Consumer wearable devices that continuously measure vital signs have been used to monitor the onset of infectious disease. Here, we show that data from consumer smartwatches can be used for the pre-symptomatic detection of coronavirus disease 2019 (COVID-19). We analysed physiological and activity data from 32 individuals infected with COVID-19, identified from a cohort of nearly 5,300 participants, and found that 26 of them (81%) had alterations in their heart rate, number of daily steps or time asleep. Of the 25 cases of COVID-19 with detected physiological alterations for which we had symptom information, 22 were detected before (or at) symptom onset, with four cases detected at least nine days earlier. Using retrospective smartwatch data, we show that 63% of the COVID-19 cases could have been detected before symptom onset in real time via a two-tiered warning system based on the occurrence of extreme elevations in resting heart rate relative to the individual baseline. Our findings suggest that activity tracking and health monitoring via consumer wearable devices may be used for the large-scale, real-time detection of respiratory infections, often pre-symptomatically.

持续测量生命体征的消费者可穿戴设备已被用来监测传染病的发作。在这里，我们展示了来自消费者智能手表的数据可用于 2019 年冠状病毒病 (COVID-19) 的症状前检测。我们分析了近 5,300 名参与者中的 32 名感染了 COVID-19 的人的生理和活动数据，发现其中 26 人 (81%) 的心率、每日步数或睡眠时间发生了变化。在我们有症状信息的 25 例检测到生理变化的 COVID-19 病例中，22 例是在症状出现之前（或当时）检测到的，其中 4 例至少在 9 天前检测到。使用回顾性智能手表数据，我们表明，根据静息心率相对于个人基线极端升高的情况，可以通过两级预警系统在症状出现之前实时检测到 63% 的 COVID-19 病例。我们的研究结果表明，通过消费者可穿戴设备进行的活动跟踪和健康监测可用于大规模、实时检测呼吸道感染，通常是在出现症状前进行检测。