i

EFFECTS OF EXPERIENCE LEVEL ON

HEAD IMPACT EXPOSURE IN YOUTH FOOTBALL

By

Casey Taylor Tierney

B.S., Brown University, 2018

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 2019

ii

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: _______________________________ Casey Tierney, 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: _______________________________ Casey Tierney, Author

iii

This thesis by Casey Tierney 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. Celinda Kofron, Reader

Date________________ Signature: _______________________________ Andrea Sobieraj, Reader

Approved by the Graduate Council

Date________________ Signature: __________________________________ Dr. Andrew G. Campbell, Dean of the

Graduate School

iv

ACKNOWLEDGEMENTS

I am incredibly grateful that I have had the opportunity to continue my education at

Brown University in pursuit of my Master’s degree. The experience has been everything I

could have ever asked for. I am thankful to everyone at the Bioengineering Lab and the

Department of Orthopaedics for their support and guidance. I am beyond grateful to have

had Dr. Joseph “Trey” Crisco as my advisor. He encouraged me to be curious and

explore my own intelligence throughout the project while still being patient and

providing guidance. Dr. Crisco is an incredible role model and his work ethic is

something I can only hope to achieve in the future. I want to acknowledge and thank

Srinidhi Bellamkonda for her positive energy, guidance, and help even when it wasn’t

required of her. I would like to thank Dr. Eric Smith at Virginia Tech University for his

statistical knowledge and for answering any questions I had throughout my analysis. I

would also like to say thank you to my readers on my thesis committee, Dr. Celinda

Kofron and Andrea Sobieraj, for their support and advice. Finally, I would like to thank

my friends at Brown for the most amazing five years and my incredible family for their

unconditional love and endless support.

v

Table of Contents

AUTHORIZATION PAGE ............................................................................................. ii

SIGNATURE PAGE ........................................................................................................ iii

ACKNOWLEDGEMENTS ............................................................................................ iv

Table of Contents .............................................................................................................. v

List of Figures ................................................................................................................... vi

List of Tables .................................................................................................................... vi

1.0 Effects of Experience Level on Head Impact Exposure in Youth Football ...... 1

1.1 Abstract ........................................................................................................................... 1

1.2 Introduction .................................................................................................................... 2

1.3 Methods and Materials .................................................................................................. 3

1.4 Statistical Analysis ......................................................................................................... 5

1.5 Results ............................................................................................................................. 6

1.6 Discussion ....................................................................................................................... 9

1.7 Conflict of Interest Disclosure .................................................................................... 13

1.8 Acknowledgement ........................................................................................................ 13

1.9 References ..................................................................................................................... 14

vi

List of Figures

Figure 1. Distribution of linear head acceleration based on experience level ................ 18

Figure 2. Distribution of rotational head acceleration based on experience level .......... 19

Figure 3. Distribution of number of impacts per player per season based on experience

level .................................................................................................................................. 20

Figure 4. Location of head impacts for the entire study.................................................. 21

Figure 5. Scatter plot of mean linear acceleration for a season in relation to experience

level and age .................................................................................................................... 22

Figure 6. Scatter plot of mean rotational acceleration for a season in relation to

experience level and age .................................................................................................. 23

Figure 7. Scatter plot of mean linear acceleration for a season in relation to experience

level and weight ............................................................................................................... 24

Figure 8. Scatter plot of mean rotational acceleration for a season in relation to

experience level and weight ............................................................................................. 25

List of Tables

Table 1: Location of impacts on the head split up by experience level (group one, two, or

three) and total number of impacts in each location for the entire study Group one, two,

and three correspond to 1-2 years of experience, 3-4 years of experience, and 5+ years of

experience respectively ..................................................................................................... 26

1

1.0 Effects of Experience Level on Head Impact Exposure in Youth Football

1.1 Abstract

Concern for football players and their safety, especially regarding head impact

exposure, has been at the forefront of research for the last twenty years. Previous research

within football has focused on high school and collegiate level players, despite the fact

that over 70% of all football players within the US are under the age of 14. This study

aimed to identify the effect of experience level on head impact exposure within youth

population ages 8 to 14. Two hundred and eighty-two different players from twenty-four

youth teams were recruited and equipped with the Head Impact Telemetry (HIT) system

enabling frequency and magnitude of head impacts to be recorded at games and practices.

Over a period of four seasons, 95,229 impacts were recorded and analyzed. These

impacts were separated by individual player and the players were then divided into group

by experience level. Experience level was defined as playing football for 1-2 years (group

1), 3-4 years (group 2), and 5 or more years (group 3). Head impact exposure included

linear acceleration, rotational acceleration, and frequency of impacts. The data showed an

increase in head impact exposure with increased experience level. Covariance analysis

also showed that age can be considered a surrogate for experience level and has the same

effects on head impact exposure, but weight had no effect. This information justifies the

use of age classes in youth football but discourages the use of weight in regards to head

impact exposure. Future research has the potential to look at weight with regard to other

potential injuries to see if the use of weight in team divisions is justified at all.

2

1.2 Introduction

Concussions and minor traumatic brain injuries (mTBIs) have become a large

concern within the public, especially when related to sports (Muth 2018; Daneshvar et al.

2011; Clarke 1998). An estimated 1.7 million sports-related concussions happen within

the United States each year (Pfister et al. 2016; Langlois, Rutland-Brown, and Wald

2006; Nathanson et al. 2016). Football garners the most concern because it has the

highest number of participants and approximately 40% of sports-related mTBI occurs as

a result of football (Campolettano, Gellner, and Rowson 2017; Dick et al. 2007; Gessel et

al. 2007; Selassie et al. 2013). Initial research was done within the high school and

college level and found that players were receiving a higher frequency of impacts and

more severe impacts as they moved up in level of competition (ie. High school to college)

and age groups (Urban et al. 2013; Crisco et al. 2012; Duma et al. 2005). However,

approximately 70% of the football participants within the United States belong to the

youth population under the age of 14. Despite the large number of youth players, most

previous head impact research was focused on high school or college level. Research

within recent years has shown increased focus on head impacts within the youth

population (Campolettano, Rowson, and Duma 2016; Cobb, Rowson, and Duma 2014;

Guskiewicz et al. 2000; Bahrami et al. 2016).

Previous research studies that have been performed on the youth level were limited

to the quantification of head impact exposure and the effects of many variables including

practice drill-types, player position, and age on head impact exposure (Kelley, Urban, et

al. 2017; Campolettano, Rowson, and Duma 2016; Campolettano, Gellner, and Rowson

2017). To our knowledge, there has only been one study conducted on the effects of

3

experience level (previous years playing football) on head impact exposure. Young et al.

studied the effect of returning players in youth football players ages 7-8 and found that

returning players had significantly more high magnitude impacts than first time players

(Young et al. 2014). The study by Young et al. only focused on one age group and

divided players into two groups based on if a player was a rookie or returning player. The

effect of experience level within different age groups and a wider range of experience

levels remains to be determined.

The divisions of play for most youth football leagues are currently determined based

on an age and weight scale (PopWarner, “Welcome to AYF”). Through this system,

different teams will have similarities in the biological ages of participants but large

differences in the amount of training (experience) each participant has had in the past.

These divisions create the possibility for a more experienced player to be playing on a

“lower” team based on his age or weight. This presents the question of whether head

impact exposure is actually increasing with age, weight, and level of competition or if

level of experience could be playing a larger role. This study aimed to investigate

potential trends in head impact exposures, by quantifying linear head acceleration,

rotational head acceleration, and location of head contact based on the experience level of

youth football players between the ages of 8 and 14.

1.3 Methods and Materials

Over the course of four years, football players from three different youth football

programs and twenty-four different teams participated in a Rhode Island Hospital, Wake

Forest, and Virginia Tech Institutional Review Board approved study. Prior to

participating in the study, written consent was acquired from the recruited players and

4

their parents and/or guardians. There were 282 players that participated in the study.

Eighty-nine players participated in more than one year of the study. These players either

participated in two years (n = 63), three years (n = 17), or four years (n = 9) of the study.

Total participants for the 2015, 2016, 2017, and 2018 seasons were 86, 103, 113, and 103

respectively. The participants had an average age of 11.9 ± 2.3 years and a mean body

mass of 52.4 ± 24.0 kg. During preseason testing, surveys were given to parents or

guardians in order to collect each player’s years of experience playing football along with

other medical and demographic data. Participants were then divided into groups for

analysis based on this information. The three groups were 1-2 years of experience (n =

120), 3-4 years of experience (n = 90), or 5+ years of experience (n = 133). A player in

his rookie season was considered to have one year of experience since analysis was run

after the season was completed. Players were excluded from analysis if years of

experience playing football was not provided by the parent/guardian.

