Optimal running form requires mobility and stability. Assessment of mobility and stability in runners has been performed previously using isolated muscle testing and muscle length tests (5). A baseline functional movement screen (FMS) that assesses the entire kinetic chain will help to identify areas that may predispose a runner to injury (14). A more functionally based approach allows the tester to grade not only the quantity of movement but also the quality of how the body moves together as a whole (1). The FMS is a simple screening tool that tests an individual's quality of movement and may determine potential injury risk. The FMS includes a series of 7 functional movements that are applicable to sport, and it examines an individual's strength, flexibility, range of motion, balance, coordination, and proprioception (13). Additionally, the FMS is scored based on the movement patterns and links within the kinetic chain, asymmetries between sides, and compensatory movements (1,13). Theoretically, if poor movement patterns are identified, they can be corrected before they become learned patterns and practiced incorrectly, which may lead to injury (1). The FMS has been shown to have good to excellent interrater reliability as reported in several studies (9,13).
The primary purpose for the development of the FMS was to screen athletes for injury (1,2). The FMS takes into account many of the functional movement patterns required in athletics, and therefore, it may be a helpful component for determining possible injury risk. Kiesel et al. (6) used the FMS to predict injury risk for football players during a National Football League season. These researchers identified an FMS score of ≤14 (perfect score is 21) to put an athlete at a greater risk of sustaining an injury (odds ratio = 11.67). Using this same premise, O'Connor et al. (10) found a score of ≤14 on the FMS that predicted any injury with a sensitivity of 0.45 and a specificity of 0.71 and serious injury with a sensitivity of 0.12 and a specificity of 0.94 in a group of 874 Marine officer candidates.
Normative data that are defined for a specific population adds validity to a test and provide a standard that the strength and conditioning professional can use to measure that population. Normative FMS values for different populations have been reported in the literature. Schneiders et al. (13) conducted a study that determined normative FMS values for a young (18–40 years), active population of 209 subjects. In their study, the mean FMS score was 15.7 ± 1.9 out of a total 21, and the researchers found no statistical difference between the composite score for women and men. O'Connor et al. (10) tested the FMS on 874 Marine officer candidates, and the average score was 16.6 ± 1.7. Kiesel et al. (6) obtained FMS scores on 46 professional football players before the start of the season and established an average score of 16.9 ± 3.0 for this cohort. Currently, no published research has examined the differences between sexes or with varying age groups using the FMS on running athletes. Injury rate varies between age and sexes depending on sport, and it would be beneficial for a screening tool to be able to pick up differences between these subgroups and possibly predict an individual's susceptibility to injury.
There is limited research on the FMS as a screening tool for runners. The purpose of this study was to determine the mean values of the FMS in a group of long-distance running athletes. The secondary aims were to investigate whether FMS performance differed between men and women, and between young and old runners.
Experimental Approach to the Problem
This study was designed to report the FMS score for a cohort of long-distance runners. Further, the goal was to determine if the FMS score differed between male and female runners. Finally, we wanted to determine if differences were present in the FMS scores between runners younger than 40 years and runners older than 40 years. The FMS scores have been reported for professional football players, young athletes, and military personnel; however, no studies have determined a mean value for long-distance runners.
Forty-three runners participated in the study, 16 women (mean age = 33.5 ± 8.7 years, height = 165.2 ± 8.0 cm, weight = 56.3 ± 5.9 kg, and body mass index [BMI] = 20.6 ± 1.8) and 27 men (mean age = 39.3 ± 12.8 years, height = 177.1 ± 6.2 cm, weight = 75.7 ± 12.4 kg, and BMI = 24.5 ± 3.5). There were 26 participants less than 40 years of age (mean age = 29.3 ± 5.8 years) and 17 participants who were older than 40 years of age (mean age = 49.1 ± 7.3 years). The descriptive data are presented in Table 1. All the subjects had been injury-free for the previous 6 weeks, and all were running a minimum of 30 km·wk−1. Exclusion criteria included previous lower extremity surgery. The age division of 40 years was determined based on a previous study that used age 40 as a cut-off for young athletes (13). This study was approved by the University of Kansas Medical Center Internal Review Board, and written informed consent was obtained from all the subjects before data collection.
The subjects were recruited through advertisement to local running clubs. The subjects were screened first over the phone for inclusion criteria (injury-free and mileage) before they were invited to participate. The data were collected by 2 physical therapists with >35 years of combined clinical experience and previous experience with the FMS. The 2 met and discussed the administering and scoring of the FMS to help standardize the testing. An interrater reliability study was conducted on a subgroup of 11 subjects, and the testing took place in the summer and fall of 2011.
After providing written informed consent, the participant's weight and height were measured. Before the start of the testing, measurements of tibia height and hand length were taken and recorded for use in the screen. The FMS as described by Cook et al. (1) was used in the study. The FMS consists of 7 fundamental movement patterns to test mobility and stability (1). The 7 tasks are the deep squat, hurdle test, in-line lunge, shoulder mobility, active straight leg raise (SLR), push-up, and rotary stability. Further description can be found in several resources (9,11). Performances were scored using standardized FMS criteria. The scoring criterion for each of the 7 tests is a 4-point scale (0–3). A score of 3 was awarded for perfect form (normal functional movement pattern), a 2 for completing the test with compensations, a 1 for not completing the test accurately, and a 0 if the subject noted any pain with the testing components. For the tests that were assessed bilaterally, the lowest score was used. The maximal score that can be achieved is 21. Each movement was practiced and then performed again for scoring, in the same standardized order and recorded on a score sheet. If the examiner was not sure about the scoring, then the movement was repeated. No warm-up was included.
For the reliability study, the 11 subjects were scored simultaneously and independently by the 2 raters. One rater, APM, instructed all the subjects during the collection of the reliability data.
Data were recorded on a score sheet. The score sheet data were entered into a Microsoft Excel (2010) spread sheet to be sorted, and the demographics were calculated. Statistical analysis included calculating the mean and SD and 95% confidence interval (CI) for the total scores and each test for each gender and age groups. For comparison of the total score, independent t-tests were used. The significance level was set at p ≤ 0.05. Contingency tables were developed for each of the 7 tests to compare between sexes and age. A 2 × 2 table was used where the values scored as “2” or “1” were collapsed together because our goal was to determine those who scored a perfect score “3” and those that did not (scored “2” or “1”). Using the contingency tables, χ2 values were calculated for each test to examine for potential differences between men and women and ages using p ≤ 0.05. Interrater reliability was statistically calculated for the total score using the intraclass correlation coefficient (ICC, model 3,1). The Kappa statistic was used to establish the interrater reliability measurement for each of the 7 test items. All calculations were performed using Stata 11.2 (Stata Corp., College Station, TX, USA).
The interrater reliability is presented in Table 2. Interrater reliability (ICC 3,1) for the composite FMS score was 0.928 demonstrating excellent reliability. Interrater reliability (Kappa) for individual test components for the majority of the FMS demonstrated a substantial to excellent agreement (7). The reliability for the scoring of shoulder mobility and SLR was excellent. There was substantial strength of agreement between the graders for deep squat, hurdle step, in-line lunge, and push-up. There was fair strength of agreement for the scoring of rotary stability.
All the subjects completed the FMS in its entirety. No participant reported pain with any of the test components. The composite scores are presented in Table 3. The mean composite FMS score was 15.4 ± 2.4 with a 95% CI of 14.7–16.1. Thirteen individuals, representing 30% of the participants, had a composite score of ≤14.
The total composite score for women was 16.2 ± 2.4, and for men it was 15.0 ± 2.4. There was no significant difference between the total score for the 2 groups (p = 0.11). The number of women with a score <14 was 2 (12%) and that for the men was 11 (41%). Significant differences were noted between men and women in the shoulder mobility and SLR tests. Contingency values are found in Table 4. For the shoulder mobility test, a majority of the women were more flexible with 88% scoring a “3” and 6% scoring a “2” or “1.” The men’s group was more distributed through the scores with 52% scoring a “3” and 48% scoring a “2” or “1.” This pattern was significantly different between groups (χ2 = 5.6205, p = 0.018). For the SLR test, women were more flexible with 56% scoring a “3”; 31% scoring a “2,” and 12% scoring a “1.” The majority of men (42%) scored a “2.” This pattern was significantly different between groups (χ2 = 3.9541, p = 0.047).
In comparing runners who are under 40 years of age and over 40 years of age, there was a significant difference in the total composite score between the 2 groups (p < 0.000). The total composite score for participants under 40 years of age was 16.4 ± 1.9 and that for the participants who were over 40 years of age was 13.9 ± 2.3. The number of participants with a score of ≤14 for the younger group was 4 (15%), whereas 9 (53%) of the participants over 40 had a score ≤14.
Significant differences between the young and older runners were noted for the deep squat, hurdle step, and lunge tests. Contingency values are found in Table 5. For the deep squat, the younger group scored better with 46% scoring a “3” and 54% scoring a “2.” No participants in the younger group scored below a “2.” The majority of the older group scored a “2” (77%) with an equal number (11%) scoring a “3” and “1.” This pattern was significantly different between groups (χ2 = 5.536, p = 0.019). Scoring for the hurdle step revealed that the younger group scored better with 58% scoring a “3” and 38% scoring a “2,” and only 4% scoring a “1.” The majority of older scored a “2” (82%). This pattern was significantly different between groups (χ2 = 15.062, p = 0.000). On the lunge test, the younger group scored better with 88% scoring a “3” and 12% scoring a “2” with no scores below a “2.” In the older group, 41% scored a “3,” 35% scored a “2,” and 24% scored a “1.” This pattern was significantly different between groups (χ2 = 10.892, p = 0.001).
A good performance test needs to be reliable from 1 tester to another. The interrater reliability of the FMS has been investigated in previous literature and has been reported to range from 0.74 to 1.00 (9). In this study, the composite scoring of the FMS was excellent between the 2 testers with an ICC of 0.928. On looking at each of the 7 skills, rotary stability was the least reliable. However, the 2 testers were able to discriminate between the individual who could perform a “3” and the ones who could not. The low reliability scoring difference was determined between the “2” and “1.” Based on the primary statistical procedure performed in this study, this low reliability did not affect the results. Additionally, within the reliability study, there were 5 subjects who scored ≤14 and the 2 testers scored these subjects below the distinguishing value.
The purpose of this study was to determine the mean values of the FMS in a cohort of long-distance running athletes. Additionally, we investigated whether FMS performance differed between men and women and between young and older runners. The FMS has been scored in other groups, such as in professional football players (6), young and active population (13), and military personnel (10). It is essential to determine the mean values for specific athletic populations because different sports require different movement patterns.
In this study, the mean FMS score for runners aged 18–40 years was 16.4 ± 1.9. The older runners averaged 13.9 ± 2.3. The score for the younger runners was higher than the values found in the study by Schneiders et al. (13) who tested an active healthy population and was slightly lower than the study by Kiesel et al. (6) who tested professional football players. The score for the older runners was similar to that in a study by Cowen (3) who studied male and female firefighters and found a mean baseline FMS score of 13.3 ± 2.3. Although the FMS includes a variety of tests that may replicate skills found in a variety of sports, it seems that the average score will vary depending on the tested cohort.
There was no significant difference for composite scores between female and male runners, although the women had a higher composite score. Although there was no difference in the composite score, there were significant differences in 2 individual tests. The female participants in this study scored significantly higher in the mobility and flexibility tests, including the shoulder mobility and active SLR. The shoulder mobility test examines shoulder range of motion, scapular motion and thoracic spine mobility, and the active SLR examines hamstring and gastrocnemius flexibility. This trend was also seen in the study of Schneiders et al. (13) and is not surprising in that women tend to have an increase in the range of motion in their joints. It is worth noting that in our study, the men on average, were older, which could also influence their flexibility.
Our study differed from others that were reported in the literature. In that study, we found no difference between men and women in the strength tests such as the push-up, squat, and rotary stability tests. This could be because most of the men fell into the older group. Further studies that age-matched runners and then investigated sex differences would be beneficial.
Most of the statistical differences that we found in the study occurred between the young and older runners. The participants over 40 years old had statistically significant lower scores on the lower extremity strength and balance exercises, including the deep squat, hurdle step, and in-line lunge. These tests require the participants to have a certain level of balance, neuromuscular control, and strength. This finding is consistent with the fact that the aging population has decreased balance, stability, and strength especially after the age of 40 years (13). Further studies to investigate the relationship between balance training and improved score on the FMS is needed.
The FMS was designed to screen for potential injuries in sport. Kiesel et al. (6) determined a score of ≤14 suggestive of an elevated risk for injury on a small sample of professional football players. Although our study was not designed to test injury rate, we did note that a fair number of the runners (30%) did score at this critical level, especially in the older runners' group. Our 30% number was similar to that of Schneider et al.'s study on young, healthy athletes. Ironically, 1 of our exclusion criteria was no history of injury in the past 6 weeks before participation; those who are more likely to sustain an injury may not have been well represented in this population sample. According to McKean et al. (8), runners over the age of 40 years are more likely to suffer a running related injury that younger runners, and also have a greater chance of suffering multiple injuries. Further research is needed to test the 14 level criteria in a group of runners.
Based on the results of this study, the FMS is a reliable baseline functional screen for the long-distance runner that can be used by the strength and conditioning professional. The screen can be used to help design specific corrective exercises for the runner that may minimize injury (14). Further, this study provided mean values for the FMS in a population of runners aged 18–60 years. These values can be used as a reference in comparing FMS scores in other runners who are screened with this tool.
We also provide new data about FMS scores in a population over the age of 40 years. The results of this study demonstrate that the runner over 40 years is more likely to have issues with balance as seen by significantly lower scores on the hurdle step and lunge. This age group would benefit from balance-type exercises that may help with running performance.
The validity of the FMS score as a predictor of performance or injury risk in the long-distance runner was not tested in this study. However, it seems that components of the FMS certainly lend themselves to a skill set that simulates running and would be beneficial to measure in this population. For example, the hurdle step and in-line lunge require single leg balance with an opposite extremity knee lift and foot placement, similar to the motor pattern involved with a running stride. The deep squat, rotary stability test, and push-up require global strength and mobility that are arguably a necessary trait for the long-distance runner (4,12). The 2 flexibility tests, the SLR and shoulder mobility tests, may be the least valid to use in the running population, because there is no solid evidence that flexibility influences running performance or injury rate.
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