Initial symptom presentation after high school football-related concussion varies by time point in a season: an initial investigation
Brett et al. Sports Medicine - Open
Initial symptom presentation after high school football-related concussion varies by time point in a season: an initial investigation
Benjamin L. Brett 0 2 3
Andrew W. Kuhn 0
Aaron M. Yengo-Kahn 0 1
Zachary Y. Kerr 4
Christopher M. Bonfield 0 1
Gary S. Solomon 0 1
Scott L. Zuckerman 0 1
0 Vanderbilt Sports Concussion Center, Vanderbilt University School of Medicine , Nashville, TN , USA
1 Department of Neurological Surgery, Vanderbilt University Medical Center , Nashville, TN , USA
2 Department Counseling, Educational Psychology and Research, University of Memphis , Memphis, TN , USA
3 Department of Psychology, Veterans Affairs Connecticut Healthcare System , West Haven, CT , USA
4 Department of Exercise and Sport Science, University of North Carolina , Chapel Hill, NC , USA
Background: Schedule-based and in-season factors (e.g., competition type) have been shown to be associated with symptom reporting patterns and injury severity in sport-related concussion (SRC). To determine if acute neurocognitive and symptom presentation following SRC differ by time point within a high school football season. Methods: Multicenter ambispective cohort of high school football players who sustained a SRC (N = 2594). Timing (early, mid, and late season) of SRC was based on median dates for the start of the pre-season, regular season, and playoffs of each states' football schedules. Analysis of covariance (ANCOVA) investigated differences across season period groups for: (1) neurocognitive test scores, (2) total symptom scores (TSS), and (3) individual symptom increases from baseline within 1-week post-injury. Results: Significant group differences were observed in TSS, F(2, 2589) = 15.40, p < 0.001, η2p = 0.01, and individual symptom increases from baseline, F(2, 2591) = 16.40, p < 0.001, η2p = 0.01. Significant increases were seen from baseline to both midseason and late season in both TSS, χ2 = 24.40, p < 0.001, Φ = 0.10 and individual symptoms, χ2 = 10.32, p = 0.006, Φ = 0.10. Post hoc tests indicated a linear trend, with late-season injured athletes reporting approximately twice the TSS (13.10 vs. 6.77) and new symptoms (5.70 vs. 2.68) as those with early-season injuries. Conclusion: In a cohort of American high school football student-athletes, those suffering SRC in the late-season time period had increased acute symptom burden. SRC sustained later in-season may require more conservative management.
Sport-related concussion; Symptoms; Modifying factors; Neurocognitive function; ImPACT testing; Sport injury
A “seasonality effect” was observed where American
high school football players who sustained a SRC
later in the season reported significantly increased
total symptom scores and number of individual
symptoms compared to earlier season SRCs.
Sports medicine professionals should be aware of
external and situational factors affecting symptom
reporting following SRC.
The symptomology and presentation of SRC may
vary significantly over the course of the football
Sport-related concussion (SRC) accounts for 25–50% of
all concussions sustained by children [
] and occurs
in roughly 1.1 to 1.9 million US athletes ≤ 18 years .
SRC has become an international health concern, with
public attention paid primarily to professional and elite
]. However, given the large number of youth
athletes participating in contact sports every year [
the overall burden of this public health problem rests at
the youth and high school level [
]. American football,
in particular, accounts for 41% of all high school SRCs
]. Two recent studies from the 2008/2009–2012/2013
and 2011/2012–2013/2014 seasons estimate the current
rate of SRC among high school football players to be
8.2–9.21/10,000 athletic exposures [
Concomitant with an increase in SRC diagnoses is
heightened public awareness, healthcare utilization, and
]. A recent report by the Institute of
Medicine called for more research surrounding SRC risk
in athletes aged 5 to 21 years [
]. Several efforts have
been directed towards the role of biopsychosocial factors
influencing SRC incidence, presentation, and recovery.
Several modifying factors have been found to influence
both SRC incidence [
] and prolong recovery [
]. In the acute post-concussion period, factors such as
sex , history of concussion [
], ADHD , and
] have been shown to increase neurocognitive
deficits, while sex or pre-existing psychiatric disorders
have been associated with higher acute total symptom
The identification of other sport-related modifying
factors, beyond biopsychosocial variables, may also prove
worthwhile. For instance, schedule-based and in-season
factors, such as competition type [
] and injury
], have been shown to be associated with
symptom reporting patterns acutely and during
One trend that has yet to be investigated is the effect
of seasonality, defined operationally as time point in the
season, on acute presentation (symptom reporting and
neurocognitive functioning) after SRC. The primary
objective of this study was to investigate the effect of
seasonality (early, mid, and late season) on acute
neurocognitive performance and symptom burden
following SRC in American high school football players.
