Athletic Training and Sports Health Care

Original Research 

Neuromotor and Neurocognitive Performance in Female American Football Players

Christopher K. Rhea, PhD; Kristen N. Schleich, MS; Leah Washington, PhD; Stephen M. Glass, PhD; Scott E. Ross, PhD; Jennifer L. Etnier, PhD; W. Geoffrey Wright, PhD; Daniel J. Goble, PhD; Donna M. Duffy, PhD

Abstract

Purpose:

To determine whether existing baseline data are appropriate for comparison with female collision sport athletes, neuromotor and neurocognitive data were recorded in the preseason from female athletes playing American tackle football and compared to previously published data of athletes participating in similar sports.

Methods:

Female football players (N = 31, mean age: 33.7 ± 6.9 years) were given assessments in the neuromotor (Modified Balance Error Scoring System [M-BESS] and BTrackS Balance Test) and neurocognitive (Standardized Assessment of Concussion [SAC] and Trail Making Test [TMT]) domains prior to the start of the season. Effect size comparisons were used to examine differences between the baseline data and appropriate comparison data.

Results:

Medium to very large effect size differences in neurocognitive performance and small to large effect size differences in neuromotor performance were observed.

Conclusions:

Previously published data from athletes participating in similar sports may be limited due to sex and age differences between the data sets.

[Athletic Training & Sports Health Care. 2019;11(5):224–233.]

Abstract

Purpose:

To determine whether existing baseline data are appropriate for comparison with female collision sport athletes, neuromotor and neurocognitive data were recorded in the preseason from female athletes playing American tackle football and compared to previously published data of athletes participating in similar sports.

Methods:

Female football players (N = 31, mean age: 33.7 ± 6.9 years) were given assessments in the neuromotor (Modified Balance Error Scoring System [M-BESS] and BTrackS Balance Test) and neurocognitive (Standardized Assessment of Concussion [SAC] and Trail Making Test [TMT]) domains prior to the start of the season. Effect size comparisons were used to examine differences between the baseline data and appropriate comparison data.

Results:

Medium to very large effect size differences in neurocognitive performance and small to large effect size differences in neuromotor performance were observed.

Conclusions:

Previously published data from athletes participating in similar sports may be limited due to sex and age differences between the data sets.

[Athletic Training & Sports Health Care. 2019;11(5):224–233.]

The Concussion in Sport Group recently modified the definition of a sport-related concussion at the 5th International Conference on Concussion in Sport and included in their consensus statement that a sport-related concussion “may be caused either by a direct blow to the head, face, neck, or elsewhere on the body with an impulsive force transmitted to the head.”1 The Centers for Disease Control and Prevention estimated that 1.6 to 3.8 million concussions occur annually in sport and recreational activities.2 Sport-related concussions primarily occur through three modes of impact: (1) head-to-head impact, (2) head-to-ground impact, or (3) head-to-equipment impact. Limited contact sports (eg, volleyball) have little to no impact prevalence, whereas contact sports (eg, soccer) have more reported concussions. The highest reported prevalence is in collision sports (eg, football, hockey, and rugby)—activities where athletes purposefully collide with each other during competition.2

American football has the highest total number of concussions compared to other sports played in the United States.2 Approximately 67.6% of concussions in football result from tackling and being tackled,3 leading to many studies focusing on concussions in football players.4–8 Because females have not traditionally participated in football and many early concussion studies focused on football athletes, there is a disparity in the number of concussion studies in which females were included. This disparity has continued in current research.9,10 In a recent systematic review examining the use of common concussion assessment tools, 19 studies were included in the analysis, yielding a total of 9,086 participants who were baseline tested prior to their season.9 Only 10.6% to 16.9% of the population studied were female (depending on the specific concussion tool) and the majority of athletes studied were male football players. This disparity between studying female and male athletes is the foundation behind recent comments from the Medical Director of the National Collegiate Athletics Association (NCAA), where he acknowledged there is still a poor understanding of the differences in concussion between sexes.11

There has been a recent increase in the number of female athletes participating in collision sports. For example, the Independent Women's Football League (IWFL) is a nationwide tackle football league with 20 teams and approximately 400 female athletes. Parallel growth in other collision sports for female athletes has also been observed, including the Women's Rugby League and the Women's Flat Track Derby Association (roller derby). Unfortunately, the expansion of female athlete collision sport opportunities has not been paralleled by equal growth in female concussion research. Although limited in quantity, the research in this area suggests that there are sex differences in concussion outcomes with respect to injury rate, symptom severity, and return-to-play duration, highlighting the need for research to address sex differences in antecedents and consequences of concussion.11–13 The aforementioned women's football leagues provide a setting to address the disparity in concussion research because, unlike men's and women's hockey, these leagues play by the same contact rules as the men's leagues, providing a unique opportunity for direct comparison between men and women in a collision sport.

To begin to address this problem, a logical place to start is to report preseason neuromotor and neurocognitive performance data (commonly used indicators for functional ability) for female collision sport athletes, providing health care professionals and researchers with an appropriate benchmark for comparison. Next, it would be useful to compare these data to existing male athlete data to determine whether the female athletes' profiles prior to the season are different than those of male athletes participating in similar sports. Although it has been suggested that an individual's own baseline data be used for comparison after a head trauma event,14 the time and resources required to record individualized baseline data for each athlete prior to the season may not be feasible in many settings.15 In this case, a normative data approach derived for an appropriate group of similar athletes can be used for comparison.14,15 However, the extent to which sex may play a factor in differentiating athlete groups with respect to neuromotor and neurocognitive performance at baseline is unknown, making it difficult to know whether normative data derived from an all (or mostly) male group is appropriate to use with female athletes. Measuring female athletes' baseline neuromotor and neurocognitive performance and then comparing them to existing men's (and combined men's and women's) data will help answer the question of whether sex differences in concussion outcomes existed prior to the injury. Previous work has used this approach to identify sex differences during a baseline assessment prior to the season in child,16 high school,17 and collegiate18 athletes when collapsing athletes across a variety of sports. In the only known study to examine baseline performance of female and male athletes playing the same contact/collision sport, Brooks et al.19 tested 13- to 17-year-old hockey athletes and showed no sex differences in neurocognitive function prior to the season. However, the observation that all participants in that study were in the top two hockey tiers within their five-tier league may partially account for the homogeneity of performance across sex.

With an increase in the number of female athletes participating in collision sports, there is a need to report baseline data in this population and determine whether these data differ from previously published baseline data that are commonly collapsed across sport classifications. Furthermore, the limited literature for females has focused on school-aged athletes. Thus, the purpose of this study was two-fold: (1) measure and present baseline neuromotor and neurocognitive data for female post-collegiate athletes participating in American tackle football and (2) use effect size comparisons to determine the magnitude of differences between female American football players and previously published baseline data in athletes playing comparable sports. Because this is the first study of its kind, previous literature could not be used to derive directional hypotheses. Thus, the analyses presented were exploratory in nature with respect to how the American football players would compare to previously published baseline data.

Methods

Participants

Female football players from two IWFL teams were recruited (N = 31, mean age: 33.7 ± 6.9 years; mean football experience: 2.7 ± 1.7 years). Participant concussion history was assessed, with a frequency of 0 (n = 19), 1 (n = 5), 2 (n = 4), 3 (n = 1), 4 (n = 0), 5 (n = 0), and 6 (n = 2) previous concussions being reported. All participants signed an informed consent approved by the University of North Carolina at Greensboro's institutional review board and participated in baseline testing prior to the 2015 season.

Protocol

The study protocol was approved by the University of North Carolina at Greensboro's institutional review board. All participants were administered the same tests during a designated preseason testing day. Test order was randomized between participants and administered on a football field (or the surrounding track) for each team. The same set of instructions was verbally given for each test to ensure consistency across players and testers.

Data Collection and Processing

Participants were given a battery of tests that included the Sport Concussion Assessment Tool 3rd Edition (SCAT3),20 Trail Making Test (TMT),21 Modified Balance Error Scoring System (M-BESS),22 and BTrackS Balance Test.23 The SCAT3 includes the Standardized Assessment of Concussion (SAC)24 and the M-BESS. The SAC and TMT assessments are neurocognitive tests, whereas the M-BESS and BTrackS Balance Test assessments are neuromotor tests.

SCAT3

The SCAT3 is a standardized tool for evaluating injured athletes for a concussion using symptom reporting and neurocognitive testing. It can be used for athletes aged 13 years and older. At the time of participant testing for this study, the SCAT3 was the latest version, superseding the original SCAT and the SCAT2 published in 2005 and 2009, respectively. In 2017, the SCAT5 was published (there is no SCAT4).25 The SCAT3 consists of six sections. For the purposes of this study, only data from the neurocognitive section (SAC) and the neuromotor section (M-BESS) were analyzed. The SAC consists of questions addressing orientation (eg, “What month is it?”), immediate memory, concentration (ie, reciting digits and months backwards), and delayed recall. A higher score indicates better performance, with a maximum score of 30. The M-BESS will be described in detail below. Comparison SAC data were derived from studies that were included in a recent SCAT2 and SCAT3 systematic review,9 along with a database search to include more recent studies.

TMT

The TMT is one of the most frequently used tests to detect cognitive impairment from different neuropsychological conditions. It is a measure of visual conceptual and visuomotor components covering motor speed and agility, simple motor-spatial skills, visual tracking, mental flexibility, and basic sequence abilities. The test has two parts and was administered using pencil and paper. Part A consists of a page with numbers 1 to 25 in circles scattered across the page. The participants were asked to draw lines connecting the numbers in sequential order as quickly as possible, making sure they hit inside each circle. Part B consists of a page with numbers 1 to 13 and letters A to L in circles scattered across the page. The participants were asked to connect sequential circles, alternating between numbers and letters (eg, 1-A-2-B-3-C) as quickly as possible. Each part is timed and, although errors are not scored, they are reflected in the total time because participants were verbally alerted by a tester if they missed a circle and were instructed to go back to follow the correct sequence. A lower time indicates a better performance. By subtracting Part B scores from Part A scores, a raw score of the cost of set-switching is obtained.26 Set switching is an executive function, which is an umbrella term encompassing cognitive processing, working memory reasoning, and cognitive flexibility. Data from this study were compared to data from studies that focused on male football players,4,7,27,28 male rugby players,29,30 and male and female lacrosse players.31

M-BESS

The BESS was developed as a systematic way to assess balance with minimal equipment.32 The BESS uses three stances (single leg, double leg, and tandem) on two surfaces (hard and foam), leading to six trials. The M-BESS was developed to shorten the test to three trials by removing the foam surface, allowing the M-BESS to be given without the need for any equipment.22 The sensitivity and specificity of the M-BESS has recently been published.33 Participants were instructed throughout each stance to place their hands on their hips, close their eyes, and maintain their position for 20 seconds. An error was counted if the participant did any of the following: (1) removed hands from the hips, (2) opened their eyes, (3) took a step, (4) had abduction/flexion of the hip beyond 30 degrees, (5) lifted the heel or forefoot off the ground, or (6) remained out of the beginning position for more than 5 seconds. Errors were tallied from all trials. A lower score indicates a better performance. Comparison data were from studies that examined male and female ice hockey players,34,35 male football players,36 male and female rugby players,37 and reports that included a variety of collision and contact sport players.38,39

BTrackS Balance Test

The BTrackS Balance Test uses a force plate deemed a class 1 medical device to measure the postural sway of a participant while standing for four 20-second trials with his or her eyes closed and feet a shoulder width apart.23 The force plate is connected to a laptop to automatically record the path length of the center of pressure measured in centimeters, an objective measurement of a person's balance ability. All BTrackS Testing was conducted on the track surrounding the football field. The first trial is a familiarization trial and the average path length of the last three trials is used to index balance ability. Lower values indicate a better performance. Normative data provided by the company (Balance Tracking Systems, Inc., San Diego, CA) and participants who were 18 years and older and who participated in collision sports were used for comparison. Athletes participating in the following collision sports were available in the database: boxing (male), football (male), ice hockey (male), lacrosse (male), roller derby (female), rugby (male), and wrestling (male).

