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Curves and kinematics: Relationship between the force-time curve and landing ability
Brayden Mifsud1,2,3, Jessica M Stephens1,2, John Warmenhoven1,4
1Faculty of Health, University of Canberra Research Institute for Sport Exercise (UCRISE), University of Canberra, Canberra, Australian Capital Territory, Australia.
Abstract:
Anterior cruciate ligament (ACL) injuries have a significant impact on athletic performance and long-term quality of life. Force plates and qualitative screening tools are feasible and effective screening methods to identify abnormal movement quality associated with increased injury risk. Comparing qualitative assessments of landing ability with force-time curves, may detect unique differences between safe and high-risk athletic movement patterns. The aim of this study was to determine low- and high-risk landing ability from qualitive landing assessments and to examine the resulting force-time curves using functional principal component analysis (fPCA). Thirty-one healthy academy athletes (10 males and 21 females) completed double- and single-leg dominant and non-dominant jump-landing-rebound tasks. All movements were filmed in multiple-planes, and vertical ground reaction forces (vGRF) were simultaneously collected. The Landing Error Scoring System (LESS) and Single-Leg Landing Error Scoring System (SL-LESS) were used to score landing footage. From these scores, athletes were categorized into low-risk and high-risk groups for further analysis. fPCA was used to examine differences between landing quality groups force-time curves. Compared to high-risk landers, low-risk landers demonstrated significantly longer contact times across all movements. Scores from fPC1 revealed safe and high-risk landing techniques expose athletes to significantly different loading patterns during double- and single-leg dominant movements. A significant positive relationship was observed between fPC1 and LESS scores, however this relationship was not observed in both single-leg landing scores. Where possible incorporating curve analysis methods like fPCA into multi-faceted screening approaches may help practitioners uncover unique insights into athletic loading strategies.
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