Steven Rowson

Linear and angular head acceleration measurements in collegiate football

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.

Head impact exposure in collegiate football players

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.

Development of the STAR Evaluation System for Football Helmets: Integrating Player Head Impact Exposure and Risk of Concussion

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.

Brain Injury Prediction: Assessing the Combined Probability of Concussion Using Linear and Rotational Head Acceleration

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.

Head impact exposure sustained by football players on days of diagnosed concussion

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.

Timing of concussion diagnosis is related to head impact exposure prior to injury

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.

Can helmet design reduce the risk of concussion in football

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 players 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.

Past, present, and future of head injury research

The article authored by Drs. Kevin M. Guskiewicz and Jason P. Mihalik, presents a very effective summary of current head injury research that will serve as an excellent resource for scientists interested in sport-related concussions (4). We agree with many of their statements, and wish to expand on three specific topics: the history of the Head Impact Telemetry (HIT) System, biomechanically based injury threshold, and accurate head impact exposure data. In 2003, the HIT System was an exciting new technology that we first implemented at Virginia Tech to collect head acceleration data for true exposure numbers for our players (2). Since then, the technology and methods have been well validated and published in leading peer-reviewed biomechanics journals (1,2,5). Many researchers began to see the value in the HIT System, and adopted similar data collection protocols. During the past 8 yr, more than 115,000 head impacts have been recorded at Virginia Tech, and more than 1,500,000 head impacts through the implementation of the HIT System at other universities and high schools. Collectively, across all institutions, these data represent the most comprehensive biomechanical data set characterizing head impact and concussion in humans (Figure). As the authors have discussed, defining a biomechanical threshold for the onset of concussion from head acceleration data has proven challenging. Concussion prediction based upon linear acceleration magnitudes, alone, is likely not to be specific enough to be accepted. This is because concussion is the result of the combination of linear head acceleration, rotational head acceleration, impact duration, and impact location and direction. One study made progress by creating a weighted metric composed of several biomechanical parameters that had improved predictive capabilities when compared with single biomechanical parameters (3). Furthermore, adaptations of the HIT System technology have been used to build upon existing results to get an idea of the tissue level response of the brain caused by head impact, which may be

Head impact exposure in youth football: elementary school ages 7-8 years and the effect of returning players

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 players 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/s2 and a 95th percentile impact of 38 ± 13 g and 2052 ± 664 rad/s2 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.

Head Impact Exposure in Youth Football: Middle School Ages 12–14 Years

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.