Due to the exploratory nature of this study, we adopted
the null hypothesis, proposing that there would be no
differences in initial presentation following SRC across
season period groups.
Study design and overview
A retrospective analysis of prospectively collected data
(ambispective design) was conducted. Participants
included 2674 student-athletes from various high schools
across the USA who underwent routine pre-season and
post-concussion neurocognitive testing using Immediate
Post-Concussion Assessment and Cognitive Testing
]. Anonymous, deidentified data were
obtained for the study from the lead programmer at
ImPACT, who was blinded to the purpose of the study.
Due to the deidentified nature of the data abstraction,
repeat injuries in the same athlete could not be
controlled. Institutional Review Board (IRB) approval was
obtained prior to analysis (IRB# 120991), and the study
was performed in accordance with the standards of
ethics outlined in the Declaration of Helsinki.
Selection of participants
Following written, informed consent by the
studentathlete and/or his parent/guardian, all participants
completed a baseline neurocognitive test as part of routine
athletic care. Baseline ImPACT testing was conducted in
group settings during the pre-season and under the
supervision of a sports medicine professional trained in
the administration of ImPACT [
]; however, group
sizes or administration procedures may have slightly
varied across sites. High school football players who
sustained a SRC from 2011 to 2016 were included in the
analysis. Football was chosen due to its highest
concussion rate among high school athletics [
football was the only sport investigated to standardize
season time points and to minimize potential confounds,
such as different season durations and game schedules.
SRC diagnoses were made in accordance with the
definition provided by the 2008 and 2012 International
Concussion in Sport Group (CISG) guidelines [
Diagnoses were made by certified athletic trainers
(ATCs) or team physicians based on the following
onfield/sideline signs or symptoms: (1) lethargy, fogginess,
headache, dizziness, nausea, visual problems,
photophobia, or phonophobia; (2) alteration in mental status; (3)
loss of consciousness; or (4) amnesia. Grading systems
of concussion severity were not utilized, based on the
aforementioned CISG guidelines [
]. All athletes
included in the study completed a post-injury ImPACT
test within 7 days of injury (M = 4.21, SD = 1.68).
Assessments that obtained a positive invalidity indicator, as
designated by ImPACT [
], were not considered as
eligible for the study and were excluded. Due to the
inclusion of only valid tests as part of the data extraction, the
precise number of invalid cases could not be specified.
Of the total 2674 individual athletes who met these
criteria, those with missing data (n = 48, 1.8%) and
reporting English as a second language (n = 32, 1.2%) were
excluded, resulting in a final sample of 2594
All data were obtained from ImPACT, including basic
demographic and biopsychosocial information, four
indices of neurocognitive functioning, and a self-reported
symptom inventory [
]. The four individual
neurocognitive indices yield composite scores for verbal
memory, visual memory, visual motor speed, and
reaction time. The self-report symptom inventory computes
a total symptom scale (TSS), comprised of 22 common
symptoms, each rated on a 0-6 Likert scale, with 0 =
none and 6 = severe.
Season period ranges (early, mid, and late season) were
defined based on published data/reports from all 50
states (+District of Columbia) for dates of high school
football pre-season, regular season, playoffs, and final
game. Following the precedent set by previous studies
investigating injury rate differences across season
periods, cut-points for group formation included
preseason (early), regular season (mid), and postseason
]. Median dates for the start of pre-season,
regular season, and playoffs across all available states
were used to determine cut points for ranges. Pre-season
was defined as training camp, exhibition games, or
scrimmages. The median pre-season start date was
August 7 (range = July 30 to August 18; data available from
33 states), which was designated as the commencement
of the early-season period. Regular season was defined as
the scheduled games for all teams. The median regular
season start date was August 27 (range = August 18 to
September 10; data available in 50 states), which was
designated as the mid-season cutoff date. Late season
was defined as the playoff games, where teams played
single elimination games, based on record and seeding.
