Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study
Open accessAbstract
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.
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One study demonstrated the utilization of resting heart rate and sleep minutes tracked using wearable devices to improve surveillance of influenza-like illness in the USA.
“One study has also demonstrated the utilization of resting heart rate and sleep minutes tracked using wearable devices to improve surveillance of influenza-like illness in the USA 60 .”
From Multimodal biomedical AI (2022) · cites this paper as [60]
Recent large-scale longitudinal studies have shown promise in leveraging wearable sensor data for health monitoring and prediction.
“While recent large-scale longitudinal studies have shown promise in leveraging wearable sensor data for health monitoring and prediction, they also introduce new challenges for data modelling.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [22]
A recent study demonstrated that influenza surveillance can be improved by incorporating resting HR and sleep durations measured by wearable PPG-based devices into prediction models.
“A recent study demonstrated that influenza surveillance can be improved by incorporating resting HR and sleep durations measured by wearable PPG-based devices into prediction models”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [31]
The authors reported that the wearable device is sufficiently accurate even with existing inaccuracies.
“Nevertheless, the authors reported that the wearable device is sufficiently accurate even with existing inaccuracies [14,143,197].”
From The impact of wearable technologies in health research: scoping review (2022) · cites this paper as [14]
Most studies using wearable sensors have been conducted in high-income settings like the US and Europe.
“However, most studies were conducted in high-income settings, like the US and Europe [19, 25, 35].”
Radin et al. used a non-clinical population, and barriers to long-term wearable use are likely different in MDD versus general population.
“In contrast to our sample, Radin et al. [52] used a non-clinical population and the barriers to long-term use of a wearable device are likely to be different in an MDD versus general population sample [21].”
The RADAR-MDD Fitbit wear-time is lower than the 22.6 h/day across two-year follow-up in Radin et al.'s US population-based Fitbit study.
“This is lower than the wear-time of 22.6 h per day across a two-year follow-up period in a recent United States population-based Fitbit study by Radin and colleagues [52].”
Radin et al. omitted missing wear time data and excluded measurements with wear-time lower than 1000 min/day, which inflates their wear-time statistics.
“However, Radin et al. omitted missing wear time data, and excluded measurements with a wear-time lower than 1000 min per day which inflates their wear-time statistics.”
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- Authors
- Radin, J. M., Wineinger, N. E., Topol, E. J., & Steinhubl, S. R.
- Affiliations
- Scripps Research Institute (US)
- Corresponding author
- Radin, Jennifer M
- Venue
- The Lancet Digital Health
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 2
- Issue
- 2
- Pages
- e85-e93
- ISSN
- 2589-7500 (ISSN-L)
- PMC
- PMC8048388
- OpenAlex
- W2999317412
- Cited by
- 300
- References
- 28
- Citations by year
- 2026: 16 · 2025: 24 · 2024: 35 · 2023: 38 · 2022: 66 · 2021: 62 · 2020: 58 · 2019: 1
- FWCI
- 40.11
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- The Lancet Digital Health (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Center for Advancing Translational Sciences (UL1TR001114, UL1TR002550)
- Scoliosis Research Society
- Topics
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Health Sciences › Medicine › Epidemiology › Influenza Virus Research Studies
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- influenza-like illness, wearable sensors, resting heart rate, Fitbit, sleep duration, real-time surveillance, influenza surveillance, respiratory infections, negative binomial regression, influenza B, influenza C, influenza D virus, influenza E, influenza F
- MeSH terms
- Wearable Electronic Devices, Adult, Female, Humans, Influenza, Human, Male, Population Surveillance, United States
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