A machine learning approach to determine resilience utilizing wearable device data: analysis of an observational cohort.

ABSTRACT:

Objective

To assess whether an individual's degree of psychological resilience can be determined from physiological metrics passively collected from a wearable device.

Materials and methods

Data were analyzed in this secondary analysis of the Warrior Watch Study dataset, a prospective cohort of healthcare workers enrolled across 7 hospitals in New York City. Subjects wore an Apple Watch for the duration of their participation. Surveys were collected measuring resilience, optimism, and emotional support at baseline.

Results

We evaluated data from 329 subjects (mean age 37.4 years, 37.1% male). Across all testing sets, gradient-boosting machines (GBM) and extreme gradient-boosting models performed best for high- versus low-resilience prediction, stratified on a median C

SUBMITTER: Hirten RP 

PROVIDER: S-EPMC10152991 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature