The research, published in The Lancet Digital Health journal, demonstrates the potential of data from wearable devices to improve surveillance of infectious disease. Resting heart rate tends to spike during infectious episodes. Wearable devices such as smartwatches and fitness trackers, that track heart rate captured the data.
Influenza results in 650,000 deaths worldwide annually. Approximately 7% of working adults and 20% of children aged under five years get flu each year. Traditional surveillance reporting takes 1-3 weeks to report, which limits the ability to enact quick outbreak response measures – such as ensuring patients stay at home, wash hands, and deploying antivirals and vaccines.
This is the first time heart rate trackers and sleep data have been used to predict flu, or any infectious disease, in real-time. With higher volumes of data, it may be possible to apply the method to more geographically refined areas, such as county or city-level.