Injury prediction in female gymnasts

Area of Science:

• Sports Medicine
• Biomechanics
• Kinesiology

Background:

• Female competitive gymnasts face a high risk of injury due to the demanding nature of the sport.
• Identifying injury-proneness is crucial for implementing preventive strategies and ensuring athlete well-being.

Purpose of the Study:

• To identify key physical and anthropometric factors associated with injury-proneness in female competitive gymnasts.
• To develop a predictive model for estimating injury risk based on measurable variables.

Main Methods:

• Assessed 20 measures of flexibility, hypermobility, spinal posture, and anthropometry in 40 competitive gymnasts.
• Derived injury scores based on the severity and extent of previous injuries and hypermobility traits.
• Utilized multiple regression analysis to determine predictors of injury-proneness.

Main Results:

• Nine variables, including weight, height, age, mesomorphy, Quetelet Index, shoulder flexion, lumbar extension, standing lumbar curvature, and total peripheral flexibility, significantly differed between low and high injury risk groups.
• A subset of five variables (weight, mesomorphy, standing lumbar curvature, age, and height) accounted for nearly 70% of the variance in injury scores (R² = 0.696).
• The predictive model achieved 70% accuracy for high-risk and 79% accuracy for low-risk gymnasts.

Conclusions:

• Simple physical and anthropometric measurements can effectively predict injury-proneness in female gymnasts.
• These findings enable practitioners to identify at-risk athletes through accessible physical assessments.
• The study provides a foundation for targeted injury prevention programs in gymnastics.