The median playoff start date was November 5 (October
14–November 29; data was available for 50 states),
which was designated as the cutoff of the late-season
period. Median data for end of season date was
November 26. Athletes were classified into season period
groups (early, mid, and late) based on their date of SRC
injury, which was extracted from post-injury ImPACT
tests. Though these times mirrored pre-season, regular
season, and playoffs, some overlap of periods likely
existed between regions. Thus, early, mid, and late
season were chosen to better represent the amalgamation
Neurocognitive and symptom outcomes
Three a priori outcomes were determined: (1)
neurocognitive test scores, (2) total symptom scores (TSS), and (3)
increase in individual symptoms. A meaningful change for
each outcome measure was defined as follows. For the
outcomes of neurocognitive scores and TSS, a reliable
change was based on meaningful change at the 80%
confidence interval level [
]. To determine increase in number
and severity of symptoms, a previously validated cutoff
score (2+ symptoms, increased 1+ point) was used to
classify athletes as meaningfully changed from baseline [
The term “symptom burden” was collectively used when
referring to both symptom outcomes (TSS and individual
Demographic and biopsychosocial data from each
athlete’s ImPACT evaluation were extracted and compared
across seasonality groups using analysis of variance
(ANOVA) tests. Variables that significantly differed
between the early-, mid-, and late-season SRC groups were
selected as covariates for subsequent analysis of
covariance (ANCOVA) tests comparing outcome variables
(i.e., post-injury scores and symptoms; Table 1). Baseline
neurocognitive composite and symptom scores were also
entered as covariates for their respective post-injury
ANCOVA in order to control for individual differences
at baseline. Separate ANCOVA tests were conducted for
each post-injury ImPACT composite score and TSS. Due
to the recent emphasis on individualized symptoms over
], differences in the number of symptoms that
increased from baseline assessments to post-injury were
compared across seasonality groups. Additionally,
ANOVA tests were performed to compare individual
symptom reporting for each of the 22 symptoms within
the PCSS across the three groups, with a
Bonferronicorrected significance (alpha) level set at 0.002 [
Separate chi-squared tests were performed to compare rates
of reliable change from baseline for neurocognitive and
symptom scores. Reliable change was based on
meaningful change at the 80% confidence interval level [
Additionally, a chi-squared test was performed as a
means to test individual symptom increases from
baseline based on a previously validated cutoff score (2+
symptoms, increased 1+ point) [
Bonferronicorrected alpha level for multiple comparisons was set
at 0.008 [
A total of 2594 athletes were included in the final
analysis for the early- (n = 418), mid- (n = 2078), and
lateseason groups (n = 98). Demographic, medical, and
neuropsychiatric history, and baseline neurocognitive
and symptom scores are summarized (Table 1), along
with accompanying between-group comparisons. Timing
from SRC to post-injury assessment, measured in days,
did not differ significantly between groups: early (M =
4.09, SD = 1.69), mid (M = 4.24, SD = 1.68), and late (M
= 4.18, SD = 1.68), F(2, 2591) = 1.52, p = 0.22. Across all
three groups, 458 (17.8%) athletes were evaluated within
the first day post injury, 1258 (49.0%) were evaluated at
approximate 3 days following injury, and 850 (33.1%)
were evaluated between 4 and 7 days. Age emerged as
the only factor that significantly differed across groups,
F(2, 2591) = 6.97, p = 0.001, and was entered into
Italics indicates selection of covariates to control for in outcome measure statistical modeling
*p values of one-way ANOVAs (continuous variables) and chi-square analyses (χ2; binary and categorical variables) for comparison of those who sustained
concussion in the early, mid, or late season
ANCOVA tests as a result. Verbal memory, F(2, 2591) =
6.69, p = 0.001, and reaction time, F(2, 2591) = 11.02, p
< 0.001, significantly differed across groups as well.
Regardless of statistical significance, each baseline test was
entered as a covariate for their respective post-injury
neurocognitive or symptom score as part of each
No significant differences between the three groups were
observed for any of the four neurocognitive composite
scores (Tables 2 and 3). Classification rates of
neurocognitive scores as injured from baseline at the 80%
confidence interval did not significantly differ between the
three groups (Table 4).
