________________
___________________________________________________________________
___________________________________________________________________

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Appendix C7
Example of ImPACT Identified Database with Identifying Information
Removed for Athlete Privacy

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Appendix C8
Data Extraction Spreadsheet

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