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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.)

Shoulder-to-helmet (STH) impacts have been shown to cause approximately twenty percent of concussions in football yet little research has investigated shoulder pad design and STH impacts. This study aimed to characterize STH impacts and identify the effect of shoulder pad design on the struck head kinematics. Additional padding was added to a shoulder pad, and was then compared to an unmodified control shoulder pad. Participants performed a series of tests where they struck a helmeted Hybrid III dummy with both shoulder pad variations to compare struck head linear and rotational kinematics. The study found the modified shoulder pad reduced peak linear acceleration by 31% (Δµ = - 9.13 g's (- ∞, - 7.25), (p = 4.10e-08)), rotational acceleration by 28% (Δµ = - 565 rad s -2 (- ∞, - 435), (p = 2.10e-07)), peak rotational velocity by 10% (Δµ = - 2.42 rad s -1 (- ∞, - 1.54), (p = 6.9e-05)), and increased impact duration by 40% (Δµ = 9.96 ms (8.06, ∞), (p = 1.142e-08)). Impact response corridors were developed for both shoulder pad conditions and can be used to establish a controlled lab test setup that replicates STH impacts. Our findings suggest that shoulder pads have the potential to reduce head injury in football and warrant further research., (© 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).)

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.

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.

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.

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.

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.

Repetitive head impact exposure sustained by athletes of contact sports has been hypothesized to be a mechanism for concussion and a possible explanation for the high degree of variability in sport-related concussion biomechanics. In an attempt to limit repetitive head impact exposure during the football preseason, the NCAA eliminated two-a-day practices in 2017, while maintaining the total number of team practice sessions. The objective of this study was to quantify head impact exposure during the preseason and regular season in Division I college football athletes to determine whether the 2017 NCAA ruling decreased head impact exposure. 342 unique athletes from five NCAA Division I Football Bowl Subdivision (FBS) programs were consented and enrolled. Head impacts were recorded using the Head Impact Telemetry (HIT) System during the entire fall preseasons and regular seasons in 2016 and 2017. Despite the elimination of two-a-day practices, the number of preseason contact days increased in 2017, with an increase in average hourly impact exposure (i.e., contact intensity), resulting in a significant increase in total head impact burden (+ 26%) for the 2017 preseason. This finding would indicate that the 2017 NCAA ruling was not effective at reducing the head impact burden during the football preseason. Additionally, athletes sustained a significantly higher number of recorded head impacts per week (+ 40%) during the preseason than the regular season, implicating the preseason as a time of elevated repetitive head impact burden. With increased recognition of a possible association between repetitive head impact exposure and concussion, increased preseason exposure may predispose certain athletes to a higher risk of concussion during the preseason and regular season. Accordingly, efforts at reducing concussion incidence in contact sports should include a reduction in overall head impact exposure.

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.

Biomechanical data collected from head impacts in football have been used to characterize exposure and predict injury risk. This study sought to specifically quantify the factors that contribute towards player head impact exposure in college football practices. All players included in this study were outfitted with football helmets instrumented with accelerometer arrays (Head Impact Telemetry System). Head impact exposure was defined by the number of head impacts each player experienced in practice, the number of practice head impacts normalized by the number of practice sessions (practice head impact rate), and the 95th percentile linear and rotational resultant head impact accelerations. Practice head impact rate was observed to vary significantly with player position (p < 0.0001; η 2  = 0.46), team (p = 0.0016; η 2  = 0.03), and the number of game impacts (p < 0.0001; η 2  = 0.03), which served as a correlate for player ability. Even after controlling for practice participation, player position, team, and ability, differences between individuals accounted for 48% of the variance in head impact exposure in practice. This work demonstrates the importance of considering head impact exposure on a subject-specific basis rather than estimating head impact exposure from aggregate data.

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.

Objective: Youth 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., Methods: A 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., Results: Players 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., Conclusions: This 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.

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.

This study aimed to compare head impact exposures between practices and games in football players ages 9 to 14 years, who account for approximately 70% of all football players in the United States. Over a period of 2 seasons, 136 players were enrolled from 3 youth programs, and 49,847 head impacts were recorded from 345 practices and 137 games. During the study, individual players sustained a median of 211 impacts per season, with a maximum of 1226 impacts. Players sustained 50th (95th) percentile peak linear acceleration of 18.3 (46.9) g, peak rotational acceleration of 1305.4 (3316.6) rad·s -2 , and Head Impact Technology Severity Profile of 13.7 (24.3), respectively. Overall, players with a higher frequency of head impacts at practices recorded a higher frequency of head impacts at games (P < .001, r 2  = .52), and players who sustained a greater average magnitude of head impacts during practice also recorded a greater average magnitude of head impacts during games (P < .001). The youth football head impact data quantified in this study provide valuable insight into the player exposure profile, which should serve as a key baseline in efforts to reduce injury.

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.

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.

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.

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.

The head impact exposure experienced by football players at the college and high school levels has been well documented; however, there are limited data regarding youth football despite its dramatically larger population. The objective of this study was to investigate head impact exposure in middle school football. Impacts were monitored using a commercially available accelerometer array installed inside the helmets of 17 players aged 12-14 years. A total of 4678 impacts were measured, with an average (±standard deviation) of 275 ± 190 impacts per player. The average of impact distributions for each player had a median impact of 22 ± 2 g and 954 ± 122 rad/s², and a 95th percentile impact of 54 ± 9 g and 2525 ± 450 rad/s². Similar to the head impact exposure experienced by high school and collegiate players, these data show that middle school football players experience a greater number of head impacts during games than practices. There were no significant differences between median and 95th percentile head acceleration magnitudes experienced during games and practices; however, a larger number of impacts greater than 80 g occurred during games than during practices. Impacts to the front and back of the helmet were most common. Overall, these data are similar to high school and college data that have been collected using similar methods. These data have applications toward youth football helmet design, the development of strategies designed to limit head impact exposure, and child-specific brain injury criteria.

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.

Head impact exposure in youth football has not been well-documented, despite children under the age of 14 accounting for 70% of all football players in the United States. The objective of this study was to quantify the head impact exposure of youth football players, age 9-12, for all practices and games over the course of single season. A total of 50 players (age = 11.0 ± 1.1 years) on three teams were equipped with helmet mounted accelerometer arrays, which monitored each impact players sustained during practices and games. During the season, 11,978 impacts were recorded for this age group. Players averaged 240 ± 147 impacts for the season with linear and rotational 95th percentile magnitudes of 43 ± 7 g and 2034 ± 361 rad/s(2). Overall, practice and game sessions involved similar impact frequencies and magnitudes. One of the three teams however, had substantially fewer impacts per practice and lower 95th percentile magnitudes in practices due to a concerted effort to limit contact in practices. The same team also participated in fewer practices, further reducing the number of impacts each player experienced in practice. Head impact exposures in games showed no statistical difference. While the acceleration magnitudes among 9-12 year old players tended to be lower than those reported for older players, some recorded high magnitude impacts were similar to those seen at the high school and college level. Head impact exposure in youth football may be appreciably reduced by limiting contact in practices. Further research is required to assess whether such a reduction in head impact exposure will result in a reduction in concussion incidence.

With the increased national concern about concussions in football, recent research has focused on evaluating the impact performance of modern football helmets. Specifically, this technical note offers a biomechanical analysis of classic leather helmets compared with modern helmets. Furthermore, modern helmets were examined to illustrate the performance differences between the better- and worse-performing ones. A total of 1224 drop tests were performed from a range of drop heights and impact locations on 11 different helmet types (10 modern and 1 leather helmet model). The resulting head acceleration was used to assess the risk of concussion for each drop test. The results of this analysis demonstrate that modern helmets are significantly and substantially superior to leather helmets in all impact scenarios, and that notable differences exist among modern helmets.

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.

The purpose of this study was to quantify the severity of head impacts sustained by individual collegiate football players and to investigate differences between impacts sustained during practice and game sessions, as well as by player position and impact location. Head impacts (N = 184,358) were analyzed for 254 collegiate players at three collegiate institutions. In practice, the 50th and 95th percentile values for individual players were 20.0 g and 49.5 g for peak linear acceleration, 1187 rad/s2 and 3147 rad/s2 for peak rotational acceleration, and 13.4 and 29.9 for HITsp, respectively. Only the 95th percentile HITsp increased significantly in games compared with practices (8.4%, p = .0002). Player position and impact location were the largest factors associated with differences in head impacts. Running backs consistently sustained the greatest impact magnitudes. Peak linear accelerations were greatest for impacts to the top of the helmet, whereas rotational accelerations were greatest for impacts to the front and back. The findings of this study provide essential data for future investigations that aim to establish the correlations between head impact exposure, acute brain injury, and long-term cognitive deficits.

The head impact exposure for athletes involved in football at the college and high school levels has been well documented; however, the head impact exposure of the youth population involved with football has yet to be investigated, despite its dramatically larger population. The objective of this study was to investigate the head impact exposure in youth football. Impacts were monitored using a custom 12 accelerometer array equipped inside the helmets of seven players aged 7-8 years old during each game and practice for an entire season. A total of 748 impacts were collected from the 7 participating players during the season, with an average of 107 impacts per player. Linear accelerations ranged from 10 to 100 g, and the rotational accelerations ranged from 52 to 7694 rad/s(2). The majority of the high level impacts occurred during practices, with 29 of the 38 impacts above 40 g occurring in practices. Although less frequent, youth football can produce high head accelerations in the range of concussion causing impacts measured in adults. In order to minimize these most severe head impacts, youth football practices should be modified to eliminate high impact drills that do not replicate the game situations.

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.)

In contrast to the publicly available data on the safety of automobiles, consumers have no analytical mechanism to evaluate the protective performance of football helmets. The objective of this article is to fill this void by introducing a new equation that can be used to evaluate helmet performance by integrating player head impact exposure and risk of concussion. The Summation of Tests for the Analysis of Risk (STAR) equation relates on-field impact exposure to a series of 24 drop tests performed at four impact locations and six impact energy levels. Using 62,974 head acceleration data points collected from football players, the number of impacts experienced for one full season was translated to 24 drop test configurations. A new injury risk function was developed from 32 measured concussions and associated exposure data to assess risk of concussion for each impact. Finally, the data from all 24 drop tests is combined into one number using the STAR formula that incorporates the predicted exposure and injury risk for one player for one full season of practices and games. The new STAR evaluation equation will provide consumers with a meaningful metric to assess the relative performance of football helmets.

The high incidence rate of concussions in football provides a unique opportunity to collect biomechanical data to characterize mild traumatic brain injury. The goal of this study was to validate a six degree of freedom (6DOF) measurement device with 12 single-axis accelerometers that uses a novel algorithm to compute linear and angular head accelerations for each axis of the head. The 6DOF device can be integrated into existing football helmets and is capable of wireless data transmission. A football helmet equipped with the 6DOF device was fitted to a Hybrid III head instrumented with a 9 accelerometer array. The helmet was impacted using a pneumatic linear impactor. Hybrid III head accelerations were compared with that of the 6DOF device. For all impacts, peak Hybrid III head accelerations ranged from 24 g to 176 g and 1,506 rad/s(2) to 14,431 rad/s(2). Average errors for peak linear and angular head acceleration were 1% ± 18% and 3% ± 24%, respectively. The average RMS error of the temporal response for each impact was 12.5 g and 907 rad/s(2).

Each year, between 1.6x10(6) and 3.8x10(6) concussions are sustained by athletes playing sports, with football having the highest incidence. The high number of concussions in football provides a unique opportunity to collect biomechanical data to characterize mild traumatic brain injury. Human head acceleration data for a range of impact severities were collected by instrumenting the helmets of collegiate football players with accelerometers. The helmets of ten Virginia Tech football players were instrumented with measurement devices for every game and practice for the 2007 football season. The measurement devices recorded linear and angular accelerations about each of the three axes of the head. Data for each impact were downloaded wirelessly to a sideline data collection system shortly after each impact occurred. Data were collected for 1712 impacts, creating a large and unbiased data set. While a majority of the impacts were of relatively low severity (<30 g and <2000 rad/s2), 172 impacts were greater than 40 g and 143 impacts were greater than 3000 rad/s2. No instrumented player sustained a clinically diagnosed concussion during the 2007 season. A large and unbiased data set was compiled by instrumenting the helmets of collegiate football players. Football provides a unique opportunity to collect head acceleration data of varying severity from human volunteers. The addition of concurrent concussive data may advance the understanding of the mechanics of mild traumatic brain injury. With an increased understanding of the biomechanics of head impacts in collegiate football and human tolerance to head acceleration, better equipment can be designed to prevent head injuries.

Objective: To determine the load-limiting capabilities of protective neck collars used in football through dynamic impact testing., Design: A 50th-percentile male Hybrid III dummy was used in 48 dynamic impact tests comparing The Cowboy Collar, Bullock Collar, and Kerr Collar. A control and each collar were tested at two velocities (5 m/s and 7 m/s), three impact locations (front, top, and side of the helmet), and two shoulder pad positions (normal and raised)., Setting: Research laboratory., Patients: None., Interventions: None. Independent variables were the neck collars, impact velocity, and shoulder pad position., Main Outcome Measurements: In addition to range of motion, upper and lower neck forces and moments were measured., Results: With the top impact location, it was found that the Kerr Collar and Bullock Collar reduced head accelerations and force transmission through the neck. With the front impact location, all the collars reduced lower neck moment. The Kerr Collar was also capable of reducing the lower neck force and upper neck moment. With the side impact location, the Kerr Collar substantially reduced lower neck moment., Conclusions: These reductions in loads correlate with the degree to which each collar restricted the motion of the head and neck. By restricting the range of motion of the neck and redistributing load to the shoulders, neck loads can be effectively lowered.

The objective of this study was to perform a dynamic biomechanical analysis of football neck collars in order to determine their effect on head and neck loading. A total of 48 tests were performed comparing the Cowboy Collar, Bullock Collar, and the Kerr Collar. A control and each collar was tested at two speeds (5 m/s and 7 m/s), three impact locations (front, top, and side of the helmet), and two shoulder pad positions (normal and raised). This paper specifically analyzes the load limiting capabilities of these collars during an impact to the side of the helmet. A 50 percentile male Hybrid III dummy was equipped with a helmet, shoulder pads, and the various neck collars mentioned. The dummy was instrumented with tri-axial accelerometers at the CG of the head. Angular rate sensors were used in the head and chest. In addition, both the upper and lower neck were instrumented with load cells. The helmet was struck with a pneumatic linear impactor to provoke rotation of the head and neck. With the side impact location, the Kerr Collar substantially reduced lower neck moment. These reductions in loads correlate with the degree to which each collar restricted the motion of the head and neck.

The objective of this study was to characterize the risk of mild traumatic brain injury (MTBI) in living humans based on a large set of head impact data taken from American football players at the collegiate level. Real-time head accelerations were recorded from helmet-mounted accelerometers designed to stay in contact with the player's head. Over 27,000 head impacts were recorded, including four impacts resulting in MTBI. Parametric risk curves were developed by normalizing MTBI incidence data by head impact exposure data. An important finding of this research is that living humans, at least in the setting of collegiate football, sustain much more significant head impacts without apparent injury than previously thought. The following preliminary nominal injury assessment reference values associated with a 10% risk of MTBI are proposed: a peak linear head acceleration of 165 g, a HIC of 400, and a peak angular head acceleration of 9000 rad/s2.