First study to evaluate de-identified data from wearable devices on resting heart rate and sleep finds improved real-time prediction of influenza-like illness in five US states compared to current surveillance methods.
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 and this is captured by wearable devices such as smartwatches and fitness trackers, that track heart rate. De-identified data from 47,249 Fitbit users, retrospectively identifies weeks with elevated resting heart rate and changes to routine sleep. Further prospective studies will need to be done to help differentiate between infectious versus non-infectious forecasting.
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.