The impact of PCA derived gait kinematic variations on estimated medial knee contact forces in a knee osteoarthritis population - Giacomo Di Raimondo; Miel Willems; Bryce Adrian Killen; Sara Havashinezhadian; Katia Turcot; Benedicte Vanwanseele; Ilse Jonkers (Scientific Reports)
Abstract Osteoarthritis (OA) is a prevalent musculoskeletal condition leading to functional limitations, especially among the elderly. Current treatments focus on pain relief and functional improvement, however there is a lack of approaches which slow disease progression. A promising approach focusses on reducing knee joint loading, as excessive loading contributes to knee OA progression. This study explores kinematic variations in the knee OA population, utilizing principal component analysis (PCA) to examine gait variations (primitives) in both healthy individuals and those with knee osteoarthritis (KOA) and their implications for knee joint loading. The KOA population exhibited 14 modes of variation representing 95% of the cumulative variance, compared to 20 in the healthy population, indicating lower variability with KOA. The relation between identified gait primitives and knee loading parameters, revealed complex relationships. Surprisingly, modes with the largest kinematic variations did not consistently correspond to the highest variations in knee loading parameters revealing degrees of freedom which may have a larger role in determining joint loading. Moreover, potential gait-retraining strategies for KOA, associating specific kinematic combinations with altered knee loading were identified. The results showed a good agreement with previously applied strategies. However, this study highlights the importance of analyzing whole-body kinematics for effective gait retraining, as opposed to focusing on one single joint variation. The study’s insights contribute to understanding the intricate interplay between gait pattern variations and knee joint loading changes in healthy and KOA populations, offering practical applications for guiding interventions and estimating loading parameters.