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Real-time alerting system for COVID-19 and other stress events using wearable data
Nature Medicine  (IF53.44),  Pub Date : 2021-11-29, DOI: 10.1038/s41591-021-01593-2
Arash Alavi, Gireesh K. Bogu, Meng Wang, Ekanath Srihari Rangan, Andrew W. Brooks, Qiwen Wang, Emily Higgs, Alessandra Celli, Tejaswini Mishra, Ahmed A. Metwally, Kexin Cha, Peter Knowles, Amir A. Alavi, Rajat Bhasin, Shrinivas Panchamukhi, Diego Celis, Tagore Aditya, Alexander Honkala, Benjamin Rolnik, Erika Hunting, Orit Dagan-Rosenfeld, Arshdeep Chauhan, Jessi W. Li, Caroline Bejikian, Vandhana Krishnan, Lettie McGuire, Xiao Li, Amir Bahmani, Michael P. Snyder

Early detection of infectious diseases is crucial for reducing transmission and facilitating early intervention. In this study, we built a real-time smartwatch-based alerting system that detects aberrant physiological and activity signals (heart rates and steps) associated with the onset of early infection and implemented this system in a prospective study. In a cohort of 3,318 participants, of whom 84 were infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), this system generated alerts for pre-symptomatic and asymptomatic SARS-CoV-2 infection in 67 (80%) of the infected individuals. Pre-symptomatic signals were observed at a median of 3 days before symptom onset. Examination of detailed survey responses provided by the participants revealed that other respiratory infections as well as events not associated with infection, such as stress, alcohol consumption and travel, could also trigger alerts, albeit at a much lower mean frequency (1.15 alert days per person compared to 3.42 alert days per person for coronavirus disease 2019 cases). Thus, analysis of smartwatch signals by an online detection algorithm provides advance warning of SARS-CoV-2 infection in a high percentage of cases. This study shows that a real-time alerting system can be used for early detection of infection and other stressors and employed on an open-source platform that is scalable to millions of users.