Total symptom score
Results revealed significant differences in TSS across all
three groups, F(2, 2589) = 15.40, p < 0.001, and partial
η2p = 0.01 (Tables 2 and 3; Fig. 1). Tukey’s honest
significant difference (HSD) post hoc tests indicated that
these differences existed at all three levels and were
linear, with mid-season athletes reporting significantly
higher TSS than early-season athletes (mean difference
= 2.54, p = 0.001) and late-season athletes reporting
significantly higher TSS than mid-season athletes (mean
difference 3.75, p = 0.01). Late-season athletes reported
significantly higher TSS than early-season athletes (mean
difference = 6.30, p < 0.001). On average, TSS from
athletes who were injured in the late-season group were
twice that of athletes who were injured early in the
season (13.10 vs. 6.77). Significant differences in being
classified as reliably changed in TSS from baseline at the
80% confidence interval were observed between the early
(15.31%), mid (23.20%), and late season (36.73%) injured
athletes, χ2 = 24.40, p < 0.001, and Φ = 0.10.
A similar trend was observed for the number and
severity of increased individual symptoms from baseline,
Italics indicates significant p value based on Bonferroni-corrected alpha levels = 0.008
aEach comparison of outcome was controlled for age and respective baseline score in ANCOVA modeling; number of symptoms increased was from baseline
report and therefore baseline number of symptoms was not included as a covariate
bIncrease in number and severity of symptoms based on a previously validated cutoff score (2+ symptoms, increased 1+ point) to classify athletes as meaningfully
changed from baseline [
cPartial η2 interpretation for effect size; small = 0.01, medium = 0.06, large = 0.14
with statistically significant differences across all three
groups, F(2, 2591) = 16.40, p < 0.001, and η2p = 0.01. The
mean increase of individual symptoms above baseline
rose in a linear fashion, from early (2.68), mid (3.61),
and late season (5.70). Significant differences were
again observed at all three levels, with mid-season
athletes reporting a greater increase in symptoms from
baseline than early-season athletes (mean difference =
0.95, p < 0.001) and late-season athletes reporting a
greater increase in symptoms from baseline than
midseason athletes following SRC (mean difference = 1.85,
p < 0.001). Late-season athletes reported significantly
more symptoms above baseline than early-season
athletes as well (mean difference = 2.80, p < 0.001). Similar
to TSS, athletes who sustained a SRC during late season
reported nearly twice the number of increased
symptoms from baseline as athletes in the early-season
period (5.70 vs. 2.68). Of the 22 individual symptoms, 9
emerged as statistically different across all three groups
(Table 4; Fig. 2). Tukey HSD revealed variation in
differences between early-to-mid and mid-to-late, with
late-season athletes demonstrating significantly greater
symptoms than early athletes for all 9 symptoms.
Similarly, significant differences in being classified as
injured from baseline based on increases in individual
symptoms (2+, 1+ cutoff ) were observed between
early(45.45%), mid- (50.05%), and late-season (63.27%)
injured athletes, χ2 = 10.32, p = 0.006, Φ = 0.10.
The purpose of the current study was to assess the effect
of seasonality on acute SRC presentation in a large cohort
of American high school football student-athletes within
1-week post-injury. Acute symptom burden, measured by
TSS and individual symptom increases, significantly
increased as the high school football season advanced.
Progressively higher measures of symptom burden were
observed across season periods, with athletes in the
lateseason period reporting twice the post-injury TSS
compared to athletes in the early-season period. Similarly,
compared to the early-season group, athletes injured in
the late-season period reported almost twice as many
individual symptom increases from baseline following SRC.
While recovery was not included as an outcome in the
current study, initial symptom burden has been identified
as one of the strongest and most consistent predictors of
Early to late
prolonged recovery [
24, 30, 48–52
]. Therefore, more
conservative management of athletes who sustain a SRC later
in the season may be warranted. Silverberg et al. [
showed that athletes with higher symptom burden
immediately following injury were at increased risk for “symptom
spikes” during return to regular activities (RTRA), such as
school. Therefore, the timing of injury during the season
may influence return-to-school decisions. Clinicians can
counsel parents and athletes on the prospect of extended
duration for return-to-school and/or return-to-play as a
result of late-season injuries, which could reduce concern
over lingering symptoms or “symptom spikes” during
The effect of season period on acute symptom burden
should also be considered in the context of other
modifying factors, such as age. Sport participation at the youth
and adolescent level places children at higher risk for SRC
] and prolonged recovery [
]. Should future
studies validate the current results, there exist additional
implications for those at increased risk of concussion and
prolonged recovery, especially youth and adolescent
]. While the reporting of acute fatigue did not
significantly differ across the three groups, the effect of
accumulated fatigue may be a factor worthy of consideration
for elite adolescent and high school athletes who play their
sport year-round. This is especially true when considering
a recent meta-analysis demonstrating that higher athletic
training loads over time were associated with increased
rates of both injury and fatigue . Alongside heightened
awareness by healthcare providers regarding how time of
injury may be associated with concussion symptomatology,
consideration should be given to the number of pre-season
games and practice schedules as well.
The reason for increased symptom burden later in the
season is not entirely clear and could be due to a myriad of
reasons. An explanation of the current findings may be due
to athletes’ willingness to report an injury that may
preclude a rapid return to play. Athletes may be less likely to
report injuries later in the season due to increased desire to
play in higher stakes games, which could result in only the
more severe injuries being reported. This possibility is
further supported given that motivation to not be withheld
from competition has been significantly associated with
underreporting of concussion [
]. Cumulative head
impact burden from the season might also cause a higher
manifestation of concussion symptoms, which may lead to
increased symptom burden at season’s end [
increased symptoms could also be influenced by other factors
coinciding with season progression, such as increased
academic demands and stress later in the school year, as
compared to the pre-season. Other hypothetical explanations
for the current findings are increased intensity of playoff
games, higher level of competition with faster and stronger
athletes, and suboptimal, colder weather conditions in
The current study is not without limitation. Our
sample of high school football athletes who underwent
neurocognitive testing may not be generalizable to athletes
from other football settings, sports, or sporting levels.
Due to the de-identified process of case selection, it
cannot be guaranteed that a post-injury assessment was
an athlete’s first concussion in a season. Given that a
history of multiple concussions has been associated with
increased symptom reporting post-injury, not being able
to ensure that a post-injury assessment was an athlete’s
first SRC in the season presents as a potential confound
]. The fact that SRC history did not significantly differ
across the three groups suggested that sample selection
likely buffered the effect of this possible confound. This
is also true of the random selection of data and robust
sample size of the study [
]. Further, while cut points
for season periods were modeled after two previous
studies examining effects of season period, due to state
differences, potential for injury time misclassification
exists. Regardless, a linear trend and variations around the
season period thresholds would not have an effect on
the progressive increases in post-injury symptom
reporting. Lastly, our findings would also be advanced further
if the exact mechanism of increased symptom reporting
through the season was identified. This could be
accomplished by incorporating measures of fatigue or
cumulative head impacts in the examination of symptom
reporting trends in different season periods.
Symptom burden following SRC progressively increases
through the advancement of the season in high school
American football. While further validation is required,
these findings suggest that SRC sustained later in-season
may require more conservative management with regard
to return-to-learn and play activities. Further study is
needed to determine the etiology of greater symptom
burden reported later in the season.
No grants or outside funding supported this work.
Availability of data and materials
Supporting data is available upon request (see the corresponding author
BLB, AWK, AMYK, ZYK, CMB, GSS, and SLZ contributed to the conception,
design, research, and writing of this manuscript. All authors read and
approved the final manuscript.
Ethics approval and consent to participate
Institutional Review Board (IRB) approval was obtained prior to analysis (IRB#
120991). Written informed consent was obtained from the student-athlete
and/or a parent/guardian.
Consent for publication
GS Solomon is a consultant for the Nashville Predators, Tennessee Titans,
and the athletic departments of Tennessee Tech University and the
University of Tennessee, fees paid to institution. He is also a consultant to
the National Football League Department of Health and Safety. CM Bonfield
serves as an unaffiliated neurotrauma consultant for the NFL. BL Brett, ZY
Kerr, AW Kuhn, SL Zuckerman, and AM Yengo-Kahn declared no conflicts of
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
1. Barlow KM , Crawford S , Stevenson A , Sandhu SS , Belanger F , Dewey D. Epidemiology of postconcussion syndrome in pediatric mild traumatic brain injury . Pediatrics . 2010 ; 126 ( 2 ): e374 - 81 .
2. Kirkwood MW , Yeates KO , Wilson PE . Pediatric sport-related concussion: a review of the clinical management of an oft-neglected population . Pediatrics . 2006 ; 117 ( 4 ): 1359 - 71 .
3. Bryan MA , Rowhani-Rahbar A , Comstock RD , Rivara F , Collaborative SSCR . Sports- and recreation-related concussions in US youth . Pediatrics . 2016 ; 138 ( 1 ). https://doi.org/10.1542/peds.2015- 4635 .
4. Lovell MR , Fazio V . Concussion management in the child and adolescent athlete . Curr Sports Med Rep . 2008 ; 7 ( 1 ): 12 - 5 .
5. Nationala : Report on trends and participation in organized youth sports . 2008 .
6. Nationalb: 2014 -15 high school athletics participation survey . 2016 .
7. Gessel L , Fields S , Collins C , Dick R , Comstock R . Concussions among United States high school and collegiate athletes . J Athl Train . 2007 ; 42 ( 4 ): 495 - 503 .
8. Collins C , LB MK , Ferketich AK , Andridge R , Xiang H , Comstock RD . Concussion characteristics in high school football by helmet age/ recondition status, manufacturer, and model . Am J Sports Med . 2016 ; 44 ( 6 ): 1382 - 90 .
9. O 'Connor KL , Baker MM , Dalton SL , Dompier TP , Broglio SP , Kerr ZY . Epidemiology of sport-related concussions in high school athletes: National Athletic Treatment, Injury and Outcomes Network (NATION ), 2011 - 2012 through 2013 -2014 . J Athl Train . 2017 ; 52 ( 3 ): 175 - 85 .
10. Baugh CM , Kroshus E , Bourlas AP , Perry KI . Requiring athletes to acknowledge receipt of concussion-related information and responsibility to report symptoms: a study of the prevalence, variation, and possible improvements . J Law Med Ethics . 2014 ; 42 ( 3 ): 297 - 313 .
11. Gibson TB , Herring SA , Kutcher JS , Broglio SP . Analyzing the effect of state legislation on health care utilization for children with concussion . JAMA Pediatr . 2015 ; 169 ( 2 ): 163 - 8 .
12. AA LR , Nelson LD , Connelly PK , Walter KD , MA MC. Sport-related concussion reporting and state legislative effects . Clin J Sport Med . 2016 ; 26 ( 1 ): 33 - 9 .
13. Institute o, Medicine: Sports-related concussions in youth: improving the science, changing the culture . 2015 ; https://www.nap.edu/resource/18377/concussionsRB.pdf. Accessed September , 2015 , 2015 .
14. National C , Athletic, Association. 2014-15 sports medicine handbook. 2015 ; http://www.ncaapublications.com/DownloadPublication. aspx?download=MD15.pdf. Accessed March 16 , 2015 .
15. Covassin T , Elbin RJ , Harris W , Parker T , Kontos A . The role of age and sex in symptoms, neurocognitive performance, and postural stability in athletes after concussion . Am J Sports Med . 2012 ; 40 ( 6 ): 1303 - 12 .
16. Covassin T , Moran R , Elbin RJ . Sex differences in reported concussion injury rates and time loss from participation: an update of the National Collegiate Athletic Association Injury Surveillance Program from 2004-2005 through 2008-2009 . J Athl Train . 2016 ; 51 ( 3 ): 189 - 94 .
17. Nelson LD , Guskiewicz KM , Marshall SW , et al. Multiple self-reported concussions are more prevalent in athletes with ADHD and learning disability . Clin J Sport Med . 2016a; 26 ( 2 ): 120 - 7 .
18. Morgan CD , Zuckerman SL , Lee YM , et al. Predictors of postconcussion syndrome after sports-related concussion in young athletes: a matched case-control study . J Neurosurg Pediatr . 2015 ; 15 ( 6 ): 589 - 98 .
19. Babcock L , Byczkowski T , Wade SL , Ho M , Mookerjee S , Bazarian JJ . Predicting postconcussion syndrome after mild traumatic brain injury in children and adolescents who present to the emergency department . JAMA Pediatr . 2013 ; 167 ( 2 ): 156 - 61 .
20. Guskiewicz K , Weaver N , Padua D , Garrett WJ . Epidemiology of concussion in collegiate and high school football players . Am J Sports Med . 2000 ; 28 ( 5 ): 643 - 50 .
21. Knox CL , Comstock RD , McGeehan J , Smith GA . Differences in the risk associated with head injury for pediatric ice skaters, roller skaters, and in-line skaters . Pediatrics . 2006 ; 118 ( 2 ): 549 - 54 .
22. Kerr ZY , Zuckerman SL , Wasserman EB , Covassin T , Djoko A , Dompier TP . Concussion symptoms and return to play time in youth, high school, and college American football athletes . JAMA Pediatr . 2016a ; 170 ( 7 ): 647 - 53 .
23. Mihalik JP , Register-Mihalik J , Kerr ZY , Marshall SW , McCrea MC , Guskiewicz KM . Recovery of posttraumatic migraine characteristics in patients after mild traumatic brain injury . Am J Sports Med . 2013 ; 41 ( 7 ): 1490 - 6 .
24. Iverson GL , Gardner AJ , Terry DP , et al. Predictors of clinical recovery from concussion: a systematic review . Br J Sports Med . 2017 ; 51 ( 12 ): 941 - 8 .
25. Sandel N , Schatz P , Goldberg K , Lazar M . Sex-based differences in cognitive deficits and symptom reporting among acutely concussed adolescent lacrosse and soccer players . Am J Sports Med . 2016 ; 45 ( 4 ): 937 - 44 .
26. Covassin T , Stearne D , Elbin R . Concussion history and postconcussion neurocognitive performance and symptoms in collegiate athletes . J Athl Train . 2008 ; 43 ( 2 ): 119 - 24 .
27. Iverson GL , Gaetz M , Lovell MR , Collins MW . Cumulative effects of concussion in amateur athletes . Brain Inj . 2004 ; 18 ( 5 ): 433 - 43 .
28. Gardner RM , Yengo-Kahn A , Bonfield CM , Solomon GS . Comparison of baseline and post-concussion ImPACT test scores in young athletes with stimulant-treated and untreated ADHD . Phys Sportsmed . 2017 ; 45 ( 1 ): 1 - 10 .
29. Field M , Collins MW , Lovell MR , Maroon J . Does age play a role in recovery from sports-related concussion? A comparison of high school and collegiate athletes . J Pediatr . 2003 ; 142 ( 5 ): 546 - 53 .
30. Brown DA , Elsass JA , Miller AJ , Reed LE , Reneker JC . Differences in symptom reporting between males and females at baseline and after a sports-related concussion: a systematic review and meta-analysis . Sports Med . 2015 ; 45 ( 7 ): 1027 - 40 .
31. Covassin T , Schatz P , Swanik CB . Sex differences in neuropsychological function and post-concussion symptoms of concussed collegiate athletes . Neurosurgery . 2007 ; 61 ( 2 ): 345 - 50 . discussion 350- 341
32. Ellis MJ , Ritchie LJ , Koltek M , et al. Psychiatric outcomes after pediatric sports-related concussion . J Neurosurg Pediatr . 2015 ; 16 ( 6 ): 709 - 18 .
33. Kerr ZY , Register-Mihalik JK , Kroshus E , Baugh CM , Marshall SW . Motivations associated with nondisclosure of self-reported concussions in former collegiate athletes . Am J Sports Med . 2016b; 44 ( 1 ): 220 - 5 .
34. Kuhn A , Zuckerman S , Yengo-Kahn A , et al. Factors associated with playing through a concussion . Clin Neurosurg . 2017 ; 64 ( Suppl 1 ): 211 - 216 . https://doi. org/10.1093/neuros/nyx294.
35. Dompier TP , Kerr ZY , Marshall SW , et al. Incidence of concussion during practice and games in youth, high school, and collegiate American football players . JAMA Pediatr . 2015 ; 169 ( 7 ): 659 - 65 .
36. Zuckerman SL , Totten D , Rubel K , et al. Mechanisms of injury as a diagnostic predictor of sport-related concussion severity in football, basketball, and soccer: results from a regional concussion registry . Neurosurgery. 2016c; 63(Suppl 1 ): 169 .
37. Immediate post -concussion assessment testing (ImPACT) test: Technical manual . . 2012 ; https://www.impacttest.com/pdf/ImPACTTechnicalManual. pdf. Accessed April 6 , 2013 .
38. Kuhn AW , Solomon GS . Supervision and computerized neurocognitive baseline test performance in high school athletes: an initial investigation . J Athl Train . 2014 ; 49 ( 6 ): 800 - 5 .
39. Daneshvar DH , Nowinski CJ , McKee AC , Cantu RC . The epidemiology of sport-related concussion . Clin Sports Med . 2011 ; 30 ( 1 ): 1 - 17 .
40. McCrory P , Meeuwisse W , Johnston K , et al. Consensus statement on concussion in sport: the 3rd International Conference on Concussion in Sport held in Zurich, November 2008 . Br J Sports Med . 2009 ; 43 ( Suppl 1 ): i76 - 90 .
