Gait Alterations and Association With Worsening Knee Pain and Physical Function: A Machine Learning Approach With Wearable Sensors in the Multicenter Osteoarthritis Study
The NIH, the American Heart Association, and the Rheumatology Research Foundation were not involved in study design, collection, analysis or interpretation of data, or the decision to submit this manuscript for publication.
The Multicenter Osteoarthritis Study (MOST) is supported by four cooperative grants (U01-AG-18820, U01-AG-18832, U01-AG-18947, and U01-AG-19069) from the NIH. Sponsors and collaborators for MOST: the University of California, San Francisco; the University of Iowa; the University of Alabama at Birmingham; Boston University; and the National Institute on Aging, NIH. Dr Bacon's work was supported by an Investigator Award from the Rheumatology Research Foundation. Research for the article was supported by the NIH (grant P30-AR-0702571 to Dr Felson, grants R21-AR-074578 and R03-AG-060272 to Dr Jafarzadeh, grants R01-HL-159620 and R21-CA-253498 to Dr Kolachalama, and grant K01-AR-06972 to Dr Kumar). Dr Kumar's work was supported by the American Heart Association (grant 20SFRN35460031).
Additional supplementary information cited in this article can be found online in the Supporting Information section (http://onlinelibrary.wiley.com/doi/10.1002/acr.25327).
Author disclosures are available at https://onlinelibrary.wiley.com/doi/10.1002/acr.25327.
Abstract
Objective
The objective of this study was to identify gait alterations related to worsening knee pain and worsening physical function, using machine learning approaches applied to wearable sensor–derived data from a large observational cohort.
Methods
Participants in the Multicenter Osteoarthritis Study (MOST) completed a 20-m walk test wearing inertial sensors on their lower back and ankles. Parameters describing spatiotemporal features of gait were extracted from these 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 two years. We then used log-binomial regression to evaluate associations of the top 10 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.
Results
Of 2,324 participants, 29% and 24% had worsening knee pain and function over two 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.
Conclusion
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.
