ArticleNPJ digital medicine2021
Passive detection of COVID-19 with wearable sensors and explainable machine learning algorithms.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
55 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Exploring the Applications of Explainability in Wearable Data Analytics: Systematic Literature Review.Journal of medical Internet research · 2024Pooled it
- The Impact of SARS-CoV-2 Infection on Heart Rate Variability: A Systematic Review of Observational Studies with Control Groups.International journal of environmental research and public health · 2023Pooled it
- Trial
- Feasibility of wearable sensor signals and self-reported symptoms to prompt at-home testing for acute respiratory viruses in the USA (DETECT-AHEAD): a decentralised, randomised controlled trial.The Lancet. Digital health · 2024Trial
- Artificial intelligence in infection surveillance: Data integration, applications and future directions.Biomedical journal · 2026Review
- Smartwatch-derived versus self-reported outcomes of physiological recovery after COVID-19, influenza, and group A streptococcus: a 2-year prospective cohort study.The Lancet. Digital health · 2026Article
- Observational
- Data science for pediatric infectious disease: utilizing COVID-19 as a model.Current opinion in infectious diseases · 2025Review
- Classification of Individuals With COVID-19 and Post-COVID-19 Condition and Healthy Controls Using Heart Rate Variability: Machine Learning Study With a Near-Real-Time Monitoring Component.Journal of medical Internet research · 2025Article
- Biorhythms derived from consumer wearables predict postoperative complications in children.Science advances · 2025Article
- Digital Health Platform for Maternal Health: Design, Recruitment Strategies, and Lessons Learned From the PowerMom Observational Cohort Study.JMIR formative research · 2025Observational
- Wearable data reveals distinct characteristics of individuals with persistent symptoms after a SARS-CoV-2 infection.NPJ digital medicine · 2025Article
- Identification of key factors related to digital health observational study adherence and retention by data-driven approaches: an exploratory secondary analysis of two prospective longitudinal studies.The Lancet. Digital health · 2025Article
- MS Pattern Explorer: interactive visual exploration of temporal activity patterns for multiple sclerosis.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Feasibility of snapshot testing using wearable sensors to detect cardiorespiratory illness (COVID infection in India).NPJ digital medicine · 2024Article
- Evaluation of Machine Learning to Detect Influenza Using Wearable Sensor Data and Patient-Reported Symptoms: Cohort Study.Journal of medical Internet research · 2024Observational
- mHealth Physical Activity and Patient-Reported Outcomes in Patients With Inflammatory Bowel Diseases: Cluster Analysis.Journal of medical Internet research · 2024Article
- Optimizing COVID-19 testing resources use with wearable sensors.PLOS digital health · 2024Article
- Research on Artificial-Intelligence-Assisted Medicine: A Survey on Medical Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2024Review
- A tree-based explainable AI model for early detection of Covid-19 using physiological data.BMC medical informatics and decision making · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Individual smartwatch or fitness band sensor data in the setting of COVID-19 has shown promise to identify symptomatic and pre-symptomatic infection or the need for hospitalization, correlations between peripheral temperature and self-reported fever, and an association between changes in heart-rate-variability and infection. In our study, a total of 38,911 individuals (61% female, 15% over 65) have been enrolled between March 25, 2020 and April 3, 2021, with 1118 reported testing positive and 7032 negative for COVID-19 by nasopharyngeal PCR swab test. We propose an explainable gradient boosting prediction model based on decision trees for the detection of COVID-19 infection that can adapt to the absence of self-reported symptoms and to the available sensor data, and that can explain the importance of each feature and the post-test-behavior for the individuals. We tested it in a cohort of symptomatic individuals who exhibited an AUC of 0.83 [0.81-0.85], or AUC = 0.78 [0.75-0.80] when considering only data before the test date, outperforming state-of-the-art algorithm in these conditions. The analysis of all individuals (including asymptomatic and pre-symptomatic) when self-reported symptoms were excluded provided an AUC of 0.78 [0.76-0.79], or AUC of 0.70 [0.69-0.72] when considering only data before the test date. Extending the use of predictive algorithms for detection of COVID-19 infection based only on passively monitored data from any device, we showed that it is possible to scale up this platform and apply the algorithm in other settings where self-reported symptoms can not be collected.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.