Use of physiological data from a wearable device to identify SARS-CoV-2 infection and symptoms and predict COVID-19 diagnosis: Observational study

Robert P. Hirten, Matteo Danieletto, Lewis Tomalin, Katie Hyewon Choi, Micol Zweig, Eddye Golden, Sparshdeep Kaur, Drew Helmus, Anthony Biello, Renata Pyzik, Alexander Charney, Riccardo Miotto, Benjamin S. Glicksberg, Matthew Levin, Ismail Nabeel, Judith Aberg, David Reich, Dennis Charney, Erwin P. Bottinger, Laurie KeeferMayte Suarez-Farinas, Girish N. Nadkarni, Zahi A. Fayad

Research output: Contribution to journalArticlepeer-review

84 Scopus citations

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 mean amplitude of the circadian pattern of the standard deviation of the interbeat interval of normal sinus beats (SDNN), an HRV metric, differed between subjects with and without COVID-19 (P=.006). The mean amplitude of this circadian pattern differed between individuals during the 7 days before and the 7 days after a COVID-19 diagnosis compared to this metric during uninfected time periods (P=.01). Significant changes in the mean and amplitude of the circadian pattern of the SDNN was observed between the first day of reporting a COVID-19–related symptom compared to all other symptom-free days (P=.01). Conclusions: Longitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can predict the diagnosis of COVID-19 and identify COVID-19–related symptoms. Prior to the diagnosis of COVID-19 by nasal swab polymerase chain reaction testing, significant changes in HRV were observed, demonstrating the predictive ability of this metric to identify COVID-19 infection.

Original languageEnglish
Article numbere26107
JournalJournal of Medical Internet Research
Volume23
Issue number2
DOIs
StatePublished - Feb 2021

Keywords

  • App
  • COVID-19
  • Data
  • Diagnosis
  • Heart rate variability
  • Identification
  • Infectious disease
  • Observational
  • Physiological
  • Prediction
  • Prediction
  • Symptom
  • Wearable
  • Wearable device

Fingerprint

Dive into the research topics of 'Use of physiological data from a wearable device to identify SARS-CoV-2 infection and symptoms and predict COVID-19 diagnosis: Observational study'. Together they form a unique fingerprint.

Cite this