Digital phenotyping of sleep patterns among heterogenous samples of Latinx adults using unsupervised learning.

ABSTRACT:

Objective

This study aimed to identify sleep disturbance subtypes ("phenotypes") among Latinx adults based on objective sleep data using a flexible unsupervised machine learning technique.

Methods

This study was an analysis of sleep data from three cross-sectional studies of the Precision in Symptom Self-Management Center at Columbia University. All studies focused on sleep health in Latinx adults at increased risk for sleep disturbance. Data on total sleep time (TST), time in bed (TIB), wake after sleep onset (WASO), sleep efficiency (SE), number of awakenings (NOA) and the mean length of nightly awakenings were collected using wrist-mounted accelerometers. Cluster analysis of the sleep data was conducted using an unsupervised machine learning approach that relies on mixtur

SUBMITTER: Ensari I 

PROVIDER: S-EPMC8439405 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature