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Fitbit data helped predict flu in real time: Here's how

Fitbit's health data has helped predict flu in real-time. Here's how the tech works.

India TV Tech Desk Written by: India TV Tech Desk New Delhi Published on: January 17, 2020 15:43 IST
fitbit, smartwatch, fitness tracker, flu, real time data

Fitbit Versa 2

In a first-ever study on wearable devices to improve surveillance of infectious disease, researchers in the US have achieved real-time flu prediction in five states, using resting heart rate and sleep tracking data from Fitbit users.

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. Influenza results in 650,000 deaths worldwide annually. And approximately 7 per cent of working adults and 20 per cent of children aged under five years get flu each year.

"Responding more quickly to influenza outbreaks can prevent further spread and infection, and we were curious to see if sensor data could improve real-time surveillance at the state level," said study author Dr Jennifer Radin from Scripps Research Translational Institute.

The researchers reviewed de-identified data from 200,000 users who wore a Fitbit wearable device that tracks users' activity, heart rate and sleep for at least 60 days during the study time. From the 200,000, 47,248 users from California, Texas, New York, Illinois and Pennsylvania wore a Fitbit device consistently during the study period, resulting in a total of 13,342,651 daily measurements evaluated. The average user was 43 years old and 60 per cent were female.

De-identified data from the users retrospectively identified weeks with elevated resting heart rate and changes to routine sleep, said the research published in The Lancet Digital Health journal.

"In the future as these devices improve, and with access to 24/7 real-time data, it may be possible to identify rates of influenza on a daily instead of a weekly basis," said Radin.

This data was compared to weekly estimates for influenza-like illness rates reported by the U.S. Centers for Disease Control (CDC). 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 greater volumes of data, it may be possible to apply the method to more geographically refined areas, such as county or city-level.

The authors identify several limitations in their study. Weekly resting heart rate averages may include days when an individual is both sick and not sick, and this may result in underestimation of illness by lowering the weekly averages.

Other factors may also increase the resting heart rate, including stress or other infections. Lastly, the authors noted that previous studies of sleep measuring devices have been found to have low accuracy, though they said that accuracy will continue to improve as technology evolves.

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