Statistical Analysis

Means and standard deviations were calculated for the neurocognitive and neuromotor tests on the female American football player participants. Next, the magnitude of difference between data from the current study and previously published comparison data were examined by calculating an effect size with the Cohen's d equation:

d=M1−M2SD12+SD222
with M1 and SD1 = mean and standard deviation of our data and M2 and SD2 = mean and standard deviation of previously published data. The magnitude of the effect size indicates the disparity between two sets of data using the following guidance: 0.2 (small), 0.5 (medium), 0.8 (large), and 1.3 (very large). For this study, a positive effect size value indicates the comparison data has a smaller mean than that of our female football athlete data and vice versa. The use of effect sizes has been suggested for the athletic training community to help determine clinical meaningfulness.40

Results

Descriptive data from the current study and relevant previously published studies are presented in Tables 12 for the neurocognitive data (SAC and TMT) and Tables 34 for the neuromotor data (M-BESS and BTrackS Balance Test). We observed medium to very large effect size differences in neurocognitive performance relative to previous data, with our female football players performing worse on the SAC but better on the TMT. Small to large effect size differences in neuromotor performance were observed, but there were no clear directional differences relative to the comparative data, with our female football players performing better on the M-BESS and BTrackS Balance Test relative to some previously published data, but worse compared to others.

Standardized Assessment of Concussion (SAC) Dataa

Table 1:

Standardized Assessment of Concussion (SAC) Data

Trail Making Test (TMT) Dataa

Table 2:

Trail Making Test (TMT) Data

Modified Balance Error Scoring System (M-BESS) Dataa

Table 3:

Modified Balance Error Scoring System (M-BESS) Data

BTrackS Balance Test (BBT) Dataa

Table 4:

BTrackS Balance Test (BBT) Data

Discussion

To our knowledge, this is the first study that reports data from female American tackle football players, providing the foundation for future research in this growing population. Relative to our study's second purpose of using effect size comparisons to determine the magnitude of differences between female American football players and previously published baseline data in athletes playing comparable sports, it should be noted that limited data were available with which direct comparisons could be made, despite the increased participation in female collision sports such as rugby, roller derby, and football. For example, USA Rugby indicates there are more than 400,000 adolescent female athletes participating in rugby in the United States alone. World Rugby estimates there are 2.2 million female athlete participants across all age groups worldwide. Despite these large numbers, little head trauma research has been conducted on female collision sport athletes, leading to a dearth of available comparison data. For the comparison data that were available, medium to very large differences were observed in neurocognitive performance relative to previous data, whereas small to large differences were observed in neuromotor performance. The specifics and interpretation of each test included in this study are outlined below.

The SAC is a neurocognitive test designed for sideline assessment. It is widely used due to its inclusion in the SCAT versions 1 to 5, leading to studies reporting SAC data from collision and/or contact sport athletes.35,37–39,41–44 Relative to the purpose of this study, it should be noted that seven of the nine comparison studies did not report the scores of the various components of the SAC, only the total SAC score. Although the total SAC score is commonly used clinically, the reporting of individual components that make up the total SAC score provides insight into neurocognitive ability across domains in a particular population. We were able to compare the total SAC scores to previous research in Table 1, but the dearth of SAC individual component scores reported in previous research minimizes our ability to make observations at that level of analysis.

Only one study reported female-only data and participants were from a mixture of collision, contact, limited contact, and non-contact sports.39 Unfortunately, data for female athletes within each sport classification were not reported. Further, only collegiate athletes participated in that study, creating a large gap between the average age of the participants in their study (19.2 years) and the female football players in our study (33.7 years). Age has been shown to affect SAC scores in previous research within a high school population,45 but it is unclear how age differences may affect SAC performance in an adult population.

For the two studies that did report the component scores of the SAC,35,37 a small effect size was observed for orientation, a very large effect size for immediate memory, a medium effect size for concentration, and a medium to very large effect size for delayed recall. In all component scores, the participants in the current study scored lower than the two previous studies, indicating worse neurocognitive performance. This observation was mirrored in the total SAC scores for all comparison studies, where a very large effect size was observed with lower scores reported for the participants in this study. However, the mean age of the participants in all of the comparison studies was at least one standard deviation less than the participants in the current study, suggesting that age may partially account for the observed differences. It should also be noted that the SAC scores for the female athletes in this study would likely be considered impaired if used clinically. This further highlights the challenge of using normative SAC data (mostly derived from males) for comparison to female athletes because their initial starting points (ie, baseline performance) are not the same, which could lead to a misdiagnosis. Future research would benefit from sex-specific normative data and reporting the individual component scores of the SAC to allow a deeper level of analysis across populations.

