智能手表成为预测COVID-19感染的好帮手
诸平
据医学快讯（Medical Xpress）网站2021年1月19日报道，美国纽约市的西奈山伊坎医学院(Icahn School of Medicine at Mount Sinai)的一组研究人员发现，有证据表明，智能手表（smart watches）可以在人们意识到自己被感染之前检测出COVID-19症状。西奈山伊坎医学院研究小组在其网站上描述了通过查看苹果手表上的数据，对297名医护人员进行测试的情况（见表1-表3所示）。相关研究结果2020年11月7日已经在medRxiv网站上发表——Robert P. Hirten, Matteo Danieletto, Lewis Tomalin, Katie Hyewon Choi, Micol Zweig, Eddye Golden, Sparshdeep Kaur, Drew Helmus, Anthony Biello, Renata Pyzik, Ismail Nabeel, Alexander Charney, Benjamin Glicksberg, Matthew Levin, David Reich, Dennis Charney, Erwin P Bottinger, Laurie Keefer, Mayte Suarez-Farinas, Girish N. Nadkarni, Zahi A. Fayad. Longitudinal Physiological Data from a Wearable Device Identifies SARS-CoV-2 Infection and Symptoms and Predicts COVID-19 Diagnosis. medRxiv (2020). DOI: 10.1101/2020.11.06.20226803
COVID-19感染的早期迹象之一是身体感染部位的炎症。当炎症开始时，身体的反应是轻微改变血液流动。血液流动的变化可以从人的心跳的细微变化中看出，可以通过苹果等智能手表检测到。通过长时间记录一个人的心跳，智能手表可以确定佩戴者的正常基线。当突然的长时间变化发生时，比如持续的心率变异性，该设备也可以检测到。在西奈山伊坎医学院的测试中，志愿者被要求全天佩戴这款智能手表，并安装一款专门检测心跳持续变化的手表App(Watch App)。研究人员发现，这些手表能够在志愿者注意到任何症状的平均7天前识别出三分之二的感染者。
美国哥伦比亚广播公司（CBS）新闻最近调查了西奈山研究小组和斯坦福大学(Stanford University)的另一项研究的结果，以及其他公司对其智能手表是否也能类似工作的调查。他们发现，在大多数情况下，答案是肯定的，智能手表通常可以在症状出现前一周检测出COVID-19感染。他们进一步指出，工程师可以为智能手表开发应用程序，提醒用户，然后用户可以自我隔离，直到测试结束。研究者进一步指出，这很可能对减缓当前和未来可能发生的流行病的传播有参考价值。更多信息请注意浏览原文或者相关报道。
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Background: Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with and observed prior to the clinical identification of infection. We performed an evaluation of this metric collected by wearable devices, to identify and predict Coronavirus disease 2019 (COVID-19) and its related symptoms.
Methods: Health care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study App which was downloaded to their smartphones. Participants wore an Apple Watch for the duration of the study measuring HRV throughout the follow up period. Survey’s assessing infection and symptom related questions were obtained daily.
Findings: Using a mixed-effect COSINOR model the mean amplitude of the circadian pattern of the standard deviation of the interbeat interval of normal sinus beats (SDNN), a HRV metric, differed between subjects with and without COVID-19 (p=0.006). The mean amplitude of this circadian pattern differed between individuals during the 7 days before and the 7 days after a COVID-19 diagnosis compared to this metric during uninfected time periods (p=0.01). Significant changes in the mean MESOR and amplitude of the circadian pattern of the SDNN was observed between the first day of reporting a COVID-19 related symptom compared to all other symptom free days (p=0.01).
Interpretation: Longitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can identify the diagnosis of COVID-19 and COVID-19 related symptoms. Prior to the diagnosis of COVID-19 by nasal PCR, significant changes in HRV were observed demonstrating its predictive ability to identify COVID-19 infection.
Funding: Support was provided by the Ehrenkranz Lab For Human Resilience, the BioMedical Engineering and Imaging Institute, The Hasso Plattner Institute for Digital Health at Mount Sinai, The Mount Sinai Clinical Intelligence Center and The Dr. Henry D. Janowitz Division of Gastroenterology.
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