American scientists have learned to predict the flu according to fitness bracelets
'20.01.2020'
Source: New Time
A new study showed that using fitness bracelets can control the incidence of influenza, writes New Time.
The work showed that evaluating heart rate and sleep information from Fitbit gadgets improved the prognosis of flu-like illnesses.
As the authors of the work found, resting heart rate tends to increase during infectious episodes, and this is captured by wearable devices, such as smart watches and fitness trackers, that track heart rate.
Data from 47 Fitbit users allow you to define weeks with an increased resting heart rate and changes in normal sleep. Further research is needed to help differentiate prognosis from infectious to non-infectious.
Study author Dr. Jennifer Radin, Scripps Research Institute, USA, says: “Faster response to outbreaks of flu can prevent further spread and infection, and we were interested to see if sensor data can improve statewide real-time surveillance.” . In the future, when these devices improve, with access to real-time data 24/7, it will be possible to determine the incidence rates daily, rather than weekly. ”
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In a new study, the average resting heart rate and sleep duration, as well as deviations from this, were calculated to help determine when these rates were outside the typical range.
Researchers selected 47 Fitbit users from California, Texas, New York, Illinois, and Pennsylvania who wore the device continuously. Their data for 2016–2018 was compared with information on the incidence of influenza for the same period.
The weeks during which Fitbit-wearers showed an increase in heart rate and sleep time almost coincided with periods of outbreaks of influenza-like illness in these regions.
By incorporating data from Fitbit trackers, statewide flu forecasts have been improved. All five states experienced improved real-time surveillance.
This is the first time that heart rate trackers and sleep data are used to predict the flu or any infectious disease in real time. With large amounts of data, it may be possible to apply the method to more geographically refined areas, such as a district.
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It is noted that Fitbit users were predominantly middle-aged adults and probably with a higher income (Fitbit gadgets are relatively expensive) than the general population, therefore they are potentially less susceptible to concomitant diseases that can make them more susceptible to serious infections.
They may also be more likely to receive flu vaccines or antiviral drugs or other medications if they become ill, which can reduce the severity of the disease, so models may need to be modified for use in other populations.
