Data from the first six weeks of a landmark study has revealed that wearable devices like Fitbit are capable of identifying cases of COVID-19 by evaluating changes in heart rate, sleep and activity levels, along with self-reported symptom data - and can identify cases with greater success than looking at symptoms alone.

In March this year, researchers at the Scripps Research Translational Institute (Jupiter, FL, USA) launched the DETECT study that uses a mobile app to collect smartwatch and activity tracker data from consenting participants, and also gathers their self-reported symptoms and diagnostic test results. With data from the app, researchers can see when participants fall out of their normal range for sleep, activity level or resting heart rate; deviations from individual norms are a sign of viral illness or infection. To know if the illness causing those changes was COVID-19, the team reviewed data from those who reported developing symptoms and were tested for the novel coronavirus. Knowing the test results enabled them to pinpoint specific changes indicative of COVID-19 versus other illnesses.

For the study, the team used health data from fitness wearables and other devices to identify -with roughly 80% prediction accuracy - whether a person who reported symptoms was likely to have COVID-19. This is a significant improvement from other models that only evaluated self-reported symptoms. As of June 7, 30,529 individuals had enrolled in the study, out of which 3,811 reported symptoms, 54 tested positive for the coronavirus and 279 tested negative. More sleep and less activity than an individual's normal levels were significant factors in predicting coronavirus infection. The predictive model under development in DETECT might someday help public health officials spot coronavirus hotspots early. It also may encourage people who are potentially infected to immediately seek diagnostic testing and, if necessary, quarantine themselves to avoid spreading the virus. The researchers are now actively recruiting more participants for the study with a goal to enroll more than 100,000 people, which will help them improve their predictions of who will get sick, including those who are asymptomatic. In addition, the team plans to incorporate data from frontline essential workers who are at an especially high risk of infection.

"One of the greatest challenges in stopping COVID-19 from spreading is the ability to quickly identify, trace and isolate infected individuals," said Giorgio Quer, PhD, director of artificial intelligence at Scripps Research Translational Institute and first author of the study. "Early identification of those who are pre-symptomatic or even asymptomatic would be especially valuable, as people may potentially be even more infectious during this period. That's the ultimate goal."

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