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Background Alzheimer's disease (AD) affects over 6 million people and is the seventh-leading cause of death in the United States. 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). Twelve traditional predictors and 8 accelerometer-based physical activity (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 6 months, 1 year, and 2 years. Results The best-performing individual predictors of incident AD were age (p  < .0001, rcvC = 0.658) and moderate-to-vigorous PA (MVPA, p  = .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  = .0030), more than all other potential risk factors considered. Conclusions 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 per day reduces the hazard substantially, equivalent to 2 years younger. [ABSTRACT FROM AUTHOR]

Background: Step counting from wrist accelerometry data is widely used in physical activity research and practice. While several open-source algorithms can estimate steps from high-resolution accelerometry data, there is a critical need to compare these algorithms and provide practical recommendations for their use in older adults., Methods: 1,282 Atherosclerosis Risk in Communities (ARIC) study participants (mean age 83.4, 60% female) wore ActiGraph GT9X wrist devices for 7 days, collecting 80Hz tri-axial accelerometry data. Five open-source step-counting algorithms (ADEPT, Oak, SDT, Verisense, and Stepcount) were applied to this data. Step count distributions and their cross-sectional associations with health outcomes were compared., Results: The estimated mean daily step counts varied widely across algorithms, ranging from 988 for ADEPT to 23,607 for SDT. Pearson correlations across methods ranged from moderate (r=0.52) to very strong (r=0.96). All step counts were highly associated with age, with an estimated decline of 119.0 to 142.8 steps/year (all p<0.001) with comparable trends observed across demographic subgroups. After z-score standardization (subtracting the population mean and dividing by the population standard deviation), the estimated steps from each algorithm exhibited similar directionality and magnitude of association with various metabolic, cardiovascular, physical performance, and cognitive outcomes (all p<0.001)., Conclusion: The estimated step counts algorithms are highly correlated, and, after z-scoring, have similar and highly significant associations with health outcomes. Because the total number of steps varies widely across algorithms, interpretation and translation of results for health monitoring and clinical use in older adults depends on the choice of step counting algorithm., (© The Author(s) 2025. Published by Oxford University Press on behalf of the Gerontological Society of America. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)