Wearable Device Data May Help Identify Flu Trends, Predict Outbreaks

January 28, 2020

Data tracked by wearable fitness sensors may help with real-time surveillance of seasonal respiratory infections, such as influenza.

Wearable fitness tracker data may help identify population trends of seasonal respiratory infections, such as influenza, according to a new study published in The Lancet Digital Health.

Acute infections can cause a higher-than-normal resting heart rate (RHR), increased sleep, and a decline in activity levels, all of which can be measured by wearable technology through HR and sleep data. Because activity monitors are increasingly being used, data from wearable devices may help improve real-time influenza surveillance.

For the study, the researchers analyzed sensor data from 200,000 individuals who used a Fitbit wearable device from March 1, 2016 to March 1, 2018 in the United States. They compared sensor data with weekly estimates of influenza-like illness (ILI) rates at the state level, as reported by the CDC, by identifying weeks in which Fitbit users displayed elevated RHRs and increased sleep levels.

Overall, the study identified 47,249 users in the top 5 states who wore a Fitbit consistently during the study period, including more than 13.3 million total RHR and sleep measures. Fitbit data significantly improved ILI predictions in all 5 states, according to the study.

Fitbit users were classified as having a week with abnormal data if their weekly average exceeded a given threshold, such as longer-than-average sleep duration and an overall elevated RHR.

The researchers noted that week-to-week changes in the proportion of Fitbit users with abnormal data were associated with week-to-week changes in ILI rates in most cases. They also found that the proportion of participants with Fitbit data above the threshold was higher during the 2017-2018 influenza season compared with the 2016-2017 season, according to the study.

Despite the findings, the researchers also noted that other external factors, other than illness, can influence an individual’s RHR and sleep. Another factor to consider is that owners of wearable devices are usually wealthier than the general population, potentially making them less likely to have comorbidities that could make them susceptible to severe infections.

As technology advances, the accuracy of wearable health trackers will continue to improve. New wearables that include continuous sensors for temperature, blood pressure, pulse oximetry, ECG, or even cough recognition, could further improve the ability to use these data for public health surveillance.

“The ability to harness wearable device data at a large scale might help to improve objective, real-time estimates of ILI rates at a more local level, giving public health responders the ability to act quickly and precisely on suspected outbreaks,” the researchers wrote in the study.


  • Radin JM, Wineinger NE, Topol EJ, et al. Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study. The Lancet Digital Health. 2020. DOI:https://doi.org/10.1016/S2589-7500(19)30222-5