The TMT is a neurocognitive test consisting of two parts. Most comparison studies included in this study reported the scores for Parts A and B. Participants in the current study performed better than previously reported data, with effect sizes ranging from medium to very large. Similar to the SAC, direct comparison data were not available, with nearly all studies included in this analysis reporting on male athletes.4,7,27–30 The one study that included females did not report data for each sex.31 Further, we report data for Part B minus Part A, which provides a measure of executive function26 and has been used in the neurocognitive literature.46 However, none of the comparison studies included these data, making it difficult to determine how executive function may differ at baseline between female and male athletes. Similar to the SAC data, the mean age of the comparison participants was at least one standard deviation lower than the participants in the current study. However, an opposite effect in neurocognitive performance was observed.

Our participants scored worse on the SAC, but scored better than the comparison data on the TMT. The comparison data for the SAC and TMT were derived from different studies, so direct linkage between these two observations cannot be made. Nevertheless, our findings highlight the unique neurocognitive constructs assessed by the SAC and TMT, and show that elevated performance may not generalize across neuro-cognitive constructs. Thus, care should be taken when selecting a neurocognitive test to ensure the construct of interest is assessed, along with using appropriate comparison data if available. In future studies, the inclusion of data for Part B minus Part A would help identify executive function differences between various groups.

The M-BESS is the neuromotor assessment portion of the SCAT and assesses balance by counting body movements (errors) when in double leg, single leg, and tandem stance conditions. Most comparison studies did not report the scores for each stance, only the total score.34,36–39 For the total score, the effect sizes were mostly small, with the exception of a male hockey player study,35 where the effect size was large and our female football players exhibited worse balance. That same male hockey player study was also the only comparison study to report the different stance scores. No difference was observed in the double leg stance (both studies report 0 errors). However, a large effect size was observed in the single leg stance and a medium effect size in the tandem stance, with our female football players performing worse in both conditions. This suggests that it may be appropriate to compare M-BESS data from female football players to other collision sport athletes, with the exception of male ice hockey players. The M-BESS was included in this study because it is commonly used in the clinical community.47 However, the subjectivity of the test has led to questions about its appropriateness,48–50 especially with an increase in the number of portable and low-cost solutions currently available to objectively measure balance performance.23,51,52 Future research may want to consider including one of these objective measures to avoid the subjectivity of the M-BESS. In line with this recommendation, we included the BTrackS Balance Test in this study.

The BTrackS Balance Test is an objective neuromotor test of balance that uses a portable force plate to measure movements while attempting to stand still.23,53 Access to the BTrackS Balance Test database allowed for the strongest comparisons to the female football players tested in this study. A small to medium effect size was observed, with the female football players exhibiting better balance when compared only to male football players 18 years and older. The effect size was small when the male football player sample size was reduced to within two standard deviations of the age of the female football players. Once the age was further reduced to within one standard deviation, the effect size was medium, but in the opposite direction, indicating that age-matched male football players exhibited better balance. When all collision sport athletes were considered, larger effect sizes were observed when comparing the male or combined male/female athlete groups relative to the female-only group. This indicates that future research should consider sex and age comparisons if baseline data from different populations are used.

A limitation of this study is the relatively small sample size reported here (N = 31) relative to the number of female athletes participating in American football. Thus, the data presented here should not be considered as normative, which would require a much larger sample size. Further, given the variety of skills and body types of American football athletes, normative data should also consider factors such as height, weight, and position when reporting normative neuromotor performance. Another limitation is that some of the reported outcomes in this study are not commonly assessed by clinicians, such as the TMT. Although we believe reporting these data provides value for future researchers, they may be less valuable to clinicians due to their time and the resources needed to administer the tests. Future researchers should consider including tests that are commonly used in clinical and field settings, along with tests typically used in laboratory and research settings to maximize their applicability across environments. Finally, the average football playing experience was 2.7 years in our female population, which is likely significantly less than the football experience in the comparison male populations. Thus, our observed differences in the football comparison data could have been due to relatively less cumulative head trauma resulting from fewer football playing/practicing experiences.

Implications for Clinical Practice

Collectively, the data show that previously published baseline data are typically not good comparison data for post-collegiate female American football athletes. This is likely due to the lack of a homogenous population from which to compare, because most collision sport athlete data available are from male athletes who are considerably younger than the population in our current study. Because appropriate comparative data are not readily available, it is suggested that baseline data for each athlete are taken so that they can serve as their own control. Debates about the use of individual baseline versus normative data have been discussed in the literature14,15 and it is recommended that individual baseline data are used when time and resources are available.1 Data from this study further solidify that recommendation because available normative data may not include participants who exhibit the same characteristics with respect to sex, age, or sport classification, potentially making the published data less applicable to the population of interest. Thus, it is recommended that individual baseline data are taken from female collision sport athletes until a larger dataset of relevant comparison data can be developed. In the absence of such comparison data, decisions about an athlete's return-to-play status could wrongfully be made by potentially clearing a player for activity before they are ready, which could compromise their short- and long-term health.

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Standardized Assessment of Concussion (SAC) Dataa

Study Sex Sport N Age (y) Orientation ES Immediate Memory ES Concentration ES Delayed Recall ES Total SAC Score ES
Current study F Football 31 33.7 (6.9) 4.91 (0.18) 13.06 (1.95) 3.29 (1.16) 3.00 (0.97) 23.56 (5.20)
Nassiri et al. (2002) M Football 298 17.5 to 23.8 NR NR NR NR 27.90 (1.60) −1.98
Gysland et al. (2012) M Football 46 19.7 (1.2) NR NR NR NR 27.88 (1.73) −1.22
Galetta et al. (2013) M Ice hockey 27 28.0 (5.0) NR 14 (NR) NR NR 28 (NR)
Weber et al. (2013) M Wrestling 32 20.0 (1.4) NR NR NR NR 27.72 (1.91) −1.07
Putukian et al. (2015) Both Variety 280 20.33 (1.74) NR NR NR NR 28.05 (1.60) −2.02
Zimmer et al. (2015) F Variety 144 19.2 (NR)b NR NR NR NR 27.63 (1.87) −1.48
Zimmer et al. (2015) M Variety 330 19.2 (NR)b NR NR NR NR 26.97 (2.05) −1.38
Hänninen et al. (2016) M Ice hockey 304 25.3 (5.2) 4.90 (0.30) 0.03 14.60 (0.60) −1.88 3.90 (0.90) −0.66 3.70 (1.10) −0.64 27.00 (1.70) −1.53
Willmott et al. (2018) Both Rugby 53 26.5 (4.2) 4.96 (0.19) −0.27 14.62 (0.77) −1.17 3.77 (1.11) −0.43 4.23 (0.96) −1.28 NR

Trail Making Test (TMT) Dataa

Study Sex Sport N Age (y) Part A ES Part B ES Part B – Part A ES
Current study F Football 31 33.7 (6.9) 16.43 (4.07) 39.06 (13.87) 22.63 (11.77)
Macciocchi et al. (1996) M Football 2,300 18 to 26 19.90 (7.10) −0.49 47.30 (14.40) −0.57 NR
Collins et al. (1999) M Football 386 20.4 (1.7) 21.00 (5.90) −0.79 55.40 (17.30) −0.96 NR
Onate et al. (2000) Both Lacrosse 59 F: 19.3 (1.3); M: 19.8 (1.2) 22.57 (5.09) −1.29 49.67 (15.53) −0.71 NR
McCrea et al. (2003) M Football 56 19.2 (1.5) NR 57.30 (18.69) −1.06 NR
Pellman et al. (2004) M Football 655 25.4 (NR) 21.40 (7.40) −0.68 55.60 (17.10) −0.97 NR
Collie et al. (2006) M Rugby 84 23.4 (3.6) NR 52.93 (16.54) −0.87 NR
Shuttleworth-Edwards et al. (2008) M Rugby 27 19.85 (1.46) 24.86 (7.05) −1.49 58.72 (17.45) −1.26 NR

