Feasibility of continuous fever monitoring using wearable devices
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Abstract
Elevated core temperature constitutes an important biomarker for COVID-19 infection; however, no standards currently exist to monitor fever using wearable peripheral temperature sensors. Evidence that sensors could be used to develop fever monitoring capabilities would enable large-scale health-monitoring research and provide high-temporal resolution data on fever responses across heterogeneous populations. We launched the TemPredict study in March of 2020 to capture continuous physiological data, including peripheral temperature, from a commercially available wearable device during the novel coronavirus pandemic. We coupled these data with symptom reports and COVID-19 diagnosis data. Here we report findings from the first 50 subjects who reported COVID-19 infections. These cases provide the first evidence that illness-associated elevations in peripheral temperature are observable using wearable devices and correlate with self-reported fever. Our analyses support the hypothesis that wearable sensors can detect illnesses in the absence of symptom recognition. Finally, these data support the hypothesis that prediction of illness onset is possible using continuously generated physiological data collected by wearable sensors. Our findings should encourage further research into the role of wearable sensors in public health efforts aimed at illness detection, and underscore the importance of integrating temperature sensors into commercially available wearables.
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For each 24-h period, distal body temperature values below the 5th percentile and above the 95th percentile were discarded to exclude periods of non-wear and clinical fever and to minimize influence from non-representative extreme values.
“For each 24-h period, we discarded wearable sensor-assessed distal body temperature values below the 5th percentile value and above the 95th percentile. This served to exclude periods of non-wear and periods of clinical fever and to minimize influence from non-representative extreme values (e.g., outliers), as done in prior research with wearable sensor-assessed metrics51,52.”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [52]
The Methods > Data preparation passage says the paper used medians of the lowest and highest 360 minutes in each 24-hour window to reduce the influence of outliers and acute extremes. It does not describe discarding values below the 5th or above the 95th percentile, or doing so to exclude non-wear and clinical fever. The cited work supports the general outlier-minimization rationale, but not the specific procedure attributed to prior research. (LLM, conf=0.88)
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Automate, Illuminate, Predict: A Universal Framework for Integrating Wearable Sensors in Healthcare
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Mobile Software Development Kit for Real Time Multivariate Blood Glucose Prediction
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Deep learning model using continuous skin temperature data predicts labor onset
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Wearables in Sports Cardiology
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Dear Watch, Should I Get a COVID-19 Test? Designing deployable machine learning for wearablesPreprint
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Dear Watch, Should I get a COVID Test? Designing deployable machine learning for wearablesPreprint
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Self-citationTowards a Dynamic Composability Approach for using Heterogeneous Systems in Remote Sensing
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AI for precision medicine must keep non-random complexity in mind to support equity in outcomes
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- Authors
- Smarr, B. L., Aschbacher, K., Fisher, S. M., Chowdhary, A., Dilchert, S., Puldon, K., Rao, A., Hecht, F. M., & Mason, A. E.
- Affiliations
- University of California San Diego (US)
- University of California, San Francisco (US)
- Baruch College (US)
- Corresponding author
- Smarr, Benjamin L.
- ORCID
- Venue
- Scientific Reports
- Published
- 2020-12-14
- Volume
- 10
- Issue
- 1
- Pages
- 21640-21640
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC7736301
- OpenAlex
- W3112300562
- Cited by
- 133
- References
- 30
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 19 · 2023: 20 · 2022: 44 · 2021: 31
- FWCI
- 7.46
- Type
- journal article
- Language
- en
- Update notices
- correction (2022-03-15)
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- CUNY Academic Works (City University of New York) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- Oura Health Oy
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Infrared Thermography in Medicine
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Keywords
- COVID-19 pandemic, fever monitoring, wearable devices, peripheral temperature, core body temperature, symptom reporting, health monitoring
- MeSH terms
- Wearable Electronic Devices, COVID-19, Adult, Aged, Feasibility Studies, Female, Fever, Humans, Male, Middle Aged, Monitoring, Physiologic, Telemedicine, Young Adult, Self Report, Thermometry
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