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Background: Early medical attention after concussion may minimize symptom duration and burden; however, many concussions are undiagnosed or have a delay in diagnosis after injury. Many concussion symptoms (eg, headache, dizziness) are not visible, meaning that early identification is often contingent on individuals reporting their injury to medical staff. A fundamental understanding of the types and levels of factors that explain when concussions are reported can help identify promising directions for intervention., Purpose: To identify individual and institutional factors that predict immediate (vs delayed) injury reporting., Study Design: Case-control study; Level of evidence, 3., Methods: This study was a secondary analysis of data from the Concussion Assessment, Research and Education (CARE) Consortium study. The sample included 3213 collegiate athletes and military service academy cadets who were diagnosed with a concussion during the study period. Participants were from 27 civilian institutions and 3 military institutions in the United States. Machine learning techniques were used to build models predicting who would report an injury immediately after a concussive event (measured by an athletic trainer denoting the injury as being reported "immediately" or "at a delay"), including both individual athlete/cadet and institutional characteristics., Results: In the sample as a whole, combining individual factors enabled prediction of reporting immediacy, with mean accuracies between 55.8% and 62.6%, depending on classifier type and sample subset; adding institutional factors improved reporting prediction accuracies by 1 to 6 percentage points. At the individual level, injury-related altered mental status and loss of consciousness were most predictive of immediate reporting, which may be the result of observable signs leading to the injury report being externally mediated. At the institutional level, important attributes included athletic department annual revenue and ratio of athletes to athletic trainers., Conclusion: Further study is needed on the pathways through which institutional decisions about resource allocation, including decisions about sports medicine staffing, may contribute to reporting immediacy. More broadly, the relatively low accuracy of the machine learning models tested suggests the importance of continued expansion in how reporting is understood and facilitated., Competing Interests: One or more of the authors has declared the following potential conflict of interest or source of funding: This work was supported by the Assistant Secretary of Defense for Health Affairs endorsed by the DoD through the Psychological Health and Traumatic Brain Injury Research Program under award No. W81XWH-20-2-0044. This publication was also made possible with support from the Grand Alliance Concussion Assessment, Research and Education (CARE) Consortium, funded in part by the National Collegiate Athletic Association (NCAA) and DoD. The US Army Medical Research Acquisition Activity is the awarding and administering acquisition office. E.K-H. has received consulting fees from the NCAA. J.K.R-M. has consulted for Allied Health Education (paid) and has received speaker honoraria and travel reimbursements for talks given. She served previously (unpaid) on USA Football’s Football Development Council. R.C.L. has received current or past funding from the National Operating Committee on Standards for Athletic Equipment and the National Football League (NFL). S.P.B. has received current or past research funding from NFL/Under Armour/GE, Simbex, and ElMindA; has consulted for US Soccer (paid), US Cycling (unpaid), the University of Calgary SHRed Concussions external advisory board (unpaid), and medicolegal litigation; and has received speaker honoraria and travel reimbursements for talks given. He is coauthor of Biomechanics of Injury (3rd ed) and has a patent pending on “Brain Metabolism Monitoring Through CCO Measurements Using All-Fiber-Integrated Super-Continuum Source” (US application No. 17/164,490). M.A.M. has current or past research funding from NFL and Abbott Laboratories. AOSSM checks author disclosures against the Open Payments Database (OPD). AOSSM has not conducted an independent investigation on the OPD and disclaims any liability or responsibility relating thereto.

Background: Few previous studies have investigated how different injury mechanisms leading to sport-related concussion (SRC) in soccer may affect outcomes., Purpose: To describe injury mechanisms and evaluate injury mechanisms as predictors of symptom severity, return to play (RTP) initiation, and unrestricted RTP (URTP) in a cohort of collegiate soccer players., Study Design: Cohort study; Level of evidence, 2., Methods: The Concussion Assessment, Research and Education (CARE) Consortium database was used. The mechanism of injury was categorized into head-to-ball, head-to-head, head-to-body, and head-to-ground/equipment. Baseline/acute injury characteristics-including Sports Concussion Assessment Tool-3 total symptom severity (TSS), loss of consciousness (LOC), and altered mental status (AMS); descriptive data; and recovery (RTP and URTP)-were compared. Multivariable regression and Weibull models were used to assess the predictive value of the mechanism of injury on TSS and RTP/URTP, respectively., Results: Among 391 soccer SRCs, 32.7% were attributed to a head-to-ball mechanism, 27.9% to a head-to-body mechanism, 21.7% to a head-to-head mechanism, and 17.6% to a head-to-ground/equipment mechanism. Event type was significantly associated with injury mechanism [χ 2 (3) = 63; P < .001), such that more head-to-ball concussions occurred in practice sessions (n = 92 [51.1%] vs n = 36 [17.1%]) and more head-to-head (n = 65 [30.8%] vs n = 20 [11.1]) and head-to-body (n = 76 [36%] vs n = 33 [18.3%]) concussions occurred in competition. The primary position was significantly associated with injury mechanism [χ 2 (3) = 24; P < .004], with goalkeepers having no SRCs from the head-to-head mechanism (n = 0 [0%]) and forward players having the least head-to-body mechanism (n = 15 [19.2%]). LOC was also associated with injury mechanism ( P = .034), with LOC being most prevalent in head-to-ground/equipment. Finally, AMS was most prevalent in head-to-ball (n = 54 [34.2%]) and head-to-body (n = 48 [30.4%]) mechanisms [χ 2 (3) = 9; P = .029]. In our multivariable models, the mechanism was not a predictor of TSS or RTP; however, it was associated with URTP ( P = .044), with head-to-equipment/ground injuries resulting in the shortest mean number of days (14 ± 9.1 days) to URTP and the head-to-ball mechanism the longest (18.6 ± 21.6 days)., Conclusion: The mechanism of injury differed by event type and primary position, and LOC and AMS were different across mechanisms. Even though the mechanism of injury was not a significant predictor of acute symptom burden or time until RTP initiation, those with head-to-equipment/ground injuries spent the shortest time until URTP, and those with head-to-ball injuries had the longest time until URTP., Competing Interests: One or more of the authors has declared the following potential conflict of interest or source of funding: This project was supported, in part, by the Grand Alliance Sport-Related Concussion Assessment, Research, and Education (CARE) Consortium, funded, in part by the National Collegiate Athletic Association (NCAA) and the DoD. The U.S. Army Medical Research Acquisition Activity is the awarding and administering acquisition office. This work was supported by the Office of the Assistant Secretary of Defense for Health Affairs through the Psychological Health and Traumatic Brain Injury Program under award No. W81XWH-14-2-0151. A.J.B. has been part of projects that have received funding from the DoD, the National Operating Committee on Standards from Athletic Equipment, the National Athletic Trainer's Association (NATA) Research and Education Foundation, and the NCAA. P.F.P. is an employee of the DoD and has previously received other DoD-related grants. T.W.M. has received research support from the NCAA, the US DoD, and the National Institutes of Health (NIH). He also reports textbook royalties from American Psychiatric Press Publishing Inc and serves on the Concussion Committee Scientific Advisory Board for the Australian Football League. M.A.M. has received research funding from the NIH, the Centers for Disease Control and Prevention (CDC), the DoD, the NCAA, the National Football League (NFL), and Abbott Laboratories. S.P.B. has received research funding from the NIH, the CDC, the DoD-USA Medical Research Acquisition Activity, the NCAA, the NATA Research and Education Foundation, the NFL/Under Armour/GE, Simbex, and ElmindA. He has consulted for US Soccer, US Cycling, the University of Calgary SHRed Concussions external advisory board, and medicolegal litigation. S.L.Z. is a member of the NFL Head, Neck, and Spine Committee. D.P.T. serves as a scientific advisor for HitIQ. He previously consulted for REACT Neuro Inc. He has a consulting practice in forensic neuropsychology, including expert testimony, involving individuals who have sustained mild traumatic brain injury (including former athletes). AOSSM checks author disclosures against the Open Payments Database (OPD). AOSSM has not conducted an independent investigation on the OPD and disclaims any liability or responsibility relating thereto.

Background: Molecular-based approaches to understanding concussion pathophysiology provide complex biological information that can advance concussion research and identify potential diagnostic and/or prognostic biomarkers of injury., Objective: The aim of this study was to identify gene expression changes in peripheral blood that are initiated following concussion and are relevant to concussion response and recovery., Methods: We analyzed whole blood transcriptomes in a large cohort of concussed and control collegiate athletes who were participating in the multicenter prospective cohort Concussion Assessment, Research, and Education (CARE) Consortium study. Blood samples were collected from collegiate athletes at preseason (baseline), within 6 h of concussion injury, and at four additional prescribed time points spanning 24 h to 6 months post-injury. RNA sequencing was performed on samples from 230 concussed, 130 contact control, and 102 non-contact control athletes. Differential gene expression and deconvolution analysis were performed at each time point relative to baseline., Results: Cytokine and immune response signaling pathways were activated immediately after concussion, but at later time points these pathways appeared to be suppressed relative to the contact control group. We also found that the proportion of neutrophils increased and natural killer cells decreased in the blood following concussion., Conclusions: Transcriptome signatures in the blood reflect the known pathophysiology of concussion and may be useful for defining the immediate biological response and the time course for recovery. In addition, the identified immune response pathways and changes in immune cell type proportions following a concussion may inform future treatment strategies., (© 2023. The Author(s).)

Background: Current protein biomarkers are only moderately predictive at identifying individuals with mild traumatic brain injury or concussion. Therefore, more accurate diagnostic markers are needed for sport-related concussion., Methods: This was a multicenter, prospective, case-control study of athletes who provided blood samples and were diagnosed with a concussion or were a matched non-concussed control within the National Collegiate Athletic Association-Department of Defense Concussion Assessment, Research, and Education Consortium conducted between 2015 and 2019. The blood was collected within 48 h of injury to identify protein abnormalities at the acute and subacute timepoints. Athletes with concussion were divided into 6 h post-injury (0-6 h post-injury) and after 6 h post-injury (7-48 h post-injury) groups. We applied a highly multiplexed proteomic technique that used a DNA aptamers assay to target 1305 proteins in plasma samples from athletes with and without sport-related concussion., Results: A total of 140 athletes with concussion (79.3% males; aged 18.71 ± 1.10 years, mean ± SD) and 21 non-concussed athletes (76.2% males; 19.14 ± 1.10 years) were included in this study. We identified 338 plasma proteins that significantly differed in abundance (319 upregulated and 19 downregulated) in concussed athletes compared to non-concussed athletes. The top 20 most differentially abundant proteins discriminated concussed athletes from non-concussed athletes with an area under the curve (AUC) of 0.954 (95% confidence interval: 0.922‒0.986). Specifically, after 6 h of injury, the individual AUC of plasma erythrocyte membrane protein band 4.1 (EPB41) and alpha-synuclein (SNCA) were 0.956 and 0.875, respectively. The combination of EPB41 and SNCA provided the best AUC (1.000), which suggests this combination of candidate plasma biomarkers is the best for diagnosing concussion in athletes after 6 h of injury., Conclusion: Our data suggest that proteomic profiling may provide novel diagnostic protein markers and that a combination of EPB41 and SNCA is the most predictive biomarker of concussion after 6 h of injury., (Copyright © 2023. Production and hosting by Elsevier B.V.)

Brain strain is increasingly being used in helmet design and safety performance evaluation as it is generally considered as the primary mechanism of concussion. In this study, we investigate whether different helmet designs can meaningfully alter brain strains using two commonly used metrics, peak maximum principal strain (MPS) of the whole brain and cumulative strain damage measure (CSDM). A convolutional neural network (CNN) that instantly produces detailed brain strains is first tested for accuracy for helmeted head impacts. Based on N = 144 impacts in 12 impact conditions from three random and representative helmet models, we conclude that the CNN is sufficiently accurate for helmet testing applications, for elementwise MPS (success rate of 98.6%), whole-brain peak MPS and CSDM (coefficient of determination of 0.977 and 0.980, with root mean squared error of 0.015 and 0.029, respectively). We then apply the technique to 23 football helmet models (N = 1104 impacts) to reproduce elementwise MPS. Assuming a concussion would occur when peak MPS or CSDM exceeds a threshold, we sweep their thresholds across the value ranges to evaluate the number of predicted hypothetical concussions that different helmets sustain across the impact conditions. Relative to the 12 impact conditions tested, we find that the "best" and "worst" helmets differ by an average of 22.5% in terms of predicted concussions, ranging from 0 to 42% (the latter achieved at the threshold value of 0.28 for peak MPS and 0.4 for CSDM, respectively). Such a large variation among helmets in strain-based concussion predictions demonstrate that helmet designs can still be optimized in a clinically meaningful way. The robustness and accuracy of the CNN tool also suggest its potential for routine use for helmet design and safety performance evaluation in the future. The CNN is freely available online at https://github.com/Jilab-biomechanics/CNN-brain-strains ., (© 2022. The Author(s) under exclusive licence to Biomedical Engineering Society.)

Sport-related concussions can result from a single high magnitude impact that generates concussive symptoms, repeated subconcussive head impacts aggregating to generate concussive symptoms, or a combined effect from the two mechanisms. The array of symptoms produced by these mechanisms may be clinically interpreted as a sport-related concussion. It was hypothesized that head impact exposure resulting in concussion is influenced by severity, total number, and frequency of subconcussive head impacts. The influence of total number and magnitude of impacts was previously explored, but frequency was investigated to a lesser degree. In this analysis, head impact frequency was investigated over a new metric called 'time delta', the time difference from the first recorded head impact of the day until the concussive impact. Four exposure metrics were analyzed over the time delta to determine whether frequency of head impact exposure was greater for athletes on their concussion date relative to other dates of contact participation. Those metrics included head impact frequency, head impact accrual rate, risk weighted exposure (RWE), and RWE accrual rate. Athletes experienced an elevated median number of impacts, RWE, and RWE accrual rate over the time delta on their concussion date compared to non-injury sessions. This finding suggests elevated frequency of head impact exposure on the concussion date compared to other dates that may precipitate the onset of concussion., (© 2022. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

The relationship between head impact and subsequent brain injury for American football players is not well-defined, especially for youth. The objective of this study is to quantify and assess Head Impact Exposure (HIE) metrics among youth and collegiate football players. This multi-season study enrolled 639 unique athletes (354 collegiate; 285 youth, ages 9-14), recording 476,209 head impacts (367,337 collegiate; 108,872 youth) over 971 sessions (480 collegiate; 491 youth). Youth players experienced 43 and 65% fewer impacts per competition and practice, respectively, and lower impact magnitudes compared to collegiate players (95th percentile peak linear acceleration (PLA, g) competition: 45.6 vs 61.9; 95th percentile PLA practice: 42.6 vs 58.8; 95th percentile peak rotational acceleration (PRA, rad·s -2 ) competition: 2262 vs 4422; 95th percentile PRA practice: 2081 vs 4052; 95th percentile HITsp competition: 25.4 vs 32.8; 95th percentile HITsp practice: 23.9 vs 30.2). Impacts during competition were more frequent and of greater magnitude than during practice at both levels. Quantified comparisons of head impact frequency and magnitude between youth and collegiate athletes reveal HIE differences as a function of age, and expanded insight better informs the development of age-appropriate guidelines for helmet design, prevention measures, standardized testing, brain injury diagnosis, and recovery management., (© 2022. The Author(s) under exclusive licence to Biomedical Engineering Society.)

Head impact measurement devices enable opportunities to collect impact data directly from humans to study topics like concussion biomechanics, head impact exposure and its effects, and concussion risk reduction techniques in sports when paired with other relevant data. With recent advances in head impact measurement devices and cost-effective price points, more and more investigators are using them to study brain health questions. However, as the field's literature grows, the variance in study quality is apparent. This brief paper aims to provide a high-level set of key considerations for the design and analysis of head impact measurement studies that can help avoid flaws introduced by sampling biases, false data, missing data, and confounding factors. We discuss key points through four overarching themes: study design, operational management, data quality, and data analysis., (© 2022. The Author(s).)

More than six million people participate in whitewater kayaking and rafting in the United States each year. Unfortunately, with these six million whitewater participants come 50 deaths annually, making it one of the highest fatality rates of all sports. As the popularity in whitewater activities grows, the number of injuries, including concussions, also increases. The objective of this study was to create a new rating system for whitewater helmets by evaluating the biomechanical performance and risk of head injury of whitewater helmets using the Summation of Tests for the Analysis of Risk (STAR) system. All watersport helmets that passed the EN: 1385: 2012 standard and that were clearly marketed for whitewater use were selected for this study. Two samples of each helmet model were tested on a custom pendulum impactor under conditions known to be associated with the highest risk of head injury and death. A 50th percentile male NOCSAE headform instrumented with three linear accelerometers and a triaxial angular rate sensor coupled with a Hybrid III 50th percentile neck were used for data collection. A total of 126 tests were performed using six different configurations. These included impacts to the front, side, and rear using two speeds of 3.1 and 4.9 m/s that modeled whitewater river flow rates. Each helmet's STAR score was calculated using the combination of exposure and injury risk that was determined from the linear and rotational head accelerations. The resulting head impact accelerations predicted a very high risk of concussion for all impact locations at the 4.9 m/s speed. The STAR score varied between helmets indicating that some helmets provide better protection than others. Overall, these results show a clear need for improvement in whitewater helmets, and the methodologies developed in this research project should provide manufacturers a design tool for improving these products., (© 2022. The Author(s) under exclusive licence to Biomedical Engineering Society.)

Background: The prevalence of unreported concussions is high, and undiagnosed concussions can lead to worse postconcussion outcomes. It is not clear how those with a history of undiagnosed concussion perform on subsequent standard concussion baseline assessments., Purpose: To determine if previous concussion diagnosis status was associated with outcomes on the standard baseline concussion assessment battery., Study Design: Cross-sectional study; Level of evidence, 3., Methods: Concussion Assessment, Research, and Education (CARE) Consortium participants (N = 29,934) self-reported concussion history with diagnosis status and completed standard baseline concussion assessments, including assessments for symptoms, mental status, balance, and neurocognition. Multiple linear regression models were used to estimate mean differences and 95% CIs among concussion history groups (no concussion history [n = 23,037; 77.0%], all previous concussions diagnosed [n = 5315; 17.8%], ≥1 previous concussions undiagnosed [n = 1582; 5.3%]) at baseline for all outcomes except symptom severity and Brief Symptom Inventory-18 (BSI-18) score, in which negative binomial models were used to calculate incidence rate ratios (IRRs). All models were adjusted for sex, race, ethnicity, sport contact level, and concussion count. Mean differences with 95% CIs excluding 0.00 and at least a small effect size (≥0.20), and those IRRs with 95% CIs excluding 1.00 and at least a small association (IRR, ≥1.10) were considered significant., Results: The ≥1 previous concussions undiagnosed group reported significantly greater symptom severity scores (IRR, ≥1.38) and BSI-18 (IRR, ≥1.31) scores relative to the no concussion history and all previous concussions diagnosed groups. The ≥1 previous concussions undiagnosed group performed significantly worse on 6 neurocognitive assessments while performing better on only 2 compared with the no concussion history and all previous concussions diagnosed groups. There were no between-group differences on mental status or balance assessments., Conclusion: An undiagnosed concussion history was associated with worse clinical indicators at future baseline assessments. Individuals reporting ≥1 previous undiagnosed concussions exhibited worse baseline clinical indicators. This may suggest that concussion-related harm may be exacerbated when injuries are not diagnosed.

Purpose: Contact sport athletes are exposed to a unique environment where they sustain repeated head impacts throughout the season and can sustain hundreds of head impacts over a few months. Accordingly, recent studies outlined the role that head impact exposure (HIE) has in concussion biomechanics and in the development of cognitive and brain-based changes. Those studies focused on time-bound effects by quantifying exposure leading up to the concussion, or cognitive changes after a season in which athletes had high HIE. However, HIE may have a more prolonged effect. This study identified associations between HIE and concussion incidence during different periods of the college football fall season., Methods: This study included 1120 athlete seasons from six National Collegiate Athletic Association Division I football programs across 5 yr. Athletes were instrumented with the Head Impact Telemetry System to record daily HIE. The analysis quantified associations of preseason/regular season/total season concussion incidence with HIE during those periods., Results: Strong associations were identified between HIE and concussion incidence during different periods of the season. Preseason HIE was associated with preseason and total season concussion incidence, and total season HIE was associated with total season concussion incidence., Conclusions: These findings demonstrate a prolonged effect of HIE on concussion risk, wherein elevated preseason HIE was associated with higher concussion risk both during the preseason and throughout the entire fall season. This investigation is the first to provide evidence supporting the hypothesis of a relationship between elevated HIE during the college football preseason and a sustained decreased tolerance for concussion throughout that season., (Copyright © 2022 Written work prepared by employees of the Federal Government as part of their official duties is, under the U.S. Copyright Act, a “work of the United States Government” for which copyright protection under Title 17 of the United States Code is not available. As such, copyright does not extend to the contributions of employees of the Federal Government.)

The objective of this study was to determine the psychometrics (reliability, validity) of the original Standardized Assessment of Concussion (SAC) in a youth sample (ages 11 to 13). Demographic factors of race, level of vocabulary knowledge, mother's level of education were also considered. Over 150 youth football athletes completed the SAC and a brief battery of NIH Toolbox cognitive tests as part of a larger study on biomechanical factors in youth sport concussion. This was a within-subjects design (pre-season, post-season assessments), and correlational analysis of convergent and discriminant validity. Between groups analysis based on demographic differences was also employed. The pre-season SAC scores were not different by age; however, SAC scores were statistically different by race: t( 155) = 3.162, p  = .002, d  = .519. Maternal level of education and participant vocabulary scores were related to racial group membership. Convergent and discriminant validity were established compared to NIH Toolbox tests of memory and speed. Pre-post-season tests for 108 participants established marginally acceptable test-retest reliability (ICC = .692). These data support the use of the original SAC in youth football although clinicians must be aware of racial differences in scores.

Importance: Concussion ranks among the most common injuries in football. Beyond the risks of concussion are growing concerns that repetitive head impact exposure (HIE) may increase risk for long-term neurologic health problems in football players., Objective: To investigate the pattern of concussion incidence and HIE across the football season in collegiate football players., Design, Setting, and Participants: In this observational cohort study conducted from 2015 to 2019 across 6 Division I National Collegiate Athletic Association (NCAA) football programs participating in the Concussion Assessment, Research, and Education (CARE) Consortium, a total of 658 collegiate football players were instrumented with the Head Impact Telemetry (HIT) System (46.5% of 1416 eligible football players enrolled in the CARE Advanced Research Core). Players were prioritized for instrumentation with the HIT System based on their level of participation (ie, starters prioritized over reserves)., Exposure: Participation in collegiate football games and practices from 2015 to 2019., Main Outcomes and Measures: Incidence of diagnosed concussion and HIE from the HIT System., Results: Across 5 seasons, 528 684 head impacts recorded from 658 players (all male, mean age [SD], 19.02 [1.25] years) instrumented with the HIT System during football practices or games met quality standards for analysis. Players sustained a median of 415 (interquartile range [IQR], 190-727) recorded head impacts (ie, impacts) per season. Sixty-eight players sustained a diagnosed concussion. In total, 48.5% of concussions (n = 33) occurred during preseason training, despite preseason representing only 20.8% of the football season (0.059 preseason vs 0.016 regular-season concussions per team per day; mean difference, 0.042; 95% CI, 0.020-0.060; P = .001). Total HIE in the preseason occurred at twice the proportion of the regular season (324.9 vs 162.4 impacts per team per day; mean difference, 162.6; 95% CI, 110.9-214.3; P < .001). Every season, HIE per athlete was highest in August (preseason) (median, 146.0 impacts; IQR, 63.0-247.8) and lowest in November (median, 80.0 impacts; IQR, 35.0-148.0). Over 5 seasons, 72% of concussions (n = 49) (game proportion, 0.28; 95% CI, 0.18-0.40; P < .001) and 66.9% of HIE (262.4 practices vs 137.2 games impacts per player; mean difference, 125.3; 95% CI, 110.0-140.6; P < .001) occurred in practice. Even within the regular season, total HIE in practices (median, 175.0 impacts per player per season; IQR, 76.0-340.5) was 84.2% higher than in games (median, 95.0 impacts per player per season; IQR, 32.0-206.0)., Conclusions and Relevance: Concussion incidence and HIE among college football players are disproportionately higher in the preseason than regular season, and most concussions and HIE occur during football practices, not games. These data point to a powerful opportunity for policy, education, and other prevention strategies to make the greatest overall reduction in concussion incidence and HIE in college football, particularly during preseason training and football practices throughout the season, without major modification to game play. Strategies to prevent concussion and HIE have important implications to protecting the safety and health of football players at all competitive levels.

Objectives: Head impact exposure (HIE) in youth football is a public health concern. The objective of this study was to determine if one season of HIE in youth football was related to cognitive changes., Method: Over 200 participants (ages 9-13) wore instrumented helmets for practices and games to measure the amount of HIE sustained over one season. Pre- and post-season neuropsychological tests were completed. Test score changes were calculated adjusting for practice effects and regression to the mean and used as the dependent variables. Regression models were calculated with HIE variables predicting neuropsychological test score changes., Results: For the full sample, a small effect was found with season average rotational values predicting changes in list-learning such that HIE was related to negative score change: standardized beta (β) = -.147, t(205) = -2.12, and p = .035. When analyzed by age clusters (9-10, 11-13) and adding participant weight to models, the R2 values increased. Splitting groups by weight (median split), found heavier members of the 9-10 cohort with significantly greater change than lighter members. Additionaly, significantly more participants had clinically meaningful negative changes: X2 = 10.343, p = .001., Conclusion: These findings suggest that in the 9-10 age cluster, the average seasonal level of HIE had inverse, negative relationships with cognitive change over one season that was not found in the older group. The mediation effects of age and weight have not been explored previously and appear to contribute to the effects of HIE on cognition in youth football players.

Importance: Validation of protein biomarkers for concussion diagnosis and management in military combative training is important, as these injuries occur outside of traditional health care settings and are generally difficult to diagnose., Objective: To investigate acute blood protein levels in military cadets after combative training-associated concussions., Design, Setting, and Participants: This multicenter prospective case-control study was part of a larger cohort study conducted by the National Collegiate Athletic Association and the US Department of Defense Concussion Assessment Research and Education (CARE) Consortium from February 20, 2015, to May 31, 2018. The study was performed among cadets from 2 CARE Consortium Advanced Research Core sites: the US Military Academy at West Point and the US Air Force Academy. Cadets who incurred concussions during combative training (concussion group) were compared with cadets who participated in the same combative training exercises but did not incur concussions (contact-control group). Clinical measures and blood sample collection occurred at baseline, the acute postinjury point (<6 hours), the 24- to 48-hour postinjury point, the asymptomatic postinjury point (defined as the point at which the cadet reported being asymptomatic and began the return-to-activity protocol), and 7 days after return to activity. Biomarker levels and estimated mean differences in biomarker levels were natural log (ln) transformed to decrease the skewness of their distributions. Data were collected from August 1, 2016, to May 31, 2018, and analyses were conducted from March 1, 2019, to January 14, 2020., Exposure: Concussion incurred during combative training., Main Outcomes and Measures: Proteins examined included glial fibrillary acidic protein, ubiquitin C-terminal hydrolase-L1, neurofilament light chain, and tau. Quantification was conducted using a multiplex assay (Simoa; Quanterix Corp). Clinical measures included the Sport Concussion Assessment Tool-Third Edition symptom severity evaluation, the Standardized Assessment of Concussion, the Balance Error Scoring System, and the 18-item Brief Symptom Inventory., Results: Among 103 military service academy cadets, 67 cadets incurred concussions during combative training, and 36 matched cadets who engaged in the same training exercises did not incur concussions. The mean (SD) age of cadets in the concussion group was 18.6 (1.3) years, and 40 cadets (59.7%) were male. The mean (SD) age of matched cadets in the contact-control group was 19.5 (1.3) years, and 25 cadets (69.4%) were male. Compared with cadets in the contact-control group, those in the concussion group had significant increases in glial fibrillary acidic protein (mean difference in ln values, 0.34; 95% CI, 0.18-0.50; P < .001) and ubiquitin C-terminal hydrolase-L1 (mean difference in ln values, 0.97; 95% CI, 0.44-1.50; P < .001) levels at the acute postinjury point. The glial fibrillary acidic protein level remained high in the concussion group compared with the contact-control group at the 24- to 48-hour postinjury point (mean difference in ln values, 0.22; 95% CI, 0.06-0.38; P = .007) and the asymptomatic postinjury point (mean difference in ln values, 0.21; 95% CI, 0.05-0.36; P = .01). The area under the curve for all biomarkers combined, which was used to differentiate cadets in the concussion and contact-control groups, was 0.80 (95% CI, 0.68-0.93; P < .001) at the acute postinjury point., Conclusions and Relevance: This study's findings indicate that blood biomarkers have potential for use as research tools to better understand the pathobiological changes associated with concussion and to assist with injury identification and recovery from combative training-associated concussions among military service academy cadets. These results extend the previous findings of studies of collegiate athletes with sport-associated concussions.

Objective: To examine the association between estimated age at first exposure (eAFE) to American football and clinical measures throughout recovery following concussion., Methods: Participants were recruited across 30 colleges and universities as part of the National Collegiate Athletic Association (NCAA)-Department of Defense Concussion Assessment, Research and Education Consortium. There were 294 NCAA American football players (age 19 ± 1 years) evaluated 24-48 hours following concussion with valid baseline data and 327 (age 19 ± 1 years) evaluated at the time they were asymptomatic with valid baseline data. Participants sustained a medically diagnosed concussion between baseline testing and postconcussion assessments. Outcome measures included the number of days until asymptomatic, Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) composite scores, Balance Error Scoring System (BESS) total score, and Brief Symptom Inventory 18 (BSI-18) subscores. The eAFE was defined as participant's age at the time of assessment minus self-reported number of years playing football., Results: In unadjusted regression models, younger eAFE was associated with lower (worse) ImPACT Visual Motor Speed ( R 2 = 0.031, p = 0.012) at 24-48 hours following injury and lower (better) BSI-18 Somatization subscores ( R 2 = 0.014, p = 0.038) when the athletes were asymptomatic. The effect sizes were very small. The eAFE was not associated with the number of days until asymptomatic, other ImPACT composite scores, BESS total score, or other BSI-18 subscores., Conclusion: Earlier eAFE to American football was not associated with longer symptom recovery, worse balance, worse cognitive performance, or greater psychological distress following concussion. In these NCAA football players, longer duration of exposure to football during childhood and adolescence appears to be unrelated to clinical recovery following concussion., (© 2020 American Academy of Neurology.)

Identifying the associations between head impact biomechanics and clinical recovery may inform better head impact monitoring procedures and identify athletes who may benefit from early treatments aimed to enhance recovery. The purpose of this study was to test whether head injury biomechanics are associated with clinical recovery of symptom severity, balance, and mental status, as well as symptom resolution time (SRT) and return-to-participation (RTP) time. We studied 45 college American football players (n = 51 concussions) who sustained an incident concussion while participating in a multi-site study. Player race/ethnicity, prior concussion, medical history, position, body mass index, event type, and impact location were covariates in our multivariable analyses. Multivariable negative binomial regression models analyzed associations between our study outcomes and (1) injury-causing linear and rotational head impact severity, (2) season repetitive head impact exposure (RHIE), and (3) injury day RHIE. Median SRT was 6.1 days (IQR 5.8 days, n = 45) and median RTP time was 12.3 days (IQR 7.8 days, n = 36) across our study sample. RTP time was 86% (Ratio 1.86, 95% CI [1.05, 3.28]) longer in athletes with a concussion history. Offensive players had SRTs 49% shorter than defensive players (Ratio 0.51, 95% CI [0.29, 0.92]). Per-unit increases in season RHIE were associated with 22% longer SRT (Ratio 1.22, 95% CI [1.09, 1.36]) but 28% shorter RTP time (Ratio 0.72, 95% CI [0.56, 0.93]). No other head injury biomechanics predicted injury recovery.

Concussion tolerance and head impact exposure are highly variable among football players. Recent findings highlight that head impact data analyses need to be performed at the subject level. In this paper, we describe a method of characterizing concussion risk between individuals using a new survival analysis technique developed with real-world head impact data in mind. Our approach addresses the limitations and challenges seen in previous risk analyses of football head impact data. Specifically, this demonstrative analysis appropriately models risk for a combination of left-censored recurrent events (concussions) and right-censored recurrent non-events (head impacts without concussion). Furthermore, the analysis accounts for uneven impact sampling between players. In brief, we propose using the Consistent Threshold method to develop subject-specific risk curves and then determine average risk point estimates between subjects at injurious magnitude values. We describe an approach for selecting an optimal cumulative distribution function to model risk between subjects by minimizing injury prediction error. We illustrate that small differences in distribution fit can result in large predictive errors. Given the vast amounts of on-field data researchers are collecting across sports, this approach can be applied to develop population-specific risk curves that can ultimately inform interventions that reduce concussion incidence.

Measuring head impacts in sports can further our understanding of brain injury biomechanics and, hopefully, advance concussion diagnostics and prevention. Although there are many head impact sensors available, skepticism on their utility exists over concerns related to measurement error. Previous studies report mixed reliability in head impact sensor measurements, but there is no uniform approach to assessing accuracy, making comparisons between sensors and studies difficult. The objective of this paper is to introduce a two-phased approach to evaluating head impact sensor accuracy. The first phase consists of in-lab impact testing on a dummy headform at varying impact severities under loading conditions representative of each sensor's intended use. We quantify in-lab accuracy by calculating the concordance correlation coefficient (CCC) between a sensor's kinematic measurements and headform reference measurements. For sensors that performed reasonably well in the lab (CCC ≥ 0.80), we completed a second phase of evaluation on-field. Through video validation of impacts measured by sensors on athletes, we classified each sensor measurement as either true-positive and false-positive impact events and computed positive predictive value (PPV) to summarize real-world accuracy. Eight sensors were tested in phase one, but only four sensors were assessed in phase two. Sensor accuracy varied greatly. CCC from phase one ranged from 0.13 to 0.97, with an average value of 0.72. Overall, the four devices that were implemented on-field had PPV that ranged from 16.3 to 91.2%, with an average value of 60.8%. Performance in-lab was not always indicative of the device's performance on-field. The methods proposed in this paper aim to establish a comprehensive approach to the evaluation of sensors so that users can better interpret data collected from athletes.

The objective of this study was to compare head impact data acquired with an impact monitoring mouthguard (IMM) to the video-observed behavior of athletes' post-collision relative to their pre-collision behaviors. A total of n = 83 college and high school American football players wore the IMM and were video-recorded over 260 athlete-exposures. Ex-athletes and clinicians reviewed the video in a two-step process and categorized abnormal post-collision behaviors according to previously published Obvious Performance Decrement (OPD) definitions. Engineers qualitatively reviewed datasets to check head impact and non-head impact signal frequency and magnitude. The ex-athlete reviewers identified 2305 head impacts and 16 potential OPD impacts, 13 of which were separately categorized as Likely-OPD impacts by the clinical reviewers. All 13 Likely-OPD impacts were in the top 1% of impacts measured by the IMM (ranges 40-100 g, 3.3-7.0 m/s and 35-118 J) and 12 of the 13 impacts (92%) were to the side or rear of the head. These findings require confirmation in a larger data set before proposing any type of OPD impact magnitude or direction threshold exists. However, OPD cases in this study compare favorably with previously published impact monitoring studies in high school and college American football players that looked for OPD signs, impact magnitude and direction. Our OPD findings also compare well with NFL reconstruction studies for ranges of concussion and sub-concussive impact magnitudes in side/rear collisions, as well as prior theory, analytical models and empirical research that suggest a directional sensitivity to brain injury exists for single high-energy impacts.

Importance: Identifying plasma biomarkers associated with the amount of time an athlete may need before they return to sport (RTS) following a sport-related concussion (SRC) is important because it may help to improve the health and safety of athletes., Objective: To examine whether plasma biomarkers can differentiate collegiate athletes who RTS in less than 14 days or 14 days or more following SRC., Design, Setting, and Participants: This multicenter prospective diagnostic study, conducted by the National Collegiate Athletics Association-Department of Defense Concussion Assessment, Research, and Education Consortium, included 127 male and female athletes who had sustained an SRC while enrolled at 6 Concussion Assessment, Research, and Education Consortium Advanced Research Core sites as well as 2 partial-Advanced Research Core military service academies. Data were collected between February 2015 and May 2018. Athletes with SRC completed clinical testing and blood collection at preseason (baseline), postinjury (0-21 hours), 24 to 48 hours postinjury, time of symptom resolution, and 7 days after unrestricted RTS., Main Outcomes and Measures: A total of 3 plasma biomarkers (ie, total tau protein, glial fibrillary acidic protein [GFAP], and neurofilament light chain protein [Nf-L]) were measured using an ultrasensitive single molecule array technology and were included in the final analysis. RTS was examined between athletes who took less than 14 days vs those who took 14 days or more to RTS following SRC. Linear mixed models were used to identify significant interactions between period by RTS group. Area under the receiver operating characteristic curve analyses were conducted to examine whether these plasma biomarkers could discriminate between RTS groups., Results: The 127 participants had a mean (SD) age of 18.9 (1.3) years, and 97 (76.4%) were men; 65 (51.2%) took less than 14 days to RTS, and 62 (48.8%) took 14 days or more to RTS. Linear mixed models identified significant associations for both mean (SE) plasma total tau (24-48 hours postinjury, <14 days RTS vs ≥14 days RTS: -0.65 [0.12] pg/mL vs -0.14 [0.14] pg/mL; P = .008) and GFAP (postinjury, 14 days RTS vs ≥14 days RTS: 4.72 [0.12] pg/mL vs 4.39 [0.11] pg/mL; P = .04). Total tau at the time of symptom resolution had acceptable discrimination power (area under the receiver operating characteristic curve, 0.75; 95% CI, 0.63-0.86; P < .001). We also examined a combined plasma biomarker panel that incorporated Nf-L, GFAP, and total tau at each period to discriminate RTS groups. Although the analyses did reach significance at each time period when combined, results indicated that they were poor at distinguishing the groups (area under the receiver operating characteristic curve, <0.7)., Conclusions and Relevance: The findings of this study suggest that measures of total tau and GFAP may identify athletes who will require more time to RTS. However, further research is needed to improve our ability to determine recovery following an SRC.

Introduction: Regulatory efforts toward reducing concussion risk have begun to focus on decreasing the number of head impacts (i.e., head impact burden) sustained by athletes in contact sports. To that end, in 2018, the NCAA decreased the number of preseason on-field team activities for Division I teams from 29 to 25. The objective of the current study was to quantify changes in practice schedule and head impact exposure between the 2017 and 2018 football preseasons., Methods: Athletes from five NCAA Division I football teams (n = 426) were consented and enrolled., Results: On average, athletes participated in 10% fewer contact practices in 2018. However, the effect of this ruling on preseason head impact burden was mixed. Across all athletes, the total preseason head impact burden was essentially the same from 2017 to 2018. However, this study revealed significant team-by-team differences in preseason head impact burden, with one team demonstrating a 35% increase in the average number of recorded head impacts from 2017 to 2018, despite a modest decrease in the number of contact practices. Other teams had similar or decreased head impact burden., Conclusions: Team-based differences in total preseason head impact burden were attributable to changes in daily practice schedule, with longer practice durations and more intense contact practice sessions contributing to increases in daily head impact exposure that, in turn, led to greater preseason head impact burden. Results of this study have highlighted the difficulty in decreasing contact sport head impact exposure through rule changes targeted at limiting on-field team activities. Future efforts aimed specifically at contact practice duration, daily head impact exposure, or limiting time in specific drills may be more effective at reducing total preseason head impact burden.

Conventional brain injury metrics are scalars that treat the whole head/brain as a single unit but do not characterize the distribution of brain responses. Here, we establish a network-based "response feature matrix" to characterize the magnitude and distribution of impact-induced brain strains. The network nodes and edges encode injury risks to the gray matter regions and their white matter interconnections, respectively. The utility of the metric is illustrated in injury prediction using three independent, real-world datasets: two reconstructed impact datasets from the National Football League (NFL) and Virginia Tech, respectively, and measured concussive and non-injury impacts from Stanford University. Injury predictions with leave-one-out cross-validation are conducted using the two reconstructed datasets separately, and then by combining all datasets into one. Using support vector machine, the network-based injury predictor consistently outperforms four baseline scalar metrics including peak maximum principal strain of the whole brain (MPS), peak linear/rotational acceleration, and peak rotational velocity across all five selected performance measures (e.g., maximized accuracy of 0.887 vs. 0.774 and 0.849 for MPS and rotational acceleration with corresponding positive predictive values of 0.938, 0.772, and 0.800, respectively, using the reconstructed NFL dataset). With sufficient training data, real-world injury prediction is similar to leave-one-out in-sample evaluation, suggesting the potential advantage of the network-based injury metric over conventional scalar metrics. The network-based response feature matrix significantly extends scalar metrics by sampling the brain strains more completely, which may serve as a useful framework potentially allowing for other applications such as characterizing injury patterns or facilitating targeted multi-scale modeling in the future.

Background: Organizations recommend that athletes should be asymptomatic or symptom-limited before initiating a graduated return-to-play (GRTP) protocol after sports-related concussion, although asymptomatic or symptom-limited is not well-defined., Hypotheses: (1) There will be a range (ie, beyond zero as indicator of "symptom-free") in symptom severity endorsement when athletes are deemed ready to initiate a GRTP protocol. (2) Baseline symptom severity scores and demographic/preinjury medical history factors influence symptom severity scores at the commencement of the GRTP protocol. (3) Greater symptom severity scores at GRTP protocol initiation will result in longer protocol duration. (4) Symptom severity scores will not differ between those who did and did not sustain a repeat injury within 90 days of their initial injury., Study Design: Cohort study; Level of evidence, 2., Methods: Across 30 universities, athletes (N = 1531) completed assessments at baseline and before beginning the GRTP protocol, as determined by local medical staff. Symptom severity scores were recorded with the symptom checklist of the Sport Concussion Assessment Tool-3rd Edition. Nonparametric comparisons were used to examine the effect of medical, demographic, and injury factors on symptom endorsement at GRTP protocol initiation, as well as differences in symptom severity scores between those who did and did not sustain a repeat injury within 90 days. A Cox regression was used to examine the association between symptom severity scores at GRTP protocol initiation and protocol duration., Results: Symptom severity scores at the time when the GRTP protocol was initiated were as follows: 0 to 5 (n = 1378; 90.0%), 6 to 10 (n = 76; 5.0%), 11 to 20 (n = 42; 3.0%), and ≥21 (n = 35; 2.0%). Demographic (sex and age), medical (psychiatric disorders, attention-deficit/hyperactivity disorder, learning disorder), and other factors (baseline symptom endorsement and sleep) were significantly associated with higher symptom severity scores at the GRTP initiation ( P < .05). The 4 GRTP initiation time point symptom severity score groups did not significantly differ in total time to unrestricted RTP, χ 2 (3) = 1.4; P = .73. When days until the initiation of the GRTP protocol was included as a covariate, symptom severity scores between 11 and 20 ( P = .02; hazard ratio = 1.44; 95% CI, 1.06-1.96) and ≥21 ( P < .001; hazard ratio = 1.88; 95% CI, 1.34-2.63) were significantly associated with a longer GRTP protocol duration as compared with symptom severity scores between 0 and 5. Symptom severity scores at GRTP initiation did not significantly differ between those who sustained a repeat injury within 90 days and those who did not ( U = 29,893.5; P = .75)., Conclusion: A range of symptom severity endorsement was observed at GRTP protocol initiation, with higher endorsement among those with higher baseline symptom endorsement and select demographic and medical history factors. Findings suggest that initiation of a GRTP protocol before an absolute absence of all symptoms is not associated with longer progression of the GRTP protocol, although symptom severity scores >10 were associated with longer duration of a GRTP protocol. Results can be utilized to guide clinicians toward optimal GRTP initiation (ie, balancing active recovery with avoidance of premature return to activity).

Importance: There is potential scientific and clinical value in validation of objective biomarkers for sport-related concussion (SRC)., Objective: To investigate the association of acute-phase blood biomarker levels with SRC in collegiate athletes., Design, Setting, and Participants: This multicenter, prospective, case-control study was conducted by the National Collegiate Athletic Association (NCAA) and the US Department of Defense Concussion Assessment, Research, and Education (CARE) Consortium from February 20, 2015, to May 31, 2018, at 6 CARE Advanced Research Core sites. A total of 504 collegiate athletes with concussion, contact sport control athletes, and non-contact sport control athletes completed clinical testing and blood collection at preseason baseline, the acute postinjury period, 24 to 48 hours after injury, the point of reporting being asymptomatic, and 7 days after return to play. Data analysis was conducted from March 1 to November 30, 2019., Main Outcomes and Measures: Glial fibrillary acidic protein (GFAP), ubiquitin C-terminal hydrolase-L1 (UCH-L1), neurofilament light chain, and tau were quantified using the Quanterix Simoa multiplex assay. Clinical outcome measures included the Sport Concussion Assessment Tool-Third Edition (SCAT-3) symptom evaluation, Standardized Assessment of Concussion, Balance Error Scoring System, and Brief Symptom Inventory 18., Results: A total of 264 athletes with concussion (mean [SD] age, 19.08 [1.24] years; 211 [79.9%] male), 138 contact sport controls (mean [SD] age, 19.03 [1.27] years; 107 [77.5%] male), and 102 non-contact sport controls (mean [SD] age, 19.39 [1.25] years; 82 [80.4%] male) were included in the study. Athletes with concussion had significant elevation in GFAP (mean difference, 0.430 pg/mL; 95% CI, 0.339-0.521 pg/mL; P < .001), UCH-L1 (mean difference, 0.449 pg/mL; 95% CI, 0.167-0.732 pg/mL; P < .001), and tau levels (mean difference, 0.221 pg/mL; 95% CI, 0.046-0.396 pg/mL; P = .004) at the acute postinjury time point compared with preseason baseline. Longitudinally, a significant interaction (group × visit) was found for GFAP (F7,1507.36 = 16.18, P < .001), UCH-L1 (F7,1153.09 = 5.71, P < .001), and tau (F7,1480.55 = 6.81, P < .001); the interaction for neurofilament light chain was not significant (F7,1506.90 = 1.33, P = .23). The area under the curve for the combination of GFAP and UCH-L1 in differentiating athletes with concussion from contact sport controls at the acute postinjury period was 0.71 (95% CI, 0.64-0.78; P < .001); the acute postinjury area under the curve for all 4 biomarkers combined was 0.72 (95% CI, 0.65-0.79; P < .001). Beyond SCAT-3 symptom score, GFAP at the acute postinjury time point was associated with the classification of athletes with concussion from contact controls (β = 12.298; 95% CI, 2.776-54.481; P = .001) and non-contact sport controls (β = 5.438; 95% CI, 1.676-17.645; P = .005). Athletes with concussion with loss of consciousness or posttraumatic amnesia had significantly higher levels of GFAP than athletes with concussion with neither loss of consciousness nor posttraumatic amnesia at the acute postinjury time point (mean difference, 0.583 pg/mL; 95% CI, 0.369-0.797 pg/mL; P < .001)., Conclusions and Relevance: The results suggest that blood biomarkers can be used as research tools to inform the underlying pathophysiological mechanism of concussion and provide additional support for future studies to optimize and validate biomarkers for potential clinical use in SRC.

Physical differences between youth and adults, which include incomplete myelination, limited neck muscle development, and a higher head-body ratio in the youth population, likely contribute towards the increased susceptibility of youth to concussion. Previous research efforts have considered the biomechanics of concussion for adult populations, but these known age-related differences highlight the necessity of quantifying the risk of concussion for a youth population. This study adapted the previously developed Generalized Acceleration Model for Brian Injury Threshold (GAMBIT) that combines linear and rotational head acceleration to model the risk of concussion for a youth population with the Generalized Acceleration Model for Concussion in Youth (GAM-CY). Survival analysis was used in conjunction with head impact data collected during participation in youth football to model risk between individuals who sustained medically-diagnosed concussions (n = 15). Receiver operator characteristic curves were generated for peak linear acceleration, peak rotational acceleration, and GAM-CY, all of which were observed to be better injury predictors than random guessing. GAM-CY was associated with an area under the curve of 0.89 (95% confidence interval: 0.82-0.95) when all head impacts experienced by the concussed players were considered. Concussion tolerance was observed to be lower for youth athletes, with average peak linear head acceleration of 62.4 ± 29.7 g compared to 102.5 ± 32.7 g for adults and average peak rotational head acceleration of 2609 ± 1591 rad/s 2 compared to 4412 ± 2326 rad/s 2 . These data provide further evidence of age-related differences in concussion tolerance and may be used for the development of youth-specific protective designs.

Studies of football athletes have implicated repetitive head impact exposure in the onset of cognitive and brain structural changes, even in the absence of diagnosed concussion. Those studies imply accumulating damage from successive head impacts reduces tolerance and increases risk for concussion. Support for this premise is that biomechanics of head impacts resulting in concussion are often not remarkable when compared to impacts sustained by athletes without diagnosed concussion. Accordingly, this analysis quantified repetitive head impact exposure in a cohort of 50 concussed NCAA Division I FBS college football athletes compared to controls that were matched for team and position group. The analysis quantified the number of head impacts and risk weighted exposure both on the day of injury and for the season to the date of injury. 43% of concussed athletes had the most severe head impact exposure on the day of injury compared to their matched control group and 46% of concussed athletes had the most severe head impact exposure for the season to the date of injury compared to their matched control group. When accounting for date of injury or season to date of injury, 72% of all concussed athletes had the most or second most severe head impact exposure compared to their matched control group. These trends associating cumulative head impact exposure with concussion onset were stronger for athletes that participated in a greater number of contact activities. For example, 77% of athletes that participated in ten or more days of contact activities had greater head impact exposure than their matched control group. This unique analysis provided further evidence for the role of repetitive head impact exposure as a predisposing factor for the onset of concussion. The clinical implication of these findings supports contemporary trends of limiting head impact exposure for college football athletes during practice activities in an effort to also reduce risk of concussive injury.

