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Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study

机译:使用可穿戴设备的生理数据以鉴定SARS-COV-2感染和症状并预测Covid-19诊断:观察研究

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摘要

BackgroundChanges in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification. ObjectiveWe performed an evaluation of HRV collected by a wearable device to identify and predict COVID-19 and its related symptoms. MethodsHealth 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. ResultsUsing 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). ConclusionsLongitudinally 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.
机译:BructractChanges在自主神经系统功能中,其特征在于心率变异性(HRV),与感染有关并在其临床鉴定之前观察到。目的术对可穿戴设备收集的HRV进行评估,以识别和预测Covid-19及其相关症状。在持续的观察研究中,使用自定义战士手表学习应用程序进行了持续的观察研究,持续下载了Sinai卫生系统的方法正期护理工作者。参与者在研究期间穿着Apple手表,在整个后续期间测量HRV。每天获得评估感染和症状相关问题的调查。加工混合效果植物学模型,昼夜间隔的标准偏差的平均振幅(SDNN),HRV指标之间的标准偏差,在具有和不具有Covid-19的受试者之间不同(P = .006) 。这种昼夜节律模式的平均幅度在未经染色的时间段期间与该度量相比,在Covid-19诊断之前的7天内和7天内的个体之间的幅度不同(P = .01)。与所有其他无症状天数相比,在报告CoVID-19相关症状的第一天之间观察到SDNN的昼夜昼夜平均模式的显着变化(P = .01)。结论来自常用的商业可穿戴设备(Apple Watch)的长长收集的HRV指标可以预测Covid-19的诊断并鉴定Covid-19相关症状。在通过鼻拭子聚合酶链反应测试诊断Covid-19之前,观察到HRV的显着变化,证明了该度量识别Covid-19感染的预测能力。

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