High-Resolution Digital Phenotypes From Consumer Wearables and Their Applications in Machine Learning of Cardiometabolic Risk Markers: Cohort Study.

ABSTRACT:

Background

Consumer-grade wearable devices enable detailed recordings of heart rate and step counts in free-living conditions. Recent studies have shown that summary statistics from these wearable recordings have potential uses for longitudinal monitoring of health and disease states. However, the relationship between higher resolution physiological dynamics from wearables and known markers of health and disease remains largely uncharacterized.

Objective

We aimed to derive high-resolution digital phenotypes from observational wearable recordings and to examine their associations with modifiable and inherent markers of cardiometabolic disease risk.

Methods

We introduced a principled framework to extract interpretable high-resolution phenotypes from wearable data record

SUBMITTER: Zhou W 

PROVIDER: S-EPMC9377462 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature