Gait alterations associated with worsening knee pain and physical function: a machine-learning approach with wearable sensors in the Multicenter Osteoarthritis Study.

The objective of this study is to identify gait alterations related to worsening knee pain, and to worsening physical function, using machine learning approaches applied to wearable-sensor derived data from a large observational cohort. Participants in the Multicenter Osteoarthritis Study (MOST) completed a 20-meter walk test wearing inertial sensors on their lower back and ankles. Parameters describing spatiotemporal features of gait were extracted from this data. We used an ensemble machine learning technique ("super learning") to optimally discriminate between those with and without worsening physical function and, separately, those with and without worsening pain over 2 years. We then used log-binomial regression to evaluate associations of the top ten influential variables selected with super-learning with each outcome. We also assessed whether the relation of altered gait with worsening function was mediated by changes in pain. Of 2324 participants, 29% and 24% had worsening knee pain and function over 2-years, respectively. From the super-learner, several gait parameters were found to be influential for worsening pain and for worsening function. After adjusting for confounders, greater gait asymmetry, longer average step length, and lower dominant frequency were associated with worsening pain, and lower cadence was associated with worsening function. Worsening pain partially mediated the association of cadence with function. We identified gait alterations associated with worsening knee pain and those associated with worsening physical function. These alterations could be assessed with wearable sensors in clinical settings. Further research should determine whether they might be therapeutic targets to prevent worsening pain and worsening function.

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