With increased COVID-19 testing and the detection of more positive cases (COVID+), many “at-risk” individuals who appear asymptomatic or only with mild symptoms are being told to self-quarantine at home. Unfortunately, there are situations where a sudden worsening of the disease occurs and symptoms progress quickly, usually within 24-48 hours. This rapid health decline often goes unreported until too late. More proactive surveillance of these COVID+ individuals could provide an alert to healthcare providers to initiate earlier intervention and save lives.

Our project adapts our current mHealth framework to the immediate needs of the COVID-19 crisis. Unlike other mHealth efforts that are using apps for contact tracing, self-reporting of symptoms, or new sensors for detecting disease onset, we focus on using mHealth to enable (safe) remote monitoring of known COVID+ patients. We originally created the Sensing for At-Risk Patients (SARP) platform to automatically collect information on older, frail patients with different conditions. SARP uses a smartwatch and different sensors to capture this information, automatically and securely sending the data to UCLA Health for review by healthcare staff. We are extending SARP by adding several FDA-approved devices (thermometer, pulse oximeter, respiratory monitor) to augment its present capabilities. SARP will be used to gather objective observations for at-home COVID+ cases, with the data reviewed by healthcare providers to assess patient health.

Capturing this data will further elucidate the myriad presentation of symptoms in COVID+ patients in a natural environment, helping establish a better understanding of this disease and the evolving pandemic. As more insight is gained about these patients and the presentation of the disease, we will explore the application of machine learning methods on this data to detect early patterns of worsening symptoms, leading to potential clinical decision support tools for physicians.