Smartphone apps in the COVID-19 pandemic
Open accessAbstract
At the beginning of the COVID-19 pandemic, analog tools such as nasopharyngeal swabs for PCR tests were center stage and the major prevention tactics of masking and physical distancing were a throwback to the 1918 influenza pandemic. Overall, there has been scant regard for digital tools, particularly those based on smartphone apps, which is surprising given the ubiquity of smartphones across the globe. Smartphone apps, given accessibility in the time of physical distancing, were widely used for tracking, tracing and educating the public about COVID-19. Despite limitations, such as concerns around data privacy, data security, digital health illiteracy and structural inequities, there is ample evidence that apps are beneficial for understanding outbreak epidemiology, individual screening and contact tracing. While there were successes and failures in each category, outbreak epidemiology and individual screening were substantially enhanced by the reach of smartphone apps and accessory wearables. Continued use of apps within the digital infrastructure promises to provide an important tool for rigorous investigation of outcomes both in the ongoing outbreak and in future epidemics.
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Many remote, site-less trials using wearables were conducted during the COVID-19 pandemic to detect coronavirus.
“Many remote, site-less trials using wearables were conducted during the COVID-19 pandemic to detect coronavirus 38 .”
From Multimodal biomedical AI (2022) · cites this paper as [38]
The Passive screening section (page unavailable) describes several pandemic-era studies and projects using wearables to detect COVID-19, including DETECT and the Stanford Wearables Study. It does not establish that many of these were site-less trials; the trial listed in the References is a study protocol, not evidence that the trial was conducted. (LLM, conf=0.86)
Experimental — accuracy not yet measuredMany mobile apps have been developed using Bluetooth and Wi-Fi technology to understand outbreak epidemiology, individual screening and contact tracing.
“Many mobile apps have been developed using such technology to understand outbreak epidemiology, individual screening and contact tracing [172–174].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [172]
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- Authors
- Pandit, J. A., Radin, J. M., Quer, G., & Topol, E. J.
- Affiliations
- Northwestern University (US)
- Scripps Research Institute (US)
- Corresponding author
- Topol, Eric J.
- Venue
- Nature Biotechnology
- Published
- 2022-06-20
- Volume
- 40
- Issue
- 7
- Pages
- 1013-1022
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC13501915
- OpenAlex
- W4283167029
- Cited by
- 101
- References
- 79
- Citations by year
- 2026: 16 · 2025: 18 · 2024: 31 · 2023: 32 · 2022: 4
- FWCI
- 2.68
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion)
- Funders
- National Institutes of Health (UL1TR002550)
- National Center for Advancing Translational Sciences
- Topics
- Physical Sciences › Computer Science › Information Systems › COVID-19 Digital Contact Tracing
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Keywords
- COVID-19 pandemic, mobile application, contact tracing, digital health, data privacy, digital health literacy, wearable devices, outbreak epidemiology, individual screening, structural inequality, pandemic response
- MeSH terms
- COVID-19, SARS-CoV-2, Humans, Contact Tracing, Pandemics, Mobile Applications
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 ›
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