Musculoskeletal injuries are a primary source of disability in the U.S. military (38,62). In 2007, musculoskeletal injuries resulted in approximately 2.4 million medical visits to military treatment facilities and accounted for $548 million in direct patient care costs (22). Second, only to spine disorders, lower-extremity injuries, both traumatic (i.e., noncontact anterior cruciate ligament tears and ankle sprains) and overuse (i.e., anterior knee pain, medial tibial stress syndrome, and plantar fasciitis) injuries, account for over 4.8 million of the 11 million limited duty days annually related to injury (4). Considering physical training and sports-related activities account for up to 90% of all injuries (31,34,35) and 80% of these injuries are overuse in nature, (2,4,60) the implications for determining injury risk and establishing potential protective measures are substantial.
Although there is currently no validated, efficient, field expedite, and cost effective way to screen or prevent lower-extremity injury risk in service members; there is emerging evidence in athletic populations that decreased dynamic balance and impaired functional movements contribute to increased injury risk (12,22,29,31,33–35,38,55,59). The lower quarter Y-Balance Test (YBT) and the functional movement screen (FMS) are specific tests proposed for use in screening injury risk in both athletic and military settings (33,42,55,63,64,69,70). The YBT measures 3 components of the Star Excursion Balance Test (54). The YBT challenges single limb stance while simultaneously moving the non-stance limb in an anterior, posterolateral and posteromedial direction. Deficits in single limb stance excursions have been documented in those with a history of lower-extremity injury such as ankle sprains, chronic ankle instability, and anterior knee pain (3,24,25). Initial evidence also suggests that high school athletes with anterior reach asymmetries >4 cm and females with total excursion in all 3 directions <94% of their limb length were 2.5 and 6.5 times more likely to sustain a future lower-extremity injury (55).
The FMS assesses the following 7 movement patterns: active straight leg raise, shoulder mobility, trunk stability push-up, rotary stability quadruped, deep squat, hurdle step, and in-line lunge. One of the goals of the FMS was to develop a test that assesses movements requiring a combination of muscle strength, flexibility, range of motion, coordination, balance, and proprioception (11). In a retrospective study involving the National Football League players, an FMS composite score ≤14 had a significantly greater chance of injury across a competitive season. Logistic regression indicated that players with an FMS score ≤14 were approximately 11 times more likely to be injured and players with an asymmetry were 3 times more likely to be injured (p ≤ 0.05). Researchers have also found that a composite score ≤14 on the FMS demonstrated limited ability to predict all future musculoskeletal injuries (traumatic or overuse) in a sample of marines with a sensitivity (Sn) of 0.45 and specificity (Sp) of 0.71; whereas the same cut-off value was able to predict a serious injury (any injury that was severe enough to remove the participant from the training program) with a Sn of 0.12 and a Sp of 0.94 (47). The FMS composite score was also predictive of injury for female athletes (55). However, the association between FMS composite score and performance is less clear. Perry and Koehle (51) found that FMS composite scores were associated with higher levels of participation in general physical exercise, whereas Parchmann and McBride (50) found that the FMS composite score was not related to athletic performance of division 1 golfers.
Although initial evidence suggests that YBT and FMS tools may be beneficial in predicting injury risk, scant evidence exists regarding their relationship with self-report and field-expedient clinical measures commonly used to assess active individuals at risk for lower-extremity injuries. Decrements in lower-extremity self-reported function (6) and limitations in flexibility (31,52,53,72), strength (10,15,20,37,46,53), power (9,28), lumbopelvic endurance (21,30,39,52,56,73), and knee valgus (52) collapse have all been advocated as potential impairments before or after lower-extremity injury. However, limited evidence exists that explores the relationship between self-report and impairment measurements with YBT and FMS scores. A better understanding of impairments associated with low YBT and FMS scores would assist strength and conditioning specialists and clinicians in developing and assessing personalized intervention programs to address those identified at high risk for injury.
