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. Author manuscript; available in PMC: 2025 Oct 1.
Published in final edited form as: Clin Neuropsychol. 2024 Feb 18;38(7):1683–1706. doi: 10.1080/13854046.2024.2315735

The Sport Concussion Assessment Tool: A Multidimensional Symptom Model for Detecting Elevated Post-Concussion Symptoms

Eric O Ingram 1, Justin E Karr 1
PMCID: PMC11330539  NIHMSID: NIHMS1978177  PMID: 38369485

Abstract

Objective:

Investigate whether a four-factor model of post-concussion symptoms (i.e., cognitive, physical, affective, and sleep-arousal) aids in identifying student-athletes with persistent concerns not reflected by a total symptom score.

Method:

Collegiate student-athletes (N=32,066) from the Concussion Assessment Research and Education consortium completed the Sport Concussion Assessment Tool, 3rd edition Symptom Evaluation at baseline and two post-injury follow-ups (i.e., beginning RTP and 6-month). Confirmatory factor analysis was used to compare a one- and four-factor model of post-concussion symptoms. Normative reference data were compared across stratifications (e.g., sex, prior concussions, and number of pre-existing conditions) using Mann-Whitney U tests, and elevation rates (i.e., ≥84th percentile) for subscales and the total score were recorded.

Results:

The four-factor model fit well before and after injury (CFIs>.95). Greater symptom severity on the subscale and total scores was associated with female sex (ps<.001, r range: .07 to .14) and more pre-existing conditions (ps<.001, η2 range: .01 to .04), while having more prior concussions was only related to total symptom scores (ps<.001, η2<.01). After a concussion, a sizeable portion of student-athletes (i.e., RTP=11.8%; 6-month=8.3%) had subscale elevations despite no total score elevation. Physical subscale elevations at RTP were the most common (i.e., 11.9%), driven by head and neck pain.

Conclusion:

After a sport-related concussion a four-factor symptom model can be used to assess persistent symptoms in collegiate student-athletes. Identifying athletes with domain-specific elevations may help clinicians identify areas for further assessment and, in some cases, personalized rehabilitation plans.

Keywords: Sports medicine; brain concussion; post-concussion syndrome; factor analysis, statistical; clinical assessment/grading scales

According to the Concussion in Sport Group’s (CISG) consensus definition (Patricios et al., 2023), a sport-related concussion (SRC) is a type of traumatic brain injury from an impulsive force transmitted to the brain during sport or exercise-related activities. Symptoms may present immediately or evolve over several hours; but, on average, individuals will see symptoms gradually resolve within 14 days (Putukian et al., 2023). Persistent symptoms (i.e., >4 weeks after injury; Patricios et al., 2023) may be due to pre-existing or concussion-related factors and are seen in around 30% of children and adults (Chadwick et al., 2022; Wiebe et al., 2022; Yeates et al., 2023). Athletes of all ages are susceptible to experiencing SRCs and the United States Centers for Disease Control and Prevention estimates that 1.6 to 3.8 million sport or recreation-related concussions occur annually (Langlois et al., 2006). Studying SRCs in student-athletes is important due to their high prevalence, and the brain being in a vulnerable state of maturation until around age 25 (Arain et al., 2013). In line with this knowledge, clinicians and researchers strive to understand the full impact of concussions and head injuries on young student-athletes.

Following a suspected SRC, post-injury assessments often include symptom questionnaires and tests of cognitive functioning that are compared to preseason baseline scores (Putukian et al., 2023). Primarily, clinical recovery from a concussion is tracked through symptom questionnaires with return-to-play (RTP) and return-to-learn decisions made, in part, based on the reduction of symptoms, although light physical activity that does not exacerbate symptoms is recommended in the early stages of recovery (Patricios et al., 2023; Putukian et al., 2023). These post-concussion-like symptoms include any combination of physical, mood, cognitive, sleep, and headache-related sequelae at varying levels of severity (Collins et al., 2014; Reynolds et al., 2014). The most common symptoms of concussion (e.g., headaches, dizziness, concentration/memory problems, irritability, fatigue, etc.) can lower quality of life and may persist past three months (Lagacé-Legendre et al., 2021).

The Sport Concussion Assessment Tool, 3rd edition (SCAT3) contains a self-report symptom questionnaire and objective measures of cognition, balance, and neurological functioning. The SCAT is the most effective evaluation in differentiating between concussed and non-concussed athletes in the acute phase of injury and is recommended by the CISG, for the concussion in sport group (Patricios et al., 2023). The SCAT-3 Symptom Evaluation is particularly useful and has the largest effect size, compared to balance and cognitive tests, in differentiating injured and non-injured athletes 24 hours after injury (Chin et al., 2016). The SCAT3 Symptom Evaluation contains 22 items rated on their current severity from None (0) to Severe (6).

Although research suggests an important link between symptom monitoring, symptom management, and return-to-learn/play decisions (McAvoy et al., 2020), it is difficult to determine which variables underlie elevations in post-concussion symptoms after injury. Many factors are known to be associated with endorsing more, or longer-lasting, post-concussion symptoms, including female sex (Anderson & Jordan, 2021; Brown et al., 2015), prior concussions (Aggarwal et al., 2020; Chrisman et al., 2013; Morgan et al., 2015; Zuckerman et al., 2016), concurrent mental health symptoms/diagnoses (Iverson et al., 2017; G. A. Thomas et al., 2021), greater acute symptoms (Makdissi et al., 2010; Putukian et al., 2021; Reynolds et al., 2014; Zuckerman et al., 2016), and pre-existing health condition (Chin et al., 2016). Despite the associations between these risk factors and post-concussion symptoms, there are mixed results when using these variables to predict recovery from an SRC. The most consistently supported predictors of prolonged recovery are pre-existing conditions, particularly mental health concerns, and acute symptom severity (Iverson et al., 2017; Meehan et al., 2013, 2014; Putukian et al., 2021, 2023).