Each of the players was equipped with a Riddell Revolution, Speed, or Speed

Flex helmet (Riddell, Elyria, OH). Each helmet was instrumented with a helmet-based

sensor unit from the Head Impact Telemetry (HIT) System (Simbex, Lebanon, NH) that

is a part of the Sideline Response System (Riddell, Elyria, OH). Each unit contains 6

orthogonal single-axis accelerometers which communicate with a sideline receiver so that

head impact accelerations and impact locations can be measured and recorded

(Greenwald et al. 2003; Duma et al. 2005; Daniel, Rowson, and Duma 2012). An impact

and its data was recorded if any of the six accelerometers exceeded 9.6g. When this

happens, the impact data is collected for a total of 40 milliseconds with 8 ms of pre-

trigger data and 32 ms of post-trigger data. The biomechanical data collected includes

5

linear head acceleration, rotational head acceleration, and location of impact. The HIT

system has been extensively validated in laboratory and on-field settings with any errors

reported (Beckwith, Greenwald, and Chu 2012; Crisco, Chu, and Greenwald 2004; Funk

et al. 2012; Rowson et al. 2011).

Practices and games were used as data recording sessions. Practices were defined

as a session in which players wore helmets and pads with a potential for impact to the

head. The practices with only helmets or only helmets and shoulder pads were also

included within the definition of a practice. Competitions and Scrimmages were both

included in the definition of a game. A player’s participation in a session was defined as a

player receiving at least one head impact in a practice or game (Bellamkonda et al. 2018).

In order to ensure impacts included in the study were valid, there was a researcher

at each game and practice to record video footage and keep a log of all activities that

occurred. This allowed for impacts during water breaks, before practice, or after practice

to be excluded from the data set. The data set was then filtered to only include impacts

with a peak linear head acceleration that was equal to or exceeded the threshold of 40g.

Impacts over 40g were verified by researchers through video verification protocol in

order to avoid error and limit overestimation when analyzing high magnitude impacts

(Cobb, Rowson, and Duma 2014).

1.4 Statistical Analysis

Data was analyzed using MATLAB (MathWorks Inc., Natick, MA) and exported

to GraphPad (GraphPad Software, San Diego, CA) and SAS (SAS Institute Inc., Cary,

NC) for statistical analysis. To evaluate significance between level of experience

(groups), the linear acceleration and rotational acceleration means were first calculated

6

for each subject and year. Statistical tests were carried out for using analysis of variance

(PROC GLM and PROC MIXED) and analysis of covariance (ANCOVA). Assumptions

for the analyses were checked using boxplot displays, scatterplots of data and normal

probability plots of residuals. Normality was evaluated using a Shapiro-Wilk test in

addition to the graphical display. The models focused on the variable of experience and

whether it was affected by the year of play, the team, type of activity (practice or

competition) as well as the weight and age of the player. Because the player may have

participated over multiple years, PROC MIXED was used to model the factors treating

player as a random factor. In addition, models were evaluated using just one year of play

for a player (either the first or last year of play), thus removing the possible temporal

dependence. A p-value less than 0.05 was considered to be statistically significant.

1.5 Results

A total of 95,229 impacts were recorded over the course of four football seasons.

The total distribution of linear acceleration for all three groups had a 50th percentile value

of 18.4 g and a 95th percentile value of 48.5 g. From the total number of impacts, 9.0% of

head impacts (n = 8528) were greater that 40g, 2.3% of head impacts (n = 2159) were

greater than 60g, and 0.66% of head impacts (n = 624) were greater than 80g. The total

distribution of rotational acceleration for all three groups had a 50th percentile value of

887.8 rad/s2 and a 95th percentile value of 2307.9 rad/s2.

When looking at the effect of experience level, the mean linear head accelerations

were 21.2 g for group one, 21.8 g for group two, and 23.0 g for group three (Figure 1).

The data shows a significantly greater mean linear acceleration for group three when

compared to group one (p < 0.0001) and group two (p = 0.0005). There was no

7

significant difference in the mean linear acceleration between groups one and two (p =

0.0753). The mean rotational head accelerations for each experience level were 979.7

rad/s2 for group one, 1038.0 rad/s2 for group two, and 1060.3 rad/s2 (Figure 2). The mean

rotational head acceleration for group one was significantly lower than both group two (p

= 0.0043) and group three (p < 0.0001). There was no significant difference between

group two and group three with respect to mean rotational head acceleration (p = 0.2610).

