TORONTO -- A new U.S. study found that using Fitbit sleep and heart-rate data improves real-time prediction of influenza-like illness compared to current tracking methods.
The study, on Thursday, reviewed data from 200,000 Fitbit users for at least 60 days from March 2016 to March 2018.
Of the 200,000 users, more than 47,000 wore a Fitbit consistently during the study period. Researchers evaluated 13,342,651 daily measurements.
They used an individual鈥檚 resting heart rate and sleep duration and noted if there were abnormalities that were outside a user鈥檚 typical range.
Lead researcher Jennifer Radin says that resting heart rate tends to spike when an individual is sick and drops back down to normal after they鈥檝e recovered.
Radin and her team compared the Fitbit data they had to the CDC鈥檚 weekly influenza surveillance report that tracks flu activity in the U.S.
鈥淥ur goal was to see when we added in this Fitbit variable, which included the proportion of people with abnormal data compared to their individual norm, whether that improved predictions in real-time,鈥� Radin, a senior staff scientist at Scripps Research Translational Institute in U.S., told CTVNews.ca in a phone interview Wednesday.
The researchers found that incorporating data from the wearable devices improved real-time flu predictions in the five states included in the study: California, Texas, New York, Illinois and Pennsylvania.
on their website states that users鈥� information could be shared for research and surveys.
In addition, Radin notes they picked Fitbit because they have a longer battery life than other devices so people aren鈥檛 taking it off as often to recharge. 鈥淭hat gives us better long-term data,鈥� she says.
Radin notes that there were a few limitations to their study, but they鈥檙e at the beginning stages of building a model that will help with real-time flu predictions.
鈥淭he main limitation we had was that we didn鈥檛 actually know which individuals in our study had influenza-like illness or the flu, we were just correlating the proportion that had abnormal Fitbit data with CDC influenza-like data,鈥� she said.
The researches also didn鈥檛 have any data on children and their heart rates.
鈥淔lu typically has the highest impact on children and the elderly and we didn鈥檛 have data on whether infants were getting elevated heart rate,鈥� Radin says.
In the study, the Fitbit users were predominately middle aged and more than half were female.
Despite the limitations, Radin says this model can help public health responders stop the spread of infection.
鈥淚f we know there鈥檚 a flu outbreak going on in a particular region, we can make sure people are getting vaccinated or encourage people to stay home if their sick so they鈥檙e not spreading the infection to a neighbour,鈥� she explained.
Radin says her team plans to do a perspective study.
鈥淲e want to enroll individuals into our study and follow them and identify who gets sick, which one doesn鈥檛 and look at the changes in their wearable devices,鈥� she said.
Radin is hopeful that with access to more data in the future, they will be able to refine their real-time flu prediction model.
鈥淲ith a more refined model, we could potentially give people a warning that their data indicates that something might be up with their health, which may or may not indicate an infection,鈥� she said.