Multidimensional symptom models are one approach to extracting greater meaning from symptom questionnaires. Because post-concussion symptoms are variable, non-specific, and influenced by a wide variety of factors, using a total score symptom interpretation (i.e., assuming a one-factor model) ignores the individual nuance of an athlete’s experience. In contrast, a multidimensional interpretation (i.e., a multifactor model) adds personalization to post-injury evaluations by examining the type of symptoms that occur, rather than the overall severity of symptoms. By devising symptom factors or subscales, researchers can group symptoms during evaluations to determine the specific areas in need of intervention. One model derived from the Post-Concussion Symptom Scale (PCSS) has somewhat consistent support for a four-factor model of cognitive, sleep, emotional, and physical/somatic symptoms (Joyce et al., 2015; Karr & Iverson, 2020; Kontos et al., 2012; Merritt & Arnett, 2014). This four-factor model is advantageous for its clinical utility compared to a total score (one-factor) approach, meaning it may better reflect the everyday symptom experience of injured student-athletes rather than their overall symptom burden. Each dimension of the four-factor symptom model examines subjective difficulties a student-athlete may have within specific functional domains, such as emotional distress/instability, cognitive/academic performance, balance impairments, and sleep disturbance. Research on the SCAT3 Symptom Evaluation’s latent structure has supported a bifactor model, through which all items load onto a general factor and subsets of items co-load onto several specific factors (i.e., vestibulo-ocular, headache, sensory, fatigue, cognitive, and emotion symptom; Brett et al., 2020). Further research is needed to determine whether a unidimensional versus multidimensional approach is more useful for addressing clinical outcomes and recovery after an SRC.

Consider a student-athlete whose total score on the SCAT3 Symptom Evaluation decreases from 30 immediately after injury to 8 at a two-week follow-up. In such a case, the student-athlete may be considered recovered and cleared for RTP if the score of 8 is reflective of their baseline pre-injury symptom burden. However, symptoms within a specific factor may have remained elevated while the severity of other unrelated symptoms decreased. Thus, the overall symptom burden may have improved while symptoms in a specific domain (e.g., sleep disturbance) remained elevated. This athlete could be cleared for RTP despite endorsing symptoms that continue to interfere with their everyday functioning. Multidimensional models may be an improvement over a total score interpretation in identifying specific functional impairments or cases of persistent post-concussion symptoms where student-athletes are elevated on a subscale, but not the total score.

To address this gap in the literature on SRC assessment, evaluation, and outcomes, this study (1) determined whether a one- or four-factor symptom model (i.e., cognitive, physical, sleep-arousal, affective ; Karr & Iverson, 2020; Merritt & Arnett, 2014) had a superior fit for the SCAT3 Symptom Evaluation, (2) prepared normative reference data for student-athletes stratified by sex, prior concussions, and pre-existing conditions, and (3) evaluated whether a multidimensional symptom approach identified more student-athletes with persistent symptoms than the total score alone. We hypothesized that (1) confirmatory factor analyses would show the four-factor model to have superior fit over a one-factor model before and after injury, (2) female sex, prior concussions, and pre-existing conditions would be associated with greater symptom severity, and (3) a sizeable portion of student-athletes experiencing an SRC will be elevated on specific subscales but not the total symptom score at RTP, relative to a baseline normative sample matched on sex, prior concussions, and number of pre-existing conditions.

Materials and Methods

This study involved a large, longitudinal dataset of student-athletes from the National Collegiate Athletic Association and the United States Department of Defense Concussion Assessment, Research, and Education (CARE) Consortium (Broglio et al., 2017). The CARE Consortium was a large, multisite study of concussions in collegiate student-athletes and military service cadets at baseline and post-injury time points (e.g., <6 hours, 24-48 hours, asymptomatic or cleared to begin RTP, cleared for unrestricted RTP, and 6 months). The CARE Consortium aimed to study the course of clinical and neurobiological recovery after concussion for student-athletes and military personnel (Broglio et al., 2017). This compiled database enables the longitudinal study of post-concussion outcomes and potentially associated neurobiological mechanisms of recovery.

Participants

Participants from the CARE consortium database were included in the study if they were administered the SCAT3 Symptom Evaluation during their baseline assessment (N=32,814), when cleared to begin RTP procedures (i.e., not yet cleared for unrestricted RTP; n=2,570), or when they were 6 months post-injury (n=1,788). Participants were then excluded from the baseline, RTP, and 6-month analyses, respectively, if they endorsed a history of autism (n=53, n=8, n=4), meningitis (n=137, n=17, n=10), seizure or epilepsy (n=128, n=19, n=13), brain surgery (n=57, n=8, n=5), stroke (n=46, n=7, n=4), memory disorder (n=233, n=29, n=16), Parkinson’s disease (n=73, n=8, n=4), Schizophrenia (n=35, n= 9, n=5), or bipolar disorder or manic episodes (n=76, n=16, n=10). These pre-existing conditions were selected as exclusion criteria due to being rare or more severe neurological or neurodevelopmental conditions for a college-aged population. Participants were also excluded for missing data on individual items of the SCAT3 Symptom Evaluation (n=7, n=0, n=0); or missing data on sex (n=142, n=24, n=9), which was used to stratify the normative data.

Overall, 32,066, 2,483, and 1,740 participants were included for the final baseline, RTP, and 6-month time point analyses, respectively. The median time since injury was 7 days (M=9.88, SD=11.4, interquartile range: 4 to 12 days, missingness=37.7%) and 181 days (M=176.96, SD=39.4, interquartile range: 172 to 190 days, missingness=29.1%) at the RTP and 6-month follow-ups, respectively. The sum of participants excluded may exceed the difference between the initial sample and the sample ultimately included for analyses due to participants meeting multiple exclusion criteria. At baseline, the sample had a mean age of 19.5 years (SD=1.5, range: 16 to 30) and was 63.0% male, 61.5% White, 8.9% Hispanic, 39.7% Freshman class, and 11.5% Football players. Full demographic information, including socioeconomic status, can be seen in Table 1 at each time point examined.

Table 1.

Participant Demographics

Baseline
(n=32,066)		 Return-to-Play
(n=2,483)		 6-Months
(n=1,740)
n (%) n (%) n (%)
Age M=19.5, SD=1.5
Range: 16 to 30		 M=19.4, SD=1.4
Range: 17 to 25		 M=19.5, SD=1.4
Range: 17 to 27
Sex
 Male 20,211 (63.0) 1,515 (61.0) 1,049 (60.3)
 Female 11,855 (37.0) 968 (39.0) 691 (39.7)
Race
 White 19,713 (61.5) 1,430 (57.6) 1,030 (59.2)
 African American 3,507 (10.9) 355 (14.3) 231 (13.3)
 Asian 1,220 (3.8) 81 (3.3) 65 (3.7)
 Multiracial 1,533 (4.8) 143 (5.8) 102 (5.9)
 No Response/Unknown 6,093 (19.0) 474 (19.1) 312 (17.9)
Ethnicity
 Hispanic or Latino 2,867 (8.9) 206 (8.3) 152 (8.7)
 Not Hispanic or Latino 25,652 (80.0) 1,996 (80.4) 1,423 (81.8)
 No Response/Unknown 3,547 (11.0) 281 (11.3) 165 (9.5)
Class
 Freshman 12,727 (39.7) 867 (34.9) 581 (33.4)
 Sophomore 5,511 (17.2) 568 (22.9) 446 (25.6)
 Junior 4,999 (15.6) 377 (15.2) 279 (16.0)
 Senior 3,089 (9.6) 218 (8.8) 154 (8.9)
 5th Year Senior 358 (1.1) 31 (1.2) 16 (0.9)
 Graduate Student 168 (.5) 11 (0.4) 5 (0.3)
 No Response/Unknown 5,214 (16.3) 411 (16.6) 259 (14.9)
Prior Concussion Status
 None/No Response 24,702 (77.0) 1,577 (63.5) 1,098 (63.1)
 One 5,612 (17.5) 650 (26.2) 460 (26.4)
 Two or more 1,752 (5.5) 256 (10.3) 182 (10.5)
Sport Participation
 Football 3,682 (11.5) 438 (17.6) 278 (16.0)
 Soccer 1,858 (5.8) 224 (9.0) 158 (9.1)
 Basketball 1,147 (3.6) 123 (5.0) 89 (5.1)
 Volleyball 632 (2.0) 93 (3.7) 59 (3.4)
 Other 11,891 (37.1) 712 (28.7) 458 (26.3)
 No Response/Unknown 12,856 (40.1) 893 (36.0) 698 (40.1)
Family Income Range
 $0-60,000 3,265 (10.1) 258 (10.4) 176 (10.1)
 $60,001-120,000 7,066 (22.0) 551 (22.2) 400 (23.0)
 $120,001-180,000 4,331 (13.5) 304 (12.2) 242 (13.9)
 $180,001-240,000 2,601 (8.1) 213 (8.6) 156 (9.0)
 $240,001-300,000 1,309 (4.1) 117 (4.7) 82 (4.7)
 $300,001+ 2,265 (7.1) 155 (6.2) 115 (6.6)
 No response 11,229 (35.0) 885 (35.6) 569 (32.7)
Pre-existing health conditions
 Headaches (past 3 months) 5,882 (18.3) 710 (28.6) 535 (30.7)
 Headache disorder 262 (0.8) 35 (1.4) 31 (1.8)
 Diabetes mellitus (Type 1 or 2) 124 (0.4) 16 (0.6) 12 (0.7)
 Sleep disorder 194 (0.6) 18 (0.7) 13 (0.7)
 Vestibular disorder 0 (0) 0 (0) 0 (0)
 Meniere's disease 0 (0) 0 (0) 0 (0)
 Learning disorder 374 (1.2) 59 (2.4) 36 (2.1)
 ADHD 976 (3.0) 144 (5.8) 81 (4.7)
 Depression 466 (1.5) 67 (2.7) 45 (2.6)
 Other psychiatric disorder 317 (1.0) 46 (1.9) 31 (1.8)
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Note. ADHD=Attention-deficit/hyperactivity disorder. M denotes the mean value and SD the standard deviation.

Measures

The SCAT3 is a broad evaluation that can be used for on-field concussion assessments and contains measures of symptom severity, cognitive ability, neurological status, and balance (Chin et al., 2016). For this study, only the self-report symptoms were used in analyses. The SCAT3 Symptom Evaluation contains 22 items rated on a seven-point scale of current severity ranging from None (0) to Severe (6). For example, some items ask respondents to rate the current severity of symptoms such as “Blurred vision” or “Drowsiness”. All items are summed to create a total symptom severity score that ranges from 0 to 132. The SCAT3 Symptom Evaluation total symptom severity score has a high internal consistency reliability estimate per prior research (α=.85; Robinson & McElhiney, 2017).

Procedure

The CARE consortium’s data is stored in the Federal Interagency Traumatic Brain Injury Research (FITBIR) database (Broglio, 2017). The CARE data was accessed upon approval by FITBIR. Since 2014, student-athletes from approximately 30 participating universities and military academies were examined regularly at preseason baseline, and following a concussion (i.e., post-injury time points: <6 hours, 24-48 hours, asymptomatic or cleared to begin RTP, cleared for unrestricted RTP, and 6 months). The consensus definition was applied in determining the presence of a concussion (Carney et al., 2014), which was diagnosed at the determination of the research and medical staff at each testing site (Broglio et al., 2017). Test administrators (e.g., athletic trainers, team physicians, researchers, etc.) facilitated the evaluations involving any standard neurocognitive assessment in addition to the Standardized Assessment of Concussion, the Balance Error Scoring System, and the SCAT3 Symptom Evaluation. In this study, demographic and health history data were compiled with SCAT3 Symptom Evaluation scores and examined cross-sectionally at baseline and post-injury time points.

Statistical Analyses

Using symptom data, confirmatory factor analyses tested a four-factor model of post-concussion symptoms, which included cognitive, physical, affective, and sleep-arousal symptom factors (Karr & Iverson, 2020), when participants were at baseline, cleared to begin RTP, and 6-months post-injury. The individual items of the SCAT-3 were assigned to each factor based on their conceptual and theoretical fit with each scale from the pre-existing model (see Table 3) (Karr & Iverson, 2020; Merritt & Arnett, 2014). The items loading onto the cognitive factor included Difficulty concentrating, Difficulty remembering, Confusion, Feeling slowed down, Feeling like “in a fog”, and “Don’t feel right”. The items loading onto the physical factor included Neck pain, Headache, “Pressure in head”, Nausea or vomiting, Balance problems, Sensitivity to light, Sensitivity to noise, Dizziness, and Blurred vision. The items loading onto the affective factor included More emotional, Irritability, Nervous or anxious, and Sadness. Lastly, the items loading onto the sleep-arousal factor included Fatigue or low energy, Drowsiness, and Trouble falling asleep.

Table 3.