The number of head impacts per player per season, including practices and games,

also changed with experience level. The mean number of impacts for a season was 200

for group one, 299 for group two, and 276 for group three (Figure 3). The ranges of

frequency of impact were quite large with 8 to 1009, 17 to 1099, and 3 to 1437 for groups

one, two, and three, respectively. Group two had the highest frequency of impacts for the

season. This difference in number of impacts was significant when compared to group

one (p = 0.0007) but not when compared to group three (p = 0.2508).

Looking at location of head impacts for the entire study, 10.17% of impacts were

to the top of the head, 18.49% of impacts were to the back of the helmet, 51.69% were to

the front of the helmet, and 19.63% were to the side of the helmet (Figure 4). Location of

head impact was also separated based on experience level (Table 1). Through the

separation of location of head impacts, it can be seen that group one players have slightly

fewer impacts to the front of the head than groups two and three.

The results of covariance analysis as function of experience level and age and

experience level and weight showed the same trends in linear and rotational acceleration

as seen with just experience level by itself. The only group in all three instances that was

significantly higher in regards mean linear accelerations was group three. Analysis of all

8

three variables also showed the mean rotational accelerations to be significantly lower in

group one, but did not differ significantly when looking at just groups two and three.

Covariance analysis as a function of experience level and age found mean linear

accelerations were 21.3 g for group one, 21.7 g for group two, and 22.9 g for group three

(Figure 5). Group three had a significantly higher mean linear acceleration than group

one (p < 0.0001) and group two (p = 0.0009), but groups one and two did not differ

significantly (p = .2309). The mean rotational accelerations were 992.7 rad/s2 for group

one, 1032.4 rad/s2 for group two, and 1044.9 rad/s2 for group three (Figure 6). Group one

mean rotational acceleration was significantly lower than group two (p = 0.0481) and

group three (p = 0.0059), but groups two and three were not found to be significantly

different (p = 0.5169). Analysis that only took into account age and the effects on head

impact exposure also found age to be a significant factor for both linear (p < 0.0001) and

rotational (p < 0.0001) acceleration.

Covariance analysis as a function of experience level and weight found mean

linear accelerations were 21.3 g for group one, 21.8 g for group two, and 23.1 g for group

three (Figure 7). Again, group three is considered to have a significantly greater mean

linear acceleration than group one (p < 0.0001) and group two (p = 0.0004). Groups one

and two were found to not differ significantly (p = 0.2717). The mean linear accelerations

were 989.1 rad/s2 for group one, 1037.2 rad/s2 for group two, and 1061.6 rad/s2 for group

three (Figure 8). Group one is significantly lower than group two (p = 0.0269) and group

three (p = 0.0002), but groups two and three were not found to be significantly different

(p = 0.2277). While covariance analysis including both weight and experience level was

significant for linear and rotational acceleration (p < 0.0001, p = 0.001), weight was not

9

significant on its own for linear acceleration (p = 0.6655) or rotational acceleration (p =

0.7868).

1.6 Discussion

The purpose of this study was to analyze experience level and the effects it has on

head impact exposure in youth football players (ages 8-14). There was an increase in

head impact exposure that occurred with an increase in player experience level.

Essentially a player with more experience received impacts with greater linear and

rotational accelerations as well as a higher frequency of impacts overall. Additionally,

age was seen to have similar effects to experience level on head impact exposure, while

weight had no effect.

Study-wide, head impact exposure data were compared to values found in other

studies. The 50th percentile linear and rotational head accelerations (18.4g, 887.8 rad/s2)

were towards the higher end of the range of values found in other youth studies by Cobb

et. al (18.0 g, 856.0 rad/s2), Young et. al (16.0 g, 686.0 rad/s2), and Kelley et. al (20.8 g,

1120.7 rad/s2)(Cobb et al. 2013; Kelley, Urban, et al. 2017; Young et al. 2014). The

difference in these values could be attributed to a higher sample size within our study and

a youth age range that is larger than the studies to which we compared. Our results were

only slightly lower than the values found by Urban et al. (21.9 g, 973.0 rad/s2) and

Schmidt et al. (20.2-26.6g, 1212.6-1655.9 rad/s2) in high school football players. (Urban

et al. 2013; Schmidt et al. 2016). Our values landing between the youth and high school

numbers could be a result of our average age of players (11.9 years) being closer to the

high end of our age range. This age would place players in middle school where they

would soon be making the transition to high school football. Our values were lower than

10

results for accelerations in collegiate players collected by Crisco et al. (20.5g, 1400

rad/s2) and Bronlinson et al. (20.9g) (Crisco et al. 2011; Brolinson et al. 2006).

