Summary
We focus on screening activities and metrics to predict ACL injury risk with the goal of developing personalized, evidence-based strategies to improve athletic performance and mitigate injury risk.
Abstract
This project is focused on screening activities and metrics that predict ACL injury risk. We report on recent work of the Wu Tsai Human Performance Alliance and Stanford Human Performance Laboratory as part of the digital athlete performance improvement and injury prevention program. We summarize findings from quantitative surveys and qualitative research interviews with nine ACL experts on biomechanical metrics and screening activities relevant to ACL injury risk prediction. ACL Study Group collaborators include Seth L. Sherman, Constance Chu, and Greg Myers.
The experts consistently emphasized the inadequacy of any single biomechanical measure and advocated for more holistic, multivariate models incorporating dynamic, sport-specific movements. Key themes included the importance of proximal (trunk/core) control, assessment of landing mechanics, and the need for standardized measurement protocols. Novel metrics such as a “dynamic valgus index”, unanticipated cuts, and the triple vertical hop task emerged as promising areas for future research. Experts identified that a comprehensive approach to injury risk assessment should consider not only biomechanics but also factors like fatigue, knee integrity and stability, and exposure to high-risk situations.
The insights gained from this survey provide a foundation for advancing ACL injury risk prediction and prevention. The next steps will prioritize translating these insights into actionable strategies. This involves developing and validating multivariate prediction models that incorporate the predictive metrics identified by the experts and data science studies. Further evaluating the feasibility of new activities and metrics is warranted. Ongoing research efforts focus on creating standardized protocols for data collection and analysis to ensure comparability and to facilitate widespread implementation with video-based biomechanical screening tools like OpenCap. Longitudinal studies to track injury rates and performance outcomes will further validate long-term effectiveness of multivariable injury risk models to predict ACL injury.