Factor Loadings, Reliability Estimates, and Inter-Factor Correlations for the Sport Concussion Assessment Tool-3 Symptom Evaluation Four-Factor Model

Baseline
(n=32,066)		 Return-to-Play
(n=2,483)		 6-Months
(n=1,740)
λ e λ e λ e
Cognitive Symptoms ω=.86, α=.86 ω=.83, α=.82 ω=.72, α=.70
 Difficulty concentrating .85 .28 .86 .27 .86 .26
 Difficulty remembering .85 .28 .77 .40 .80 .36
 Confusion .87 .25 .84 .30 .63 .61
 Feeling slowed down .88 .22 .90 .20 .90 .19
 Feeling like ‘in a fog’ .86 .26 .86 .26 .85 .28
 “Don’t feel right” .88 .23 .92 .16 .83 .32
Physical Symptoms ω=.79, α=.78 ω=.83, α=.79 ω=.60, α=.55
 Neck pain .67 .56 .56 .68 .66 .56
 Headache .76 .43 .85 .28 .79 .37
 “Pressure in head” .82 .33 .84 .30 .86 .26
 Nausea or vomiting .75 .44 .76 .42 .61 .63
 Balance problems .76 .43 .82 .33 .51 .74
 Sensitivity to light .75 .44 .84 .29 .75 .44
 Sensitivity to noise .82 .33 .84 .30 .73 .46
 Dizziness .88 .23 .87 .24 .83 .31
 Blurred vision .78 .39 .73 .46 .71 .50
Affective Symptoms ω=.83, α=.83 ω=.79, α=.78 ω=.73, α=.72
 More emotional .90 .19 .91 .18 .87 .25
 Irritability .85 .27 .91 .17 .88 .23
 Nervous or anxious .82 .33 .83 .31 .85 .27
 Sadness .92 .16 .90 .20 .87 .24
Sleep-Arousal Symptoms ω=.78, α=.69 ω=.76, α=.71 ω=.69, α=.61
 Fatigue or low energy .89 .20 .91 .17 .90 .20
 Drowsiness .86 .26 .87 .25 .82 .33
 Trouble falling asleep .63 .60 .75 .45 .68 .54
Inter-factor Correlations r SE r SE r SE
 Cognitive-Physical .83 .01 .92 .02 .75 .03
 Cognitive-Affective .77 .01 .77 .04 .75 .04
 Cognitive-Sleep-Arousal .87 .00 .86 .02 .84 .03
 Physical-Affective .68 .01 .64 .04 .54 .04
 Physical-Sleep-Arousal .69 .01 .78 .02 .74 .03
 Affective-Sleep-Arousal .76 .01 .70 .04 .73 .04
Total Score ω=.91, α=.90 ω=.91, α=.90 ω=.81, α=.80
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Note. Estimates of the internal consistency of subscale and total scores are denoted by omega (ω) and alpha (α) values. The inter-factor correlations between subscales are denoted with r, with the standard error of these correlations denoted by SE. Individual loadings of response items onto a subscale factor are denoted by λ, with the corresponding error variances denoted by e.

Confirmatory factor analyses were conducted in RStudio® using the lavaan package (Rosseel, 2012) to assess the fit of the four-factor model for the SCAT3 Symptom Evaluation. The SCAT3 Symptom Evaluation is comprised of ordinal items, so in place of maximum likelihood estimation, weighted least squares with mean and variance adjusted (WLSMV) was used instead. WLSMV is designed for ordinal data and assumes that categorical variables have an underlying normal latent distribution (Li, 2016). Chi-square goodness-of-fit statistics were reported, for which a non-significant value indicates a good fit; however, this approach is biased by the large sample size of the study (Shi et al., 2018). To accommodate this, alternative fit indices were calculated, including the comparative fit index (CFI), Tucker Lewis Index (TLI), and root mean square error of approximation (RMSEA). Standard conventions for the CFI and TLI suggest values greater than .90 indicate acceptable model fit (Bentler & Bonett, 1980) and values greater than .95 indicate good model fit (Hu & Bentler, 1999). For the RMSEA, values close to .06 indicate an acceptable fit and values less than .05 indicate a good fit (Hu & Bentler, 1999; Kim et al., 2016). Estimates of internal consistency were obtained using both alpha (α) and omega (ω). Standard conventions state that values above ≥.70 and ≥.65 indicate acceptable reliability, and values ≥.85 and >.80 indicate strong reliability for alpha and omega, respectively (Kalkbrenner, 2021). To compare the utility of a one- versus four-factor model, a second confirmatory factor analysis was fitted with all 22 items loading onto a single factor representing the total score. The CFI was then compared to determine which factor structure had a better quantitative fit. A CFI difference between the one- and four-factor models ≥.01 suggested a significant improvement in model fit (Cheung & Rensvold, 2002), which was calculated for each of the model comparison time points.

A series of Mann-Whitney U tests were used to examine pairwise differences in baseline total and subscale symptom severity scores by sex and specific numbers of prior concussions and pre-existing conditions within each sex. An r statistic was computed as an associated effect size for the non-parametric statistic, calculated as Z/√N (Fritz et al., 2012). An r value of .10, .30, or .50 was interpreted as being a small, medium, or large effect size, respectively (Jacob Cohen, 1988). A series of Kruskal-Wallis H tests for non-parametric analyses of variance were conducted comparing baseline total and subscale symptom severity scores based on the number of prior concussions (i.e., 0, 1, and 2 or more) and pre-existing conditions (i.e., 0, 1, 2, and 3 or more). Pre-existing health conditions included self-reported history of headaches in the past three months, headache disorder, diabetes mellitus (type 1 or 2), sleep disorder, vestibular disorder, Meniere’s disease, learning disorder, attention deficit disorder or attention-deficit/hyperactivity disorder, depression, or any other psychiatric disorder (e.g., anxiety, eating disorders, post-traumatic stress disorder, etc.). See Table 1 for the self-report frequencies of preexisting conditions at each assessment time point. Symptom severity scores were compared based on the number of prior concussions and pre-existing conditions separately for males and females. A η2 statistic was calculated as an omnibus effect size based on the formula (H-k+1)/(n-k). A η2 value of .01, .06, or .14 corresponded to a small, moderate, or large effect size, respectively (Miles & Shevlin, 2001). A Bonferroni correction was used to account for multiple comparisons in differentiating the symptom severity of those with prior concussions and pre-existing conditions (Sedgwick, 2012). The critical value of .05 was divided by 6 (i.e., number of pairwise comparisons) to arrive at a threshold of <.008 for significant group differences based on pre-existing conditions.

The distribution of baseline, RTP, and 6-month symptom severity scores for student-athletes was obtained, and values that corresponded to the 50th, 75th, 84th, 91st, and 98th percentiles were recorded. To account for the relationships between demographic and health variables and symptom reporting, these percentiles were stratified by sex, prior concussions (i.e., zero, one, and two or more) and the total number of pre-existing conditions (i.e., zero, one, two, and three or more) for each sex. Therefore, the symptom percentiles for females and males with any number of prior concussions or pre-existing conditions were calculated separately. To determine post-injury elevation status on a subscale or total score, each student-athlete was matched into a norming group based on their sex, number of prior concussions, and number of pre-existing conditions. Student-athletes were considered elevated if their symptom severity score was equal to or greater than the 84th percentile for their respective norming group at baseline. The cutoff for symptom elevations was chosen, in part, based on consensus guidelines for labeling test scores that suggest results between the 9th and 24th percentile should be considered ‘low average’ (Guilmette et al., 2020). For an oppositely oriented scale, this would reflect the 76th percentile as a cutoff for a score outside of the normal range. The 84th percentile was chosen as a conservative midpoint within this range and corresponds to a score that is one standard deviation above the mean, which reflects a cutoff commonly applied in clinical practice. Elevation rates were calculated for each subscale as well as the total score, and separate elevation rates were calculated for exclusive subscale elevations. That is, the percentage of student-athletes who were elevated on any subscale without being elevated on the SCAT3 total score. Rates of overall and exclusive elevations were recorded for both the RTP and 6-month time points, as well as the frequency of student-athletes obtaining elevations in specific subscales.

