Predictive Utility of the Functional Movement Screen and Y-Balance Test: Current Evidence and Future Directions

Abstract

Musculoskeletal injury (MSI) risk screening has gained significant attention in rehabilitation, sports, and fitness due to its ability to predict injuries and guide preventive interventions. This review analyzes the Functional Movement Screen (FMS) and the Y-Balance Test (YBT) landscape. Although these instruments are widely used because of their simplicity and ease of access, their accuracy in predicting injuries is inconsistent. Significant issues include reliance on broad scoring systems, varying contextual relevance, and neglecting individual characteristics such as age, gender, fitness levels, and past injuries. Meta-analyses reveal that the FMS and YBT overall scores often lack clinical relevance, exhibiting significant variability in sensitivity and specificity among different groups. Findings support the effectiveness of multifactorial models that consider modifiable and non-modifiable risk factors such as workload ratios, injury history, and fitness data for better prediction outcomes. Advances in machine learning (ML) and wearable technology, including inertial measurement units (IMUs) and intelligent monitoring systems, show promise by capturing dynamic and personalized high-dimensional data. Such approaches enhance our understanding of how biomechanical, physiological, and contextual injury aspects interact. This review discusses the problems of conventional movement screens, highlights the necessity for workload monitoring and personalized evaluations, and promotes the integration of technology-driven and data-centered techniques. Adopting tailored, multifactorial models could significantly improve injury prediction and prevention across varied populations. Future research should refine these models to enhance their practical use in clinical and field environments.

Topics & Concepts