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.

传染病的早期发现对于减少传播和促进早期干预至关重要。在这项研究中，我们构建了一个基于智能手表的实时警报系统，该系统检测与早期感染发作相关的异常生理和活动信号（心率和步数），并在一项前瞻性研究中实施了该系统。在一个由 3,318 名参与者组成的队列中，其中 84 人感染了严重急性呼吸系统综合症冠状病毒 2 (SARS-CoV-2)，该系统在 67 人 (80%) 中生成了症状前和无症状 SARS-CoV-2 感染的警报。感染者。在症状出现前 3 天的中位数观察到症状前信号。检查参与者提供的详细调查回复显示，其他呼吸道感染以及与感染无关的事件，例如压力、饮酒和旅行，也可能触发警报，尽管平均频率要低得多（每人 1.15 个警报天数，而 2019 年冠状病毒病病例为每人 3.42 个警报天数）。因此，通过在线检测算法对智能手表信号的分析可以在很大一部分病例中提供 SARS-CoV-2 感染的预警。这项研究表明，实时警报系统可用于早期检测感染和其他压力源，并在可扩展到数百万用户的开源平台上使用。通过在线检测算法对智能手表信号进行分析，在高比例病例中提供了 SARS-CoV-2 感染的预警。这项研究表明，实时警报系统可用于早期检测感染和其他压力源，并在可扩展到数百万用户的开源平台上使用。通过在线检测算法对智能手表信号进行分析，在高比例病例中提供了 SARS-CoV-2 感染的预警。这项研究表明，实时警报系统可用于早期检测感染和其他压力源，并在可扩展到数百万用户的开源平台上使用。