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Regression model for predicting knee flexion angles using ankle plantar flexion angles, body mass index and generalised joint laxity
Citation
Teng, P. S. P., Leong, K. F., & Kong, P. W. (2024). Regression model for predicting knee flexion angles using ankle plantar flexion angles, body mass index and generalised joint laxity. Sports Biomechanics, 23(11), 1990-2005. https://doi.org/10.1080/14763141.2021.1989480
Abstract
Increased knee flexion angles are associated with reduced non-contact anterior cruciate ligament (ACL) injury risks. Ankle plantar flexion angles and internal risk factors could influence knee flexion angles, but their correlations are unknown. This study aimed to establish and validate a regression model to predict knee flexion angles using ankle plantar flexion angles, body mass index (BMI) and generalised joint laxity (GJL) at initial contact of single-leg drop landings. Thirty-two participants performed single-leg drop landings from a 30-cm-high platform. Kinematics and vertical ground reaction forces were measured using a motion capture system and force plate. A multiple regression was performed, and it was validated using a separate data set. The prediction model explained 38% (adjusted R2) of the change in knee flexion angles at initial contact (p = 0.001, large effect size). However, only the ankle plantar flexion angle (p < 0.001) was found to be a significant predictor of knee flexion angles. External validation further showed that the model explained 26% of knee flexion angles (large effect size). The inverse relationship between ankle plantar flexion and knee flexion angles suggests that foot landing strategies could be used to increase knee flexion angles, thereby reducing non-contact ACL injury risks.
Date Issued
2024
Publisher
Taylor & Francis
Journal
Sports Biomechanics
Dataset
https://doi.org/10.25340/R4/Z2LEZL
Grant ID
S11-1191-IDS
Funding Agency
Institute for Sports Research