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Using the Functional Movement Screen™ to Evaluate the Effectiveness of Training

Frost, David M.; Beach, Tyson A.C.; Callaghan, Jack P.; McGill, Stuart M.

Journal of Strength and Conditioning Research: June 2012 - Volume 26 - Issue 6 - p 1620–1630
doi: 10.1519/JSC.0b013e318234ec59
Original Research
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Frost, DM, Beach, TAC, Callaghan, JP, and McGill, SM. Using the functional movement screen™ to evaluate the effectiveness of training. J Strength Cond Res 26(6): 1620–1630, 2012—The Functional Movement Screen™ (FMS) has demonstrated some efficacy in the prediction of injuries and is thus used by many practitioners to make recommendations for exercise. However, questions remain regarding its utility as a means to evaluate the effectiveness of training. Sixty firefighters volunteered to participate, and their FMS scores were examined before and after 12 weeks of training. Individuals were graded on how they chose to perform rather than how they could perform. The participants were assigned to 1 of 3 groups: intervention 1, intervention 2, or control. The 2 intervention groups received three 1.5-hour training sessions each week and differed in the emphasis that was placed on movement quality. Sagittal and frontal plane videos were used to grade the FMS with 3 methods: the standard 0–3 scale, a 100-point scale that weighted specific compensations (research standard), and a modified 100-point scale whereby grades were assigned based on the total number of compensations present. There were no significant differences in the total FMS scores for any group posttraining. However, the scores of 85% of the firefighters who did not receive training did change. The 100-point scale methods resulted in more FMS score changes posttraining, but the between-group interactions were identical to those found with the standard scoring method. The control group's scores were not consistent pretraining and posttraining; thus, the influence of each intervention could not be evaluated. Currently, the FMS might provide a momentary impression of general movement quality, although further efforts would likely assist in the development of better ways to implement the test, interpret the results, and generate reliable scores.

1Department of Kinesiology, University of Waterloo, Waterloo, Ontario, Canada

2Faculty of Physical Education and Health, University of Toronto, Toronto, Ontario, Canada

Address correspondence to David M. Frost, d3frost@uwaterloo.ca.

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Introduction

Flexibility (3,20,24,26,32), strength (24), passive joint range of motion (45), motor patterns (48), and core stability and proprioception (46,47) have each been cited as possible risk factors for injury; however, the mere presence of a risk factor is not sufficient to infer causation (1). When the body is considered as a series of interconnected segments, basic mechanical concepts can be exploited to understand how such movement constraints could limit performance potential or promote injurious loading patterns. The presence of overt joint mobility- or control-limiting factors effectively limit the number of whole-body movement strategies available to affected individuals.

Over the past 10 years, tremendous progress has been made in the prevention of ACL injuries by contrasting the movement patterns employed during noncontact ACL injury events to ACL loading mechanisms. Researchers who have used this knowledge and focused their efforts on task execution—how an athlete jumps or lands—have been able to change movement patterns and attenuate joint loading (16,38) and through the implementation of long-term interventions have reduced the incidence and relative risk of ACL injury (15,29). In comparison, general strength-training programs that have been implemented without emphasizing how the tasks were performed did not change jump-landing mechanics (14,31), and therefore, may have less impact on the relative risk of injury.

Given the arguments presented above, scientists and practitioners have begun using movement-based screens to expose “faulty” or “aberrant” patterns that might predispose individuals to any injury (6,12,27,28,36,38,40,41). There are, however, several considerations that must be addressed if attempting to grade movement quality. First, it is not clear what tasks should be graded. As described above, knee kinematics and kinetics measured during drop jumps may provide information related to ACL injury risk but do not necessarily yield direct information pertaining to the risk of suffering other common musculoskeletal injuries associated with a broad range of occupational and athletic activities. A second challenge in using movement screens is that the way individuals perform a given task can be influenced by a number of factors including coaching or feedback (9,10,13,33,39), thus making it difficult to establish reliable criteria by which to rank movement quality over multiple testing sessions. Lastly, clear distinctions must be made between what individuals can do (i.e., movement abilities) and what they choose to do (i.e., habitual movement strategies). Just because individuals can perform a particular task in a certain way (given specific instructions in a controlled setting) does not mean they will perform in a similar manner when in the unpredictable environment of their occupation or sport. If injury prediction is the desired outcome of movement screening, it may be more important to evaluate the “ingrained” or “natural” strategies used to perform general, occupational, or athletic tasks (5,14,37).

The Functional Movement Screen™ (FMS) is a movement grading method that has received widespread recognition because of associations between FMS scores and athletic injuries (22,23). The 7-task test is used to grade whole-body movement quality on a 4-point ranking system; scores of 0–3 are assigned for each test based on published criteria and a cumulative grade is given out of 21 (6,7). The interrater and intrarater reliabilities have been shown to be high when grades were assigned from video (34,43) and authors have recently reported improvements in the total FMS scores after various training interventions (8,11,21). However, several important questions remain unexplored, the foremost being the repeatability of the test itself (i.e., within subject). In contrast to the 2 injury studies and the 3 training studies reporting positive results, there have also been 4 investigations that found no relationship between FMS scores and injury (4,18,42,43). Whether the results reflect differences in the number or type of participants (i.e., football players, firefighters, and runners), the method of scoring (i.e., paper and video) or feedback given during the test, they are difficult to interpret without knowing how the same person would be graded on multiple days. Second, some of the tests have less clearly defined descriptors of midrange performance (i.e., score of 2), which allows for a broad range of movement patterns to be categorized with the same score (4). Given the many ways an individual could choose to move during the performance of an FMS task and therefore the possibility of between-day scoring differences, it may be challenging to individualize recommendations for exercise or to evaluate movement quality outcomes of a training program; however, such a contention is yet to be addressed.

This investigation sought to examine the utility of the FMS as a means to evaluate changes in an individual's movement patterns after an exercise intervention. To address this objective, firefighters were assigned to one of two 12-week training programs (i.e., fitness oriented and movement oriented) or a control group and screened with the FMS before and after the 12-week period. It was hypothesized that training (particularly the movement-oriented intervention) would improve screen scores beyond any changes exhibited by the control group (within-subject variation), and therefore, the FMS could be used as a tool to evaluate the effectiveness of an intervention. Three scoring methods were also tested to examine the number of participants demonstrating screen score changes posttraining when the grading scheme was expanded to reflect all task performance criteria. It was hypothesized that any differences noted among the intervention groups would become more apparent when a broader scoring range was used.

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Methods

Experimental Approach to the Problem

Firefighters from the Pensacola Fire Department volunteered to participate, and their FMS scores were examined before and after a 12-week training intervention. Each individual was graded on how they chose to perform (self-selected movement pattern) rather than how they could perform given coaching or feedback. Upon the completion of the baseline screens, the participants were assigned to 1 of 3 groups: intervention 1, intervention 2, or control. The 2 intervention groups received three 1.5-hour training sessions each week and were coached by strength and conditioning professionals. Each program differed in the emphasis that was placed on movement quality because it related to task performance. After the 12 weeks of training, each firefighter was screened a second time. Sagittal and frontal plane video was used to grade each FMS using 3 different methods: the standard 0–3 scale, a 100-point scale that weighted specific compensations (research standard), and a modified 100-point scale whereby grades were assigned based on the number of compensations present. The number of participants exhibiting screen score differences provided an estimate as to how sensitive a specific test or grading scheme might be to changes in movement.

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Subjects

Sixty professional firefighters (men) from the Pensacola Fire Department volunteered to participate in this investigation. All the men were free of musculoskeletal injury or pain at the time of testing and were on full active duty. Their mean (SD) age, height, and body mass were 37.5 (9.8) years, 1.80 (0.06) m, and 90.3 (14.3) kg, respectively. The University's Office of Research Ethics, the Baptist Hospital Institutional Review Board, and the City of Pensacola each approved the investigation, and all the participants gave their informed consent before the data collection began.

