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Symptom inventories are generally only collected after a suspected concussion, but regular in-season monitoring may allude to clinical symptoms associated with repetitive subconcussive impacts and potential undiagnosed concussions. Despite sex-specific differences in symptom presentation and outcome of concussion, no return-to-play protocol takes sex into account. The objective of this study was to monitor a cohort of contact-sport athletes and compare the frequency and severity of in-season concussion-like symptom reporting between sexes. Graded symptom checklists from 144 female and 104 male athlete-seasons were administered weekly to quantify the effect of subconcussive impacts on frequency and severity of in-season symptom reporting. In-season, mean symptom severity score (SSS) (p = 0.026, mean difference of 1.8), mean number of symptoms (p = 0.044, mean difference of 0.9), max SSS (p < 0.001, mean difference of 19.2), and max number of symptoms (p < 0.001, mean difference of 6.8) were higher in the females. The females' survey results showed differences between elevated and concussed SSS (p < 0.005, mean difference of 28.1) and number of symptoms reported (p = 0.001, mean difference of 6.6). The males did not have a difference in SSS (p = 0.97, mean difference of 1.12) nor in number of symptoms (p = 0.35, mean difference of 1.96) from elevated to concussed athletes. Rugby players report concussion-like symptoms in the absence of a diagnosed concussion in-season. Female athletes reported elevated symptom frequencies with greater severities than the males, but both sexes reported considerable levels throughout the season. Published version

The best way to prevent severe head injury when cycling is to wear a bike helmet. To reduce the rate of head injury in cycling, knowing the nature of real-world head impacts is crucial. Reverse engineering real-world bike helmet impacts in a laboratory setting is an alternative to measuring head impacts directly. This study aims to quantify bike helmet damage using computed tomography (CT) and reconstruct real-world damage with a custom, oblique test rig to recreate real-world impacts. Damaged helmets were borrowed from a helmet manufacturer who runs a helmet warranty program. Each helmet was CT-scanned and the damage metrics were quantified. Helmets of the same model and size were used for in-lab reconstructions of the damaged helmets where normal velocity, tangential velocity, peak linear acceleration (PLA) and peak rotational velocity (PRV) could be measured. The damage metrics of the in-lab dropped helmets were quantified using the same CT scanning process. For each case, a multiple linear regression (MLR) equation was created to define a relationship between the quantified damage metrics of the in-lab tested helmets and the associated measured impact velocities and kinematics. These equations were used to predict the impact kinematics and velocities from the corresponding real-world damaged helmet based on the damage metrics from the original damaged helmet. Average normal velocity (3.5 m/s), tangential velocity (2.5 m/s), PLA (108.0 g), PRV (15.7 rad/s) were calculated based on a sample of 23 helmets. Within these head impact cases, five notes reported a concussion. The difference between the average PLA and PRV for concussive cases versus other impacts were not significantly different, although the average impact kinematics for the concussive cases (PLA = 111.4 g, PRV = 18.5 rad/s) were slightly higher than the remaining cases (PLA = 107.1 g, PRV = 15.0 rad/s). The concussive cases were not indicative of high magnitude impact kinematics.

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 (95(th) percentile peak linear acceleration (PLA, g) competition: 45.6 vs 61.9; 95(th) percentile PLA practice: 42.6 vs 58.8; 95(th) percentile peak rotational acceleration (PRA, rads·(−2)) competition: 2262 vs 4422; 95(th) percentile PRA practice: 2081 vs 4052; 95(th) percentile HITsp competition: 25.4 vs 32.8; 95(th) 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.

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.

A youth-specific football helmet testing standard has been proposed to address the physical and biomechanical differences between adult and youth football players. This study sought to relate the proposed youth standard-defined laboratory impacts to on-field head impacts collected from youth football players. Head impact data from 112 youth football players (ages 9–14) were collected through the use of helmet-mounted accelerometer arrays. These head impacts were filtered to only include those that resided in corridors near prescribed National Operating Committee on Standards for Athletic Equipment (NOCSAE) impact locations. Peak linear head acceleration and peak rotational head acceleration magnitudes collected from pneumatic ram impactor tests as specified by the proposed NOCSAE youth standard were compared to the distribution of on-field head impacts. All laboratory impact tests were among the top 10% in terms of magnitude for Severity Index and peak rotational acceleration of matched location head impacts experienced by youth football players. As concussive head impacts are among the most severe impacts experienced on the field, a safety standard geared toward mitigating concussion should assess the most severe on-field head impacts. This proposed testing standard may be refined as more becomes known regarding the biomechanics of concussion among youth athletes.

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.

The absence of evidence-based guidelines make medical disqualification (MDQ) following concussion one of the most challenging decision-making processes faced by sports medicine professionals. We aimed to compare premorbid and postmorbid factors between student-athletes that were and were not medically disqualified from sport following a concussion. Among 1832 student-athletes diagnosed with concussion within the CARE Consortium, 53 (2.9%) were medically disqualified (MDQ +) and 1779 (97.1%) were not medically disqualified (MDQ−). We used contingency tables and descriptive statistics for an initial evaluation of a broad list of premorbid and postmorbid factors. For those factors showing association with MDQ status, we calculated odds ratios and 95% confidence intervals for the odds of being MDQ + in the presence of the identified factor. History of 2 (OR: 3.2, 95% CI 1.5, 6.9) or 3 + (OR: 7.4, 95% CI 3.4, 15.9) previous concussions; 1 + headaches in past 3 months (OR: 1.8, 95% CI 1.0, 3.2); immediate removal from play (OR: 2.4, 95% CI 1.2, 4.9); alcohol (OR: 2.6, 95% CI 1.2, 5.4), tobacco (OR: 3.3, 95% CI 1.1, 9.5), or marijuana use since injury (OR: 5.4, 95% CI 1.5, 19.0); as well as prolonged recovery due to mental health alterations (OR: 5.3, 95% CI 2.0, 14.1) or motivation/malingering (OR: 7.5, 95% CI 3.3, 17.0) increased odds of being MDQ + . The MDQ + group took longer to become asymptomatic relative to the MDQ− group (MDQ + : 23.5 days, 95% CI 15.8, 31.2; MDQ−: 10.6 days, 95% CI 9.5, 11.6; p

Youth football helmet testing standards have served to largely eliminate catastrophic head injury from the sport. These standards, though, do not presently consider concussion and do not offer consumers the capacity to differentiate the impact performance of youth football helmets. This study adapted the previously developed Summation of Tests for the Analysis of Risk (STAR) equation for youth football helmet assessment. This adaptation made use of a youth-specific testing surrogate, on-field data collected from youth football players, and a concussion risk function developed for youth athletes. Each helmet is subjected to 48 laboratory impacts across 12 impact conditions. Peak linear head acceleration and peak rotational head acceleration values from each laboratory impact are aggregated into a single STAR value that combines player exposure and risk of concussion. This single value can provide consumers with valuable information regarding the relative performance of youth football helmets.

To reduce head impact exposure, a number of youth football organizations have begun teaching tackling technique. This study sought to develop and utilize a set of tackling form grading criteria for both tacklers and ball carriers as well as to relate tackling form to head acceleration outcome. It was hypothesized that players exhibiting better form would experience lower head accelerations in individual tackles. Three teams consisting of 67 players (20, 29, and 18 players per team; age 12.7 ± 0.95, age range 11–14 years, body mass 55.1 ± 16.2 kg) were instrumented with helmet-mounted accelerometer arrays for one season. Videos of close-range tackling drills were used to develop tackling form criteria. Two raters used these criteria to each score 105 impacts. Six of the seven categories were found to have good inter-rater reliability metrics (total percent agreement ≥ 79%, agreement coefficient ≥ 0.65). Bending at both the hips and knees, leading with the shoulder or arm, and initiating contact were found to reduce risk of high-magnitude (>40 g) head impacts in tacklers. Keeping the eyes up and avoiding dropping the head reduced the risk of high-magnitude head impacts in ball carriers. This study shows the potential effectiveness of training both tacklers and ball carriers in proper technique for collisions in youth football to minimize head impacts.

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.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 hours 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.A total of 140 athletes with concussion (79.3% male; aged 18.71 ± 1.10 years, mean ± SD) and 21 non-concussed athletes (76.2% male; 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 of 0.954 (95% confidence interval: 0.922‒0.986). Specifically, after 6 h of injury, the individual area under the curve of plasma erythrocyte membrane protein band 4.1 (EPB41) and alpha-synuclein were 0.956 and 0.875, respectively. The combination of EPB41 and alpha-synuclein provided the best area under the curve (1.000), which suggests this combination of candidate plasma biomarkers is best for diagnosing concussion in athletes after 6 h of injury.Our data suggest that proteomic profiling may provide novel diagnostic protein markers and that a combination of EPB41 and alpha-synuclein is the most predictive biomarker of concussion after 6 h of injury.

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 .

ObjectivesAssess the validity and feasibility of current instrumented mouthguards (iMGs) and associated systems.MethodsPhase I; four iMG systems (Biocore-Football Research Inc (FRI), HitIQ, ORB, Prevent) were compared against dummy headform laboratory criterion standards (25, 50, 75, 100 g). Phase II; four iMG systems were evaluated for on-field validity of iMG-triggered events against video-verification to determine true-positives, false-positives and false-negatives (20±9 player matches per iMG). Phase III; four iMG systems were evaluated by 18 rugby players, for perceptions of fit, comfort and function. Phase IV; three iMG systems (Biocore-FRI, HitIQ, Prevent) were evaluated for practical feasibility (System Usability Scale (SUS)) by four practitioners.ResultsPhase I; total concordance correlation coefficients were 0.986, 0.965, 0.525 and 0.984 for Biocore-FRI, HitIQ, ORB and Prevent. Phase II; different on-field kinematics were observed between iMGs. Positive predictive values were 0.98, 0.90, 0.53 and 0.94 for Biocore-FRI, HitIQ, ORB and Prevent. Sensitivity values were 0.51, 0.40, 0.71 and 0.75 for Biocore-FRI, HitIQ, ORB and Prevent. Phase III; player perceptions of fit, comfort and function were 77%, 6/10, 55% for Biocore-FRI, 88%, 8/10, 61% for HitIQ, 65%, 5/10, 43% for ORB and 85%, 8/10, 67% for Prevent. Phase IV; SUS (preparation-management) was 51.3–50.6/100, 71.3–78.8/100 and 83.8–80.0/100 for Biocore-FRI, HitIQ and Prevent.ConclusionThis study shows differences between current iMG systems exist. Sporting organisations can use these findings when evaluating which iMG system is most appropriate to monitor head acceleration events in athletes, supporting player welfare initiatives related to concussion and head acceleration exposure.

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.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.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.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.

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. National Institute of Neurological Disorders and Stroke of the National Institutes of Health [1R01NS126449]; Grand Alliance Concussion Assessment, Research, and Education (CARE) Consortium - National Collegiate Athletic Association (NCAA); Department of Defense (DOD); Office of the Assistant Secretary of Defense for Health Affairs [W81XWH-14-2-0151]; Veterans Affairs Medical Research Published version Research reported in this publication was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under Award Number 1R01NS126449. 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 the Department of Defense (DOD). The U.S. Army Medical Research Acquisition Activity, 820 Chandler Street, Fort Detrick MD 21702-5014 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. Opinions, interpretations, conclusions and recommendations are those of the author and are not necessarily endorsed by the Department of Defense (DHP funds). This study was also supported by Veterans Affairs Medical Research. The authors would also like to thank Jody Harland, MS, Janetta Matesan, BA, Michael Menser, Larry Riggen, MS (Indiana University School of Medicine); Ashley Rettmann, BS, Nicole L'Heureux, MBA (University of Michigan); Melissa Koschnitzke, MA (Medical College of Wisconsin); Michael Jarrett, MBA, Vibeke Brinck, MS and Bianca Byrne, BA (Quesgen); Melissa Baker, BS (Datalys Center for Sports Injury Research and Prevention); and the research and medical staff at each of the CARE participation sites. The team would also like to acknowledge the HIT System operators that participated in this study: Julia Ford (UNC), Philippe Gagnon-Joseph (UNC), Miranda Kruse (UNC), Gus Hendricks (UCLA), Quinn Lukens (UW), Brett Griesemer (VT), Alex Black (VT), Lewis Young (VT), Dom Calhoun (USAFA), Mike Aderman (USMA), Sean Roach (USMA), and Jeremy Ross (USMA).

Background: Concern for head injuries is widespread and has been reported by the media to be the number one cause of decreased participation in football among the American youth population. Identifying player mechanisms associated with intentional, or purposeful, head impacts should provide critical data for rule modifications, educational programs, and equipment design. Purpose: To investigate the frequency of intentional and unintentional head impacts and to examine the player mechanisms associated with intentional high-magnitude head impacts by comparing the impact mechanism distributions among session type, player position, and ball possession. Study Design: Cross-sectional study; Level of evidence, 3. Methods: Head impact sensors and video footage of 68 players were used to analyze and classify 1319 high-magnitude impacts recorded over 1 season of youth football. Results: In total, 80% of the high-magnitude head impacts were classified as being caused by intentional use of the head. Head-to-head impact was the primary impact mechanism (n = 868; 82.7%) within the 1050 intentional high-magnitude impacts, with classifiable mechanisms, followed by head-to-body (n = 139; 13.2%), head-to-ground (n = 34; 3.2%), and head-to-equipment (n = 9; 0.9%). Head-to-head impacts also accounted for a greater proportion of impacts during practices (n = 625; 88.9%) than games, for linemen (n = 585; 90.3%) than perimeters and backs, and for ball carriers (n = 72; 79.1%) than tacklers. Conclusion: Overall, the majority of high-magnitude head impacts were intentional and resulted from head-to-head contact. The proportion of head-to-head contact was significantly higher for practices than games, linemen than backs and perimeter players, and ball carriers than tacklers.

