Not just a fitness fad, wearable fitness trackers (such as Fitbit) can be a crucial ally in effective, real-time flu surveillance, US researchers from the Scripps Research Translational Institute have reported in The Lancet Digital Health journal.
Resting heart rate tends to spike during infectious episodes. This is captured by wearable devices such as smartwatches and fitness trackers which track heart rate. De-identified data from 47,249 Fitbit users retrospectively identified for weeks those with elevated resting heart rate and changes to routine sleep to make assessments about infection bouts.
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. According to the National Centre for Disease Control (NCDC), there were 28,714 cases of flu in India in 2019 and 1,216 deaths. Experts say the real numbers may be far higher, as many cases go unreported.
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 deploy antivirals and vaccines.
The researchers concluded: “Activity and physiological trackers are increasingly used in the USA and globally to monitor individual health. By accessing these data, it could be possible to improve real-time and geographically refined influenza surveillance. This information could be vital to enact timely outbreak response measures to prevent further transmission of influenza cases during outbreaks.” This is the first time heart rate trackers and sleep data have been used to predict flu, or any infectious disease, in real time.
Study author Dr Jennifer Radin, Scripps Research Translational Institute, USA, said: “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. We demonstrate the potential for metrics from wearable devices to enhance flu surveillance and consequently improve public health responses. 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 weekly basis.”
The researchers reviewed de-identified data from 2,00,000 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 March 2016 to March 2018. Among them, 47,248 users 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% were female.
Users’ data and its deviations from average that pointed to infectious spells were compared to weekly estimates for influenza-like illness rates reported by the US Centers for Disease Control (CDC). In all five states there was an improvement in real-time surveillance, and the closest alignment with CDC data was found when abnormal resting heart rate was defined as half a standard deviation above normal and sleep more than half a standard deviation below.
In a Comment article, Dr Cécile Viboud of the Fogarty International Center, National Institutes of Health, USA, said: “The study by Radin et al is a promising first step towards integrating wearable measurements in predictive models of infectious diseases. […] we anticipate the large amount of real-time data generated by Fitbit and other devices will prove highly useful for public health and augment traditional surveillance systems. ”
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