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Functional Movement Screen

The FMS is a 7-task test comprising fundamental movement patterns that require a balance of joint mobility and neuromuscular control (6). It was designed as a simple tool that could be used to identify compensatory motions, imbalances, or asymmetries before the onset of exercise. The FMS is not to be treated as a battery of exercises or an evaluation of technique; its recommended purpose is to “red-flag” problems in the movement system that may predispose an individual to injury. The 7 screening tasks are (a) Deep squat (SQT)—a dowel is placed over the head with the arms outstretched and the individual squats as low as possible; (b) Hurdle step (HRD)—a dowel is placed across the shoulders, and the individuals steps over a hurdle placed directly in front of them; (c) In-line lunge (LNG)—with the feet aligned and a dowel contacting the head, back, and sacrum, the individual performs a split squat; (d) Shoulder mobility (SHDR)—the individuals attempt to touch their fists together behind their back (internal and external shoulder rotation); (e) Active straight leg raise (SLR)—while lying supine with their head on the ground, the individuals actively raise 1 leg as high as possible; (f) Trunk stability push-up (PU)—the individuals perform a push-up with their hands shoulder width apart; (g) Rotary stability (ROT)—the individuals assume a quadruped position and attempts to touch their knee and elbow, first on the same side of the body and then on the opposite. “Clearing” tests are also included with the SHDR, PU, and ROT to expose other potential sources of pain that may be overlooked during the primary task performance. Additional details of each task have been published previously (6,7).

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Testing Procedures

Each participant was screened with approximately 50 general and job-specific tests, 7 of which comprised the FMS. Instruction and administration of the FMS were completed in accordance with previously published guidelines (6,7) by an FMS certified instructor. Aside from the standard verbal instructions (6,7), no specific cues were given, and the participants were blinded to the test objectives, scoring criteria, and their screen results. The firefighters were graded on how they chose to perform rather than how they could perform the tasks given feedback or coaching. No rationale was given as to the general purpose of the screen to ensure that each individual's task performance was as natural as possible. Video was synchronously collected from the sagittal and frontal planes (Nexus 1.4, Vicon, Centennial, CO, USA), and 4 repetitions (2 forwards and 2 backwards) of each task were performed. As recommended (6,7), the “best” repetition was graded.

Upon completion of baseline testing, the participants were assigned to 1 of 3 groups: intervention 1 (INT1; n = 21), intervention 2 (INT2; n = 19), or control (CTL; n = 20), each matched for age, height, body mass, and total FMS score (given the number of participants and the nature of the study, true randomization was not possible). The 2 interventions comprised 12-week exercise programs designed to improve firefighter fitness and performance. The participants attended three 1.5-hour sessions 3 times per week at a local performance facility and were coached by strength and conditioning professionals (1 per group). The performance coaches were blinded to the results of the FMS and instructed to refrain from sharing the test objectives or scoring criteria with their group of firefighters. Individual-specific mobility and neuromuscular control training (based on FMS scores) was given to the participants in INT1; however, the decisions made regarding exercise selection and progression were made by coaches other than those responsible for training. Both programs comprised multiple phases (INT1—4 phases, INT2—3 phases) prescribed in a periodized fashion but differed with regard to exercise selection and the emphasis placed on movement quality as it related to task performance (via coaching cues). The INT1 coach focused on whole-body coordination and control during task execution rather than on performance metrics alone (i.e., emphasis was placed on how and how much). Strength, power, and aerobic capacity development remained high priorities in the INT1 group; however, so too was injury prevention. The INT1 incorporated several evidence-based strategies that have been previously hypothesized or demonstrated to reduce the risk of injury (9,10,15,25,29,30,44). For example, firefighters were made aware of the potential implications surrounding excessive frontal plane knee motion during the performance of all relevant exercises. The main objective of INT2 was simply to make the firefighters as “fit” as possible. Exercise technique was monitored, and feedback was provided when necessary (for safety purposes), but the primary emphasis was on maximizing performance and fitness outcomes (i.e., how much). The participants were required to attend a minimum of 30/36 training sessions to be included in the analyses. The control group received no feedback or guidance with regard to exercise and was asked to refrain from making any changes to their physical activity habits for the 12-week period. After the intervention, each firefighter was screened a second time.

