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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
- Source :
- Journal of Medical Internet Research, Vol 23, Iss 2, p e26107 (2021), Journal of Medical Internet Research
- Publication Year :
- 2021
- Publisher :
- JMIR Publications, 2021.
-
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.
- Subjects :
- 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
Subjects
Details
- Language :
- English
- ISSN :
- 14388871
- Volume :
- 23
- Issue :
- 2
- Database :
- OpenAIRE
- Journal :
- Journal of Medical Internet Research
- Accession number :
- edsair.doi.dedup.....e36f7d9c53e20512d4f58e19d8dfed1e