Preseason Functional Movement Screen Predicts Risk of... : The Journal of Strength & Conditioning Research (original) (raw)
Introduction
Rugby union is one of the most popular team sports globally, with 7.23 million participants in 120 countries registered with World Rugby (2,28). It is played by men and women of all ages (2,28).
With reported injury rates of 68–218 injuries per 1,000 player-hours at the professional level and 15–93 injuries per 1,000 player-hours at the club-level, rugby union has one of the highest reported incidences of injury in sports, exceeding that of ice hockey, soccer, and collegiate American football (1,5). This may be attributed to the body contacts and collisions that are integral to the game, characterized by tackles, rucks, mauls, and scrums, and the lack of protective equipment that can be worn within the rules of the sport (2). Additionally, previous injury, which often results in dysfunctional movement patterns (8), poses an increased risk for injury in rugby union (2,23). Consequently, the need for effective injury risk assessments and injury prevention strategies, within the context of movement quality, is warranted.
The Functional Movement Screen (FMS) is a noninvasive, inexpensive, quick, and easily administered screening tool that assesses multiple functional movement patterns of an individual to identify movement limitations and asymmetries, which are suspected to influence risk of sport-related injury (8,15). The FMS involves performing 7 functional movements, each of which is scored on a scale of 0–3 to yield a composite score (FMS composite score) out of 21 (8,9). Five of the 7 functional movements in the FMS, including the hurdle step, in-line lunge, shoulder mobility, active straight leg raise, and rotary stability, are bilateral movement tasks which provide for the identification of asymmetries in functional movement (8,9).
The effectiveness of the FMS as a predictor of injury risk has previously been demonstrated in a number of physically active populations, such as professional American football players (14,15), NCAA athletes (7,16), Marine Corps officer candidates (18,21), and firefighters (4). Additionally, the presence of one of more asymmetries has been linked to a higher risk of injury in professional American football players (14). Conversely, some studies have posited limited utility of the FMS to predict injury risk in basketball players (20,24) and firefighters (3), while others have asserted that the FMS may lack the accuracy required to effectively assess individualized injury risk (11,29). A recent review has suggested that a lack of evidence prevents the FMS from being regarded as an injury prediction tool (19).
Nevertheless, limitations in movement quality, such as mobility and stability imbalances, are risk factors for injury that are made observable by the FMS (8). The ability of the FMS to predict injury risk in rugby players has yet to be explored. The low-cost, timely identification of rugby union athletes at higher risk of injury during preseason can lead to effective prehabilitation efforts that help reduce the risk of injury for these athletes. The purpose of this study was to determine the relationship between FMS score and the risk of time-loss injury in experienced male rugby union athletes. In addition, this study explored the relationship between FMS-determined asymmetries and the risk of time-loss injury in these athletes.
Methods
Experimental Approach to the Problem
This study used a cohort design similar to those used in epidemiological research. Functional Movement Screen data were collected immediately before the start and at the halfway point of an 8-month competitive season, representing the inherent characteristics (i.e., impairments in stability and mobility, asymmetries, and dysfunctional movement patterns) hypothesized to affect the incidence of time-loss injury. Injury data were collected prospectively throughout the course of the season. The first half of the season (H1) took place in September–December, whereas the second half (H2) took place in January–April. The outcome measure, incidence of time-loss injury, was compared between the 2 groups: (a) players exhibiting FMS scores below the experimentally determined cut-off and (b) those exhibiting FMS scores above the cut-off. The FMS composite cut-off score used to separate the 2 cohorts was established using a receiver-operator characteristic (ROC) curve after all FMS and injury data had been collected.
Subjects
Seventy-six experienced male rugby union athletes (age 22 ± 3 years) actively participating in club-, representative-, and international-level 15-aside rugby competition volunteered for the study. Given the lack of professional rugby union leagues in Canada, this group of participants is reflective of the level of play in Canadian club-level rugby. All participants were at least 18 years old and had played at least 3 full seasons of rugby union before participating in the study. Additionally, although all participants had sustained previous injury before their involvement in the study, none were injured at the time of the first data collection. Of the total participant pool, 68 athletes competed in the first half of the season. Eleven of these athletes did not participate in the second half of the season for various reasons, including injury, taking time away from rugby, or moving away from the region. To compensate for the loss of participants after H1, 8 athletes were recruited to join the study before the start of H2 to yield 65 athletes. To remain consistent with previous related research, participants were required to be free from any injury or surgical procedure that excluded or limited their participation in practice and/or competition 30 days before testing (7). Those who missed more than 3 games in either H1 or H2 for reasons other than rugby-related injury or who began regular mobility interventions (e.g., yoga) midway through the season were excluded from the statistical analyses. This study was conducted with institutional human research ethics board approval. Participants were informed of the benefits and risks of participation in the investigation before providing signed informed consent.
Procedures
The surveillance time for this study was one 8-month competitive season (bisected by a 4-week winter break) during which time-loss injury data were collected. Functional Movement Screen data were obtained during the 2 weeks before the commencement of both H1 and H2. Additionally, participants completed preseason injury history and demographic questionnaires.
Functional Movement Screen Data
Individual participant results on the 7 FMS movement tasks were collected by the same experienced, certified FMS rater. Administration of the FMS occurred before rugby training, or on a non-training day, in accordance to guidelines outlined by Cook, Burton and Hoogenboom (8,9). Functional Movement Screen data were collected twice throughout the study (during preseason and between H1 and H2) because individual functional movement patterns have been observed to change over the course of a competitive season in collegiate athletes (25).
To confirm the reliability and accuracy of the FMS measurements, the FMS rater scored 10 athletes alongside another experienced, certified FMS rater in concurrent, blind, parallel tests. These athletes were selected on the basis of convenience and were not participants in the study itself. In the context of real-time simultaneous testing, the FMS score demonstrated an intraclass correlation coefficient value of 0.930 (95% confidence interval [CI]: 0.752–0.982).
Injury Data
Injury data were collected from all rugby competitions in which each participant took part. Injury was defined as “any physical complaint… that was sustained by a player during a rugby match or rugby training, irrespective of the need for medical attention or time-loss from rugby activities” that “results in a player being unable to take a full part in future rugby training or match play” (12). Team medical personnel and participants provided information to the researcher to complete injury report forms. A modified version of the injury report form provided in the International Rugby Board's Consensus Statement on Injury Definitions and Data Collection Procedures for Studies of Injuries in Rugby Union was used to collect all injury data (12).
Statistical Analyses
Statistical analyses (IBM SPSS Statistics, Version 22.0; Armonk, NY, USA) for H1 and H2 were conducted independently. Significance was set at the p ≤ 0.05 level.
Functional Movement Screen Score
Dependent _t_-tests were conducted for both H1 and H2 to test for differences in FMS scores in those who sustained a time-loss injury and those who did not. Pearson correlation and linear regression analyses were used to determine the relationship between FMS composite score and the incidence of injury among participants. Binomial logistic regression analysis was used to assess the association between time-loss injury status (binomial dependent variable) and the independent variables of FMS composite score, and scores for each of the 7 individual FMS components.
An ROC curve was plotted for each half of the season to determine the FMS cut-off score that maximized sensitivity and specificity of the FMS composite score as a screening test for time-loss injury. This cut-off value most effectively discriminated between those participants at greater risk of time-loss injury from those at lower risk of injury on the basis of FMS composite score.
The FMS cut-off score was used to evaluate the relationship between lower and higher FMS composite scores and time-loss injury risk within each half of the season. A 2 × 2 contingency table was generated for each half of the season, dichotomizing those athletes with FMS composite scores above the cut-off from those at or below the cut-off, and those who suffered a time-loss injury from those who did not. Fisher's exact tests were used in analyzing the contingency tables. Sensitivity, specificity, diagnostic odds ratios (ORs), and likelihood ratios (−LR, +LR) were also calculated. Additionally, contingency tables and corresponding Fisher's exact tests were used to assess the relationship between scoring a 1 or 0 on any of the 7 FMS components and time-loss injury risk.
Movement Asymmetries
Correlation and regression analyses were used to evaluate the relationship between FMS-determined movement asymmetries and the incidence of time-loss injury among participants. Additionally, ROC curves were plotted to determine the most appropriate cut-off value of asymmetries. Subsequent 2 × 2 contingency tables were created for the cut-off values to calculate ORs while Fisher's exact tests were performed to determine significance between groups above and below the cut-off values.