Missing Data

Missing data on the 22 individual items of the SCAT3 Symptom Evaluation were observed for seven participants at baseline, and no participants at the post-injury time points. Because the percentage of examinees missing data on the SCAT3 Symptom Evaluation was well less than 1%, listwise deletion was used and the seven participants were excluded from analyses.

Results

The four-factor model of the SCAT3 Symptom Evaluation had a superior fit compared to the one-factor model for the full sample of student-athletes examined at preseason baseline (CFI=0.953; ΔCFI=.07), RTP (CFI=0.979; ΔCFI=.03), and 6-month (CFI=0.986; ΔCFI=.03) time points. See Table 2 for the full comparison of fit indices between the one-factor and four-factor models, including CFI, TLI, RMSEA, and chi-square statistics. Collectively, the CFI, TLI, RMSEA, and ΔCFI offer concordant evidence to suggest the four-factor model provides a better fit than the one-factor model before and after a concussion. The individual factor loadings and residuals are provided in Table 3 for the four-factor model at all time points. Internal consistency reliability estimates were also recorded for the total and subscale scores of the SCAT3 Symptom Evaluation. The reliability for the total score, cognitive, physical, sleep-arousal, and affective subscales suggest acceptable to strong reliability for the total and subscale symptom scores at preseason baseline and RTP (α range: .69 to .90; ω range: .76 to .91), with poorer reliability 6 months after injury (α range: .55 to .80; ω range: .60 to .81).

Table 2.

Comparison of a One- and Four-Factor Model of the Sport Concussion Assessment Tool-3 Symptom Evaluation

Baseline
(n=32,066)		 Return-to-Play
(n=2,483)		 6-Months
(n=1,740)
One-
Factor		 Four-
Factor		 One-
Factor		 Four-
Factor		 One-
Factor		 Four-
Factor
CFI .887 .953* .953 .979* .959 .986*
TLI .875 .946 .948 .976 .955 .984
RMSEA
[95% CI]		 .076 [.075, .076] .050 [.049, .050] .043 [.041, .045] .029 [.027, .032] .025 [.021, .028] .014 [.010, .019]
χ 2 38,706.06 16,247.09 1,165.69 636.08 429.52 276.55
p <.001 <.001 <.001 <.001 <.001 <.001
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Note. CFI=Comparative Fit Index. TLI=Tucker Lewis Index. RMSEA=Root Mean Square Error of Approximation. CI=Confidence Interval.

*

Indicates superior model fit demonstrated by a CFI difference ≥.01 between the one- and four-factor models

To examine whether baseline symptom reporting differed by sex and number of prior concussions and pre-existing conditions, the distributions of scores were stratified and compared using Mann-Whitney U Tests and Kruskal-Wallace H Tests. The omnibus tests showed significant differences in total symptom severity by sex (z=22.3, ps<.001, r=.12), prior concussions in males (H=19.4, p<.001, η2<.001) and females (H=14.1, p<001, η2=.001), as well as pre-existing conditions in males (H=639.4, p<.001, η2=.03) and females (H=447.8, p<.001, η2 =.04), although all had small effect sizes. The distribution of subscale symptom severity also differed significantly by sex (ps<.001, r range: .07 to .14) and pre-existing conditions in males (ps<.001, η2 range: .01 to .03) and females (ps<.001, η2 range: .02 to .04) with small to moderate effect sizes. However, subscale symptom severity differed by prior concussion status only for the physical subscale in both males (ps<.001, η2<.01) and females (ps<.001, η2<.01).

A series of Mann-Whitney U tests identified pairwise differences in severity scores between specific numbers of prior concussion (e.g., 0 vs. 1 concussion, 1 vs. 2 or more concussions, etc.) and pre-existing conditions (e.g., 0 vs. 1 condition, 1 vs. 2 conditions, etc.). See Table 4 for the full comparison of subscale and total symptom severity scores among each stratification. Tables 1-3 in the Supplementary Materials section show stratified normative reference data for each subscale and the total symptom severity score at baseline, RTP, and 6-Months, with reference data separated by individuals with zero, one, or two or more prior concussions. These normative reference tables reflect the differences derived from Kruskal-Wallace H and Mann-Whitney U tests. For example, the total symptom severity score for males with no prior concussions but 3 or more pre-existing conditions showed substantially greater symptom burden relative to those with no prior concussions and no pre-existing conditions (e.g., 84th percentile: no conditions=7, 3 or more conditions=24).

Table 4.

Comparison of Baseline Symptom Severity Differences by Sex, and Number of Prior Concussions/Pre-existing Conditions

