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Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study
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...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7901594/ https://www.ncbi.nlm.nih.gov/pubmed/33529156 http://dx.doi.org/10.2196/26107 |
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author | Hirten, Robert P Danieletto, Matteo Tomalin, Lewis Choi, Katie Hyewon Zweig, Micol Golden, Eddye Kaur, Sparshdeep Helmus, Drew Biello, Anthony Pyzik, Renata Charney, Alexander Miotto, Riccardo Glicksberg, Benjamin S Levin, Matthew Nabeel, Ismail Aberg, Judith Reich, David Charney, Dennis Bottinger, Erwin P Keefer, Laurie Suarez-Farinas, Mayte Nadkarni, Girish N Fayad, Zahi A |
author_facet | Hirten, Robert P Danieletto, Matteo Tomalin, Lewis Choi, Katie Hyewon Zweig, Micol Golden, Eddye Kaur, Sparshdeep Helmus, Drew Biello, Anthony Pyzik, Renata Charney, Alexander Miotto, Riccardo Glicksberg, Benjamin S Levin, Matthew Nabeel, Ismail Aberg, Judith Reich, David Charney, Dennis Bottinger, Erwin P Keefer, Laurie Suarez-Farinas, Mayte Nadkarni, Girish N Fayad, Zahi A |
author_sort | Hirten, Robert P |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7901594 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-79015942021-03-02 Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study Hirten, Robert P Danieletto, Matteo Tomalin, Lewis Choi, Katie Hyewon Zweig, Micol Golden, Eddye Kaur, Sparshdeep Helmus, Drew Biello, Anthony Pyzik, Renata Charney, Alexander Miotto, Riccardo Glicksberg, Benjamin S Levin, Matthew Nabeel, Ismail Aberg, Judith Reich, David Charney, Dennis Bottinger, Erwin P Keefer, Laurie Suarez-Farinas, Mayte Nadkarni, Girish N Fayad, Zahi A J Med Internet Res Original Paper 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. JMIR Publications 2021-02-22 /pmc/articles/PMC7901594/ /pubmed/33529156 http://dx.doi.org/10.2196/26107 Text en ©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 Keefer, Mayte Suarez-Farinas, Girish N Nadkarni, Zahi A Fayad. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 22.02.2021. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Hirten, Robert P Danieletto, Matteo Tomalin, Lewis Choi, Katie Hyewon Zweig, Micol Golden, Eddye Kaur, Sparshdeep Helmus, Drew Biello, Anthony Pyzik, Renata Charney, Alexander Miotto, Riccardo Glicksberg, Benjamin S Levin, Matthew Nabeel, Ismail Aberg, Judith Reich, David Charney, Dennis Bottinger, Erwin P Keefer, Laurie Suarez-Farinas, Mayte Nadkarni, Girish N Fayad, Zahi A Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study |
title | Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study |
title_full | Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study |
title_fullStr | Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study |
title_full_unstemmed | Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study |
title_short | Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study |
title_sort | use of physiological data from a wearable device to identify sars-cov-2 infection and symptoms and predict covid-19 diagnosis: observational study |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7901594/ https://www.ncbi.nlm.nih.gov/pubmed/33529156 http://dx.doi.org/10.2196/26107 |
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