Results
Data from 68 participants in H1 and 65 participants in H2, of whom 57 competed in both seasons, were included in the statistical analyses. Participant characteristics are provided in Table 1. The mean FMS composite score for each half of the season were not significantly different (15.2 ± 1.94 and 15.4 ± 2.05, respectively). In both H1 and H2, the mean FMS composite score did not differ significantly between injured (15.04 ± 2.15 and 15.15 ± 2.30 in H1 and H2, respectively) and uninjured (15.55 ± 1.27 and 15.90 ± 1.21 in H1 and H2, respectively) athletes. The distribution of the FMS composite scores for injured and uninjured athletes are illustrated in Figure 1 (H1) and Figure 2 (H2).
Anthropometric characteristics, age, years of rugby union playing experience of the study population (all male, n = 76).
Half 1 distribution of composite Functional Movement Screen (FMS) scores, indicating those who sustained injury and those who remained uninjured (n = 68).
Half 2 distribution of composite Functional Movement Screen (FMS) scores, indicating those who sustained injury and those who remained uninjured (n = 65).
Functional Movement Screen Score and Injury Risk
No significant relationship between FMS composite score and the incidence of injury was demonstrated through correlation and linear regression analysis. Binomial logistic regression did not identify scores from any one the 7 individual FMS components of the FMS composite score to be significantly predictive of injury status.
In H1, the FMS cut-off score of 14.5 maximized sensitivity and specificity of the FMS composite score (Figure 3), whereas for H2 (Figure 4), the FMS composite cut-off scores of 14.5 and 15.5 both maximized sensitivity and specificity. Since ROC curve analyses identified cut-off scores of 14.5 and 15.5, which cannot be achieved on the FMS (an FMS composite score must be a whole number from 0 to 21), the cut-off scores are expressed as ≤14 and ≤15, respectively. The FMS composite cut-off score of ≤14 was applied to the injury data from both H1 and H2 to evaluate the risk of injury associated with participants exhibiting FMS composite scores below and above the cut-off value. Tables 2 and 3 demonstrate the effectiveness of the FMS cut-off score of ≤14 as a predictor of injury risk in dichotomizing those with FMS composite scores ≤14 from those >14, and those who sustained injury from those who were uninjured.
Receiver-operator characteristic (ROC) curve for Functional Movement Screen (FMS) composite score and injury status in half 1. Coordinates of the ROC curve indicate that the FMS composite score value that lies nearest to the upper left corner of the 1 − specificity versus sensitivity graph is 14.5 (in bold), justifying its determination as the FMS composite cut-off score (10). Cut-off values are the averages of 2 consecutive ordered observed test values.
Receiver-operator characteristic (ROC) curve for Functional Movement Screen (FMS) composite score and injury status in half 2. Coordinates of the ROC curve indicate that the FMS composite score values of 14.5 and 15.5 (in bold) lie near to the upper left corner of the 1 − specificity versus sensitivity graph. Because of the presence of 2 potential FMS composite cut-off scores that maximized sensitivity and specificity, statistical analyses were conducted for both scores.
H1 2 × 2 contingency table dichotomizing those above from those below the cut-off Functional Movement Screen (FMS) score of ≤14, and those who suffered a time-loss injury from those who did not.
H2 2 × 2 contingency table dichotomizing those above from those below the cut-off Functional Movement Screen (FMS) score of ≤14, and those who suffered a time-loss injury from those who did not.
Additionally, because 2 cut-off values were identified for H2, the FMS cut-off score of ≤15 was used to evaluate the risk of injury for participants above and below this cut-off score in H2.
