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.

The Functional Movement Screen as a Predictor of Injury in National Collegiate Athletic Association Division II Athletes

“…Our findings refute those of O'Connor et al 8 and our meta-analysis, 11 both of which demonstrated the FMS to have lower sn and higher sp. The current data indicate that the FMS was more sensitive than specific.…”

“…11 O'Connor et al established the AUC for the injury classifications of any injury (0.58), overuse injury (0.52), and serious injury (0.53) of the FMS to be slightly better than chance in predicting injury in each of the 3 categories. Our meta-analysis results 11 established the comprehensive AUC of the included FMS literature at 0.587. Based on the results of these 3 studies, we can establish that the FMS was slightly better than chance in predicting injury.…”

“…Those reports demonstrated differences between injured and uninjured participants in comprehensive FMS scores and individual FMS test scores (eg, deep squat). However, a recent systematic review and meta-analysis 11 brought into question the ability of the current body of research to effectively evaluate the predictive validity of the FMS. The FMS was more specific (0.85; 95% confidence interval [CI] ¼ 0.77, 0.91) than sensitive (0.24; 95% CI ¼ 0.15, 0.36), with a positive predictive value (PVþ) of 0.42 (95% CI ¼ 0.23, 0.64) and a negative predictive value (PVÀ) of 0.72 (95% CI ¼ 0.67, 0.76).…”

“…The previous use of an inconsistent reference standard (ie, injury definition) in the FMS literature may have limited our understanding of the test's ability to assess risk. 11 Therefore, the purpose of this current research project was to examine the prognostic accuracy of the FMS in predicting injury in National Collegiate Athletic Association (NCAA) Division II athletes and to evaluate how variations of the reference standard (ie, musculoskeletal injury [MI], overall injury [OI], and severe injury [SI]) may affect the prognostic values and accuracy of the FMS.…”

Red, Amber, or Green? Athlete Monitoring in Team Sport: The Need for Decision-Support Systems

“…Despite the abovementioned methodological issues pertaining to injury prediction, nonetheless there has been a range of research investigating the relationships between the incidence of injury with player wellness, 20 musculoskeletal screening test scores, 21 fitness levels, 22 and training load. 23 As many elite team sport athletes are being assessed in some capacity on an almost daily basis, the ability to analyze these athletes at the individual level has never been more feasible.…”