Multidimensional Sleep Profiles via Machine learning and Risk of Dementia and Cardiovascular Disease.

ABSTRACT:

Importance

Sleep health comprises several dimensions such as duration and fragmentation of sleep, circadian activity, and daytime behavior. Yet, most research has focused on individual sleep characteristics. Studies are needed to identify sleep profiles incorporating multiple dimensions and to assess how different profiles may be linked to adverse health outcomes.

Objective

To identify actigraphy-based 24-hour sleep/circadian profiles in older men and to investigate whether these profiles are associated with the incidence of dementia and cardiovascular disease (CVD) events over 12 years.

Design

Data came from a prospective sleep study with participants recruited between 20032005 and followed until 2015-2016.

Setting

Multicenter population-based cohort study.

SUBMITTER: Cavailles C 

PROVIDER: S-EPMC11370502 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature