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used anonymous data collected from wearable devices, such as Fitbits and other smartwatches and fitness trackers, to retrospectively identify weeks with elevated resting heart rate and changes to routine sleep. Resting heart rate tends to increase during infectious episodes, allowing tracking and prediction of influenza-like illness.

Dr Rosalind Eggo is Assistant Professor in Public Health Epidemiology at the ������app of Hygiene & Tropical Medicine, working on infectious disease modelling to predict how diseases like influenza, Ebola and zika spread through populations and how they can be controlled.

Commenting on this study, Dr Eggo said:

“Wearable technologies have promise to provide new data sources for tracking infectious diseases such as influenza. This study is an example of an interesting use of this data for flu surveillance.

“However, it is critical to fully understand the strengths and weaknesses of these types of data before relying on them for monitoring infectious diseases. Further analysis is required to gauge how reliable these data are over time, how specific these measurements are for flu, and how representative Fitbit users are of the whole population.”

In December, Dr Eggo was invited to take part in The Royal Institution Christmas Lectures, where she explained how infectious diseases spread through a population, and how this spread can be slowed or halted completely through vaccination. Watch her talk from 22:05 .

Past studies using crowdsourced data, such as Google Flu Trends and Twitter have found variable success on their own as these methods tend to overestimate rates during epidemics.

According to The Lancet Digital Health, further prospective studies will need to be done to help differentiate between infectious versus non-infectious forecasting.

Publication:

Jennifer M Radin, Nathan E Wineinger, Eric J Topol, Steven R Steinhubl. The Lancet Digital Health. . DOI: