Accelerometry-Derived Activity and Sleep Patterns in the NIH All of Us Cohort: Insights and Predictive Potential for Inflammatory Arthritis.

Abstract

Objective: The relationship between physical activity and sleep with inflammatory arthritis (IA) is understudied, and existing research has relied largely on self-report or short-term assessments. The NIH All of Us database provides long-term accelerometry data, enabling more precise estimation of the association between lifestyle behaviors and IA.

Methods: Participants from the All of Us database who shared electronic health record and Fitbit data were included. Daily activity and sleep metrics were compared between individuals with and without IA using multiple linear regression. Cox proportional hazards regression was used to examine the association of activity and sleep patterns with incident IA in a 10-year follow-up period.

Results: A total of 23,855 participants were included, 200 of whom had IA. Participants with IA took fewer daily steps (P < 0.001) and had greater sleep variability (P < 0.001) compared to those without IA. 122 individuals had incident IA. Every 1,000 extra daily steps were associated with a 7% lower risk of IA (hazard ratio [HR] 0.93 [95% confidence interval (CI) 0.87-0.99], P = 0.02). Compared to those who walked <5,000 steps daily, those who walked 5,000 to 10,000 steps and 10,000+ steps had a 41% (HR 0.59 [95% CI 0.39-0.91], P = 0.02) and 50% (HR 0.50 [95% CI 0.29-0.86], P = 0.01) reduction in risk of IA.

Conclusion: Individuals with IA had reduced step counts and more sleep variability compared to those without IA, highlighting how physical activity and sleep contribute to IA. Additionally, increased daily step count was associated with decreased risk of incident IA, suggesting a possible research intervention for those at high risk of IA.