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Repetitive head impacts do not affect postural control following a competitive athletic season
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International Journal of Psychophysiology journal homepage: www.elsevier.com/locate/ijpsycho
Repetitive head impacts do not affect postural control following a competitive athletic season Nicholas G. Murraya,⁎, Katelyn E. Grimesa, Eric D. Shifletta, Barry A. Munkasya, Nathan R. D'Amicob, Megan E. Mormilea, Douglas W. Powellc, Thomas A. Buckleyd,e a
School of Health and Kinesiology, College of Health and Human Services, Georgia Southern University, P.O. Box 8073, Statesboro, GA 30458, United States Department of Health, Human Performance, and Recreation, Office for Sport Concussion Research, University of Arkansas, Fayetteville, AR, United States c School of Health Studies, University of Memphis, 106 Elma Roane Fieldhouse, Memphis, TN 28152, United States d Department of Kinesiology and Applied Physiology, University of Delaware, 540 College Avenue, Newark, DE 19716, United States e Interdisciplinary Program in Biomechanics and Movement Science, University of Delaware, 540 College Avenue, Newark, DE 19716, United States b
A R T I C L E I N F O
A B S T R A C T
Keywords: Subconcussive Repetitive head impacts Concussion Postural control
Evidence suggests that Repetitive Head Impacts (RHI) directly influence the brain over the course of a single contact collision season yet do not significantly impact a player's performance on the standard clinical concussion assessment battery. The purpose of this study was to investigate changes in static postural control after a season of RHI in Division I football athletes using more sensitive measures of postural control as compared to a non-head contact sports. Fourteen Division I football players (CON) (age = 20.4 ± 1.12 years) and fourteen non-contact athletes (NON) (2 male, 11 female; age = 19.85 ± 1.21 years) completed a single trial of two minutes of eyes open quiet upright stance on a force platform (1000 Hz) prior to athletic participation (PRE) and at the end of the athletic season (POST). All CON athletes wore helmets outfitted with Head Impact Telemetry (HIT) sensors and total number of RHI and linear accelerations forces of each RHI were recorded. Center of pressure root mean square (RMS), peak excursion velocity (PEV), and sample entropy (SampEn) in the anteroposterior (AP) and mediolateral (ML) directions were calculated. CON group experienced 649.5 ± 496.8 mean number of impacts, 27.1 ± 3.0 mean linear accelerations, with ≈ 1% of total player impacts exceeded 98 g over the course of the season. There were no significant interactions for group x time RMS in the AP (p = 0.434) and ML (p = 0.114) directions, PEV in the AP (p = 0.262) and ML (p = 0.977) directions, and SampEn in the AP (p = 0.499) and ML (p = 0.984) directions. In addition, no significant interactions for group were observed for RMS in the AP (p = 0.105) and ML (p = 0.272) directions, PEV in the AP (p = 0.081) and ML (p = 0.143) directions, and SampEn in the AP (p = 0.583) and ML (p = 0.129) directions. These results suggest that over the course of a single competitive season, RHI do not negatively impact postural control even when measured with sensitive non-linear metrics.
1. Introduction Sport-related concussions are a major health concern that effect all levels of athletic play including measurable deficits such as postural instability, cognitive impairment, and neurologic symptoms (e.g., headaches, dizziness) (McCrory et al., 2017). The effect of multiple concussions over the course of an athletic career can lead to an increased risk of early onset Alzheimer's disease, mild cognitive impairment, depression, and overall later-life cognitive impairments (Guskiewicz et al., 2005; Guskiewicz et al., 2007). However, repetitive head impacts (RHI), independent of concussion, have been associated with the development of the progressive degenerative brain disease
⁎
known as Chronic Traumatic Encephalopathy (CTE) (McKee et al., 2016; McKee et al., 2013; McKee et al., 2016). This is supported by a recent report that has confirmed the presence of CTE in 99% of postmortem retired National Football League (NFL) players and 87% of all American football players across different levels of play (Mez et al., 2017); however these results must be viewed cautiously given an acknowledged selection bias in the studies. However, the role of RHI in the development neurodegenerative pathologies remains inconclusive (Bailes et al., 2013; Solomon et al., 2016). Standard clinical post-concussion assessments include postural stability, cognition, and self-reported symptoms which are components of the Sport Concussion Assessment Tool-5 (SCAT-5) (McCrory et al.,
Corresponding author at: School of Health & Kinesiology, Georgia Southern University, P.O. Box 8076, Statesboro, GA 30460-8076, United States. E-mail address: [email protected] (N.G. Murray).
http://dx.doi.org/10.1016/j.ijpsycho.2017.09.018 Received 15 December 2016; Received in revised form 28 July 2017; Accepted 25 September 2017 0167-8760/ © 2017 Elsevier B.V. All rights reserved.
Please cite this article as: Murray, N.G., International Journal of Psychophysiology (2017), http://dx.doi.org/10.1016/j.ijpsycho.2017.09.018
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measures of postural stability can detect changes in athletes who experience RHI over the course of a single athletic season when common clinical tests fail to identify differences. Therefore, the purpose of this study was to investigate changes in static postural control following a season of Division I football compared to a non-head contact sports using both traditional and entropic measures of postural control. It was hypothesized that RHI athletes would experience lower regularity as measured by SampEn following post-season measurements, whereas no differences would be observed in sway magnitude values.
2017). Multifaceted assessment batteries are highly sensitive to acute concussion (≤ 0.96) (Broglio et al., 2014); however, these assessments have failed to identify differences following a season of RHI (Gysland et al., 2012; Miller et al., 2007; McAllister et al., 2012). Conversely, neuroimaging studies have identified alterations in brain physiology (e.g., reduced white matter diffusion) following a single athletic season (Breedlove et al., 2014; McAllister et al., 2012) which may be dose dependent for impacts (Bazarian et al., 2014). Thus, these findings seem to indicate that a season of RHI has adverse effects on the brain when measured by neuroimaging, but not standard concussion related clinical measures. This finding is not particularly surprising, as recent evidence supports the argument that physiological recovery persists well beyond apparent clinical recovery (Kamins et al., 2017). Furthermore, there has been limited investigation of the association between RHI and postural control. Postural control requires the integration of multiple components of the nervous system including the motor, sensory, and cognitive systems, and thus is a marker of neurological health (Winter, 1995). Impaired postural control is a cardinal sign of a concussion and multiple methods exist to quantify the magnitude of instability (Murray et al., 2014; Buckley et al., 2016). While the SCAT-5 recommends the modified Balance Error Scoring System (BESS) (Riemann and Guskiewicz, 2000) despite limited psychometrics (McCrory et al., 2017; Buckley et al., 2017), clinically the original BESS is the most commonly utilized although the substantial limitations, most notably a practice affect, reduces the clinical applicability (Buckley et al., 2015; Burk et al., 2013). The Sensory Organization Test (SOT), a force platform-based exam with challenging sensory perturbations, has identified impaired postural control up to 96 hours post-concussion, but also likely suffers from practice effects (Cavanaugh et al., 2006; Reed-Jones et al., 2014; Buckley et al., 2016). Other more sensitive methods of measuring the postural control system suggest that those with a prior concussion history have impaired postural control reflected as alterations in gait and postural stability (Martini et al., 2011; Buckley et al., 2015). However, these approaches have either failed to identify or have not evaluated changes in postural control from RHI over the course of a football season with both measures actually showing large performance improvements potentially due to the practice effects (Gysland et al., 2012). This suggests that standard clinical postural control measures are insensitive to potential changes, if any, associated with football related RHI. To improve postural control sensitivity and objectively, center of pressure (CoP) kinematics and entropic measures have been utilized to identify impaired postural control well beyond clinically measured recovery (Gao et al., 2011; Sosnoff et al., 2011; Powers et al., 2014; Murray et al., 2014). Traditional measures of postural stability (e.g., sway kinematics) provide information regarding mechanical balance performance, while nonlinear measures may be more sensitive to the dynamics of the underlying neuromuscular strategy (Stergiou and Decker, 2011; Williams et al., 2016). Nonlinear measurements such as approximate entropy (ApEn) and sample entropy (SampEn) may detect subtle differences in the moment-to-moment regularity within the center of pressure time-series and be sensitive to subtle variations in the characteristics of CoP profiles (Cavanaugh et al., 2006; Cavanaugh et al., 2005; Stergiou and Decker, 2011; Williams et al., 2016). Furthermore, research has indicated that these non-linear measures are capable of detecting subtle differences in the time-series center of pressure profile even in the absence of notable differences in traditional measures such as sway excursions (Cavanaugh et al., 2006; Stergiou and Decker, 2011). As such, the use of nonlinear measures in postural stability offers a unique quantitative analysis that provides a distinct perspective relating to the adaptability of the underlying neuromuscular system (Buckley et al., 2016). The RHI-related changes in postural stability over the course of an athletic season have not been previously investigated using linear and non-linear measures. As such, it is of interest to examine if more these
2. Methods 2.1. Participants Fourteen National Collegiate Athletic Association (NCAA) Division I football players wearing instrumented helmets (age = 20.40 ± 1.12 years, concussion history: 0.5 ± 0.8 concussions) and fourteen noncontact competitive cheerleaders (3 male, 11 female; mean age = 19.85 ± 1.21 years, with no prior history of concussion) participated in this study. All participants were free of current musculoskeletal and/or neuromuscular injury beyond the documented concussion injury, had no self-reported history of psychiatric illness, Attention Deficit Hyperactivity Disorder and/or seizures. In addition, no participant had experienced a concussion within the 6 months prior to the initial test nor presented with a clinically unresolved concussion at either testing session and all participants were medically cleared for full unrestricted participation at the time of testing. All participants provided written informed consent as approved by the institutional review board. 2.2. Instrumentation Kinetic data were collected from a single force platform (1000 Hz, AMTI Inc., Model OR-6, Watertown, MA. USA) embedded level with the laboratory floor. Force platform technology is considered the criterion method for CoP measurements (Winter, 1995; Guskiewicz et al., 2000). Football participant's helmets were instrumented with the Helmet Impact Telemetry System (HITS) (Ridell, Chicago, IL. USA) which records the frequency, location, and magnitude of impacts sustained. The helmet unit consists of six uniaxial accelerometers embedded within the helmet (Beckwith et al., 2012). When an impact acceleration of > 10 gravitational forces (g) is registered on any of the six accelerometers, accelerations were recorded for a period of 40 ms (8 ms prior to the impact and 32 ms following the impact) at 1000 Hz (Crisco et al., 2004; Duma et al., 2005; Breedlove et al., 2014). Recorded impact data is transmitted via a signal transducer within the helmet unit to a receiver and laptop on the sideline where data is displayed in real time. Data is also stored within the helmet with a magnitude > 10 g in the event the controller loses contact with or is out of range of the signal receiver (Duma et al., 2005; Crisco et al., 2004). 2.3. Procedures All participants completed testing on two occasions: (1) Within 4 weeks prior to the start of the athletic season (PRE), and (2) within 72 h of the conclusion of their respective athletic seasons (POST). At each testing session, participants completed a single quiet standing trial of 120 seconds barefoot feet together on top of a force platform (Gao et al., 2011). Each participant was instructed to remain as still as possible with their hands resting comfortably at their sides for the entire trial. Raw CoP coordinates were recorded and exported via Vicon Nexus 1.8.5 (Vicon Ltd., Oxford, UK) and further analyzed using a custom software (MATLAB 2016a, MathWorks, Inc., Natick, MA, USA). Postural control was characterized by root mean square (RMS), peak excursion velocity (PEV), and sample entropy (SampEn) in the anteroposterior (AP) and mediolateral (ML) directions. The HIT System Sideline 2
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Fig. 1. Pre-Post Athletic Season Center of Pressure Root Mean Square in the Anteroposterior and Mediolateral directions. Please note a lack of significant interaction for time (pre to post) and between groups as determined by mixed model ANOVA. Notes: PRE = pre-athletic season measurement, POST = post-athletic season measurement.
