ArticlePreventive medicine2022
Wearable technology for early detection of COVID-19: A systematic scoping review.
Article in Preventive medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 3 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
24 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- A Systematic Review of the Accuracy of Machine Learning Models for Diagnosing Pulmonary Tuberculosis: Implications for Nursing Practice and Implementation.Nursing & health sciences · 2025Pooled it
- Real-World Accuracy of Wearable Activity Trackers for Detecting Medical Conditions: Systematic Review and Meta-Analysis.JMIR mHealth and uHealth · 2024Pooled it
- Does deidentification of data from wearable devices give us a false sense of security? A systematic review.The Lancet. Digital health · 2023Pooled it
- The utility of bluetooth and smartphone technology to detect peer contact.Experimental and clinical psychopharmacology · 2026Article
- Artificial intelligence in infection surveillance: Data integration, applications and future directions.Biomedical journal · 2026Review
- Privacy rights and improving knowledge are not hierarchical needs: data protection and good epidemiologic standard (DP_GOES) checklist for retrospective observational studies using secondary data.BMC medical research methodology · 2026Article
- Opportunities and Challenges in Gas Sensor Technologies for Accurate Detection of COVID-19.Biosensors · 2025Review
- Remote Patient Monitoring for Global Emergencies: Case Study in Patients With COVID-19.JMIR formative research · 2025Article
- Physiological Sensors Equipped in Wearable Devices for Management of Long COVID Persisting Symptoms: Scoping Review.Journal of medical Internet research · 2025Article
- Investigation and Validation of New Heart Rate Measurement Sites for Wearable Technologies.Sensors (Basel, Switzerland) · 2025Article
- Septic shock in the immunocompromised cancer patient: a narrative review.Critical care (London, England) · 2024Review
- Designing a Hybrid Energy-Efficient Harvesting System for Head- or Wrist-Worn Healthcare Wearable Devices.Sensors (Basel, Switzerland) · 2024Article
- Continuous Monitoring of Heart Rate Variability in Free-Living Conditions Using Wearable Sensors: Exploratory Observational Study.JMIR formative research · 2024Article
- Detection of Common Respiratory Infections, Including COVID-19, Using Consumer Wearable Devices in Health Care Workers: Prospective Model Validation Study.JMIR formative research · 2024Article
- Data-driven prediction model for periodontal disease based on correlational feature analysis and clinical validation.Heliyon · 2024Article
- Innovations in public health surveillance: An overview of novel use of data and analytic methods.Canada communicable disease report = Releve des maladies transmissibles au Canada · 2024Article
- Self-supervised learning for human activity recognition using 700,000 person-days of wearable data.NPJ digital medicine · 2024Article
- Health 4.0 in the medical sector: a narrative review.Revista da Associacao Medica Brasileira (1992) · 2024Article
- Predicting Deterioration from Wearable Sensor Data in People with Mild COVID-19.Sensors (Basel, Switzerland) · 2023Article
- Opportune warning of COVID-19 in a Mexican health care worker cohort: Discrete beta distribution entropy of smartwatch physiological records.Biomedical signal processing and control · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wearable technology is an emerging method for the early detection of coronavirus disease 2019 (COVID-19) infection. This scoping review explored the types, mechanisms, and accuracy of wearable technology for the early detection of COVID-19. This review was conducted according to the five-step framework of Arksey and O'Malley. Studies published between December 31, 2019 and December 15, 2021 were obtained from 10 electronic databases, namely, PubMed, Embase, Cochrane, CINAHL, PsycINFO, ProQuest, Scopus, Web of Science, IEEE Xplore, and Taylor & Francis Online. Grey literature, reference lists, and key journals were also searched. All types of articles describing wearable technology for the detection of COVID-19 infection were included. Two reviewers independently screened the articles against the eligibility criteria and extracted the data using a data charting form. A total of 40 articles were included in this review. There are 22 different types of wearable technology used to detect COVID-19 infections early in the existing literature and are categorized as smartwatches or fitness trackers (67%), medical devices (27%), or others (6%). Based on deviations in physiological characteristics, anomaly detection models that can detect COVID-19 infection early were built using artificial intelligence or statistical analysis techniques. Reported area-under-the-curve values ranged from 75% to 94.4%, and sensitivity and specificity values ranged from 36.5% to 100% and 73% to 95.3%, respectively. Further research is necessary to validate the effectiveness and clinical dependability of wearable technology before healthcare policymakers can mandate its use for remote surveillance.
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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 Socratic graph.