Seshadri Dhruv R, Davies Evan V, Harlow Ethan R, Hsu Jeffrey J, Knighton Shanina C, Walker Timothy A, Voos James E, Drummond Colin K
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States.
Department of Electrical Engineering, Case Western Reserve University, Cleveland, OH, United States.
Front Digit Health. 2020 Jun 23;2:8. doi: 10.3389/fdgth.2020.00008. eCollection 2020.
The COVID-19 pandemic has brought into sharp focus the need to harness and leverage our digital infrastructure for remote patient monitoring. As current viral tests and vaccines are slow to emerge, we see a need for more robust disease detection and monitoring of individual and population health, which could be aided by wearable sensors. While the utility of this technology has been used to correlate physiological metrics to daily living and human performance, the translation of such technology toward predicting the incidence of COVID-19 remains a necessity. When used in conjunction with predictive platforms, users of wearable devices could be alerted when changes in their metrics match those associated with COVID-19. Anonymous data localized to regions such as neighborhoods or zip codes could provide public health officials and researchers a valuable tool to track and mitigate the spread of the virus, particularly during a second wave. Identifiable data, for example remote monitoring of cohorts (family, businesses, and facilities) associated with individuals diagnosed with COVID-19, can provide valuable data such as acceleration of transmission and symptom onset. This manuscript describes clinically relevant physiological metrics which can be measured from commercial devices today and highlights their role in tracking the health, stability, and recovery of COVID-19+ individuals and front-line workers. Our goal disseminating from this paper is to initiate a call to action among front-line workers and engineers toward developing digital health platforms for monitoring and managing this pandemic.
新冠疫情让利用和借助数字基础设施进行远程患者监测的必要性变得极为突出。由于目前病毒检测和疫苗研制进展缓慢,我们认为需要更强大的疾病检测以及对个人和群体健康的监测,而可穿戴传感器对此能有所帮助。虽然这项技术已被用于将生理指标与日常生活及人类表现相关联,但将此类技术用于预测新冠疫情的发病率仍然很有必要。当与预测平台结合使用时,可穿戴设备的用户在其指标变化与新冠相关指标相匹配时会收到警报。诸如社区或邮政编码区域等本地化的匿名数据可为公共卫生官员和研究人员提供一个宝贵工具,以追踪和缓解病毒传播,尤其是在第二波疫情期间。可识别数据,例如对与新冠确诊患者相关的群体(家庭、企业和机构)进行远程监测,可提供诸如传播加速和症状出现等有价值的数据。本文描述了目前可从商用设备测量的临床相关生理指标,并强调了它们在追踪新冠病毒感染者和一线工作者的健康、稳定性及康复情况方面的作用。我们撰写本文的目的是呼吁一线工作者和工程师行动起来,开发用于监测和管理这场疫情的数字健康平台。