HEAD IMPACT EXPOSURE IN YOUTH FOOTBALL PLAYERS AT PRACTICES AND GAMES By Srinidhi Bellamkonda B.S., Rutgers, The State University of New Jersey, 2015 Thesis Submitted in partial fulfillment of the requirements for the Degree of Master of Science in the Department of Biomedical Engineering at Brown University PROVIDENCE, RHODE ISLAND MAY 2017 i AUTHORIZATION TO LEND AND REPRODUCE THE THESIS As the sole author of this thesis, I authorize Brown University to lend it to other institutions or individuals for the purpose of scholarly research. Date________________ Signature: _______________________________ Srinidhi Bellamkonda, Author I further authorize Brown University to reproduce this thesis by photocopying or other means, in total or in part, at the request of other institutions or individuals for the purpose of scholarly research. Date________________ Signature: _______________________________ Srinidhi Bellamkonda, Author ii This thesis by Srinidhi Bellamkonda is accepted in its present form by the Center of Biomedical Engineering as satisfying the thesis requirements for the degree of Masters of Science Date________________ Signature: _______________________________ Dr. Joseph J. (Trey) Crisco, Advisor Date________________ Signature: _______________________________ Dr. Brian C. Burke, Reader Date________________ Signature: _______________________________ Dr. Diana M. Horrigan, Reader Approved by the Graduate Council Date________________ Signature: __________________________________ Dr. Andrew G. Campbell, Dean of the Graduate School iii CURRICULUM VITAE SRINIDHI BELLAMKONDA Education Brown University, Providence, RI Masters of Science in Biomedical Engineering, May 2017(Expected) Specialized in Bioengineering Rutgers University, New Brunswick, NJ Bachelors of Science in Biomedical Engineering May 2015 Professional Experience Vet Innovations, West Greenwich, RI Medical device - Design Intern June 2016 – August 2016 • Investigated and designed test fixtures of cat and dog paws for testing pet food dispensers • Composed assemblies of designed parts to clearly illustrate the drawings for manufacturers Rhode Island Hospital Orthopaedic Foundation, Providence, RI Co-op Intern January 2016 – May 2016 • Assisted an established bioengineering testing facility that provides unique solutions and assistance in device failure analysis, research and development and regulatory processes • Developed methods to analyze image and numerical data obtained from simulated mechanical tests Brown University, Providence, RI Academic Coach October 2015 – December 2015 • Galvanized undergraduate freshmen to improve their time management and problem solving skills iv • Enabled and collaborated with students to develop strategies to enhance productivity and work efficiency Rutgers University, New Brunswick, NJ Learning Assistant- Physics Sept 2013 – May 2015 • Organized and conducted workshops to familiarize and enhance the understanding of physics concepts • Created a collaborative atmosphere among 20 engineering freshmen to inculcate robust study techniques Research Experience Brown University, Providence, RI Thesis Research, Orthopaedic Bioengineering Department August 2015 - Present • Assisted in a cross functional team to conduct research in a front line NIH funded biomechanics program known for its advanced work in bone kinematics, sports injury and mechanics • Communicated and recruited human research subjects for data collection adhering to strict eligibility requirements with the purpose of investigating differences in head impact exposure in youth football players • Interviewed subjects in-person to assess changes in neuro-cognitive function • Analyzed head impact data using MATLAB, Sigma Plot and Graph Pad Rutgers University, New Brunswick, NJ Senior Design, Biomedical Engineering Department September 2014 – May 2105 • Designed an affordable and portable medical device to screen for vascular stiffness in hypertensive patients • Prototyped and tested the device on subjects and analyzed and interpreted results using Biopac Technical Skills MATLAB, AutoCAD, Solid Works, Sigma Plot, Graph Pad, R, Zotero, NIH Toolbox Presentations v  Bellamkonda S, Wilcox BJ., Duma S, Stitzel JD, Greenwald RM, Crisco JJ. “Head Impact Exposure Varies By Team in Youth Football” The 40th Annual Meeting of the American Society of Biomechanics, Raleigh, NC, 2016.  