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310 Digital Physical Activity Phenotype before Cerebrovascular Disease: A Retrospective Study of Accelerometer-Measured Behavior in UK Biobank Observational Cohort

Published online by Cambridge University Press:  03 April 2024

Stephanie Zawada
Affiliation:
Mayo Clinic
Ali Ganjizadeh
Affiliation:
Mayo Clinic
Gian Marco Conte
Affiliation:
Mayo Clinic
Bart Demaerschalk
Affiliation:
Mayo Clinic
Bradley Erickson
Affiliation:
Mayo Clinic
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Abstract

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OBJECTIVES/GOALS: To investigate digital behavior patterns before cerebrovascular disease (CeVD), we compared accelerometer-measured physical activity (PA) phenotypes of future CeVD patients versus controls in UK Biobank. METHODS/STUDY POPULATION: Accelerometer data from 76,525 eligible participants monitored for 7-days (Jan. 2013-Dec. 2015) was classified into four categories: sedentary, light PA (LPA), moderate-to-vigorous PA (MVPA), and sleep. Covariables and diagnoses were defined using baseline data and patient records. Daily PA patterns associated with incident CeVD were compared to controls using negative binomial regression models. RESULTS/ANTICIPATED RESULTS: Adult participants with future CeVD (n = 2,163) spent 4.4% less time in MVPA (Incident Rate-Ratio (IRR) 0.956; 95% CI = 0.923-0.992; p = 0.016) compared to controls. During 0:00-5:59h (midnight to 5:59AM), future CeVD patients were less likely to sleep (IRR = 0.985; 95% CI = 0.977-0.992; p <0.001) but more likely to be sedentary (IRR = 1.189; 95% CI = 1.098-1.290; p <0.001) or in LPA (IRR = 1.108; 95% CI = 1.015-1.211; p <0.001). In subgroup analyses, decreased MVPA was observed in current/former smokers (IRR = 0.887; 95% CI = 0.819-0.963), males (IRR = 0.931; 95% CI = 0.870-0.997), and the unemployed/retired (IRR = 0.923; 95% CI = 0.856-0.998), an effect more pronounced in depressed patients (p for interaction = 0.044) and prolonged (> 2 hr/day) screen users (p for interaction = 0.018). DISCUSSION/SIGNIFICANCE: The digital phenotype of PA prior to CeVD is characterized by less sleep during 0:00-5:59h and less daily MVPA, demonstrating the utility of accelerometer data in identifying candidates for preventative intervention.

Type
Informatics and Data Science
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s), 2024. The Association for Clinical and Translational Science