Objectively measured physical activity using wrist-worn accelerometers as a predictor of incident Alzheimer’s Disease in the UK Biobank

Abstract

Background Alzheimer’s disease (AD) affects over 6 million people and is the seventh-leading cause of death in the US. This study compares wrist-worn accelerometry-derived PA measures against traditional risk factors for incident AD in the UK Biobank. Methods Of 42,157 UK Biobank participants 65 years and older who had accelerometry data and no prior AD diagnosis, 157 developed AD by April 1, 2021 (264,988 person-years or on average 6.2 years of follow-up). 12 traditional predictors and 8 accelerometer-based PA measures were used in single- and multivariate Cox models. Their predictive performances for future AD diagnosis were compared across models using the repeated cross-validated concordance (rcvC). To account for potential reverse causality, sensitivity analyses were conducted by removing dropouts and cases within the first six months, one year, and two years. Results The best-performing individual predictors of incident AD were age (p < 0.0001, rcvC = 0.658) and moderate-to-vigorous PA (MVPA, p = 0.0001, rcvC = 0.622). Forward selection produced a model that included age, diabetes, and MVPA, rcvC = 0.681). Adding MVPA to the model containing age and diabetes improved its rcvC from 0.665 to 0.681 (p = 0.0030), more than all other potential risk factors considered. Conclusion Objective PA summaries are the best single predictors among modifiable risk factors with a predictive performance close to that of age. Adding PA summaries to traditional risk factors for AD substantially increases the predictive performance of these models. Increasing MVPA by 14.5 minutes/day reduces the hazard substantially, equivalent to two years younger.