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.

Fitbit-informed influenza forecasts

“…The authors report positive correlations between historical patterns in Fitbit-derived resting heart rate data and weekly surveillance time series for ILI in five US states in 2016-18. 4 Peaks in aggregated resting heart rate were well defined and generally coincided with ILI outbreaks, lending support to the use of these data in capturing short-term changes in population health. The authors also developed short-term prediction models for ILI activity using resting heart rate indicators and autoregressive terms as covariates.…”

“…In The Lancet Digital Health, Jennifer Radin and colleagues 4 analyse data obtained from 47 249 Fitbit wearable device users to forecast influenza-like illness (ILI) activity. Previous work has shown that an individual's resting heart rate tends to spike during infectious episodes, which can be accurately captured by Fitbit devices.…”

“…5 An increase in the proportion of Fitbit users experiencing elevated resting heart rate might signal the occurrence of an infectious disease outbreak. The innovative study by Radin and colleagues 4 is the first to use wearable device data to study influenza outbreaks.…”

The Use of Digital Health in the Detection and Management of COVID-19

“…Both medical devices and wearables have the potential to be repurposed to detect emerging patterns that are indicative of disease outbreaks. For example, Fitbit devices have been used to inform timely and accurate models of population-level influenza trends [12]. Additionally, smart thermometers have provided a novel source of information for influenza surveillance and forecasting [13].…”

Sustained suppression

“…Beyond testing and proximity-tracing technology, healthcare apps that allow people to self-report symptoms and confirmed infections anonymously could aid public health systems to find blind spots of disease transmission. Future privacy-minded apps that share data from smartwatches (elevated resting heart rate and sleep duration helped detect influenza-like illness in real time 12 , and the DETECT study (https://detectstudy. org), which has now enrolled over 20,000 people, might eventually do the same for COVID-19) could also help to detect early spots of disease to then target them with increased testing efforts and public health measures.…”