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Statistical Analyses

Video was used to objectively assign FMS task scores using 3 methods: (a) standard (STD); (b) research standard (RES); and (c) modified (MOD). The STD method was graded using an ordinal scale of 0–3 whereby scores of 3, 2, 1, and 0 represented “performed without compensation” (according to relevant criteria), “performed with compensation,” “could not perform” (according to relevant criteria) and “pain,” respectively (6,7). For example, if the SQT was performed with the board (a compensation) and the hips were below parallel, the participants would receive a 2, permitting that no other compensation (e.g., dowel was not behind toes) was present. If any additional compensation was noted (Table 1), the SQT was scored a one. Tasks requiring the performances of the left and right sides of the body were given a grade equal to that of the lowest score. The cumulative sum of all the 7 tasks represented the total STD score (21 was the best possible score). The RES criteria were identical to those of the STD; however, specific compensations and tests were weighted, the left and right sides were treated independently, and a cumulative score was given out of 100 (best possible score). Task weightings incorporated in the RES method were based on work by Hickey et al. (17). With this scoring method, if the squat was performed without the board and participants' hips were below parallel (criteria to use W1; Table 1, panel A), they could achieve a maximum score of 18; 6 points were given if the tibia and torso were parallel, and 4 points were awarded if the knees were aligned with the toes, the squat was symmetrical and the dowel was behind the toes. If the board was used, the maximum score was reduced to 8, and 2 points were awarded for each criterion (W2 was used; Table 1, panel A). With the MOD scoring method, each task was assigned 1 primary objective (OBJ; Table 1, panel A), corresponding to a specific STD criterion (e.g., squat depth), and secondary compensations (COMP; Table 1, panel A). If the test objective was met, the task score was made to equal the total number of compensations present by assigning them each one point (a score of 0 was perfect). If the objective was not met, the baseline score was raised and made to be 1 point higher than the total number of compensations possible (e.g., 7 for the SQT). One additional point was given for each of the compensations present. The left and right sides were treated separately. The scores for each task were evenly weighted, and a cumulative sum was given out of 100 (0 was the best possible score). Using the SQT as an example once again, the primary objective (OBJ) was defined as the hips breaking parallel. If this was achieved, the best possible score was 0, and each compensation present (e.g., tibia/torso not parallel, knees not aligned with toes) added 1 point to total. If the hips did not break parallel, the best score was defined as 7 (equal to the total number of compensations possible), and again, each compensation raised the score by 1 point. Asymmetries for each grading scheme were defined as left and right side screen score differences. A detailed list of all testing criteria is outlined in Table 1. An individual with 7 years of experience using the FMS and who had attended numerous FMS practical workshops graded all the tests. The rater was involved with data collection; however, the screens were graded with video 8 months after the investigation. No identifying documentation regarding group membership was present in the video.

Table 1-a

Table 1-a

Table 1-b

Table 1-b

The distribution of FMS scores was examined for normality, and it was determined that the between-group differences would be best compared with a Kruskal-Wallis 1-way analysis of variance. Wilcoxon signed-rank tests were used to identify significant changes in the scores posttraining. The number of participants exhibiting screen score differences provided an estimate as to how sensitive a specific test or grading scheme might be to changes in movement. One-way Chi-square tests were performed to determine whether or not the between-group and between-method changes were significant. Spearman Rank Order Correlations were used to provide an additional measure of between-day repeatability among the CTL (total score only). Statistical significance was set at the level of p < 0.05 for all the tests.

