Abstract
This 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.
Key words
injury prevention - hockey - valgus knee moments - training - anterior cruciate ligament
