ArticleHeliyon2023
The functional movement screen predicts sports injuries in Chinese college students at different levels of physical activity and sports performance.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 15 citations in OpenAlex.
- Nonlinear association between physical fitness and sports injury risk among Chinese high school students: a cross-sectional study.BMC public health · 2025Article
- Exploring injury profiles in professional football: evidence from a five-year study and the role of the functional movement screen.BMC sports science, medicine & rehabilitation · 2025Article
- Prevention of Overhead Shoulder Injuries in Throwing Athletes: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- The application of modified functional movement screen as predictor of training injury in athletes.Heliyon · 2024Article
- Machine learning-based optimization of contract renewal predictions in Korea Baseball organization.Heliyon · 2023Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Functional Movement Screen (FMS) is used to evaluate the movement quality of an individual. However, the FMS composite score used to predict sports injuries is currently ambiguous. Further refinement of the FMS scoring method may be required to more accurately predict sports injuries. Objectives: To investigate whether FMS scores could accurately predict sports injuries in college students with different levels of physical activity (PA) and sports performance (SP). Methods: One hundred eighty-seven college students aged 18 to 22 were prospectively screened by the FMS test and grouped by the levels of PA and SP. Sports injury occurrences were monitored and collected 12 months later. Spearman's rank coefficients and binary logistic regression were used to identify the risk factors for sports injuries. The receiver operating characteristic (ROC) curve and the total area under the curve (AUC) value were used to determine the optimal FMS cut-off point for sports injuries. Results: The FMS composite score (sum of the seven FMS tests) exhibited a fair association with sports injuries (r = -0.434, Conclusions: The FMS composite score could be used to predict sports injuries in college students with an FMS cut-off value of 17.5. Population stratification by the levels of PA and SP seems to influence the predictive accuracy of the FMS.
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Registered trials
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