Research Review By Dr. Demetry Assimakopoulos©

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Date Posted:

January 2016

Study Title:

Evaluation of the Functional Movement Screen as an injury prediction tool among active adult populations: a systematic review and meta-analysis

Authors:

Dorrel BS, Long T, Shaffer S et al.

Author's Affiliations:

Northwest Missouri State University, School of Health Science, Maryville, Missouri; Academy of Health Sciences, Graduate School, US Army Baylor Doctoral Program in Physical therapy, Fort Sam Houston, Texas; Division of Sports Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA.

Publication Information:

Athletic Training 2015; 7(6): 532-537.

Background Information:

Due to the high number of injuries that occur at all levels of competition (1, 2) in virtually all sports and activities, many clinicians and researchers dedicate an inordinate amount of time and resources towards identifying risk factors for future injury. As such, a number of clinical ‘tools’ have been proposed and developed. One such tool is the The Functional Movement Screen (FMS). Specifically, the FMS is purported to:
  1. Evaluate kinetic chain stability and mobility during whole body movements.
  2. Expose left/right movement asymmetry.
  3. Identify poor movement patterns.
  4. Determine movement competency.
  5. Screen active individuals for future injury.
The authors of this study utilized meta-analysis methodology to evaluate the injury prediction capability of the FMS in various active adult populations. Essentially, they sough to answer the question: Can the FMS predict injury?

Pertinent Results:

Seven studies were selected for review. Many studies defined an FMS “cut score”. The FMS stipulates that if an athlete scores below the cut score, they might have a greater predisposition to injury. Conversely, if the individual scores above the cut score, their likelihood for future injury may be lower.

Risk of Bias & Quality of Evidence:
  • Many of the included studies had significant threats to validity, which impaired their quality. Specifically, 5 of the 7 studies had poorly defined study methods, statistical methods and statistical reporting.
  • QUADAS-2 bias assessment: 3 of the included studies had low risk of bias. Two had a high risk of bias, and the remaining 2 were unclear due to lack of methodological reporting.
  • Risk of bias for the FMS (index test): 3 scored a low risk of bias. The remaining 4 studies were unclear due to lack of methodological reporting.
  • Risk of bias for the injury diagnosis/injury definition: 4 studies had a low risk of bias, and 2 were unclear due to lack of methodological reporting.
  • Potential bias for flow and timing: the risk level for all 7 studies was unclear, because of failure to report patient attrition rates and if any subjects were excluded from the data set.
  • Six of the 7 studies were prospective.
  • All included studies failed to adequately describe patient blinding, data collector blinding and outcome assessor blinding.
Meta-analysis:
  • Six of the 7 included studies were used in the meta-analysis.
  • Specificity was defined as the ability of the FMS to accurately classify subjects who scored above the cut score and did not sustain injury. Each study defined its own cut score. The meta-analysis showed the FMS has an overall specificity of 0.85 (95% CI 0.77-0.91).
  • Sensitivity was defined as the ability of the test to accurately classify the subjects who scored at or below the FMS cut score and sustained an injury. The analysis showed that the FMS has a sensitivity of only 0.24 (95% CI 0.15-0.36).
  • The Positive Predictive Value (PPV) was defined as the likelihood that a subject with a positive test actually has the target condition. The analysis showed that the FMS has a PPV of 0.42.
  • The Area Under the Curve (AUC) is the ability of the test to accurately discriminate between those at risk and those not at risk. The AUC was scored at 0.58 (95% CI 0.42-0.77).
  • The Positive Likelihood Ratio was 1.65.
  • The Negative Likelihood Ratio was 0.87.
  • Relative Risk was 1.5.
  • Effect Size was 0.67.
All of the included studies used the FMS as the index test, and injury as the reference standard. However, there was a lack of overall consistency in the definition of an injury. Inconsistent definitions of injury may limit the insight that can be drawn from aggregated data and can negatively affect the meta-analysis. Additionally, many studies borrowed FMS cut scores from studies that used a different definition of injury. Thus, each study may have failed to identify an optimal cut score for their respective population, which threatens the validity of the study’s results.

Clinical Application & Conclusions:

The meta-analysis showed that the FMS provides satisfactory specificity, but low sensitivity. These findings suggest that the FMS can satisfactorily predict who might not sustain an injury, if they score above the defined cut score. However, the FMS is NOT powerful enough to predict whether or not a person will sustain an injury if they score below the defined cut score (EDITOR’S NOTE: unfortunately, this is how the FMS is often used and interpreted in both clinical and athletic environments).

Further, the analysis showed that the accuracy of the FMS, determined by the AUC, is only slightly above chance. The poor AUC score, in addition to the poor positive and negative likelihood ratios, indicate that the FMS’ ability to accurately predict injury is quite low. However, the poor quality of the included studies decreased the validity of the dataset, and limits the interpretation of the meta-analysis results.

Study Methods:

Functional Movement Screen Review:

The FMS includes 7 individual foundational tests:
  1. Deep Squat
  2. In-line Lunge
  3. Hurdle Step
  4. Straight Leg Raise
  5. Trunk Stability Push Up
  6. Shoulder Flexibility
  7. Rotary Trunk Stability
FMS Scoring: Each test is given score from 0-to-3, to create a composite, total score between 0-21:
  • 0 – Pain during movement
  • 1 – Poor Performance
  • 2 – Good Performance
  • 3 – Excellent Performance
The authors accessed a number of databases for their article search, including PubMed, EBSCOhost, Google Scholar and the Cochrane Review. They also reviewed the article reference lists of accessed articles.

The inclusion criteria allowed for peer-reviewed publications studying active populations (ex. firefighters, athletes, soldiers etc.). They collected information on the general study type, methodology, number of subjects, injury classification definition, FMS cut score, sensitivity, specificity, odds ratios, likelihood ratios, predictive values, receiver operator characteristic (ROC), area under the curve (AUC) and whether the study demonstrated a significant difference between the FMS scores for injured and uninjured subjects.

The study authors performed a meta-analysis on the studies that met the inclusion criteria. They also calculated the mean sensitivity and specificity, positive and negative predictive values, effect size, ROC summary, AUC summary, and positive and negative likelihood ratios.

Study Strengths / Weaknesses:

Weaknesses:
  • The majority of the dataset failed to adequately include injury definition reference standards. The majority also failed to identify a population-specific cut score, and report AUC. Reporting the AUC is important, as it determines the test’s overall diagnostic accuracy.
  • As reported above, many of the included studies used cut scores from previous articles that utilized a different study population, which might negatively affect the validity of the dataset.
Strengths:
  • Meta-analysis is the most superior statistical assessment method.
  • The dataset included studies from a number of active populations.
  • The authors searched and accessed a large number of relevant journal databases.

Additional References:

  1. Hootman JM, Dick R & Agel J. Epidemiology of collegiate injuries for 15 sports: Summary and recommendations for injury prevention initiatives. J Athl Train 2007; 42: 311-319.
  2. Shankar PR, Fields SK, Collins CL et al. Epidemiology of high school and collegiate football injuries in the United States, 2005-2006. Am J Sports Med 2007; 35: 1295-1303.