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Updated: Feb 13, 2026

Movement Retraining using Real-time Feedback of Performance
Published on: January 17, 2013
The learning process of gait retraining using real-time feedback in patients with medial knee osteoarthritis
Rosie Richards1, Martin van der Esch2, Josien C van den Noort3
1VU University Medical Center, Department of Rehabilitation Medicine, Amsterdam Movement Sciences, Amsterdam, Netherlands.
Abstract:
The objective of this study was to investigate the learning process of knee osteoarthritis (KOA) patients learning to change their foot progression angle (FPA) over a six-week toe-in gait training program. Sixteen patients with medial KOA completed a six-week toe-in gait training program with real-time biofeedback. Patients walked on an instrumented treadmill while receiving real-time feedback on their foot progression angle (FPA) with reference to a target angle. The FPA difference (difference between target and actual FPA) was analyzed during i) natural walking, ii) walking with feedback, iii) walking without feedback and iv) walking with a dual-task at the start and end of the training program. Self-reported difficulty and abnormality and time spent walking and training were also analyzed. The FPA difference during natural walking was significantly decreased from median 6.9 to median 3.6° i.e. by 3.3° in week six (p < 0.001); adding feedback reduced FPA difference to almost zero. However the dual-task condition increased the FPA difference at week one compared to the feedback condition (median difference: 1.8°, p = 0.022), but after training this effect was minimized (median difference: 0.6°, p = 0.167). Self-reported abnormality and difficulty decreased from median 5 to 3 and from median 6 to 3 on the NRS respectively (p < 0.05). Patients with medial KOA could reduce the FPA difference during natural walking after the gait retraining program, with some evidence of a reduction in the cognitive demand needed to achieve this. Automation of adaptions might need support from more permanent feedback using wearable technologies.
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