Researchers have been collecting head impact data from instrumented football players to characterize the biomechanics of concussion for the past 15 years, yet the link between biomechanical input and clinical outcome is still not well understood. We have previously shown that even though concussive biomechanics might be unremarkable in large datasets of head impacts, the impacts causing injury are of high magnitude for the concussed individuals relative to their impact history. This finding suggests a need to account for differences in tolerance at the individual level. In this study, we identified control subjects for our concussed subjects who demonstrated traits we believed were correlated to factors thought to affect injury tolerance, including height, mass, age, race, and concussion history. A total of 502 college football players were instrumented with helmet-mounted accelerometer arrays and provided complete baseline assessment data, 44 of which sustained a total of 49 concussion. Biomechanical measures quantifying impact frequency and acceleration magnitude were compared between groups. On average, we found that concussed subjects experienced 93.8 more head impacts (p = 0.0031), 10.2 more high magnitude impacts (p = 0.0157), and 1.9 × greater risk-weighted exposure (p = 0.0175) than their physically matched controls. This finding provides further evidence that head impact data need to be considered at the individual level and that cohort wide assessments may be of little value in the context of concussion.

Previous studies have shown that mild traumatic brain injury (mTBI) can cause abnormalities in clinically relevant magnetic resonance imaging (MRI) sequences. No large-scale study, however, has prospectively assessed this in athletes with sport-related concussion (SRC). The aim of the current study was to characterize and compare the prevalence of acute, trauma-related MRI findings and clinically significant, non-specific MRI findings in athletes with and without SRC. College and high-school athletes were prospectively enrolled and participated in scanning sessions between January 2015 through August 2017. Concussed contact sport athletes ( n  = 138; 14 female [F]; 19.5 ± 1.6 years) completed up to four scanning sessions after SRC. Non-concussed contact ( n  = 135; 15 F; 19.7 ± 1.6) and non-contact athletes ( n  = 96; 15 F; 20.0 ± 1.7) completed similar scanning sessions and served as controls. Board-certified neuroradiologists, blinded to SRC status, reviewed T 1 -weighted and T 2 -weighted fluid-attenuated inversion recovery and T 2 *-weighted and T 2 -weighted images for acute (i.e., injury-related) or non-acute findings that prompted recommendation for clinical follow-up. Concussed athletes were more likely to have MRI findings relative to contact (30.4% vs. 15.6%; odds ratio [OR] = 2.32; p  = 0.01) and non-contact control athletes (19.8%; OR = 2.11; p  = 0.04). Female athletes were more likely to have MRI findings than males (43.2% vs. 19.4%; OR = 2.62; p  = 0.01). One athlete with SRC had an acute, injury-related finding; group differences were largely driven by increased rate of non-specific white matter hyperintensities in concussed athletes. This prospective, large-scale study demonstrates that <1% of SRCs are associated with acute injury findings on qualitative structural MRI, providing empirical support for clinical guidelines that do not recommend use of MRI after SRC.

During normal participation in football, players are exposed to repetitive subconcussive head impacts, or impacts that do not result in signs and symptoms of concussion. To better understand the effects of repetitive subconcussive impacts, the biomechanics of on-field head impacts and resulting brain deformation need to be well characterized. The current study evaluates local brain response to typical youth football head impacts using the atlas-based brain model (ABM), an anatomically accurate brain finite element (FE) model. Head impact kinematic data were collected from three local youth football teams using the Head Impact Telemetry (HIT) System. The azimuth and elevation angles were used to identify impacts near six locations of interest, and low, moderate, and high acceleration magnitudes (5th, 50th, and 95th percentiles, respectively) were calculated from the grouped impacts for FE simulation. Strain response in the brain was evaluated by examining the range and peak maximum principal strain (MPS) values in each element. A total of 40,538 impacts from 119 individual athletes were analyzed. Impacts to the facemask resulted in 0.18 MPS for the high magnitude impact category. This was 1.5 times greater than the oblique impact location, which resulted in the lowest strain value of 0.12 for high magnitude impacts. Overall, higher strains resulted from a 95th percentile lateral impact (41.0 g , 2556 rad/sec 2 ) with two predominant axes of rotation than from a 95th percentile frontal impact (67.6 g , 2641 rad/sec 2 ) with a single predominant axis of rotation. These findings highlight the importance of accounting for directional dependence and relative contribution of axes of rotation when evaluating head impact response.

Kinematic measurements of head impacts are sensitive to sports concussion, but not highly specific. One potential reason is these measures reflect input conditions only and may have varying degrees of correlation to regional brain tissue deformation. In this study, previously reported head impact data recorded in the field from high school and collegiate football players were analyzed using two finite element head models (FEHM). Forty-five impacts associated with immediately diagnosed concussion were simulated along with 532 control impacts without identified concussion obtained from the same players. For each simulation, intracranial response measures (max principal strain, strain rate, von Mises stress, and pressure) were obtained for the whole brain and within four regions of interest (ROI; cerebrum, cerebellum, brain stem, corpus callosum). All response measures were sensitive to diagnosed concussion; however, large inter-athlete variability was observed and sensitivity strength depended on measure, ROI, and FEHM. Interestingly, peak linear acceleration was more sensitive to diagnosed concussion than all intracranial response measures except pressure. These findings suggest FEHM may provide unique and potentially important information on brain injury mechanisms, but estimations of concussion risk based on individual intracranial response measures evaluated in this study did not improve upon those derived from input kinematics alone.

Background: Timely removal from activity after concussion symptoms remains problematic despite heightened awareness. Previous studies indicated potential adverse effects of continuing to participate in physical activity immediately after sustaining a concussion. Hypothesis/Purpose: The purpose was to determine the effect of timing of removal from play after concussion on clinical outcomes. It was hypothesized that immediate removal from activity after sport-related concussion (SRC) would be associated with less time missed from sport, a shorter symptomatic period, and better outcomes on acute clinical measures., Study Design: Cohort study; Level of evidence, 3., Methods: Data were reported from the National Collegiate Athletic Association and Department of Defense Grand Alliance: Concussion Awareness, Research, and Education (CARE) Consortium. Participants with 506 diagnosed SRCs from 18 sports and 25 institutions and military service academies were analyzed and classified as either immediate removal from activity (I-RFA) or delayed removal from activity (D-RFA). Outcomes of interest included time missed from sport attributed to their SRC, symptom duration, and clinical assessment scores., Results: There were 322 participants (63.6%) characterized as D-RFA. I-RFA status was associated with significantly less time missed from sport ( R 2 change = .022-.024, P < .001 to P = .001) and shorter symptom duration ( R 2 change = .044-.046, P < .001 [all imputations]) while controlling for other SRC recovery modifiers. These athletes missed approximately 3 fewer days from sport participation. I-RFA athletes had significantly less severe acute SRC symptoms and were at lower risk of recovery taking ≥14 days (relative risk = .614, P < .001, small-medium effect size) and ≥21 days (relative risk = .534, P = .010, small effect size)., Conclusion: I-RFA is a protective factor associated with less severe acute symptoms and shorter recovery after SRC. Conveying this message to athletes, coaches, and others involved in the care of athletes may promote timely injury reporting.

Postural control testing is often used by clinicians and athletic trainers to assess the health of athletes during recovery from a concussion. Characterization of postural control as a clinical tool for use with youth athletes is limited though. The objective of this study was to compare performance on the Balance Error Scoring System (BESS) and a force plate protocol at the beginning and end of a season of football within a cohort of 34 healthy youth football players (average age of 9.9 ± 0.6 y). A secondary aim was to investigate if changes in measures of balance from the postseason to the preseason were correlated with head impact exposure. Players completed testing at the beginning and end of the youth football season. There were no significant differences between BESS scores before the season and after the season (P = .54). Performance on the BESS was not associated with any of the center of pressure metrics considered in this study. No correlation was observed between measures of balance and head impact exposure for the season. Further research is required to determine the viability of postural control testing with this population.

Concussion is a brain injury induced by biomechanical forces that is broadly defined as a complex pathophysiological process affecting the brain. The intricate link between biomechanical input and concussion injury response is poorly understood. We aimed to test the hypothesis that greater biomechanical forces would result in the presentation of more concussion-related symptoms that would take longer to resolve. The objective of this study was to investigate the relationship between an array of biomechanical parameters measured for concussive impacts and the presentation and resolution of concussion symptoms. A total of 319 collegiate football players from six universities were recruited to participate in this study. Certified athletic trainers and/or team physicians at each site diagnosed and treated concussions sustained by subjects through participation in football. The subjects' helmets were instrumented with accelerometer arrays that measured linear and rotational head accelerations for each impact experienced during games and practices. Correlations between biomechanical measurements associated with concussion symptom presentation and recovery were quantified. A total of 22 subjects collectively sustained 25 concussions, with three subjects sustaining two concussions each. Biomechanical measures associated with injury were not found to be correlated with number of symptoms, Sport Concussion Assessment Tool 3 Symptom Severity Score, or time to symptom resolution. Linear and rotational accelerations associated with injury were not correlated with symptom severity for any of the 22 individual symptoms evaluated. Further, we found no association between impact location and presence of any individual symptom when ignoring severity grade. While concussive impacts did not stand out relative to impacts that did not result in injury, concussive impacts were among the most severe for each individual player. This suggests tolerance to head acceleration might be individual-specific, meaning similar biomechanical inputs can produce different injury presentations between individuals. Future investigations should consider individual-specific analyses of tolerance to head acceleration and injury response.

