Athletic Training and Sports Health Care

Original Research 

Frequency and Location of Head Impacts in Division I Men's Lacrosse Players

Theresa Miyashita, PhD, ATC; Eleni Diakogeorgiou, MBA, ATC; Kaitlyn Marrie, MS, ATC; Rosemary Danaher, PhD

Abstract

Gaining a better understanding of head impact exposures may lead to an improved comprehension of the possible effects of repeated subconcussive impacts. This cohort study aimed to quantify the frequency, magnitude, and location of head impacts among player positions between practices and games. Forty-two Division I men's lacrosse players wore lacrosse helmets instrumented with sensors while playing on the collegiate lacrosse field to measure the frequency and linear acceleration of head impacts over the course of the 2014 spring season. The number of head impacts greater than 20 units of linear acceleration (g) and the location of the head impacts were recorded and analyzed by session type and player position. A total of 11,403 impacts were recorded for the season. A statistically significant difference was found among the six player positions and the average linear acceleration per impact (F[5, 11,397] = 39.53, P < .001); the average linear acceleration per impact was highest in goalies and lowest in face-off players. The most common head impact location for all six player positions was the front of the head. Players sustained 2.3 times as many head impacts during games. Player position and session type appear to be determining factors for the head impact data collected. Frequency data collected on men's lacrosse players allow for a better understanding of the biomechanics associated with a concussion injury and support the investigation of the cumulative effect of subconcussive impacts. [Athletic Training & Sports Health Care. 2016;8(5):202–208.]

Abstract

Gaining a better understanding of head impact exposures may lead to an improved comprehension of the possible effects of repeated subconcussive impacts. This cohort study aimed to quantify the frequency, magnitude, and location of head impacts among player positions between practices and games. Forty-two Division I men's lacrosse players wore lacrosse helmets instrumented with sensors while playing on the collegiate lacrosse field to measure the frequency and linear acceleration of head impacts over the course of the 2014 spring season. The number of head impacts greater than 20 units of linear acceleration (g) and the location of the head impacts were recorded and analyzed by session type and player position. A total of 11,403 impacts were recorded for the season. A statistically significant difference was found among the six player positions and the average linear acceleration per impact (F[5, 11,397] = 39.53, P < .001); the average linear acceleration per impact was highest in goalies and lowest in face-off players. The most common head impact location for all six player positions was the front of the head. Players sustained 2.3 times as many head impacts during games. Player position and session type appear to be determining factors for the head impact data collected. Frequency data collected on men's lacrosse players allow for a better understanding of the biomechanics associated with a concussion injury and support the investigation of the cumulative effect of subconcussive impacts. [Athletic Training & Sports Health Care. 2016;8(5):202–208.]

Although the research on sport-related concussion injuries continues to grow, an abundance of information remains questionable. One specific area receiving debate and varied results in the literature is the theoretical impact threshold for sustaining a concussion.1,2 Pellman et al.1 suggested that those who participate in helmeted sports and sustain an impact of 70 to 75 units of linear acceleration (g) are likely to have a concussion. Gaining a better understanding of the biomechanical factors in a variety of sports that contribute to mild traumatic brain injury may lead to a better understanding of impact threshold and help individuals predict outcomes following a concussion. Researchers have found that linear and rotational accelerations play important roles in producing diffuse injury to the brain3,4 and the direction of an impact could affect the presentation of the clinical signs and symptoms associated with concussions.5,6 Monitoring impacts and severity of impacts may help to identify high-risk events and alert appropriate medical personnel to perform a concussion evaluation.7

An additional aspect of concussion research receiving attention is the potential cumulative effect of subconcussive impacts. Killam et al.8 found that athletes who participated in contact sports with no history of concussions scored lower in memory domains and total scores on the Repeatable Battery assessment when compared to controls. Subconcussions are an under-recognized phenomenon that may lead to neurological deficits.9 Research looking at the effect of cumulative subconcussive impacts on neurocognitive function has produced conflicting results. Miller et al.10 and Gysland et al.11 did not find deficits in neurological function correlating to subconcussive impacts in collegiate football players within either of their studies. However, McAllister et al.12 found poorer scores on a post-season assessment of cognitive functioning correlating with greater head impact exposures. Breedlove et al.13 found progressive neurophysiological changes with accumulated blows to the head in high school football players. Further long-term analysis will help us gain a better understanding of the potential effects of cumulative subconcussive head impacts.

The number of reported concussions has continued to rise over the years, with the greatest increases seen in football, men's lacrosse, and women's soccer. Football has the largest number of athletes with concussions when compared to other sports, followed closely by men's lacrosse.14 In the equipment-intensive sport of men's lacrosse, little research has been conducted investigating head impacts over a competitive season. Men's lacrosse is the fastest growing team sport in the United States.15 With the increase in popularity comes an increase in injury frequency and the need for proper injury management and prevention strategies. Men's lacrosse players are four times more likely to sustain an injury during a game versus practice, and 11.7% of all injuries are head/neck injuries occurring during games, with 6.2% occuring during practice.16

An analysis of impact data in men's lacrosse could lead to a greater understanding of concussion risk factors and may help helmet manufacturers design helmets to decrease the impact of a linear force. The purpose of our study was to investigate head impact data in individual players over the course of one competitive season. We tested the hypothesis that impact frequency and linear acceleration would differ by event type (game vs practice), player position, and head impact location. For the purpose of this study, we only analyzed linear acceleration.

Methods

Participants

Forty-two Division I men's lacrosse players (age = 19.54 ± 1.09 years; height = 71.7 ± 2.56 inches; weight = 181.93 ± 16.03 lbs) from one university participated in this study. The team comprised 5 goalies, 3 face-off specialists, 15 midfielders, 2 long-stick midfielders, 7 attack players, and 10 defensemen. The study was approved by the university's internal review board, and all participants signed an informed consent.

Instrumentation

All players wore the Warrior Sports TII helmets instrumented with a GForce tracker (Artaflex, Markham, ON) sensor. This sensor has been shown to be a valid instrument in the measurement of linear acceleration and rotational velocity.17 The GForce tracker was affixed internally to the jaw guard of the helmet and calibrated prior to use. The GForce tracker contains a triaxial accelerometer and triaxial gyroscope to measure linear acceleration and rotational velocity. This sensor has a sampling frequency of 3 kHz and recorded data when an impact exceeded the preset threshold of 20 g. In this study, 40 ms of data per impact (8 ms pre-trigger and 32 ms post-trigger) were recorded. We elected to set the threshold at 20 g to minimize potential “noise” from players dropping helmets. The current literature available presents with a variety of threshold levels in which the accelerometers are being set.18–24 The sensors also provided information regarding the impact's location on the head, divided into front, back, right, left, top, and bottom locations. All sensors were calibrated prior to use and after being affixed to the helmets. The sensor was attached to a computer via USB connection and calibrated via GForce tracker's downloaded software client. To ensure proper calibration, all instrumented helmets were moved into various positions as commanded by the GForce tracker's software client (ie, right side, back, and neutral). Once the calibration process was complete, the software client described the location of the sensor in the helmet. This description and the actual location of the sensor had to match for the instrumented helmet to be properly calibrated.

Procedures and Data Collection

The GForce tracker was internally affixed to each player's helmet via 3-mm double-sided adhesive at the jaw guard and calibrated. Players would manually turn on and off the sensor before/after each practice/game. The coaching staff and athletic training staff assisted in verbally reminding each player of this task.

GForce trackers, while remaining attached to the helmet, were charged every other day. After the sensor was fully charged, the data were downloaded from the sensor via USB connection and exported to a secure central server. All sensors were charged after each game to ensure proper battery life for the following practice. There were a total of 17 games and 40 practices in which participants could have been involved. There was an average of 36 ± 10.9 exposures per player for the season. An exposure was defined as the time when a player participated in a practice or game with an instrumented helmet. A Spearman's rho correlation statistic determined there was not a significant correlation between number of exposures and player positions (rs[40] = .085, P = .594). Players did not participate in all sessions for a multitude of reasons, including injury and class schedules.

Statistical Analysis

Because the assumption of normality was violated, Spearman's rho was calculated to determine the correlation between season exposure rates and player position, average linear acceleration, and total number of impacts. Because equal variances cannot be assumed, analysis of variance statistics were computed with Games–Howell post-hoc tests to determine if there were significant differences between player positions and impact locations with linear acceleration. A paired samples t test was used to determine if there was a significant difference in linear acceleration between practice and games. All data were analyzed with SPSS for Macintosh software (version 23; IBM Corp., Armonk, NY). Statistical significance was established a priori as an alpha value of .05 or less.

Results

A total of 11,403 impacts were collected over the course of 40 practices and 17 games by 42 lacrosse players. A Spearman's rho correlation determined there was no correlation between exposure rate and player position (rs[40] = .085, P = .594) and exposure rate and average linear acceleration (rs[40] = −.02, P = .925). There was a correlation between exposure rates and the total number of impacts (rs[40] = .646, P < .001). The positive direction indicates that athletes who have a greater number of exposures will experience a greater number of impacts. Figure 1 illustrates the frequency distribution of all 11,403 impacts. A majority of the impacts (66.8%) occurred between the 20 and 50 g range.


Frequency distribution of the 11,403 impacts by linear accelerations.

Figure 1.

Frequency distribution of the 11,403 impacts by linear accelerations.

An analysis of variance indicated that the player positions differed significantly on average linear acceleration per impact (F[5, 11,397] = 39.53, P < .001). Post hoc Games–Howell tests indicate that goalies experienced higher average linear accelerations per impact when compared to all other player positions (P < .001) and face-off players experienced the lowest average linear acceleration per impact compared to all other player positions (P < .001) (Table 1).


Average Linear Acceleration by Player Position

Table 1:

Average Linear Acceleration by Player Position

A statistically significant difference was found among the six head impact locations and average linear acceleration (F[5, 11,397] = 27.69, P < .001). Post hoc Games–Howell tests indicated that impacts to the front of the head resulted in higher average linear accelerations when compared to impacts to the back (P < .001), right (P < .001), and left (P < .001). Impacts to the top and bottom of the head resulted in higher average linear accelerations when compared to impacts to the right (P < .001) and left (P < .001). Impacts to the back of the head resulted in lower average linear accelerations when compared to impacts to the top (P < .001) and bottom (P < .001) (Table 2).


Average Linear Acceleration by Impact Location

Table 2:

Average Linear Acceleration by Impact Location

To investigate whether there was a statistically significant association between impact locations and player position, a correlation was computed. There was no significant correlation between player position and impact location (r[11,401] = .005, P = .614) (Table 3). The greatest number of impacts at each position was seen at the front of the helmet.


Number of Head Impacts by Location and Player Position

Table 3:

Number of Head Impacts by Location and Player Position

When averaging all players' linear acceleration data, a statistically significant difference was found regarding the average linear acceleration per impact between practices and games (t[11,402] = 134.78, P < .001, d = 1.26). The difference is both statistically significant and larger than typical according to Cohen's (1988) guidelines. A higher linear acceleration per impact was seen during practices (56.72 g) compared to games (48.86 g). A total of 5,759 impacts occurred over the course of 40 practices, with 144 impacts per practice and 3.35 impacts per player per practice. A total of 5,644 impacts occurred over the course of 17 games, with 332 impacts per game and 7.7 impacts per player per game.

Discussion

It has recently been theorized that an increase in the linear acceleration of head impacts may be the greatest factor for the observed increase in concussion incidence rates.18 Our study found that players would sustain approximately 3.35 impacts per practice with an average of 56.72 g per impact and 7.7 impacts per game with an average of 48.86 g per impact. The average of each impact should be carefully evaluated because a majority of impacts occurred below 50 g, and our primary concern with these data is the number of impacts sustained. Our participants experienced 2.3 times as many impacts in games as in practice. With knowledge that concussion incident rates are 6 to 8 times higher in games versus practice,19 sustaining 2.3 times as many impacts during a game is of great concern. Our findings correlate with Crisco et al.'s20 findings in that the number of impacts sustained in a football game was approximately two times greater than the number of impacts sustained during a practice. However, our findings differed from those of Mihalik et al.,21 who found that the total number of impacts sustained in a college football practice was approximately twice the number seen in games. The number of impacts sustained by our participants was lower compared to what other researchers found in football players.22,23 Duma et al.22 found, on average, football players sustained 7.6 impacts per practice and 15 impacts per game, and Schnebel et al.23 found football players to sustain, on average, 13 impacts per session.

Research regarding average linear acceleration per impact, primarily collected in football, varies among studies. Duma et al.22 found the average linear impact to be 32 g in collegiate football players, whereas Pellman et al.24 found the average linear impact in professional football players to be 60 g per impact. Our players sustained an average of 52.83 g per impact, which is more closely related to Pellman et al.'s findings in professional football players. Our numbers may be slightly inflated compared to other studies that set their impact threshold under 20 g. We elected to set our threshold to 20 g to remain consistent with currently ongoing men's lacrosse research to allow for equal comparison of our data. Pellman et al.1 reported that linear impacts of 95 g were the threshold for 75% of humans to report concussion symptoms, and that helmeted impacts of 70 to 75 g are likely to result in a concussion. Our impact data by event also differed from Mihalik et al.'s25 findings in youth ice hockey players. They found that impacts occurring in a game resulted in higher linear accelerations compared to impacts sustained during a practice. Our results indicated that, on average, impacts during practices resulted in a higher linear acceleration, but that more impacts were sustained during games. The reason for the differences seen in our study may be attributed to sensor placement, style of the game, size differences among players, and different equipment. We compared our data to other sports because there is no literature currently available on men's lacrosse impact data. Further, the vastly different styles of play and equipment must be taken into consideration when comparing impact data across sports.

We also investigated head impact locations by linear acceleration and player position. Researchers have suggested that impact location and duration of an impact affect concussion risk.2,26,27 We found that a majority of head impacts sustained by our athletes occurred at the front of their head, regardless of position. These results were similar to Crisco et al.'s20 and Breedlove et al.'s13 findings in football players, but different from King et al.'s28 findings, which stated that the right side of the head in amateur rugby players sustained the greatest number of impacts. In regard to linear acceleration by head impact location, we found that the highest linear accelerations were seen with impacts to the front of the head (mean = 58.03 g). These findings were again different from King et al.'s,28 who found the greatest linear acceleration to occur at the back of the head with an average of 28 g. These differences could be attributed to the different styles of play and equipment each sport requires.

Our findings regarding the greatest number of impacts occurring to the front of the head, in addition to the highest linear accelerations seen with frontal impacts, is a concern that should be addressed through player behavior modification and helmet manufacturers. New rules in men's lacrosse dictate that players will be penalized if they hit another player in the head, but it is of critical importance that coaches properly instruct their players and officials penalize players who do not adhere to the new rule.

Current helmet designs are not constructed to protect against concussions.29 However, research has suggested that football helmet designs may have an impact on concussion incident rates.30,31 Rowson et al.30 found a decrease in concussion incidence rates and high magnitude impacts with a newer helmet design in football players. Collins et al.31 saw a decrease in concussion incidence rates with newer helmets offering greater offset and padding. Our findings may assist lacrosse helmet manufacturers in future helmet designs because our impact data would demonstrate areas of the helmet most frequently hit and the areas of the helmet where the highest linear accelerations most frequently occur.

Newer helmet designs could also have an impact on the effect of cumulative subconcussive impacts. This particular area of research has garnered various results, with some finding neuropsychological deficits due to cumulative subconcussive impacts11,13,31 and others not finding a correlation.10,32,33 Although this area of research continues to evolve, the goal of reducing the total number of impacts and linear acceleration per impact should remain a consideration for all contact/collision sports and equipment manufacturers.

Similar to the results seen in football studies,20–23 our position players also varied on the average linear acceleration per impact. Goalies sustained significantly higher average linear impacts compared to all other positions (71.48 g), with face-off specialists sustaining the lowest average linear acceleration per impact (44.09 g). For the entire season, the greatest number of impacts were sustained by midfielders (5,056) with an average of 53.63 g per impact. The higher number of impacts for this position player could be attributed to the increased number of players at that position. This information could assist helmet manufacturers with customized, player-position designs; more importantly, it could assist on-site medical personnel. With the knowledge that goalies, on average, sustain higher average linear accelerations per impact, it may be important to be more aware of the onset of concussion symptoms after a hit to the head.

Further analysis of detailed exposures and mechanisms of injury across a variety of sports, positions, and between genders is important to understand injury susceptibility, risk thresholds, and the potential role of subconcussive impacts. As with many research studies, our study had limitations. Our minimal threshold was set to 20 g, whereas previous studies have been set to 10 to 15 g, thus potentially inflating our numbers. Additionally, we did not cross-reference our impact readings with video analysis to determine if these were all true head impacts versus an impact reading when the players were not wearing their helmet (ie, the helmet being dropped).

Implications for Clinical Practice

Health care professionals who work with lacrosse players should be familiar with the intricacies of the sport, including relevant frequency data that may aid in injury prevention. We found that our players sustained 2.3 times as many impacts during games compared to practices, but the linear acceleration per impact was lower in the impacts sustained during a game. We were also able to show that the greatest number of impacts was to the front of the head and the greatest linear accelerations were seen with impacts to the front of the head. Compared to all other position players, goalies sustained the highest linear acceleration per impact. These data could aid health care professionals and lacrosse helmet manufacturers alike in terms of injury prevention strategies. Further analysis could help to determine potential injury thresholds and effect of subconcussive impacts.

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  33. Eckner JT, Sabin M, Kutcher JS, Broglio SP. No evidence for a cumulative impact effect on concussion injury threshold. J Neurotrauma. 2011;28:2079–2090. doi:10.1089/neu.2011.1910 [CrossRef]

Average Linear Acceleration by Player Position

POSITIONNMEANSD95% CI

LOWHIGH
Goalie64271.48a64.6566.4776.49
Face-off1,10544.09b30.8142.2745.91
Midfielder5,05653.6339.7152.5354.72
Long-stick midfielder63150.4938.6647.4753.52
Attack1,98851.8137.4950.1653.46
Defense1,98151.3739.3249.6453.10
Total11,40352.8340.6252.0853.57

Average Linear Acceleration by Impact Location

IMPACT LOCATIONNMEANSD95% CI

LOWHIGH
Front2,81658.0344.3356.459.67
Back1,97049.735.2248.1451.26
Top1,33957.7744.2355.460.14
Bottom1,76654.4743.7352.4356.51
Right1,60547.8037.2645.9849.63
Left1,90747.635.4346.049.19
Total11,40352.8340.6252.0853.57

Number of Head Impacts by Location and Player Position

POSITIONFRONTBACKTOPBOTTOMRIGHTLEFTTOTAL
Goalie15811690879893642
Face-off2751561741271911821,105
Midfielder1,3028625538276638495,056
Long-stick midfielder14312383959295631
Attack4483171983592753911,988
Defense4903962412712862971,981
Authors

From Sacred Heart University, Fairfield, Connecticut.

The authors have no financial or proprietary interest in the materials presented herein.

Correspondence: Theresa Miyashita, PhD, ATC, Sacred Heart University, 5151 Park Ave., Fairfield, CT 06824. E-mail: miyashitat@sacredheart.edu

Received: July 27, 2015
Accepted: January 27, 2016
Posted Online: May 16, 2016

10.3928/19425864-20160503-01

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