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

• DOI: 10.2196/26107
• Publication Date: 2021-02-02T19:29:54Z
• Authors: 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 Glicks...; Matthew Levin; Ismail Nabeel; Judith Aberg; David Reich; Dennis Charney; Erwin P Bottinger; Laurie Keefer; Mayte Suarez-Farinas; Girish N Nadkarni; Zahi A Fayad
• Keywords: 2019-20 coronavirus outbreak

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 a wearable device identify predict COVID-19 related symptoms. Health care workers the Mount Sinai System were prospectively followed ongoing observational study using custom Warrior Watch Study app, which was downloaded their smartphones. Participants wore Apple for duration study, measuring throughout follow-up period. Surveys assessing symptom-related questions obtained daily. Using mixed-effect cosinor model, mean amplitude circadian pattern standard deviation interbeat interval normal sinus beats (SDNN), metric, differed between subjects without (P=.006). The this individuals during 7 days before after diagnosis compared metric uninfected time periods (P=.01). Significant changes SDNN first day reporting COVID-19-related symptom all other symptom-free Longitudinally metrics from commonly worn commercial (Apple Watch) can Prior nasal swab polymerase chain reaction testing, significant observed, demonstrating predictive ability infection.