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Can Functional Movement Assessment Predict Football Head Impact Biomechanics?


Medicine & Science in Sports & Exercise: June 2018 - Volume 50 - Issue 6 - p 1233–1240
doi: 10.1249/MSS.0000000000001538

Purpose The purposes of this study was to determine functional movement assessments’ ability to predict head impact biomechanics in college football players and to determine whether head impact biomechanics could explain preseason to postseason changes in functional movement performance.

Methods Participants (N = 44; mass, 109.0 ± 20.8 kg; age, 20.0 ± 1.3 yr) underwent two preseason and postseason functional movement assessment screenings: 1) Fusionetics Movement Efficiency Test and 2) Landing Error Scoring System (LESS). Fusionetics is scored 0 to 100, and participants were categorized into the following movement quality groups as previously published: good (≥75), moderate (50–75), and poor (<50). The LESS is scored 0 to 17, and participants were categorized into the following previously published movement quality groups: good (≤5 errors), moderate (6–7 errors), and poor (>7 errors). The Head Impact Telemetry (HIT) System measured head impact frequency and magnitude (linear acceleration and rotational acceleration). An encoder with six single-axis accelerometers was inserted between the padding of a commercially available Riddell football helmet. We used random intercepts general linear-mixed models to analyze our data.

Results There were no effects of preseason movement assessment group on the two Head Impact Telemetry System impact outcomes: linear acceleration and rotational acceleration. Head impact frequency did not significantly predict preseason to postseason score changes obtained from the Fusionetics (F1,36 = 0.22, P = 0.643, R2 = 0.006) or the LESS (F1,36 < 0.01, P = 0.988, R2 < 0.001) assessments.

Conclusions Previous research has demonstrated an association between concussion and musculoskeletal injury, as well as functional movement assessment performance and musculoskeletal injury. The functional movement assessments chosen may not be sensitive enough to detect neurological and neuromuscular differences within the sample and subtle changes after sustaining head impacts.

1Matthew Gfeller Sport-Related Traumatic Brain Injury Research Center, Department of Exercise and Sport Science, The University of North Carolina at Chapel Hill, Chapel Hill, NC;

2Curriculum in Human Movement Science, Department of Allied Health Sciences, School of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC;

3Baylor Athletics, Baylor University, Waco, TX; and

4Sports Medicine Research Laboratory, Department of Exercise and Sport Science, The University of North Carolina at Chapel Hill, Chapel Hill, NC

Address for correspondence: Jason P. Mihalik, Ph.D., C.A.T.(C), A.T.C., Department of Exercise and Sport Science, University of North Carolina at Chapel Hill, 2201 Stallings-Evans Sports Medicine Complex, Campus Box 8700, Chapel Hill, NC 27599-8700; E-mail:

Submitted for publication July 2017.

Accepted for publication December 2017.

As many as 3.8 million traumatic brain injuries are estimated to annually occur in sport and recreational activities (1). Concussions are a traumatic brain injury subset defined as trauma-induced alterations in mental status (2). A multifaceted approach is often used to diagnose concussion due to the injury’s complexity. Although several clinical tests are established for diagnosis and tracking recovery, there has been surprisingly little research to develop tools for identifying athletes possessing the highest concussion injury risk. Football results in the highest overall concussion frequency and is among the sports with the highest injury rates sustained by National Collegiate Athletics Association athletes (3,4). Given football’s nature and current playing rules, this is very likely due to the high head impact frequency players sustain during participation (5). Research has identified factors influencing head impact frequency and magnitude to understand their relationship to concussion risk. These factors include, but are not limited to, event type (3,6,7), collision anticipation (8,9), and play type (10). Earlier studies suggested that peak linear accelerations exceeding 70g to 75g may be associated with greater concussion risk (11). However, more contemporary research has emphasized that no definitive head injury threshold exists (12,13). Understanding concussion injury mechanics is a growing concern because of the potential long-term neurological consequences associated with concussions and repetitive head impacts—subconcussive impacts—that may not result in any acute observable signs resulting in a clinically diagnosed concussion (14–16). Given these potential long-term implications, studying head impacts using innovative head impact monitoring systems allow clinicians and scientists to better understand these phenomena and may identify mechanisms by which injury risk can be mitigated.

