背景:呼吸道感染的早期检测可以改善对暴发的反应。可穿戴设备可以使用纵向生理信号提供对健康和福祉的见解。
目的:本研究的目的是前瞻性评估基于消费者可穿戴生理学的呼吸道感染检测算法在医护人员中的性能。
方法:在本研究中,我们评估了以前开发的预测COVID-19或其他上呼吸道感染的系统的性能.该系统使用从智能手表记录的生理信号生成实时警报。静息心率,呼吸频率,和在睡眠期间测量的心率变异性用于预测。基线记录后,当参与者收到来自系统的通知时,他们被要求在Northwell卫生系统站点接受测试。参与者被要求在研究期间自我报告任何阳性测试。使用呼吸道感染结果(实验室结果或自我报告)评估模型预测的准确性,和通报后调查用于评估潜在的混杂因素。
结果:2022年1月6日至2022年7月20日,共有577名来自纽约NorthwellHealth的参与者参加了这项研究。其中,470成功地完成了这项研究,89没有提供足够的生理数据来接收来自模型的任何预测,18退出。在完成研究并按照16周研究持续时间的要求佩戴智能手表的470名参与者中,该算法产生了665个正面警报,其中153人(23.0%)没有接受呼吸道病毒检测。在涉及呼吸道病毒面板测试的512例阳性警报中,63人确认了呼吸道感染结果(即,使用聚合酶链反应或家庭测试检测到COVID-19或其他呼吸道感染),其余449例上呼吸道感染测试结果为阴性。在所有案件中,基于每天预测的估计假阳性率为2%,在这个特定人群中,阳性预测值从4%到10%不等,观察到的发病率为每周每100,000例198例。在收到积极警报后填写的问卷的详细检查显示,身体或情绪压力事件,比如剧烈运动,睡眠不好,压力,过度饮酒,可能会导致假阳性结果。
结论:实时警报系统可对呼吸道病毒感染以及其他可能导致生理信号变化的身体或情绪应激事件提供预警。这项研究表明,具有嵌入式警报系统的可穿戴设备具有提供健康措施信息的潜力。
BACKGROUND: The early detection of respiratory infections could improve responses against outbreaks. Wearable devices can provide insights into health and well-being using longitudinal physiological signals.
OBJECTIVE: The purpose of this
study was to prospectively evaluate the performance of a consumer wearable physiology-based respiratory infection detection algorithm in health care workers.
METHODS: In this
study, we evaluated the performance of a previously developed system to predict the presence of COVID-19 or other upper respiratory infections. The system generates real-time alerts using physiological signals recorded from a smartwatch. Resting heart rate, respiratory rate, and heart rate variability measured during the sleeping period were used for prediction. After baseline recordings, when participants received a notification from the system, they were required to undergo testing at a Northwell Health System site. Participants were asked to self-report any positive tests during the
study. The accuracy of model prediction was evaluated using respiratory infection results (laboratory results or self-reports), and postnotification surveys were used to evaluate potential confounding factors.
RESULTS: A total of 577 participants from Northwell Health in New York were enrolled in the study between January 6, 2022, and July 20, 2022. Of these, 470 successfully completed the study, 89 did not provide sufficient physiological data to receive any prediction from the model, and 18 dropped out. Out of the 470 participants who completed the study and wore the smartwatch as required for the 16-week
study duration, the algorithm generated 665 positive alerts, of which 153 (23.0%) were not acted upon to undergo testing for respiratory viruses. Across the 512 instances of positive alerts that involved a respiratory viral panel test, 63 had confirmed respiratory infection results (ie, COVID-19 or other respiratory infections detected using a polymerase chain reaction or home test) and the remaining 449 had negative upper respiratory infection test results. Across all cases, the estimated false-positive rate based on predictions per day was 2%, and the positive-predictive value ranged from 4% to 10% in this specific population, with an observed incidence rate of 198 cases per week per 100,000. Detailed examination of questionnaires filled out after receiving a positive alert revealed that physical or emotional stress events, such as intense exercise, poor sleep, stress, and excessive alcohol consumption, could cause a false-positive result.
CONCLUSIONS: The real-time alerting system provides advance warning on respiratory viral infections as well as other physical or emotional stress events that could lead to physiological signal changes. This
study showed the potential of wearables with embedded alerting systems to provide information on wellness measures.