The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Pre-symptomatic detection of COVID-19 from smartwatch data
Open accessPublished version of a preprint
Abstract
Consumer wearable devices that continuously measure vital signs have been used to monitor the onset of infectious disease. Here, we show that data from consumer smartwatches can be used for the pre-symptomatic detection of coronavirus disease 2019 (COVID-19). We analysed physiological and activity data from 32 individuals infected with COVID-19, identified from a cohort of nearly 5,300 participants, and found that 26 of them (81%) had alterations in their heart rate, number of daily steps or time asleep. Of the 25 cases of COVID-19 with detected physiological alterations for which we had symptom information, 22 were detected before (or at) symptom onset, with four cases detected at least nine days earlier. Using retrospective smartwatch data, we show that 63% of the COVID-19 cases could have been detected before symptom onset in real time via a two-tiered warning system based on the occurrence of extreme elevations in resting heart rate relative to the individual baseline. Our findings suggest that activity tracking and health monitoring via consumer wearable devices may be used for the large-scale, real-time detection of respiratory infections, often pre-symptomatically. Analysis of physiological and activity data from consumer smartwatches enables real-time detection, often before symptom onset, of COVID-19, as well as other respiratory illnesses and stress inducers.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Harris, P. A., Taylor, R., Thielke, R., Payne, J., Gonzalez, N., & Conde, J. G. (2009) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2008.08.010 · Cited by 51,384
Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249
Lauer, S. A., Grantz, K. H., Bi, Q., Jones, F. K., Zheng, Q., Meredith, H. R., Azman, A. S., Reich, N. G., & Lessler, J. (2020) · Annals of Internal Medicine · doi:10.7326/m20-0504 · Cited by 5,970
Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation
Perez, M. V., Mahaffey, K. W., Hedlin, H., Rumsfeld, J. S., Garcia, A., Ferris, T., Balasubramanian, V., Russo, A. M., Rajmane, A., Cheung, L., et al. (2019) · New England Journal of Medicine · doi:10.1056/nejmoa1901183 · Cited by 1,917
Mathew, D., Giles, J. R., Baxter, A. E., Oldridge, D. A., Greenplate, A. R., Wu, J. E., Alanio, C., Kuri-Cervantes, L., Pampena, M. B., D’Andrea, K., et al. (2020) · Science · doi:10.1126/science.abc8511 · Cited by 1,509
Personal omics profiling reveals dynamic molecular and medical phenotypes
Chen, R., Mias, G. I., Li-Pook-Than, J., Jiang, L., Lam, H. Y. K., Chen, R., Miriami, E., Karczewski, K. J., Hariharan, M., Dewey, F. E., et al. (2012) · Cell · doi:10.1016/j.cell.2012.02.009 · Cited by 1,291
Self-citationWearables and the Medical Revolution
Dunn, J., Runge, R., & Snyder, M. (2018) · Personalized Medicine · doi:10.2217/pme-2018-0044 · Cited by 654
Self-citationAccuracy of Wristband Fitbit Models in Assessing Sleep: Systematic Review and Meta-Analysis
Haghayegh, S., Khoshnevis, S., Smolensky, M. H., Diller, K. R., & Castriotta, R. J. (2019) · Journal of Medical Internet Research · doi:10.2196/16273 · Cited by 446
Li, X., Dunn, J., Salins, D., Zhou, G., Zhou, W., Schüssler-Fiorenza Rose, S. M., Perelman, D., Colbert, E., Runge, R., Rego, S., et al. (2017) · PLOS Biology · doi:10.1371/journal.pbio.2001402 · Cited by 431
Self-citationPrehospital pulse oximetry: A red flag for early detection of silent hypoxemia in COVID-19 patients
Jouffroy, R., Jost, D., & Prunet, B. (2020) · Critical Care · doi:10.1186/s13054-020-03036-9 · Cited by 99
Research works printed but not linked: 20
- Sethuraman, N., Jeremiah, S. S. & Ryo, A. Interpreting diagnostic tests for SARS-CoV-2. J. Am. Med. Assoc. https://doi.org/10.1001/jama.2020.8259 (2020). · DOI · Paper page
- Marinsek, N. et al. Measuring COVID-19 and influenza in the real world via person-generated health data. Preprint at medRxiv https://doi.org/10.1101/2020.05.28.20115964 (2020). · doi:10.1101/2020.05.28.20115964
- Radin, J. M., Wineinger, N. E., Topol, E. J. & Steinhubl, S. R. Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study. Lancet Digital Health 2, e85–e93 (2020). · DOI · Paper page
- Seshadri, D. R.et al. Wearable sensors for COVID-19: a call to action to harness our digital infrastructure for remote patient monitoring and virtual assessments. Front. Digital Health https://doi.org/10.3389/fdgth.2020.00008 (2020). · DOI · Paper page
- Arons, M. M. et al. Presymptomatic SARS-CoV-2 infections and transmission in a skilled nursing facility. N. Engl. J. Med. 382, 2081–2090 (2020). · DOI · Paper page