Although the long-term effects resulting from incident concussions and/or subconcussive impacts remain largely unknown, studying acute effects resulting from incident concussions has focused almost entirely on clinical manifestation including symptoms, balance, and neurocognition (2,17–20). The clinical measures used to assess these manifestations may be limited. For example, lingering deficits in dynamic balance, voluntary movement, and reaction times—required for adapting to changing environments in sport participation—remain impaired beyond recovery in traditional static balance testing (21,22). Given this, it is not surprising concussed athletes are two times more likely to sustain a musculoskeletal injury compared with his or her own preinjury musculoskeletal injury risk (4,21,22). Concussed athletes are also more likely to sustain a musculoskeletal injury after returning to play as compared with their nonconcussed counterparts. Given the growing link between concussion and impaired neuromuscular control, it is possible that repetitive head impacts could negatively influence functional movement.

Movement assessments are commonly used to clinically evaluate functional movement quality and could help inform the relationship between movement quality (23) and concussion injury risk, including head impact severity mitigation. Movement assessments are used in clinical settings to identify movement compensations including muscular imbalances, decreased flexibility, and balance deficits associated with musculoskeletal injury risk (23). Such mechanisms identifying an individual as a poor mover may also place them at greater concussion risk by limiting impending collision avoidance. Fusionetics is a movement assessment incorporating upper and lower extremity movement patterns to identify injury risk (24). The Landing Error Scoring System (LESS) is a dynamic movement assessment evaluating jump landing biomechanics (25).

Scientific data support that functional movement screening can identify patients at a higher musculoskeletal injury risk (23,25–28). Studying the association between functional movement and head impact biomechanics is a necessary progression in understanding the growing links between concussion and musculoskeletal injury risk. Therefore, the overall study purpose was twofold: 1) to determine the association between movement assessment performance (Fusionetics and the LESS) and head impact biomechanics (frequency and magnitude), and 2) to identify whether head impact biomechanics predicted preseason to postseason changes in functional movement.

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We used a prospective cohort study design whereby all participants underwent preseason and postseason movement assessments to derive changes in movement assessment scores. All participants wore instrumented helmets to collect head impact biomechanics during a single National Collegiate Athletics Association Division 1A football season.

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We recruited 44 male college football players (mass, 109.0 ± 20.8 kg; age, 20.0 ± 1.3 yr). Table 1 provides a breakdown for each position group from this original sample. Participants were permitted to enroll in the study unless they met any of the following exclusion criteria: 1) they had an injury rendering them unable to complete preseason movement testing, 2) they wore a helmet unable to accommodate our head impact monitoring system device, 3) excluded from postseason testing if they sustained a significant injury throughout the season resulting in time loss of ≥4 wk, or 4) if they sustained a minor injury for which they were not cleared for full return to participation before their scheduled postseason assessment. Our institution’s Office of Human Research Ethics review board approved this research study, and all participants provided informed consent before participating in the study.



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Fusionetics Movement Efficiency Test

The Fusionetics Movement Efficiency Test comprises the following: 1) an overhead squat, 2) overhead squat with heel lift, 3) single-leg squat, 4) push-up, 5) glenohumeral range of motion, 6) lumbar spine range of motion, and 7) cervical spine range of motion. Errors at the feet, knees, trunk, shoulders, and cervical spine were identified (Table 2). Individual scores for each movement pattern were given along with a total movement score calculated by Fusionetics proprietary algorithm. Scores range from 0 to 100. Higher scores indicate better movement quality. Fusionetics movement assessment scores categorized participants into one of three movement quality groups: 1) good (score exceeding 75), 2) moderate (scores ranging from 50 to 75), and 3) poor (scores lower than 50). The Fusionetics software predetermined grouping cutoffs.



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The LESS is a jump-landing task used to assess dynamic lower extremity movement patterns. A depth camera (Microsoft Kinect Sensor Version 1; Microsoft Corporation, Redmond, WA) recorded human kinematics while participants performed the LESS tasks. PhysiMax Athletic Movement Assessment software (PhysiMax Technologies Ltd., Tel Aviv, Israel) assessed the video data for errors or compensations at the feet, knees, or trunk and scored each completed LESS trial (Table 3). PhysiMax has shown good agreement with expert LESS raters (prevalence-adjusted bias-adjusted κ = 0.71 ± 0.27) (22). Scores range from 0 to 17. Lower scores indicate better movement quality. In addition, LESS errors categorize participants into one of three movement quality groups: 1) good (<5 errors), 2) moderate (6–7 errors), and 3) poor (>7 errors) (25). Total error frequency was averaged over three trials to give the subject their final LESS movement score.



