Wearable Sensors for COVID-19: A Call to Action to Harness Our Digital Infrastructure for Remote Patient Monitoring and Virtual Assessments

Abstract

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.

Keywords: COVID-19; pandemic; predictive analytics; remote patient monitoring; wearable sensors.

Figure 1

Correlating clinical symptoms to the immune response of COVID-19 and when the implementation of wearable technology fits into the virus timeline. (A) Correlating symptoms to the immunological pathway of COVID-19 described in this figure. Wearable sensors can be used to detect changes in physiological metrics before a formal diagnosis. (B) Image from the WHOOP application correlating a decrease in recovery with a diagnosis by an individual diagnosed with COVID-19 on Wednesday the 18th (red bar). (C) Schematic detailing an example of an iPhone application collecting physiological data from a wearable sensor and translating those metrics to alert an individual on his/her overall health status. Figure reproduced and modified from Azuravesta Design (10), (A) and Team (11), (B).

Figure 2

Clinical pathway summarizing the role of wearable sensor technology and predictive analytics for monitoring COVID-19. (A) Physiological metrics currently capable of being measured from commercial wearable sensors. (B) Changes in physiological metrics can be inputted into an early detection algorithm for COVID-19 monitoring. The goal of such algorithms is to ensure the true positive rate is robust to support the use of the analytics for real-time clinical decision making. (C) Integrated analytics to monitor COVID-19 can be used to monitor individual or population health. HRV, heart rate variability; Resp Rate, respiration rate; SpO₂, blood oxygen saturation; Temp, temperature; TP, True Positive; FP, False Positive; TN, True Negative; FN, False Negative.