Cognitive Physical Sleep-Arousal Affective Total
Z r Z r Z r Z r Z r
Males x Females (n=32,066) −11.9* −0.07 −16.4* −0.09 −15.9* −0.09 −25.1* −0.14 −22.3* −0.12
Males (K-W) H=431.4* η2=0.02 H=577.0* η2=0.03 H=416.5* η2=0.02 H=270.3* η2=0.01 H=639.4* η2=0.03
 0 vs. 1 condition (n=19,690) −18.2* −0.13 −22.052* −0.16 −19.3* −0.14 −14.9* −0.11 −23.1* −0.16
 0 vs. 2 conditions (n=16,663) −8.7* −0.07 −9.5* −0.07 −6.8* −0.05 −5.3* −0.04 −9.4* −0.07
 0 vs. 3 conditions (n=16,350) −8.2* −0.06 −7.1* −0.06 −4.6* −0.04 −6.4* −0.05 −7.5* −0.06
 1 vs. 2 conditions (n=3,861) −1.3 −0.02 −0.8 −0.01 −0.7 −0.01 −0.4 −0.01 −0.4 −0.01
 1 vs. 3 conditions (n=3,548) −3.8* −0.06 −2.4 −0.04 −1.0 −0.02 −3.0* −0.05 −3.1* −0.05
 2 vs. 3 conditions (n=521) −2.7* −0.12 −1.8 −0.08 −1.3 −0.06 −2.9* −0.13 −2.7* −0.12
Males (K-W) H=8.3 η2<.001 H=50.2* η2<.01 H=1.4 η2<.001 H=2.8 η2<.001 H=19.4* η2<.001
 0 vs. 1 concussion (n=19,141) −1.0 −0.01 −4.9* −0.04 −1.3 −0.01 −0.8 −0.01 −3.5* −0.03
 0 vs. 2 concussions (n=16,540) −2.8* −0.02 −5.7* −0.04 −1.2 −0.01 −0.9 −0.01 −3.1* −0.02
 1 vs. 2 concussions (n=4,741) −2.0 −0.03 −2.5 −0.04 −0.3 −0.004 −0.4 −0.01 −1.0 −0.01
Females (K-W) H=297.1* η2=0.02 H=405.4* η2=0.03 H=232.6* η2=0.02 H=228.2* η2=0.02 H=447.8* η2=0.04
 0 vs. 1 condition (n=11,260) −13.1* −0.12 −17.1* −0.16 −11.9* −0.11 −9.8* −0.09 −16.6* −0.16
 0 vs. 2 conditions (n=9,118) −8.4* −0.09 −9.4* −0.10 −8.2* −0.09 −8.4* −0.09 −11.1* −0.12
 0 vs. 3 conditions (n=8,893) −10.3* −0.11 −9.4* −0.10 −7.6* −0.08 −9.9* −0.10 −10.4* −0.11
 1 vs. 2 conditions (n=2,962) −2.2 −0.04 −1.7 −0.03 −2.7* −0.05 −3.6* −0.07 −3.9* −0.07
 1 vs. 3 conditions (n=2,737) −5.7* −0.11 −3.7* −0.07 −3.9* −0.08 −6.50* −0.12 −5.9* −0.11
 2 vs. 3 conditions (n=595) −3.5* −0.14 −2.0 −0.08 −1.9 −0.08 −3.3* −0.13 −3.0* −0.12
Females (K-W) H=8.4 η2=<.001 H=62.7* η2<.01 H=2.9 η2<.001 H=2.0 η2<.001 H=14.1* η2=.001
 0 vs. 1 concussion (n=11,173) −0.4 −0.004 −3.9* −0.04 −0.9 −0.01 −1.4 −0.01 −0.5 −0.01
 0 vs. 2+ concussions (n=9,914) −2.8* −0.03 −7.3* −0.07 −1.0 −0.01 −0.9 −0.01 −3.8* −0.04
 1 vs. 2+ concussions (n=2,623) −2.7* −0.05 −4.1* −0.08 −1.4 −0.03 −1.5 −0.03 −3.0* −0.06
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Note. All omnibus group comparisons were conducted using Kruskal-Wallis (K-W) H tests from which the η2 measure of effect size was calculated as (H-k+1)/(n-k). Pairwise comparisons were conducted with Mann-Whitney U tests which were used to derive the Z-statistic. The r effect size was calculated as Z/√N.

*

Indicates significance at p<.008 to account for multiple comparisons

The frequency of athletes obtaining elevated symptom severity scores was recorded at the time they were cleared to begin RTP procedures and the 6-month post-injury follow-up. Most student-athletes experienced no subscale or total symptom elevations when cleared to begin RTP (83.7%) and after 6 months (89.8%). To further examine the utility of a four- vs. one-factor approach, exclusive subscale elevations (i.e., elevations on a subscale but not the total score) were calculated and observed in 11.8% and 8.3% of student-athletes at RTP and 6-months, respectively (Table 5). Compared to the overall elevation rates, relatively fewer athletes had exclusive subscale elevations, and all symptom elevations were less common 6-months after injury (Table 6). AT RTP, the physical subscale was the most common exclusive elevation (i.e., 8.3%), compared to the cognitive (3.8%), sleep-arousal (1.0%), and affective (1.9%) domains. A single elevated subscale was the most common (i.e., RTP=9.7%, 6-month=7.1%) and it was increasingly uncommon for athletes to be elevated on 2 (i.e., RTP=3.8%, 6-month=2.0%), 3 (i.e., RTP=2.1%, 6-month=1.1%), or 4 (i.e., RTP=0.6%, 6-month=0.1%) subscales.

Table 5.

Comparison of Subscale and Total Score Elevation Rates for the Sport Concussion Assessment Tool-3 Symptom Evaluation

Total Sample Elevated on
Total Score
n (%) 		Not Elevated on
Total Score
n (%)
Return-to-Play (n=2,483) - -
Elevated on a Subscale (%) 111 (4.5) 293 (11.8)
Not Elevated on a Subscale (%) 0 2,079 (83.7)
6-Months (n=1,740) - -
Elevated on a Subscale (%) 32 (1.8) 145 (8.3)
Not Elevated on a Subscale (%) 0 1,563 (89.8)
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Note. Participants were considered elevated if their symptom scores matched or exceeded the 84th percentile of any subscale or the total symptom severity score at baseline (see Supplementary Materials).

Table 6.

Sport Concussion Assessment Tool-3 Total and Subscale Score Elevation Rates After Concussion

Cognitive Physical Sleep-
Arousal		 Affective Total
Score
n (%) n (%) n (%) n (%) n (%)
Return-to-Play (n=2,483)
 Elevation Rates 190 (7.7) 296 (11.9) 73 (2.9) 90 (3.6) 111 (4.5)
 Exclusive Elevation Rates 90 (3.8) 197 (8.3) 24 (1.0) 44 (1.9) -
6-Month (n=1,740)
 Elevation Rates 60 (3.4) 86 (4.9) 58 (3.3) 48 (2.8) 32 (1.8)
 Exclusive Elevation Rates 38 (2.2) 70 (4.0) 32 (1.8) 34 (2.0) -
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Note. A participant was considered elevated on a subscale or the total score if they matched or exceeded the 84th percentile for their norming group at baseline (see Supplementary Materials). Participants with exclusive elevations were elevated on at least one symptom subscale without being elevated on the Sport Concussion Assessment Tool-3 total score. Student-athletes may be elevated on multiple subscales at a time, given that they are not elevated on the total score.

Following initial analyses suggesting the physical subscale was the most likely to remain elevated after injury, post hoc analyses were conducted among participants elevated on the physical subscale when cleared to begin RTP, which was the time point when physical subscale elevations were the most pronounced. The endorsement rates (i.e., a rating of 1 or higher on an individual symptom) for items on the physical subscale show that Headache and Pressure in Head were the most frequently endorsed items at 77.7% and 56.9%, respectively. These symptoms were the only items on the physical subscale that were endorsed by the majority of student-athletes with a physical subscale elevation at RTP. The next most commonly endorsed symptom was Neck Pain at 37.6%, collectively indicating physical sequelae of head or neck injury.

