Skip to main content
PMC home page
Springer logoLink to Springer
. 2013 Jul 24;41(12):2463–2473. doi: 10.1007/s10439-013-0867-6

Head Impact Exposure in Youth Football: Elementary School Ages 9–12 Years and the Effect of Practice Structure

Bryan R Cobb 1, Jillian E Urban 2,3, Elizabeth M Davenport 2,3, Steven Rowson 1,✉, Stefan M Duma 1, Joseph A Maldjian 2,4, Christopher T Whitlow 4,5, Alexander K Powers 6,7, Joel D Stitzel 2,7
PMCID: PMC3825505  PMID: 23881111

Abstract

Head impact exposure in youth football has not been well-documented, despite children under the age of 14 accounting for 70% of all football players in the United States. The objective of this study was to quantify the head impact exposure of youth football players, age 9–12, for all practices and games over the course of single season. A total of 50 players (age = 11.0 ± 1.1 years) on three teams were equipped with helmet mounted accelerometer arrays, which monitored each impact players sustained during practices and games. During the season, 11,978 impacts were recorded for this age group. Players averaged 240 ± 147 impacts for the season with linear and rotational 95th percentile magnitudes of 43 ± 7 g and 2034 ± 361 rad/s2. Overall, practice and game sessions involved similar impact frequencies and magnitudes. One of the three teams however, had substantially fewer impacts per practice and lower 95th percentile magnitudes in practices due to a concerted effort to limit contact in practices. The same team also participated in fewer practices, further reducing the number of impacts each player experienced in practice. Head impact exposures in games showed no statistical difference. While the acceleration magnitudes among 9–12 year old players tended to be lower than those reported for older players, some recorded high magnitude impacts were similar to those seen at the high school and college level. Head impact exposure in youth football may be appreciably reduced by limiting contact in practices. Further research is required to assess whether such a reduction in head impact exposure will result in a reduction in concussion incidence.

Keywords: Concussion, Brain injury, Biomechanics, Helmet, Linear, Rotational, Acceleration, Pediatrics, Children, Sports

Introduction

In recent years, football has come under increased scrutiny because of the concern for player safety and the risk of injury, especially related to concussion. Researchers estimate that between 1.6 and 3.8 million cases of sports related concussion occur each year in the United States, with football having the highest rate of injury among team sports.14,19 While the long term effects of sports concussions are still under investigation, links may exist between the accumulation of head impacts over a playing career and increased risk of neurodegenerative diseases later in life, among other health concerns.30 The majority of the biomechanics research investigating concussions in football has been focused on high school, college, and professional players, despite that more than two-thirds of football players are under the age of 14.11

In order to understand the biomechanics associated with concussion, numerous studies have been conducted over the last decade to investigate player exposure and tolerance to head impacts in football.3,4,7,9,10,13,16,17,22,23,25–27,31–34 Many of these studies have utilized commercially available helmet-mounted accelerometer arrays (Head Impact Telemetry (HIT) System, Simbex, Lebanon, NH) to measure head kinematics resulting from head impact in real-time during live play. The accelerometer arrays collect data from each head impact a player experiences while instrumented, allowing researchers to get a more complete view of the biomechanical response of a player’s head to impacts across a wide range of magnitudes. Since 2003, more than 1.5 million impacts have been recorded using the HIT system, primarily at the high school and college level.7–10,12 From these data, strategies to reduce head impact exposure through rule changes and methods to evaluate protective equipment have been developed.8–10,25 Unfortunately, little research has focused on youth football, where the head impact exposure is still not well understood.11 A single study has investigated head impact exposure at the youth level. That study found that 7 and 8 year old players sustained an average of 107 impacts over the course of a season, with the majority of high magnitude impacts occurring in practice.11 This work was one factor contributing to youth football organizations updating contact restrictions during practice.28

An estimated 5 million athletes participate in organized football in the United States annually. Children, age 6–13 years, account for around 3.5 million of these participants, compared to just 2000 in the National Football League (NFL), 100,000 in college, and 1.3 million in high school.11,18,24 Despite making up 70% of the football playing population, just one study has investigated head impact exposure experienced by youth football players under 14 years old. The objective of this study was to quantify the head impact exposure of youth football players, aged 9–12 years, for all practices and games over the course of single season. These data, along with future research, may be used in the development of scientifically based strategies for head injury mitigation.

Materials and Methods

On-field head impact data were collected from 50 players, age 9–12 years, on three youth tackle football teams instrumented with the HIT system for a single fall football season. The three teams consisted of a juniors team (team A, 9–11 years old), a pee wee team (team B, 10–12 years old), and a junior pee wee team (team C, 9–11 years old). Further description of the three teams is provided in Table 1. Players were monitored during each of the teams’ games and contact practices. Approval for this study was given by the Virginia Tech and Wake Forest University Institutional Review Boards (IRBs). Each player provided assent and their parent/guardian gave written consent for participation in the study.

Table 1.

Description of subject groups investigated in this study

Team Player mass (kg) Player age (years) Number of players Number of impacts
A 37.6 ± 5.7 9.8 ± 0.8 14 2206
B 50.1 ± 3.9 12.2 ± 0.5 17 5005
C 43.9 ± 5.9 10.9 ± 0.6 19 4767
Combined 44.2 ± 7.2 11.0 ± 1.1 50 11,978
Open in a new tab

The HIT system consists of an array of six non-orthogonally mounted single-axis accelerometers oriented normal to the surface of the head. The arrays, designed to fit in medium or large Riddell Revolution helmets, were installed between the existing padding inside the helmets. Each accelerometer is mounted on an elastic base so that they remain in contact with the head throughout the duration of head impact, allowing for the measurement of head acceleration rather than that of the helmet.20 Any time an instrumented player experienced a head impact that resulted in a single accelerometer measuring 14.4 g during games and practices, data acquisition was triggered to record 40 ms of data at 1000 Hz, including 8 ms of pre-trigger data. Data from the helmet-mounted accelerometers were then transmitted wirelessly to a computer on the sideline, where the data were stored and processed to compute resultant linear head acceleration and peak rotational head acceleration using previously described methods.6,27 In addition, impact location was generalized into 1 of 4 impact locations (front, side, top, or back) based on the acceleration vectors from the linear accelerometers.15 Impacts were verified using video from practice and game sessions to ensure they occurred while players were wearing the helmets. The HIT system has previously been found to reliably determine linear acceleration, peak rotational acceleration, and impact location.1

Empirical cumulative distribution functions (CDF) for both linear and rotational head acceleration were determined. Head impact exposure was quantified in terms of impact frequency and 50th and 95th percentile head accelerations. Acceleration duration was measured from the local minimum before peak linear acceleration and the local minimum after the peak, while time to peak linear acceleration was measured from the local minimum before peak linear acceleration to the peak. The data were sorted by generalized impact location and session type (practice or game). A Kruskal–Wallis one-way analysis of variance was conducted to evaluate for between-group differences in head impact exposure associated with the three teams and two session types. A threshold of p < 0.05 was used to determine statistical significance. In the event that more than two groups were compared, p values were calculated for all pairs and the most conservative p value was reported. All data analysis was conducted on an individual player basis and then averaged to represent the exposure level of a typical 9–12 year old football player. Head impact exposure levels were then compared with those of other levels of play that have been previously described in the literature.

Results

A total of 11,978 impacts were measured, ranging from linear accelerations of 10–126 g and rotational accelerations of 4–5838 rad/s2. The distribution of linear acceleration had a median value of 19 g and a 95th percentile value of 46 g. The distribution of rotational acceleration had a median value of 890 rad/s2 and a 95th percentile value of 2081 rad/s2. CDFs of linear and rotational acceleration magnitudes for the season were determined (Fig. 1). The acceleration distributions are right-skewed and heavily weighted toward lower magnitude impacts. The impact durations measured were 8.82 ± 2.97 ms (average ± standard deviation) with a time to peak linear acceleration of 4.67 ± 1.73 ms. Resultant linear acceleration is plotted vs. time for several impacts recorded in this study as, examples of a typical acceleration pulse (Fig. 2).

Figure 1.

Figure 1

Open in a new tab

Cumulative distribution plots of linear acceleration (left) and rotational acceleration (right) magnitudes for impacts collected during the season

Figure 2.

Figure 2

Open in a new tab

Resultant linear acceleration vs. time for several impacts of various magnitudes recorded from 9 to 12 year old football players

On average, instrumented players sustained 240 ± 147 impacts during the season, with values ranging from 26 to 585 impacts. The average instrumented player sustained 10.6 ± 5.2 impacts per session while participating in 21.8 ± 5.7 sessions. The median impact sustained by instrumented players resulted in accelerations of 18 ± 2 g and 856 ± 135 rad/s2. The 95th percentile impact sustained by instrumented players resulted in accelerations of 43 ± 7 g and 2034 ± 361 rad/s2. Head impact exposure was quantified on an individual player basis by session type (Table 2). A total of 961 impacts (8.0%) greater than 40 g, 160 impacts (1.3%) greater than 60 g, and 36 impacts (0.3%) greater than 80 g were recorded throughout the season. The average player sustained 19.2 ± 20.1 impacts greater than 40 g, 3.2 ± 4.4 impacts greater than 60 g, and 0.7 ± 1.2 impacts greater than 80 g.

Table 2.

Expanded head impact exposure data for each player, split up by session type for each team: (a) team A, (b) team B, and (c) team C

Player ID Practice Games Season
Sessions Number of Impacts Linear Acceleration (g) Rotational Acceleration (rad/s2) Sessions Number of Impacts Linear Acceleration (g) Rotational Acceleration (rad/s2) Sessions Number of Impacts Linear Acceleration (g) Rotational Acceleration (rad/s2)
Total Per Session 50% 95% 50% 95% Total Per Session 50% 95% 50% 95% Total Per Session 50% 95% 50% 95%
(a)
 A1 8 53 6.6 14 26 319 982 6 46 7.7 11 17 269 1004 14 99 7.1 13 25 305 1051
 A2 3 19 6.3 15 34 667 1247 7 73 10.4 20 53 973 2401 10 92 9.2 17 54 890 2348
 A3 8 52 6.5 21 41 996 2019 8 176 22 20 42 940 2139 16 228 14.3 20 42 943 2130
 A4 10 87 8.7 16 42 448 1915 7 286 40.9 19 46 702 2186 17 373 21.9 18 45 633 2156
 A5 9 36 4 16 33 647 1321 6 25 4.2 15 44 603 2650 15 61 4.1 16 35 637 1826
 A6 10 53 5.3 19 30 770 1530 8 54 6.8 18 33 903 1826 18 107 5.9 19 33 854 1570
 A7 8 44 5.5 16 27 642 1489 7 45 6.4 18 35 838 1474 15 89 5.9 17 30 785 1494
 A8 9 39 4.3 17 38 698 1891 7 109 15.6 18 45 791 1883 16 148 9.3 18 44 777 1892
 A9 7 16 2.3 16 29 669 1392 8 56 7 21 58 1114 3310 15 72 4.8 20 53 1002 3115
 A10 7 78 11.1 19 32 669 1509 7 160 22.9 17 32 633 1778 14 238 17 17 32 664 1738
 A11 9 80 8.9 16 30 714 1227 6 160 26.7 16 43 811 2252 15 240 16 16 36 780 1796
 A12 9 56 6.2 17 36 752 1461 8 96 12 19 39 813 2068 17 152 8.9 18 38 774 1953
 A13 5 26 5.2 16 33 670 1541 8 61 7.6 18 48 576 2151 13 87 6.7 17 47 613 1724
 A14 6 35 5.8 17 32 728 1455 8 185 23.1 18 36 808 1696 14 220 15.7 18 35 788 1696
 Ave. 7.7 48 6.2 17 33 671 1499 7.2 109 15.2 18 41 770 2059 14.9 158 10.5 17 39 746 1892
 SD 1.9 21 2.1 2 5 147 274 0.8 71 10.1 2 10 199 524 1.9 87 5.3 2 8 166 456
(b)
 B1 18 104 5.8 19 34 913 1944 6 9 1.5 23 46 983 2183 24 113 4.7 19 39 920 2077
 B2 15 129 8.6 17 48 866 1769 7 21 3 22 35 879 1929 22 150 6.8 18 47 874 1796
 B3 15 114 7.6 20 41 1018 2020 7 21 3 17 36 865 2169 22 135 6.1 19 41 1011 2072
 B4 21 338 16.1 21 58 1004 2732 9 199 22.1 25 64 1121 2907 30 537 17.9 22 59 1061 2801
 B5 13 146 11.2 21 42 1045 2164 5 25 5 18 41 705 1627 18 171 9.5 19 42 994 2083
 B6 17 248 14.6 18 45 980 2152 7 74 10.6 21 44 1051 2097 24 322 13.4 19 46 988 2154
 B7 8 107 13.4 19 42 864 1660 5 45 9 21 48 923 2342 13 152 11.7 19 48 895 1974
 B8 18 314 17.4 22 44 974 2159 9 84 9.3 20 45 856 2235 27 398 14.7 21 44 956 2177
 B9 15 87 5.8 16 32 788 1606 9 50 5.6 18 36 938 1675 24 137 5.7 17 33 835 1629
 B10 15 197 13.1 19 45 861 2059 8 218 27.3 21 50 977 2605 23 415 18 19 48 924 2437
 B11 14 128 9.1 19 37 969 1693 8 37 4.6 18 35 917 1795 22 165 7.5 18 37 964 1718
 B12 18 423 23.5 21 49 900 2001 8 87 10.9 21 48 975 1989 26 510 19.6 21 49 904 2005
 B13 21 484 23 24 49 1170 2474 8 101 12.6 21 51 1044 2136 29 585 20.2 24 49 1137 2437
 B14 18 341 18.9 17 42 734 1901 8 98 12.3 17 48 753 2113 26 439 16.9 17 42 743 1918
 B15 16 116 7.3 18 38 859 2151 7 27 3.9 18 36 888 1958 23 143 6.2 18 38 881 2128
 B16 18 192 10.7 16 35 834 1808 7 40 5.7 18 46 923 2411 25 232 9.3 16 37 837 1881
 B17 19 246 12.9 19 48 904 2177 7 155 22.1 20 52 935 2459 26 401 15.4 19 50 915 2379
 Ave. 16.4 218 12.9 19 43 923 2028 7.4 76 9.9 20 45 925 2155 23.8 294 12 19 44 932 2098
 SD 3 119 5.4 2 6 102 282 1.2 61 7.3 2 7 99 319 3.9 159 5.2 2 6 90 284
(c)
 C1 18 258 14.3 19 43 931 1873 6 67 11.2 20 45 989 1954 24 325 13.5 19 44 940 1951
 C2 22 377 17.1 20 47 1012 2511 8 191 23.9 23 49 1152 2411 30 568 18.9 21 47 1050 2463
 C3 18 152 8.4 16 37 825 1766 9 36 4 16 37 837 1537 27 188 7 16 37 835 1690
 C4 17 125 7.4 18 41 858 1866 7 85 12.1 18 44 821 2449 24 210 8.8 18 43 850 2179
 C5 18 143 7.9 17 38 842 2137 8 56 7 16 31 748 1687 26 199 7.7 16 36 818 1911
 C6 21 286 13.6 17 46 852 2164 9 244 27.1 20 47 945 2128 30 530 17.7 19 47 898 2153
 C7 17 154 9.1 18 39 874 1765 7 30 4.3 19 32 852 1444 24 184 7.7 18 38 874 1745
 C8 18 125 6.9 16 37 757 1519 5 19 3.8 17 50 731 2654 23 144 6.3 16 38 755 1844
 C9 18 171 9.5 18 47 925 2321 8 33 4.1 17 27 882 1624 26 204 7.8 18 46 921 2276
 C10 21 283 13.5 17 40 801 1587 8 114 14.3 19 40 851 1895 29 397 13.7 18 40 821 1652
 C11 17 187 11 17 37 823 1778 8 122 15.3 19 34 888 1772 25 309 12.4 18 36 845 1783
 C12 20 148 7.4 17 51 745 2196 9 55 6.1 17 43 783 2224 29 203 7 17 49 759 2206
 C13 16 155 9.7 18 47 876 2190 7 84 12 19 51 1001 1926 23 239 10.4 19 48 957 2073
 C14 19 210 11.1 18 40 894 2190 8 30 3.8 18 47 976 2662 27 240 8.9 18 40 899 2254
 C15 21 122 5.8 19 54 929 2705 9 26 2.9 18 50 824 2387 30 148 4.9 19 54 885 2715
 C16 18 268 14.9 20 42 985 2100 9 177 19.7 19 47 921 2307 27 445 16.5 20 44 946 2140
 C17 15 66 4.4 21 48 1013 2180 8 46 5.8 20 51 913 2344 23 112 4.9 21 50 963 2367
 C18 13 81 6.2 16 36 765 1464 5 15 3 20 37 967 1724 18 96 5.3 17 37 780 1601
 C19 7 17 2.4 15 47 681 2456 5 9 1.8 15 57 777 3259 12 26 2.2 16 56 742 2565
 Ave. 17.6 175 9.5 18 43 863 2040 7.5 76 9.6 18 43 887 2126 25.1 251 9.5 18 44 870 2083
 SD 3.3 85 3.8 2 5 89 334 1.4 64 7.3 2 8 101 451 4.3 141 4.6 1 6 80 311
Open in a new tab

In games, the average player had a median linear acceleration value of 19 ± 2 g and a 95th percentile value of 43 ± 8 g. The average player had a median linear acceleration value of 18 ± 2 g and 95th percentile value of 40 ± 7 g in practices. Both the difference in median (p = 0.0289) and 95th percentile (p = 0.0463) linear acceleration magnitudes between games and practices were significant. For rotational acceleration, the average player had a median value of 867 ± 149 rad/s2 and a 95th percentile value of 2117 ± 436 rad/s2 for games. In practices, the average player had a median rotational acceleration value of 829 ± 152 rad/s2 and a 95th percentile value of 1884 ± 385 rad/s2. As with linear acceleration, the difference between game and practice 95th percentile rotational acceleration (p = 0.0099) was significant. The average player sustained 154 ± 113 impacts in 14.4 ± 5.2 contact practices and 85 ± 68 impacts in 7.4 ± 1.2 games. On a per session basis, players experienced 9.7 ± 4.9 impacts per practice and 11.3 ± 8.7 impacts per game. While players experienced significantly more impacts in practices than games (p = 0.0011) throughout the season, the difference in the number of impacts per session for practices and games (p = 0.9423) was not significant.

Substantial differences existed among the three teams in this study for both impact frequency and acceleration magnitude (Table 3). Players on team A accumulated fewer impacts in practices during the season (p < 0.0001) than those on teams B and C, as well as fewer impacts on a per practice basis (p < 0.0097). Furthermore, team A players sustained appreciably lower magnitude accelerations than their team B and C counterparts (Fig. 3). For linear acceleration magnitude, the 95th (p < 0.0001) percentile differences between team A and the other two was significant for practices. Likewise, the difference in rotational acceleration magnitudes between team A and teams B and C was significant for the median (p < 0.0001) and 95th percentile (p < 0.002) values for practices. In games, impact frequency and acceleration magnitudes were not significantly different among the teams. Team A players sustained significantly fewer impacts throughout the season compared to team B players (p = 0.0045) due to practice differences. While team A players also sustained fewer impacts during the season than team C players, the difference was not significant (p = 0.0742).

Table 3.

Summary comparison of three teams of 9–12 year old players

Team Practices Games Season
Impacts Linear acceleration (g) Rotational acceleration (rad/s2) Impacts Linear acceleration (g) Rotational acceleration (rad/s2) Impacts Linear acceleration (g) Rotational acceleration (rad/s2)
Total Per session Median (50%) 95% Median (50%) 95% Total Per session Median (50%) 95% Median (50%) 95% Total Per session Median (50%) 95% Median (50%) 95%
A 48 6.2 17 33 671 1499 109 15.2 18 41 770 2059 158 10.5 17 39 746 1892
B 218 12.9 19 43 923 2028 76 9.9 20 45 925 2155 294 12.0 19 44 932 2098
C 175 9.5 18 43 863 2040 76 9.6 18 43 887 2126 251 9.5 18 44 870 2083
Open in a new tab

Figure 3.

Figure 3

Open in a new tab

Player 95th percentile acceleration magnitude vs. number of impacts per session for practices (left) and games (right). Individual players are shown in gray while team averages are displayed in black with error bars showing standard deviation

Impacts to the front of the helmet were the most common, representing 41% of all impacts, followed by those to the back at 25% and side at 23% (Table 4). The least frequently impacted location was the top of the helmet, representing 11% of all impacts. Impacts to the top of the helmet resulted in the highest magnitude linear accelerations with a median value of 21 g and a 95th percentile value of 46 g. For rotational acceleration, impacts to the front had the highest values while those to the top had the lowest.

Table 4.

Head impact frequency and magnitude by location for 9–12 year old players

Location Percentage of impacts (%) Linear acceleration (g) Rotational acceleration (rad/s2)
50th 95th 50th 95th
Front 52 19 41 951 2049
Side 19 16 34 810 1715
Rear 18 18 41 790 2030
Top 10 21 46 388 1040
Open in a new tab

Among the three teams participating in this study, four instrumented players sustained concussions diagnosed by physicians: two on the pee wee team (B4 and B6) and one on each of the other two teams (A8 and C18). The impact associated with player A8’s concussion was to the front of the helmet and had a linear acceleration of 58 ± 9 g and rotational acceleration of 4548 ± 1400 rad/s2. For player B4, the concussion was associated with an impact to the back of the helmet with linear and rotational acceleration magnitudes of 64 ± 10 g and 2830 ± 900 rad/s2. No impacts were recorded for B6 on the day of his concussion due to a battery failure in the sensor array. Player C18’s concussion was linked to an impact to the side of the helmet with linear and rotational acceleration magnitudes of 26 ± 4 g and 1552 ± 500 rad/s2.

Discussion

Previous studies have investigated the frequency and magnitude of head impacts in other tackle football populations, including youth (7–8 years), high school (14–18 years), and college (18–23 years) in the last decade (Table 5).5,11,25,27 Data from these studies show a trend of increasing acceleration magnitude and impact frequency with increasing level of play. Not surprisingly, the 9–12 year old players in this study were found to experience linear acceleration magnitudes between those found in 7–8 year old players and high school players. For rotational acceleration, the 95th percentile magnitude found in this study was less than that found previously in younger players.11 Rotational acceleration tends to correlate well with linear acceleration, though impact location can heavily influence the relationship.27 Players in this study experienced more impacts to the front of their helmets and fewer to the side than the 7–8 year old players studied by Daniel et al.11 In that study, impacts to the front of player’s helmets were associated with lower rotational acceleration magnitudes, while those to the side were associated with higher magnitudes.

Table 5.

Comparison of head impact exposure across various levels of play3,5,11,25,27

Level of play Number of impacts per season Linear acceleration (g) Rotational acceleration (rad/s2)
Median (50%) 95% Median (50%) 95%
Youth (7–8 years) 107 15 40 672 2347
Youth (9–12 years) 240 18 43 856 2034
High school (14–18 years) 565 21 56 903 2527
College (19–23 years) 1000 18 63 981 2975
Open in a new tab

As with magnitude, the impact frequency reported in this study fell between those of 7–8 year old and high school athletes. In this study, the average player experienced 240 impacts throughout the season compared to 107 impacts per season for 7–8 year old players and 565 for high school players.3,5,11 This trend can be partially attributed to the number of sessions (practices and games) increasing as the level of play increases. The 7–8 year old team studied by Daniel et al.11 experienced impacts in 9.4 practices and 4.7 games for a total of 14.1 sessions. Players in this study participated in an average of 14.4 contact practices and 7.4 games, for a total of 21.8 sessions. Compared to the high school team studied by Broglio et al.,3 the teams in this study participated in fewer practices and games in addition to experiencing fewer impacts per session. High school players experienced on average 15.9 impacts per session whereas the 9–12 year old players in this study experienced 10.6 impacts per session. The age related differences reported among these three age groups are most likely due to increased size, athleticism, and aggression in older players.

Players experienced slightly greater impact frequencies and acceleration magnitudes in games than in practice, similar to findings of high school and college football studies.4,7,9,29 For example, a group of high school players, experienced a mean linear acceleration magnitude of 23 g in practices and 25 g in games while the players in this study had a mean linear acceleration magnitude of 22 g in practices and 23 g in games.5 With regard to impact frequency, players in this study experienced a similar number of impacts per practice as per game. The rate of impact in practice was similar to the 9.2 impacts per practice that Broglio et al.5 reported for high school football players. However, the high school players sustained 24.5 impacts per game. These data suggest that high school players experience fewer impacts in practice than in games, while the 9–12 year old players in this study had roughly equal numbers of impacts per session for the two session types.

Substantial differences in impact frequency were observed between team A and the other two teams. For the entire season, players on team A experienced an average of 37–46% fewer impacts than players on teams B and C, though only the difference between teams A and B was statistically significant. This difference is largely due to players on teams B and C participating in 2.1–2.3 times more contact practices than players on team A. The average number of games each player participated in was nearly the same for all three teams, and team A actually had the highest average number of impacts per game at 15.2. Team B and C players averaged 9.9 and 9.6 impacts per game, respectively. Since team A had fewer players than the other two teams, their players may have had more playing time leading to more impacts per game, though other factors such as playing style or skill may have also played a role. For practices, team A players averaged just 6.2 impacts per session compared to 12.9 and 9.5 for teams B and C. Furthermore, players from teams B and C participated in twice as many practice sessions as those from team A. As a result of the higher rate of impact in practices and greater number of practices, team B and C players experienced 219 and 175 impacts during practices, while team A players averaged 48 impacts.

Several factors may have played a role in reducing the head impact exposure observed in team A players relative to teams B and C in this study. First, Pop Warner mandated two rule changes for the 2012 football season that applied to all of their affiliates: (1) a mandatory minimum play rule, where coaches are required to give each player a certain amount of playing time, and (2) a limit on contact in practice, where no more than one-third of weekly practice time and no more than 40 min of a single session can involve contact drills.28 While no team in this study was affiliated with Pop Warner, the league in which team A competed enforced the same rule changes, whereas teams B and C had no such restrictions. Second, special teams plays, including kickoffs and punts, were live plays for teams B and C, similar to high school, college, and professional football. Alternatively, team A’s special teams plays were controlled situations where no contact was allowed. Data from previous studies suggest that players on special teams are more susceptible to large magnitude head accelerations, which may lead to higher incidence of concussion on these plays.2,18,21 Third, all three teams played approximately the same number of games during the season, but teams B and C played 11 and 12 week seasons while team A had a 9 week season. With more time between games, teams generally practice at a higher frequency and intensity. Fourth, player skill, athleticism, and maturity could have implications on the level of exposure. Even within teams, variability among players is apparent, with some players experiencing substantially more impacts than the team average. No significant differences were found in game acceleration magnitudes or impact frequency, suggesting practice differences were not due to player differences among teams. Instrumented players ranged from experiencing 72 to 585 head impacts. Fifth, coaching style has major influence on factors such as the types of drills used in practice and the plays called in games. These coaching variations would likely contribute to the differences in the head impact exposure that players experienced.

Two of the impacts (A8 and B4) associated with diagnosed concussions were substantially greater than the player’s season 95th percentile linear acceleration magnitude. Furthermore, the acceleration magnitudes were consistent with concussive values reported in previous studies, albeit at the lower end of the range.16,25,27 For player A8, the impact was the third highest linear acceleration magnitude he experienced during the season and second highest magnitude resulting from an impact to the front of the helmet. The two highest magnitude impacts that this player experienced were similar in magnitude to the concussive impact. For player B4, the concussive impact was his highest magnitude impact to the back of the helmet for the season. This player also accumulated the third highest number of impacts during the season among all study participants. The third impact associated with a concussion (C18) was in the top 20% of linear acceleration magnitudes for that player throughout the season. Although the acceleration magnitude was relatively low for a concussion, it was the player’s second highest magnitude resulting from an impact to the side of the helmet.

The data collected in this study may have applications towards improving the safety of youth football through rule changes, coach training, and equipment design. Prior to the 2012 season, many youth football organizations, including the league in which team A competed, modified rules, and provided coaches with practice guidelines to reduce head impacts in practice. The data collected in this study suggest that head impact exposure over the course of a season can be reduced significantly by limiting contact in practices to levels below those experienced in games. In addition to guiding future rules for youth football, this study can be used to aid designers in developing youth-specific football helmets that may be able to better reduce head accelerations due to head impacts for young football players. Impact location, frequency, and head acceleration magnitudes can be used to optimize helmet padding to maximize protection while keeping factors such as helmet size and mass to age appropriate levels.

A number of limitations should be noted about this study. First, the HIT system used for data collection is associated with some measurement error for linear and rotational acceleration. On average, the HIT system overestimates linear acceleration by 1% and rotational acceleration by 6% when compared to the Hybrid III headform. The correlation between the HIT system and Hybrid III measurements of head acceleration is R 2 = 0.903 for linear acceleration and R 2 = 0.528 for rotational acceleration.1 Individual data points have uncertainty values due to random error as well; however, the analysis presented here primarily examined distributions of data sets, rather than individual points. Uncertainty values that account for the random error are included with the three concussive data points presented. Second, this study followed three teams consisting of 9–12 year old players with a total of 50 players with large variations in head impact exposure among the different teams and players. Head impact exposure is likely dependent on other factors, in addition to age.

Real-time head impact kinematic data were collected from youth football players, age 9–12 years, during practice and game sessions for an entire season. The data show, on average, that players experienced greater head impact exposure, through more frequent and higher magnitude impacts, than 7–8 year old players, but less than that of high school players. Furthermore, players experienced similar levels of head impact exposure in practice and game sessions on a per-session basis. The vast majority of head impacts recorded in both games and practices were below acceleration magnitudes generally associated with concussions; though, some high magnitude impacts, similar to those seen among older players, did occur. The data presented in this study suggest that head impact exposure at the youth level may effectively be reduced by limiting contact in practices. Future studies are required to determine how rule modifications, coaching style, and other factors influence player impact exposure in practice. Furthermore, additional research is required to determine how reducing head impact exposure in practice affects concussion risk in youth football. Researcher should continue to collect head impact kinematic data in youth football across all age groups to establish the level of head impact exposure a typical player experiences, in a season and career, in order to improve player safety in youth football.

Acknowledgments

The authors would like to thank the Childress Institute for Pediatric Trauma at Wake Forest Baptist Medical Center and the National Highway Traffic Safety Administration for providing support for this study as well as Elizabeth Lillie, Matt Bennett, Amanda Dunn, and the South Fork Panthers and Blacksburg youth football programs for their involvement.

References

  • 1.Beckwith JG, Greenwald RM, Chu JJ. Measuring head kinematics in football: correlation between the head impact telemetry system and Hybrid III headform. Ann. Biomed. Eng. 2012;40(1):237–248. doi: 10.1007/s10439-011-0422-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Boden BP, Tacchetti RL, Cantu RC, Knowles SB, Mueller FO. Catastrophic head injuries in high school and college football players. Am. J. Sports Med. 2007;35(7):1075–1081. doi: 10.1177/0363546507299239. [DOI] [PubMed] [Google Scholar]
  • 3.Broglio SP, Schnebel B, Sosnoff JJ, Shin S, Fend X, He X, Zimmerman J. Biomechanical properties of concussions in high school football. Med. Sci. Sports Exerc. 2010;42(11):2064–2071. doi: 10.1249/MSS.0b013e3181dd9156. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Broglio SP, Sosnoff JJ, Shin S, He X, Alcaraz C, Zimmerman J. Head impacts during high school football: a biomechanical assessment. J. Athl. Train. 2009;44(4):342–349. doi: 10.4085/1062-6050-44.4.342. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Broglio SP, Surma T, Ashton-Miller JA. High school and collegiate football athlete concussions: a biomechanical review. Ann. Biomed. Eng. 2012;40(1):37–46. doi: 10.1007/s10439-011-0396-0. [DOI] [PubMed] [Google Scholar]
  • 6.Crisco JJ, Chu JJ, Greenwald RM. An algorithm for estimating acceleration magnitude and impact location using multiple nonorthogonal single-axis accelerometers. J. Biomech. Eng. 2004;126(6):849–854. doi: 10.1115/1.1824135. [DOI] [PubMed] [Google Scholar]
  • 7.Crisco JJ, Fiore R, Beckwith JG, Chu JJ, Brolinson PG, Duma S, McAllister TW, Duhaime AC, Greenwald RM. Frequency and location of head impact exposures in individual collegiate football players. J. Athl. Train. 2010;45(6):549–559. doi: 10.4085/1062-6050-45.6.549. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Crisco JJ, Greenwald RM. Let’s get the head further out of the game: a proposal for reducing brain injuries in helmeted contact sports. Curr. Sports Med. Rep. 2011;10(1):7–9. doi: 10.1249/JSR.0b013e318205e063. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Crisco JJ, Wilcox BJ, Beckwith JG, Chu JJ, Duhaime AC, Rowson S, Duma SM, Maerlender AC, McAllister TW, Greenwald RM. Head impact exposure in collegiate football players. J. Biomech. 2011;44(15):2673–2678. doi: 10.1016/j.jbiomech.2011.08.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Crisco JJ, Wilcox BJ, Machan JT, McAllister TW, Duhaime AC, Duma SM, Rowson S, Beckwith JG, Chu JJ, Greenwald RM. Magnitude of head impact exposures in individual collegiate football players. J. Appl. Biomech. 2012;28(2):174–183. doi: 10.1123/jab.28.2.174. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Daniel RW, Rowson S, Duma SM. Head impact exposure in youth football. Ann. Biomed. Eng. 2012;40(4):976–981. doi: 10.1007/s10439-012-0530-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Duma SM, Rowson S. Past, present, and future of head injury research. Exerc. Sport Sci. Rev. 2011;39(1):2–3. doi: 10.1097/JES.0b013e318203dfdb. [DOI] [PubMed] [Google Scholar]
  • 13.Funk JR, Rowson S, Daniel RW, Duma SM. Validation of concussion risk curves for collegiate football players derived from hits data. Ann. Biomed. Eng. 2012;40(1):79–89. doi: 10.1007/s10439-011-0400-8. [DOI] [PubMed] [Google Scholar]
  • 14.Gilchrist J, Thomas K, Xu L, McGuire L, Coronado V. Nonfatal traumatic brain injuries related to sports and recreation activities among persons aged ≤19 years-United States, 2001–2009. Morb. Mortal. Wkly Rep. 2011;60(39):1337–1342. [PubMed] [Google Scholar]
  • 15.Greenwald RM, Gwin JT, Chu JJ, Crisco JJ. Head impact severity measures for evaluating mild traumatic brain injury risk exposure. Neurosurgery. 2008;62(4):789–798. doi: 10.1227/01.neu.0000318162.67472.ad. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Guskiewicz KM, Mihalik JP. Biomechanics of sport concussion: quest for the elusive injury threshold. Exerc. Sport Sci. Rev. 2011;39(1):4–11. doi: 10.1097/JES.0b013e318201f53e. [DOI] [PubMed] [Google Scholar]