Reporting System (SRS) was set-up on the sideline of each practice and competition, where subsequent impacts were recorded. If a player traveled outside of the radius of SRS, up to 100 raw impacts were stored within the helmet sensors and downloaded at later date.
burden (CIB) by totaling the linear accelerations over the course of the entire season, separated by practice and competition.
2.4. Data analysis
(1)
An a priori power analysis determined a total of 12 participants per group were deemed sufficient to find significance at the desired power level (1 − β = 0.80). The lowest derived effect size of 0.8 which was calculated from pilot testing and prior research findings (Cavanaugh et al., 2006; Murray et al., 2014) using G*Power (Faul et al., 2007). Six mixed model ANOVAs (2 groups × 2 assessment periods) were analyzed for dependent static measures after they were determined to be normally distributed: RMS A/P, RMS M/L, PEV A/P, PEV M/L, SampEn A/P, and SampEn M/L. A Holm-Bonferroni correction was applied for a priori comparisons (Holm, 1979). An alpha level of 0.05 was set a priori.
(2)
3. Results
2.5. Statistical analysis
Raw AP and ML CoP time-series data were filtered using a fourthorder, zero-lag Butterworth low-pass digital filter with a 50 Hz cut-off frequency. Custom software was used to quantify postural stability from the filtered CoP time-series in each experimental condition using root mean square (RMS), peak excursion velocity (PEV) and sample entropy (SampEn). RMS and PEV were calculated as described by Eqs. (1) and (2).
RMSAP =
1 N
∑ [AP(n)2]1/2
PEVAP = [AP(n + 1) − AP(n)] × fs
where AP is the anteroposterior CoP time-series, N is the length of the time-series, n is an individual observation within AP and fs is the sampling frequency. SampEn was used to quantify the regularity of postural sway via the time-dependent structure of the CoP signal. SampEn values approaching zero reflect a highly regular, predictable time-series, such as a sinusoidal wave. Larger SampEn values are associated with an irregular signal, in which a small chance exists of similar patterns of data being repeated. SampEn is reflects the probability that a sequence of data points, having repeated itself within a tolerance r for a window length m will also repeat itself for m + 1 points, without allowing selfmatches (Richman and Moorman, 2000). In the current study, SampEn was calculated as the negative natural log of an estimate of the conditional probability that epochs of length m (m = 2) match pointwise within a tolerance of r (r = 0.2 ∗ SD) also match the next point. The SampEn values for AP and ML CoP time-series were calculated using the algorithm denoted in Eq. (3):
CON group experienced 649.5 ± 496.8 mean number of impacts, 27.1 ± 3.0 mean linear accelerations, with ≈ 1% of total player impacts exceeded 98 g over the course of the season. There were no significant interactions for group x time RMS in the AP (F(1,26) = 0.633, p = 0.434, η2 = 0.024) and ML (F(1,26) = 2.674, p = 0.114, η2 = 0.093) directions (Fig. 1), PEV in the AP (F(1,26) = 1.313, p = 0.262, η2 = 0.048) and ML (F(1,26) = 0.001, p = 0.977, η2 < 0.001) directions (Fig. 2), and SampEn in the AP (F (1,26) = 0.471, p = 0.499, η2 = 0.018) and ML (F(1,26) < 0.0001, p = 0.984, η2 < 0.001) directions (Fig. 3). In addition, no significant interactions for group were observed for RMS in the AP (F (1,26) = 0.2814, p = 0.105, η2 = 0.098) and ML (F(1,26) = 1.258, p = 0.272, η2 = 0.046) directions, PEV in the AP (F(1,26) = 3.315, p = 0.081, η2 = 0.113) and ML (F(1,26) = 2.278, p = 0.143, η2 = 0.081) directions, and SampEn in the AP (F(1,26) = 0.309, p = 0.583, η2 = 0.012) and ML (F(1,26) = 2.460, p = 0.129, η2 = 0.086) directions.
N −m
SampEn(m, r , N ) = ln
∑i = 1 n′im N −m ∑i = 1
n′im + 1
4. Discussion
(3)
Raw recorded impact data was uploaded to Riddell Redzone System (Riddell, Chicago, IL), where it was processed using a custom algorithm to filter non-head impacts from the data series. Impact accelerations that exceeded 98 g were flagged during the data analysis process in order to track the regularity of impacts. The processed linear accelerations were analyzed for skewness and kurtosis using SPSS (IBM v. 21, Armonk, NY). The processed impact data was determined to be negatively skewed. Therefore the processed data were transformed using a natural log function in Microsoft Office 16 Excel (Microsoft Corporations, Redmond, WA) to correct for skewness. The transformed linear accelerations were then used to calculate cumulative impact
The purpose of this study was to investigate changes in postural control during quiet standing following a season of RHI in Division I football athletes compared to a non-head contact sports. The main finding of this study was no significant group by time interactions for any of the dependent variables. This major finding occurred in spite of the football participants experiencing a mean of 650 head impacts and cumulative linear acceleration loads of over 17,000 g during the course of the season. These results are similar to previously published clinical experiments which have failed to identify changes in neurological health, when measured with typical clinical tools, following a season of collision sports at the college level (Gysland et al., 2012; Miller et al., 3
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Fig. 2. Pre-Post Athletic Season Center of Pressure peak excursion velocity in the Anteroposterior and Mediolateral directions. Please note a lack of significant interaction for time (pre to post) and between groups as determined by mixed model ANOVA. Notes: PRE = preathletic season measurement, POST = post-athletic season measurement.
have focused on other signs and symptoms of concussion, specifically the lack of changes in commonly utilized clinical testing following a competitive season (Kelly et al., 2014, Buckley et al., 2015). Existing evidence revealed no differences on computerized or pen and paper cognitive testing (Miller et al., 2007; Gysland et al., 2012; McAllister et al., 2012), standard assessment of concussion cognitive test (Miller et al., 2007; Gysland et al., 2012), near point convergence (Kawata et al., 2016), or BESS performance (Gysland et al., 2012; Kawata et al., 2016). Similarly, there are generally no meaningful changes in total number of self-reported symptoms or symptom burden (Gysland et al., 2012; McAllister et al., 2012; Kawata et al., 2016). Beyond common clinical screening batteries, no differences were noted in electrophysiological measures of attention (Wilson et al., 2015). Interestingly, several studies have actually demonstrated improvements in balance and cognitive testing following the completion of an athletic season (Miller et al., 2007; Gysland et al., 2012; McAllister et al., 2012; Burk et al., 2013). In contrast to clinical screening batteries, neuroimaging has routinely identified changes in neurological function following a competitive athletic season using a variety of imaging modalities; however a vast majority of this work has been performed in high school athletes (Breedlove et al., 2012; Davenport et al., 2014; Talavage et al., 2014; McAllister et al., 2012). For example, within collegiate football and hockey student-athletes, diffusion tensor imaging identified changes in mean diffusivity of the corpus callosum and in fractional anisotropy of the amygdala suggesting that a relationship exists between repeated head impacts and white matter diffusion metrics (McAllister et al., 2014). Similarly, a small study of collegiate football players reported a
2007; McAllister et al., 2012). In the current study, football participants demonstrated stable performance on an instrumented quiet standing task over the course of the competitive athletic season. Similarly, Gysland et al. (2012) noted a small improvement (1.97) in SOT composite score following a football season which was significantly predicted by the number of years playing college football and the number of impacts to the top of the head. The SOT utilizes force platform technology to assess postural control; however, the test protocol (e.g., 20-s stances periods combined with a moving environment) differs from the quiet stance protocol implemented in the current study. However, both studies demonstrated that there were no apparent postural control impairments following a competitive football season. Previous research also showed that BESS scores, the most commonly utilized clinical tool improved by ~5 errors over the course a season (Gysland et al., 2012; Kelly et al., 2014; Buckley et al., 2015). When considered in conjunction with the current results, these data strongly suggest that participation in an intercollegiate athletic season does not adversely affect performance on static stance test batteries in collision or non-collision sports (American football versus cheerleading). However, these findings are in contrast to previous research findings that have focused longer term investigations of concussion history which have demonstrated impaired postural control reflected as altered gait and postural stability (Martini et al., 2011; Buckley et al., 2015). Future investigations may consider transitional and locomotor tasks which have successfully identified postconcussion impairments (Parker et al., 2007; Buckley et al., 2013; Howell et al., 2016; Oldham et al., 2016). The results of this study are consistent with previous findings that
Fig. 3. Pre-Post Athletic Season Center of Pressure Sample Entropy in the Anteroposterior and Mediolateral directions. Please note a lack of significant interaction for time (pre to post) and between groups as determined by mixed model ANOVA. Notes: PRE = pre-athletic season measurement, POST = post-athletic season measurement.
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Second, the control sample was dominated by females. Research has noted that physical differences between participants such as body mass index, height or weight can influence the reliability of temporal-distance CoP measures such as mean velocity (Chiari et al., 2002). In addition, the amount of trials or the length of data collection (above 60s) aid in minimizing the effects of height and weight and its influence of CoP velocity metrics (Chiari et al., 2002). As such, it is possible that the PEV CoP metric in the current study was influenced by the morphological differences between the groups yet this is unlikely given the 120-s data collection trial employed by the current study and the lack of a difference noted at pre-season testing. Nevertheless, future research should attempt to recruit a more closely matched group of non-contact athletes while taking into account body morphology. Third, the inclusion of participants with a prior history of concussion as well as prior exposure to RHI within the RHI group may have influenced the baseline measures of the study. However, no differences were noted at PRE between the groups which may indicate that these factor are more anecdotal and should be investigated further. Finally, other research has provided a regression model to determine if any dependent variables could predict the independent variable of RHI (Gysland et al., 2012). Unfortunately, our study was underpowered to run a proper regression model without violating its primary assumptions. It is the goal of future research to collect additional data in order to regress the variables to RHI. Overall these results indicate that static postural control was not interrupted following RHI over the course of a single athletic season even when measured with sensitive measures of postural control. The results of this study are consistent with lack of changes in commonly utilized clinical testing following a competitive season. Further research is needed over multiple seasons of RHI along with mid-season assessments.
correlation between white matter changes and helmet impact forces, however these changes were independent of concussion and clinical outcome measures (Bazarian et al., 2014). This pattern of findings, whereby imaging identifies alterations which are not matched by clinical measures raises three possibilities: 1) either the changes do not reflect a pathological state, 2) adaptive neural plasticity allows for successful allocation of neurological resources to achieve successful performance (i.e., compensatory strategies), or 3) they represent a pathological state which current clinical testing fails to identify (Chen et al., 2004; Bazarian et al., 2014). Thus, future studies should continue to investigate the relationships of neuroimaging and with both clinical and laboratory test batteries. The football athletes participating in the current study experienced a moderate number of head impacts (mean = 650) compared to prior studies in which the means are typically about 1000 impacts per year with an expected variability based on positions (McAllister et al., 2014; Reynolds et al., 2016). These noted differences could be due to the limited hitting practices instated by coaching staff at the measured university. The mean linear acceleration for the participants (27.1 g's) was similar to prior football head impact studies (Mihalik et al., 2007; Reynolds et al., 2016). These results suggest that the cumulative load was likely lower for the participants in this study, which may explain the lack of differences in postural control measures. Although no differences were observed in postural control metrics, the results of this study were similar to other research conducted in the area. Powers et al. (2014) and Murray et al. (2014) measured CoP RMS and/or PEV in the AP and ML directions with collegiate athletes (which included American rules football players) at pre-season and following a sport-related concussion. The results of the current study were closely matched (approximately 1 to 2 mm of difference between studies) to Powers et al. and Murray et al. pre-season measurements for both PRE and POST time points. Thus, the results of the current support prior research findings that RHI did not significantly impact the postural system as measured by traditional measures of CoP. Lastly, the non-linear metric of CoP SampEn was notably more regular (≈0.1 unit difference) than other reported studies in the AP and ML direction for the non-contact group at both PRE and POST (Quatman-Yates et al., 2016; Powell and Williams, 2015; Williams et al., 2016; Sosnoff et al., 2011). This could be due to the non-contact group being made up cheerleaders, a sport that requires stability and fluidity between dynamic and static postural states. The unique balance requirements associated with training in cheerleading could provide a distinct training effect that influences the regularity of the time-series data. The contact group SampEn in the AP direction closely matched results (≤ 0.03 unit difference) reported by other studies healthy controls at both PRE and POST (Quatman-Yates et al., 2016; Sosnoff et al., 2011). The other reported SampEn results of this study for both groups in the ML direction at both PRE and POST were similar to other reported literature pre-season measurements (Quatman-Yates et al., 2016, Sosnoff et al., 2011). These results could indicate that the Central Nervous Systems control of posture exerted through supraspinal pathways was not influenced following a season of RHI (Iqbal, 2011; Guskiewicz, 2011; Murray, 2014). Although research has noted brain alterations specifically in the amygdala, that have projections to areas responsible for supraspinal control of posture, following a season of RHI, the postural control in the current study was unchanged from PRE to POST (Iqbal, 2011). As such, the results of this study are consistent with limited changes in commonly utilized clinical testing following a competitive season. Several limitations exist in the current study that should be addressed in future research. First, participants were assessed at pre- and post-season, but no testing was performed in-season or immediately following high magnitude or frequency of impacts. Further research should measure postural control immediately after these impacts throughout the competitive season, as they have been suggested to be associated with elevated risk of concussion (Gysland et al., 2012).
Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. References Bailes, J.E., Petraglia, A.L., Omalu, B.I., Nauman, E., Talavage, T., 2013. Role of subconcussion in repetitive mild traumatic brain injury: a review. J. of Neurosurg. 119 (5), 1235–1245. Bazarian, J.J., Zhu, Tong, Jianhui, Z., Damir, J., Eric, R., Roberts, A., Javien, H., Merchant-Borna, K., Abar, B., Blackman, E.G., 2014. Persistent, long-term cerebral white matter changes after sports-related repetitive head impacts. PLoS One 9, 1–12. Beckwith, J., Greenwald, R., Chu, J., 2012. Measuring head kinematics in football: correlation between the head impact telemetry system and hybrid III headform. Ann. Biomed. Eng. 40, 237–248. Breedlove, E.L., Robinson, M., Poole, V.N., King, J.R., Rosenberger, P., Rasmussen, M., Talavage, T.M., Leverenz, L.J., Nauman, E.A., 2014. Detecting neurocognitive and neurophysiological changes as a result of subconcussive blows among high school football athletes. Athl. Train and Sports Health C 6 (3), 119–127. Breedlove, E.L., Robinson, M., Talavage, T., Morigaki, K., Yoruk, U., Dye, A.E., O'Keefe, K., King, J., Leverenz, L.J., Gilger, J.W., Nauman, E.A., 2012. Biomechanical correlates of symptomatic and asymptomatic neurophysiological impairment in high school football. J. Biomech. 45, 1265–1272. Broglio, S.P., Cantu, R.C., Gioia, G.A., Guskiewicz, K.M., Kutcher, J., Palm, M., Valovich McLeod, T.C., 2014. National athletic trainers' association position statement: management of sport concussion. J. Athl. Train. 49, 245–265. Buckley, T.A., Munkasy, B.A., Clouse, B.P., 2017. Sensitivity and specificity of the modified balance error scoring system in concussed collegiate student athletes. Clin. J. Sport Med (Epub ahead of print). Buckley, T.A., Munkasy, B.A., Tapia-Lovler, T.G., Wikstrom, E.A., 2013. Altered gait termination strategies following a concussion. Gait Posture 38, 549–551. Buckley, T.A., Oldham, J.R., Caccese, J.B., 2016. Postural control deficits identify lingering post-concussion neurological deficits. J. Sport Health Sci. 5, 61–69. Buckley, T.M., Evans, K., Munkasy, B.A., 2015. Lingering impairments in postural control despite clinical concussion recovery. Neurology 84 (pp. S27–001). Burk, J.M., Munkasy, B.A., Joyner, A.B., Buckley, T.A., 2013. Balance error scoring system performance changes after a competitive athletic season. Clin. J. Sport Med. 23, 312–317. Cavanaugh, J.T., Guskiewicz, K., Giuliani, C., Marshall, S., Mercer, V.S., Stergiou, N., 2006. Recovery of postural control after cerebral concussion: new insights using approximate entropy. J. Athl. Train. 41, 305–313.
