Introduction and Objective: The health and injury risk of female athletes are influenced by a range of female-specific factors, training load, recovery status, and neuromuscular and cognitive components. Despite the importance of these factors, integrated and context-specific models for predicting injury risk among Iranian female athletes remain limited. Therefore, the present study aims to develop and validate a multidimensional injury risk prediction model for Iranian female athletes.


Methods: This study will be conducted using a prospective cohort design. At baseline, Iranian female athletes from selected sports will be assessed in terms of female-specific health indicators, sleep and recovery status, training load, neuromuscular performance, and cognitive performance. Sports injuries occurring during the follow-up period will then be systematically recorded. Following data preprocessing and validation, factors associated with injury will be examined using statistical methods and machine learning algorithms, and a predictive model will subsequently be developed and validated.


Expected Results: The multidimensional model is expected to identify athletes at higher risk of injury with acceptable predictive accuracy and to demonstrate superior predictive performance compared with single-factor models.


Conclusion: The development of an evidence-based, context-specific model may facilitate personalized health monitoring, early identification of injury risk, and decision support for coaches and sports science professionals, thereby contributing to improved health and sustained participation of Iranian women in sport.