Musculoskeletal injuries are among the leading causes of morbidity and mortality in working aged adults as well as service members (3,15,16). For example, Cohen et al. (3) recently evaluated the etiology of medical evacuations from Operation Iraqi Freedom and Operation Enduring Freedom and found that, of 34,000 evacuations from January 2004 to December 2007, the most common reason was musculoskeletal disorders (24%); combat injuries were a distant second at 14%. Studies in basic trainees across services report high injury rates (15,20). Injuries among 22,000 male recruits undergoing 12 wk of basic training at Marine Corps Recruit Depot, San Diego, CA, resulted in more than 53,000 lost training days, which was estimated to cost more than $16.5 million per year (1). The morbidity of musculoskeletal injury directly translates into lost duty days, missed training, early attrition from the service, and diminished combat effectiveness in theaters of operation. To reduce the high incidence of injury, multiple studies have evaluated factors associated with injuries, with low levels of previous occupational and leisure time physical activity, previous injury history, high running mileage, low physical fitness, cigarette smoking, older age, and biomechanical factors being major risk factors (2,15,16,20,29,30).
Numerous epidemiologic studies have identified low physical fitness levels, tobacco use, sedentary lifestyle, and a history of prior injury as some of the stronger predictors for future risk of musculoskeletal injury (8,9,20,21,30). Recently, the sports medicine community has become interested in functional movement and core stability programs (10,26) because it is generally believed that these programs may improve fitness and performance and also assist in injury prevention (10,18). Despite this widely held viewpoint, few large-scale prospective studies have validated the concept that screening or correcting functional deficits will either predict or minimize injury or improve performance.
Functional movement screening (FMS) is a series of movements designed to assess the quality of fundamental movement patterns and presumably identify an individual’s functional limitations or asymmetries. Previous small studies have demonstrated that low FMS scores (≤14) are associated with serious injury in American football players and that FMS scores can be improved following a standardized intervention (17,18). In addition, a large interventional study in firefighters suggested that FMS assessment followed by an 8-wk program to enhance functional movement reduced time lost to injury by 62% when compared with historical injury rates (28).
The purpose of the study reported here was to document the distribution of FMS scores and assess the predictive value of the FMS by comparing entry scores with subsequent injury in Marine Corps Officer candidates during Officer Candidate School (OCS) training. Specifically, we hypothesized that FMS scores would predict injury rates, in particular, overuse injuries, and that FMS scores would predict injury better than physical fitness scores. Thus, the study also examined relationships among FMS, physical fitness, and injury.
This project was a prospective cohort study approved by the institutional review boards at the National Naval Medical Center and the Uniformed Services University of the Health Sciences, Bethesda, MD. Before enrollment, all subjects were thoroughly briefed about the project including benefits and risks, and those choosing to volunteer provided written informed consent and signed Health Insurance Portability and Accountability Act authorization forms permitting the use of protected health information for research.
The study participants were male candidates aged 18–30 yr, enrolled in officer candidate training during the summer of 2009, who gave informed consent. There were two classes: the first was a 6-wk short-cycle (SC: 38 d) and the second was a 10-wk-long cycle (LC: 68 d). SC participants are generally enrolled in collegiate ROTC programs, whereas LC candidates are not and seek direct military commissions. Although both LC and SC programs have comparable training intensities and volumes with candidates expected to be extremely fit for successful participation, the training is generally considered to be somewhat more condensed and intensive during the SC.
Before the study, volunteers were briefed on the FMS, given a demonstration on the movements, and those interested volunteered to participate. At 1 or 2 d after the briefing, all candidates underwent medical screening at the OCS medical facility. The screening updated medical records and immunizations, as well as ensured that candidates had no significant changes from their commissioning physical examinations before beginning the rigorous training program. Those with a history of orthopedic injuries and/or surgery received additional evaluation by the physical therapy section to ensure that they had recovered sufficiently to undergo the upcoming rigorous training. The FMS and a survey, which asked about age, tobacco use, exercise history, and prior injury, were incorporated as part of the medical screening for the volunteers.
