Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study
| العنوان: | Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study |
|---|---|
| المؤلفون: | Katie Hyewon Choi, Drew Helmus, Renata Pyzik, Sparshdeep Kaur, Erwin P. Bottinger, Micol Zweig, Benjamin S. Glicksberg, Riccardo Miotto, Ismail Nabeel, Dennis S. Charney, Anthony Biello, Laurie Keefer, Mayte Suárez-Fariñas, David Reich, Eddye Golden, Zahi A. Fayad, Matteo Danieletto, Lewis Tomalin, Girish N. Nadkarni, Judith A. Aberg, Matthew A. Levin, Robert Hirten, Alexander W. Charney |
| المصدر: | Journal of Medical Internet Research, Vol 23, Iss 2, p e26107 (2021) Journal of Medical Internet Research |
| بيانات النشر: | JMIR Publications, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Adult, Male, 0301 basic medicine, medicine.medical_specialty, Coronavirus disease 2019 (COVID-19), diagnosis, infectious disease, Health Personnel, physiological, Wearable computer, wearable device, Health Informatics, lcsh:Computer applications to medicine. Medical informatics, wearable, Wearable Electronic Devices, 03 medical and health sciences, COVID-19 Testing, 0302 clinical medicine, Heart Rate, Internal medicine, medicine, Humans, Heart rate variability, observational, 030212 general & internal medicine, Circadian rhythm, app, Original Paper, SARS-CoV-2, business.industry, lcsh:Public aspects of medicine, heart rate variability, COVID-19, lcsh:RA1-1270, prediction, symptom, Circadian Rhythm, Autonomic nervous system, 030104 developmental biology, data, identification, lcsh:R858-859.7, Female, Observational study, Metric (unit), business, Interbeat interval |
| الوصف: | 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. |
| اللغة: | English |
| تدمد: | 1438-8871 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e36f7d9c53e20512d4f58e19d8dfed1e https://www.jmir.org/2021/2/e26107 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....e36f7d9c53e20512d4f58e19d8dfed1e |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 14388871 |
|---|