The general trend seen throughout the study is increasing linear and rotational head

acceleration with increasing years of football experience. A rise is linear and rotational

accelerations with experience level could be expected when considering the increased

confidence and skill that comes with an increase in experience. Arguments could also be

made for an expected decrease in linear and rotational accelerations with increase in

experience, because a more experienced player should have better knowledge of tackling

and therefore an ability to keep the head out of the play. Young et al. had the same results

as us with linear accelerations and rotational accelerations increasing with experience.

Returning players within their study also had significantly more high magnitude impacts

(impacts greater than 40g) than the rookie players (Young et al. 2014). Our study allows

the trend of increased accelerations with increased experience level to be stated with

more confidence due to the larger sample size and age range within our study.

Frequency of impacts per season also increased significantly for players that were no

longer considered novice players (group two and three). This could be expected based on

increased confidence and training that comes from years of experience, as well as

potential for increased play time. Young et al. found the same results, increased

frequency of impacts with increased experience, with rookie players receiving 75 impacts

per season while veteran players were receiving 211 impacts per season (Young et al.

2014). Players within our study received 255 impacts per season compared to high school

players and collegiate players who were found to have 520 impacts and 1000 impacts

respectively. This large increase that occurs in high school and college can be attested to

11

the larger number of practices and games that occur at these levels of competition

(Rowson and Duma 2011; Broglio, Surma, and Ashton-Miller 2012).

When dividing the head impact location analysis based upon group, group one

showed a slightly lower amount of impacts to the front of the head for a novice player

versus a player with more experience. This could be attested to the fact that more novice

players are not anticipating the impacts as much as a veteran player due to lack of

experience. However, interpretation of this data becomes difficult because the head

impact locations differ with team and activity, therefore further analysis is needed.

However, the youth players within our study had significantly more impacts to the front

of the helmet (51.7%) compared to high school (45.3%) and collegiate players (34.01%)

(Broglio, Surma, and Ashton-Miller 2012; Urban et al. 2013). This is an interesting trend

to note with significant drops in impacts occurring to the front of the head at each level

up in competition.

The consistent trend seen in analysis with experience level and age shows that age

may be a good surrogate for experience level in head impact exposure. However, weight

had no effect on head impact exposure without the inclusion of experience level in a

covariance analysis. This becomes important when looking at the way that youth football

teams are currently being divided. The Pop Warner youth football league and the

American Youth Football league both coordinate teams and divisions based on age and

weight (PopWarner, “Welcome to AYF”). This study of experience level allowed for the

ability to see if team divisions could be separated by a more evidence based decision that

would involve age, weight, and experience level, thus creating a three-tier system. The

findings within the study supported the use of age in order to separate teams, because the

12

trends seen in head impact exposure from experience level were able to be substituted for

the trends seen in increasing age. Analysis showed that weight should not be considered

in team divisions and could be taken out of the process completely, but this analysis is

only taking into account head impact exposure. The recommendation of using only age

and experience level for team divisions cannot truly be made without making sure that

weight is not an important safety factor for other purposes such as other physical injuries.

This study has several limitations. One potential limitation in our study analysis is

the repetition of individual players over multiple seasons. The initial assumption of

independence could be considered invalid because of these repeated players, so further

analysis was done to evaluate the sensitivity of the assumptions. The two analyses used

were to only include each individual player’s first year in the study or to only include

each individual player’s last year in the study while the others are omitted. Both of these

analyses showed that the repeating players had very little effect on results and

independence of individuals could still be assumed. Another potential limitation is that

weight analysis is slightly biased. The teams that were analyzed within this study were

divided based on weight so we had less participants toward the higher end of the weight

spectrum. This could have effects on analysis that included weight.

Overall, this study suggests that experience level has effects on linear head

acceleration, rotational head acceleration, and frequency of head impacts within a season.

Linear head acceleration increases with experience at the youth levels but becomes most

relevant in players with 5 or more years of experience. Perhaps players become more

aggressive and are more willing to sustain higher magnitude impacts once reaching a

certain point in football experience. Alternatively, rotational head acceleration and

13

frequency of head impacts per season both showed tendencies that players with

experience over 1-2 years had an increased number of head impacts and an increased

magnitude of head impacts overall. Increases in both of these numbers could be a result

of players getting over fear that occurs when at the novice level of the sport or the

increase in playing time that likely occurs once a player is more experienced.

