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. Author manuscript; available in PMC: 2022 Jun 1.
Published in final edited form as: Med Sci Sports Exerc. 2021 Jun 1;53(6):1245–1251. doi: 10.1249/MSS.0000000000002567

Variations in Head Impact Rates in Male and Female High School Soccer

Colin M Huber 1,2, Declan A Patton 2, Divya Jain 1,2, Christina L Master 2,3,4, Susan S Margulies 5, Catherine C McDonald 2,6, Kristy B Arbogast 2,4
PMCID: PMC8122001  NIHMSID: NIHMS1646972  PMID: 33986230

Abstract

Introduction:

Repetitive head impacts in soccer have been linked to short-term neurophysiological deficits, and female soccer players have higher concussion rates than males. These findings have inspired investigation into gender differences in head impact exposure and how head impact rate contributes to the cumulative effect of head impact exposure on neurological outcomes. Various periods of exposure have been used to calculate head impact rates, including head impacts per season, game, and player-hour.

Purpose:

The aim of this study was to apply different methodological approaches to quantify and compare head impact rates by gender for two seasons of high school varsity soccer.

Methods:

Video review was used to confirm all events recorded by a headband-mounted impact sensor and calculate playing time for all players. Impact rates were calculated per athlete exposure (presence and participation) and per player-hour (scheduled game time, individual play time, and absolute time).

Results:

Impact rates per athlete exposure ranged from 2.5–3.2 for males and 1.4–1.6 for females, and impact rates per player-hour ranged from 2.7–3.8 for males and 1.0–1.6 for females. The exposure calculation method significantly affected head impact rates; however, regardless of approach, the head impact rate for males was higher, up to three-fold, than for females. Individual head impact exposure varied substantially within a team with one in five players experiencing no impacts.

Conclusions:

Overall, the gender differences found in this study indicate that males experience higher head impact exposure compared to females. Future studies are needed to understand potential clinical implications of variability in head impact exposure and reconcile higher female concussion rates with the reduced head impact rates presented herein.

Keywords: head injuries/concussion, pediatric sports medicine, injury prevention, football (soccer)

Introduction

Repetitive head impacts in soccer have been linked with short-term neurophysiological deficits during controlled heading studies (1–3) and the potential for increased risk of long-term neurological outcomes in former professional athletes (4). An important question is how the number of head impacts and the time between head impacts contribute to the cumulative effect of head impact exposure on neurological outcomes. Head impact kinematic sensors, coupled with video and/or observer confirmation methods (5–7), provide the opportunity to measure head impact exposure quantitatively.

Gender differences in concussion rates, with female soccer players having higher rates than male players (8,9), have spawned investigations of gender differences in head impact exposure. As research in this area has expanded, various denominators have been used to calculate head impact rates including head impacts per season, game, athlete exposure (AE), player-hour, and play (6,10–12). These various approaches make it difficult to compare sports, genders, age, and level of play across studies. In this study, multiple approaches to calculate head impact rate were explored to compare male and female high school soccer, i.e. rates per AE (presence and participation) and per player-hour (scheduled game time, individual play time, and absolute time).

Similar to epidemiological studies of concussion rates (8,9), studies often represent head impact rates per AE. An AE is typically defined as a single athlete session (13). However, some studies use the number of athletes on a roster at a session, i.e. presence (14), whereas others use the number of athletes actively playing in a session, i.e. participation (15,16). The head impact rate based on presence represents the impact rate associated with being on the team while the rate based on participation represents the impact rate associated with taking part in the game. Head impact rates have also been calculated per time period (11,12). Scheduled time is the theoretical team exposure time in a session, accounting for the length of the practice, drill, or game. Individual play time is the active participation in a drill or game for individual players and allows individual or positional play style comparisons (11,12,17). Absolute time is the real time of an individual player session start and stop time accounting for play stoppages and halftime to analyze intervals between impacts relevant to physiological recovery periods.

Head impact rates are similar across levels of play in inter-study comparisons: collegiate female (0.7–2.2 impacts per AE), collegiate male (1.7 impacts per AE), high school female (1.6 impacts per AE), high school male (1.3 impacts per AE), youth female (0.4–3.3 impacts per AE), and youth male (1.1–2.6 impacts per AE) (6,18–24). In most studies assessing both male and female teams, males had a higher head impact rate (1.4–2.2 times higher) (12,18,21,24). However, one study of high school soccer found a similar but higher head impact rate for females (1.6 impacts per AE) compared to males (1.3 impacts per AE) (20). Due to additional video analysis complexity, few studies have calculated individual play time in soccer required to quantify head impact rates on a per time period basis at an individual level (25). However, the impact rates of individual players vary substantially and correlate with position and age (12,25).

There is limited previous literature exploring gender differences in soccer head impact rates at the high school level and how different methods for calculating head impact rates influence gender comparisons. Therefore, the aim of this study was to use these different methodological approaches to quantify and compare head impact rates for male and female high school soccer.

Methods

A prospective observational cohort study design was used to investigate head impacts in male and female high school varsity soccer during the 2017 and 2018 seasons. Athletes were recruited from a suburban private high school. Junior varsity athletes that were rostered for fewer than six varsity games were not included in analysis. The current study was reviewed by the Institutional Review Board at Children’s Hospital of Philadelphia (IRB-17-013875), who granted a waiver of consent due to the fact that SIM-G usage was part of participation on that school’s soccer team, independent of the study, and no study-specific procedures were conducted.

Head Impact Quantification

All participants were assigned SIM-G head impact sensors (Triax Technologies, Inc., Norwalk, CT) throughout the season and wore them during regular and post-season games. Sensors were secured in a neoprene headband and positioned just above the greater occipital protuberance. The SIM-G device comprises a high- and low-g triaxial accelerometer for linear acceleration measurement (3–150 g) and a triaxial gyroscope for angular velocity measurement (26). An event recording is triggered by a 16 g linear acceleration threshold, and the sensor records time series data at 1000 Hz for 62 ms: 10 ms pre-trigger to 52 ms post-trigger. The SIM-G has been evaluated for accuracy in rigidly attached rotations (27), human head surrogate linear impactor events (28,29) and on-field head impact counts (5,19). As an impact counter, the SIM-G consistently recorded >85% of head impacts in two human head surrogate studies (28,30). Triax has a processing algorithm that labels each sensor-recorded event as either a “valid” or “spurious” impact (26); however, Patton et al. found the algorithm was insufficient to identify false positives (5). Therefore, the algorithm was not used to remove sensor recordings for the current study.

Prior to each game, sensors were confirmed to be connected and time synchronized with the central data collection SKYi box. Sensors were distributed before the players began warm-ups, and the SKYi box was placed at the center of the field and initiated before the game. After the game, data collection on the SKYi was ceased, and sensors were returned to study staff and deactivated. Data were uploaded to the cloud, processed by proprietary manufacturer software, and downloaded with event and time identifiers for video analysis.

As per a previously published protocol (5), video review was used to confirm sensor-recorded events and track substitutions (unlimited substitutions allowed at the high school level) for calculating playing time for all players. Before the start and end of each half, a few seconds of a world clock website was filmed (timeanddate.com), which provided a timestamp to align the video footage with the sensor data. Sensor-recorded events were removed if the associated player was off the field or out of the video frame during the event time. Only sensor-recorded events associated with a head acceleration event (e.g. ball striking the head, head striking the ground, player-to-player contact without direct head contact) were quantified as head impacts. Observable sensor-recorded events categorized as trivial events (e.g. player adjusting headband) or non-events (e.g. player stationary and not touching headband) were removed from analysis.

Head Impact Rate Definition and Calculation

Head impact rate was measured as the number of head impacts per period of exposure. Five approaches to calculate period of exposure were used (Table 1): AE presence (i.e. player present at a game), AE participation (i.e. player played in a game), scheduled time (i.e. scheduled team session time; soccer game = 80 minutes x 11 players), play time (i.e. on-field play time of an individual player), and absolute time (i.e. real time of the session including halftime and stoppages).

Table 1.