Bellamkonda S, Crisco JJ. “Head Impact Exposure in Youth Football Players at Practices and Games” Bioengineering Research Partnership for the Biomechanical Basis of Pediatric mTBI Due to Sports Related Concussion, Blacksburg, VA, 2017 vi ACKNOWLEDGEMENTS The opportunity to pursue my Master’s at Brown University was an overall incredibly rewarding and humbling experience. I have had the privilege to work alongside some of the brightest people and I am thankful to everyone in the Bioengineering Lab, Dr.Michael Ehrlich, and the Department of Orthopaedics. I am sincerely grateful for having Dr. Joseph J. (Trey) Crisco as my advisor. He gave me the freedom to be curious and ask questions and was extremely patient with me throughout the process. I admire his work ethic and I have gained confidence as an engineer and a researcher under his guidance. Dr. Diana M. Horrigan and Dr. Brian C. Burke served as readers on my thesis committee. They have given me great support and advice in both professional and academic situations. I further extend to my gratitude to Dr. Burke, for helping me get a summer internship at Vet Innovations, a medical device start-up. I would like to thank Dr. Jacquelyn Schell, Dr. Beth A Zielinksi-Habershaw, Dr.Jeffrey R Scott, Beverly Ehrich and Dr.Bethany J. Wilcox for guiding and encouraging me through my two years at Brown. Finally, I would like to thank my wonderful family and friends for their unconditional love and endless support throughout my academic career. vii Table of Contents AUTHORIZATION PAGE .......................................................................................... ii SIGNATURE PAGE ................................................................................................... iii CURRICULUM VITAE .............................................................................................. iv ACKNOWLEDGEMENTS ........................................................................................ vii Table of Contents ....................................................................................................... viii List of Figures .............................................................................................................. ix List of Tables ............................................................................................................... ix 1.0 Head Impact Exposure in Youth Football Players at Practices and Games 1.1 Abstract............................................................................................................1 1.2 Introduction .....................................................................................................2 1.3 Methods and Materials ....................................................................................4 1.4 Statistical Analysis ...........................................................................................6 1.5 Results ..............................................................................................................6 1.6 Discussion.........................................................................................................7 1.7 Conflict of Interest Disclosure ....................................................................... 13 1.8 Acknowledgement.......................................................................................... 13 1.9 References ...................................................................................................... 14 viii List of Figures Figure 1. For individual players, their impacts per game correlated linearly (r 2 = 0.52, p<0.0001) with their impacts per practice............................................................................... 19 Figure 2. Distribution of total number of impacts per player per season differed substantially among the six teams............................................................................................ 20 Figure 3. Distribution of impact frequency per practice and per game for individual players by team........................................................................................................................... 21 Figure 4. Relationship between head impacts during practice and games for median and 95th percentile peak linear acceleration (g) (a-b), peak rotational acceleration (rad/s2) (c-d) and peak HITsp (e-f) ................................................................................................................. 22 List of Tables Table 1: Shapiro-Wilk normality test at a significance level of 0.05 for frequency and magnitude variables to test normality of their distrubiton.............................................................................. 23 Table 2: Multiple linear regression analysis for the significance between impacts per practice and impacts per game for each of the six observed teams................................................................... 24 Table 3: Median [25-75th percentile] impacts per season, 50th and 95th percentile peak linear acceleration, peak rotational acceleration and HITsp for the season, practices and games........... 25 Table 4: Games vs practices linear regression analysis results (Slope, p-value and correlation coefficient), 50th and 95th percentile peak linear acceleration, peak rotational acceleration and HITsp............................................................................................................................................ 