1.7 Conflict of Interest Disclosure

Joseph J. Crisco, Richard M. Greenwald 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). We appreciate and acknowledge the researchers and

institutions from which the data were collected: Virginia Tech University, Wake Forest

University, Brown University. We also acknowledge Simbex for the coordination of all

data collection.

14

1.9 References

Bahrami, Naeim, Dev Sharma, Scott Rosenthal, Elizabeth M. Davenport, Jillian E. Urban, Benjamin Wagner, Youngkyoo Jung, et al. 2016. “Subconcussive Head Impact Exposure and White Matter Tract Changes over a Single Season of Youth Football.” Radiology 281 (3): 919–26. https://doi.org/10.1148/radiol.2016160564.

Beckwith, Jonathan G, Richard M Greenwald, and Jeffrey J Chu. 2012. “Measuring Head Kinematics in Football: Correlation between the Head Impact Telemetry System and Hybrid III Headform.” Annals of Biomedical Engineering 40 (1): 237–48. https://doi.org/10.1007/s10439-011-0422-2.

Bellamkonda, Srinidhi, Samantha J. Woodward, Eamon Campolettano, Ryan Gellner, Mireille E. Kelley, Derek A. Jones, Amaris Genemaras, et al. 2018. “Head Impact Exposure in Practices Correlates with Exposure in Games for Youth Football Players.” Journal of Applied Biomechanics, April, 1–22. https://doi.org/10.1123/jab.2017-0207.

Broglio, Steven P, Tyler Surma, and James A Ashton-Miller. 2012. “High School and Collegiate Football Athlete Concussions: A Biomechanical Review.” Annals of Biomedical Engineering 40 (1): 37–46. https://doi.org/10.1007/s10439-011-0396-0.

Brolinson, P Gunnar, Sarah Manoogian, David McNeely, Mike Goforth, Richard Greenwald, and Stefan Duma. 2006. “Analysis of Linear Head Accelerations from Collegiate Football Impacts.” Current Sports Medicine Reports 5 (1): 23–28.

Campolettano, Eamon T., Ryan A. Gellner, and Steven Rowson. 2017. “High-Magnitude Head Impact Exposure in Youth Football.” Journal of Neurosurgery. Pediatrics 20 (6): 604–12. https://doi.org/10.3171/2017.5.PEDS17185.

Campolettano, Eamon T., Steven Rowson, and Stefan M. Duma. 2016. “Drill-Specific Head Impact Exposure in Youth Football Practice.” Journal of Neurosurgery. Pediatrics 18 (5): 536–41. https://doi.org/10.3171/2016.5.PEDS1696.

Clarke, K. S. 1998. “Epidemiology of Athletic Head Injury.” Clin Sports Med 17: 1-12. Cobb, Bryan R., Steven Rowson, and Stefan M. Duma. 2014. “Age-Related Differences

in Head Impact Exposure of 9-13 Year Old Football Players.” Biomedical Sciences Instrumentation 50: 285–90.

Cobb, Bryan R, Jillian E Urban, Elizabeth M Davenport, Steven Rowson, Stefan M Duma, Joseph A Maldjian, Christopher T Whitlow, Alexander K Powers, and Joel D Stitzel. 2013. “Head Impact Exposure in Youth Football: Elementary School Ages 9-12 Years and the Effect of Practice Structure.” Annals of Biomedical Engineering, July. https://doi.org/10.1007/s10439-013-0867-6.

Crisco, Joseph J, Jeffrey J Chu, and Richard M Greenwald. 2004. “An Algorithm for Estimating Acceleration Magnitude and Impact Location Using Multiple Nonorthogonal Single-Axis Accelerometers.” Journal of Biomechanical Engineering 126 (6): 849–54.

Crisco, Joseph J, Bethany J Wilcox, Jonathan G Beckwith, Jeffrey J Chu, Ann-Christine Duhaime, Steven Rowson, Stefan M Duma, Arthur C Maerlender, Thomas W McAllister, and Richard M Greenwald. 2011. “Head Impact Exposure in Collegiate Football Players.” Journal of Biomechanics 44 (15): 2673–78. https://doi.org/10.1016/j.jbiomech.2011.08.003.

15

Crisco, Joseph J, Bethany J Wilcox, Jason T Machan, Thomas W McAllister, Ann-Christine Duhaime, Stefan M Duma, Steve Rowson, Jonathan G Beckwith, Jeffrey J Chu, and Richard M Greenwald. 2012. “Magnitude of Head Impact Exposures in Individual Collegiate Football Players.” Journal of Applied Biomechanics 28 (2): 174–83.

Daneshvar, Daniel H., Christopher J. Nowinski, Ann McKee, and Robert C. Cantu. 2011. “The Epidemiology of Sport-Related Concussion.” Clinics in Sports Medicine 30 (1): 1–17. https://doi.org/10.1016/j.csm.2010.08.006.

Daniel, Ray W, Steven Rowson, and Stefan M Duma. 2012. “Head Impact Exposure in Youth Football.” Annals of Biomedical Engineering 40 (4): 976–81. https://doi.org/10.1007/s10439-012-0530-7.

Dick, Randall, Michael S Ferrara, Julie Agel, Ron Courson, Stephen W Marshall, Michael J Hanley, and Fred Reifsteck. 2007. “Descriptive Epidemiology of Collegiate Men’s Football Injuries: National Collegiate Athletic Association Injury Surveillance System, 1988–1989 Through 2003–2004.” Journal of Athletic Training 42 (2): 221–33.

Duma, Stefan M, Sarah J Manoogian, William R Bussone, P Gunnar Brolinson, Mike W Goforth, Jesse J Donnenwerth, Richard M Greenwald, Jeffrey J Chu, and Joseph J Crisco. 2005. “Analysis of Real-Time Head Accelerations in Collegiate Football Players.” Clinical Journal of Sport Medicine: Official Journal of the Canadian Academy of Sport Medicine 15 (1): 3–8.

Funk, James R, Steven Rowson, Ray W Daniel, and Stefan M Duma. 2012. “Validation of Concussion Risk Curves for Collegiate Football Players Derived from HITS Data.” Annals of Biomedical Engineering 40 (1): 79–89. https://doi.org/10.1007/s10439-011-0400-8.

Gessel, Luke M, Sarah K Fields, Christy L Collins, Randall W Dick, and R Dawn Comstock. 2007. “Concussions among United States High School and Collegiate Athletes.” Journal of Athletic Training 42 (4): 495–503.

Greenwald, R., J. Chu, J.J. Crisco, and J. Finkelstein. 2003. “Head Impact Telemetry System (HITSTM) for Measurement of Head Acceleration in the Field.” In 27th American Society of Biomechanics Meeting. Toledo, OH. file://P:/mTBI/mTBI Refs/Sports 120108.Data/PDF/Greenwald_R_2003_HITSmeasureHeadAccel_ASBM-4245275648/Greenwald_R_2003_HITSmeasureHeadAccel_ASBM.pdf.

Guskiewicz, K. M., N. L. Weaver, D. A. Padua, and W. E. Garrett. 2000. “Epidemiology of Concussion in Collegiate and High School Football Players.” The American Journal of Sports Medicine 28 (5): 643–50.

Kelley, Mireille E., Joeline M. Kane, Mark A. Espeland, Logan E. Miller, Alexander K. Powers, Joel D. Stitzel, and Jillian E. Urban. 2017. “Head Impact Exposure Measured in a Single Youth Football Team during Practice Drills.” Journal of Neurosurgery. Pediatrics 20 (5): 489–97. https://doi.org/10.3171/2017.5.PEDS16627.

Kelley, Mireille E., Jillian E. Urban, Logan E. Miller, Derek A. Jones, Mark A. Espeland, Elizabeth M. Davenport, Christopher T. Whitlow, Joseph A. Maldjian, and Joel D. Stitzel. 2017. “Head Impact Exposure in Youth Football: Comparing Age- and

16

Weight-Based Levels of Play.” Journal of Neurotrauma 34 (11): 1939–47. https://doi.org/10.1089/neu.2016.4812.

Langlois, Jean A., Wesley Rutland-Brown, and Marlena M. Wald. 2006. “The Epidemiology and Impact of Traumatic Brain Injury: A Brief Overview.” The Journal of Head Trauma Rehabilitation 21 (5): 375–78.

Munce, Thayne A., Jason C. Dorman, Paul A. Thompson, Verle D. Valentine, and Michael F. Bergeron. 2014. “Head Impact Exposure and Neurologic Function of Youth Football Players.” Medicine and Science in Sports and Exercise 47 (8): 1567–76. https://doi.org/10.1249/MSS.0000000000000591.

Muth, Christopher C. 2018. “Sport-Related Concussion.” JAMA 319 (8): 840–840. https://doi.org/10.1001/jama.2018.0436.

Nathanson, John T., James G. Connolly, Frank Yuk, Alex Gometz, Jonathan Rasouli, Mark Lovell, and Tanvir Choudhri. 2016. “Concussion Incidence in Professional Football.” Orthopaedic Journal of Sports Medicine 4 (1). https://doi.org/10.1177/2325967115622621.

Pfister, Ted, Ken Pfister, Brent Hagel, William A. Ghali, and Paul E. Ronksley. 2016. “The Incidence of Concussion in Youth Sports: A Systematic Review and Meta-Analysis.” Br J Sports Med 50 (5): 292–97. https://doi.org/10.1136/bjsports-2015-094978.

Popwarner. “Ages and Weights.” Pop Warner Little Scholars, www.popwarner.com/ Default.aspx?tabid=1476162.

Rowson, Steven, Jonathan G Beckwith, Jeffrey J Chu, Daniel S Leonard, Richard M Greenwald, and Stefan M Duma. 2011. “A Six Degree of Freedom Head Acceleration Measurement Device for Use in Football.” Journal of Applied Biomechanics 27 (1): 8–14.

Rowson, Steven, and Stefan M Duma. 2011. “Development of the STAR Evaluation System for Football Helmets: Integrating Player Head Impact Exposure and Risk of Concussion.” Annals of Biomedical Engineering 39 (8): 2130–40. https://doi.org/10.1007/s10439-011-0322-5.

Schmidt, Julianne D., Kevin M. Guskiewicz, Jason P. Mihalik, J. Troy Blackburn, Gunter P. Siegmund, and Stephen W. Marshall. 2016. “Head Impact Magnitude in American High School Football.” Pediatrics 138 (2): e20154231. https://doi.org/10.1542/peds.2015-4231.

Selassie, Anbesaw W., Dulaney A. Wilson, E. Elisabeth Pickelsimer, Delia C. Voronca, Nolan R. Williams, and Jonathan C. Edwards. 2013. “Incidence of Sport-Related Traumatic Brain Injury and Risk Factors of Severity: A Population-Based Epidemiologic Study.” Annals of Epidemiology 23 (12): 750–56. https://doi.org/10.1016/j.annepidem.2013.07.022.

Urban, Jillian E., Elizabeth M. Davenport, Adam J. Golman, Joseph A. Maldjian, Christopher T. Whitlow, Alexander K. Powers, and Joel D. Stitzel. 2013. “Head Impact Exposure in Youth Football: High School Ages 14 to 18 Years and Cumulative Impact Analysis.” Annals of Biomedical Engineering 41 (12): 2474–87. https://doi.org/10.1007/s10439-013-0861-z.

“Welcome to AYF.” American Youth Football, www.americanyouthfootball.com/foot ball/.

17

Young, Tyler J., Ray W. Daniel, Steven Rowson, and Stefan M. Duma. 2014. “Head Impact Exposure in Youth Football: Elementary School Ages 7-8 Years and the Effect of Returning Players.” Clinical Journal of Sport Medicine: Official Journal of the Canadian Academy of Sport Medicine 24 (5): 416–21. https://doi.org/10.1097/JSM.0000000000000055.

18

FIGURE 1. Distribution of linear head acceleration differed based on experience level.

19

FIGURE 2. Distribution of rotational head acceleration differed based on experience level.

20

FIGURE 3. Distribution of number of impacts per player per season based on experience level.

21

FIGURE 4. Location of head impacts for the entire study.

22

FIGURE 5. Scatter plot of mean linear acceleration for a season with relation to experience level and age.

23

FIGURE 6. Scatter plot of mean rotational acceleration for a season with relation to experience level and age.

24

FIGURE 7. Scatter plot of mean linear acceleration for a season with relation to experience level and weight.

25

FIGURE 8. Scatter plot of mean rotational acceleration for a season with relation to experience level and weight.

26

Table of Group by General Location

Group General Location (%)

Back Front Left Right Top Total

31 20.51

50.80

9.33

9.76

9.60

2 17.51

52.77

9.65

10.18

9.88

3 17.89

51.50

9.20

10.66

10.77

Total (impacts) 16212

45327

8218

8999

8921

87677

Table 1. Location of impacts on the head split up by experience level (group one, two, or three) and total number of impacts in each location for the entire study. Group one, group two, and group three correspond to 1-2 years of experience, 3-4 years of experience, and 5+ years of experience respectively.