Wearable sensor data and self-reported symptoms for COVID-19 detection
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Abstract
Traditional screening for COVID-19 typically includes survey questions about symptoms and travel history, as well as temperature measurements. Here, we explore whether personal sensor data collected over time may help identify subtle changes indicating an infection, such as in patients with COVID-19. We have developed a smartphone app that collects smartwatch and activity tracker data, as well as self-reported symptoms and diagnostic testing results, from individuals in the United States, and have assessed whether symptom and sensor data can differentiate COVID-19 positive versus negative cases in symptomatic individuals. We enrolled 30,529 participants between 25 March and 7 June 2020, of whom 3,811 reported symptoms. Of these symptomatic individuals, 54 reported testing positive and 279 negative for COVID-19. We found that a combination of symptom and sensor data resulted in an area under the curve (AUC) of 0.80 (interquartile range (IQR): 0.73-0.86) for discriminating between symptomatic individuals who were positive or negative for COVID-19, a performance that is significantly better (P < 0.01) than a model 1 that considers symptoms alone (AUC = 0.71; IQR: 0.63-0.79). Such continuous, passively captured data may be complementary to virus testing, which is generally a one-off or infrequent sampling assay.
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A follow-up study from the DETECT Health study showed that jointly considering participant self-reported symptoms and sensor metrics improved performance relative to either modality alone, reaching an area under the receiver operating curve value of 0.80 (95% confidence interval 0.73-0.86) for classifying COVID-19-positive versus COVID-19-negative status.
“A follow-up study from this program showed that jointly considering participant self-reported symptoms and sensor metrics improved performance relative to either modality alone, reaching an area under the receiver operating curve value of 0.80 (95% confidence interval 0.73-0.86) for classifying COVID-19-positive versus COVID-19-negative status 61 .”
From Multimodal biomedical AI (2022) · cites this paper as [61]
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Experimental — accuracy not yet measuredStudies have demonstrated the potential utility of smartwatch data for improving detection of COVID-19 beyond that provided by symptom data alone and for presymptomatic detection of COVID-19.
“Studies have demonstrated the potential utility of smartwatch data for improving detection of COVID-19 beyond that provided by symptom data alone [215] and for presymptomatic detection of COVID-19 [216].”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [215]
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- Authors
- Quer, G., Radin, J. M., Gadaleta, M., Baca-Motes, K., Ariniello, L., Ramos, E., Kheterpal, V., Topol, E. J., & Steinhubl, S. R.
- Affiliations
- Scripps Research Institute (US)
- CareEvolution (United States) (US)
- Corresponding author
- Quer, Giorgio
- ORCID
- Venue
- Nature Medicine
- Published
- 2020-10-29
- Volume
- 27
- Issue
- 1
- Pages
- 73-77
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W3095708005
- Semantic Scholar
- c15a93ad91e17412f464e4447ba0fd3e70544bbb
- Cited by
- 447
- References
- 31
- Citations by year
- 2026: 22 · 2025: 51 · 2024: 56 · 2023: 75 · 2022: 123 · 2021: 115 · 2020: 5
- FWCI
- 74.87
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Nature Medicine (publishedVersion)
- medRxiv (acceptedVersion)
- PubMed (publishedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences (UL1TR002550)
- Topics
- Physical Sciences › Computer Science › Information Systems › COVID-19 Digital Contact Tracing
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 detection and testing
- Keywords
- COVID-19 pandemic, SARS-CoV-2, wearable sensors, smartwatch data, mobile application, self-reported symptoms, infection detection, symptom screening, continuous monitoring
- MeSH terms
- Wearable Electronic Devices, COVID-19, Adult, Aged, Carrier State, Female, Heart Rate, Humans, Male, Mass Screening, Middle Aged, Monitoring, Physiologic, Sleep, United States, Self Report
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