Stanford’s Michael Snyder and colleagues have used smartwatch data to detect early, presymptomatic COVID-19 in 31 individuals out of a cohort of 5,000.
They demonstrated that COVID-19 infections are associated with alterations in heart rate, steps and sleep in 80% of cases. Physiological alterations were detected prior to, or at, symptom onset in 85% of the positive cases, in some cases nine or more days before symptoms.
A method to detect onset of COVID-19 infection in real-time was developed, which detected 67% of infection cases at or before symptom onset.
The study intends to provide a roadmap to a rapid and universal diagnostic method for the large-scale detection of respiratory viral infections in advance of symptoms.
Click to view Professor Snyder’s talk at the 2019 ApplySci conference at Stanford.