26 ix 1.0 Head Impact Exposure in Youth Football Players at Practices and Games 1.1 Abstract Despite over 70% of all football players in the US being under the age of 14, previous research has focused primarily on high school and collegiate football players. With the goal of learning more about the distribution of head impact exposure in the youth population, this study aimed to compare head impact exposure data (frequency and magnitude) between practices and games on football players ages 9 to 14. One hundred thirty-six players from six teams were recruited and equipped with the HIT (Head Impact Telemetry) system enabling impact exposure data collection at every practice and game for a total of 482 sessions. Over a period of two seasons, 49,847 impacts from 345 practices and 137 games were recorded. Individual players sustained a median of 211 impacts with a highest of 1226 impacts per season, with a 50th and 95th percentile peak linear acceleration of 18.3 g and 46.9 g. The 50th and 95th percentile peak rotational acceleration were 1305.4 rad/s2 and 3316.6 rad/s2 respectively and the 50th and 95th peak percentile HITsp, a severity measure, were 13.7 and 24.3, respectively. Overall, players with a higher frequency of head impacts at practice recorded a higher frequency of head impacts at games. While there were differences in total number of head impacts an individual player received per season among each of the six teams, there was a positive correlation between the head impact frequencies players sustained at practices and at games. Moreover, players with higher magnitudes of head impacts during practice also recorded higher magnitudes of head impacts during games. Essentially, for every individual player, there was a positive significant linear relationship between the head impact exposure values for games and for practices. 1 1.2 Introduction Sports-related brain injuries, especially mild traumatic brain injury (MTBI), have garnered public attention and led to questions over how to make sports safer for the athletes (Gerberding 2003; Langlois, Rutland-Brown, and Wald 2006; Thunnan, Branche, and Sniezek 1998; Powell JW and Barber-Foss KD 1999; Powell and Barber-Foss 1999; Collins MW, Lovell MR, and Mckeag DB 1999). Even though the real number of brain injuries is assumed to be greater due to underreporting (Langlois, Rutland-Brown, and Wald 2006; McCrea et al. 2004; Gerberding 2003), the CDC estimates an annual occurrence of 1.6 to 3.8 million sports-related brain injuries (Centers for Disease Control and Prevention 1997). Studies suggest that a single concussion increases a player’s risk of sustaining another by 3-5 times (Centers for Disease Control and Prevention 1997) and that multiple head injuries increase a player’s risk of developing long-term sequelae (Centers for Disease Control and Prevention 1997; Guskiewicz KM et al. 2003; Broglio, Surma, and Ashton-Miller 2012; Buckley et al. 2016; Singh et al. 2014). While researchers are gaining an understanding of the signs, symptoms, and sequelae of concussion injuries, documenting head impact exposures that may lead to concussion or long-term cognitive deficits, including impact magnitude and frequency remains an important challenge. Previous studies on football players that have quantified the frequency and severity of head impacts have focused primarily on high school and collegiate athletes (Broglio et al. 2009a; Mihalik et al. 2007; Schnebel et al. 2007; Crisco et al. 2010; Crisco et al. 2011; Kerr et al. 2014). However, researchers recently have started investigating head impact exposure in the youth population as players under the age of 14 account for 2 over 70 % of the 5 million participating in organized football each year (Daniel, Rowson, and Duma 2012; Cobb et al. 2013; Cobb, Rowson, and Duma 2014; Guskiewicz et al. 2000; Wong, Wong, and Bailes 2014). A study in 2012 reported an increase in magnitude and frequency of impacts as kids progressed to higher age and weight groups (Daniel, Rowson, and Duma 2012). The findings from previous studies were in agreement when comparing age groups 7-8, 9-13, 14-18 and 18-22 (Young et al. 2014; Munce et al. 2014b; Urban et al. 2013; Reynolds et al. 2017). Studies have also explored comparisons in head impact exposure between practice and game sessions, reporting that players receive more impacts during games than practices (Urban et al. 2013; Reynolds et al. 2017), while other studies have reported the opposite finding (Mihalik et al. 2007; Cobb, Rowson, and Duma 2014), with specific drills having the highest rate of high magnitude impacts (Campolettano, Rowson, and Duma 2016). Wong et al. studied exposure in practices and games for individual players in the youth population, but their study was limited by a small sample size and exclusion of all hits below 30 g (Wong, Wong, and Bailes 2014), which would approximately eliminate over 50 % of the impacts reported by previous studies (Broglio et al. 2009; Mihalik et al. 2007; Brolinson et al. 2006; Naunheim et al. 2000; Daniel, Rowson, and Duma 2012; Duma et al. 2005; Schnebel et al. 2007; Urban et al. 2013; Crisco et al. 2012). With no rigorous diagnostic tool or treatment for concussions injury, the best approach for reducing the risk of concussion maybe in reducing the head impact exposures (Crisco et al. 2011). Reducing head impact during practices, due to