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/s2 compared to 4412 ± 2326 rad/s2. These data provide further evidence of age-related differences in concussion tolerance and may be used for the development of youth-specific protective designs.

Cycling is a leading cause of mild traumatic brain injury in the US. While bicycle helmets help protect cyclists who crash, limited biomechanical data exist differentiating helmet protective capabilities. This paper describes the development of a bicycle helmet evaluation scheme based in real-world cyclist accidents and brain injury mechanisms. Thirty helmet models were subjected to oblique impacts at six helmet locations and two impact velocities. The summation of tests for the analysis of risk (STAR) equation, which condenses helmet performance from a range of tests into a single value, was used to summarize measured linear and rotational head kinematics in the context of concussion risk. STAR values varied between helmets (10.9-25.3), with lower values representing superior protection. Road helmets produced lower STAR values than urban helmets. Helmets with slip planes produced lower STAR values than helmets without. This bicycle helmet evaluation protocol can educate consumers on the relative impact performance of various helmets and stimulate safer helmet design.

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. Published version

OBJECTIVEYouth football attracts approximately 3.5 million participants every year, but concern has recently arisen about the long-term effects of experiencing repetitive head accelerations from a young age due to participation in football. The objective of this study was to quantify total involvement in high-magnitude impacts among individual players in youth football practices. The authors explored the relationship between the total number of high-magnitude accelerations in which players were involved (experienced either by themselves or by other players) during practices and the number of high-magnitude accelerations players experienced.METHODSA local cohort of 94 youth football players (mean age 11.9 ± 1.5, mean body mass 50.3 ± 16.4 kg) from 4 different teams were recruited and outfitted with helmet-mounted accelerometer arrays. The teams were followed for one season each for a total of 128 sessions (practices, games, and scrimmages). All players involved in high-magnitude (greater than 40g) head accelerations were subsequently identified through analysis of practice film.RESULTSPlayers who experienced more high-magnitude accelerations were more likely to be involved in impacts associated with high-magnitude accelerations in other players. A small subset of 6 players (6%) were collectively involved in 230 (53%) high-magnitude impacts during practice, were involved in but did not experience a high-magnitude acceleration 78 times (21% of the 370 one-sided high-magnitude impacts), and experienced 152 (30%) of the 502 high-magnitude accelerations measured. Quarterbacks/running backs/linebackers were involved in the greatest number of high-magnitude impacts in practice and experienced the greatest number of high-magnitude accelerations. Which team a player was on was an important factor, as one team showed much greater head impact exposure than all others.CONCLUSIONSThis study showed that targeting the most impact-prone players for individualized interventions could reduce high-magnitude acceleration exposure for entire teams. These data will help to further quantify elevated head acceleration exposure and enable data-driven interventions that modify exposure for individual players and entire teams.

Although bicycle helmets are an effective countermeasure against head injury, many cyclists do not wear one. One avenue for facilitating widespread helmet use is through community-driven helmet safety initiatives, which often give away or subsidize wholesale helmet models that are manufactured at a low price point. However, the impact performance of such helmets during real-world accident conditions has yet to be explored. The present study seeks to investigate trends between wholesale bicycle helmet price and protective capabilities.Nine common wholesale helmet models (price range $3.65-$12.95) were evaluated according to the bicycle Summation of Tests for the Analysis of Risk (STAR) methodology, which analyzes helmet performance in 24 oblique impact tests reflecting common cyclist head impact conditions. Resulting head peak linear acceleration (PLA) and peak rotational velocity (PRV) were collected and used to predict risk of concussion. Concussion risks were then combined using the STAR algorithm in order to summarize each model's risks into a single, weighted metric.Large ranges in kinematic results led to large variations in concussion risks between helmets, and in turn, large variations in STAR values (13.5-26.2). Wholesale helmet price was not significantly associated with STAR, although incorporating 30 previous bicycle helmet STAR results produced a weak negative correlation between price and STAR overall. Nonetheless, the best-performing wholesale helmet produced one of the lowest overall STAR values for a price of $6.45. Helmet style was instead a superior predictor of STAR, with multi-sport style helmets producing significantly higher linear accelerations and resulting STAR values than bike style helmets.These results show that the impact performance of wholesale helmets ranges considerably despite their low price-points. Results can also guide helmet safety promotion organizers toward distributing wholesale bicycle helmet models associated with lower overall concussion risks.

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 (

Background Dual-task assessments can identify changes in postural control during recovery from a concussion. However, developing postural control in children presents a challenge when using adult balance assessments to examine children. Purpose The purpose of this study was to investigate the reliability of a cognitive dual-task postural control testing protocol among a youth sample with no history of concussion or exposure to head impacts. Study design Reliability pilot study. Methods Testing comprised nine 120 second trials of standing on a force plate collecting data at 250 Hz. Test conditions included no dual-task, counting backwards by 2, counting backwards by 3, listening, and the Stroop test. Subjects completed each test with open and closed eyes, except for the Stroop test. The force plate was used to measure the subjects' center of pressure (COP) trajectory. Results Nine healthy, youth subjects (average age: 11.6 ± 0.5 years) with no history of concussion or exposure to head impacts participated. Reliability was good (>0.6) or excellent (>0.75) for COP speed, sway, and sample entropy measures for several test conditions. The eyes open, no task condition produced the lowest COP measures. No differences were observed between the other dual-task conditions. Conclusion Given its high measures of reliability, this dual-task protocol might be able to detect postural control changes in concussed youth athletes. Evidence level 2.

Static postural control testing is often conducted by clinicians and athletic trainers for use with athletes who have sustained a concussion. Dynamic postural control involves the body’s response to perturbation of the center of mass and may offer additional insight that static testing cannot capture. The objective of this study was to assess the reliability and feasibility of a balance protocol consisting of both static and dynamic postural control assessments with a healthy, adult population. Subjects stood in both unipedal and bipedal stances on a force plate to capture quantitative data regarding the center of pressure over time. Further, subjects completed the Balance Error Scoring System (BESS), a static measure, and a modified version of the Star Excursion Balance Test (SEBT), a dynamic measure. Reliability with the BESS was limited, while moderate to strong reliability was obtained for the modified SEBT. Unipedal stances were associated with a greater variance than bipedal stances for both the BESS and force plate protocol. These assessments will be applied within a pediatric populations to determine the validity of their use. Further postural control research is necessary to determine the most viable assessments for use within an active, pediatric population.

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.

Key Points Question Are plasma biomarkers associated with a return-to-sport period of less than 14 days vs 14 days or more in male and female collegiate athletes following a sport-related concussion? Findings This diagnostic study, which included 127 collegiate athletes who had sustained a sports-related concussion, found that higher total tau concentrations 24 to 48 hours after injury and at the time of symptom resolution as well as lower glial fibrillary acidic protein levels acutely postinjury were associated with return-to-sport decisions. Meaning In this study, total tau and glial fibrillary acidic protein levels were associated with return to sport in male and female collegiate athletes following a sports-related concussion., This diagnostic study examines whether plasma biomarkers can differentiate collegiate athletes who return to sport in less than 14 days vs 14 days or more following a sports-related concussion., 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

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.

Although head injuries are common in cycling, exact conditions associated with cyclist head impacts are difficult to determine. Previous studies have attempted to reverse engineer cyclist head impacts by reconstructing bicycle helmet residual damage, but they have been limited by simplified damage assessment and testing. The present study seeks to enhance knowledge of cyclist head impact conditions by reconstructing helmet damage using advanced impact testing and damage quantification techniques. Damage to 18 helmets from cyclists treated in emergency departments was quantified using computed tomography and reconstructed using oblique impacts. Damage metrics were related to normal and tangential velocities from impact tests as well as peak linear accelerations (PLA) and peak rotational velocities (PRV) using case-specific regression models. Models then allowed original impact conditions and kinematics to be estimated for each case. Helmets were most frequently damaged at the front and sides, often near the rim. Concussion was the most common, non-superficial head injury. Normal velocity and PLA distributions were similar to previous studies, with median values of 3.4 m/s and 102.5 g. Associated tangential velocity and PRV medians were 3.8 m/s and 22.3 rad/s. Results can inform future oblique impact testing conditions, enabling improved helmet evaluation and design.

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.

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, χ2(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).

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

Question Is sport-related concussion associated with levels of traumatic brain injury biomarkers in collegiate athletes? Findings In this case-control study of 504 collegiate athletes with concussion, contact sport control athletes, and non-contact sport athletes, the athletes with concussion had significant elevations in multiple traumatic brain injury biomarkers compared with preseason baseline and with 2 groups of control athletes without concussion during the acute postinjury period. Meaning These 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 sport-related concussion. This case-control study examines the association between sport-related concussion and levels of traumatic brain injury biomarkers in collegiate athletes. 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 x visit) was found for GFAP (F-7,F-1507.36 = 16.18, P < .001), UCH-L1 (F-7,F-1153.09 = 5.71, P < .001), and tau (F-7,F-1480.55 = 6.81, P < .001); the interaction for neurofilament light chain was not significant (F-7,F-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 (beta = 12.298; 95% CI, 2.776-54.481; P = .001) and non-contact sport controls (beta = 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. Grand Alliance Concussion Assessment, Research, and Education (CARE) Consortium - NCAA; DODUnited States Department of Defense; Office of the Assistant Secretary of Defense for Health Affairs through the Psychological Health and Traumatic Brain Injury Program [W81XWH-14-2-0151] This work received support from the Grand Alliance Concussion Assessment, Research, and Education (CARE) Consortium funded in part by the NCAA and the DOD andwas supported by award W81XWH-14-2-0151 from the Office of the Assistant Secretary of Defense for Health Affairs through the Psychological Health and Traumatic Brain Injury Program.

Infield softball masks are intended to reduce facial fracture risk, but are rarely worn. The objective of this study was to evaluate the effectiveness of infield masks' ability to attenuate facial fracture risk over a range of designs and materials. To simulate batted ball impacts, a customized pitching machine was used to propel softballs at 24.6 +/- 0.51m/s. The balls impacted locations centered over the maxilla and zygoma bones of a FOCUS headform. The FOCUS headform was attached to a 50th percentile Hybrid III neck and secured to a slider table. Facial fracture risk of each facial bone was compared between masks and impact locations using peak resultant forces. Analysis of these data showed that the mask material and the distance between the mask and the impacted facial bone were key factors in determining a mask's performance. The effectiveness of masks varied. It was found that a metal mask with a separation distance 35mm away from the maxilla and 25mm away from the zygoma best reduced facial fracture risk for these test configurations. Plastic masks performed worse because they excessively deformed allowing ball contact with the face. This study assesses various mask designs for their ability to reduce facial fracture and suggests design recommendations based on the impact configurations tested. Institute for Critical Technology and Applied Science at Virginia Tech The authors would like to thank Craig McNally and Drew Richard for their help in constructing the projectile system and the Institute for Critical Technology and Applied Science at Virginia Tech for their support.

Advances in low-cost wearable head impact sensor technology provide potential benefits regarding sports safety for both consumers and researchers. However, previous laboratory evaluations are not directly comparable and do not incorporate test conditions representative of unhelmeted impacts. This study addresses those limitations. The xPatch by X2 Biosystems and the SIM-G by Triax Technologies were placed on a National Operating Committee on Standards for Athletic Equipment (NOCSAE) headform with a Hybrid III neck which underwent impact tests using a pendulum. Impact conditions included helmeted, padded impactor to bare head, and rigid impactor to bare head to represent long- and short-duration impacts seen in helmeted and unhelmeted sports. The wearable sensors were evaluated on their kinematic accuracy by comparing results to reference sensors located at the headform center of gravity. Statistical tests for equivalence were performed on the slope of the linear regression between wearable sensors and reference. The xPatch gave equivalent measurements to the reference in select longer-duration impacts, whereas the SIM-G had large variance leading to no equivalence. For the short-duration impacts, both wearable sensors underpredicted the reference. This error can be improved with increases in sampling rate from 1 to 1.5 kHz. Follow-up evaluations should be performed on the field to identify error in vivo.

Objective: The purpose of this study was to investigate relative differences in impact attenuation capabilities of bicycle helmets under real-world impact conditions and safety standard-specified conditions using a standard rig. Methods: A Consumer Product Safety Commission (CPSC) test rig was used to impact 10 helmet models of varied design. Impact configurations included 2 locations and 2 velocities. A frontal rim location (inferior to the standard-defined test area) and a temporal location were selected to reflect common cyclist impacts. An impact velocity of 3.4 m/s, an average normal impact velocity in cyclist accidents, was selected, as well as the CPSC standard velocity of 6.2 m/s. Four samples per helmet model were subjected to each of the 4 impact configurations once (randomized test order per sample), resulting in 160 drop tests. Peak linear acceleration (PLA) and head injury criterion (HIC)-based Abbreviated Injury Scale (AIS) ≥ 4 brain injury risk were determined and compared across helmets and impact configurations using analysis of variance. Other impact characteristics such as duration, effective liner stiffness, and energy dissipated were also calculated from acceleration data. Results: Helmet performance varied significantly between models. PLA ranged from 78 to 169 g at 3.4 m/s (0–2% AIS ≥ 4 brain injury risk) and 165–432 g (10–100% risk) at 6.2 m/s. Temporal impacts resulted in higher PLAs than frontal impacts, likely due to increased effective liner stiffness. However, 2 helmets exceeded the CPSC pass–fail threshold (300 g) at the frontal rim location, producing >70% risk. Force–displacement curves suggest that bottoming-out occurred in these impacts. Aside from bottoming-out cases, helmets that performed worse in one impact configuration tended to perform worse in others, with non-road-style helmets among the worst. Conclusions: The 10 bicycle helmets tested produced considerable differences in their protective capabilities under both real-world and standard-specified conditions on the CPSC rig. Risk of severe brain injury varied widely between helmets at the standard impact velocity, whereas the common, lower severity impacts produced PLAs associated with concussion. Helmets of a nonroad style generally performed worse across configuration. The temporal location produced higher risks for most helmets, although some helmets were found to offer inadequate protection at the helmet rim. Because this is a commonly impacted location in cyclist accidents, there may be benefit to expanding the testable area in standards to include the rim. Results from this study demonstrate the value in testing nonstandard conditions and can be used to inform standards testing and helmet design.

In the United States, all bicycle helmets must comply with the standard created by the Consumer Product Safety Commission (CPSC). In this standard, bike helmets are only required to by tested above an established test line. Unregulated helmet performance below the test line could pose an increased risk of head injury to riders. This study quantified the impact locations of damaged bike helmets from real-world accidents and tested the most commonly impacted locations under CPSC bike helmet testing protocol. Ninety-five real-world impact locations were quantified. The most common impact locations were side-middle (31.6%), rear boss-rim (13.7%), front boss-rim (9.5%), front boss-middle (9.5%), and rear boss-middle (9.5%). The side-middle, rear boss-rim, and front boss (front boss-middle and front boss-rim regions combined) were used for testing. Two of the most commonly impacted regions were below the test line (front boss-rim and rear boss-rim). Twelve purchased helmet models were tested under CPSC protocol at each location for a total of 36 impacts. An ANOVA test showed that impact location had a strong influence on the variance of peak linear acceleration (PLA) ( p = 0.002). A Tukey HSD post hoc test determined that PLA at the side-middle (214.9 ± 20.8 g) and front boss (228.0 ± 39.6 g) locations were significantly higher than the PLA at the rear boss-rim (191.5 ± 24.2 g) location. The highest recorded PLA (318.8 g) was at the front boss-rim region. This was the only test that exceeded the 300 g threshold. This study presented a method for quantifying real-world impact locations of damaged bike helmets. Higher variance in helmet performance was found at the regions on or below the test line than at the region above the test line.

Current youth football helmets, intended for players under the age of 14 years old, are similar in design and are tested under the same standard as varsity football helmets. This study evaluated the impact performance of matched youth and adult varsity football helmets. Eight helmet models were evaluated using an impact pendulum, with a modified National Operating Committee on Standards for Athletic Equipment (NOCSAE) medium sized headform mounted on a Hybrid III 50th percentile neck. Four locations on the helmet shell at three impact velocities were tested for three trials, for a total of 576 impact tests. Linear acceleration, rotational acceleration, and a concussion correlate were recorded for each test and a comparison between the youth and varsity helmets were made. It was found that the age group the helmet is intended for did not have a significant effect on the impact performance of the helmet in either linear acceleration, rotational acceleration, or concussion correlate. These results are likely due to the similarities in helmet design resulting from being tested to the same standard. Although it is unknown how a youth helmet should differ from a varsity helmet, differences in impact exposure, anthropometry, physiology, and injury tolerance are factors to consider. These data serves as a reference point for future youth-specific helmet design and helmet standards.

Youth football helmets currently undergo the same impact testing and must satisfy the same criteria as varsity helmets, although youth football players differ from their adult counterparts in anthropometry, physiology, and impact exposure. This study aimed to relate football helmet standards testing to on-field head impact magnitudes for youth and varsity football helmets. Head impact data, filtered to include only impacts to locations in the current National Operating Committee on Standards for Athletic Equipment standard, were collected for 48 collegiate players (ages 18–23 years) and 25 youth players (ages 9–11 years) using helmet-mounted accelerometer arrays. These on-field data were compared to a series of National Operating Committee on Standards for Athletic Equipment standard drop tests with a youth and varsity Riddell Speed helmet. In the on-field data, the adult players had a higher frequency of impact than the youth players, and a significant difference in head acceleration magnitude only existed at the top location (p < 0.001). In the laboratory drop tests, the only significant difference between the youth and varsity helmets was at the 3.46 m/s (61 cm) impact to the front location (p = 0.0421). Drop tests generated head accelerations within the top 10% of measured on-field impacts, at all locations and drop heights, demonstrating that drop tests are representative of the most severe head impacts experienced by youth and adult football players on the field. Current standards have been very effective at eliminating skull fracture and severe brain injury in both populations. This analysis suggests that there is not currently a need for a youth-specific drop test standard. However, there may be such a need if helmet testing standards are updated to address concussion, paired with a better understanding of differences in concussion tolerance between youth and adult populations.

This study sought to evaluate the suitability of angular rate sensors for quantifying angular acceleration in helmeted headform impacts. A helmeted Hybrid III headform, instrumented with a 3-2-2-2 nine accelerometer array and angular rate sensors, was impacted (n = 90) at six locations and three velocities (3.1, 4.9, and 6.4 m/s). Data were low-pass filtered using Butterworth four-pole phaseless digital filters which conform to the specifications described in the Society of Automotive Engineers J211 standard on instrumentation for impact tests. Nine accelerometer array data were filtered using channel frequency class 180, which corresponds to a −3 db cutoff frequency of 300 Hz. Angular rate sensor data were filtered using channel frequency class values ranging from 5 to 1000 Hz in increments of 5 Hz, which correspond to −3 db cutoff frequencies of 8 to 1650 Hz. Root-mean-square differences in peak angular acceleration between the two instrumentation schemes were assessed for each channel frequency class value. Filtering angular rate sensor data with channel frequency class values between 120 and 205 all produced mean differences within ±5%. The minimum root-mean-square difference of 297 rad/s2 was found when the angular rate sensor data were filtered using channel frequency class 175. This filter specification resulted in a mean difference of 28 ± 297 rad/s2 (1.8% ± 8.6%). Condition-specific differences (α=0.05) were observed for 11 of 18 test conditions. A total of 4 of those 11 conditions were within ±5%, and 10 were within ±10%. Furthermore, the nine accelerometer array and angular rate sensor methods demonstrated similar levels of repeatability. These data suggest that angular rate sensor may be an appropriate alternative to the nine accelerometer array for measuring angular head acceleration in helmeted head impact tests with impactor velocities of 3.1–6.4 m/s and impact durations of approximately 10 ms.

Evaluating and improving helmet design play a crucial role in reducing sports-related concussions. Despite widespread use of facemasks by football and hockey players, no helmet standards currently exist to test helmets equipped with facemasks. The purpose of this study was to determine the effect that attached facial protection has on the head kinematics resulting from impacts to the helmet shell. Helmets were fit to a modified NOCSAE (National Operating Committee on Standards for Athletic Equipment) headform and subjected to blows from a pneumatic impactor. A total of 240 impact tests were performed to evaluate the effect of the facemask on four helmet models (two for football, two for hockey). For each helmet model, one sample was tested with a facemask and another without a facemask. Tests were conducted at two impact velocities (6, 9 m/s) and three impact locations (front, side, and rear boss) for a total of six impact conditions. Five trials were performed for each helmet sample at each condition. Two-factor analyses of variance were used to quantify effects on linear and rotational head acceleration and Severity Index due to impact location and facemask presence. Significant effects varied by helmet model and impact location and were more commonly associated with football helmets. Differences in facemask effects between sports are likely attributed to differences in facemask-shell attachment mechanisms, and differences in the structure of the facemask itself. The effects of the facemask on linear and rotational acceleration were small, approximately 5% for both football and hockey helmets. On average, peak accelerations were decreased with the addition of a facemask, but individual differences were mixed and varied by helmet type and impact location. These small differences would not greatly affect impact performance tests in the lab. The results of this study have direct applications toward helmet standard development.

OBJECTIVE: The aim of this study was to quantify head impact exposure for a collegiate women's soccer team over the course of the 2014 season. Design: Observational and prospective study. Setting: Virginia Tech women's soccer games and practices. Participants: Twenty-six collegiate level women's soccer players with a mean player age of 19 +/- 1. Interventions: Participating players were instrumented with head impact sensors for biomechanical analysis. Video recordings of each event were used to manually verify each impact sustained. Main Outcome Measures: Head impact counts by player position and impact situation. Results: The sensors collected data from a total of 17 865 accelerative events, 8999 of which were classified as head impacts. Of these, a total of 1703 impacts were positively identified (19% of total real impacts recorded by sensor), 90% of which were associated with heading the ball. The average number of impacts per player per practice or game was 1.86 +/- 1.42. Exposure to head impact varied by player position. Conclusions: Head impact exposure was quantified through 2 different methods, which illustrated the challenges associated with autonomously collecting acceleration data with head impact sensors. Users of head impact data must exercise caution when interpreting on-field head impact sensor data. Copyright (C) 2016 Wolters Kluwer Health, Inc. All rights reserved. Language: en

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.