Can Wearable Tech Help Detect COVID-19 Progression?

Researchers at the Tampa-based University of South Florida will examine how wearable technology can help the body’s physiological response to COVID-19, with the goal to develop an early warning system for those most at risk of severe infection.

University researchers point out that while  the total rate of COVID-19 infection “is alarming, the percentage of those patients who experience severe illness from the virus remains relatively low.” As such, they note, experts say certain factors, such as age and health history, appear to play a role in overall outcomes. Now, USF researchers are hoping to better understand the differences in physiological changes between those patients who experience severe effects and those who do not.

Utilizing existing medical monitoring technology, researchers will monitor the physiological conditions of more than 100 study participants, each having tested positive for COVID-19. The wearable device, which is being provided by Shimmer Research, Inc., a private company partnering with USF for the study, will track a variety of markers, including skin temperature, thoracic bioimpedance, oxygen saturation (SpO2) and more.

Once the data is collected, scientists will use machine learning and artificial intelligence to synthesize the information and find patterns within the physiological fluctuations. These patterns will then be used to develop various profiles for potential patient outcomes, according to university officials.

“When you look at the viral progression across a population of people, it is very hard to anticipate which people will be severely affected by this virus. There are many different cases of individuals who are otherwise healthy, yet still have a violent reaction,” said principal investigator Matt Mullarkey. “We are confident that by examining certain markers, we can find physiological patterns that can help identify patients who are headed toward serious complications.”

Once the physiological profiles are developed, researchers believe they can then use them to identify patients who may be at risk for severe infection.

“We want to give medical professionals and patients as early an indicator as possible, an early warning system if you will, that a particular person, who is normally healthy but who has been exposed to the virus, fits a physiological profile for negative outcomes,” said Asa Oxner, M.D., co-principal investigator of the study. “If we can alert medical professionals early about the viral progression, the hope is they can take the appropriate medical interventions to save lives.”