OBJECTIVE Even in the absence of a clinically diagnosed concussion, research suggests that neurocognitive changes may develop in football players as a result of frequent head impacts that occur during football games and practices. The objectives of this study were to determine the specific situations in which high-magnitude impacts (accelerations exceeding 40 g) occur in youth football games and practices and to assess how representative practice activities are of games with regard to high-magnitude head impact exposure. METHODS A total of 45 players (mean age 10.7 ± 1.1 years) on 2 youth teams (Juniors [mean age 9.9 ± 0.6 years; mean body mass 38.9 ± 9.9 kg] and Seniors [mean age 11.9 ± 0.6 years; mean body mass 51.4 ± 11.8 kg]) wore helmets instrumented with accelerometer arrays to record head impact accelerations for all practices and games. Video recordings from practices and games were used to verify all high-magnitude head impacts, identify specific impact characteristics, and determine the amount of time spent in each activity. RESULTS A total of 7590 impacts were recorded, of which 571 resulted in high-magnitude head impact accelerations exceeding 40 g (8%). Impacts were characterized based on the position played by the team member who received the impact, the part of the field where the impact occurred, whether the impact occurred during a game or practice play, and the cause of the impact. High-magnitude impacts occurred most frequently in the open field in both games (59.4%) and practices (67.5%). "Back" position players experienced a greater proportion of high-magnitude head impacts than players at other positions. The 2 teams in this study structured their practice sessions similarly with respect to time spent in each drill, but impact rates differed for each drill between the teams. CONCLUSIONS High-magnitude head impact exposure in games and practice drills was quantified and used as the basis for comparison of exposure in the 2 settings. In this cohort, game impact rates exceeded those for practice. Back players, who were often positioned in the open field, were shown to experience elevated levels of head impact exposure relative to players at other positions. The analysis also suggests that practice intensity, which may be influenced by coaching style, may also affect high-magnitude head impact exposure. Future studies should investigate this aspect as a factor affecting head impact exposure.

Context: With an estimated 3.8 million sport- and recreation-related concussions occurring annually, targeted prevention and diagnostic methods are needed. Biomechanical analysis of head impacts may provide quantitative information that can inform both prevention and diagnostic strategies., Objective: To assess available head-impact devices and their clinical utility., Data Sources: We performed a systematic search of the electronic database PubMed for peer-reviewed publications, using the following phrases: accelerometer and concussion, head impact telemetry, head impacts and concussion and sensor, head impacts and sensor, impact sensor and concussion, linear acceleration and concussion, rotational acceleration and concussion, and xpatch concussion. In addition to the literature review, a Google search for head impact monitor and concussion monitor yielded 15 more devices., Study Selection: Included studies were performed in vivo, used commercially available devices, and focused on sport-related concussion., Data Extraction: One author reviewed the title and abstract of each study for inclusion and exclusion criteria and then reviewed each full-text article to confirm inclusion criteria. Controversial articles were reviewed by all authors to reach consensus., Data Synthesis: In total, 61 peer-reviewed articles involving 4 head-impact devices were included. Participants in boxing, football, ice hockey, soccer, or snow sports ranged in age from 6 to 24 years; 18% (n = 11) of the studies included female athletes. The Head Impact Telemetry System was the most widely used device (n = 53). Fourteen additional commercially available devices were presented., Conclusions: Measurements collected by impact monitors provided real-time data to estimate player exposure but did not have the requisite sensitivity to concussion. Proper interpretation of previously reported head-impact kinematics across age, sport, and position may inform future research and enable staff clinicians working on the sidelines to monitor athletes. However, head-impact-monitoring systems have limited clinical utility due to error rates, designs, and low specificity in predicting concussive injury.

OBJECTIVE Although 70% of football players in the United States are youth players (6-14 years old), most research on head impacts in football has focused on high school, collegiate, or professional populations. The objective of this study was to identify the specific activities associated with high-magnitude (acceleration > 40g) head impacts in youth football practices. METHODS A total of 34 players (mean age 9.9 ± 0.6 years) on 2 youth teams were equipped with helmet-mounted accelerometer arrays that recorded head accelerations associated with impacts in practices and games. Videos of practices and games were used to verify all head impacts and identify specific drills associated with each head impact. RESULTS A total of 6813 impacts were recorded, of which 408 had accelerations exceeding 40g (6.0%). For each type of practice drill, impact rates were computed that accounted for the length of time that teams spent on each drill. The tackling drill King of the Circle had the highest impact rate (95% CI 25.6-68.3 impacts/hr). Impact rates for tackling drills (those conducted without a blocker [95% CI 14.7-21.9 impacts/hr] and those with a blocker [95% CI 10.5-23.1 impacts/hr]) did not differ from game impact rates (95% CI 14.2-21.6 impacts/hr). Tackling drills were observed to have a greater proportion (between 40% and 50%) of impacts exceeding 60g than games (25%). The teams in this study participated in tackling or blocking drills for only 22% of their overall practice times, but these drills were responsible for 86% of all practice impacts exceeding 40g. CONCLUSIONS In youth football, high-magnitude impacts occur more often in practices than games, and some practice drills are associated with higher impact rates and accelerations than others. To mitigate high-magnitude head impact exposure in youth football, practices should be modified to decrease the time spent in drills with high impact rates, potentially eliminating a drill such as King of the Circle altogether., Competing Interests: The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper.

Computerized neuropsychological testing has become an important tool in the identification and management of sports-related concussions; however, the psychometric effect of repeat testing has not been studied extensively beyond test-retest statistics. The current study analyzed data from Division I collegiate athletes who completed Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) baseline assessments at four sequential time points that varied over the course of their athletic careers. Administrations were part of a larger National Institutes of Health (NIH) study. Growth curve modeling showed that the two memory composite scores increased significantly with successive administrations: Change in Verbal Memory was best represented with a quadratic model, while a linear model best fit Visual Memory. Visual Motor Speed and Reaction Time composites showed no significant linear or quadratic growth. The results demonstrate the effect of repeated test administrations for memory composite scores, while speed composites were not significantly impacted by repeat testing. Acceptable test-retest reliability was demonstrated for all four composites as well.

Concussions can occur in any sport. Often, clinical and biomechanical research efforts are disconnected. This review paper analyzes current concussion issues in sports from a biomechanical perspective and is geared toward Sports Med professionals. Overarching themes of this review include the biomechanics of the brain during head impact, role of protective equipment, potential population-based differences in concussion tolerance, potential intervention strategies to reduce the incidence of injury, and common biomechanical misconceptions.

Objective: To provide data describing the head impact exposure of 7- to 8-year-old football players., Design: Head impact data were collected from 19 players over the course of 2 seasons using helmet-mounted accelerometer arrays., Setting: Data were collected from 2 youth football teams in Blacksburg, VA, spanning 2 seasons., Participants: A total of 19 youth football players aged 7-8 years., Independent Variables: Type of session (practice or game) and the player's experience., Main Outcome Measures: Head impact frequency, acceleration magnitude, and impact location for games, practices, and the season as a whole were measured., Results: The average instrumented player sustained 9 ± 6 impacts per practice, 11 ± 11 impacts per game, and 161 ± 111 impacts per season. The average instrumented player had a median impact of 16 ± 2 g and 686 ± 169 rad/s and a 95th percentile impact of 38 ± 13 g and 2052 ± 664 rad/s throughout a season. Impacts of 40 g or greater tended to occur more frequently in practices than in games, and practices had a significantly higher 95th percentile impact magnitude than games (P = 0.023). Returning players had significantly more impacts than first time players (P = 0.007)., Conclusions: These data are a further step toward developing effective strategies to reduce the incidence of concussion in youth football and have applications toward youth-specific football helmet designs.

Of all sports, football accounts for the highest incidence of concussion in the US due to the large number of athletes participating and the nature of the sport. While there is general agreement that concussion incidence can be reduced through rule changes and teaching proper tackling technique, there remains debate as to whether helmet design may also reduce the incidence of concussion. A retrospective analysis was performed of head impact data collected from 1833 collegiate football players who were instrumented with helmet-mounted accelerometer arrays for games and practices. Data were collected between 2005 and 2010 from 8 collegiate football teams: Virginia Tech, University of North Carolina, University of Oklahoma, Dartmouth College, Brown University, University of Minnesota, Indiana University, and University of Illinois. Concussion rates were compared between players wearing Riddell VSR4 and Riddell Revolution helmets while controlling for the head impact exposure of each player. A total of 1,281,444 head impacts were recorded, from which 64 concussions were diagnosed. The relative risk of sustaining a concussion in a Revolution helmet compared with a VSR4 helmet was 46.1% (95% CI 28.1%-75.8%). When controlling for each player's exposure to head impact, a significant difference was found between concussion rates for players in VSR4 and Revolution helmets (χ(2) = 4.68, p = 0.0305). This study illustrates that differences in the ability to reduce concussion risk exist between helmet models in football. Although helmet design may never prevent all concussions from occurring in football, evidence illustrates that it can reduce the incidence of this injury.

Recent research has suggested possible long term effects due to repetitive concussions, highlighting the importance of developing methods to accurately quantify concussion risk. This study introduces a new injury metric, the combined probability of concussion, which computes the overall risk of concussion based on the peak linear and rotational accelerations experienced by the head during impact. The combined probability of concussion is unique in that it determines the likelihood of sustaining a concussion for a given impact, regardless of whether the injury would be reported or not. The risk curve was derived from data collected from instrumented football players (63,011 impacts including 37 concussions), which was adjusted to account for the underreporting of concussion. The predictive capability of this new metric is compared to that of single biomechanical parameters. The capabilities of these parameters to accurately predict concussion incidence were evaluated using two separate datasets: the Head Impact Telemetry System (HITS) data and National Football League (NFL) data collected from impact reconstructions using dummies (58 impacts including 25 concussions). Receiver operating characteristic curves were generated, and all parameters were significantly better at predicting injury than random guessing. The combined probability of concussion had the greatest area under the curve for all datasets. In the HITS dataset, the combined probability of concussion and linear acceleration were significantly better predictors of concussion than rotational acceleration alone, but not different from each other. In the NFL dataset, there were no significant differences between parameters. The combined probability of concussion is a valuable method to assess concussion risk in a laboratory setting for evaluating product safety.

