Health care workers work on the front lines of the COVID-19 pandemic, so early detection of COVID-19 infection is especially important in this population in order to prevent the spread of disease among the health care workforce, their patients, and their families. However, standard symptomatic COVID-19 testing is insufficient to protect health care workers and prevent the spread of disease. This study utilizes continuous physiological data from wearable sensors and surveys completed via smartphone technology, in conjunction with biospecimens, in order to assist health care workers in their self-monitoring for COVID-19 infection. Subjects will wear smart watches and temperature monitoring patches to collect continuous heart rate and temperature data. Subjects will also complete baseline and exit surveys, in addition to daily mood and symptom logging surveys. Finally, subjects will provide biospecimens - nasal and saliva samples and optional blood samples. Using wearable sensor data in conjunction with survey data and biospecimens, this study aims to prevent the spread of COVID-19 among health care workers, their patients, and their families, and to eventually develop a predictive model for early detection of COVID-19 infection.
This prospective study of health care workers utilizes wearable sensors, surveys and symptom logs, and biospecimens in an effort to improve self-monitoring practices for COVID-19 among health care workers and to provide key data for the development of a predictive model for early detection of COVID-19 infection.