The FMS is a comprehensive screen to assess the quality of fundamental movement patterns for presumably identifying an individual’s physical limitations or asymmetries. The FMS includes seven tests that are scored on a 0–3 ordinal scale. The seven tests are the squat, hurdle step, lunge, shoulder mobility, active straight leg raise, push-up, and rotary stability (5,18). A score of 3 indicates that the movement was completed as instructed and is free of movement compensation and pain. A score of 2 indicates that the subject could complete the movement pain-free but with some level of compensation; a score of 1 indicates that the subject could not complete the movement as instructed; a 0 is assigned if the subject experiences pain with any portion of the movement. Of the seven tests that comprise the FMS, five of them (hurdle step, lunge, shoulder mobility, active straight leg raise, and rotary stability) are performed and scored separately for the right and left sides of the body. When assigning a score to a test that incorporates both left and right sides, the lesser of the two scores is used for a final event score. Overall FMS scores can range from 0 to 21. To maximize interrater reliability, all members of the research team were certified in the FMS before participation in this project by an instructor from Functional Movement Systems™. FMS certification involved active participation in a certified workshop, practical application, and successful performance on a written examination administered through Functional Movement Systems™.
The research team set up a total of nine stations; one designated for check-in, seven stations to conduct the FMS, and the last for check-out. At the check-in station, volunteers were rebriefed on the research proposal, ensured an informed consent, and issued an FMS assessment sheet, which was completed by an evaluator at each of the next seven stations. Participants would then proceed through the seven FMS evaluation stations, at which time they were reeducated on successful performance of the station and then assessed.
Physical fitness test score.
Within 1 wk of starting the training program, candidates completed a physical fitness test. The test consisted of pull-ups to exhaustion, 2-min abdominal crunch, and a 3-mile run for time, conducted in that order. Points were assigned to various levels of performance on each event (100 points maximum), and a composite score was calculated by summing the three event scores. Details of the test events and scoring system can be found in a Marine Corps publication (24).
Data on injuries were collected daily during the training cycle at one medical facility. Medical care providers who were not part of the investigation saw subjects with medical problems and electronically recorded all medical encounters using the military’s electronic medical record system (Armed Forces Health Longitudinal Technology Application [AHLTA]). Physicians who were part of the research team examined each subject’s medical encounter in AHLTA and determined whether the encounter was for an injury or for other medical care. For each injury encounter, the diagnosis was extracted from the AHLTA record. An injury case was a subject who sustained physical damage to the body secondary to physical training (11,12) and sought medical care one or more times during the study period. Injuries were grouped by “type,” which was determined from descriptive information in the medical notes and by the specific diagnosis. Injury types included 1) overuse injury, 2) traumatic injury, 3) any injury, and 4) serious injury. Overuse injuries were presumably due to or related to long-term repetitive energy exchanges that resulted in cumulative microtrauma. Specific overuse diagnoses included musculoskeletal pain (not otherwise specified), stress fractures, tendonitis, bursitis, fasciitis, muscle injury presumably due to overuse (strain), joint injury presumably due to overuse (sprain), retropatellar pain syndrome, impingement, degenerative joint conditions, and shin splints. A traumatic injury was presumably due to sudden energy exchanges (acute event), which resulted in abrupt overload and consequent tissue damage. Traumatic injury diagnoses included pain associated with an acute event, muscle injury (strain) due to an acute event, joint injury (sprain) due to an acute event, dislocation, fracture, blister, abrasion, laceration, contusions, and/or closed head injury/concussion. “Any injury” was considered a combination of overuse and trauma diagnoses as described above. The “any injury” type included primarily musculoskeletal injuries but also included dermatological insults (e.g., blisters, abrasions, lacerations). A subject could have experienced both traumatic and overuse injuries and be counted in both categories, but only once in any injury. Serious injuries were defined as any type of injury (traumatic or overuse) that was severe enough to remove the subject from the training program (i.e., the individual attrited from training due to injury).
A sample size estimate was performed using nQuery by assuming an α of 0.05, a power of 80%, and projections from previous OCS injury data. These criteria and data from prior studies indicated that a sample of 280 candidates would be required to detect a 20% difference in injury rates (29).
Questionnaire data, physical fitness scores, injuries, and FMS scores were analyzed by using Statistical Package for the Social Sciences version 16.0 (SPSS, Inc., Chicago, IL). Comparisons between LC and SC subjects were made using t-tests (for continuous variables) or χ2 (for ordinal, nominal, or discrete variables).