Head Impact Exposure Rate Definitions

Type Definition Period Data needed Calculation Method
Presence Single player present at single session Athlete exposure Roster for session Number of uninjured rostered players present at the game that day
Participation Single player playing in single session Athlete exposure List of players who played Number of players that actively participated in the game that day (via video review)
Scheduled time Scheduled session time (e.g. soccer game = 1.33 hours) Player-hour Duration of scheduled session time Multiplying the number of players on the field by the scheduled session length (e.g. soccer game = 11 players × 1.33 hours = 14.67 hours)
Play time Cumulative play time on an individual player basis Player-hour Track player substitutions Game clock time that a player was on the field
Absolute Time Absolute time (including halftime, breaks, etc.) for individual players Player-hour Track absolute time and player substitutions Real time difference from a player’s first entry to the game and last exit
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For all exposure period calculations, each sensor must have been active and worn by the athlete to be included in the analysis. Sensors were fully charged and active before warm-ups; however, it is possible that the battery may have died, or the sensor removed by the player before the end of the game. To address this potential limitation, exposure time was removed from analysis for a player on a given day if two or more head impacts were observed on video without a confirmed sensor-recorded head impact, which was used as a surrogate for sensor failure.

AE presence was quantified by counting the number of rostered players present at the game that day. AE participation was quantified by counting via video review the number of players that actively participated in the game that day. Scheduled time is a theoretical account of team exposure time during a single high school varsity game; it was calculated by multiplying the number of players on the field by the scheduled game time length. This method estimates the head impact rate for a theoretical player based on the assumption that head impact sensor data were collected for all players on the field for the entire game. Play time was quantified on an individual basis by the game clock time that a player was on the field by tracking substitution times. Absolute time was quantified for players who actively played in the game based on the real time difference (as measured by video timestamp alignment with a real time clock) from when a player entered the game for the first time and exited the game for the last time. Additionally, to study the proximity in time of repeated head impacts, the absolute timestamp of each impact was used to calculate the time between impacts for all games in which a player sustained two or more impacts.

Data Analysis

Data analysis was completed exclusively on video-confirmed head impact sensor data and summarized by gender. Each of the five head impact rates were calculated for each gender by pooling the total number of head impacts and total period of exposure for all players. Head impact rate ratios (RR) with 95% confidence intervals were calculated to determine gender differences in head impact rate within rate type and rate type differences within each gender. A Kolmogorov–Smirnov test (α=0.05) was used to determine differences between males and females for the distribution of absolute time between impacts and distribution of individual athlete season head impact rates.

Results

Head impact sensor data were recorded from 53 high school varsity soccer players (21 female and 32 male) for 75 athlete seasons (31 female and 44 male) (Table 2). During 41 games, 1312 sensor-recorded events were confirmed via video as head impact events. Female athletes sustained 271 head impacts during 18 games and male athletes sustained 1041 head impacts during 23 games. Indirect head accelerations (e.g. those caused by player-to-player collision without direct head contact) comprised 5% of head impact events for females and 10% for males.

Table 2.

Data Collection Summary

Total Female Male
Participants 53 21 32
Games 41 18 23
Athlete Seasons 75 31 44
Head Impacts 1312 271 1041
Presence (AE) 606 196 410
Participation (AE) 493 167 326
Scheduled Time (hr) 601.3 264.0 337.3
Play Time (hr) 444.2 167.3 276.9
Absolute Time (hr) 602.1 220.4 381.7
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Head Impact Rate per Athlete Exposure – Presence vs Participation

Head impact rates per AE were compared for males and females (Figure 1). When accounting for players that entered the game, 410 AEs presence were reduced to 326 AEs participation for males and from 196 AEs presence to 167 AEs participation for females. The head impact rate increased significantly for males from per AE presence to per AE participation (rate ratio = 1.26 and 95% confidence interval [1.07, 1.44]) but not for females (RR = 1.17 [0.93, 1.42]). For both AE calculation methods, males had a significantly higher head impact rate than females (presence: RR = 1.84 [1.52, 2.15] and participation: RR = 1.97 [1.60, 2.33]).

Figure 1.

Figure 1.

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Head impact rates per AE were compared across males and females for both AE presence and AE participation. Head impact rate calculated per AE participation was significantly higher for males (RR = 1.26 [1.07, 1.44]) but not females (RR = 1.17 [0.93, 1.42]) than per AE presence. Males had higher head impact rates than females for both rate methods (*p < 0.05). AE: athlete exposure.

Head Impact Rate per Player-Hour – Scheduled vs Play Time

To account for the length of a game and individual play time, head impact rates per player-hour were compared (Figure 2). Total scheduled time was 264.0 hours for females and 337.3 hours for males, but the total play time was 167.3 hours for females and 276.9 hours for males. Head impact rates per player-hour of play time were significantly higher than impact rates per player-hour of scheduled time for males (RR = 1.22 [1.03, 1.40]) and females (RR = 1.58 [1.26, 1.89]). Males had significantly higher head impact rates than females for scheduled time (RR = 3.01 [2.52, 3.49]) and play time (RR = 2.32 [1.88, 2.77]).

Figure 2.

Figure 2.

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Head impact rates per player-hour were compared across males and females for both scheduled time and play time. Head impact rates per player-hour of play time were significantly higher than impact rates per player-hour of scheduled time for males (RR = 1.22 [1.03, 1.40]) and females (RR = 1.58 [1.26, 1.89]). Male teams had higher head impact rates than females for both rate methods (*p < 0.05).

Individual Variability in Impacts per Player-Hour of Play Time

Individual head impact rates per player-hour of play time calculated for each athlete season varied greatly within gender (Figure 3). The male and female individual impact rate distributions were significantly different as the female distribution was more heavily weighted toward low head impact rates (p < 0.001). No head impacts were recorded for 18% and 26% of males and females, respectively, for the entire season. Within each gender, the higher magnitude head impact rates were skewed toward only a few players. For the 2017 male season, just 4 of 23 players (17%) accounted for over half of the impacts recorded and 5 of 21 (24%) for the 2018 season. For the 2017 female season, just 2 of 15 players (13%) accounted for over half of the impacts recorded and 3 of 16 (19%) for the 2018 season.

Figure 3.

Figure 3.

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The distribution of individual athlete-season head impact rates calculated per player-hour of play time was compared across males and females. Higher impact rates were skewed towards a few players for males and females. The distribution of head impact rate differed between males and females (p < 0.001).

Head Impact Rate per Player-Hour of Absolute Time

To account for play stoppages and halftime, head impact rates per player-hour of absolute time were calculated (Figure 4A). Absolute time for male players was 381.7 hours and 220.4 hours for females. The head impact rates calculated per player-hour of absolute time were significantly lower than the impact rates per player-hour of play time for both males (RR = 0.73 [0.61, 0.84]) and females (RR = 0.76 [0.61, 0.91]). Males had higher head impact rates per player-hour of absolute time than females (RR = 2.22 [1.85, 2.59]).

Figure 4.

Figure 4.

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(A) Head impact rates per player-hour of absolute time were compared between males and females. Male players had higher head impact rates than females (*p < 0.05). (B) The distribution of absolute time between sensor-recorded impacts differed between males and females (*p < 0.05).

Of the 326 male AEs participation, players had multiple impacts for 204, a single impact for 38, and zero impacts for 84. Of the 167 female AEs participation, players had multiple impacts for 35, a single impact for 28, and zero impacts for 104. The distribution of time between impacts is presented for both males and females (Figure 4B). Male and female distributions were significantly different (p < 0.003); male impacts occurred closer in time than females. For males, 50% of the impacts occurred less than 6.9 minutes apart, while for females, 50% of the impacts were less than 9.8 minutes apart.

Discussion

In this study, head impact rates were quantified for male and female high school soccer players using various methods of defining exposure period. Males had significantly higher head impact rates for all exposure period methods. However, the calculation method for exposure period significantly affected head impact rate magnitudes and differentially affected data collected from male and female teams. These findings are relevant to analysis of gender differences in multiple aspects of high school soccer and have implications in the interpretation of cross study comparison of head impact rates across sports, gender, age, and level of play.

Gender Differences in Head Impact Rates

High school males had significantly higher head impact rates than females for all rate calculation methods in this study (1.8–3.0 times higher). Studies at the youth and collegiate levels consistently have found that males experience head impact rates at 1.4–2.2 times higher than females (12,18,21,24). However, an earlier analysis of high school soccer using the xPatch sensor by Nevins et al. found a head impact rate per AE participation similar to this study for females (1.57 impacts per athlete per game) but a lower rate than this study for male soccer (1.3 impacts per athlete per game) (20). However, Nevins et al. found that males had a higher direct head impact rate, and females had more indirect head accelerations from body-to-body player contacts (20), while this study found that males had higher direct head impact and indirect head acceleration rates. Future studies are needed to further examine variability introduced by sensor choice and recording threshold (e.g. 10 vs. 16 g), competitive level, and individual team characteristics to confirm gender differences in impact rates.

The comparison between males and females was altered by the different approaches to calculating exposure as rate ratios ranged from 1.8–3.0. Males had a relatively higher impact rate than females for calculations per player-hour compared to calculations per AE. Therefore, when directly comparing on-field active play per player-hour of play time, males experience an even higher head impact rate than females relative to a more general estimate of the team experience calculated by AE. Future studies should employ the same sensor and methods in males and females across multiple sports to control for some aspects of variability in sport-to-sport comparisons.

While male soccer players had higher head impact rates than female players in this study, previous epidemiological studies have found that females have a concussion rate nearly twice as high as males in soccer (8,9). Further analyses are required to quantify the magnitude of head impacts in order to help understand important gender differences related to injury risk. Additionally, to help understand if there are gender differences in vulnerability to injury, future studies could measure physiological changes after controlled head impact events (e.g. soccer heading at consistent velocities).

Head Impact Rate per Athlete Exposure – Presence vs Participation

In this study, the male soccer rate calculated per AE participation was significantly higher (26%) than the impact rate per AE presence, highlighting the fact that the two rates are not directly comparable. The female rates did not significantly differ between AE presence and AE participation. The difference in presence and participation between genders in this study is likely related to the size of the team; the male team was larger (22 vs. 16 athletes) leading to more players not participating in games. Head impact rates per AE presence in this study were within the range of previous head impact rates of youth, high school, and collegiate soccer for females (0.4–2.2 impacts per AE) and males (1.2–2.4 impacts per AE) (6,18–24), but rates per AE participation were higher for males in the current study compared to earlier work (6,18–24). Previous studies were unclear in their approach to defining athlete exposure, making direct comparisons difficult to interpret. Counting AE presence at practices and/or games is straightforward to quantify based on the roster (14); however, quantifying AE participation requires additional information about active play for each athlete (15,16). For head impact sensor studies, not all members of a team may be enrolled in the sensor study and/or not all sensors may be functioning for all players during every game (15,22), so quantifying AE participation in the context of this information provides a more accurate representation of exposures for potential sensor-recorded head impacts. Future studies should clearly define AE quantification for head impact rate interpretation.

Head Impact Rate per Player-Hour – Scheduled vs Play Time

To compare active sport play, head impact rates were calculated per player-hour accounting for the length of a session and individual playing time. This study of high school soccer found lower head impact rates than a previous study of collegiate female soccer using the xPatch sensor and observer-confirmation of head impact recordings: 4.5 impacts per player-hour of scheduled time and 4.8 impacts per player-hour of play time (25). In the current study, the impact rate per player-hour of play time was significantly higher than the impact rate per player-hour of scheduled time for both males and females, emphasizing that the two rates are not equivalent. While scheduled time is easier to calculate than individual play time, scheduled time represents a theoretical player based on overall team experience and assumes data were collected for all players for the entire game. Therefore, play time provides a more accurate metric of on-field active play for most studies and allows for analysis at the player level. Furthermore, head impact rates per player-hour cannot be compared to rates per AE because the latter encompasses variable durations of participation up to a full game length. Detailing individual periods of exposure for play time calculations may provide a more consistent metric of on-field head impact rates by accounting for variability introduced by differing amounts of playing time across players and team-specific substitution dynamics associated with AE quantification.

Individual Variability in Impacts per Player-Hour of Play Time

Player head impact exposure varied substantially across the players on a given team in this study with 21% of athletes sustaining no head impacts throughout the course of the season and only a few athletes accounting for more than half the impacts. This was emphasized by the medians of both teams (F = 0.8, M = 2.6 impacts per player-hour of play time) being substantially below the pooled team average (F = 1.6, M = 3.8 impacts per player-hour of play time). For the female team, the skew toward zero impacts was even more pronounced as a larger proportion of the team experienced relatively low head impact rates. Individual factors, such as position (6) and age (21), may influence head impact rate. Therefore, when the safety of the sport as a whole is evaluated via calculation of team head impact exposure (either in terms of AE or per player-hour), head impact rates of individual players may be substantially underestimated or overestimated.

Head Impact Rate per Player-Hour of Absolute Time

In this study, both males and females had lower head impact rates per player-hour of absolute time than per player-hour of play time, and males had higher head impact rates per player-hour of absolute time than females. Absolute time accounts for game stoppages and halftime to quantify impacts experienced in real time, and therefore, rates were expected to be lower than rates per player-hour of play time. Shorter times between repeat injuries in preclinical rodent models have been linked to worse neurological outcomes (31,32), and subclinical repeated head impacts may have a similar cumulative effect in humans. Since soccer has a continuously running clock with minimal play stoppages, soccer may have shorter periods between impacts compared to other sports, such as American football and basketball, which have frequent play clock stoppages and quantified larger differences between absolute time and play/scheduled time (33).

For both genders, impacts occurred in quick succession, with over half of impacts occurring within 10 minutes of each other for a given player. Head impacts occurred closer in time for males than females. It is unknown how these short intervals between impacts at a sub-injury level affect the brain. However, the quantified time between impacts provides a basis to design the timing of laboratory-based repetitive head loading studies (e.g. soccer heading) (1–3), which have predominantly analyzed impacts occurring 30–60 seconds apart, to link on-field impact frequency with relevant clinical and physiological outcomes.

Limitations

Several limitations of the current study exist. First, only high school soccer was analyzed in this study. The principles of each methodological approach transfer to all sports; however, the specific head impact rates and differences between rate types likely vary by sport, team, and level of play. Second, this study used a headband-mounted sensor with a 16 g linear acceleration threshold. The sensor and recording threshold influence the number of impacts recorded (34,35); therefore, head impact rates must be interpreted and compared between studies in the context of the threshold and sensor used. Third, in our calculation of individual impact rates per player-hour of play time, each athlete-season was represented independently despite some individuals participating in the study both years. While inherent individual characteristics may influence the frequency of head impacts, high school athletes and team dynamics change substantially from year to year affecting individual play time, playing style, and position, providing support for treating each athlete season independently. Lastly, the methodological approaches described in this study do not include all possible methods for calculating impact rates. For example, head impact rates per play have also been used to describe American football (10). The results of the current study demonstrate the need for clearly defined methods to compare impact rates across studies.

Summary

The current study quantified head impact rates in high school male and female soccer using several methodological approaches. The choice of head impact rate calculation method affected rate magnitude. Regardless of approach, the head impact rate for males was always higher than that for females. Individual head impact exposure varied substantially within a team with many players experiencing no head impacts and only a few responsible for over half the impacts. Many head impacts occurred in close time proximity, providing insight into appropriate test conditions for future studies on the physiological effects of repetitive head impacts. Overall, the differences in head impact rates found in this study emphasize that future studies should clearly define the method used to quantify exposure periods and that meta-analyses must carefully interpret each study’s methods to appropriately compare head impact rates across studies.

Acknowledgments

The authors thank the students and parents from the Shipley School for their participation. The authors appreciate the support from the Shipley School administration and athletic department: Steve Piltch, Michael Turner, Mark Duncan, Katelyn Taylor, Dakota Carroll, Kimberly Shaud, and Kayleigh Jenkins.

Research reported in this publication was supported by National Institute of Neurologic Disorders and Stroke of the National Institutes of Health under award number R01NS097549 and the Pennsylvania Department of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health and the Pennsylvania Department of Health.

Footnotes

Conflict of Interest Statement: The authors have no conflicts to disclose. The results of the present study are solely the responsibility of the authors and does not constitute endorsement by ACSM. The authors affirm that the results are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation.

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