Feasibility Analysis of Functional Movement Screen in Predicting Sports Injury among tactical athlete：Based on Meta-analysis of Diagnostic Tests

Objective Systematically evaluate the predictive efficacy of the Functional Movement Screen (FMS) for musculoskeletal injuries in tactical athletes. Methods The study protocol was retrospectively registered via the PROSPERO platform (registration number: CRD420251186285). A computerized search of databases, including PubMed, Web of Science, Cochrane Library, Embase, CNKI, and the Scopus, identified diagnostic studies evaluating the FMS for predicting sports injuries in tactical athletes from 2005 to 2025. The study used the Meta-disc 1.4 tool to investigate threshold effects and the QUADAS-2 tool to evaluate the quality of the literature. STATA 15.0 software was used to calculate pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and odds ratio. The area under the SROC curve (AUC) and Fagan's histogram were plotted to assess FMS accuracy in predicting sports injuries within specific occupational groups. Meta-regression and subgroup analysis evaluated heterogeneity and explored factors influencing FMS predictive efficacy. Deeks' funnel plots were used to assess publication bias. Results The Spearman correlation coefficient between sensitivity and specificity was 0.425 (P = 0.078), indicating no threshold effect. The area under the ROC curve (AUC) was 0.67 (95% CI: 0.63–0.71), with pooled sensitivity of 0.47 (95% CI: 0.38–0.57), specificity of 0.74 (95% CI: 0.68–0.80), positive likelihood ratio of 1.83 (95% CI: 1.51–2.33), negative likelihood ratio of 0.71 (95% CI: 0.61–0.82), diagnostic score of 0.95 (95% CI: 0.63–1.26), and odds ratio of 2.58 (95% CI: 1.88–3.53), with Q-test P < 0.01 and I² ≥ 50, indicating significant heterogeneity. Subgroup analysis revealed that threshold selection methods, injury types, occupation, cycle, and nationality were primary sources of heterogeneity. The Deeks funnel plot showed no evidence of publication bias (P = 0.22 > 0.05) or other confounding factors. Conclusion The Functional Movement Screen (FMS) demonstrates moderate utility in predicting injury risk among tactical athletes. While higher scores correlate with relatively lower injury probability, the association between lower scores and higher injury risk remains insufficiently supported by evidence. From a movement pattern perspective, this screening may serve as an auxiliary tool for injury prevention, but it is not recommended as a direct or sole basis for injury prediction. Factors such as different sports, injury types, ethnicity, and threshold selection methods may all influence predictive efficacy. We recommend combining the FMS with multiple injury prediction tools to enhance the predictive accuracy of injury risk assessment for tactical athletes.