Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study.
Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study.
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DOI：
10.2196/26107
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发表时间：
2021-02-22
影响因子：
7.4
通讯作者：
Fayad ZA
中科院分区：
文献类型：
--
作者：
Hirten RP;Danieletto M;Tomalin L;Choi KH;Zweig M;Golden E;Kaur S;Helmus D;Biello A;Pyzik R;Charney A;Miotto R;Glicksberg BS;Levin M;Nabeel I;Aberg J;Reich D;Charney D;Bottinger EP;Keefer L;Suarez-Farinas M;Nadkarni GN;Fayad ZA
以心率变异性(HRV)为特征的自主神经系统功能的变化与感染有关，并在临床鉴定之前被观察到。我们对可穿戴设备收集的心率变异性进行了评估，以识别和预测新冠肺炎及其相关症状。在一项正在进行的观察性研究中，西奈山健康系统的医护人员被前瞻性地跟踪调查，该研究使用定制的Warrior Watch研究应用程序，该应用程序被下载到他们的智能手机上。参与者在研究期间佩戴Apple Watch，在整个随访期内测量HRV。每天都会获得评估感染的调查和与症状相关的问题。采用混合效应余弦模型，心率变异性指标--正常窦性心搏搏动间期标准差昼夜节律的平均幅度在有无新冠肺炎的受试者中有所不同(P=0.006)。在确诊为新冠肺炎的前7天和后7天，这种昼夜节律模式的平均幅度与未感染时间段的这一指标相比有所不同(P=0.01)。在报告新冠肺炎相关症状的第一天与所有其他无症状日相比，SDNN昼夜节律的平均值和幅度发生了显著变化(P=0.01)。从经常佩戴的商用可穿戴设备(苹果手表)纵向收集的心率变异指标可以预测新冠肺炎的诊断，并识别新冠肺炎相关症状。在通过鼻拭子聚合酶链式反应检测诊断新冠肺炎之前，观察到心率变异的显著变化，证明了这一指标对识别新冠肺炎感染的预测能力。
Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification. We performed an evaluation of HRV collected by a wearable device to identify and predict COVID-19 and its related symptoms. 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. Surveys assessing infection and symptom-related questions were obtained daily. 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), an HRV metric, differed between subjects with and without COVID-19 (P=.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=.01). Significant changes in the mean 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=.01). Longitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can predict the diagnosis of COVID-19 and identify COVID-19–related symptoms. Prior to the diagnosis of COVID-19 by nasal swab polymerase chain reaction testing, significant changes in HRV were observed, demonstrating the predictive ability of this metric to identify COVID-19 infection.
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DOI：
10.1136/bmj.m3582
发表时间：
2020-10-28
期刊：
BMJ (Clinical research ed.)
影响因子：
--
作者：
Shah ASV;Wood R;Gribben C;Caldwell D;Bishop J;Weir A;Kennedy S;Reid M;Smith-Palmer A;Goldberg D;McMenamin J;Fischbacher C;Robertson C;Hutchinson S;McKeigue P;Colhoun H;McAllister DA
通讯作者：
McAllister DA
影响因子：
11.8
作者：
Kang, Min;Song, Tie;Zhang, Yonghhui
通讯作者：
Zhang, Yonghhui
影响因子：
82.9
作者：
He, Xi;Lau, Eric H. Y.;Leung, Gabriel M.
通讯作者：
Leung, Gabriel M.
影响因子：
3.1
作者：
Hirten, Robert P.;Stanley, Stephanie;Sands, Bruce E.
通讯作者：
Sands, Bruce E.
影响因子：
82.9
作者：
Quer, Giorgio;Radin, Jennifer M.;Steinhubl, Steven R.
通讯作者：
Steinhubl, Steven R.