Wrist-worn health devices may detect COVID-19 before symptoms start: Hamilton-based researcher

Using wrist-worn health devices, like smartwatches and bracelets, to count our steps, track calories, and respond to text messages is an incredible technological achievement. But what if those same devices could be used to detect infections like COVID-19 as early as two days before symptoms appear?

David Conen is a medical researcher at McMaster University in Hamilton. He and a team of experts across Europe have done just that.

The COVI-GAPP study examined whether existing wearable health devices could be paired with machine learning technology to detect presymptomatic and asymptomatic COVID-19 infections.

“When the pandemic started in March 2020,” Conen explained to McMaster’s Jesse Dorey, “we quickly thought about how we could… contribute to the knowledge gain and help to prevent and treat patients with COVID and help to avoid or better treat and better handle future pandemics.”

COVID-19 symptoms typically take a few days to appear, during which time the infected person can unknowingly spread the virus to others.

“If you can isolate those patients with COVID or whatever future infection there is, then this could have great implications for public health,” says Conen, an associate professor of medicine in the Faculty of Health Sciences and a scientist at the Population Health Research Institute.

“This (study) is related to multiple different infections or other diseases where you can use those algorithms to identify people early and either try to prevent complications, isolate the patients when it’s a very contagious disease, and change treatments.”

Data was collected from 1,163 participants from March 2020 until April 2021 while they wore an AVA fertility tracker — a commercially available, FDA and European agency-approved health bracelet that monitors breathing rate, heart rate, heart rate variability, skin temperature, and blood flow.

The bracelet was synched to a modified mobile app to record any activity that might affect the body’s central nervous system, such as alcohol use and prescription or recreational drug intake, as well as any potential COVID-19 symptoms.

127 participants tested positive for COVID-19 during the study, and the bracelet identified noticeable changes in all five physiological indicators during all stages of infection.

Based on the information that was provided by the patients, an algorithm was created to detect COVID-19 symptoms in 70 per cent of the participants who tested positive for the virus, and 73 per cent of confirmed positive cases were detected up to two days before symptoms appeared.

“That an existing medical device is able to be used in a different meaning [shows] that wearables have a promising future,” says Conen.

He adds, however, that the original study was conducted with a relatively small group of participants, and the group lacked diversity in ethnicity, age, and geographic location.

To further test the efficacy of wearable health devices and machine learning in COVID-19 detection, a much larger study of 20,000 participants is being conducted in the Netherlands. The results are expected to be published later this year.

The COVI-GAPP study, based on a larger research project based in Lichtenstein, was conducted by researchers from McMaster, the Dr. Risch Medical Laboratory, the University of Basel in Switzerland, and Imperial College London.