Background: The progression of medial knee osteoarthritis seems closely related to a high external knee adduction moment, which could be reduced through gait retraining. We aimed to determine the retraining strategy that reduces this knee moment most effective during gait, and to determine if the same strategy is the most effective for everyone. Methods: Thirty-seven healthy participants underwent 3D gait analysis. After normalwalking was recorded, participants received verbal instructions on four gait strategies (Trunk Lean, Medial Thrust, Reduced Vertical Acceleration, Toe Out). Knee adduction moment and strategy-specific kinematics were calculated for all conditions. Findings: The overall knee adduction moment peak was reduced by Medial Thrust (−0.08 Nm/Bw·Ht) and Trunk Lean (−0.07 Nm/Bw·Ht), while impulse was reduced by 0.03 Nms/Bw·Ht in both conditions. Toeing out reduced late stance peak and impulse significantly but overall peakwas not affected. Reducing vertical acceleration at initial contact did not reduce the overall peak. Strategy-specific kinematics (trunk lean angle, knee adduction angle, first peak of the vertical ground reaction force, foot progression angle) showed that multiple parameters were affected by all conditions. Medial Thrust was the most effective strategy in 43% of the participants, while Trunk Lean reduced external knee adduction moment most in 49%. With similar kinematics, the reduction of the knee adduction moment peak and impulse was significantly different between these groups. Interpretation: Although Trunk Lean and Medial Thrust reduced the external knee adduction moment overall, individual selection of gait retraining strategy seems vital to optimally reduce dynamic knee load during gait.
Background The gait modification strategies Trunk Lean and Medial Thrust have been shown to reduce the external knee adduction moment (EKAM) in patients with knee osteoarthritis which could contribute to reduced progression of the disease. Which strategy is most optimal differs between individuals, but the underlying mechanism that causes this remains unknown. Research question Which gait parameters determine the optimal gait modification strategy for individual patients with knee osteoarthritis? Methods Forty-seven participants with symptomatic medial knee osteoarthritis underwent 3-dimensional motion analysis during comfortable gait and with two gait modification strategies: Medial Thrust and Trunk Lean. Kinematic and kinetic variables were calculated. Participants were then categorized into one of the two subgroups, based on the modification strategy that reduced the EKAM the most for them. Multiple logistic regression analysis with backward elimination was used to investigate the predictive nature of dynamic parameters obtained during comfortable walking on the optimal modification gait strategy. Results For 68.1 % of the participants, Trunk Lean was the optimal strategy in reducing the EKAM. Baseline characteristics, kinematics and kinetics did not differ significantly between subgroups during comfortable walking. Changes to frontal trunk and tibia angles correlated significantly with EKAM reduction during the Trunk Lean and Medial Thrust strategies, respectively. Regression analysis showed that MT is likely optimal when the frontal tibia angle range of motion and peak knee flexion angle in early stance during comfortable walking are high (R2Nagelkerke = 0.12). Significance Our regression model based solely on kinematic parameters from comfortable walking contained characteristics of the frontal tibia angle and knee flexion angle. As the model explains only 12.3 % of variance, clinical application does not seem feasible. Direct assessment of kinetics seems to be the most optimal strategy for selecting the most optimal gait modification strategy for individual patients with knee osteoarthritis.
MULTIFILE
Injuries and lack of motivation are common reasons for discontinuation of running. Real-time feedback from wearables can reduce discontinuation by reducing injury risk and improving performance and motivation. There are however several limitations and challenges with current real-time feedback approaches. We discuss these limitations and challenges and provide a framework to optimise real-time feedback for reducing injury risk and improving performance and motivation. We first discuss the reasons why individuals run and propose that feedback targeted to these reasons can improve motivation and compliance. Secondly, we review the association of running technique and running workload with injuries and performance and we elaborate how real-time feedback on running technique and workload can be applied to reduce injury risk and improve performance and motivation. We also review different feedback modalities and motor learning feedback strategies and their application to real-time feedback. Briefly, the most effective feedback modality and frequency differ between variables and individuals, but a combination of modalities and mixture of real-time and delayed feedback is most effective. Moreover, feedback promoting perceived competence, autonomy and an external focus can improve motivation, learning and performance. Although the focus is on wearables, the challenges and practical applications are also relevant for laboratory-based gait retraining.
