A Reliable Video-based ACL Injury Screening Tool for Female Team Sport Athletes

Abstract

AbstractThis study aimed to develop a 2-dimensional (2D) video screening tool capable of predicting an athlete’s peak 3-dimensional (3D) knee moments during unplanned sidestepping. 2D video-based kinematic measures were simultaneously captured with 3D peak knee moments for 30 female field hockey players (15 junior, 15 senior). Intra- and intertester repeatability of 2D kinematic measures was performed. Then, linear regression models were used to model 3D knee moments from 2D kinematic variables utilizing 80% of the sample (n=24). Regression equations were then validated on the remaining 20% of the sample (n=6). Angular 2D measures had good-excellent intra- (ICC=0.936–0.998) and intertester (ICC=0.662–0.949) reliability. Displacement measures had poor-excellent intra- (ICC=0.377–0.539) and inter-tester (ICC=0.219–0.869) reliability. Significant independent predictors of peak knee moments were dynamic knee valgus, knee flexion angle at foot strike, trunk flexion range of motion (ROM), trunk lateral flexion, hip abduction and knee flexion ROM (P<0.05). Regression equations generated from these models effectively predicted peak knee extension, valgus and internal rotation moments (i. e., were not different from measured values P>0.05, ES<0.4) in the 20% subsample. 2D video-based measurements of an athlete's full body kinematics during unplanned sidestepping provide a reliable, specific, sensitive and cost-effective means for screening female team sport athletes.