Modified Balance Error Scoring System (M-BESS) Dataa

Study Sex Sport N Age Double Leg ES Single Leg ES Tandem ES Total ES
Current study F Football 31 33.7 (6.9) 0.00 (0.00) 3.19 (2.32) 1.29 (1.53) 4.53 (3.48)
Echlin et al. (2012) Both Ice hockey 45 Collegiate NR NR NR 4.85 (5.36) −0.07
Putukian et al. (2015) Both Variety 280 20.3 (1.7) NR NR NR 3.45 (3.21) 0.33
Zimmer et al. (2015) F Variety 144 19.2 (NR) NR NR NR 4.06 (3.90) 0.12
Zimmer et al. (2015) M Variety 330 19.2 (NR) NR NR NR 4.51 (4.14) 0.00
Hänninen et al. (2016) M Ice hockey 304 25.3 (5.2) 0.00 (0.00) 0.00 1.40 (1.80) 0.97 0.60 (1.50) 0.46 2.00 (2.50) 0.97
Mathiasen et al. (2018) M Football 48 Collegiate NR NR NR 5.00 (4.00) −0.04
Willmott et al. (2018) Both Rugby 53 26.5 (4.2) NR NR NR 2.60 (1.76) 0.34

BTrackS Balance Test (BBT) Dataa

Study Sex and Age Category Sport N Age (y) BBT ES
Current study Female Football 31 33.7 (6.9) 22.65 (7.26)
BTrackS database Male age 18+ Football 1,237 20.1 (2.0) 24.56 (7.55) −0.41
BTrackS database Male age ± 2 SD of current study Football 575 21.6 (2.0) 24.14 (7.11) −0.21
BTrackS database Male age ± 1 SD of current study Football 13 31.1 (4.3) 19.46 (5.68) 0.47
BTrackS database Female and male age 18+ All Collision Sports 1,452 20.6 (3.6) 24.44 (7.44) −0.24
BTrackS database Male age 18+ All Collision Sports 1,431 20.4 (2.9) 24.47 (7.45) −0.24
BTrackS database Female age 18+ All Collision Sports 21 34.9 (9.7) 22.33 (6.64) 0.05
BTrackS database Female and male age ± 2 SD of current study All Collision Sports 691 22.2 (3.5) 24.02 (7.06) −0.19
BTrackS database Male age ± 2 SD of current study All Collision Sports 674 21.9 (2.9) 24.08 (7.09) −0.20
BTrackS database Female age ± 2 SD of current study All Collision Sports 17 32.9 (6.8) 21.41 (5.34) 0.19
BTrackS database Female and male age ± 1 SD of current study All Collision Sports 34 31.7 (3.5) 21.91 (5.56) 0.12
BTrackS database Male age ± 1 SD of current study All Collision Sports 24 31.0 (3.2) 24.46 (6.06) −0.27
BTrackS database Female age ± 1 SD of current study All Collision Sports 10 33.4 (3.7) 22.30 (4.40) 0.05
Authors

From the Department of Kinesiology, University of North Carolina at Greensboro, Greensboro, North Carolina (CKR, SER, JLE, DMD), the School of Health Sciences (KNS) and the Department of Exercise Science (LW), Elon University, Elon, North Carolina; the Department of Otolaryngology, Ohio State University, Columbus, Ohio (SMG); the Department of Physical Therapy, Temple University, Philadelphia, Pennsylvania (WGW); and the School of Health Sciences, Oakland University, Rochester, Michigan (DJG).

Supported in part by the Women's Football Foundation (CKR, DMD).

Dr. Goble is eligible for royalties from a pending patent (OMB 0651-0032) related to the technology used in this study and has an equity stake (stock options) in Balance Tracking Systems, Inc. The remaining authors have no financial or proprietary interest in the materials presented herein.

The authors thank the participants of this study for their time commitment.

Correspondence: Christopher K. Rhea, PhD, Department of Kinesiology, University of North Carolina at Greensboro, 237-A Coleman Building, 1408 Walker Ave., Greensboro, NC 27412. E-mail: ckrhea@uncg.edu

Received: March 15, 2018
Accepted: August 22, 2018
Posted Online: January 29, 2019

10.3928/19425864-20181101-01

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