Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study.

ABSTRACT:

Background

Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification.

Objective

We performed an evaluation of HRV collected by a wearable device to identify and predict COVID-19 and its related symptoms.

Methods

Health care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study app, which was downloaded to their smartphones. Participants wore an Apple Watch for the duration of the study, measuring HRV throughout the follow-up period. Surveys assessing infection and symptom-related questions were obtained daily.

Results

Using a mixed-effect cosinor model, the

SUBMITTER: Hirten RP 

PROVIDER: S-EPMC7901594 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature