Evaluation of the Functional Movement Screen as an Injury Prediction Tool Among Active Adult Populations

Abstract

Context:

The Functional Movement Screen (FMS) is an assessment tool for quality of human movement. Research reports a significant difference between FMS scores of subjects who later experienced injury and those who remain uninjured.

Objective:

To systematically review literature related to predictive validity of the FMS. From the aggregated data, a meta-analysis was conducted to determine the prognostic accuracy of the FMS.

Data Sources:

PubMed, Ebscohost, Google Scholar, and the Cochrane Review databases were searched between 1998 and February 20, 2014.

Study Selection:

Identified studies were reviewed in full detail to validate inclusion criteria. Seven of the 11 identified studies were included. Articles were reviewed for inclusion criteria, then bias assessment and critical analysis were conducted.

Study Design:

Systematic review and meta-analysis.

Level of Evidence:

Level 3.

Data Extraction:

Extracted data included the following: study type, methodology, study subjects, number of subjects, injury classification definition, FMS cut score, sensitivity, specificity, odds ratios, likelihood ratios (LR), predictive values, receiver operator characteristic (ROC) analysis, and area under the curve (AUC).

Results:

Overall bias for the included 7 studies was low with respect to patient selection. Quality assessment scored 1 study 5 of a possible 7, 2 studies were scored 3 of 7, and 4 studies were scored 2 of 7. The meta-analysis indicated the FMS was more specific (85.7%) than sensitive (24.7%), with a positive predictive value of 42.8% and a negative predictive value of 72.5%. The area under the curve was 0.587 (LR+, 1.7; LR–, 0.87; 95% CI, 0.6-6.1) and the effect size was 0.68.

Conclusion:

Based on analysis of the current literature, findings do not support the predictive validity of the FMS. Methodological and statistical limitations identified threaten the ability of the research to determine the predictive validity of FMS.