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Head Impact Telemetry System

The Head Impact Telemetry (HIT) System measured head impact biomechanics for impacts sustained to the head while football players participated in games and practices. An encoder with six single-axis accelerometers was inserted inside commercially available Riddell Revolution (sizes: M, L, or XL), Speed (sizes: M, L, or XL), and Flex football helmets. This study used a recording threshold of 10g. The accelerometers collected data at 1 kHz for a period of 40 ms (data collected 8 ms before trigger and 32 ms after trigger). A radiofrequency telemetry link permits time-stamped data transmission from the accelerometers to a sideline computer. In the event players were out of range from the sideline computer, data from 100 separate impacts could be stored in the encoder’s nonvolatile on-board memory. The HIT System’s proprietary algorithm reduced, processed, and calculated the peak linear and rotational accelerations, in addition to other measures not evaluated in this study including Gadd Severity Index, Head Injury Criterion, and head impact location. Cumulative peak linear and rotational acceleration were defined as the sum of the peak linear and rotational accelerations associated with each individual head impact sustained over the course of the season (29). The HIT System has been validated in a laboratory setting using hybrid dummies equipped with football helmets (30).

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Participants underwent functional movement assessments before starting the preseason. Participants performed a standardized warm-up before all functional movement assessment tests. The warm-up included cycling for 5 min on a stationary bike at 80 rpm, dynamic muscle stretching (hamstrings, quadriceps, hip flexors, gluteals, calves, and shoulders), and static stretching (hamstrings, quadriceps, hip flexors, gluteals, iliotibial band, leg adductors, and latissimus dorsi). Dynamic (10 yards) and static (one time for 30 s) upper and lower extremity stretches were completed. Movement quality was then assessed using the LESS followed by Fusionetics. All participants completed the movement assessment in the same test order. The same clinician administered both assessments for all participants. Test–retest reliability was not assessed for this clinician; however, the clinician was trained in administering and interpreting these evaluations, and had considerable experience implementing these measures in the study population before the study.

Participants performed six LESS trials (three practice and three collected trials). The participant started on a 30-cm box, and then jumped horizontally without any upward motion into a target landing area located at a distance 50% of their height from the box front. The participant then immediately performed a maximal vertical jump after landing in the target area. Participants received no coaching on their form. A Kinect camera was placed 11 ft from the box front and measured participant’s landing and jumping kinematics as previously described.

After completing the LESS testing, participants proceeded to complete the Fusionetics movement assessment. This assessment began with participants performing an overhead squat. They were instructed to perform five repetitions with their arms overhead, squatting as low as they could as if sitting in a chair. The overhead squat was repeated after adding a heel lift. Next, participants completed three single-leg squat repetitions on each leg. They were instructed to squat to an approximate chair seat height with their arms and opposite leg positioned wherever the participant felt most comfortable. The push-up test (three push-ups) was then performed with the participant’s hands in a comfortable position. Three trials for the following range of motion measures were evaluated in a standing position: 1) glenohumeral (flexion, horizontal abduction, internal rotation, and external rotation), 2) lumbar spine (rotation and lateral flexion), and 3) cervical spine (rotation and lateral flexion). Fusionetics test components were administered in the same order during preseason and postseason testing given that this is controlled by the Fusionetics platform.

Participants wore helmets instrumented with the HIT System. Players had a unique identification number assigned to their HIT System sensor. Professional equipment managers properly fitted the helmet before preseason camps ensuring the accelerometers made head contact to measure head acceleration, not helmet acceleration. The HIT System collected, computed, and stored real-time linear acceleration and rotational acceleration data during all games and practices within a single season. We collected head impact biomechanics data during 74 practices and 13 competitions. The HIT System was programmed to begin collecting data when a practice or competition began, and programmed to stop at the session completion. This reduced our chances for including possible impacts sustained during times extending beyond participation.

