ArticleNPJ digital medicine2019
Patients' views of wearable devices and AI in healthcare: findings from the ComPaRe e-cohort.
Article in NPJ digital medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 109 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.
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Who cites it
109 citing papers in PubMed, 2 syntheses or guidelines pooled it, 282 citations in OpenAlex.
- Impact of Digital Interventions on the Treatment Burden of Patients With Chronic Conditions: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Identification of Patient Perceptions That Can Affect the Uptake of Interventions Using Biometric Monitoring Devices: Systematic Review of Randomized Controlled Trials.Journal of medical Internet research · 2020Pooled it
- Assessing the impact of information on patient attitudes toward artificial intelligence-based clinical decision support (AI/CDS): a pilot web-based SMART vignette study.Journal of medical ethics · 2025Trial
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- Personal Health Data in Healthcare: Important Factors Considered by Health Students-A Qualitative Study.Healthcare (Basel, Switzerland) · 2026Article
- Constructs Influencing Patient Perceptions of Use of AI in Medical Imaging Analysis: Systematic Review.Interactive journal of medical research · 2026Review
- Patient Preferences for Artificial Intelligence in Medical Imaging: A Single-Center Cross-Sectional Survey.Journal of imaging informatics in medicine · 2026Article
- Patient perspectives on the use of artificial intelligence to support treatment decision making in renal cancer: findings from the KATY project.BMC medical informatics and decision making · 2026Article
- Patient Perceptions of Artificial Intelligence in Diabetes Self-Management: Cross-Sectional Survey Study.JMIR formative research · 2026Article
- Article
- Facilitators and Barriers of Using an Artificial Intelligence Agent in Chronic Disease Management: A Normalization Process Theory-Guided Qualitative Study of Older Patients with COPD.Healthcare (Basel, Switzerland) · 2026Article
- Key Information Influencing Patient Decision-Making About AI in Health Care: Survey Experiment Study.Journal of medical Internet research · 2026Article
- Development of a scale for measuring the perception of artificial intelligence among mental health consumers.PloS one · 2026Article
- Artificial Intelligence in Ocular Oncology: Differentiating Choroidal Melanocytic Lesions.Ophthalmology science · 2026Article
- Factors that influence the implementation of AI-driven lifestyle monitoring in long-term care for older adults.The Gerontologist · 2025Article
- Artificial Intelligence-Driven Wearable and Connected Technology for Allergy: Real-Time Monitoring and Predictive Management for Personalized Care.The journal of allergy and clinical immunology. In practice · 2025Review
- Perception of AI Use in Youth Mental Health Services: Qualitative Study.Journal of participatory medicine · 2025Article
- Smart Clothing and Medical Imaging Innovations for Real-Time Monitoring and Early Detection of Stroke: Bridging Technology and Patient Care.Diagnostics (Basel, Switzerland) · 2025Review
- Bridging technology and medicine: artificial intelligence in targeted anticancer drug delivery.RSC advances · 2025Review
- Insights into motor impairment assessment using myographic signals with artificial intelligence: a scoping review.Biomedical engineering letters · 2025Review
49 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wearable biometric monitoring devices (BMDs) and artificial intelligence (AI) enable the remote measurement and analysis of patient data in real time. These technologies have generated a lot of "hype," but their real-world effectiveness will depend on patients' uptake. Our objective was to describe patients' perceptions of the use of BMDs and AI in healthcare. We recruited adult patients with chronic conditions in France from the "Community of Patients for Research" (ComPaRe). Participants (1) answered quantitative and open-ended questions about the potential benefits and dangers of using of these new technologies and (2) participated in a case-vignette experiment to assess their readiness for using BMDs and AI in healthcare. Vignettes covered the use of AI to screen for skin cancer, remote monitoring of chronic conditions to predict exacerbations, smart clothes to guide physical therapy, and AI chatbots to answer emergency calls. A total of 1183 patients (51% response rate) were enrolled between May and June 2018. Overall, 20% considered that the benefits of technology (e.g., improving the reactivity in care and reducing the burden of treatment) greatly outweighed the dangers. Only 3% of participants felt that negative aspects (inadequate replacement of human intelligence, risks of hacking and misuse of private patient data) greatly outweighed potential benefits. We found that 35% of patients would refuse to integrate at least one existing or soon-to-be available intervention using BMDs and AI-based tools in their care. Accounting for patients' perspectives will help make the most of technology without impairing the human aspects of care, generating a burden or intruding on patients' lives.
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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.