Longitudinal Physiological Data from a Wearable Device Identifies SARS-CoV-2 Infection and Symptoms and Predicts COVID-19 Diagnosis

Publication Title
Publication/Creation Date
November 7 2020
Icahn School Of Medicine At Mount Sinai (creator)
Robert Hirten (creator)
Matteo Danieletto (creator)
Lewis Tomalin (creator)
Katie Hyewon Choi (creator)
Micol Zweig (creator)
Eddye Golden (creator)
Sparshdeep Kaur (creator)
Drew Helmus (creator)
Anthony Biello (creator)
Renata Pyzik (creator)
Ismail Nabeel (creator)
Alexander Charney (creator)
Benjamin Glicksberg (creator)
Matthew Levin (creator)
David Reich (creator)
Erwin Bottinger (creator)
Laurie Keefer (creator)
Mayte Suarez-Farinas (creator)
Girish N. Nadkarni (creator)
Zahi Fayad (creator)
Media Type
Journal Article
Persuasive Intent

Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with and observed prior to the clinical identification of infection. We performed an evaluation of this metric collected by wearable devices, to identify and predict Coronavirus disease 2019 (COVID-19) and its related symptoms.

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. Survey’s assessing infection and symptom related questions were obtained daily.

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), a HRV metric, differed between subjects with and without COVID-19 (p=0.006). 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=0.01). Significant changes in the mean MESOR 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=0.01).

Longitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can identify the diagnosis of COVID-19 and COVID-19 related symptoms. Prior to the diagnosis of COVID-19 by nasal PCR, significant changes in HRV were observed demonstrating its predictive ability to identify COVID-19 infection.


HCI Platform
Location on Body
Marketing Keywords
Apple, Apple Watch

Related Collections
COVID-19 Tech (2020-2021)
Date archived
January 18 2021
Last edited
January 18 2021
How to cite this entry
Icahn School of Medicine at Mount Sinai, Robert Hirten, Matteo Danieletto, Lewis Tomalin, Katie Hyewon Choi, Micol Zweig, Eddye Golden, Sparshdeep Kaur, Drew Helmus, Anthony Biello, Renata Pyzik, Ismail Nabeel, Alexander Charney, Benjamin Glicksberg, Matthew Levin, David Reich, Erwin Bottinger, Laurie Keefer, Mayte Suarez-Farinas, Girish N. Nadkarni, Zahi Fayad. (November 7 2020). "Longitudinal Physiological Data from a Wearable Device Identifies SARS-CoV-2 Infection and Symptoms and Predicts COVID-19 Diagnosis". medRxiv. Fabric of Digital Life. https://fabricofdigitallife.com/Detail/objects/5051