which there are few injuries and a higher opportunity for players to recover between games, has been the goal of recent changes in the Ivy League (“Ivy League to Limit Full-Contact Football Practice” 2011; Sullivan 2017; “Ivy League 3 Coaches Approve Banning Full-Contact Football Practices | News | The Harvard Crimson” 2017; Belson 2016). The relationship between head impact exposure at practices and games for individual players at the youth level remains unknown. Therefore, this study aimed to investigate if individual players who sustain higher frequency of impacts per practice also sustain higher frequency of impacts per game, as well as if individual players who receive higher magnitude of impacts during practice also receive higher magnitude of impacts during games. We sought to examine these relationships across multiple teams across multiple youth programs. 1.3 Methods and Materials After IRB approval, one hundred and thirty-six male players (mean 11.4 + 1.3 years, range 9-14 years) were recruited from a total of six teams (Teams A – F) across three youth programs after informed assent was obtained from them and informed consent was obtained from their parents. Data collection was conducted throughout two seasons (2015 and 2016) during practices (n = 345) and games (n = 137). All players wore Riddell Revolution or Speed (Riddell, Chicago IL) football helmets that were instrumented with the Head Impact Telemetry (HIT) System (Simbex, Lebanon, NH). The HIT System is a head impact monitoring device that computes and records linear and rotational acceleration of the center of gravity (CG) of the head, as well as impact location on the helmet (Beckwith, Chu, and Greenwald 2007; Crisco, Chu, and Greenwald 2004; Crisco et al. 2011, 2012; Manoogian et al. 2006). The HIT system includes a sideline receiver unit with a laptop connected to a radio receiver and a sensor 4 unit for each helmet. The impacts were recorded over a 40-millisecond duration, including 8-milliseconds of pre-trigger data. A session was defined as either a practice or a game. Practices were defined as sessions with a potential of contact to the head in which players wore helmets and pads. Games included both competitions and scrimmages. Participation in a session was defined as a player receiving at least one impact for a given practice or game. Head impact data from all six teams were uploaded to a secure central server provided by Simbex (Simbex, Lebanon, NH) and subsequently exported for analysis. The biomechanical data consisted of head-impact events that exceeded a peak resultant linear acceleration of 10g. Video footage was collected at all sessions and impacts in the top 5 % of linear acceleration were validated through video identification. Five measures of head impact frequency were analyzed for each player: number of practice impacts, the total number of head impacts a player sustained during all practices; number of game impacts, the total number of head impacts a player sustained during all games; impacts per season, the total number of head impacts for a player during all team sessions in a single season; impacts per practice, the average number of head impacts a player sustained during practices; and impacts per game, the average number of head impacts a player sustained during games (Crisco et al. 2010). Each individual player’s distribution of peak linear acceleration (g), peak rotational acceleration (rad/s2), and peak HITsp were quantified by the 50th and 95th percentile value for each session type. HITsp is a non-dimensional measure of head impact severity (Greenwald et al. 2008). It is measured by transforming the computed head impact measures of peak linear and peak angular acceleration into a single latent variable using 5 Principal Component Analysis and applying a weighting factor based on impact location (Greenwald et al. 2008). 1.4 Statistical Analysis Data was analyzed using Matlab (Mathworks,Natick,MA), and exported to SigmaPlot (Systat Software, Chicago, IL) and GraphPad (GraphPad Software, San Diego, CA) for statistical analysis. Measures of impact frequency were expressed as median values and [25 % - 75 %] inter-quartile range, as the study measures were not normally distributed, according to the Shapiro-Wilk Normality test (Table 1). Correlations in head impact exposure measures between practices and games were assessed using linear regression analysis. Paired t-tests were performed for magnitude measures at practices and games to analyze significance in difference of means. Differences in impacts per season across teams were examined using a Kruskal-Wallis one-way ANOVA on ranks. 1.5 Results A total of 49,847 head impacts were recorded over the two seasons within 482 sessions (345 practices and 137 games). Players participated in a median of 19.0 [15.0- 23.3] practices and 9.0 [7.0-10. 