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Statistical Analysis

Only impacts with a peak resultant linear acceleration greater than 10g were included in the analyses (5,31,32). We applied natural logarithmic transformations to our linear and rotational acceleration data to conform to the assumptions of data normality for our analyses that required this. For our study purposes, separate random intercepts general linear-mixed models were performed predicting peak linear and rotational acceleration from preseason functional movement quality (good, moderate, and poor). We also evaluated the effect of impact frequency on preseason to postseason change in functional movement quality for Fusionetics and the LESS, as well as the effects of average and cumulative impact magnitude on postseason movement scores in a series of regression models. We also describe the 90th percentile linear and rotational accelerations to further explore head impact magnitude outcomes and to illustrate the threshold for the highest magnitude head impacts sustained across all movement assessment groups for both the Fusionetics and the LESS. All analyses were carried out in SAS (version 9.4; SAS Institute Inc., Cary, NC). Statistical significance was set a priori with an alpha not exceeding 0.05.

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Forty-four participants underwent preseason movement testing. Thirty-eight subsequently completed postseason movement testing (Table 4). Six players completed preseason movement testing but did not complete postseason movement testing for the following reasons: 1) sustained season ending injuries (n = 2), 2) injuries rendering them unable to complete postseason movement testing (n = 2), and 3) did not attend postseason movement testing (n = 2). During the data collection period, we collected 29,747 head impacts (11.6 impacts per game per player and 8.8 impacts per practice per player). Our participants were classified into poor, moderate, or good movement categories based on their performance in preseason Fusionetics and LESS testing. Of the 38 participants who completed both test sessions, no one tested poor for Fusionetics, whereas there were 21 moderate movers (7 Bigs, 3 Big Skill, 10 Skill, and 1 Special Team) and 17 good movers (7 Bigs, 4 Big Skill, 4 Skill, and 2 Special Teams). There was no association between the position group and the LESS performance group (χ2 = 2.66, P < 0.05). Using the LESS, there were 17 poor movers (7 Bigs, 2 Big Skill, 6 Skill, and 2 Special Teams), 14 moderate movers (4 Bigs, 1 Big Skill, 8 Skill, and 1 Special Team), and 7 good movers (3 Bigs and 4 Big Skill). There was no association between the position group and the LESS performance group (χ2 = 12.53, P < 0.05).



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Preseason movement assessment as a predictor of head impact biomechanics

Table 5 provides all the descriptive and statistical data related to our primary purpose. In summary, there were no effects of Fusionetics preseason movement assessment categorization on the two HIT System impact outcomes: linear acceleration (F1,42 = 0.76, P = 0.453) and rotational acceleration (F1,42 = 1.32, P = 0.193). Similar results were obtained for the LESS preseason movement assessment categorization on these same two measures: linear acceleration (F2,41 = 0.83, P = 0.442) and rotation acceleration (F2,41 = 0.23, P = 0.793).



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Preseason to postseason movement assessment change and head impact frequency

Overall, we found very small nonsignificant changes in preseason to postseason movement assessment scores (Fusionetics: t37 = −1.00, P = 0.32 (mean change, 1.22 ± 7.53); LESS: t37 = 0.45, P = 0.65 (mean change, 0.18 ± 2.50; Table 4)). Moreover, head impact frequency was not associated with preseason to postseason change in Fusionetics movement performance scores (F1,36 = 0.22, P = 0.643, R2 = 0.006) or with changes in LESS scores (F1,36 < 0.01, P = 0.988, R2 < 0.001).

We also tested whether impacts sustained during the season predicted postseason movement scores. Tested in separate regression models, there were no effects of average linear acceleration (F1,36 = 0.13, P = 0.73) or cumulative linear acceleration (F1,36 = 0.75, P = 0.39) on postseason Fusionetics assessment scores. Similar results for average linear acceleration (F1,36 = 0.02, P = 0.90) and cumulative linear acceleration (F1,36 = 0.21, P = 0.65) were observed for postseason LESS scores. Similarly, neither average rotational acceleration (F1,36 = 0.75, P = 0.39) nor cumulative rotational acceleration (F1,36 = 0.62, P = 0.44) had an effect on postseason Fusionetics scores. Average rotational acceleration (F1,36 = 0.73, P = 0.40) and cumulative rotational acceleration (F1,36 = 0.18, P = 0.68) also did not have an effect on postseason LESS scores.

