Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study

Abstract

Background: Acute infections can cause an individual to have an elevated resting heart rate (RHR) and change their routine daily activities due to the physiological response to the inflammatory insult. Consequently, we aimed to evaluate if population trends of seasonal respiratory infections, such as influenza, could be identified through wearable sensors that collect RHR and sleep data.

Methods: We obtained de-identified sensor data from 200 000 individuals who used a Fitbit wearable device from March 1, 2016, to March 1, 2018, in the USA. We included users who wore a Fitbit for at least 60 days and used the same wearable throughout the entire period, and focused on the top five states with the most Fitbit users in the dataset: California, Texas, New York, Illinois, and Pennsylvania. Inclusion criteria included having a self-reported birth year between 1930 and 2004, height greater than 1 m, and weight greater than 20 kg. We excluded daily measurements with missing RHR, missing wear time, and wear time less than 1000 min per day. We compared sensor data with weekly estimates of influenza-like illness (ILI) rates at the state level, as reported by the US Centers for Disease Control and Prevention (CDC), by identifying weeks in which Fitbit users displayed elevated RHRs and increased sleep levels. For each state, we modelled ILI case counts with a negative binomial model that included 3-week lagged CDC ILI rate data (null model) and the proportion of weekly Fitbit users with elevated RHR and increased sleep duration above a specified threshold (full model). We also evaluated weekly change in ILI rate by linear regression using change in proportion of elevated Fitbit data. Pearson correlation was used to compare predicted versus CDC reported ILI rates.

Findings: We identified 47 249 users in the top five states who wore a Fitbit consistently during the study period, including more than 13·3 million total RHR and sleep measures. We found the Fitbit data significantly improved ILI predictions in all five states, with an average increase in Pearson correlation of 0·12 (SD 0·07) over baseline models, corresponding to an improvement of 6·3-32·9%. Correlations of the final models with the CDC ILI rates ranged from 0·84 to 0·97. Week-to-week changes in the proportion of Fitbit users with abnormal data were associated with week-to-week changes in ILI rates in most cases.

Interpretation: 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.

Funding: Partly supported by the US National Institutes of Health National Center for Advancing Translational Sciences.

Conflict of interest statement

Declaration of interests

We declare no competing interests.

Copyright © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.

Figures

Figure 1:. Study profile

RHR=resting heart rate.

Figure 2:. Percentage of participants with weekly data above threshold of the mnaive model (A) and average daily wear time against number of users (B)

Data are from March 15, 2016, to March 1, 2018. (A) Measurements from 144 360 users from all states were included. Measurements with missing wear time, wear time less than 1000 min/day or missing RHR were excluded, as well as weeks with fewer than four RHR measurements and users with less than 100 total RHR measurements. Model 1 thresholds were used: participants were over the threshold for any given week if they had a sleep time that was greater than 0·5 SD below their overall average and an RHR that was 0·5 SD above their overall average. (B) Measurements from 186 656 users from all states were included. Measurements with missing wear time, wear time less than 1000 min/day, and missing RHR were excluded for this analysis. The sharp downwards spike in wear time in March, 2017, is the result of daylight saving time. RHR=resting heart rate.

Figure 3:. Weekly CDC ILI rates, predicted ILI rates from the baseline mabs,H0 model, and predicted rates and 95% CIs for the mabs,H1 model, by state

Model 1 is used, with the lower heart rate cutoff. Data are from March 16, 2016, to March 1, 2018. CDC=Centers for Disease Control and Prevention. ILI=influenza-like illness.