Predictive Models for Injury Risk Across Body Regions and Sport Types in Physically Active Students: Cross-Sectional Design

TL;DR

This study identifies how age and body composition affect injury risk in amateur athletes, showing that these factors vary by sport type and body region.

Contribution

The study introduces a novel integrated approach to analyzing injury risk by simultaneously considering multiple body regions and sport-specific contexts.

Findings

Age had protective effects against upper and lower limb injuries in some groups but increased risk in male team-sport athletes.

Body composition variables like SMI, MFR, and FMI predicted injury risk differently across sex and sport type.

Injury risk profiles were highly context-dependent, requiring tailored prevention strategies.

Abstract

Background/Objectives: Previous studies have typically investigated injury risk factors either by body region or sport type in isolation, limiting their practical applicability to real-world settings where multiple factors interact. However, injury risk is inherently multifactorial—shaped by a complex interplay of demographic, physiological, and training-related characteristics that differ by anatomical site and sport context. This study addresses that gap by simultaneously analyzing predictors across multiple body regions and sport-specific environments. This integrated approach is critical for developing more precise, evidence-based injury prevention strategies tailored to the specific demands and risk profiles of amateur athletes. This study aimed to identify key predictors of injury risk across various body regions and sport-specific contexts among amateur athletes. Specifically, we sought to (1) develop predictive models that include demographic and body composition variables, and (2) compare the relative predictive strength of these variables across models, highlighting differences in their influence by injury location and sport type. Methods: A total of 454 amateur athletes (219 males and 235 females) participated. Data on anthropometry, body composition, training load were collected. Injury history was obtained via self-administered questionnaires, with participants reporting injuries that had occurred during the 12 months prior to the time of data collection. Logistic regression models were used to identify significant predictors, and Nagelkerke’s R2 was calculated to assess model fit. Results: Overall, 49.78% of athletes experienced injuries, with a higher proportion in females (54.47%) than in males (44.75%). Age demonstrated divergent effects: it was protective against both upper and lower limb injuries in male individual-sport athletes (OR = 0.62 and OR = 0.69, respectively) and in female athletes across sport types (ORs = 0.75–0.64), but conversely increased the risk of upper limb injuries in male team-sport athletes (OR = 1.88). In female individual athletes, higher Skeletal Muscle Index (SMI) predicted upper limb injuries (OR = 1.18, p = 0.034). In female team athletes, higher Muscle-to-Fat Ratio (MFR) (OR = 2.46, p = 0.017) and BMI (OR = 1.67, p = 0.008) predicted upper limb injuries, while higher Fat Mass Index (FMI) predicted lower limb injuries (OR = 1.70, p = 0.009). Models showed moderate explanatory power (Nagelkerke’s R2 ranging from 0.03 to 0.33). Conclusions: These findings suggest that injury risk profiles are highly context-dependent. Preventive strategies should be tailored by sex and sport type, for example, younger athletes in team sports may benefit from age-sensitive load monitoring, while in female team athletes, targeted interventions addressing both fat and muscle balance could be essential. Age, body composition, and sport-specific demands should be considered in individualized injury prevention planning.

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