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October 2016. Br. J. Sports Med. http://dx.doi.org/10.1136/bjsports-2017-097699. (April). McKee, A.C., Cairns, N.J., Dickson, D.W., Folkerth, R.D., Keene, C.D., Litvan, I., 2016. The first NINDS/NIBIB consensus meeting to define neuropathological criteria for the diagnosis of chronic traumatic encephalopathy. Acta Neuropathol. 131, 75–86. McKee, A.C., Stern, R.A., Nowinski, C.J., Stein, T.D., Alvarez, V.E., Daneshvar, D.H., 2013. The spectrum of disease in chronic traumatic encephalopathy. Brain 136, 43–64. McKee, L.A., Yorgos, T., Jarnagin, J., Baugh, C.M., Martin, B., Chaisson, C.E., 2016. Repetitive Head Impact Exposure and Later-life Plasma Total Tau in Former NFL players. Alzheimer's & Dement: Diagnos, Assess & Dis Monitoring. pp. 1–8. Mez, J.M., Daneshvar, D.H., Kiernan, P.T., Abdolmohammadi, B.A., Alvarez, V.E., ... Mckee, A.D., 2017. Clinicopathological evaluation of chronic traumatic encephalopathy in players of american football. J. Am. Med. Assoc. 318, 360–370. Mihalik, J.P., Bell, D.R., Marshall, S.W., Guskiewicz, K.M., 2007. Measurement of head impacts in collegiate football players: an investigation of positional and event-type differences. Neurosurgery 61, 1229–1235. Miller, J.R., Adamson, G.J., Pink, M.M., Sweet, J.C., 2007. Comparison of preseason, midseason, and postseason neurocognitive scores in uninjured collegiate football players. Am. J. Sports Med. 35, 1284–1288. Murray, N.G., 2014. Evaluation of Static and Dynamic Postural Control in Athletes With Concussions During Recovery, ETD Collection for University of Texas, El Paso. Paper: AAI3636291. Murray, N.G., Salvatore, A.P., Powell, D.W., Reed-Jones, R., 2014. Reliability and validity evidence of multiple balance assessments in concussed athletes. J. Athl. Train. 49, 1–10. Oldham, J., Evans, K., Wikstrom, E., Buckley, T., 2016. Conservative gait termination strategy following concussion. Neurology 86, 300–329. Parker, T.M., Osternig, L.R., van Donkelaar, P., Chou, L., 2007. Recovery of cognitive and dynamic motor function following concussion. Br. J. Sports Med. 41, 868–873. Powell, D.W., Williams, D.S., 2015. Athletes trained using stable compared to unstable surfaces exhibit distinct postural control profiles when assessed by traditional and nonlinear measures. Hum. Mov. Sci. 44, 73–80. Powers, K.C., Kalmar, J.M., Cinelli, M.E., 2014. Recovery of static stability following a concussion. Gait Posture 39, 611–614. Quatman-Yates, C.C., Bonnette, S., Hugentobler, J.A., Mede, B., Kiefer, A.W., Kurowski, B.G., Riley, M.A., 2016. Postconcussion postural sway variability changes in youth: the benefit of structural variability analysis. Pediatr. Phys. Ther. 27, 316–327. Reed-Jones, R.J., Murray, N.G., Powell, D.W., 2014. Clinical assessment of balance in adults with concussion. Semin. Speech Lang. 35, 186–195. Reynolds, B.B., Patrie, J., Henry, E.J., Goodkin, H.P., Broshek, D.K., Wintermark, M., Druzgal, T.J., 2016. Practice type effects on head impact in collegiate football. J. Neurosurg. 124, 501–510. Richman, J.S., Moorman, J.R., 2000. Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol. 278, 2039–2049. Riemann, B.L., Guskiewicz, K.M., 2000. Effects of mild head injury on postural stability as measured through clinical balance testing. J. Athl. Train. 35, 19. Solomon, G.S., Kuhn, A.W., Zuckerman, S.L., Casson, I.R., Viano, D.C., Lovell, M.R., Sills, A.K., 2016. Participation in pre-high school football and neurological, neuroradiological, and neuropsychological findings in later life: a study of 45 retired National Football League players. Am. J. Sports Med. 44, 1106–1115. Sosnoff, J.J., Broglio, S.P., Shin, S., Ferrara, M.S., 2011. Previous mild traumatic brain injury and postural-control dynamics. J. Athl. Train. 46 (1), 85–91. Stergiou, N., Decker, L.M., 2011. Human movement variability, nonlinear dynamics, and pathology: is there a connection? Hum. Mov. Sci. 30, 869–888. Talavage, T.M., Nauman, E.A., Breedlove, E.L., Yoruk, U., Dye, A.E., Morigaki, K.E., Feuer, H., Leverenz, L.J., 2014. Functionally-detected cognitive impairment in high school football players without clinically-diagnosed concussion. J. Neurotrauma 31, 327–338. Williams, D.S., Murray, N.G., Powell, D.W., 2016. Training paradigm effects postural responses to balance perturbations. J. Sport Health Sci. 5, 70–76. Wilson, M.J., Harkrider, A.W., King, K.A., 2015. Effect of repetitive, subconcussive impacts on electrophysiological measures of attention. South. Med. J. 108, 559–566. Winter, D.A., 1995. Human balance and posture control during standing and walking. Gait Posture 3, 193–214.
Cavanaugh, J.T., Guskiewicz, K.M., Giuliani, C., Marshall, S., Mercer, V., Stergiou, N., 2005. Detecting altered postural control after cerebral concussion in athletes with normal postural stability. Br. J. Sports Med. 39, 805–811. Chen, J.K., Johnston, K.M., Frey, S., Petrides, M., Worsley, K., Ptito, A., 2004. Functional abnormalities in symptomatic concussed athletes: an fMRI study. NeuroImage 22, 68–82. Chiari, L., Capello, A., Lenzi, D., Della Croce, U., 2002. An improved technique for the extraction of stochastic parameters from stabilograms. Gait Posture 12, 225–234. Crisco, J.J., Chu, J.J., Greenwald, R.M., 2004. An algorithm for estimating acceleration magnitude and impact location using multiple nonorthogonal single-axis accelerometers. J. Biomech. Eng. 126, 849–854. Davenport, E.M., Whitlow, C.T., Urban, J.E., Espeland, M.A., Jung, Y., Rosenbaum, D.A., Gioia, G.A., Powers, A.K., Stitzel, J.D., Maldjian, J.A., 2014. Abnormal white matter integrity related to head impact exposure in a season of high school varsity football. J. Neurotrauma 31, 1617–1624. Duma, S.M., Manoogian, S.J., Bussone, W.R., 2005. Analysis of real-time head accelerations in collegiate football players. Clin. J. Sport Med. 15, 3–8. Faul, F., Erdfelder, E., Lang, A.G., Buchner, A., 2007. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav. Res. Methods 39, 175–191. Gao, J., Hu, J., Buckley, T.A., White, K., Hass, C.J., 2011. Shannon and Renyi entropies to classify effects of mild traumatic brain injury on postural sway. PLoS One 6, 1–8. Guskiewicz, K.M., 2011. Balance assessment in the management of sport-related concussion. Clin. J. Sport Med. 30, 89–102. Guskiewicz, K.M., Marshall, S.W., Bailes, J., McCrea, M., Harding Jr., H.P., Matthews, A., Mihalik, J.R., Cantu, R.C., 2007. Recurrent concussion and risk of depression in retired professional football players. Med. Sci. Sports Exerc. 39, 903–909. Guskiewicz, K.M., Marshall, S.W., Bailes, J., et al., 2005. Association between recurrent concussion and late-life cognitive impairment in retired professional football players. Neurosurgery 57, 719–726. Guskiewicz, K.M., Weaver, N.l., Padua, D.A., Garrett Jr., W.E., 2000. Epidemiology of concussion in collegiate and high school football players. Am. J. Sports Med. 28, 643–650. Gysland, S.M., Mihalik, J., Register-Mihalik, J.K., Trulock, S.C., Shields, E.W., Guskiewicz, K.M., 2012. The relationship between subconcussive impacts and concussion history on clinical measures of neurologic function in collegiate football players. Ann. Biomed. Eng. 40, 14–22. Holm, S., 1979. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 65–70. Howell, D.R., Hanson, E., Sugimoto, D., Stracciolini, A., Meehan III, W.P., 2016. Assessment of the postural stability of female and male athletes. Clin. J. Sport Med. Iqbal, K., 2011. Mechanisms and models of postural stability and control. Engin. in Med. and Biology Society 7837–7840. Kamins, J., Bigler, E., Covassin, T., Henry, L., Kemp, S., Leddy, J.J., Mayer, A., et al., 2017. What is the physiological time to recovery after concussion? A systematic review. Br. J. Sport Med. 51, 935–940. http://dx.doi.org/10.1136/bjsports-2016097464. Kawata, K., Tierney, R., Phillips, J., Jeka, J.J., 2016. Effect of repetitive sub-concussive head impacts on ocular near point of convergence. Int. J. Sports Med. 37, 405–410. Kelly, K.C., Jordan, E.M., Joyner, A.B., Burdette, G.T., Buckley, T.A., 2014. National collegiate athletic association division i athletic trainers' concussion-management practice patterns. J. Athl. Train. 49, 665–673. Martini, D.N., Sabin, M.J., DePesa, S.A., Leal, E.W., Negrete, T.N., Sosnoff, J.J., Broglio, S.P., 2011. The chronic effects of concussion on gait. Arch. Phys. Med. Rehabil. 92, 585–589. McAllister, T.W., Flashman, L.A., Maerlender, A., Greenwald, R.M., Beckwith, J.G., Tosteson, T.D., Crisco, J.J., Brolinson, P.G., Duma, S.M., Duhaime, A.C., Grove, M.R., 2012. Cognitive effects of one season of head impacts in a cohort of collegiate contact sport athletes. Neurology 78, 1777–1784. McAllister, T.W., Ford, J.C., Flashman, L.A., Maerlender, A., Greenwald, R.M., Beckwith, J.G., Bolander, R.P., Tosteson, T.D., Turco, J.H., Raman, R., Jain, S., 2014. Effect of head impacts on diffusivity measures in a cohort of collegiate contact sport athletes. Neurology 82, 63–69. McCrory, P., Meeuwisse, W., Dvorak, J., et al., 2017. Consensus statement on concussion in sport-the 5(th) international conference on concussion in sport held in Berlin,
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Contents lists available at ScienceDirect
International Journal of Psychophysiology journal homepage: www.elsevier.com/locate/ijpsycho
Repetitive head impacts do not affect postural control following a competitive athletic season Nicholas G. Murraya,⁎, Katelyn E. Grimesa, Eric D. Shifletta, Barry A. Munkasya, Nathan R. D'Amicob, Megan E. Mormilea, Douglas W. Powellc, Thomas A. Buckleyd,e a
School of Health and Kinesiology, College of Health and Human Services, Georgia Southern University, P.O. Box 8073, Statesboro, GA 30458, United States Department of Health, Human Performance, and Recreation, Office for Sport Concussion Research, University of Arkansas, Fayetteville, AR, United States c School of Health Studies, University of Memphis, 106 Elma Roane Fieldhouse, Memphis, TN 28152, United States d Department of Kinesiology and Applied Physiology, University of Delaware, 540 College Avenue, Newark, DE 19716, United States e Interdisciplinary Program in Biomechanics and Movement Science, University of Delaware, 540 College Avenue, Newark, DE 19716, United States b
A R T I C L E I N F O
A B S T R A C T
Keywords: Subconcussive Repetitive head impacts Concussion Postural control
Evidence suggests that Repetitive Head Impacts (RHI) directly influence the brain over the course of a single contact collision season yet do not significantly impact a player's performance on the standard clinical concussion assessment battery. The purpose of this study was to investigate changes in static postural control after a season of RHI in Division I football athletes using more sensitive measures of postural control as compared to a non-head contact sports. Fourteen Division I football players (CON) (age = 20.4 ± 1.12 years) and fourteen non-contact athletes (NON) (2 male, 11 female; age = 19.85 ± 1.21 years) completed a single trial of two minutes of eyes open quiet upright stance on a force platform (1000 Hz) prior to athletic participation (PRE) and at the end of the athletic season (POST). All CON athletes wore helmets outfitted with Head Impact Telemetry (HIT) sensors and total number of RHI and linear accelerations forces of each RHI were recorded. Center of pressure root mean square (RMS), peak excursion velocity (PEV), and sample entropy (SampEn) in the anteroposterior (AP) and mediolateral (ML) directions were calculated. CON group experienced 649.5 ± 496.8 mean number of impacts, 27.1 ± 3.0 mean linear accelerations, with ≈ 1% of total player impacts exceeded 98 g over the course of the season. There were no significant interactions for group x time RMS in the AP (p = 0.434) and ML (p = 0.114) directions, PEV in the AP (p = 0.262) and ML (p = 0.977) directions, and SampEn in the AP (p = 0.499) and ML (p = 0.984) directions. In addition, no significant interactions for group were observed for RMS in the AP (p = 0.105) and ML (p = 0.272) directions, PEV in the AP (p = 0.081) and ML (p = 0.143) directions, and SampEn in the AP (p = 0.583) and ML (p = 0.129) directions. These results suggest that over the course of a single competitive season, RHI do not negatively impact postural control even when measured with sensitive non-linear metrics.
