Stanford has launched a new COVID-19 Wearables Data Study that aims to detect the coronavirus using the Apple Watch and other wearable devices.
"Smartwatches and other wearables make many, many measurements per day — at least 250,000, which is what makes them such powerful monitoring devices," said Michael Snyder, PhD, professor and chair of genetics at the Stanford School of Medicine. "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."
In 2017, Snyder and postdoctoral scholar Xiao Li, PhD, now an assistant professor in the Center for RNA Science and Therapeutics at Case Western Reserve University, created an algorithm that could detect infection using data — specifically, data from a change in heart rate — from a smartwatch. Specific patterns of heart rate variation can indicate illness even when an individual is asymptomatic.
“I feel confident based on our former study that we’ll be able to detect some signal of infection based off of the wearables’ data,” Snyder said. “And I’m hopeful that as our study picks up, we may even have the granularity to anticipate the severity of viral infection based on smart device data. This tool may end up being a plus for both diagnosis and for prognosis.”
To qualify for the study, you must own a wearable device and:
● Have had a confirmed or suspected case of COVID-19, or
● Have been exposed to somebody who has known or suspected COVID-19, or
● Are at higher risk of exposure (like healthcare workers or grocery store workers).
You can enroll at the link below!
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"Smartwatches and other wearables make many, many measurements per day — at least 250,000, which is what makes them such powerful monitoring devices," said Michael Snyder, PhD, professor and chair of genetics at the Stanford School of Medicine. "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."
In 2017, Snyder and postdoctoral scholar Xiao Li, PhD, now an assistant professor in the Center for RNA Science and Therapeutics at Case Western Reserve University, created an algorithm that could detect infection using data — specifically, data from a change in heart rate — from a smartwatch. Specific patterns of heart rate variation can indicate illness even when an individual is asymptomatic.
“I feel confident based on our former study that we’ll be able to detect some signal of infection based off of the wearables’ data,” Snyder said. “And I’m hopeful that as our study picks up, we may even have the granularity to anticipate the severity of viral infection based on smart device data. This tool may end up being a plus for both diagnosis and for prognosis.”
To qualify for the study, you must own a wearable device and:
● Have had a confirmed or suspected case of COVID-19, or
● Have been exposed to somebody who has known or suspected COVID-19, or
● Are at higher risk of exposure (like healthcare workers or grocery store workers).
You can enroll at the link below!
Read More