  • 17.Guskiewicz KM, Mihalik JP, Shankar V, Marshall SW, Crowell DH, Oliaro SM, Ciocca MF, Hooker DN. Measurement of head impacts in collegiate football players: relationship between head impact biomechanics and acute clinical outcome after concussion. Neurosurgery. 2007;61(6):1244–1253. doi: 10.1227/01.neu.0000306103.68635.1a. [DOI] [PubMed] [Google Scholar]
  • 18.Guskiewicz KM, Weaver NL, Padua DA, Garrett WE., Jr Epidemiology of concussion in collegiate and high school football players. Am. J. Sports Med. 2000;28(5):643–650. doi: 10.1177/03635465000280050401. [DOI] [PubMed] [Google Scholar]
  • 19.Langlois JA, Rutland-Brown W, Wald MM. The epidemiology and impact of traumatic brain injury: a brief overview. J. Head Trauma Rehabil. 2006;21(5):375–378. doi: 10.1097/00001199-200609000-00001. [DOI] [PubMed] [Google Scholar]
  • 20.Manoogian S, McNeely D, Duma S, Brolinson G, Greenwald R. Head acceleration is less than 10 percent of helmet acceleration in football impacts. Biomed. Sci. Instrum. 2006;42:383–388. [PubMed] [Google Scholar]
  • 21.Pellman EJ, Powell JW, Viano DC, Casson IR, Tucker AM, Feuer H, Lovell M, Waeckerle JF, Robertson DW. Concussion in professional football: epidemiological features of game injuries and review of the literature—part 3. Neurosurgery. 2004;54(1):81–94. doi: 10.1227/01.NEU.0000097267.54786.54. [DOI] [PubMed] [Google Scholar]
  • 22.Pellman EJ, Viano DC, Tucker AM, Casson IR. Concussion in professional football: location and direction of helmet impacts—part 2. Neurosurgery. 2003;53(6):1328–1340. doi: 10.1227/01.NEU.0000093499.20604.21. [DOI] [PubMed] [Google Scholar]
  • 23.Pellman EJ, Viano DC, Tucker AM, Casson IR, Waeckerle JF. Concussion in professional football: reconstruction of game impacts and injuries. Neurosurgery. 2003;53(4):799–812. doi: 10.1093/neurosurgery/53.3.799. [DOI] [PubMed] [Google Scholar]
  • 24.Powell JW, Barber-Foss KD. Traumatic brain injury in high school athletes. JAMA. 1999;282(10):958–963. doi: 10.1001/jama.282.10.958. [DOI] [PubMed] [Google Scholar]
  • 25.Rowson S, Duma SM. Development of the star evaluation system for football helmets: integrating player head impact exposure and risk of concussion. Ann. Biomed. Eng. 2011;39(8):2130–2140. doi: 10.1007/s10439-011-0322-5. [DOI] [PubMed] [Google Scholar]
  • 26.Rowson S, Duma SM. Brain injury prediction: assessing the combined probability of concussion using linear and rotational head acceleration. Ann. Biomed. Eng. 2013;41(5):873–882. doi: 10.1007/s10439-012-0731-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Rowson S, Duma SM, Beckwith JG, Chu JJ, Greenwald RM, Crisco JJ, Brolinson PG, Duhaime AC, McAllister TW, Maerlender AC. Rotational head kinematics in football impacts: an injury risk function for concussion. Ann. Biomed. Eng. 2012;40(1):1–13. doi: 10.1007/s10439-011-0392-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Rules changes regarding practice & concussion prevention. http://www.popwarner.com/About_Us/Pop_Warner_News/Rule_Changes_Regarding_Practice___Concussion_Prevention_s1_p3977.htm, 2012.
  • 29.Schnebel B, Gwin JT, Anderson S, Gatlin R. In vivo study of head impacts in football: a comparison of National Collegiate Athletic Association Division I versus high school impacts. Neurosurgery. 2007;60(3):490–495. doi: 10.1227/01.NEU.0000249286.92255.7F. [DOI] [PubMed] [Google Scholar]
  • 30.Stern RA, Riley DO, Daneshvar DH, Nowinski CJ, Cantu RC, McKee AC. Long-term consequences of repetitive brain trauma: chronic traumatic encephalopathy. PM&R. 2011;3(10):S460–S467. doi: 10.1016/j.pmrj.2011.08.008. [DOI] [PubMed] [Google Scholar]
  • 31.Viano DC, Halstead D. Change in size and impact performance of football helmets from the 1970s to 2010. Ann. Biomed. Eng. 2012;40(1):175–184. doi: 10.1007/s10439-011-0395-1. [DOI] [PubMed] [Google Scholar]
  • 32.Viano DC, Withnall C, Halstead D. Impact performance of modern football helmets. Ann. Biomed. Eng. 2012;40(1):160–174. doi: 10.1007/s10439-011-0384-4. [DOI] [PubMed] [Google Scholar]
  • 33.Viano DC, Withnall C, Wonnacott M. Effect of mouthguards on head responses and mandible forces in football helmet impacts. Ann. Biomed. Eng. 2012;40(1):47–69. doi: 10.1007/s10439-011-0399-x. [DOI] [PubMed] [Google Scholar]
  • 34.Viano DC, Withnall C, Wonnacott M. Football helmet drop tests on different fields using an instrumented Hybrid III head. Ann. Biomed. Eng. 2012;40(1):97–105. doi: 10.1007/s10439-011-0377-3. [DOI] [PubMed] [Google Scholar]

Articles from Annals of Biomedical Engineering are provided here courtesy of Springer

RESOURCES