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Given the mounting evidence linking musculoskeletal injury to both concussion and functional movement assessment performance in non–football studies, we sought to determine whether functional movement performance could predict head impact biomechanics in college football players. We found that athletes’ movement abilities as assessed by Fusionetics and the LESS largely did not predict head impact magnitude throughout the season, as evidenced by nonsignificant findings and consistent outcomes at even the highest magnitude head impacts (i.e., 90th percentile). Previous research including college non–football club athletes (men’s and women’s rugby, men’s lacrosse, cheerleading, and ultimate Frisbee) has demonstrated no relationship between concussion history and Functional Movement Screen score (33). Elite youth soccer athletes with LESS scores exceeding 5 experience an increased anterior cruciate ligament injury risk (25), and prospective studies report a 15% increase in stress fracture incidence with every 1-point increase in LESS score in a study at a US Service Academy (34). These devastating and limiting injuries are less commonly seen in football. We submit that future research to explain the link between movement assessments and musculoskeletal injuries common to football is warranted. Our focus in the current article was restricted to the relationship between functional movement performance and head impact magnitude.

Functional movement compensations observed during Fusionetics and LESS assessments may not translate to all on-field activities. Although they may point to an athlete’s ability to evade an unexpected body collision (e.g., side step a tackle), it is unlikely to play a role in the majority of football head impacts occurring in the open field. Previous research has identified players are at greater risk for sustaining severe head impacts when closing distances between the striking player and the struck player exceeds 10 yd (10). Decreased visual and sensory performances are also linked to more severe head impact biomechanics (32). These examples demonstrate some modifiable factors associated with increasing head impact magnitude for which Fusionetics and the LESS are simply not equipped to evaluate. In addition, movement assessments are typically performed in controlled environments, and it is believed that many concussion and musculoskeletal injuries are likely to occur from unanticipated events difficult to replicate off the playing field. We studied head impact biomechanics and did not study concussion incidence in this study. We submit that this would be a valuable opportunity to explore in future research. Specific to head impact biomechanics, our data support current research in that a high head impact frequency was classified as mild (95.5% of all linear accelerations) (5,6).

Sport-specific outcomes should be considered when choosing a movement assessment. We chose Fusionetics because it incorporates the upper extremity and cervical spine, and involves slower movements, which we believe reveal a person’s potential to engage in dynamic activities and ultimately open the door for studying neuromuscular deficits. We chose the LESS because it was a more dynamic movement pattern and has previously been a predictor for musculoskeletal injury risk (25,34). We found that Fusionetics and LESS demonstrate poor agreement in categorizing a participant’s movement performance as poor, moderate, or good. We initially hypothesized that they would agree because they are widely used and independent of each other in clinical settings, that is, one or the other, but rarely both. However, it is perhaps not surprising that they lack agreement given the innate differences between the two assessments. Fusionetics incorporates the upper extremity, whereas the LESS does not. Although the LESS predicts anterior cruciate ligament injury risk in soccer players (25), football players are at a relatively greater risk for sustaining upper extremity injuries (35,36). In addition, the LESS is a dynamic test (jump-landing task) that may be more sensitive to poor movement compared with the relatively static features demonstrated by the Fusionetics assessment. Our preseason assessment results support this observation, with the LESS categorizing 18 participants (41%) as poor movers, whereas Fusionetics did not categorize anyone as poor. Fusionetics uses several tasks (Table 2), with an emphasis on the double-leg squat. The double-leg squat is traditionally trained by college football players during year-round conditioning workouts and thus likely inflated athlete Fusionetics performance as a result. In combination, our data beg the question: Which assessment is correctly identifying football players at greater musculoskeletal injury risk, if any? Given our study emphasis on head impact biomechanics—and not musculoskeletal injury—we were unable to track musculoskeletal injuries sustained throughout the season to determine if either functional movement assessment was able to successfully predict musculoskeletal injuries in a college football cohort. It is important to note that although we did not observe strong agreement between the two functional movement assessments, the two assessments did agree in that neither predicted head impact biomechanics.

