Predictive Utility of the Functional Movement Screen and Y-Balance Test: Current Evidence and Future Directions
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 th...
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MDPI AG
2025-02-01
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| Series: | Sports |
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| Online Access: | https://www.mdpi.com/2075-4663/13/2/46 |
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| author | Adam C. Eckart Pragya Sharma Ghimire James Stavitz Stephen Barry |
| author_facet | Adam C. Eckart Pragya Sharma Ghimire James Stavitz Stephen Barry |
| author_sort | Adam C. Eckart |
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| description | 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. |
| format | Article |
| id | doaj-art-05f2b2ec49204f6aa91c9ce1854369c4 |
| institution | OA Journals |
| issn | 2075-4663 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Sports |
| spelling | doaj-art-05f2b2ec49204f6aa91c9ce1854369c42025-08-20T02:01:24ZengMDPI AGSports2075-46632025-02-011324610.3390/sports13020046Predictive Utility of the Functional Movement Screen and Y-Balance Test: Current Evidence and Future DirectionsAdam C. Eckart0Pragya Sharma Ghimire1James Stavitz2Stephen Barry3Department of Exercise Science, Kean University, 1000 Morris Avenue, Union, NJ 07083, USADepartment of Exercise Science, Kean University, 1000 Morris Avenue, Union, NJ 07083, USADepartment of Athletic Training, Kean University, 1000 Morris Avenue, Union, NJ 07083, USADepartment of Exercise Science, Kean University, 1000 Morris Avenue, Union, NJ 07083, USAMusculoskeletal 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.https://www.mdpi.com/2075-4663/13/2/46musculoskeletal injuriesfunctional movement screenY-Balance Testinjury risk factorsworkloadinjury prediction |
| spellingShingle | Adam C. Eckart Pragya Sharma Ghimire James Stavitz Stephen Barry Predictive Utility of the Functional Movement Screen and Y-Balance Test: Current Evidence and Future Directions Sports musculoskeletal injuries functional movement screen Y-Balance Test injury risk factors workload injury prediction |
| title | Predictive Utility of the Functional Movement Screen and Y-Balance Test: Current Evidence and Future Directions |
| title_full | Predictive Utility of the Functional Movement Screen and Y-Balance Test: Current Evidence and Future Directions |
| title_fullStr | Predictive Utility of the Functional Movement Screen and Y-Balance Test: Current Evidence and Future Directions |
| title_full_unstemmed | Predictive Utility of the Functional Movement Screen and Y-Balance Test: Current Evidence and Future Directions |
| title_short | Predictive Utility of the Functional Movement Screen and Y-Balance Test: Current Evidence and Future Directions |
| title_sort | predictive utility of the functional movement screen and y balance test current evidence and future directions |
| topic | musculoskeletal injuries functional movement screen Y-Balance Test injury risk factors workload injury prediction |
| url | https://www.mdpi.com/2075-4663/13/2/46 |
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