Diagnostic OR analyses revealed that participants who scored ≤14 were 10.42 times (95% CI: 1.28–84.75) more likely to have sustained injury in H1 (+LR = 7.08, −LR = 0.72, specificity = 0.95, sensitivity = 0.35) and 4.97 times (95% CI: 1.02–24.19) in H2 (+LR = 3.56, −LR = 0.71, specificity = 0.90, sensitivity = 0.36) than those with higher FMS scores. Fisher's exact tests confirmed that participants with FMS composite scores ≤14 were significantly more likely to sustain injury in both halves of the season (p ≤ 0.05). The majority of players with FMS composite scores ≤14 sustained one or more injuries in both H1 (94.44%) and H2 (88.89%). Additionally, participants with FMS composite scores ≤14 sustained significantly more injuries (1.72 ± 1.72 and 1.22 ± 0.65 injuries per participant in H1 and H2, respectively) than those scoring >14 (0.96 ± 0.95 and 0.79 ± 0.75 injuries in H1 and H2, respectively). Participants scoring ≤15 on the FMS were also at significantly greater risk of injury than their higher scoring counterparts, exhibiting a risk of injury 3.37 times (95% CI: 1.12–10.14, Fisher's exact test, one-tailed, p = 0.027) greater than players with higher FMS scores in H2 (+LR = 1.84, −LR = 0.55, specificity = 0.65, sensitivity = 0.64) but not in H1. Fisher's exact tests revealed no significant relationship between scoring ≤1 on any of the 7 individual FMS component and time-loss injury status in either H1 and H2.
Movement Asymmetries and Injury Risk
Correlation and regression analysis demonstrated no significant relationship between the presence of FMS-determined movement asymmetries and the incidence of injury. The occurrence of asymmetries was not significantly predictive of injury status. Furthermore, no cut-off number of asymmetries was able to adequately distinguish between those at significantly greater risk of time-loss injury and those at lower risk.
Discussion
Lower composite FMS scores have been previously associated with significantly greater risks of sports and occupational injury (4,7,14–16,18,21). Additionally, FMS-determined movement asymmetries have been associated with an increased likelihood of injury in American football players (14). The primary finding of this study was that an FMS score ≤14 distinguished experienced male rugby union athletes as being at significantly greater risk of sustaining time-loss injuries than those with higher FMS scores. Athletes with FMS scores ≤14 exhibited a 10- and 5-fold increased likelihood of time-loss injury in H1 and H2, respectively. The presence of asymmetries was not associated with increased likelihood of time-loss injury.
The results from this investigation support those of Kiesel, Plisky and Voight (15), who used an ROC curve and diagnostic ORs analysis to determine an eleven-fold increased likelihood of serious injury in professional American football players scoring ≤14 on the FMS. Additionally, this cut-off point has been reported to indicate significantly greater likelihood of injury in firefighter training (4), Marine Corps officer training (18,21) and female NCAA soccer, basketball and volleyball (7). The current study provides additional evidence of the utility of an FMS composite cut-off score of ≤14 in identifying individuals at risk of injury, extending the generalizability of this FMS composite cut-off score to male rugby union athletes.
The FMS's ability to quantify the various potential and confirmed risk factors for injury that are observable during the functional movement evaluation helps explain its utility at predicting risk of time-loss injury in male rugby union athletes. A significant risk factor for injury in rugby union is previous injury (2,23). Rugby union athletes with low FMS scores are likely to demonstrate dysfunctional movement patterns which often result from previous injuries (8), particularly those that have not been fully rehabilitated. Since previous injury remains one of the most important risk factors for injury, not only in rugby union (2,23), but sport in general (14,26), these individuals are likely at greater risk of injury because of injury histories which may underlie low FMS scores. This is an important consideration in developing effective strength and conditioning rehabilitation programs for rugby players.
A number of potential risk factors for injury in rugby union are relevant to FMS performance. These include the presence of dysfunctional movement patterns and limitations to functional movement quality, trunk and core strength and stability, muscular strength, balance, motor control, range of motion, neuromuscular coordination, and static and dynamic flexibility (8,22). Specifically, trunk displacement after sudden force release, a factor of dynamic stability, has been linked to knee injury in collegiate athletes (30). Another factor of dynamic stability, trunk muscle response latency to perturbation, has been linked to lower back injuries in collegiate athletes (6). Impaired core proprioception has been associated with knee injury, though only in female athletes (31). The on-field interplay of these risk factors for injury helps to explain the findings of this study, and also highlights the importance of strength and conditioning training in the prevention of injury. The perpetuation of movement dysfunction as a result of previous injury, in addition to these risk factors, best explains the association between FMS scores ≤14 and time-loss injury in the participants.