- He, X. et al. Temporal dynamics in viral shedding and transmissibility of COVID-19. Nat. Med. 26, 672–675 (2020). · DOI · Paper page
- Witt, D., Kellogg, R., Snyder, M. & Dunn, J. Windows into human health through wearables data analytics. Curr. Opin. Biomed. Eng. 9, 28–46 (2019). · DOI
- Arias-Castro, E., Castro, R. M., Tánczos, E. & Wang, M. Distribution-free detection of structured anomalies: permutation and rank-based scans. J. Am. Stat. Assoc. 113, 789–801 (2018). · doi:10.1080/01621459.2017.1286240
- Rousseeuw, P. J. & Van Driessen, K. A. Fast algorithm for the minimum covariance determinant estimator. Technometrics 41, 212–223 (1999). · DOI · Paper page
- Backer, J. A., Klinkenberg, D. & Wallinga, J. Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20–28 January 2020. Eurosurveillance 25, 2000062 (2020). · DOI · Paper page
- Bi, Q. et al. Epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close contacts in Shenzhen, China: a retrospective cohort study. Lancet Infect. Dis. https://doi.org/10.1016/s1473-3099(20)30287-5 (2020). · DOI · Paper page
- Arias-Castro, E. & Wang, M. Distribution-free tests for sparse heterogeneous mixtures. Test 26, 71–94 (2017). · DOI
- Page, E. S. Cumulative sum charts. Technometrics 3, 1–9 (1961). · DOI · Paper page
- Levin, B. & Kline, J. The CuSum test of homogeneity with an application in spontaneous abortion epidemiology. Stat. Med. 4, 469–488 (1985). · DOI
- Xie, J. et al. Association between hypoxemia and mortality in patients with COVID-19. Mayo Clin. Proc. 95, 1138–1147 (2020). · DOI · Paper page
- Lorente-Ros, A. et al. Myocardial injury determination improves risk stratification and predicts mortality in COVID-19 patients. Cardiol. J. https://doi.org/10.5603/CJ.a2020.0089 (2020). · DOI
- Miller, D. J. et al. Analyzing changes in respiratory rate to predict the risk of COVID-19 infection. Preprint at medRxiv https://doi.org/10.1101/2020.06.18.20131417 (2020). · DOI
- Siordia, J. A. Epidemiology and clinical features of COVID-19: a review of current literature. J. Clin. Virol. 127, 104357 (2020). · DOI · Paper page
- Zhou, W. et al. Longitudinal multi-omics of host–microbe dynamics in prediabetes. Nature 569, 663–671 (2019). · DOI · Paper page
- Wickham, H. Ggplot2: Elegant Graphics for Data Analysis (Springer, 2016). · DOI · Paper page
Cited by
End-to-end design of wearable sensors
Ates, H. C., Nguyen, P. Q., Gonzalez-Macia, L., Morales-Narváez, E., Güder, F., Collins, J. J., & Dincer, C. (2022) · Nature Reviews Materials · doi:10.1038/s41578-022-00460-x · Cited by 1,306
A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields
Ko, H., Lee, S., Park, Y., & Choi, A. (2022) · Electronics · doi:10.3390/electronics11010141 · Cited by 658
Wearable sensor data and self-reported symptoms for COVID-19 detection
Quer, G., Radin, J. M., Gadaleta, M., Baca-Motes, K., Ariniello, L., Ramos, E., Kheterpal, V., Topol, E. J., & Steinhubl, S. R. (2021) · Nature Medicine · doi:10.1038/s41591-020-1123-x · Cited by 447
Artificial intelligence-powered electronic skin
Xu, C., Solomon, S. A., & Gao, W. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00760-z · Cited by 259
Wearable Photoplethysmography for Cardiovascular Monitoring
Charlton, P. H., Kyriacou, P. A., Mant, J., Marozas, V., Chowienczyk, P., & Alastruey, J. (2022) · Proceedings of the IEEE · doi:10.1109/jproc.2022.3149785 · Cited by 250
Metadata
- Authors
- Mishra, T., Wang, M., Metwally, A. A., Bogu, G. K., Brooks, A. W., Bahmani, A., Alavi, A., Celli, A., Higgs, E., Dagan-Rosenfeld, O., Fay, B., Kirkpatrick, S., Kellogg, R., Gibson, M., Wang, T., Hunting, E. M., Mamic, P., Ganz, A. B., Rolnik, B., Li, X., & Snyder, M. P.
- Venue
- Nature Biomedical Engineering
- Published
- 2020-11-18
- Volume
- 4
- Issue
- 12
- Pages
- 1208-1220
- ISSN
- 2157-846X (ISSN-L)
- OpenAlex
- W3104096625
- Cited by
- 463
- References
- 13
- FWCI
- 23.59
- Type
- journal article
- Language
- en
- Open access
- bronze
- Licence
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Pre-symptomatic detection of COVID-19 from smartwatch data. https://app.cochat.ai/papers/10.1038/s41551-020-00640-6
- The paper
- Mishra, T., Wang, M., Metwally, A. A., Bogu, G. K., Brooks, A. W., Bahmani, A., Alavi, A., Celli, A., Higgs, E., Dagan-Rosenfeld, O., Fay, B., Kirkpatrick, S., Kellogg, R., Gibson, M., Wang, T., Hunting, E. M., Mamic, P., Ganz, A. B., Rolnik, B., . . . Snyder, M. P. (2020). Pre-symptomatic detection of COVID-19 from smartwatch data. Nature Biomedical Engineering. https://doi.org/10.1038/s41551-020-00640-6
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