Additionally, in the military setting, there is a lack of evidence linking identified neuromusculoskeletal impairments for lower-extremity injuries with injury preventive strategies. A better understanding of the relationship between clinical and field measures that may be associated with performance on the YBT and FMS in healthy service members may help inform the design of group-based preventive neuromuscular training programs for the lower extremity. Specifically, understanding the specific impairments typically associated with poor performance on these more global screening tools (i.e., YBT and FMS tests) would provide a foundation for possible group-based exercises that physical fitness leaders could incorporate. The inclusion of the lower extremity functional scale (LEFS) (6) also provided an opportunity to examine the relationship of self-reported activities on dynamic balance and functional movement. The purpose of our study was to determine the association between specific measures of power, strength, flexibility, balance, and endurance compared with more global measures of dynamic balance, using the YBT, and functional movement, using the FMS, in healthy soldiers.
Experimental Approach to the Problem
This study consisted of a single cohort and was conducted in a laboratory setting. After being screened for eligibility, each participant completed assessments of flexibility, strength, endurance, power, agility, dynamic balance, and functional movement. All tests selected were based on a review of the existing literature, the ability to perform the test in the field setting, and the requirement of minimal equipment. Given the multifactorial nature of accurately predicting injuries (38) and lack of research involving injury screening and preventive strategies in military service members, we elected to include efficient and cost effective clinical measures that may be associated with performance on the YBT and FMS tests.
Assessments were obtained using 7 testing stations. Participants completed the test stations in a counterbalanced order to minimize the influence of an order effect. The stations were organized to ensure efficiency of the screening process by allowing multiple participants to be screened simultaneously; similar to implementation in a military setting. To minimize learning effect, instructions and practice trials were standardized based on the best practices outlined in the literature. For example, during the YBT, participants viewed an instructional video and practiced 6 trials on each leg in each of the 3 reach directions (anterior, posteromedial, and posterolateral) to minimize the influence of a learning effect (58). All testing was completed during the early morning hours (6:00 to 10:00 AM) based on the standard military physical fitness training hours. Hydration was available during testing but was not standardized. Additional details on the independent and dependent measures are provided in the Procedure section.
Raters consisted of 29 physical therapy students enrolled in their second and third semesters of a doctor of physical therapy training program before their 1-year clinical internship. Each rater was assigned to a particular test; with 2–4 raters assigned to each test. The physical therapy students were guided by 4 faculty members and 1 research assistant. The training period consisted of 20 hours of hands-on training before testing. Test-retest reliability of the raters used in this study will be summarized in Procedure section and have been previously published (47,65).
A convenience sample of 64 active duty service members (53 men, 11 women) with a mean age of 25.2 ± 3.8 years and body mass index of 25.1 ± 3.1 kg·m−2 were enrolled in this study (Table 1). Participants were recruited over an 8- to 12-week period from service members assigned to military training at Fort Sam Houston, TX After receiving a briefing on the study, students who were interested in participating in the study were screened for inclusion and exclusion criteria. Participants were eligible for inclusion if they were an active duty service member between the ages of 18 and 35 years (or a 17-year-old emancipated minor), fluent in English, had no current complaint of lower extremity pain, spine pain, or neuromusculoskeletal disorders that limited participation in work or exercise in the past 6 months. Participants were excluded if they were currently seeking medical care for lower-extremity injuries or had medical history that included any surgery for lower-extremity injuries. Participants were also excluded if they were unable to participate in physical training because of other musculoskeletal injuries and if they had a history of fracture (stress or traumatic) in the femur, pelvis, tibia, fibula, talus, or calcaneus. Female participants were excluded if they were pregnant. All participants completed all aspects of the study. All participants were informed of the aims and testing procedures of the study and provided written consent that was approved by the Brooke Army Medical Center's Institutional Review Board.
Researchers have associated inadequate flexibility in various athletes with the development of anterior knee pain and lower extremity injuries (5,17,31,36,44,45,52,65). Flexibility of the hamstrings, quadriceps, gastrocnemius, soleus, and iliotibial band (ITB)/tensor fascia latae was assessed through length testing. A general overview of the techniques used to assess flexibility is provided below; additional details are provided elsewhere (65). The average of 2 consecutive measurements was used for analysis. For all measurements, flexibility was obtained on the leg the participant stated was dominant (which leg would you use to kick a ball?). Hamstring flexibility was assessed in supine with the testing side hip flexed to 90° and stabilized; the leg not tested was fully relaxed in terminal extension (44,45). A bubble inclinometer just distal to the tibial tuberosity measured knee extension. Quadriceps flexibility was assessed with the participant in prone position. Passive knee flexion was measured using a bubble inclinometer on the distal anterior tibia while the examiner stabilized the pelvis with the other hand (17,52). Gastrocnemius/soleus flexibility was tested with the participants lying prone with their ankles just off the edge of the plinth (36,52). Maximal dorsiflexion was assessed with a goniometer with the knee in full extension and in 90° of flexion to assess gastrocnemius and soleus flexibility, respectively (5,36). The modified Ober's test was used to assess ITB/tensorfascia latae flexibility. The modified Ober's test was performed with the patient in side lying, with the nontested leg bent slightly to minimize body rotation (7,32,52,57). The examiner supported the weight of the leg being as it was passively brought into hip extension. Stabilizing the trunk for lateral flexion and ensuring the femur was in a neutrally rotated position, a bubble inclinometer was placed along the lateral thigh over the distal ITB just proximal to the knee. The examiner released support of the testing leg and recorded the difference between the participant's thigh and the horizontal plane. Measures of flexibility had intraclass correlation coefficient (ICC) (2, 2) values ranging from 0.27 to 0.59 with SEM ranging from 4.1 to 9.9° (65).
Hip muscle strength, with specific emphasis on hip external rotation (ER) and abduction (ABD), has been associated with lower-extremity overuse injuries in runners sustaining knee pain (10,15,20,37,46,53). Hip ER strength was assessed with the participant in a prone position with the knee of the dominant leg flexed to 90°, the hip in neutral rotation, and the thighs of both legs kept closely together. The nondominant leg was in full knee extension and the thighs of both legs kept closely together (52). Belts were used at the level of the posterior superior iliac spines and over the distal posterior thighs to minimize substitutions. The examiner stood on the side of the dominant leg and placed the hand-held dynamometer (HHD; Lafayette Instrument, Lafayette, TN, USA) just proximal to the medial malleolus of the participant. The participant was asked to press into the dynamomenter, thus generating an ER torque. Hip ABD strength was assessed with the participant in a side lying position (nondominant leg down) with the bottom knee bent for support (20,52). The examiner positioned dominant leg in 20° of hip ABD, 5° of hip extension, and slight ER. Using cushions under the foot and ankle for support, a belt fitted with a HHD was positioned just proximal and slightly posterior to the lateral malleolus. The subject was instructed to lift their leg off the cushions, which generated an ABD force into the HHD for recording. Two trials were performed on the dominant leg with 1-minute rest between trials. If the first 2 trials varied by more than 10%, a third trial was performed. Measures of hip strength had ICC (2, 3) values ranging from 0.61 to 0.82 with SEM ranging from 1.3 to 3.0 kg (65).
Poor lumbopelvic endurance has been associated with lower-extremity injuries (21,30,39,53,56,73). Although poor lumbopelvic endurance is unlikely to directly influence performance on the YBT and FMS, it was hypothesized that there may be an association between these measures in those with increased injury risk for prolonged or repetitive activities. In this study, endurance was assessed by testing the ability to hold the following static postures: trunk flexion, extension, and lateral flexion (65). To ensure proper postures, participants received standardized feedback (e.g., verbal cues) in 20-second intervals. If a participant deviated more than the accepted range in a given posture, corrective feedback was provided. In the event of a second deviation, the test was terminated. Additionally, if a participant exceeded holding a posture for more than 240 seconds, the test was terminated.
Trunk flexion endurance was tested by having the participants lay supine on an examination table with arms at their sides palm down. The subject was instructed to lift hold their feet 10–20 cm off the table without pushing down through their arms for assistance. The duration of time the participant was able to hold both legs (with knees extended and legs together) 10–20 cm off the surface of the plinth was recorded (71).
Trunk extension endurance was tested on an examination table with participants in a prone position with their anterior-superior iliac spine (ASIS) positioned as close to the edge of the examination table as possible. Three belts (placed at proximal ankle, popliteal fossa, and greater trochanters) were used as a counter balance; stabilizing the lower body to the plinth while they actively maintained their trunk and upper body in a neutral position, ±10° (9,40,41). Timing began when the participant released his or her hands from the resting position and folded the arms across the chest. To ensure test position was maintained, an inclinometer was placed between the inferior borders of the scapula, and the participants were instructed to position the upper body so that the inclinometer reads zero.
Lateral trunk flexion endurance was tested on the dominant side, with participants on a plinth in side lying. Participants were asked to move into a side plank position, supported by their elbow and feet, and hold static as long as they could. Timing began when the participant moved into the testing position. A composite score was calculated as the sum of all 3 endurance test times. Measures of lumbopelvic endurance had ICC (2, 1) values ranging from 0.77 to 0.79 with SEM ranging from 18.3 to 24.5 seconds (65).
An indirect measure of power was assessed using the 6-m timed hop test and the triple crossover hop test (18,19,65). These tests have been found to be a good predictor of lower limb power, strength, and return to function after an injury (9,27). Additionally, it was hypothesized that decreased balance and agility would also negatively influence performance on these tests. Although an isokinetic dynamometer would provide a more direct measure of power; the 2 hop tests were selected based on the ability to perform these tests in a field setting with minimal equipment. Three trials were performed for each hop test. The average time and the number of hops to cover the 6-m distance were recorded. The average distance covered in the crossover hop test was measured from the start line to the heel strike on the landing of the third hop. The hop tests had ICC (2, 3) values ranging from 0.78 to 0.93 with SEM values of 0.2 seconds and 27.4 cm (65).
Balance was assessed using the YBT (55). Participants viewed an instructional video and practiced 6 trials on each leg in each of the 3 reach directions (anterior, posteromedial, and posterolateral) to minimize the influence of a learning effect (58). Participants were provided a 30-second rest period between practice trials on each leg. Participants stood on the center foot plate with the distal aspect of the foot at the starting line. While maintaining single leg stance, the participant reached the free limb in the anterior, posteromedial, and posterolateral directions by pushing a reach indictor box as far as possible. The reach distance was measured as the distance to the edge of the reach indicator to the nearest 0.5 cm. If the participant failed to maintain unilateral stance on the platform, failed to maintain contact with the reach indicator, used the reach indicator for stance support, or failed to return the reach foot to the starting position under control, the trial was not included. Participants were allowed a maximum of 6 trials to complete 3 successful trials (12,13). The maximal and average distance reached of the 3 successful trials in each direction were recorded. The composite score was calculated by summing the maximal distance from each direction. Consistent with the existing literature, the reach distance was normalized to the lower-extremity length (55). Lower-extremity length was measured from the ASIS to the most distal portion of the medial malleolus. Asymmetry was assessed as the difference between the right and left reach distance for each direction. The YBT-normalized composite score served as the dependent measure for the first regression equation. The YBT variables served as independent measures for the regression analysis associated with the FMS composite score. Interrater reliability of the maximal reach had an ICC (1, 2) range of 0.80–0.85 with an SEM ranging from 3.1 to 4.2 cm for the 3 reach directions. Interrater reliability of the average reach of 3 trails had an ICC (2, 3) range of 0.85–0.93 with an associated SEM ranging from 2.0 to 3.5 cm for the 3 reach directions (65).
Functional measures were assessed using the LEFS (6), the lateral step-down test (52), and the FMS (12,13). The LEFS is a 20-item questionnaire about a person's ability to perform everyday tasks, recreational, and occupational activities. The LEFS score ranges from 0 to 80; higher scores represent higher function (6). Although the LEFS is a survey tool, it was included in this battery of tests to determine its association with performance on the YBT and FMS. Specifically, if lower LEFS scores were associated with poor performance on the YBT or FMS, then this survey tool may be more cost and time efficient mechanism to screen for injury risk than performance measures.
The lateral step-down test assesses the ability of a participant to maintain good alignment and biomechanics of the lower extremity during a single leg step down from a 20-cm step. The lateral step-down test was included because it provides a field expedient test to assess medial collapse of the knee; which has been shown to increase the risk of lower-extremity injury (65). Each step down is scored from 0 to 5 based on whether they use various balance strategies (arm strategy, trunk movement, pelvis plane, and knee position). The participant's tibial tuberosity and second toe are marked to assist with scoring. The average of 5 trials is used to represent the participants' movement with 0 indicating good quality of movement and 5 indicating poor quality of movement (52).
The FMS is composed of 7 component tests (deep squat, hurdle step, in-line lunge, shoulder mobility, active straight leg raise, trunk stability push-up, and quadruped rotary stability tests) used to assess different fundamental movement patterns (11–13). Each of the FMS component tests is scored on a scale of 0–3 based on the quality of movement, and their sum creates a FMS composite score ranging from 0 to 21 points. A score of 3 on a component test demonstrates quality movement, a score of 2 indicated that the participant required some type of compensation or was unable to complete the entire movement, a score of 1 indicated that the participant was unable to remain in the movement position throughout the movement, lost balance during the test, or did not meet the minimum criteria to score a 2. Pain during any of the FMS component tests indicated a score of 0. Five of the 7 component tests assess asymmetry by measuring the test bilaterally. If discrepancies exist between the left and right sides, asymmetry is noted for that component test and the lower of the 2 scores is included in the FMS composite score (11). All participants were allowed to perform each component test up to 3 times, and the maximal score achieved was recorded. Additional details on scoring of each of the component tests and the composite score are provided elsewhere (12,13).
The FMS has been shown to have adequate reliability (12,13). Specifically, the FMS composite score had an ICC (2, 1) of 0.74 (95% CI: 0.60–0.83), with an SEM of 1 point on the 21-point scale. The interrater agreement of the component scores ranged from moderate to excellent (κw = 0.45–0.82) (65). The reliability was consistent with the previous publications (16,23,42,48,61). The FMS component and composite measures served as independent measures for the regression analysis associated with the YBT normalized composite score. The FMS composite score served as the dependent measure for the second regression analysis.
Two models were developed to help describe the association of FMS composite score (0–21 points) or YBT composite score normalized to limb length (sum of the % limb length reached in all 3 directions) with the 17 tests and the 38-associated potential independent variables that measured strength, power, flexibility, endurance, balance, and functional movement. To decrease the number of potential independent variables, univariate Pearson correlation coefficients were calculated between the dependent measures (FMS composite score and YBT composite score normalized to limb length) and the variables associated with flexibility, strength, power, endurance, balance, and functional movement. Measures of functional movement were considered as potential independent variables when YBT normalized composite score was the dependent measure. However, no other measures of balance were included when YBT normalized composite score was the dependent measure. When FMS composite score was the dependent measure, YBT scores were considered potential independent variables. However, FMS component scores were not included in this analysis. The original set of potential independent variables was narrowed to a smaller group of interest by only retaining those variables with a univariate correlation with an absolute value ≥0.20.
The independent variables with a correlation ≥0.20 were then entered into a hierarchical stepwise backwards regression to determine the most parsimonious set of variables associated with dependent measure. When YBT normalized composite score was the dependent measure, the independent measures of power, strength, flexibility, endurance, and functional measures with a correlation ≥0.20 were entered sequentially and assessed using 5 blocks, respectively. When FMS composite score was the dependent measure, the independent measures of power, strength, flexibility, endurance, balance, and the non-FMS functional movement measures with a correlation ≥0.20 were entered sequentially and assessed in 6 blocks, respectively. A significance level of p ≤ 0.05 was required to enter into the model, and p > 0.06 was the criterion for removal (66,67).
Descriptive statistics between the predicted and measured values were calculated to provide an estimate of the error in the regression equation. Regression diagnostics were performed to assess the resulting regression equation. The desired minimum number of 60 participants for this study was determined using the “rule of thumb” approach for regression studies (63), which specifies the need for 15 participants per variable in the final model. Additionally, descriptive statistics were calculated to summarize the demographic and clinical measurements used in this study. All statistical analyses were performed using SPSS software, version 17.0 (SPSS, Inc., Chicago, IL, USA).
Y-Balance Test Analysis
The mean YBT composite reach score was 241.3 ± 23.3 cm, and the mean YBT normalized composite reach score was 87.9 ± 8.8%. Pearson product moment correlations (r ≥ 0.2, p ≤ 0.01) yielded 13 variables of interest (Table 2). The hierarchical stepwise backward linear regression analysis of the remaining variables resulted in 4 variables of interest based on a significance level of p ≤ 0.05 to enter the model and p > 0.06 to remove the variable (Table 3; Figure 1). The resulting 4-variable model (F = 13.413, p < 0.001) has an R = 0.72, R2 = 0.51, and an adjusted R2 = 0.47. The measured mean ± SD YBT-normalized composite reach score was 87.9 ± 8.8%, and the predicted YBT-normalized composite reach score was 87.9 ± 6.3%. The difference between the measured and predicted Y-balance score was 0.00 ± 6.1%. The 4-variable model had a Durbin-Watson score of 1.7; all variance inflation factor (VIF) values were <1.1; indicating acceptable and low levels of multicollinearity within the final model. Variables related to power, functional movement, flexibility, and upper trunk mobility remained in the final model. Better performance on the YBT composite score normalized to leg length was associated with better performance on the FMS in-line lunge (r = 0.40, p = 0.001), FMS shoulder/upper trunk mobility (r = 0.29, p = 0.017), decreased number of hops required during a 6-m hop test (r = −0.35, p = 0.004), and greater gastrocnemius flexibility (r = 0.38, p = 0.004).
Functional Movement Screen Analysis
The mean FMS composite score was 15.7 ± 2.0 points. Pearson product moment correlations (r ≥ 0.2, p ≤ 0.01) yielded 19 potential variables of interest (Table 2). The hierarchical stepwise backward linear regression analysis of the remaining variables resulted in 4 variables of interest based on a significance level of p ≤ 0.05 to enter the model and p > 0.06 to remove the variable (Table 4; Figure 2). The resulting 4-variable model (F = 11.813, p < 0.001) had an R = 0.70, R2 = 0.50, and an adjusted R2 = 0.45. The measured FMS composite score was 15.7 ± 2.0 points, and the predicted FMS composite score was 15.7 ± 1.4 points. The difference between the measured and predicted FMS score was 0.1 ± 1.5 on the 21-point FMS scale. The 4-variable model had a Durbin-Watson score of 1.96, and all VIF values were <1.5; indicating acceptable and low levels of multicollinearity within the final model. Better performance on FMS composite scores was associated with greater anterior reach on the YBT (r = 0.49, p < 0.001), greater distance measured for crossover hop test (r = 0.24, p = 0.05), increased hamstring flexibility (r = −0.28, p < 0.001), and higher levels of self-reported lower-extremity function through the LEFS (r = 0.27, p = 0.03).
Understanding the relationship between self-report (LEFS) and clinical measures that contribute to performance on the YBT and FMS in healthy individuals may assist in the design of preventive neuromuscular training programs for the lower extremity that target impairments associated with decreased dynamic balance and functional movement. Additionally, understanding the variables associated with decreased dynamic balance and functional movement provides researchers and clinicians with additional variables that potentially lead to enhanced injury prediction screening protocols that identify those at highest risk for injury. Ideally, tests that would be added to an injury prediction screening protocol would enhance the currently reported low-sensitivity (0.12–0.54) values associated with the FMS (33,47).
Individuals who performed better on the YBT demonstrated superior performance on the FMS in-line lunge test, greater mobility of the shoulder and upper thoracic spine on the FMS shoulder/upper trunk mobility test, fewer hops to complete the 6-m hop test, and greater ankle dorsiflexion range of motion. The multivariate model was able to explain 51% of the variation in YBT composite scores. The variables in the model have face validity and are consistent with previous research that has demonstrated the association between balance deficits with fatigue and impaired neuromuscular control of the lumbopelvic region musculature (24–26,68), asymmetries during hopping, jumping, squatting (49), and foot structure (14). Strength and conditioning coaches and healthcare providers could assess the individual's ability to perform an in-line lunge, shoulder and upper thoracic mobility, single-leg hopping skill, and ankle mobility to determine if impairments exist. If impairments exist, therapeutic interventions could be tailored to the individual that focused on both balance training and the additional impairments identified. Although deficits in single-limb stance excursions have been documented in those with a history of lower-extremity injury (1,3,24,25), more investigation is needed to determine how variables associated with YBT performance are related with injury risk and if improvements in these variables result in decreased injuries.
Superior functional movement as measured by the FMS composite score was associated with greater single limb reach during the YBT anterior reach test, increased power and agility while performing the crossover hop test, greater hamstring flexibility, and higher levels of self-reported function using the LEFS. The multivariate model developed was able to explain 50% of the variation in the FMS composite score. The overall model is consistent with Perry and Koehle (51) who found that the FMS composite scores were positively associated with higher levels of physical activity but in disagreement with Parchmann and McBride (50) who found that the FMS was not associated with athletic performance. These results are consistent with Myer et al. (43) who demonstrated that deficits in the crossover hop test were persistent in athletes who returned to sport after anterior cruciate ligament reconstruction. Theoretically, increased anterior reach distances on the YBT may assist with improved performance on the in-line lunge and hurdle step because these tests require either single limb or tandem stance. Hamstring flexibility is also critical for enhanced performance on the active straight leg raise test and may contribute to deep squat and hurdle step performance. The need for adequate mobility to perform the FMS tests is reinforced in a recent study by Butler et al. (8), who demonstrated that improved performance on the deep squat was associated with greater dorsiflexion, greater peak knee flexion, and greater hip flexion. Lower self-reported function in our study was also associated with decreased performance on FMS tests. The significant association (r = 0.27, p = 0.03) between the FMS and LEFS reinforces the association between self-report of 20 measures of activity and participation (ranging from rolling to running on uneven ground) and objective measures of basic functional movement (i.e., squatting, lunges, hurdle step). Informed by the model, strength and conditioning coaches and healthcare providers could assess the individual's ability to perform a single-leg balance during an anterior reach, power and agility during a crossover hop test, and hamstring flexibility to determine if impairments exist. If impairments exist, therapeutic interventions could be tailored to the individual that focused on both motor control training and the additional impairments identified. Although poor performance on the FMS has been associated with increased injury risk for NFL players (33), female athletes (55), and marine officer candidates (47), more research is needed to determine how variables associated with FMS composite score are associated with injury risk and if enhancement of these variables would result in decreased injury risk or improvement in the FMS composite score.
Although the YBT and FMS have been found to be predictive of injury, there is scant evidence for their association with other physical performance measures. The multivariate models developed in this study may help clinicians assess individualized injury risk and inform the prescription of tailored prevention programs. Specifically, the YBT and FMS are measures in which decreased performance may be associated with a variety of underlying impairments. Although the results of this study are preliminary, the variables identified in these models should be considered in future injury prevention research and may ultimately aid clinicians to design more effective individualized injury prevention programs. Additionally, for patients who have already sustained a lower-extremity injury, clinicians might assess these variables to guide a more focused treatment plan to improve functional outcomes, enhance athletic performance, and prevent injury recurrence. Future research should determine whether these variables or their individual components (e.g., YBT anterior reach, FMS deep squat) are independently associated with injury risk or if the addition of these variables to multivariate models enhances the ability to accurately identify those at risk for injury. From a military perspective, the ability to screen for injury risk upon initial entry into the military and during annual physicals could help identify high-risk service members, the results of which might better inform decision making and reduce the costs associated with lower-extremity injuries in the military. Although this study presents just 1 component in this line of research, the ultimate goal is to allow policymakers to work with clinicians to develop appropriate training methods that safeguard the health of service members; thereby minimizing the negative impact of injuries on military readiness and healthcare costs.
Although the models developed in this study were able to explain at least 50% of the variance in performance of the YBT and FMS, replication of these models with an independent sample is warranted. Because the regression technique used in this study is descriptive and not inferential, further research also should be conducted to determine whether the variables in these models are merely associated with performance on the YBT and FMS tests or whether a cause and effect mechanism exists. A standardized warm-up was not used in this study based on how these tests would be performed in a field setting. Theoretically, performance may have improved on some of these measures if a standardized warm-up was included. However, the impact is probably minimal based on the standardization across all participants and the counterbalanced order of testing. Finally, future research could inform the understanding as to whether improvements in the variables included in the final models result in improved activity and participation. Given that the limited number of females enrolled in this study and the mean age of participants in this study was 25.2 ± 3.8 years, the relevance of these variables for female and older service members must be determined.
Musculoskeletal injures are a primary source of disability in the civilian and military environments, and injury prevention programs are necessary to reduce the impact of musculoskeletal injury. Physical fitness leaders and clinicians could use these models to inform decision making when developing and assessing the outcomes of a personalized intervention program for those with low FMS and YBT scores. The results of this study are initial steps in a line of research designed to mitigate injury risk, reduce disability, enhance military readiness, and reduce the financial impact associated with overuse lower-extremity injuries in the military. Specifically, the multivariate models developed in this study provide insight into the relationship between specific measures of power, strength, flexibility, endurance, and self-report measures with gross tests of dynamic balance using the YBT and functional movement using the FMS. Application of these additional specific measures in addition to the more global measures of FMS and YBT before, during, and after injury can be used to assess whether improvements in these measures are associated with enhanced YBT and FMS performance, improvements in performance tasks, and ultimately decreased risk of musculoskeletal injuries.
This study was performed in collaboration with research assistants from the Physical Therapy Department, University of Texas Health Science Center, San Antonio, TX: Mark Bauernfeind, Francis Bisagni, Jordan Boldt, Cindy Boyer, Cara Dobbertin, Steve Elliot, Angela Gass, Germaine Herman, Lacey Jung, Jake Mitchess, Teddy Ortiz, Kelly Rabon, Jason Smith, Megan Swint, Joshua Trock, and Jerry Yeung. Additional research assistant from Department of Physical Therapy, U.S. Army Medical Department Center and School, U.S. Army-Baylor University, San Antonio, TX: CPT Dustin Donofry, Lt Joshua Halfpap, CPT Sarah Hill, CPT Crystal Straseske, CPT Rick Warren, CPT Samantha Wood. Additionally, we thank Dr. Jessie Dugan for her administrative support and Jennifer Prye for her assistance with facilitating the manuscript. No professional relationships exist with companies or manufacturers who will benefit from the results of this study. The results of this study do not constitute endorsement of the product by the authors or the NSCA. The views expressed herein are those of the authors and do not reflect the official policy or position of Brooke Army Medical Center, the U.S. Army Medical Research and Materiel Command, the U.S. Army Medical Department, the U.S. Army Office of the Surgeon General, the Department of the Army, Department of the Air Force, Department of Defense, or the U.S. Government.
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Keywords:Copyright © 2014 by the National Strength & Conditioning Association.
injury prevention; functional movement screen; Y-Balance Test