Wearables Could Improve Flu Forecasting, but They Miss the Most Vulnerable
Flu cases can be predicted from heart rate and sleep information
In 2017, a group of Stanford researchers found that wearable devices for tracking exercise could also tell when you’re getting sick based on abnormal measures of heart rate, skin temperature, and other biometrics.
The results intrigued Jennifer Radin, an epidemiologist and digital medicine expert at the Scripps Research Translational Institute in La Jolla, California. She wondered whether it was possible to pull together data from many wearable users to predict cases of flu. “Across my own wearables, I noticed my heart rate rose when I got sick, so I was really interested to see if I could apply this to a population level,” Radin told OneZero.
Using de-identified data from more than 47,000 Fitbit users, Radin and her colleagues improved flu forecasting in five states compared to current surveillance methods. The findings were published Thursday in the Lancet Digital Health. But crucially missing from this forecast were people who don’t own wearables, which are most likely to include children, the elderly, and lower-income people. These groups are also more susceptible to serious complications that can arise from the flu.