1. Introduction Sport-related concussions are a major health concern that effect all levels of athletic play including measurable deficits such as postural instability, cognitive impairment, and neurologic symptoms (e.g., headaches, dizziness) (McCrory et al., 2017). The effect of multiple concussions over the course of an athletic career can lead to an increased risk of early onset Alzheimer's disease, mild cognitive impairment, depression, and overall later-life cognitive impairments (Guskiewicz et al., 2005; Guskiewicz et al., 2007). However, repetitive head impacts (RHI), independent of concussion, have been associated with the development of the progressive degenerative brain disease
⁎
known as Chronic Traumatic Encephalopathy (CTE) (McKee et al., 2016; McKee et al., 2013; McKee et al., 2016). This is supported by a recent report that has confirmed the presence of CTE in 99% of postmortem retired National Football League (NFL) players and 87% of all American football players across different levels of play (Mez et al., 2017); however these results must be viewed cautiously given an acknowledged selection bias in the studies. However, the role of RHI in the development neurodegenerative pathologies remains inconclusive (Bailes et al., 2013; Solomon et al., 2016). Standard clinical post-concussion assessments include postural stability, cognition, and self-reported symptoms which are components of the Sport Concussion Assessment Tool-5 (SCAT-5) (McCrory et al.,
Corresponding author at: School of Health & Kinesiology, Georgia Southern University, P.O. Box 8076, Statesboro, GA 30460-8076, United States. E-mail address: [email protected] (N.G. Murray).
http://dx.doi.org/10.1016/j.ijpsycho.2017.09.018 Received 15 December 2016; Received in revised form 28 July 2017; Accepted 25 September 2017 0167-8760/ © 2017 Elsevier B.V. All rights reserved.
Please cite this article as: Murray, N.G., International Journal of Psychophysiology (2017), http://dx.doi.org/10.1016/j.ijpsycho.2017.09.018
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measures of postural stability can detect changes in athletes who experience RHI over the course of a single athletic season when common clinical tests fail to identify differences. Therefore, the purpose of this study was to investigate changes in static postural control following a season of Division I football compared to a non-head contact sports using both traditional and entropic measures of postural control. It was hypothesized that RHI athletes would experience lower regularity as measured by SampEn following post-season measurements, whereas no differences would be observed in sway magnitude values.
2017). Multifaceted assessment batteries are highly sensitive to acute concussion (≤ 0.96) (Broglio et al., 2014); however, these assessments have failed to identify differences following a season of RHI (Gysland et al., 2012; Miller et al., 2007; McAllister et al., 2012). Conversely, neuroimaging studies have identified alterations in brain physiology (e.g., reduced white matter diffusion) following a single athletic season (Breedlove et al., 2014; McAllister et al., 2012) which may be dose dependent for impacts (Bazarian et al., 2014). Thus, these findings seem to indicate that a season of RHI has adverse effects on the brain when measured by neuroimaging, but not standard concussion related clinical measures. This finding is not particularly surprising, as recent evidence supports the argument that physiological recovery persists well beyond apparent clinical recovery (Kamins et al., 2017). Furthermore, there has been limited investigation of the association between RHI and postural control. Postural control requires the integration of multiple components of the nervous system including the motor, sensory, and cognitive systems, and thus is a marker of neurological health (Winter, 1995). Impaired postural control is a cardinal sign of a concussion and multiple methods exist to quantify the magnitude of instability (Murray et al., 2014; Buckley et al., 2016). While the SCAT-5 recommends the modified Balance Error Scoring System (BESS) (Riemann and Guskiewicz, 2000) despite limited psychometrics (McCrory et al., 2017; Buckley et al., 2017), clinically the original BESS is the most commonly utilized although the substantial limitations, most notably a practice affect, reduces the clinical applicability (Buckley et al., 2015; Burk et al., 2013). The Sensory Organization Test (SOT), a force platform-based exam with challenging sensory perturbations, has identified impaired postural control up to 96 hours post-concussion, but also likely suffers from practice effects (Cavanaugh et al., 2006; Reed-Jones et al., 2014; Buckley et al., 2016). Other more sensitive methods of measuring the postural control system suggest that those with a prior concussion history have impaired postural control reflected as alterations in gait and postural stability (Martini et al., 2011; Buckley et al., 2015). However, these approaches have either failed to identify or have not evaluated changes in postural control from RHI over the course of a football season with both measures actually showing large performance improvements potentially due to the practice effects (Gysland et al., 2012). This suggests that standard clinical postural control measures are insensitive to potential changes, if any, associated with football related RHI. To improve postural control sensitivity and objectively, center of pressure (CoP) kinematics and entropic measures have been utilized to identify impaired postural control well beyond clinically measured recovery (Gao et al., 2011; Sosnoff et al., 2011; Powers et al., 2014; Murray et al., 2014). Traditional measures of postural stability (e.g., sway kinematics) provide information regarding mechanical balance performance, while nonlinear measures may be more sensitive to the dynamics of the underlying neuromuscular strategy (Stergiou and Decker, 2011; Williams et al., 2016). Nonlinear measurements such as approximate entropy (ApEn) and sample entropy (SampEn) may detect subtle differences in the moment-to-moment regularity within the center of pressure time-series and be sensitive to subtle variations in the characteristics of CoP profiles (Cavanaugh et al., 2006; Cavanaugh et al., 2005; Stergiou and Decker, 2011; Williams et al., 2016). Furthermore, research has indicated that these non-linear measures are capable of detecting subtle differences in the time-series center of pressure profile even in the absence of notable differences in traditional measures such as sway excursions (Cavanaugh et al., 2006; Stergiou and Decker, 2011). As such, the use of nonlinear measures in postural stability offers a unique quantitative analysis that provides a distinct perspective relating to the adaptability of the underlying neuromuscular system (Buckley et al., 2016). The RHI-related changes in postural stability over the course of an athletic season have not been previously investigated using linear and non-linear measures. As such, it is of interest to examine if more these
2. Methods 2.1. Participants Fourteen National Collegiate Athletic Association (NCAA) Division I football players wearing instrumented helmets (age = 20.40 ± 1.12 years, concussion history: 0.5 ± 0.8 concussions) and fourteen noncontact competitive cheerleaders (3 male, 11 female; mean age = 19.85 ± 1.21 years, with no prior history of concussion) participated in this study. All participants were free of current musculoskeletal and/or neuromuscular injury beyond the documented concussion injury, had no self-reported history of psychiatric illness, Attention Deficit Hyperactivity Disorder and/or seizures. In addition, no participant had experienced a concussion within the 6 months prior to the initial test nor presented with a clinically unresolved concussion at either testing session and all participants were medically cleared for full unrestricted participation at the time of testing. All participants provided written informed consent as approved by the institutional review board. 2.2. Instrumentation Kinetic data were collected from a single force platform (1000 Hz, AMTI Inc., Model OR-6, Watertown, MA. USA) embedded level with the laboratory floor. Force platform technology is considered the criterion method for CoP measurements (Winter, 1995; Guskiewicz et al., 2000). Football participant's helmets were instrumented with the Helmet Impact Telemetry System (HITS) (Ridell, Chicago, IL. USA) which records the frequency, location, and magnitude of impacts sustained. The helmet unit consists of six uniaxial accelerometers embedded within the helmet (Beckwith et al., 2012). When an impact acceleration of > 10 gravitational forces (g) is registered on any of the six accelerometers, accelerations were recorded for a period of 40 ms (8 ms prior to the impact and 32 ms following the impact) at 1000 Hz (Crisco et al., 2004; Duma et al., 2005; Breedlove et al., 2014). Recorded impact data is transmitted via a signal transducer within the helmet unit to a receiver and laptop on the sideline where data is displayed in real time. Data is also stored within the helmet with a magnitude > 10 g in the event the controller loses contact with or is out of range of the signal receiver (Duma et al., 2005; Crisco et al., 2004). 2.3. Procedures All participants completed testing on two occasions: (1) Within 4 weeks prior to the start of the athletic season (PRE), and (2) within 72 h of the conclusion of their respective athletic seasons (POST). At each testing session, participants completed a single quiet standing trial of 120 seconds barefoot feet together on top of a force platform (Gao et al., 2011). Each participant was instructed to remain as still as possible with their hands resting comfortably at their sides for the entire trial. Raw CoP coordinates were recorded and exported via Vicon Nexus 1.8.5 (Vicon Ltd., Oxford, UK) and further analyzed using a custom software (MATLAB 2016a, MathWorks, Inc., Natick, MA, USA). Postural control was characterized by root mean square (RMS), peak excursion velocity (PEV), and sample entropy (SampEn) in the anteroposterior (AP) and mediolateral (ML) directions. The HIT System Sideline 2
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Fig. 1. Pre-Post Athletic Season Center of Pressure Root Mean Square in the Anteroposterior and Mediolateral directions. Please note a lack of significant interaction for time (pre to post) and between groups as determined by mixed model ANOVA. Notes: PRE = pre-athletic season measurement, POST = post-athletic season measurement.
Reporting System (SRS) was set-up on the sideline of each practice and competition, where subsequent impacts were recorded. If a player traveled outside of the radius of SRS, up to 100 raw impacts were stored within the helmet sensors and downloaded at later date.
burden (CIB) by totaling the linear accelerations over the course of the entire season, separated by practice and competition.
2.4. Data analysis
(1)
An a priori power analysis determined a total of 12 participants per group were deemed sufficient to find significance at the desired power level (1 − β = 0.80). The lowest derived effect size of 0.8 which was calculated from pilot testing and prior research findings (Cavanaugh et al., 2006; Murray et al., 2014) using G*Power (Faul et al., 2007). Six mixed model ANOVAs (2 groups × 2 assessment periods) were analyzed for dependent static measures after they were determined to be normally distributed: RMS A/P, RMS M/L, PEV A/P, PEV M/L, SampEn A/P, and SampEn M/L. A Holm-Bonferroni correction was applied for a priori comparisons (Holm, 1979). An alpha level of 0.05 was set a priori.
(2)
3. Results
2.5. Statistical analysis
Raw AP and ML CoP time-series data were filtered using a fourthorder, zero-lag Butterworth low-pass digital filter with a 50 Hz cut-off frequency. Custom software was used to quantify postural stability from the filtered CoP time-series in each experimental condition using root mean square (RMS), peak excursion velocity (PEV) and sample entropy (SampEn). RMS and PEV were calculated as described by Eqs. (1) and (2).
RMSAP =
1 N
∑ [AP(n)2]1/2
PEVAP = [AP(n + 1) − AP(n)] × fs
where AP is the anteroposterior CoP time-series, N is the length of the time-series, n is an individual observation within AP and fs is the sampling frequency. SampEn was used to quantify the regularity of postural sway via the time-dependent structure of the CoP signal. SampEn values approaching zero reflect a highly regular, predictable time-series, such as a sinusoidal wave. Larger SampEn values are associated with an irregular signal, in which a small chance exists of similar patterns of data being repeated. SampEn is reflects the probability that a sequence of data points, having repeated itself within a tolerance r for a window length m will also repeat itself for m + 1 points, without allowing selfmatches (Richman and Moorman, 2000). In the current study, SampEn was calculated as the negative natural log of an estimate of the conditional probability that epochs of length m (m = 2) match pointwise within a tolerance of r (r = 0.2 ∗ SD) also match the next point. The SampEn values for AP and ML CoP time-series were calculated using the algorithm denoted in Eq. (3):
CON group experienced 649.5 ± 496.8 mean number of impacts, 27.1 ± 3.0 mean linear accelerations, with ≈ 1% of total player impacts exceeded 98 g over the course of the season. There were no significant interactions for group x time RMS in the AP (F(1,26) = 0.633, p = 0.434, η2 = 0.024) and ML (F(1,26) = 2.674, p = 0.114, η2 = 0.093) directions (Fig. 1), PEV in the AP (F(1,26) = 1.313, p = 0.262, η2 = 0.048) and ML (F(1,26) = 0.001, p = 0.977, η2 < 0.001) directions (Fig. 2), and SampEn in the AP (F (1,26) = 0.471, p = 0.499, η2 = 0.018) and ML (F(1,26) < 0.0001, p = 0.984, η2 < 0.001) directions (Fig. 3). In addition, no significant interactions for group were observed for RMS in the AP (F (1,26) = 0.2814, p = 0.105, η2 = 0.098) and ML (F(1,26) = 1.258, p = 0.272, η2 = 0.046) directions, PEV in the AP (F(1,26) = 3.315, p = 0.081, η2 = 0.113) and ML (F(1,26) = 2.278, p = 0.143, η2 = 0.081) directions, and SampEn in the AP (F(1,26) = 0.309, p = 0.583, η2 = 0.012) and ML (F(1,26) = 2.460, p = 0.129, η2 = 0.086) directions.
N −m
SampEn(m, r , N ) = ln
∑i = 1 n′im N −m ∑i = 1
n′im + 1
4. Discussion
(3)
Raw recorded impact data was uploaded to Riddell Redzone System (Riddell, Chicago, IL), where it was processed using a custom algorithm to filter non-head impacts from the data series. Impact accelerations that exceeded 98 g were flagged during the data analysis process in order to track the regularity of impacts. The processed linear accelerations were analyzed for skewness and kurtosis using SPSS (IBM v. 21, Armonk, NY). The processed impact data was determined to be negatively skewed. Therefore the processed data were transformed using a natural log function in Microsoft Office 16 Excel (Microsoft Corporations, Redmond, WA) to correct for skewness. The transformed linear accelerations were then used to calculate cumulative impact
The purpose of this study was to investigate changes in postural control during quiet standing following a season of RHI in Division I football athletes compared to a non-head contact sports. The main finding of this study was no significant group by time interactions for any of the dependent variables. This major finding occurred in spite of the football participants experiencing a mean of 650 head impacts and cumulative linear acceleration loads of over 17,000 g during the course of the season. These results are similar to previously published clinical experiments which have failed to identify changes in neurological health, when measured with typical clinical tools, following a season of collision sports at the college level (Gysland et al., 2012; Miller et al., 3
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Fig. 2. Pre-Post Athletic Season Center of Pressure peak excursion velocity in the Anteroposterior and Mediolateral directions. Please note a lack of significant interaction for time (pre to post) and between groups as determined by mixed model ANOVA. Notes: PRE = preathletic season measurement, POST = post-athletic season measurement.
have focused on other signs and symptoms of concussion, specifically the lack of changes in commonly utilized clinical testing following a competitive season (Kelly et al., 2014, Buckley et al., 2015). Existing evidence revealed no differences on computerized or pen and paper cognitive testing (Miller et al., 2007; Gysland et al., 2012; McAllister et al., 2012), standard assessment of concussion cognitive test (Miller et al., 2007; Gysland et al., 2012), near point convergence (Kawata et al., 2016), or BESS performance (Gysland et al., 2012; Kawata et al., 2016). Similarly, there are generally no meaningful changes in total number of self-reported symptoms or symptom burden (Gysland et al., 2012; McAllister et al., 2012; Kawata et al., 2016). Beyond common clinical screening batteries, no differences were noted in electrophysiological measures of attention (Wilson et al., 2015). Interestingly, several studies have actually demonstrated improvements in balance and cognitive testing following the completion of an athletic season (Miller et al., 2007; Gysland et al., 2012; McAllister et al., 2012; Burk et al., 2013). In contrast to clinical screening batteries, neuroimaging has routinely identified changes in neurological function following a competitive athletic season using a variety of imaging modalities; however a vast majority of this work has been performed in high school athletes (Breedlove et al., 2012; Davenport et al., 2014; Talavage et al., 2014; McAllister et al., 2012). For example, within collegiate football and hockey student-athletes, diffusion tensor imaging identified changes in mean diffusivity of the corpus callosum and in fractional anisotropy of the amygdala suggesting that a relationship exists between repeated head impacts and white matter diffusion metrics (McAllister et al., 2014). Similarly, a small study of collegiate football players reported a
2007; McAllister et al., 2012). In the current study, football participants demonstrated stable performance on an instrumented quiet standing task over the course of the competitive athletic season. Similarly, Gysland et al. (2012) noted a small improvement (1.97) in SOT composite score following a football season which was significantly predicted by the number of years playing college football and the number of impacts to the top of the head. The SOT utilizes force platform technology to assess postural control; however, the test protocol (e.g., 20-s stances periods combined with a moving environment) differs from the quiet stance protocol implemented in the current study. However, both studies demonstrated that there were no apparent postural control impairments following a competitive football season. Previous research also showed that BESS scores, the most commonly utilized clinical tool improved by ~5 errors over the course a season (Gysland et al., 2012; Kelly et al., 2014; Buckley et al., 2015). When considered in conjunction with the current results, these data strongly suggest that participation in an intercollegiate athletic season does not adversely affect performance on static stance test batteries in collision or non-collision sports (American football versus cheerleading). However, these findings are in contrast to previous research findings that have focused longer term investigations of concussion history which have demonstrated impaired postural control reflected as altered gait and postural stability (Martini et al., 2011; Buckley et al., 2015). Future investigations may consider transitional and locomotor tasks which have successfully identified postconcussion impairments (Parker et al., 2007; Buckley et al., 2013; Howell et al., 2016; Oldham et al., 2016). The results of this study are consistent with previous findings that
Fig. 3. Pre-Post Athletic Season Center of Pressure Sample Entropy in the Anteroposterior and Mediolateral directions. Please note a lack of significant interaction for time (pre to post) and between groups as determined by mixed model ANOVA. Notes: PRE = pre-athletic season measurement, POST = post-athletic season measurement.
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Second, the control sample was dominated by females. Research has noted that physical differences between participants such as body mass index, height or weight can influence the reliability of temporal-distance CoP measures such as mean velocity (Chiari et al., 2002). In addition, the amount of trials or the length of data collection (above 60s) aid in minimizing the effects of height and weight and its influence of CoP velocity metrics (Chiari et al., 2002). As such, it is possible that the PEV CoP metric in the current study was influenced by the morphological differences between the groups yet this is unlikely given the 120-s data collection trial employed by the current study and the lack of a difference noted at pre-season testing. Nevertheless, future research should attempt to recruit a more closely matched group of non-contact athletes while taking into account body morphology. Third, the inclusion of participants with a prior history of concussion as well as prior exposure to RHI within the RHI group may have influenced the baseline measures of the study. However, no differences were noted at PRE between the groups which may indicate that these factor are more anecdotal and should be investigated further. Finally, other research has provided a regression model to determine if any dependent variables could predict the independent variable of RHI (Gysland et al., 2012). Unfortunately, our study was underpowered to run a proper regression model without violating its primary assumptions. It is the goal of future research to collect additional data in order to regress the variables to RHI. Overall these results indicate that static postural control was not interrupted following RHI over the course of a single athletic season even when measured with sensitive measures of postural control. The results of this study are consistent with lack of changes in commonly utilized clinical testing following a competitive season. Further research is needed over multiple seasons of RHI along with mid-season assessments.
correlation between white matter changes and helmet impact forces, however these changes were independent of concussion and clinical outcome measures (Bazarian et al., 2014). This pattern of findings, whereby imaging identifies alterations which are not matched by clinical measures raises three possibilities: 1) either the changes do not reflect a pathological state, 2) adaptive neural plasticity allows for successful allocation of neurological resources to achieve successful performance (i.e., compensatory strategies), or 3) they represent a pathological state which current clinical testing fails to identify (Chen et al., 2004; Bazarian et al., 2014). Thus, future studies should continue to investigate the relationships of neuroimaging and with both clinical and laboratory test batteries. The football athletes participating in the current study experienced a moderate number of head impacts (mean = 650) compared to prior studies in which the means are typically about 1000 impacts per year with an expected variability based on positions (McAllister et al., 2014; Reynolds et al., 2016). These noted differences could be due to the limited hitting practices instated by coaching staff at the measured university. The mean linear acceleration for the participants (27.1 g's) was similar to prior football head impact studies (Mihalik et al., 2007; Reynolds et al., 2016). These results suggest that the cumulative load was likely lower for the participants in this study, which may explain the lack of differences in postural control measures. Although no differences were observed in postural control metrics, the results of this study were similar to other research conducted in the area. Powers et al. (2014) and Murray et al. (2014) measured CoP RMS and/or PEV in the AP and ML directions with collegiate athletes (which included American rules football players) at pre-season and following a sport-related concussion. The results of the current study were closely matched (approximately 1 to 2 mm of difference between studies) to Powers et al. and Murray et al. pre-season measurements for both PRE and POST time points. Thus, the results of the current support prior research findings that RHI did not significantly impact the postural system as measured by traditional measures of CoP. Lastly, the non-linear metric of CoP SampEn was notably more regular (≈0.1 unit difference) than other reported studies in the AP and ML direction for the non-contact group at both PRE and POST (Quatman-Yates et al., 2016; Powell and Williams, 2015; Williams et al., 2016; Sosnoff et al., 2011). This could be due to the non-contact group being made up cheerleaders, a sport that requires stability and fluidity between dynamic and static postural states. The unique balance requirements associated with training in cheerleading could provide a distinct training effect that influences the regularity of the time-series data. The contact group SampEn in the AP direction closely matched results (≤ 0.03 unit difference) reported by other studies healthy controls at both PRE and POST (Quatman-Yates et al., 2016; Sosnoff et al., 2011). The other reported SampEn results of this study for both groups in the ML direction at both PRE and POST were similar to other reported literature pre-season measurements (Quatman-Yates et al., 2016, Sosnoff et al., 2011). These results could indicate that the Central Nervous Systems control of posture exerted through supraspinal pathways was not influenced following a season of RHI (Iqbal, 2011; Guskiewicz, 2011; Murray, 2014). Although research has noted brain alterations specifically in the amygdala, that have projections to areas responsible for supraspinal control of posture, following a season of RHI, the postural control in the current study was unchanged from PRE to POST (Iqbal, 2011). As such, the results of this study are consistent with limited changes in commonly utilized clinical testing following a competitive season. Several limitations exist in the current study that should be addressed in future research. First, participants were assessed at pre- and post-season, but no testing was performed in-season or immediately following high magnitude or frequency of impacts. Further research should measure postural control immediately after these impacts throughout the competitive season, as they have been suggested to be associated with elevated risk of concussion (Gysland et al., 2012).
Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. References Bailes, J.E., Petraglia, A.L., Omalu, B.I., Nauman, E., Talavage, T., 2013. Role of subconcussion in repetitive mild traumatic brain injury: a review. J. of Neurosurg. 119 (5), 1235–1245. Bazarian, J.J., Zhu, Tong, Jianhui, Z., Damir, J., Eric, R., Roberts, A., Javien, H., Merchant-Borna, K., Abar, B., Blackman, E.G., 2014. Persistent, long-term cerebral white matter changes after sports-related repetitive head impacts. PLoS One 9, 1–12. Beckwith, J., Greenwald, R., Chu, J., 2012. Measuring head kinematics in football: correlation between the head impact telemetry system and hybrid III headform. Ann. Biomed. Eng. 40, 237–248. Breedlove, E.L., Robinson, M., Poole, V.N., King, J.R., Rosenberger, P., Rasmussen, M., Talavage, T.M., Leverenz, L.J., Nauman, E.A., 2014. Detecting neurocognitive and neurophysiological changes as a result of subconcussive blows among high school football athletes. Athl. Train and Sports Health C 6 (3), 119–127. Breedlove, E.L., Robinson, M., Talavage, T., Morigaki, K., Yoruk, U., Dye, A.E., O'Keefe, K., King, J., Leverenz, L.J., Gilger, J.W., Nauman, E.A., 2012. Biomechanical correlates of symptomatic and asymptomatic neurophysiological impairment in high school football. J. Biomech. 45, 1265–1272. Broglio, S.P., Cantu, R.C., Gioia, G.A., Guskiewicz, K.M., Kutcher, J., Palm, M., Valovich McLeod, T.C., 2014. National athletic trainers' association position statement: management of sport concussion. J. Athl. Train. 49, 245–265. Buckley, T.A., Munkasy, B.A., Clouse, B.P., 2017. Sensitivity and specificity of the modified balance error scoring system in concussed collegiate student athletes. Clin. J. Sport Med (Epub ahead of print). Buckley, T.A., Munkasy, B.A., Tapia-Lovler, T.G., Wikstrom, E.A., 2013. Altered gait termination strategies following a concussion. Gait Posture 38, 549–551. Buckley, T.A., Oldham, J.R., Caccese, J.B., 2016. Postural control deficits identify lingering post-concussion neurological deficits. J. Sport Health Sci. 5, 61–69. Buckley, T.M., Evans, K., Munkasy, B.A., 2015. Lingering impairments in postural control despite clinical concussion recovery. Neurology 84 (pp. S27–001). Burk, J.M., Munkasy, B.A., Joyner, A.B., Buckley, T.A., 2013. Balance error scoring system performance changes after a competitive athletic season. Clin. J. Sport Med. 23, 312–317. Cavanaugh, J.T., Guskiewicz, K., Giuliani, C., Marshall, S., Mercer, V.S., Stergiou, N., 2006. Recovery of postural control after cerebral concussion: new insights using approximate entropy. J. Athl. Train. 41, 305–313.
5
International Journal of Psychophysiology xxx (xxxx) xxx–xxx
N.G. Murray et al.
October 2016. Br. J. Sports Med. http://dx.doi.org/10.1136/bjsports-2017-097699. (April). McKee, A.C., Cairns, N.J., Dickson, D.W., Folkerth, R.D., Keene, C.D., Litvan, I., 2016. The first NINDS/NIBIB consensus meeting to define neuropathological criteria for the diagnosis of chronic traumatic encephalopathy. Acta Neuropathol. 131, 75–86. McKee, A.C., Stern, R.A., Nowinski, C.J., Stein, T.D., Alvarez, V.E., Daneshvar, D.H., 2013. The spectrum of disease in chronic traumatic encephalopathy. Brain 136, 43–64. McKee, L.A., Yorgos, T., Jarnagin, J., Baugh, C.M., Martin, B., Chaisson, C.E., 2016. Repetitive Head Impact Exposure and Later-life Plasma Total Tau in Former NFL players. Alzheimer's & Dement: Diagnos, Assess & Dis Monitoring. pp. 1–8. Mez, J.M., Daneshvar, D.H., Kiernan, P.T., Abdolmohammadi, B.A., Alvarez, V.E., ... Mckee, A.D., 2017. Clinicopathological evaluation of chronic traumatic encephalopathy in players of american football. J. Am. Med. Assoc. 318, 360–370. Mihalik, J.P., Bell, D.R., Marshall, S.W., Guskiewicz, K.M., 2007. Measurement of head impacts in collegiate football players: an investigation of positional and event-type differences. Neurosurgery 61, 1229–1235. Miller, J.R., Adamson, G.J., Pink, M.M., Sweet, J.C., 2007. Comparison of preseason, midseason, and postseason neurocognitive scores in uninjured collegiate football players. Am. J. Sports Med. 35, 1284–1288. Murray, N.G., 2014. Evaluation of Static and Dynamic Postural Control in Athletes With Concussions During Recovery, ETD Collection for University of Texas, El Paso. Paper: AAI3636291. Murray, N.G., Salvatore, A.P., Powell, D.W., Reed-Jones, R., 2014. Reliability and validity evidence of multiple balance assessments in concussed athletes. J. Athl. Train. 49, 1–10. Oldham, J., Evans, K., Wikstrom, E., Buckley, T., 2016. Conservative gait termination strategy following concussion. Neurology 86, 300–329. Parker, T.M., Osternig, L.R., van Donkelaar, P., Chou, L., 2007. Recovery of cognitive and dynamic motor function following concussion. Br. J. Sports Med. 41, 868–873. Powell, D.W., Williams, D.S., 2015. Athletes trained using stable compared to unstable surfaces exhibit distinct postural control profiles when assessed by traditional and nonlinear measures. Hum. Mov. Sci. 44, 73–80. Powers, K.C., Kalmar, J.M., Cinelli, M.E., 2014. Recovery of static stability following a concussion. Gait Posture 39, 611–614. Quatman-Yates, C.C., Bonnette, S., Hugentobler, J.A., Mede, B., Kiefer, A.W., Kurowski, B.G., Riley, M.A., 2016. Postconcussion postural sway variability changes in youth: the benefit of structural variability analysis. Pediatr. Phys. Ther. 27, 316–327. Reed-Jones, R.J., Murray, N.G., Powell, D.W., 2014. Clinical assessment of balance in adults with concussion. Semin. Speech Lang. 35, 186–195. Reynolds, B.B., Patrie, J., Henry, E.J., Goodkin, H.P., Broshek, D.K., Wintermark, M., Druzgal, T.J., 2016. Practice type effects on head impact in collegiate football. J. Neurosurg. 124, 501–510. Richman, J.S., Moorman, J.R., 2000. Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol. 278, 2039–2049. Riemann, B.L., Guskiewicz, K.M., 2000. Effects of mild head injury on postural stability as measured through clinical balance testing. J. Athl. Train. 35, 19. Solomon, G.S., Kuhn, A.W., Zuckerman, S.L., Casson, I.R., Viano, D.C., Lovell, M.R., Sills, A.K., 2016. Participation in pre-high school football and neurological, neuroradiological, and neuropsychological findings in later life: a study of 45 retired National Football League players. Am. J. Sports Med. 44, 1106–1115. Sosnoff, J.J., Broglio, S.P., Shin, S., Ferrara, M.S., 2011. Previous mild traumatic brain injury and postural-control dynamics. J. Athl. Train. 46 (1), 85–91. Stergiou, N., Decker, L.M., 2011. Human movement variability, nonlinear dynamics, and pathology: is there a connection? Hum. Mov. Sci. 30, 869–888. Talavage, T.M., Nauman, E.A., Breedlove, E.L., Yoruk, U., Dye, A.E., Morigaki, K.E., Feuer, H., Leverenz, L.J., 2014. Functionally-detected cognitive impairment in high school football players without clinically-diagnosed concussion. J. Neurotrauma 31, 327–338. Williams, D.S., Murray, N.G., Powell, D.W., 2016. Training paradigm effects postural responses to balance perturbations. J. Sport Health Sci. 5, 70–76. Wilson, M.J., Harkrider, A.W., King, K.A., 2015. Effect of repetitive, subconcussive impacts on electrophysiological measures of attention. South. Med. J. 108, 559–566. Winter, D.A., 1995. Human balance and posture control during standing and walking. Gait Posture 3, 193–214.
Cavanaugh, J.T., Guskiewicz, K.M., Giuliani, C., Marshall, S., Mercer, V., Stergiou, N., 2005. Detecting altered postural control after cerebral concussion in athletes with normal postural stability. Br. J. Sports Med. 39, 805–811. Chen, J.K., Johnston, K.M., Frey, S., Petrides, M., Worsley, K., Ptito, A., 2004. Functional abnormalities in symptomatic concussed athletes: an fMRI study. NeuroImage 22, 68–82. Chiari, L., Capello, A., Lenzi, D., Della Croce, U., 2002. An improved technique for the extraction of stochastic parameters from stabilograms. Gait Posture 12, 225–234. Crisco, J.J., Chu, J.J., Greenwald, R.M., 2004. An algorithm for estimating acceleration magnitude and impact location using multiple nonorthogonal single-axis accelerometers. J. Biomech. Eng. 126, 849–854. Davenport, E.M., Whitlow, C.T., Urban, J.E., Espeland, M.A., Jung, Y., Rosenbaum, D.A., Gioia, G.A., Powers, A.K., Stitzel, J.D., Maldjian, J.A., 2014. Abnormal white matter integrity related to head impact exposure in a season of high school varsity football. J. Neurotrauma 31, 1617–1624. Duma, S.M., Manoogian, S.J., Bussone, W.R., 2005. Analysis of real-time head accelerations in collegiate football players. Clin. J. Sport Med. 15, 3–8. Faul, F., Erdfelder, E., Lang, A.G., Buchner, A., 2007. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav. Res. Methods 39, 175–191. Gao, J., Hu, J., Buckley, T.A., White, K., Hass, C.J., 2011. Shannon and Renyi entropies to classify effects of mild traumatic brain injury on postural sway. PLoS One 6, 1–8. Guskiewicz, K.M., 2011. Balance assessment in the management of sport-related concussion. Clin. J. Sport Med. 30, 89–102. Guskiewicz, K.M., Marshall, S.W., Bailes, J., McCrea, M., Harding Jr., H.P., Matthews, A., Mihalik, J.R., Cantu, R.C., 2007. Recurrent concussion and risk of depression in retired professional football players. Med. Sci. Sports Exerc. 39, 903–909. Guskiewicz, K.M., Marshall, S.W., Bailes, J., et al., 2005. Association between recurrent concussion and late-life cognitive impairment in retired professional football players. Neurosurgery 57, 719–726. Guskiewicz, K.M., Weaver, N.l., Padua, D.A., Garrett Jr., W.E., 2000. Epidemiology of concussion in collegiate and high school football players. Am. J. Sports Med. 28, 643–650. Gysland, S.M., Mihalik, J., Register-Mihalik, J.K., Trulock, S.C., Shields, E.W., Guskiewicz, K.M., 2012. The relationship between subconcussive impacts and concussion history on clinical measures of neurologic function in collegiate football players. Ann. Biomed. Eng. 40, 14–22. Holm, S., 1979. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 65–70. Howell, D.R., Hanson, E., Sugimoto, D., Stracciolini, A., Meehan III, W.P., 2016. Assessment of the postural stability of female and male athletes. Clin. J. Sport Med. Iqbal, K., 2011. Mechanisms and models of postural stability and control. Engin. in Med. and Biology Society 7837–7840. Kamins, J., Bigler, E., Covassin, T., Henry, L., Kemp, S., Leddy, J.J., Mayer, A., et al., 2017. What is the physiological time to recovery after concussion? A systematic review. Br. J. Sport Med. 51, 935–940. http://dx.doi.org/10.1136/bjsports-2016097464. Kawata, K., Tierney, R., Phillips, J., Jeka, J.J., 2016. Effect of repetitive sub-concussive head impacts on ocular near point of convergence. Int. J. Sports Med. 37, 405–410. Kelly, K.C., Jordan, E.M., Joyner, A.B., Burdette, G.T., Buckley, T.A., 2014. National collegiate athletic association division i athletic trainers' concussion-management practice patterns. J. Athl. Train. 49, 665–673. Martini, D.N., Sabin, M.J., DePesa, S.A., Leal, E.W., Negrete, T.N., Sosnoff, J.J., Broglio, S.P., 2011. The chronic effects of concussion on gait. Arch. Phys. Med. Rehabil. 92, 585–589. McAllister, T.W., Flashman, L.A., Maerlender, A., Greenwald, R.M., Beckwith, J.G., Tosteson, T.D., Crisco, J.J., Brolinson, P.G., Duma, S.M., Duhaime, A.C., Grove, M.R., 2012. Cognitive effects of one season of head impacts in a cohort of collegiate contact sport athletes. Neurology 78, 1777–1784. McAllister, T.W., Ford, J.C., Flashman, L.A., Maerlender, A., Greenwald, R.M., Beckwith, J.G., Bolander, R.P., Tosteson, T.D., Turco, J.H., Raman, R., Jain, S., 2014. Effect of head impacts on diffusivity measures in a cohort of collegiate contact sport athletes. Neurology 82, 63–69. McCrory, P., Meeuwisse, W., Dvorak, J., et al., 2017. Consensus statement on concussion in sport-the 5(th) international conference on concussion in sport held in Berlin,
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