Discussion

This study examined a four-factor model of post-concussion symptoms (i.e., cognitive, physical, sleep-arousal, and affective; Karr & Iverson, 2020; Merritt & Arnett, 2014) using the SCAT3 to develop normative reference data and assess its utility in identifying persistent post-concussion symptoms in collegiate student-athletes following a recent concussion. Self-reported symptoms are an integral part of concussion assessment and management, though clinicians typically focus on the overall severity of symptoms rather than the types of symptoms that may be problematic for an individual. Researchers estimate around 30% of student-athletes exhibit persistent symptoms after a concussion (Chadwick et al., 2022; Makdissi et al., 2013; Wiebe et al., 2022; Yeates et al., 2023), and a multidimensional approach to symptom interpretation may aid in identifying athletes with symptom elevations in a specific domain. The four-factor symptom model showed a superior fit to a one-factor model, which represented the total score, before and after injury. The normative reference data developed showed significant differences such that females and those with more pre-existing health conditions had higher symptom endorsement on subscales and the total score, while prior concussion status was significantly associated with total and physical symptom severity only. An interpretation of symptom subscales identified 11.8% of student-athletes who remained elevated on a subscale despite not having a total score elevation when beginning RTP, and elevations were most commonly observed on the physical subscale, driven by reported headache and neck pain.

Utility of Normative Reference Data

Concussion management guidelines emphasize heterogeneity in the clinical signs and symptoms a student-athlete may exhibit following an SRC (Doolan et al., 2012; McCrory et al., 2017). Additionally, post-concussion-like symptoms are non-specific to head injuries and common across unrelated disorders and diagnoses (Champigny et al., 2020; Garden & Sullivan, 2010; Ingram & Karr, 2022; Iverson, 2006; Iverson et al., 2015; Schneider, 2019; G. A. Thomas et al., 2021). Normative reference data may help clinicians distinguish between symptoms that emerge due to a recent head injury, as opposed to those that reflect personal characteristics (e.g., sex, prior concussions, and pre-existing conditions). Normative data may also benefit student-athletes who do not have baseline assessment data available for post-injury comparison. For student-athletes without a baseline reference score, normative data can be used to standardize clinical judgment.

For example, consider a female student-athlete with 3 pre-existing conditions (e.g., a learning disorder, generalized anxiety disorder, and headache disorder) and no prior concussions that has a total score of 10 on the SCAT3 Symptom Evaluation following concussion. The symptom severity score may be elevated compared to all student-athletes (i.e., 84th percentile; Supplementary Materials Table 1), or may be considered typical compared to female athletes with the 3 pre-existing health conditions (i.e., 50th percentile; Supplementary Materials Table 1). The normative reference data provided should not be used to dismiss their concerns but should be used as an aid to understand and contextualize symptoms. This female student-athlete may show a single domain of concern (e.g., cognitive=8, 91st percentile; physical=0; affective=1, 75th percentile; sleep-arousal=1, 50th percentile) that would otherwise be missed by examining the total score only. Elevated cognitive symptoms may reflect the preexisting learning disorder or may indicate domain-specific difficulties arising from their concussion and as such, the athlete may benefit from compensatory cognitive strategies that help her focus and retain information during coursework. However, in general, RTP decisions should consider multiple sources of information (e.g., athlete’s perception of recovery, degree of interference that symptoms have on functioning, objective cognitive test performances), and not rely on a single symptom subscale score alone.

Elevated Symptom Severity Scores

Using normative data to gauge symptom elevations, the study found that 16.3% of student-athletes (i.e., 404 of 2,483) had lingering symptoms when cleared to begin the RTP process after a recent concussion. Most of these student-athletes (i.e., n=293) would not be identified using the total score alone. Although guidelines state that individuals should return to competition when asymptomatic (Doolan et al., 2012; McCrory et al., 2017; Patricios et al., 2023), athletes may participate in light physical activity if their symptoms remain mild and are not exacerbated. Thus, these findings do not suggest individuals necessarily require a delayed RTP, but rather inform RTP and clinical decision-making for the types of symptoms commonly persisting after a concussion.

Notably, and perhaps counterintuitively, the RTP normative reference data (Supplementary Materials Table 2) generally reflected lower symptom severity scores than the baseline reference data (Supplementary Materials Table 1). This important finding highlights some of the unique context clinicians may consider when conducting sport-related concussion assessments, such as hesitancy in athletes to endorse symptoms that may potentially delay their RTP. Student-athletes at baseline may also report symptoms from other non-specific causes, such as headaches or sleep problems that occur commonly among college students; whereas, at RTP, they may be reporting only symptoms that they attribute to their recent concussion. That is, they may have sleep problems in general but not personally attribute these problems to their recent concussion, and therefore not report it on the SCAT3. Another possible explanation for the contradictory finding is that athletes are likely more physically active in school at RTP compared to when baseline examinations occur before the season, and rehabilitation efforts for ongoing symptoms may be successfully reducing symptom severity scores. As symptom severity scores decrease from baseline to RTP, elevated scores are more notable and uncommon. An athlete without a prior concussion having a total RTP symptom severity score of 11 exceeds the 84th percentile of the baseline normative data (Supplementary Materials Table 1) but is above the 95th percentile based on the RTP normative reference data (Supplementary Materials Table 2). Clinicians may wish to compare athletes to what is typical when RTP decisions are made, although there is heterogeneity in the progression of RTP across athletic departments, making these data more variable in their time and context of measurement than preseason baseline data.

The normative data from athletes at the 6-month follow-up may be a useful reference for the typical long-term recovery of an athlete from SRC. That said, these scores are also variable, in that the recovery experiences and interventions received vary across athletic departments. The 6-month symptom scores again followed the trend of being lower than baseline scores and even RTP scores. That means an athlete that would be elevated based on a baseline normative comparison would have a symptom severity score at an even higher percentile in comparison to 6-month data. Again, at 6-month follow-up, athletes may respond differently long after a concussion, compared to when uninjured or when being evaluated during removal from play and rehabilitation. The RTP normative reference data likely provides the more sensitive approach to identifying elevated symptoms following a recent injury, in that there is a lower threshold for identifying elevated symptom severity scores. The RTP normative data would likely detect more athletes with potential unresolved symptoms, who may remain out-of-play for longer to ensure symptom resolution and avoid reinjury while still recovering. However, this approach may be confounded by the variability in post-concussion management and protocols across settings. In the context of a student-athletes who is much further from their injury, such as being evaluated more than six months post injury, the baseline data may better reflect the context of their evaluation, which is well beyond typical symptom resolution. As such, the normative data used depends on the timing and context of the evaluation, as well as the judgment of the clinician.