Cumulative injury incidence was defined as the proportion (%) of candidates who had one or more injuries during their training cycle (injury incidence = ∑candidates with ≥1 injury/∑all candidates). The injury incidence rate was the proportion of candidates with one or more injuries divided by the time at risk (incidence rate = ∑candidates with ≥1 injury/∑time (d) in training for all candidates).
Receiver operating characteristic (ROC) curves were calculated by pairing FMS and PT scores with any, overuse, and serious injuries.
χ2 statistics were used to evaluate differences in injury risk among those with FMS scores above and below the cut point and by physical fitness test scores. A χ2 for person–time was used to compare injury incidence rates. Because the LC and SC subjects had different exposure times, and similar, although somewhat unique training programs, associations between FMS and injury were evaluated separately in these groups.
A total of 874 male candidates consented, completed questionnaires, and had the FMS performed during medical in-processing. Of the 874, 427 volunteers were in the LC and 447 volunteers were in the SC.
Characteristics of the LC and SC groups indicated the groups were comparable (Table 1). Compared with the SC group, the LC group was about 1 yr older and scored four fewer points on the fitness test than the SC group. However, no significant differences were noted in baseline exercise frequency, smoking history, or functional return from a prior injury.
Cumulative injury incidence and injury incidence rates.
Because of the longer training cycle and longer exposure time, the LC candidates had significantly higher cumulative injury incidences for any, overuse, traumatic, and serious injuries (Table 2). When the groups were compared as a function of injuries per 1000 person-days, the SC candidates had higher injury incidence rates for any and traumatic injuries; the two groups did not differ on overuse or serious injury incidence rates.
The mean FMS score among all candidates was 16.6 ± 1.7 with a range of 6–21. The mean FMS scores for the LC and SC were 16.5 ± 1.5 and 16.7 ± 1.9, respectively (P = 0.07) (Fig. 1). Only 0.2% of candidates had a perfect score of 21, and only 0.2% had scores ≤10. The most frequent score among candidates was 17 (23%). Figure 2 presents the distribution of scores for each movement: 8.4% and 12.8% of the LC and SC, respectively, had scores ≤14, and 25.3% and 36.7% of the LC and SC, respectively, had scores ≥18. Shoulder mobility was the movement with the highest frequency of 1 as a score (7.6%), and push-ups was the movement with the highest frequency of 3 as a score (84.5%). The squat, hurdle step, and rotary stability were the movements with the highest frequency of 2.
FMS and injuries.
In the SC cohort, candidates with an FMS score of ≤14 had a 1.91 times (95% confidence interval (CI) = 1.21–3.01, P < 0.01) higher any injury incidence rate compared with a score >14 (Table 3, upper panel). Candidates in the LC group were 1.65 times more likely to sustain an injury with an FMS score ≤14 (95% CI = 1.05–2.59, P = 0.03), compared with those with a score >14. When the LC and SC groups were combined, the relative risk was 1.5 times greater for any injury with a FMS score ≤14 (P = 0.003): 45.8% of persons with scores ≤14 suffered an injury compared with 30.6% of those with scores >14. In contrast, FMS scores were not associated with the incidence of overuse injuries. Overall, 12.5% of persons with scores ≤14 had overuse injuries compared with 10.6% with scores >14 (P = 0.6). Mean FMS scores of 16.7 ± 1.7 for those who had “no” injury were comparable to those who suffered “any” injury (16.7 ± 1.8).
Although the intent of the study was to dichotomize the data, as has been done in the past, we found that cumulative injury incidence was higher at FMS scores of 18 (LC = 46.7%, n = 75; SC = 32.2%, n = 90) compared with FMS scores of 17 (LC = 27.6%, n = 116; SC = 18.1%, n = 94). Thus, injury risk of LC and SC groups was examined according to FMS categories of ≤14, 15–17, and ≥18. When grouped in this way, and using the 15–17 as the reference, the risk of injury was significantly higher in the ≤14 group, as before, but also significantly higher for the ≥18 category for the LC group, which suggests a bimodal distribution (Table 3, lower panel).
FMS cut point determination.