Recent studies demonstrate that concussed athletes are twice as likely to sustain a lower extremity injury after concussion compared with before concussion, and twice as likely to sustain a lower extremity injury within 90 d of their return to play as compared with their nonconcussed counterparts (4,22). Given the growing link between concussion and neuromuscular control, we hypothesized that movement assessment score would decrease after sustaining multiple head impacts throughout a competitive season. We did not identify any such differences and posit that the gross movement patterns that Fusionetics and LESS incorporate may not be sensitive to functional movement changes occurring after head impacts. Any neurological decline manifesting as neuromuscular impairments may be too subtle for conventional movement assessments to identify within a single season; the sensitivity of these assessments in detecting long-term declines is an area worthy of further study. For example, the Functional Movement Screen comprises gross upper and lower extremity movement patterns that may not detect subtle changes in neuromuscular control. The gait literature interestingly identifies subtle gait changes and static postural control insufficiencies in those with a concussion history (37,38). On the surface, dynamic movements (gait and balance) seem more sensitive to identifying subtle neuromuscular changes compared with gross motor functional movement assessments. Future studies should complement these functional movement assessments with advanced research instrumentation (e.g., completing the LESS on force platforms) to inform possible enhancements designed to address subtle functional impairments not previously possible.

A discussion and deeper understanding of the functional movement assessments we used is warranted given the theme of the present study. Although not a primary purpose of the study, we did observe that Fusionetics and LESS had poor agreement on categorizing an individual’s preseason movement quality as good, moderate, or poor movers (κ = 0.0435 (95% confidence interval, −0.1166 to 0.2036); P < 0.001); thus, our data do not support an either/or implementation. Rather, we feel that both offer unique clinical insights and should be used to complement each other. We recognize that the human, physical, and time resources may not always permit implementing multiple movement assessments in any given clinical setting. We submit that the LESS is time efficient, trials are quickly performed, and including the Kinect camera and PhysiMax software made data retrieval easy. The LESS easily accommodated our large football program. Fusionetics identifies movement compensations and creates corrective exercises to address muscular imbalances, decreased flexibility, and asymmetries. Fusionetics can assess functional movement quality (39,40), nutrition, and recovery, but did not include the latter two portions in our study (41–43). As research in this area continues, we are interested to see if preseason functional movement assessments assessed by the tools we administered in our study or any others will ever predict head impact magnitude. The assessments we used are designed to identify individuals with limits to their movement patterns, which we believe could then limit a person’s ability to absorb or avoid head impacts. Our data would suggest that, for the most part, our sample had sufficient movement quality to participate in competitive football. It is very likely that other factors will carry greater predictive capacity than functional movement assessments. Regardless, subsequent studies are needed because the resulting data will no doubt inform intervention studies designed to mitigate head impact magnitude—and concussion injury risk—by correcting movement inefficiencies, enhancing overall athlete conditioning, and improving on-field awareness and playing techniques.

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Our observational study may have benefited from a control group. In our current study design, the control cohort would have helped us interpret the expected change in functional movement observed in our study. We also studied an elite athlete sample participating in a single Division 1 college football program. Unfortunately, we did not assess test–retest reliability for the clinician; however, the clinician was trained in administering and interpreting these evaluations, and had considerable experience implementing these measures in the study population before the study. The functional movement assessments chosen may not be sensitive enough to detect neurological and neuromuscular differences within this sample. Greater performance heterogeneity will be more likely observed in high school athletes because of varying training programs and skill levels pervasive to football at that competitive level.

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A growing link between increasing musculoskeletal injury risk and both concussion and functional movement performance has been previously published. We sought to better understand whether a link between functional movement performance and head impact magnitude, a surrogate for concussion injury risk, existed. Our data do not seem to support that functional movement performance alone is a strong predictor of head impact magnitude. We identify several limitations with existing functional movement assessments that our scientific colleagues will need to consider before thoroughly exploring the link between neuromuscular function and head injury risk.

The authors thank Laura Stanley and Barnett Frank for providing technical expertise, as well as Clapp and Aaron Smith for assisting with data collection. There are no funding sources to disclose.

The University of North Carolina at Chapel Hill has previously received a gift from Fusionetics, which did not go to support the current study. The authors have no conflicts to disclose. The results of the present study do not constitute endorsement by the American College of Sports Medicine. The results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation.

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