Digital phenotyping by consumer wearables identifies sleep-associated markers of cardiovascular disease risk and biological aging.

ABSTRACT: Sleep is associated with various health outcomes. Despite their growing adoption, the potential for consumer wearables to contribute sleep metrics to sleep-related biomedical research remains largely uncharacterized. Here we analyzed sleep tracking data, along with questionnaire responses and multi-modal phenotypic data generated from 482 normal volunteers. First, we compared wearable-derived and self-reported sleep metrics, particularly total sleep time (TST) and sleep efficiency (SE). We then identified demographic, socioeconomic and lifestyle factors associated with wearable-derived TST; they included age, gender, occupation and alcohol consumption. Multi-modal phenotypic data analysis showed that wearable-derived TST and SE were associated with cardiovascular disease risk markers such

SUBMITTER: Teo JX 

PROVIDER: S-EPMC6778117 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature