ArticleMedical & biological engineering & computing2015
Automatic messaging for improving patients engagement in diabetes management: an exploratory study.
Article in Medical & biological engineering & computing, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- Tailored Communication Within Mobile Apps for Diabetes Self-Management: A Systematic Review.Journal of medical Internet research · 2017Pooled it
- A vital physiological parameter mobile monitoring system for elderly patients in remote areas.BMC medical informatics and decision making · 2026Article
- A systematic review of strategies in digital technologies for motivating adherence to chronic illness self-care.npj health systems · 2025Article
- Article
- Effects of an Electronic Medical Records-Linked Diabetes Self-Management System on Treatment Targets in Real Clinical Practice: Retrospective, Observational Cohort Study.Endocrinology and metabolism (Seoul, Korea) · 2024Observational
- Multimedia Automation Access Control of Big Data Open Resources Based on Blockchain.Computational intelligence and neuroscience · 2022Article
- Patient-Generated Data Analytics of Health Behaviors of People Living With Type 2 Diabetes: Scoping Review.JMIR diabetes · 2021Article
- Mobile Health in Chronic Disease Management and Patient Empowerment: Exploratory Qualitative Investigation Into Patient-Physician Consultations.Journal of medical Internet research · 2021Article
- mHealth and Engagement Concerning Persons With Chronic Somatic Health Conditions: Integrative Literature Review.JMIR mHealth and uHealth · 2020Review
- A Survey of Healthcare Internet-of-Things (HIoT): A Clinical Perspective.IEEE internet of things journal · 2020Article
- User Centered Design to Improve Information Exchange in Diabetes Care Through eHealth : Results from a Small Scale Exploratory Study.Journal of medical systems · 2019Article
- Conformity of Diabetes Mobile apps with the Chronic Care Model.BMJ health & care informatics · 2019Article
- An expandable approach for design and personalization of digital, just-in-time adaptive interventions.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Assessment of Psychological Dimensions in Telemedicine Care for Gestational Diabetes Mellitus: A Systematic Review of Qualitative and Quantitative Studies.Frontiers in psychology · 2019Article
- Artificial Intelligence for Diabetes Management and Decision Support: Literature Review.Journal of medical Internet research · 2018Review
- Special issue on emerging technologies for the management of diabetes mellitus.Medical & biological engineering & computing · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mobile health systems aiming to promote adherence may cost-effectively improve the self-management of chronic diseases like diabetes, enhancing the compliance to the medical prescription, encouraging and stimulating patients to adopt healthy life styles and promoting empowerment. This paper presents a strategy for m-health applications in diabetes self-management that is based on automatic generation of feedback messages. A feedback assistant, representing the core of architecture, delivers dynamic and automatically updated text messages set up on clinical guideline and patient's lifestyle. Based on this strategy, an m-health adherence system was designed, developed and tested in a small-scale exploratory study with T1DM and T2DM patients. The results indicate that the system could be feasible and well accepted and that its usage increased along with adherence to prescriptions during the 4 weeks of the study. A more extensive research is pending to corroborate these outcomes and to establish a clear benefit of the proposed solution.
Indexed as
Identifiers
25564181What Socratic 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 Socratic graph.