ROC curves were developed for overuse, serious, and any injury. Analysis of the ROC curves yielded areas under the curve of 0.58, 0.52, and 0.53 for any, overuse, and serious injuries, respectively. No ROC curve provided a point that maximized specificity and sensitivity. Table 4 presents the odds ratios (OR), CI, sensitivity, and specificity for the dichotomized FMS score (≤14 vs >14) as a predictor of injury. In addition, we examined whether a combination of only four movements would yield comparable ORs: we chose the deep squat, shoulder mobility, active straight leg, and hurdle step because they had the highest frequency of scores less than 3.
Physical fitness scores, FMS scores, and injury.
The relationship between physical fitness (PT) and FMS scores was also examined. PT scores were dichotomized as ≥280 (high fitness) versus <280 (moderate fitness): the best possible score was 300 and 33.3% of the sample had scores ≥280. Analysis of the ROC curves for PT yielded areas under the curve comparable to FMS scores—0.57, 0.53, and 0.52 for any, overuse, and serious injuries, respectively. Again, no ROC curve provided a point that maximized specificity and sensitivity. Table 4 presents the association between fitness, FMS scores, and injuries. As shown candidates with PT scores <280 were 2.2 times more likely to have FMS scores ≤14 and significantly more likely to sustain an injury across all types of injuries. No biomodal distribution was noted for PT scores when classified by quintiles (data not shown).
Because musculoskeletal injuries are so common and associated with significant morbidity, clinicians and researchers have been seeking approaches for identifying those as highest risk. To date, no large prospective study using FMS had been conducted. In the present study, we applied the FMS, which has been used to identify functional imbalances and weaknesses in football players, to candidates undergoing training to determine whether scores would predict injury. The risk of any injury was 2.0 times higher among those with FMS scores ≤14, but it remains to be determined whether treating the identified imbalances would have prevented those injuries. Although the limited predictability might reflect the low percentage of persons with scores ≤14, this cannot be determined from the present data. Importantly, PT scores were just as predictive of future injury as FMS scores and had a higher sensitivity.
The FMS, first introduced in 2001 by Cook (4) and described in more detail in 2006 (6,7), has altered the paradigm of screening for static biomechanical deficiencies: comprehensive functional movements and core stability are assessed to establish an individual’s functional platform. Core stability may serve a role in injury prediction and prevention (13,22,23,33), but its role in predicting injury or improving performance is questionable (13,27). Limited data are available on the use of the FMS for screening, but the interrater reliability of the FMS has been established in recent studies, with weighted κ values ranging from 0.45 to 1.00 (25,32). Preliminary studies with FMS have demonstrated injury predictability in a small number of NFL football players (17,28). Kiesel et al. (18) retrospectively analyzed the relationship between FMS scores for National Football League (NFL) football players and the likelihood of serious injury. FMS scores were obtained before the start of the season for 46 NFL players, and a score of ≤14 was found to positively predict serious injury with a specificity of 0.91 and sensitivity of 0.54; the odds of sustaining a serious injury was 11.7 times higher in those with an FMS score ≤14 compared with those with a score >14. Kiesel et al. (18) also noted lower scores among those who had been injured (14.3 ± 2.3) compared with those without injury (17.4 ± 3.1). In the present study, when we compared entry FMS scores by no injury versus any injury, the scores were the same (16.7 ± 1.7) and the OR for sustaining a serious injury was 2.0 (95% CI = 1.0–3.8); the sensitivity and specificity were 0.19 and 0.90, respectively. Interestingly, two groups have reported that FMS did not predict injury: one study was with 60 marathon runners (14) and another was on 112 basketball players (31). Hoover et al. (14) reported an 8.3% sensitivity and 94.5% specificity for marathon runners, whereas Sorenson’s (31) data yielded a sensitivity and specificity of 53.8% and 52.3%, respectively, for basketball players. The low sensitivity is problematic because a sensitivity above 50% is desirable so those predisposed to injury can be identified early on and potentially rehabilitated before injury. Although specificity seems to be high, this is in large part explained by the small proportion of the cohort with scores ≤14.
One major difference between our study and those of Kiesel et al. (17,18) was the percent with scores ≤14. In one study, 21.7% of the football players had low scores compared with only 10.6% of candidates (18). In another study, Kiesel et al. (17) found that 90.1% of football players had scores ≤14. It is possible that the low percentage of scores ≤14 limited the predictive value of our data. One other important observation from the present study was that, although no single test alone was significantly associated with injuries, combinations of selected movements yielded ORs comparable to those for the seven tests. Likewise, although asymmetries were assessed and recorded, no statistical evidence supported asymmetry as a risk factor for injury in this cohort. However, correcting asymmetries and remediating problematic movements might be essential for any intervention to mediate risk of injury.
Several studies have investigated whether using FMS scores to dictate treatment for identified weaknesses is effective. Peate et al. (28) assessed core strength and flexibility in 443 firefighters and found lost time to and the number of injuries was reduced by 62% and by 42%, respectively, after an intervention to improve flexibility and strength in those with FMS scores <17. In addition, Goss et al. (10) used FMS scores to dictate training, and after 6 wk, performance on functional tests and FMS scores improved significantly, but no improvement in pain scores was noted. Kiesel et al. (17) examined the effect of a 7-wk off-season intervention program on changes in the number of asymmetries and FMS scores in football players. After intervention, 51.6% had scores >14 compared with the 9.9% at preintervention and the number of asymmetries declined by 35%. However, 20 subjects undergoing the intervention failed to improve their score enough to exceed the threshold of 14. The authors concluded that, although a standardized intervention did change overall movement patterns, further research would be necessary to determine whether changes in movement patterns translated into reduced injury risk (17). The high percentage of persons in their study with scores ≤14 is striking, given only 10% of our volunteers had low scores.
Among our SC and LC groups, no significant differences in injury risk factors were noted, to include baseline exercise frequency, smoking history, or functional return from a prior injury. Whereas the LC groups scored slightly higher (about 1%) on the PT test, this small difference was unlikely to have contributed to injury risk. However, scores on the PT test were significantly associated with injury, such that those with high PT scores were significantly less likely to suffer any, overuse, or serious injuries compared with those with lower PT scores, which is consistent with previous military studies (15,16,19,20,29). In addition, PT scores were comparable to FMS scores about predicting injury: the OR for PT scores ranged from 2.1 to 2.5 and combining the two scores did not significantly improve the prediction. Although the ability to predict injury in this population seems to be comparable between PT and FMS scores, the FMS offers the potential advantage of a rehabilitative intervention. However, further study will be needed to address the higher injury rates in persons with scores ≥18.
Several factors should be considered in interpreting the data from the present study. First, our sample of candidates represents highly fit young men who have been previously challenged and screened in the Marine Corps and, as such, represent a relatively homogeneous population. In the present study, the average FMS score was 16.7 ± 1.7, whereas the average score was only 12.6 ± 2.1 in one NFL study (17). However, mean scores of 16.7 ± 3.0 (18) and 15.1 (10) have also been reported for football players and military personnel, respectively. Another consideration is the distribution of scores. In our study, 10.3% had scores ≤14 with only 1.3% of scores ≤12; among the NFL football players studied, 22% had scores ≤14 such that the distribution of their scores was skewed to the left (17). Clearly, more work will be needed with different populations.
In summary, FMS scores ≤14 were associated with increased injury risk, although the sensitivity was low. Nonetheless, data from this study suggest that further investigations are warranted. Future studies should seek to evaluate a military cohort entering basic training because they are likely to represent a more heterogeneous population than Marine candidates. In addition, the present study has demonstrated that FMS screening can be accomplished as part of medical in-processing.
The authors acknowledge that this research was performed under a research grant award from the American Medical Society for Sports Medicine.
The authors report no conflict of interest.
The authors thank Dr. Bruce Jones from the US Army Public Health Command for his insights, careful reading, and assistance with the statistics; Drs. Megan Raleigh and Devin McFadden for screening of the medical records; and Mr. Tyson Grier for his assistance with constructing the database. The authors also thank COL Richard Mancini, MAJ Brad Kroll, and Mr. Jess Vera Cruz from Officer Candidate School, Quantico, VA, as well as Francesca Cariello, PhD, RN, CCRN, and Richard Blumling, MSN, CDR, USN, from the Naval Health Clinic Quantico. Finally, the authors also thank Mr. Brian McGuire, Marine Corps Training and Education Command, for his assistance in facilitating this project at Quantico.
The results of this study do not constitute endorsement of functional movement screening by the authors, the US Department of Defense, or the American College of Sports Medicine.
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