0] games per season. The median total number of head impacts received by an individual player was 211 [122.3-371.5] with a maximum of 1225.5 impacts per season. The median total number of impacts sustained per season by an individual player during practices was 132.0 [69.5-235.5], with a maximum of 660.0 and during games was 71.3 [36.0-139.0], with a maximum of 581.5. The median number of impacts received by a player was 7.2 [4.6-10.5] per practice session and 7.9 [4.6-15.4] per game. 6 The number of head impacts per practice had a significant positive correlation (p < 0.0001) with the number of head impacts per game for individual players (Figure 1). This significant relationship between impacts per practice and impacts per game across individual players remained for five (p = 0.0159) of the six teams (Figure 3 & Table 2) even though the total number of impacts were significantly (p < 0.001) different for each team (Figure 2). Overall, 10.0 % of head impacts (n = 4959.0) were greater than 40 g, 2.8% of impacts (n = 1397.0) were greater than 60 g, and 0.8 % of impact (n = 414.0) were greater than 80 g. By location, 51.6 % of head impacts were to the front of the helmet, 20.0 % were to the back of the helmet, 9.7 % were to the top of the helmet and 18.6 % were to the side of the helmet. As the exposure data for right and left head impact locations did not differ significantly (p = 0.346), they were combined and reported as head impacts to the side. There was a significant difference in the magnitude of head impacts between practices and games for 50th percentile peak linear (p = 0.0333) and rotational (p = 0.0450) acceleration. However, there was no significant difference in the magnitude of impacts between practices and games for 95th percentile peak linear acceleration (p = 0.8891), 95th percentile peak rotational acceleration (p = 0.0644), or 50th (p = 0.2999) and 95th (p = 0.0989) percentile peak HITsp (Table 3). There was a significant positive correlation (p < 0.0001) within the study population between practices and games for the 50th and 95th linear acceleration, rotational acceleration and HITsp (Table 4 & Figure 4). 1.6 Discussion 7 The purpose of this study was to quantify head impact exposure in youth football players age 9-14 and analyze the relationships between impact frequency and magnitude per individual player by session type. There was a positive correlation between the number of impacts per practice and per game for every individual player, essentially a player with a higher number of impacts at practice also had higher number of impacts at games. Additionally, players who sustained greater magnitude impacts during practices also sustained greater magnitude impacts during games. Overall, the average number of head impacts recorded per player was 7.2 per practice and 7.9 per game. These values are slightly higher than the values reported by Daniel et al. (6.7, 5.8), lower than the values reported by Cobb et al. (9.7, 11.3), Young et al. (9, 11) and Munce et al. (9, 12) at the youth level (Daniel, Rowson, and Duma 2012; Cobb et al. 2013; Young et al. 2014; Munce et al. 2014b), and substantially higher than the 1.5 impacts per player per practice and 3.7 impacts per player per game, reported in a previous study (Wong, Wong, and Bailes 2014). The total number of players in a team and the number of practices and games that players participate may have accounted for the differences in values of impacts per practice and impacts per game between studies. It should be noted that the HIT System registers impacts that exceed a threshold of 10g (Beckwith, Greenwald, and Chu 2012), while the binary switch based impact sensor (Shockbox Impact Alert Sensor, Impakt Protective Inc., Canada) utilized in Wong et al. registers impacts with a linear acceleration greater than 30g. The study also preemptively estimated that by having a threshold of 30g they would be excluding about 80-85% of the impacts reported in previous studies(Wong, Wong, and Bailes 2014). Hence it is not alarming that the median linear acceleration of impacts for the current study (18.4 g) was 8 lower than the value reported by Wong et al. by a factor of 2.5 (Wong, Wong, and Bailes 2014). Head impact exposure is reported to increase with the age and level of play (youth to high school, high school to college) (Daniel, Rowson, and Duma 2012; Kelley et al. 2017; Fitness 2015). Our results were consistent with the findings, as the frequency of impacts by session for players in this youth study was less than the range of impacts reported in previous studies for high school (5.3-9.4 impacts per practice and 13-15.5 impacts per game) and collegiate players (6.3-13.2 impacts per practice and 14.3-24.2 impacts per game) (Duma et al. 2005; Schnebel et al. 2007; Crisco et al. 2010; Reynolds et al. 2017; Urban et al. 2013; Talavage et al. 2014). Cobb et al., postulated that this increase in impact frequency from youth to high school and high school to collegiate level was due to an increase in size, strength and speed of the players (Cobb, Rowson, and Duma 2014). It is also interesting to note that, even though the total number of head impacts significantly differed across the six observed teams, the players from five out of the six teams played similarly at practices as in games. The reason for lack of positive significant correlation between impacts per practice and game for Team E (p = 0.1146) could be due to the small subject size (n = 10), while the rest of the five teams had subject sizes ranging between 16-37 players. As for the magnitude variables, the median linear acceleration for practices (18.2 g) and games (18.4 g) measured in this study were similar to those reported by Cobb et al. (18 g, 19 g), Young et al. (17 g, 17 g) and Munce et al. (19.9 g, 20.9 g) at the youth level (Cobb et al. 2013; Munce et al. 2014b; Young et al. 2014). Additionally, the players in this youth study also had comparable median linear and rotational acceleration values 9 to that of high school (20.5-27.1g, 942-1468.58 rad/s2) and collegiate players (20.5-32 g, 981-1400 rad/s2), but had comparatively lower 95th percentile magnitude values (53.7- 62.7 g, ~4378rad/s2) (Crisco et al. 2011; Reynolds et al. 2017; Broglio et al. 2009; Urban et al. 2013; Eckner et al. 2011; Breedlove et al. 2012; Mihalik et al. 2007; Brolinson et al. 2006). This indicates that while youth players are sustaining fewer impacts in frequency and 95th percentile peak magnitudes, they continue to receive impacts with similar median magnitudes as their high school and collegiate counterparts. Moreover, high magnitude impacts, in conjunction with lower body mass and neck strength among other factors, can make youth players more vulnerable to injury (Broglio, Surma, and Ashton- Miller 2012; Farrey 2012). It is not necessary for youth athletes to sustain a concussion to suffer a brain injury (Munce et al. 2014a). Although there have been no large scale longitudinal studies investigating head impacts exposure at practices and games for individual players at the youth level, evaluating the correlation of the frequency and magnitude variables between practice and game for individual players provided a unique opportunity to view impact trends over the season and will hopefully be used to aid in screening for players at higher risk for injury. The advantages aside, the study also has several limitations. While the top 5 % of the highest magnitude head impacts were validated by video, every impact reported herein was not confirmed on video because of the challenges involved in identifying impacts among a group of players, along with some impacts occurring outside the frame of the camera. It should be noted that the HIT System has been validated and error associated with the system has been extensively reported (Rowson et al. 2012; Daniel, Rowson, and Duma 2012; Rowson et al. 2011; Beckwith, Greenwald, and Chu 10 2012). Even though the battery for the sensor has the capacity to store up to 100 impacts if the sideline receiver is not on the field, this does not protect the stored impact data if the sensor battery runs out of charge. However, meticulous and frequent changing and recharging of the sensor batteries helped contain the loss of data due to battery discharges to a minimum. While player position is a strong factor in head impact exposures with high school and collegiate population (Mihalik et al. 2007; Dick et al. 2007; Crisco et al. 2010, 2011; Reynolds et al. 2017; Broglio et al. 2009; Schnebel et al. 2007), it was not used as a factor to analyze impact exposure for the current study because the youth players on these six teams participated in offense, defense and special teams, including different positions within each unit. Accordingly, due to the lack of assigned positions for the youth population, impact location by position could not be analyzed. While differences in head impact exposures were found among the observed six teams and the analysis of these factors was not performed, however, based on previous studies these differences could be attributed to variation in style of play (Kucera et al. 2017; Z. Y. Kerr et al. 2015), practice drills (Campolettano, Rowson, and Duma 2016), coaching style (Zachary Y. Kerr et al. 2017) and difference in number of players per team. However, despite the differences among teams, the positive correlation in head impact exposures between practices and games existed across all teams. As one may assume practices drills are structured in a manner to reduce exposure by allowing players to anticipate impacts and increasing the percentage of noncontact play, whereas during games there is an increased level of unpredictability in the impacts sustained by the players and hence practices and games can be considered to be 11 fundamentally different. Despite these differences in structure of each session, the players who experienced higher impact exposures during practice also experienced higher impact exposure during games. One of the reasons could be that, over a course of a season a player on average participates in more practices than games, hence sustains a higher frequency of head impacts during practices (Young et al. 2014; Daniel, Rowson, and Duma 2012; Mihalik et al. 2007; Wong, Wong, and Bailes 2014). But tackling style is also reported to be a major contributor for the high frequency