Purpose: Concussions are commonly undiagnosed in an athletic environment because the postinjury signs and symptoms may be mild, masked by the subject, or unrecognized. This study compares measures of head impact frequency, location, and kinematic response before cases of immediate and delayed concussion diagnosis., Methods: Football players from eight collegiate and six high school teams wore instrumented helmets during play (n = 1208), of which 95 were diagnosed with concussion (105 total cases). Acceleration data recorded by the instrumented helmets were reduced to five kinematic metrics: peak linear and rotational acceleration, Gadd severity index, head injury criterion, and change in head velocity (Δv). In addition, each impact was assigned to one of four general location regions (front, back, side, and top), and the number of impacts sustained before injury was calculated over two periods (1 and 7 days)., Results: All head kinematic measures associated with injury, except peak rotational acceleration (P = 0.284), were significantly higher for cases of immediate diagnosis than delayed diagnosis (P < 0.05). Players with delayed diagnosis sustained a significantly higher number of head impacts on the day of injury (32.9 ± 24.9, P < 0.001) and within 7 d of injury (69.7 ± 43.3, P = 0.006) than players with immediate diagnosis (16.5 ± 15.1 and 50.2 ± 43.6). Impacts associated with concussion occurred most frequently to the front of the head (46%) followed by the top (25%), side (16%), and back (13%) with the number of impacts by location independent of temporal diagnosis (χ(3) = 4.72, P = 0.19)., Conclusions: Concussions diagnosed immediately after an impact event are associated with the highest kinematic measures, whereas those characterized by delayed diagnosis are preceded by a higher number of impacts.

Purpose: This study compares the frequency and severity of head impacts sustained by football players on days with and without diagnosed concussion and to identify the sensitivity and specificity of single-impact severity measures to diagnosed injury., Methods: One thousand two hundred eight players from eight collegiate football teams and six high school football teams wore instrumented helmets to measure head impacts during all team sessions, of which 95 players were diagnosed with concussion. Eight players sustained two injuries and one sustained three, providing 105 injury cases. Measures of head kinematics (peak linear and rotational acceleration, Gadd severity index, head injury criteria (HIC15), and change in head velocity (Δv)) and the number of head impacts sustained by individual players were compared between days with and without diagnosed concussion. Receiver operating characteristic curves were generated to evaluate the sensitivity and specificity of each kinematic measure to diagnosed concussion using only those impacts that directly preceded diagnosis., Results: Players sustained a higher frequency of impacts and impacts with more severe kinematic properties on days of diagnosed concussion than on days without diagnosed concussion. Forty-five injury cases were immediately diagnosed after head impact. For these cases, peak linear acceleration and HIC15 were most sensitive to immediately diagnosed concussion (area under the curve = 0.983). Peak rotational acceleration was less sensitive to diagnosed injury than all other kinematic measures (P = 0.01), which are derived from linear acceleration (peak linear, HIC15, Gadd severity index, and Δv)., Conclusions: Players sustained more impacts and impacts of higher severity on days of diagnosed concussion than on days without diagnosed concussion. In addition, of historical measures of impact severity, those associated with peak linear acceleration are the best predictors of immediately diagnosed concussion.

Object: Concussive head injuries have received much attention in the medical and public arenas, as concerns have been raised about the potential short- and long-term consequences of injuries sustained in sports and other activities. While many student athletes have required evaluation after concussion, the exact definition of concussion has varied among disciplines and over time. The authors used data gathered as part of a multiinstitutional longitudinal study of the biomechanics of head impacts in helmeted collegiate athletes to characterize what signs, symptoms, and clinical histories were used to designate players as having sustained concussions., Methods: Players on 3 college football teams and 4 ice hockey teams (male and female) wore helmets instrumented with Head Impact Telemetry (HIT) technology during practices and games over 2-4 seasons of play. Preseason clinical screening batteries assessed baseline cognition and reported symptoms. If a concussion was diagnosed by the team medical staff, basic descriptive information was collected at presentation, and concussed players were reevaluated serially. The specific symptoms or findings associated with the diagnosis of acute concussion, relation to specific impact events, timing of symptom onset and diagnosis, and recorded biomechanical parameters were analyzed., Results: Data were collected from 450 athletes with 486,594 recorded head impacts. Forty-eight separate concussions were diagnosed in 44 individual players. Mental clouding, headache, and dizziness were the most common presenting symptoms. Thirty-one diagnosed cases were associated with an identified impact event; in 17 cases no specific impact event was identified. Onset of symptoms was immediate in 24 players, delayed in 11, and unspecified in 13. In 8 cases the diagnosis was made immediately after a head impact, but in most cases the diagnosis was delayed (median 17 hours). One diagnosed concussion involved a 30-second loss of consciousness; all other players retained alertness. Most diagnoses were based on self-reported symptoms. The mean peak angular and rotational acceleration values for those cases associated with a specific identified impact were 86.1 ± 42.6g (range 16.5-177.9 g) and 3620 ± 2166 rad/sec( 2 ) (range 183-7589 rad/sec( 2 )), respectively., Conclusions: Approximately two-thirds of diagnosed concussions were associated with a specific contact event. Half of all players diagnosed with concussions had delayed or unclear timing of onset of symptoms. Most had no externally observed findings. Diagnosis was usually based on a range of self-reported symptoms after a variable delay. Accelerations clustered in the higher percentiles for all impact events, but encompassed a wide range. These data highlight the heterogeneity of criteria for concussion diagnosis, and in this sports context, its heavy reliance on self-reported symptoms. More specific and standardized definitions of clinical and objective correlates of a "concussion spectrum" may be needed in future research efforts, as well as in the clinical diagnostic arena.

Some reports have shown that head injuries in baseball may comprise up to 18.5% of all competitive sports-related head injuries. The objective of this study was to evaluate the response of catcher and umpire masks to impacts at these different regions to discover the impact conditions that represent the greatest risk of injury. A series of 10 events in which a catcher or umpire in Major League Baseball, who experienced a foul ball to the mask that resulted in a concussion, were analyzed through video and data on pitch characteristics. It was found that the impacts were distributed across the face, and the median plate speed was approximately 38 m/s (84 mph). To determine the relative severity of each identified impact location, an instrumented Hybrid III head outfitted with a catcher or umpire mask was impacted with baseballs. Testing at 27 and 38 m/s (60 and 84 mph) suggested that impacts to the center-eyebrow and chin locations were the most severe. Peak linear and rotational accelerations were found to be lower than the suggested injury thresholds. While impacts to a mask result in head accelerations which are near or below levels commonly associated with the lower limits for head injury, the exact injury mechanism is unclear, as concussions are still experienced by the mask wearers.

Recent research has suggested a possible link between sports-related concussions and neurodegenerative processes, highlighting the importance of developing methods to accurately quantify head impact tolerance. The use of kinematic parameters of the head to predict brain injury has been suggested because they are indicative of the inertial response of the brain. The objective of this study is to characterize the rotational kinematics of the head associated with concussive impacts using a large head acceleration dataset collected from human subjects. The helmets of 335 football players were instrumented with accelerometer arrays that measured head acceleration following head impacts sustained during play, resulting in data for 300,977 sub-concussive and 57 concussive head impacts. The average sub-concussive impact had a rotational acceleration of 1230 rad/s(2) and a rotational velocity of 5.5 rad/s, while the average concussive impact had a rotational acceleration of 5022 rad/s(2) and a rotational velocity of 22.3 rad/s. An injury risk curve was developed and a nominal injury value of 6383 rad/s(2) associated with 28.3 rad/s represents 50% risk of concussion. These data provide an increased understanding of the biomechanics associated with concussion and they provide critical insight into injury mechanisms, human tolerance to mechanical stimuli, and injury prevention techniques.

For several years, Virginia Tech and other schools have measured the frequency and severity of head impacts sustained by collegiate American football players in real time using the Head Impact Telemetry (HIT) System of helmet-mounted accelerometers. In this study, data from 37,128 head impacts collected at Virginia Tech during games from 2006 to 2010 were analyzed. Peak head acceleration exceeded 100 g in 516 impacts, and the Head Injury Criterion (HIC) exceeded 200 in 468 impacts. Four instrumented players in the dataset sustained a concussion. These data were used to develop risk curves for concussion as a function of peak head acceleration and HIC. The validity of this biomechanical approach was assessed using epidemiological data on concussion incidence from other sources. Two specific aspects of concussion incidence were addressed: the variation by player position, and the frequency of repeat concussions. The HIT System data indicated that linemen sustained the highest overall number of head impacts, while skill positions sustained a higher number of more severe head impacts (peak acceleration > 100 g or HIC > 200). When weighted using injury risk curves, the HIT System data predicted a higher incidence of concussion in skill positions compared to linemen at rates that were in strong agreement with the epidemiological literature (Pearson's r = 0.72-0.87). The predicted rates of repeat concussions (21-39% over one season and 33-50% over five seasons) were somewhat higher than the ranges reported in the epidemiological literature. These analyses demonstrate that simple biomechanical parameters that can be measured by the HIT System possess a high level of power for predicting concussion.

In American football, impacts to the helmet and the resulting head accelerations are the primary cause of concussion injury and potentially chronic brain injury. The purpose of this study was to quantify exposures to impacts to the head (frequency, location and magnitude) for individual collegiate football players and to investigate differences in head impact exposure by player position. A total of 314 players were enrolled at three institutions and 286,636 head impacts were recorded over three seasons. The 95th percentile peak linear and rotational acceleration and HITsp (a composite severity measure) were 62.7g, 4378rad/s(2) and 32.6, respectively. These exposure measures as well as the frequency of impacts varied significantly by player position and by helmet impact location. Running backs (RB) and quarter backs (QB) received the greatest magnitude head impacts, while defensive line (DL), offensive line (OL) and line backers (LB) received the most frequent head impacts (more than twice as many than any other position). Impacts to the top of the helmet had the lowest peak rotational acceleration (2387rad/s(2)), but the greatest peak linear acceleration (72.4g), and were the least frequent of all locations (13.7%) among all positions. OL and QB had the highest (49.2%) and the lowest (23.7%) frequency, respectively, of front impacts. QB received the greatest magnitude (70.8g and 5428rad/s(2)) and the most frequent (44% and 38.9%) impacts to the back of the helmet. This study quantified head impact exposure in collegiate football, providing data that is critical to advancing the understanding of the biomechanics of concussive injuries and sub-concussive head impacts., (Copyright © 2011 Elsevier Ltd. All rights reserved.)