41. McCrory P , Meeuwisse WH , Aubry M , et al. Consensus statement on concussion in sport: the 4th International Conference on Concussion in Sport held in Zurich, November 2012 . Br J Sports Med . 2013 ; 47 ( 5 ): 250 - 8 .
42. Steiner ME , Berkstresser BD , Richardson L , Elia G , Wang F . Full-contact practice and injuries in college football . Sports Health . 2016 ; 8 ( 3 ): 217 - 23 .
43. Hootman JM , Dick R , Agel J . Epidemiology of collegiate injuries for 15 sports: summary and recommendations for injury prevention initiatives . J Athl Train . 2007 ; 42 ( 2 ): 311 - 9 .
44. Iverson GL , Lovell MR , Collins MW . Interpreting change on ImPACT following sport concussion . Clin Neuropsychol . 2003 ; 17 ( 4 ): 460 - 7 .
45. Hearps SJ , Takagi M , Babl FE , et al. Validation of a score to determine time to postconcussive recovery . Pediatrics . 2017 ; 139 ( 2 )
46. Feddermann-Demont N , Echemendia RJ , Schneider KJ. , et al. What domains of clinical function should be assessed after sport-related concussion? A systematic review . Br J of Sport Med . 2017 ; 51 ( 11 ): 903 - 18 .
47. Dunn O. Multiple comparisons among means . J Am Stat Assoc . 1961 ; 56 ( 293 ): 52 - 64 .
48. Heyer GL , Schaffer CE , Rose SC , Young JA , McNally KA , Fischer AN . Specific factors influence postconcussion symptom duration among youth referred to a sports concussion clinic . J Pediatr . 2016 ; 174 : 33 - 8 : e32 .
49. Iverson G . Predicting slow recovery from sport-related concussion: the new simple-complex distinction . Clin J Sport Med . 2007 ; 17 ( 1 ): 31 - 7 .
50. McCrory P , Meeuwisse W , Dvorak J , et al. Consensus statement on concussion in sport-the 5(th) international conference on concussion in sport held in Berlin, October 2016 . Br J Sports Med . 2017 ; 51 ( 11 ): 838 - 47 .
51. Zemek R , Barrowman N , Freedman SB , et al. Clinical risk score for persistent Postconcussion symptoms among children with acute concussion in the ED . JAMA. 2016 ; 315 ( 10 ): 1014 - 25 .
52. Zuckerman SL , Yengo-Kahn AM , Buckley TA , Solomon GS , Sills AK , Kerr ZY . Predictors of postconcussion syndrome in collegiate student-athletes . Neurosurg Focus . 2016 ; 40 ( 4 ): E13 .
53. Silverberg ND , Iverson GL , McCrea M , Apps JN , Hammeke TA , Thomas DG . Activity-related symptom exacerbations after pediatric concussion . JAMA Pediatr . 2016 ; 170 ( 10 ): 946 - 53 .
54. Abrahams S , Fie SM , Patricios J , Posthumus M , September AV . Risk factors for sports concussion: an evidence-based systematic review . Br J Sports Med . 2014 ; 48 ( 2 ): 91 - 7 .
55. Nelson LD , Guskiewicz KM , Barr WB , et al. Age differences in recovery after sport-related concussion: a comparison of high school and collegiate athletes . J Athl Train. 2016b;51 ( 2 ): 142 - 52 .
56. Jones CM , Griffiths PC , Mellalieu SD . Training load and fatigue marker associations with injury and illness: a systematic review of longitudinal studies . Sports Med . 2017 ; 47 ( 5 ): 943 - 74 .
57. McCrea M , Hammeke T , Olsen G , Leo P , Guskiewicz K. Unreported concussion in high school football players: implications for prevention . Clin J Sport Med . 2004 ; 14 ( 1 ): 13 - 7 .
58. Broglio SP , Eckner JT , Kutcher JS . Field-based measures of head impacts in high school football athletes . Curr Opin Pediatr . 2012 ; 24 ( 6 ): 702 - 8 .
59. Silverberg ND , Lange RT , Millis SR , et al. Post-concussion symptom reporting after multiple mild traumatic brain injuries . J Neurotrauma . 2013 ; 30 ( 16 ): 1398 - 404 .
60. Coombs W , Algina J , Altman D . Univariate and multivariate omnibus hypothesis tests selected to control type I error rates when population variances are not necessarily equal . Rev Educ Res . 1996 ; 66 ( 2 ): 137 - 79 .