of impacts, as it is responsible for approximately 50% of the head injuries in high school players (Shankar et al. 2007; Badgeley et al. 2013; Kontos et al. 2013). Therefore, in attempt to reduce the high frequency of impacts, Pop Warner introduced rule changes to officially limit contact during practice (Farrey 2012). Part of the reform was banning full speed head-on blocking or tackling drills along with reducing the amount of contact drills during each practice to a maximum of ⅓ of the practice time. Since this study started after the implementation of the rules, consistent data is not readily available to investigate their effectiveness on head impact exposure within the current dataset. In conclusion, the findings of this study suggest that the frequency and magnitude of impacts that players sustain during practice have a positive association with the impacts they sustain during games. Perhaps aggressive players are more inclined to sustain impacts than less aggressive players regardless of the session type. Alternatively, it can be speculated that rule changes may have affected the frequency of impacts by reducing the ratio of hits per practice vs per game but this is currently undetectable because of lack of extensive data. Either way, a more detailed analysis of tackling style followed at the youth level and practice drills causing the majority of the head impacts 12 during practice may give us an insight regarding the distribution of head impacts at practice and games and further guide and aid researchers, coaches and authorities to make the sport safer. 1.7 Conflict of Interest Disclosure Joseph J. Crisco, Richard M. Greenawald and Simbex have a financial interest in the instruments (HIT System, Sideline Response System (Riddell, Inc)) that were used to collect the biomechanical data reported in this study. 1.8 Acknowledgement Research reported in this publication was supported by the National Institutes of Health under the Award Number NIH R01NS094410. HIT System technology was developed in part under NIH R44HD40743 and research and development support from Riddell, Inc. (Chicago, IL). 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For individual players, their impacts per game correlated linearly (r 2 = 0.52, P < 0.0001) with their impacts per practice. 19 Figure 2. Distribution of total number of impacts per player per season differed substantially among the six teams. 20 Figure 3. Distribution of Impact frequency per practice and per game for individual players by team. 21 Figure 4. Relationship between head impacts during practice and games for median and 95th percentile peak linear acceleration (g) (a-b), peak rotational acceleration (rad/s2) (c-d) and peak HITsp (e-f). 22 Variable Normality Test Result Impacts per Practice Failed (p<0.001) Impacts per Game Failed (p<0.001) Practice Passed (p=0.672) Median linear acceleration Game Failed (p<0.001) Practice Failed (p<0.001) 95th Percentile peak linear acceleration Game Passed (p=0.067) Practice Failed (p<0.001) Median rotational acceleration Game Failed (p<0.001) Practice Failed (p<0.001) th 95 Percentile peak rotational acceleration Game Failed (p<0.001) Practice Failed (p<0.001) Median HITsp Game Failed (p<0.001) Practice Failed (p<0.001) th 95 Percentile peak HITsp Game Passed (p=0.150) Table 1: Shapiro-wilk normality test at a significance level of 0.05 for frequency and magnitude variables to test normality of their distrubiton 23 Team A Team B Team C Team D Team E Team F Number of players on 37 27 19 33 10 16 the team Regression < 0.0001 <0.0001 0.0009 0.0004 0.1146 0.0159 r2 0.55 0.61 0.48 0.33 0.28 0.35 Table 2: Multiple linear regression analysis for the significance between impacts per practice and impacts per game for each of the six observed teams. 24 Season Practice Game th 50 Linear 18.3 [17.1-19.3] 18.2 [16.6-19.4] 18.4 [16.8-19.8] Percentile Acceleration (g) Peak Rotational 1305.4 [1188.2- 1290.4 [1153.1- 1336.7 [1218.6- Value Acceleration 1397.4] 1403.4] 1440.2] (rad/s2) HITsp 13.7 [13.2-14.2] 13.6 [13.0-14.2] 13.8 [13.2-14.5] 95th Linear 46.9 [40.5-52.5] 46.4 [39.2-54.1] 46.5 [40.8-54.1] Percentile Acceleration (g) Peak Rotational 3316.6 [2852.3- 3187.0 [2776.7- 3277.8 [2845.3- Value Acceleration 3664.9] 3657.1] 3915.3] (rad/s2) HITsp 24.3 [22.0-27.3] 24.1 [21.4-27.2] 24.9 [21.5-28.2] Table 3: 50th and 95th percentile peak linear acceleration, peak rotational acceleration and HITsp for the season, practices and games (Median [25th-75th]) 25 Slope of the best Regression r2 fit line 50th Linear Acceleration (g) 0.54 ± 0.09 P < 0.0001 0.21 Percentile Peak Rotational Acceleration 0.79 ± 0.07 P < 0.0001 0.46 Value (rad/s2) HITsp 0.83 ± 0.08 P < 0.0001 0.42 95th Linear Acceleration (g) 0.46 ± 0.06 P < 0.0001 0.28 Percentile Rotational Acceleration 0.49 ± 0.09 P < 0.0001 0.18 Peak (rad/s2) Value HITsp 0.60± 0.07 P < 0.0001 0.34 Table 4: Games vs Practices Linear Regression analysis results (Slope, p-value and correlation coefficient), 50th and 95th percentile peak linear acceleration, peak rotational acceleration and HITsp 26