Wearable sensor data and self-reported symptoms for COVID-19 detection
Wearable sensor data and self-reported symptoms for COVID-19 detection
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DOI：
10.1038/s41591-020-1123-x
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发表时间：
2020-10-29
期刊：
影响因子：
82.9
通讯作者：
Steinhubl, Steven R.
中科院分区：
文献类型：
--
作者：
Quer, Giorgio;Radin, Jennifer M.;Steinhubl, Steven R.
一款智能手机应用程序将智能手表和活动追踪器数据与自我报告的症状结合起来，可以持续监测新冠病毒感染情况。传统的COVID-19筛查通常包括关于症状和旅行史的调查问题，以及温度测量。在这里，我们探讨了随着时间的推移收集的个人传感器数据是否有助于识别表明感染的细微变化，例如COVID-19患者。我们开发了一款智能手机应用程序，可以收集美国个人的智能手表和活动追踪器数据，以及自我报告的症状和诊断测试结果，并评估了症状和传感器数据是否可以区分有症状个体的COVID-19阳性病例和阴性病例。我们在2020年3月25日至6月7日期间招募了30,529名参与者，其中3,811人报告了症状。在这些有症状的人中，54人报告COVID-19检测呈阳性，279人报告呈阴性。我们发现，症状和传感器数据的结合导致区分COVID-19阳性或阴性的有症状个体的曲线下面积（AUC）为0.80（四分位数间距（IQR）： 0.73-0.86），这一性能明显优于单独考虑症状的模型(1)（AUC = 0.71; IQR: 0.63-0.79）。这种连续的、被动捕获的数据可能是对病毒测试的补充，病毒测试通常是一次性或不频繁的抽样分析。
A smartphone app that combines smartwatch and activity tracker data together with self-reported symptoms allows continuous monitoring of SARS-CoV-2 infection.Traditional screening for COVID-19 typically includes survey questions about symptoms and travel history, as well as temperature measurements. Here, we explore whether personal sensor data collected over time may help identify subtle changes indicating an infection, such as in patients with COVID-19. We have developed a smartphone app that collects smartwatch and activity tracker data, as well as self-reported symptoms and diagnostic testing results, from individuals in the United States, and have assessed whether symptom and sensor data can differentiate COVID-19 positive versus negative cases in symptomatic individuals. We enrolled 30,529 participants between 25 March and 7 June 2020, of whom 3,811 reported symptoms. Of these symptomatic individuals, 54 reported testing positive and 279 negative for COVID-19. We found that a combination of symptom and sensor data resulted in an area under the curve (AUC) of 0.80 (interquartile range (IQR): 0.73-0.86) for discriminating between symptomatic individuals who were positive or negative for COVID-19, a performance that is significantly better (P < 0.01) than a model(1) that considers symptoms alone (AUC = 0.71; IQR: 0.63-0.79). Such continuous, passively captured data may be complementary to virus testing, which is generally a one-off or infrequent sampling assay.