Multifactorial Risk Profiling of Overuse Injuries in Elite High School Basketball Players in Japan: A Cluster Analysis Approach

TL;DR

This study identifies risk profiles for overuse injuries in high school basketball players using factors like training duration and physical function, showing that imbalances increase injury risk.

Contribution

The study introduces a cluster analysis approach to create multifactorial risk profiles for overuse injuries in high school basketball players.

Findings

Cluster 3, with low physical function and long training hours, had the highest overuse injury rate (56.2%).

Logistic regression showed cluster 3 had a 25.7 times higher injury risk compared to others.

The model demonstrated good predictive accuracy with an AUC of 0.77.

Abstract

Objective

This study aimed to comprehensively evaluate multiple factors, including body composition, physical function, and training characteristics, in high school basketball players to identify the risk factors associated with overuse injuries, develop risk profiles using cluster analysis, and examine the relationship between these profiles and injury occurrence.

Materials and methods

Eighty male high school basketball players participated in this study. Data were collected on physical function, including grip strength, vertical jump, side-step test, and quadriceps muscle thickness; body composition, including skeletal muscle mass, total body water, and body fat percentage; training characteristics; and demographic variables. Overuse injury was defined as chronic pain or discomfort caused by sports activities within the past six months, diagnosed by a medical professional, and resulting in limitations in sports participation. Risk factors were identified using univariate analysis. A hierarchical cluster analysis was conducted to classify players into distinct risk profiles based on the extracted variables. The association between these cluster profiles and the incidence of overuse injuries was examined using the chi-square test and logistic regression analysis. Predictive accuracy was further assessed using receiver operating characteristic (ROC) curve analysis.

Results

Univariate analysis identified daily training duration (p = 0.034), vertical jump performance, and skeletal muscle mass index (p = 0.071) as factors associated with overuse injuries. Based on these variables, the cluster analysis categorized players into four distinct risk profiles. Cluster 3, characterized by low physical function and longer training hours, demonstrated the highest incidence of overuse injuries (56.2%). Logistic regression analysis revealed a significantly elevated injury risk in cluster 3 (OR = 25.7; 95% CI: 2.7-241.1; p = 0.005). ROC analysis showed good predictive performance of the model (AUC = 0.77).

Conclusions

Imbalances between physical function and training load are key contributors to overuse injuries in high school basketball players. Risk profiling using cluster analysis is a practical and effective method for identifying individuals at risk and may facilitate the development of personalized injury prevention strategies for athletes.

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