Annals of Biomedical EngineeringThe Journal of the Biomedical Engineering Society
© The Author(s) 2013
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. Cobb1, Jillian E. Urban2, 3, Elizabeth M. Davenport2, 3, Steven Rowson , Stefan M. Duma1, Joseph A. Maldjian2, 4, Christopher T. Whitlow4, 5, Alexander K. Powers6, 7 and Joel D. Stitzel2, 7
(1)
School of Biomedical Engineering & Sciences, Virginia Tech-Wake Forest University, 440 ICTAS Building, Stanger St., Blacksburg, VA 24061, USA
(2)
School of Biomedical Engineering & Sciences, Virginia Tech-Wake Forest University, Medical Center Blvd., Winston-Salem, NC 27157, USA
(3)
Wake Forest School of Medicine, Medical Center Blvd., Winston-Salem, NC 27157, USA
(4)
Department of Radiology (Neuroradiology), Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
(5)
Translational Science Institute, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
(6)
Department of Neurosurgery, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
(7)
Childress Institute for Pediatric Trauma, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
 
 
Steven Rowson
Received: 1 March 2013Accepted: 8 July 2013Published online: 24 July 2013
Associate Editor Peter E. McHugh oversaw the review of this article.
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.3,4,7,9,10,13,16,17,22,23,2527,3134 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.710,12 From these data, strategies to reduce head impact exposure through rule changes and methods to evaluate protective equipment have been developed.810,25 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
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).
/static-content/images/285/art%253A10.1007%252Fs10439-013-0867-6/MediaObjects/10439_2013_867_Fig1_HTML.gif
Figure 1
Cumulative distribution plots of linear acceleration (left) and rotational acceleration (right) magnitudes for impacts collected during the season
/static-content/images/285/art%253A10.1007%252Fs10439-013-0867-6/MediaObjects/10439_2013_867_Fig2_HTML.gif
Figure 2
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)
Sessions
Number of Impacts
Linear Acceleration (g)
Sessions
Number of Impacts
Linear Acceleration (g)
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
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
/static-content/images/285/art%253A10.1007%252Fs10439-013-0867-6/MediaObjects/10439_2013_867_Fig3_HTML.gif
Figure 3
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
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
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.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
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