July 2014

Functional movement tests and injury risk in athletes

functional-mainEfficient, low-cost, field-ready functional movement tests may be useful in preparticipation physicals to identify and provide preventive treatment for individuals with an elevated risk of injury, as well as aiding return-to-sport decisions when used at discharge from rehabilitation.

By Kathryn Schwartzkopf-Phifer, PT, DPT, OCS, CSCS, and Kyle Kiesel, PT, PhD, ATC

It is well known that the most common sports injuries occur in the lower extremity, most notably the ankle, knee, and hamstring.1-3 Researchers have identified multiple risk factors associated with athletic injury in efforts to reduce injury rates, the associated disability, and healthcare costs.4 The majority of research to date has focused on individual risk factors associated with specific injuries while controlling for the most common and strongest risk factor, previous injury.

Although previous injury in and of itself is not modifiable,5-8 many of the residual effects of injury, such as changes in motor control,9,10-13 are. For example, researchers have observed delays in the activation of the hip musculature in patients with a history of ankle instability.10,11,13 These subtle changes in motor control at the hip, regardless of the status of the ankle, may be a contributing factor for subsequent injury. Additionally, Cholewicki et al have shown that excessive outer core-muscle activation patterns are present in athletes with a history of low back pain, even though they were pain free at the time they were tested.14,15

These excessive activation patterns resulted in fewer outer core muscles being shut off after a response to a sudden load, with athletes with a history of low back pain shutting muscles off more slowly than those without a history of low back pain. These altered core activation strategies are also related to subsequent knee ligamentous injury.16 Taken collectively, these data suggest that an alteration in the timing and coordination of core and hip muscles exists in athletes following lower extremity injury and may be worthy of consideration when screening for future injury risk.

It is challenging to efficiently screen or test for core and hip motor control dysfunction, as this involves time-consuming and highly technical lab testing. Therefore, it may be desirable to perform alternate tests of fundamental movement that assess how the individual coordinates and controls his or her own body weight. Measurement of performance on such basic tasks involving a single body-weight movement may provide a better understanding of an athlete’s intrinsic motor control that may have been compromised by previous injury.

Such basic movement tests could provide insight into injury risk and serve as standardized assessments practitioners could perform when patients are discharged from rehabilitation in an effort to reduce the effects of previous injury on injury risk.17

The FMS and Y-balance test

Researchers have identified two reliable field-expedient measures involving motor control of body-weight movement tasks that have been associated with identifying athletes at increased risk of injury. While the majority of athletes who demonstrate dysfunction on these basic body-weight tests do have a history of injury, the tests are also useful for identifying those with poor underlying fundamental movement regardless of injury history. We speculate that improper strength and conditioning practices may be related to the development of dysfunctional movement.

Research has shown that the Functional Movement Screen (FMS) (Figure 1) has acceptable to excellent reliability18-24 and that clinicians can use it efficiently in a mass screening environment.25 The FMS ranks seven fundamental movement patterns, incorporates three clearing tests, and is designed to screen for major movement limitations and asymmetries26,27 (see Table 1).

functional-fig1Several injury risk studies have established predictive validity of the FMS. A composite score of less than or equal to 14 on the FMS, for example, is predictive of injury in professional football players,28,29 female collegiate athletes,30 firefighters,31 and military personnel.32,33 The presence of any asymmetrical movement is related to increased injury risk in professional football players.29

Another reliable motor control test is the lower quarter Y-balance test (YBT-LQ)34,35 (Figure 2). The YBT-LQ requires single-limb dynamic balance while the contralateral limb completes an open chain excursion in the anterior, posteromedial, and posterolateral directions. The greatest reach is normalized to limb length, and testers also report each of the three directions on the right and left sides as well as right and left composite scores. This provides an objective measure of how individuals perform near their limits of stability.

functional-table1Research has shown that performance on the YBT-LQ is related to injury risk in several populations, including female high school basketball players, in whom symmetry and scoring poorly compared with age-matched norms increased the likelihood of injury.34 Poor performance has also been found to be predictive of ankle sprains in college-aged individuals,36 and researchers have reported that poor performance has a relationship with lower extremity injury risk in college football players.37 Investigators have published a systematic review of YBT-LQ research.38

Because of the wide variety of known risk factors for lower extremity injury, and because most studies examine only a single diagnosis (such as an anterior cruciate ligament [ACL] tear) and frequently require lab-based testing, it previously has been challenging to determine injury risk using field-based screening and testing. An option proposed in the literature39 involves synthesizing multiple evidence-based factors and using an algorithmic approach to categorize an individual’s risk. For example, if an athlete presents with multiple known risk factors, such as recent injury, poor movement, and asymmetrical movement, that athlete would be categorized as being at greater risk than an athlete demonstrating just one of those factors. Each factor can also be weighted based on the strength of evidence from the current literature regarding that factor.

This approach was developed by researchers and tested in a 2013 study of collegiate athletes.39 The study examined the relationship between the risk category calculated by the Move2Perform injury risk algorithm and noncontact lower extremity time-loss injury. The algorithm weights a variety of evidenced-based factors that can be collected in an efficient manner in a standard preparticipation physical mass screening setting, including FMS score and asymmetry, YBT-LQ performance, injury history, current pain, gender, and sport.

The computerized algorithm places each individual into one of four risk categories that include Substantial and Moderate (considered “high risk”) and Slight and Normal (considered “low risk”). The study demonstrated that athletes placed in the high-risk group had a relative risk (RR) of 3.4 (95% CI: 2.0 to 6.0), indicating they were 3.4 times more likely to be injured over the course of their respective sport seasons than those in the low-risk group.

functional-fig2Although poor performance on the FMS and YBT-LQ are predictive of injury, evidence suggests these risk factors are also modifiable. Investigators collected FMS scores for 62 professional football players prior to their beginning a supervised off-season strength and conditioning program.40 They prescribed athletes individualized exercise programs designed to address their specific movement deficits or asymmetries based on their FMS results. Exercises included self and partner stretching, treatment of trigger points, and “corrective exercises,” which emphasized use of the improved mobility as well as appropriate core activation. Athletes did these exercises for seven weeks and investigators then repeated FMS testing. Of the 55 athletes who scored 14 or lower on the composite threshold at pretesting, 32 improved their scores to more than 14 after the intervention period. Forty two athletes were free of asymmetry at post-test, compared with 31 at pretest.

Significant improvements in FMS scores also have been demonstrated in other active populations. After completing six weeks of a yoga-based program, mean FMS scores improved from 13.25 to 16.55 in a sample of firefighters, who were more likely to be injured during training if their FMS score was less than or equal to 14.41 An average FMS score improvement of 2.5 points was also noted in special operations soldiers following a six-week functional training program that focused on improving agility, core strength, balance, and power.42

A randomized controlled trial by Bodden et al demonstrated a significant change in the number of athletes scoring above the threshold of 14, as well as the number of athletes with any asymmetry on the FMS following a four-week individualized exercise program in mixed martial arts.43

A multimodal training approach has also been effective for improving dynamic balance in female youth soccer players as measured by the star excursion balance test (SEBT), a motor control test similar to the YBT-LQ. Athletes did an injury prevention program (FIFA 11+), which consisted of agility training, strengthening, plyometrics, balance exercises, and running, approximately two times per week for seven to 11 weeks. Although all athletes experienced an improvement in SEBT scores, those athletes who were highly adherent to the program had a 72% lower injury risk than those who were less adherent.44 Athletes also performed exercises under three different conditions. A control group received access to the FIFA 11+ website only, while another group received an instructional workshop for the program in addition to website access. The final group received the instructional workshop, website access, plus contact with a physiotherapist who attended weekly sessions to assist with appropriate technique and exercise progression. Significantly greater improvement was noted in the physiotherapist- assigned group than the control group, particularly in the anterior reach direction of the SEBT.

functional-fig3It should be noted that previous research has emphasized the importance of the anterior reach in injury prediction.34 Therefore, a multimodal training program was not only effective at improving scores on the SEBT, but this improvement also led to decreased injury risk.

A pilot study45 has also demonstrated that an intervention program can change an athlete’s injury risk category. Using the algorithm approach described previously, Huebner et al45 reported a significant number of high school female soccer players changed from higher to lower risk categories following an eight-week supervised intervention program that included individual movement correction exercises and a group jump training program.

Collectively, the results of these studies indicate that, through a variety of intervention strategies, athletes can improve FMS score and YBT performance, and potentially reduce their injury risk.

A recent study has also suggested a relationship exists between FMS scores and performance.46 Chapman et al collected FMS scores on 121 elite track and field athletes and compared their best performance between two consecutive seasons. Athletes scoring greater than 14 on the FMS had a significantly different change in performance (+.41 ± 2.5%) between seasons compared with those scoring 14 or less (–.51 ± 2.3%). Performance changes for athletes demonstrating at least one asymmetry on any of the bilateral movements was –.26 ± 2.1% compared with +.60 ± 2.86% for athletes without asymmetry. Additionally, athletes scoring a three on the deep squat had a significantly greater improvement in performance compared with those scoring one or two. This is an interesting finding, as previous research isolating deep squat scores demonstrates that athletes were nearly five times more likely to fail to improve total FMS scores if they scored a one on the deep squat. While total FMS score, and even individual components of the FMS, have been predictive of injury and improvement potential, there is some evidence that its use can be extended to predict performance as well.

Clinical application

Injuries come at a cost, not only to athletes and their families, but also to the schools, universities, or organizations for which they play. If athletes at high risk for injury can be identified during a preseason screen, appropriate interventions can be implemented and the financial burden of these injuries can be avoided. The FMS and the YBT-LQ are field-expedient, reliable tools that can be combined with demographic information to predict lower extremity injuries in active populations.

Athletes can then be placed into categories of injury risk based on the presence of known evidence-based risk factors. Identification of high-risk athletes can have time and cost-saving benefits by allowing clinicians to focus preventive efforts on those who need it most. High-risk athletes can receive one-on-one care from clinicians, addressing specific areas of deficit to improve performance in fundamental movements and dynamic stability. Ultimately, these improvements lead to a change in category of risk and limit the effects of the previously named risk factors. Once a lower injury risk category is achieved, these athletes can receive instruction in a generalized preventive program, along with other low-risk athletes, with minimal supervision to improve performance and decrease injury risk. Coaching staff can implement and monitor these preventive programs, thereby reducing demands on clinical staff.

These tests are advantageous, not only because of the information to be gained, but also because they are quickly administered and easily used and interpreted by medical and coaching personnel (Figure 3). Staff and funding are limited at many institutions and under these circumstances, these tests can facilitate evidence-based, cost-effective allocation of resources.

Conclusion

Lower extremity injuries are common in sports, and the strongest risk factor associated with these injuries is previous injury. Because of the well-known motor control changes that occur with injury, basic single body-weight, movement-oriented testing may be helpful in determining how previous injury has affected the motor control of basic body-weight movement tasks. An injury prediction algorithm has been proposed that utilizes efficient, low-cost, field-ready tests and brief historic injury data to categorize an athlete’s injury risk. The initial research on this algorithm is promising, and further research is warranted. Ultimately, the tests used with the injury prediction algorithm may be useful in preparticipation physicals to identify individuals with an elevated risk of injury and to aid return-to-sport decisions when used at discharge from rehabilitation.

Kathryn Schwartzkopf-Phifer, PT, DPT, OCS, CSCS, is an instructor in the Department of Physical Therapy at the University of Evansville, IN, and a doctoral student in the Rehabilitation Sciences Program at the University of Kentucky in Lexington. Kyle Kiesel, PT, PhD, ATC, is a professor of physical therapy at the University of Evansville.

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