Concussion Subtypes and Individualized Intervention Plans

Post hoc analyses suggest that the most common elevations on the physical subscale during RTP were primarily driven by the individual items Headache and Pressure in Head, followed by Neck Pain. These findings align with prior research that found post-traumatic headaches to be a common and central symptom of concussion (Blume, 2015; Lucas, 2011; Schwedt, 2021; Zasler, 2015). The prevalence of concurrent head and neck pain in student-athletes beginning RTP procedures may be suggestive of cervicogenic headaches, one of several symptom subtypes of SRC that may be caused by injury, inflammation, or dysfunction in the muscles and nerves of the cervical spine (Ellis, Leddy, et al., 2016; Kristjansson & Treleaven, 2009; Passatore & Roatta, 2006). In general, concussion subtypes are defined by both symptom presentations and pathophysiological mechanisms deduced through testing and physical examinations (Ellis et al., 2015; Ellis, Leddy, et al., 2016; Ellis, Ritchie, et al., 2016; Kristjansson & Treleaven, 2009; Leddy, Baker, et al., 2016; Leddy et al., 2007, 2012; Leddy, Hinds, et al., 2016; Leddy & Willer, 2013; Ventura et al., 2014, 2015), with interventions that correspond to each subtype.

For example, manual neck strengthening or stability training (Ellis et al., 2015; Jull et al., 2002; Kristjansson & Treleaven, 2009; Schneider et al., 2014) and light aerobic exercise (Ellis, Leddy, et al., 2016; Leddy, Baker, et al., 2016; Schneider et al., 2014) are recommended for athletes with cervicogenic headaches. Those with persistent emotional or sleep-related complaints may benefit from different cognitive behavioral therapy protocols (Al Sayegh et al., 2010; Chen et al., 2020; Conder et al., 2020; Kapadia et al., 2019; Kostyun et al., 2015; Podlog et al., 2020; R. Thomas et al., 2017). An individual with complaints regarding balance, gait, and dizziness may display a vestibulo-ocular subtype (Ellis, Leddy, et al., 2016) with recommended vestibular therapy involving balance exercises and gaze stabilization (Ellis et al., 2015; Leddy et al., 2012; Schneider et al., 2014). Cognitive rehabilitation treatments teaching compensatory strategies are beneficial in reducing post-concussion symptoms (Leddy, Baker, et al., 2016; Leddy et al., 2012) and improving cognition and attentional processes during recovery (Helmick, 2010; Heslot et al., 2021; Leddy, Baker, et al., 2016; Leddy et al., 2012). Prior evidence of concussion symptom subtypes, in addition to the persistent domain-specific symptoms recorded in this study, may support the utility of using a four-factor symptom model to guide post-injury rehabilitation.

Beyond planning interventions, subscale elevations may help clinicians identify areas for further assessment. An elevated physical subscale may prompt a clinician to assess for headache types (e.g., migraine vs. cervicogenic) (Ellis, Leddy, et al., 2016; Headache Classification Committee of the International Headache Society (IHS), 2013), or elevated affective symptoms may cue a clinician to administer a specific psychopathology screener, such as the PHQ-9 or GAD-7 (Ellis, Leddy, et al., 2016; Riegler et al., 2019). It is important to note that symptom severity elevations at RTP are not necessarily indicative of poor clinical recovery or severe symptomatology. Rather, elevated scores reflect abnormality in symptom severity relative to other athletes at RTP. A student-athlete with symptoms exceeding the 84th percentile in a given domain may still subjectively experience their symptoms as mild. Thus, symptom severity cutoff scores alone should not be used to determine if an athlete requires a delayed RTP. Rather, they provide another point of information to help guide clinical decision-making in the best interests of the athlete. The provided subscale elevation rates give context to the types of symptoms that may persist after injury and can be connected to areas of further assessment or domain-specific rehabilitation plans for athletes.

Limitations and Future Directions

This study has several limitations that affect the generalizability of its findings. The participants in this study were collegiate student-athletes evaluated during the normal course of play and recovery. As such, their symptom experience likely differs from student-athletes who may present to a specialty concussion clinic with more severe and persistent symptomology, or significant functional impairments. The results also may not apply to non-collegiate athletes, such as those involved in youth or professional sports, as well as non-athletes who may present to the emergency department or experience a concussion due to a different mechanism of injury (e.g., veterans, survivors of intimate partner violence, etc.). Although the SCAT3 is now outdated, the newly available SCAT6 Symptom Evaluation (Echemendia et al., 2023) contains the same items as the SCAT3, so the stratified normative data provided will apply to the most current version of the measure. Finally, when preparing normative data, participants were matched on their total number of pre-existing conditions rather than each specific individual condition. While this approach reduced the specificity of the normative reference data, it allowed the results to be more generalizable and the aggregate categories retained larger sample sizes for pairwise comparisons.

Future research should examine the correspondence between subscale scores and objective measures of functioning within specific domains in the acute and long-term phases of recovery (e.g., the relation between physical subscale scores and scores on the Balance Error Scoring System or Vestibular Ocular Motor Screening). Additionally, research should aim to determine if self-report questionnaires help predict the type of personalized intervention a student-athlete may benefit from after injury. Domain-specific interventions may help to manage the health and recovery of collegiate student-athletes, a population at unique risk for experiencing a concussion (Buki et al., 2015; McKee et al., 2013; Munakomi & Puckett, 2023; Tsushima et al., 2019) and persistent post-concussion symptoms (Ellis, Leddy, et al., 2016; Makdissi et al., 2013).

Supplementary Material

Supp 1

Acknowledgments

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): 10.23718/FITBIR/1504074. This manuscript reflects the views of the authors and may not reflect the opinions or views of the DOD, NIH, or the Submitters submitting original data to the FITBIR Informatics System. This work was supported by the University of Kentucky, through the UNited in True racial Equity Predoctoral Fellowship in conjunction with the Lyman T. Johnson Fellowship. This work was also supported, in part, by a Building Interdisciplinary Research Careers in Women's Health (BIRCWH) grant (#K12-DA035150) from the National Institute on Drug Abuse (NIDA) of the National Institutes of Health.

Footnotes

Declaration of Interest Statement

The authors report there are no competing interests to declare.

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