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Results

Functional Movement Screen (Standard Scoring Method)

The total FMS scores were not significantly different between the groups before training (p = 0.838) (Table 2). On average, the SQT and LNG were the worst and best scored tasks, respectively (Figure 1); all the participants scored either a 2 (41 total) or 3 (19 total) on the LNG, whereas 68% of all SQT screens were scored a 1 (INT1—14, INT2—12, CTL—15). The mean score for each of the other 5 tasks was approximately 2, although it should be noted that 93% of the HRD screens received a 2 (Figure 1). No one received a zero (i.e., pain) on any test.

Table 2

Table 2

Figure 1

Figure 1

There were no significant posttraining changes in the total FMS score for any group (p > 0.176) (Table 2). When the individual scores were examined, however, the group means were not found to be representative of all the participants, particularly among those acting as controls. Seventeen participants did receive the same score pretraining and posttraining (INT1—9, INT2—5, CTL—3), but 26 improved (INT1—9, INT2—9, CTL—8), and 17 got worse (INT1—3, INT2—5, CTL—9). In fact, the scores of 85% of the participants in the control group actually changed (Figure 2). Further complicating the matter, the SHDR screen, in which there was only 1 criterion (i.e., range of motion), was found to be more variable than all other tasks; 15 of the 20 CTL participants either increased or decreased their SHDR score after 12 weeks. In comparison, only 4 and 6 changes were noted among the INT1 and INT2 groups, respectively (p < 0.001). Additionally, 8 of the 20 CTL participants received different SLR and PU scores posttraining, and 9 changed their ROT screen score. The SQT, HRD, and LNG were found to be the most consistently graded screens (4, 4, and 3 changes were noted among the control group, respectively); however, there were also fewer changes found on these tasks among the participants who received training (p > 0.114). The total number of asymmetries present before and after training was found to be just as variable as the individual screen scores (Figure 2), and there were no differences between groups (p = 0.528).

Figure 2

Figure 2

Interestingly, changes were noted among the CTL group irrespective of the baseline score (Figure 3). However, the distribution of total FMS score changes (e.g., number of increases) was dependent on the grade of the initial screen (p = 0.008). Seventeen of the 26 participants exhibiting an increase were given an initial grade <13, whereas 14 of the 17 who received a lower total posttraining score had a baseline FMS ≥13. There were no differences in the distribution of scores between groups (p = 0.653).

Figure 3

Figure 3

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Scoring Method

When the FMS was graded with the STD and RES methods, there were no significant changes noted in the pre-post FMS scores for any group (p > 0.060) (Table 2). Differences were found when the MOD method was applied to the same data (p < 0.049), although the changes were evident in all the groups (CTL included). As was hypothesized, modifying the range of possible grades by using all task criteria resulted in a larger number of FMS screen score changes posttraining (Table 3). Fifty-nine and 60 participants changed their total scores for the MOD and RES methods, respectively, in comparison with 43 for the STD (p < 0.001). The SQT, HRD, LNG, SLR, and ROT were each more sensitive (p < 0.035) to changes in movement (Table 3). The total number of asymmetry score changes was not significantly different between methods (STD—33, RES—41, MOD—44). Significant (p < 0.002) between-day correlations were found among the CTL for all 3 scoring methods (STD—0.759, RES—0.645, MOD—0.669).

Table 3

Table 3

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Discussion

Although not initially intended to be a reliability study, the findings of this research raise questions about the ability of the FMS (as it is currently used) to characterize meaningful changes in movement quality over multiple testing sessions. Joint mobility restrictions and neuromuscular control deficits can be viewed as performance-limiting factors (2), but they can also offer a mechanical rationale as to why certain demographics report increased rates of injury (15). For these reasons, numerous performance and injury prevention specialists have adopted the FMS as a tool to grade movement quality (i.e., to uncover limitations in joint mobility and neuromuscular control) and to provide recommendations for exercise. In this study, the FMS was used to evaluate the movement quality of firefighters before and after 12 weeks of training. It was hypothesized that the FMS scores of trainees would exhibit changes more consistent and greater than what would be expected in a control group. However, the data collected did not support this hypothesis, regardless of whether the grading criteria were expanded so as to reflect all compensations present.

Since Kiesel et al. (23) first reported a relationship between composite FMS scores and injury, 3 studies have adopted the screen as a means to monitor the effects of training (8,11,21). Study participants were football players (21), firefighters (8), and soldiers (11), and the exercise interventions comprised either functional training (11,21) or yoga (8). Baseline FMS scores were reportedly highest for the group of soldiers (15.1) and lowest for the football players (12.5), although the yoga-based intervention was found to have had the greatest impact on the total FMS scores posttraining (+3.2). Interestingly, although the 2 functional training programs lasted 6 and 7 weeks and emphasized all the aspects of performance (e.g., strength, endurance, power, flexibility), the yoga-based study comprised an average of only 4 sessions and focused on breathing, relaxation, and posture. It is difficult, however, to interpret these findings, particularly in light of the fact that comparisons were not made to a control group in any of the abovementioned studies. The changes in scores could reflect improved coordination and neuromuscular control and a lower risk of injury, but in light of the findings from this study (i.e., no group change), they could just as easily be because of prior exposure to the test, specific feedback received, or coaching.

In this study, it was hypothesized that training (especially INT1) would improve FMS scores beyond any changes experienced by the control group. Measures were taken to ensure that each participant received no feedback and was unaware of the overall rationale behind movement-based evaluations to discourage efforts to “train for the test.” For the same reasons, the strength and conditioning professionals responsible for both interventions were not privy to the screening process and were instructed to avoid making references to the FMS tasks. The objective was to evaluate the influence of training on the execution (i.e., joint mobility and neuromuscular control) of movement-related tasks (i.e., the FMS) that did not overtly appear to be exercise-related. Surprisingly, the average composite FMS scores did not change after 12 weeks of training, and there were no differences among the 3 groups. At first glance, it appeared that the supervised exercise programs simply failed to influence movement quality. If the control group did not change and the interventions had no impact on scores, the only logical explanation would be that training did not transfer to nonexercise task performance (i.e., FMS). However, this was not the case; the control group did change, just not in a systematic fashion. Eighty-five percent of the firefighters who did not participate in training had a different composite FMS score after 12 weeks. In fact, on the 3 tasks (i.e., SQT, SHDR, and SLR) found to be responsible for the majority of changes among the soldiers mentioned above (11), nearly twice as many improvements were reported for firefighters in the control group (CTL—17, INT1—9, INT2—10). These findings make it impossible to use the standard ordinal scale to evaluate the influence of training on the movement quality of the firefighters in this study.

Although the FMS is simplistic by design, it was also intended to assist in the development of individualized recommendations for exercise (6). There may be valuable information to be gained from each individual's screen that can and should be used for this very purpose. At present, it is not clear whether descriptors of movement such as ‘2’ or ‘14’ provide sufficient information to make educated decisions regarding exercise prescription, particularly if the movements are not consistently performed or scored. Because of the nature of each FMS task and the number of associated criteria, there are 2 primary issues that need to be considered: (a) a broad range of movement patterns can be categorized with the same score, and (b) scores may not reflect an individual's task performance. The first issue was addressed in the current investigation by using 2 additional grading schemes to evaluate the FMS. Originally, the tasks comprising the most criteria were found to be the least variable, both on the initial screen (e.g., 56 firefighters received a 2 on the HRD) and with regard to the number of changes posttraining (e.g., 54 firefighters received the same score on the HRD). As it turns out, this was simply a reflection of the scoring method. When the MOD approach was used and each criterion was able to influence the task score, a similar number of changes were noted for all the tasks. However, contrary to our initial hypothesis, both modified scoring schemes failed to discriminate between the number of changes experienced by firefighters receiving training and those in the control group. Consequently, it may have been more appropriate to focus our efforts on the specific criteria being used to describe the execution of each task rather than the scoring system itself.

By using a defined set of criteria to describe each FMS screen, subtle changes in task execution may influence a score (increase or decrease) without reflecting true or meaningful changes in joint mobility or neuromuscular control. For example, the dowel position in the SQT or the rear foot position in the LNG may be inherently variable descriptors of movement, particularly in the absence of coaching or task-specific feedback. It is therefore possible that the between-test differences seen among the control group reflected normal motor output variability (19) rather than bona fide changes in movement quality, although the same could be said for the firefighters who received training. Conversely, perhaps the decision to move in a specific manner was because of improved body awareness acquired during the intervention. The firefighters who received 12 weeks of coaching may have chosen to squat above parallel because they were conscious of the fact that they would not be able to maintain adequate control of their trunk or hips by going any deeper.

The decision to grade each firefighter on how they chose to perform was made so that each screening task could serve as a “transfer test” to evaluate the specific movement-related adaptations to training. Verbal feedback was avoided because it has been shown to alter the kinetics and kinematics of similar movements (9,33,39) and thus would have made it impossible to differentiate between the influence of exercise and FMS-specific coaching. As it turns out, this may have been a limitation of the original study design. Had the firefighters been informed of specific grading criteria before they completed the baseline test the scores among the control group may have been less variable. Screening results would have better reflected what each individual could do, potentially providing more reliable and direct indicators of movement quality or ability (e.g., joint mobility and neuromuscular control). However, our decision to grade how the firefighters chose to perform FMS tasks was based on our a priori assumption that the natural movement strategies observed might better predict how individuals would perform when performing physical activities of daily living. For example, if individuals elected to control their lumbar spine posture during the FMS push-up task (by activating “core” muscles before and throughout task execution), it is possible that the said individual would exhibit a similar neuromuscular control strategy when required to resist an external low-back extensor moment in life (e.g., when pushing a cart or a door). On the other hand, if an individual did not choose or was unable to generate the requisite muscle force and stiffness to maintain a neutral lumbar posture during the push-up task, the observed movement pattern could indicate that the individual lacks the necessary awareness or physical capacity to avoid potentially injurious loading scenarios in life. If specific instructions or coaching (i.e., real-time feedback) were provided to the firefighters during the push-up task, it would have been difficult to interpret whether the training interventions had an effect on their natural movement and control strategies. Interestingly, Moreside (35) recently found that training-induced improvements in the hip range of motion (i.e., changing what the participants could do after 6 weeks of passive and active stretching) had no influence on how participants chose to move during a battery of screening tasks.

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Practical Applications

There is likely great value in using movement-based tests such as the FMS to evaluate movement behavior. Screening can expose issues related to movement ability or awareness and provide the basis from which to make recommendations for exercise or to gauge the transfer of training. It is important, however, to acknowledge the fact that many factors can influence the ways in which individuals move, be it verbal feedback, external task constraints (e.g., load magnitude and rate), or adaptations to training. Furthermore, there is the possibility of inherent day-to-day or repetition-to-repetition variation in the very criteria being used to grade the quality of movement. Tools such as the FMS may reveal important information for injury prevention and performance specialists, but further questions related to the implementation and interpretation of the FMS should be explored in the context of exercise prescription and long-term progression to best ensure that the training strategies are consistent, effective, and sustainable.

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Acknowledgments

The authors would like to thank performance coaches Anthony Hobgood and Andrew Fisher for their contributions and are grateful to Athletes' Performance and the Andrews-Paulos Research and Education Institute for the use of their facilities and for their support. The authors would also like to extend their gratitude to each member of Pensacola Fire Department for their commitment to this work. Funding for this project was provided by the Center of Research Expertise for the Prevention of Musculoskeletal Disorders and the Natural Sciences and Engineering Research Council of Canada. The results of this study do not constitute endorsement for or against the FMS by the authors or the National Strength and Conditioning Association.

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Keywords:

firefighter; FMS; injury; performance; repeatable

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