In order for the FMS to be deemed an effective tool for predicting injury risk in rugby union athletes, its ability to accurately identify athletes at high risk of injury is critical. High specificity values in H1 (0.95) and H2 (0.90) indicate that the composite cut-off score of ≤14 is conservative in its time-loss injury risk classification, minimizing false positive errors by making positive classifications only with strong evidence (10). Consequently, when strength and conditioning coaches and medical staff use the composite cut-off score of ≤14, the composite FMS score can confidently identify male rugby union athletes at high risk of time-loss injury.
Those working with such athletes should be aware that despite being highly specific, the FMS offers limited capability in ruling out time-loss injury in athletes identified as being at low risk of injury, as indicated by weak sensitivity values in H1 (0.35) and H2 (0.36). Kiesel, Plisky and Voight (15) found that the FMS offered limited sensitivity (0.54) when using the cut-off score of ≤14 in American football. Weak sensitivity values indicate that the FMS cannot be used alone to accurately rule out the risk of injury in rugby union athletes, preventing the test from being a stand-alone injury risk assessment tool. Despite the finding that participants scoring ≤15 on the FMS were also at significantly greater risk of injury than their higher scoring counterparts in H2, limited sensitivity (0.64) and specificity (0.65) values prevent the secondary cut-off score of ≤15 from accurately ruling out time-loss injury in athletes identified as being at low risk of injury and ruling in time-loss injury for athletes identified as being at high risk of injury.
A recent critical review posited that along with an overall lack of research on the topic of movement screens and injury risk, methodological issues, including ambiguous injury definitions and lack of exposure time, deterred movement screens such as the FMS from being considered an effective injury prediction tool (19). The current study aimed to minimize these methodological issues by using an injury definition that is standard to rugby union and collecting injury data over the entirety of an 8-month season. Despite these efforts, the results of this study should be considered in light of a number of limitations. Because FMS data were only collected twice during the 8-month competitive season, it is possible that any injury experienced between FMS testing periods could have impacted functional movement patterns which would have been overlooked. Ideally, each participant would have been retested on the FMS after injury before returning to full participation. Still, retesting athletes half-way through the season, at H2, provided more accurate assessment of functional movement status than would one single pre-season assessment. Additionally, although Fisher's exact tests revealed a nonrandom association between FMS composite scores ≤14 and increased injury risk, wide CIs indicated a large breadth of uncertainty around the OR estimates for H1 (OR: 10.42, 95% CI: 1.28–84.75) and H2 (OR: 4.97, 95% CI: 1.02–24.19). Studying a larger group of rugby union athletes could produce more certain OR estimates indicated by more precise CIs. In addition, the manual administration of the FMS used in this study has been criticized as being susceptible to error when compared with more objective assessment strategies such as inertial measurement units (27). The use of an objective strategy could have produced more accurate FMS scores had this technology been available to the research team; however, this study aimed to provide evidence for the utility of the FMS when administered manually, as this is currently the dominant mode of scoring across athletic populations. When administered manually, the FMS is a very practical tool for strength and conditioning and medical professionals when assessing a team of athletes.
Despite the evidence from several studies linking injury risk to the FMS composite score (4,7,14–16,18,21), analyses of the psychometric properties of the FMS have revealed that the 7 components of the FMS have low internal consistency and are not representative of a single construct underlying functional movement performance in elite athletes (17) and Marine Officer Candidates (13). These findings question the utility of the FMS composite score and suggest that the individual component scores are more meaningful when interpreting FMS scores (13,17). In consideration of such findings, this study analyzed the composite score and each of the 7 individual FMS components. Despite revealing an association between FMS composite score and injury risk, binomial logistic regression did not identify any one of the 7 FMS components to be predictive of time-loss injury in this study. Moreover, when dichotomizing athletes who scored ≤1, out of a possible maximum of 3, on any of the individual 7 FMS components from those who scored >1 on all 7 FMS components, and those who suffered a time-loss injury from those who did not, Fisher's exact tests indicated no significant association between scoring ≤1 on any FMS component and injury status. Still, it should be recognized that there are several ways in which athletes can obtain a given FMS composite score and the risk of injury for these athletes may differ depending on the way their composite scores are made up.
