Gait retraining for medial compartment knee osteoarthritis
Research type
Research Study
Full title
Gait retraining for medial compartment knee osteoarthritis
IRAS ID
245234
Contact name
Ruth Nicholson
Contact email
Sponsor organisation
Imperial College London JRCO
Duration of Study in the UK
3 years, 0 months, 1 days
Research summary
Summary of Research: Osteoarthritis (OA) affects approximately 8.5 million people in the UK alone and is the most common cause of chronic pain, with social and economical costs amounting to approximately £5.5 billion per year for arthritis and related conditions. OA of the knee is associated with increased loading at the medial compartment of the joint, which has been shown to correlate with the risk of disease progression.
This finding has raised the prospect of intervention to reduce medial compartment loading, in order to slow disease progression. Attempts to do so have thus far been hampered by the slow speed of modern complex musculoskeletal models. Using neural networks in place of these complex models has been shown to offer significant speedup without loss of accuracy. This pilot project aims to exploit this speedup to provide gait retraining to patients with OA using the magnitude of knee joint loading as biofeedback. In addition, the feasibility of using neuromuscular electrical stimulation (NMES) to augment retraining regimes will be tested.
The experimental protocol comprises two separate experimental sessions. In the first, participants will undergo motion capture; these data will subsequently be used to refine a statistical model trained to estimate joint forces during walking. In the second session, the model will be deployed to the gait lab and used to provide real time biofeedback on the magnitude of joint loading during gait. The subject will be encouraged to modify gait in order to reduce this loading, and the success of these attempts will be verified post-hoc using a customised musculoskeletal model. During both sessions, data from portable accelerometers and video cameras will also be collected, and NMES will be applied. These data will be used to build further statistical models with the aim of validating their use for the estimation of muscle and joint forces during walking.Summary of Results: Forces experienced at the knee joint are linked to osteoarthritis (degenerative disease). This study aimed to determine personal factors influencing knee forces and by doing so inform a personalised retraining programme to reduce disease risk. Personalised computational models were built using patient data, then used to guide improved walking patterns. We found that individual differences in baseline walking, body type and musculature led to specific differences in the best retraining strategy for each patient.
REC name
South Central - Hampshire A Research Ethics Committee
REC reference
18/SC/0458
Date of REC Opinion
19 Sep 2018
REC opinion
Further Information Favourable Opinion