Background: Knee osteoarthritis (OA) is a leading cause of pain and disability globally. Pain from knee OA leads to functional limitation. Physical activity (PA), e.g., taking more steps/day, reduces the risk of functional limitation. However, little is known about patterns of activity throughout the day and to what extent such patterns may be associated with the functional limitation. This is a major gap given activity patterns can be modified and may be an important to consider for treatment to further address functional limitation associated with knee OA. Objectives: The purpose of this study was to investigate patterns of PA in individuals with knee OA and to examine the association of patterns with incident slow gait speed over 2 years. Methods: We utilized data from the Osteoarthritis Initiative (OAI) in this analysis. Accelerometer data was collected at the 4-year follow-up visit. We calculated the total activity counts per hour reported from the accelerometer (ActiGraph GT1M). We utilized four days per participant and included the hours of 8 am to 9 pm. A multidimensional (12 hours) multilevel (4 days) Functional Principal Components Analysis, a dimensionality reduction technique, was applied to investigate major sample activity patterns. An individual’s activity patterns were summarized into numerical scores called individual level principal components (PC) scores. These individual scores indicate the extent to which a particular individual’s activity is explained by the sample’s corresponding activity pattern. Gait speed was measured at baseline (year 4) and again at 2-year follow-up (year 6). A speed of <1 m/s during a 20-meter walk was classified as a slow gait speed. Demographic information, radiographic OA presence, knee pain, and depressive symptoms were also collected. We examined the association of PC scores, categorized into quartiles, with incident slow gait speed over a 2-year follow-up period. We calculated risk ratios (RR) and 95% confidence intervals (CI) for the association of the top quartile each PC with the bottom quartile (referent) with incident slow gait speed adjusted for potential confounders. Results: Of the 1457 participants included in the analytic sample (Age 60.1 years (8.8), 53.3% Women, BMI 28.2 kg/m2 (4.5), 53.8% with radiographic knee OA), we found four patterns of activity. Figure 1 displays loadings of the first four-person level PCs that explain about 82% of participant level variation. PC1 represents an overall increase over the sample’s mean activity such that positive PC1 values represent being more active throughout the day compared to the sample mean. PC2 shows decreased activity from morning to afternoon and increased activity in the evening. PC3 shows decreased activity levels in the middle of the day with increased activity in the morning and evening. PC4 shows low levels of activity in the early morning with fluctuating activity levels throughout the remainder of the day. Participants in the highest quartile of PC4, meaning those whose daily patterns of activity best match PC4, were found to have 2.76 times the risk of developing slow gait speed compared to those in the lowest quartile of PC4 (Table 1). Participants in the highest quartile of PC3 had 72% less risk of developing slow gait speed compared to those in the lowest quartile of PC3 which met statistical significance. Conclusion: Four preliminary patterns of activity were identified among adults with or at high risk of knee OA. Adults whose activity level fluctuates constantly throughout the day could be at increased risk for developing slow gait speed, while those who are active in the mornings and evenings could be at decreased risk. Future studies should examine how these patterns of activity could be related to other OA-related outcomes.Table 1. Incident slow gait speed development rate according to the corresponding Principal Component*Adjusted for age, sex, race, pain intensity, presence of radiographic OA and Center for Epidemiologic Studies Depression Scale REFERENCES: NIL. Acknowledgements: NIL. Disclosure of Interests: None declared.Figure 1Graphs of Patterns of PA counts per hour for each Principal Component (PC)*Large positive loadings show that a particular hour has a strong positive relationship to the corresponding PC
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