Abstract
Injury rates in student athletes are high and often unpredictable. Injury risk factors are not agreed upon and often not validated. Here, we present a random-forest machine learning methodology for identifying the most significant injury risk factors and develop a model of lower extremity musculoskeletal injury risk in student athletes with physical performance metrics spanning joint strength measured with force transducers, postural stability measured using a force plate, and flexibility, measured with a goniometer, combined with previous injury metrics and athlete demographics. We tested our model in a population of 122 student athletes with performance metrics for the lower extremity musculoskeletal system and achieved an injury risk accuracy of 79% and identified significant injury risk factors, that could be used to increase accuracy of injury risk assessments, implement timely interventions, and decrease the number of career-ending or chronic injuries among student athletes.
Highlights
Musculoskeletal injuries are a significant problem in student athlete populations with some reports indicating that 90% of student athletes report a sports-related injury during their athletic career as a student (Research Report: Changing the Culture of Youth Sports, 2014)
We developed a model for injury risk in student athletes using random forest machine learning
Our secondary validation with k-fold cross validation resulted in an average receiver operating characteristic (ROC) area under the curve (AUC) of 68.90%
Summary
Musculoskeletal injuries are a significant problem in student athlete populations with some reports indicating that 90% of student athletes report a sports-related injury during their athletic career as a student (Research Report: Changing the Culture of Youth Sports, 2014). Student athletes are 2.5 times more likely to report a major injury and chronic injuries than non-athletes: 67% of former Division I athletes sustain a major injury and 50% reported chronic injuries in a survey of 232 former Division I athletes (Simon and Docherty, 2014; Cowee and Simon, 2019). These injuries will have chronic effects that may influence lifelong physical activity behavior (Hrysomallis, 2007) and have profound financial implications for student athletes including costly medical bills and potential loss of scholarship (Dixon, 2017). There is a critical need to identify risk factors for lower extremity musculoskeletal injuries
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