Utilizing Wearable Device Data for Syndromic Surveillance: A Fever Detection Approach
Open accessAbstract
Commercially available wearable devices (wearables) show promise for continuous physiological monitoring. Previous works have demonstrated that wearables can be used to detect the onset of acute infectious diseases, particularly those characterized by fever. We aimed to evaluate whether these devices could be used for the more general task of syndromic surveillance. We obtained wearable device data (Oura Ring) from 63,153 participants. We constructed a dataset using participants’ wearable device data and participants’ responses to daily online questionnaires. We included days from the participants if they (1) completed the questionnaire, (2) reported not experiencing fever and reported a self-collected body temperature below 38 °C (negative class), or reported experiencing fever and reported a self-collected body temperature at or above 38 °C (positive class), and (3) wore the wearable device the nights before and after that day. We used wearable device data (i.e., skin temperature, heart rate, and sleep) from the nights before and after participants’ fever day to train a tree-based classifier to detect self-reported fevers. We evaluated the performance of our model using a five-fold cross-validation scheme. Sixteen thousand, seven hundred, and ninety-four participants provided at least one valid ground truth day; there were a total of 724 fever days (positive class examples) from 463 participants and 342,430 non-fever days (negative class examples) from 16,687 participants. Our model exhibited an area under the receiver operating characteristic curve (AUROC) of 0.85 and an average precision (AP) of 0.25. At a sensitivity of 0.50, our calibrated model had a false positive rate of 0.8%. Our results suggest that it might be possible to leverage data from these devices at a public health level for live fever surveillance. Implementing these models could increase our ability to detect disease prevalence and spread in real-time during infectious disease outbreaks.
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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 [51]
The Materials and Methods section (page unavailable) describes classifying fever days using self-reported symptoms and a temperature threshold of 38 °C. Neither it nor the retrieved passages describes discarding wearable temperature values below the 5th or above the 95th percentile for each 24-hour period. Searches for those percentiles, non-wear, and outlier removal did not establish the attributed method. (LLM, conf=0.74)
Experimental — accuracy not yet measuredDM can affect circadian rhythm stability.
“since DM can affect circadian rhythm stability51”
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 [51]
The Discussion mentions deviations from expected circadian rhythms as features used in other illness-detection work, but does not connect them to DM or rhythm stability. Searches for DM and diabetes mellitus with circadian rhythm stability found no passage addressing that relationship in our copy. (LLM, conf=0.73)
Experimental — accuracy not yet measured
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References 35 · Cited by 13
Research works printed but not linked: 1
- [15]Merrill M.A. Safranchik E. Kolbeinsson A. Gade P. Ramirez E. Schmidt L. Foschini L. Althoff T. Homekit2020: A Benchmark for Time Series Classification on a Large Mobile Sensing Dataset with Laboratory Tested Ground Truth of Influenza Infections Proceedings of the Conference on Health, Inference, and Learning Cambridge, MA, USA 13 June 2023 207 228
- Authors
- Kasl, P., Keeler Bruce, L., Hartogensis, W., Dasgupta, S., Pandya, L. S., Dilchert, S., Hecht, F. M., Gupta, A., Altintas, I., Mason, A. E., & Smarr, B. L.
- Affiliations
- University of California San Diego (US)
- UC San Diego Health Department of Biomedical Informatics, University of California San Diego, San Diego, CA 92093-0021, USA
- University of California, San Francisco (US)
- San Diego Supercomputer Center (US)
- Baruch College (US)
- San Diego Supercomputer Center, University of California San Diego, San Diego, CA 92093-0021, USA
- Corresponding author
- Kasl, Patrick
- ORCID
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2024-03-12
- Volume
- 24
- Issue
- 6
- Pages
- 1818
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC10975930
- OpenAlex
- W4392716064
- Cited by
- 13
- References
- 35
- Citations by year
- 2026: 7 · 2025: 6
- FWCI
- 3.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Department of Defense (MTEC-20-12-COVID19-D.-023, 7029991, 134650, K24AT007827)
- #StartSmall foundation (MTEC-20-12-COVID19-D.-023, 7029991, 134650, K24AT007827)
- Oura Health Oy (MTEC-20-12-COVID19-D.-023, 7029991, 134650, K24AT007827)
- National Institutes of Health, USA (MTEC-20-12-COVID19-D.-023, 7029991, 134650, K24AT007827)
- Topics
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- wearable devices, syndromic surveillance, fever detection, body temperature, skin temperature, heart rate, sleep, tree-based classifiers, infectious disease outbreaks, real-time disease surveillance, Oura Ring
- MeSH terms
- Wearable Electronic Devices, Routinely Collected Health Data, Fever, Humans, Monitoring, Physiologic, Sentinel Surveillance, Self Report
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