The efficacy of multi-task relative rankings in screening for anterior cruciate ligament injury risk

Biomechanical factors are poor predictors of Anterior Cruciate Ligament (ACL) injury risk making the screening of athletes questionable [1]. However, these predictions are often based on a single parameter observed in a single task. This study aimed to determine if parameters measured across multiple tasks could in theory better stratify individuals with task-independent “high risk” behaviours. Four experimentally collected biomechanical risk factors were used to calculate multi-task covariances for Monte-Carlo simulations which varied the number of tasks and risk factors. Results showed that inter-task covariance facilitated improved identification of undesirable characteristics across multiple tasks.