Stanford Medicine using wearable devices' health data to catch early signs of viral infections

Stanford (Calif.) Medicine is teaming up with Scripps Research and Fitbit on a new study that aims to detect viral infections such as COVID-19 through data collected from wearable devices.

Fitbit will donate 1,000 smartwatches to the research project, which is being led by Michael Snyder, PhD, genetics chair at the Stanford School of Medicine. Dr. Snyder's research is based on an algorithm that he created in 2017 with Xiao Li, PhD, an assistant professor in Cleveland-based Case Western Reserve University's Center for RNA Science and Therapeutics. The algorithm showed it is possible to detect infection by using data from a change in heart rate.

"Smartwatches and other wearables make many, many measurements per day — at least 250,000, which is what makes them such powerful monitoring devices," Dr. Snyder said in the news release. "My lab wants to harness that data and see if we can identify who's becoming ill as early as possible — potentially before they even know they're sick."

Dr. Snyder is collecting data from five different wearable devices, including a smart ring and various smartwatches. He and his team will develop five new algorithms to potentially detect when someone is getting sick.

The research team is recruiting study participants through Dr. Snyder's lab's personal health dashboard. The study combines research already underway from Stanford Medicine and the Scripps Research Institute, which last month launched a program to analyze patients' wearable health data to identify signs of coronavirus and other fast-spreading viral illnesses.


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