Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA; email:
physIQ Inc., Chicago, Illinois, USA.
Annu Rev Biomed Eng. 2022 Jun 6;24:1-27. doi: 10.1146/annurev-bioeng-103020-040136. Epub 2021 Dec 21.
Mounting clinical evidence suggests that viral infections can lead to detectable changes in an individual's normal physiologic and behavioral metrics, including heart and respiration rates, heart rate variability, temperature, activity, and sleep prior to symptom onset, potentially even in asymptomatic individuals. While the ability of wearable devices to detect viral infections in a real-world setting has yet to be proven, multiple recent studies have established that individual, continuous data from a range of biometric monitoring technologies can be easily acquired and that through the use of machine learning techniques, physiological signals and warning signs can be identified. In this review, we highlight the existing knowledge base supporting the potential for widespread implementation of biometric data to address existing gaps in the diagnosis and treatment of viral illnesses, with a particular focus on the many important lessons learned from the coronavirus disease 2019 pandemic.
越来越多的临床证据表明，病毒感染可导致个体正常生理和行为指标发生可检测的变化，包括心率和呼吸频率、心率变异性、体温、活动量和睡眠，甚至在无症状个体中也是如此。虽然可穿戴设备在真实环境中检测病毒感染的能力尚未得到证实，但多项近期研究已经证实，可以轻松获取来自一系列生物特征监测技术的个体连续数据，并且通过使用机器学习技术，可以识别生理信号和预警信号。在这篇综述中，我们重点介绍了现有的知识库，这些知识库支持广泛应用生物特征数据来解决病毒疾病诊断和治疗方面的现有差距，特别关注了从 2019 冠状病毒病大流行中吸取的许多重要经验教训。