The FMS should be a considered by strength and conditioning specialists and medical professionals as a valuable component of a comprehensive preseason injury risk assessment protocol for its ability to accurately and rapidly identify at-risk male rugby union athletes when using the composite cut-off value of ≤14. In addition to the FMS, team staff should incorporate into a preseason protocol indicators of a wide scope of confirmed and potential risk factors for injury in rugby union, including injury history, objective measures of physiologic and physical function such as dynamic stability, and demographic information such as playing experience. Team trainers and other staff should note that individual fundamental movement patterns have been demonstrated to change throughout the course of one competitive season, indicating the need for repeat FMS testing during a competitive season (25).
In summary, the findings of this study indicate that the quality of fundamental movement, as assessed by the FMS composite score, is predictive of time-loss injury risk in experienced rugby union athletes and should be considered an important preseason player assessment tool. The FMS is highly specific and can confidently be used to quickly and accurately rule in the likelihood of injury in rugby athletes scoring ≤14. However, low sensitivity for injury values indicate that the FMS should not be used alone to rule out the risk of injury in male rugby union athletes. The inclusion of female athletes in future research is an important step in rugby union injury prevention, as female participation in the sport is rapidly growing. The FMS would be most effectively used in conjunction with other means of injury-risk assessment, as injury in rugby union and other sports is determined by many factors other than fundamental movement quality.
Practical Applications
The FMS provides an inexpensive method for strength and conditioning specialists and medical professionals to rapidly and accurately identify club-level rugby union athletes at risk of time-loss injury, although it should be noted that the FMS is limited in its ability to identify players who are at low risk of injury. This assessment can be used to guide pre- and rehabilitation programs designed to correct movement dysfunction and reduce injury risk. Strength and conditioning coaches, kinesiologists, athletic therapists, physiotherapists, and athletic trainers working with these athletes should focus on improving the functional movement status of players scoring ≤14 on the FMS, as prehabilitation efforts that improve movement deficits may reduce the likelihood of injury. Although no link between time-loss injury and athletes scoring ≤1 on any of the 7 individual FMS components was established in this study, improving low scores on individual FMS components will contribute to a higher composite score, which influences injury risk in a number of sports (7,14–16). Prehabilitation programs should be tailored to improve particular movement tasks in which athletes received low scores, which indicate movement dysfunctions and contribute to a lower FMS composite score. Strength and conditioning coaches must interpret the individual movement task scores in addition to the FMS composite score when creating prehabilitation programs that aim to reduce the likelihood of injury.
An important potential application of the FMS is in the assessment for return-to-play protocols, as previous injury can influence functional movement performance (8). When players sustain time-loss injury, strength and conditioning coaches or medical staff can periodically administer the FMS throughout the process of injury rehabilitation to provide an assessment of the athletes' quality of functional movement. The comparison of composite and individual FMS component scores during the rehabilitation course to baseline scores collected during preseason may serve as an indicator of readiness to compete. It should be noted that returning to a baseline FMS composite score does not necessarily indicate that a particular injury has been rehabilitated, as there are multiple ways by which an athlete can achieve the same composite FMS score. Further research on this application of the FMS is warranted.
Evaluating the utility of injury prediction models incorporating multiple risk factors for injury, such as injury history, demographic information, and measures of physiologic function in addition to the FMS will provide for superior, comprehensive injury risk assessments. This will lead to the development of preseason assessments that accurately rule in and rule out the likelihood of injury in athletes, thereby informing injury prevention strategies. The multifactorial nature of injury risk in sport warrants holistic injury risk assessments and injury prevention strategies that consider multiple risk factors for injury.
Acknowledgments
The authors express their gratitude to Danielle Mah (PT), Traci Vander Byl (AT), and the athletes, coaches, and team medical staff involved in this study for their valuable support and cooperation. Appreciation is also extended to Dr. David Docherty for his valuable insight during the development of this article. The results from this study do not constitute endorsement of the product by the authors or the National Strength and Conditioning Association.
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Keywords:
FMS; asymmetry; injury-risk; prevention
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