Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study

• Published in: Journal of medical Internet research Vol. 23; no. 2; p. e26107
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: Canada Gunther Eysenbach MD MPH, Associate Professor 22.02.2021; JMIR Publications
• Subjects: Adult; Asymptomatic; Autonomic nervous system; Between-subjects design; Central nervous system; Chemical analysis; Circadian rhythm; Circadian Rhythm - physiology; Coronaviruses; COVID-19; COVID-19 - diagnosis; COVID-19 - physiopathology; COVID-19 - virology; COVID-19 Testing - methods; Diagnostic tests; Disease transmission; Female; Health care; Health Personnel; Heart rate; Heart Rate - physiology; Humans; Hypothesis testing; Infections; Male; Medical diagnosis; Medical personnel; Multimedia; Nervous system; Observational studies; Original Paper; Physiology; Predictions; Questionnaires; SARS-CoV-2 - genetics; SARS-CoV-2 - isolation & purification; Severe acute respiratory syndrome coronavirus 2; Smartphones; Symptoms; Wearable computers; Wearable Electronic Devices; Workers; app; observational; wearable; data; diagnosis; COVID-19; wearable device; symptom; identification; heart rate variability; infectious disease; prediction; physiological
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/26107

Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification. We performed an evaluation of HRV collected by a wearable device to identify and predict 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. Surveys 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), 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). 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.