Trial reportBMC medical informatics and decision making2020
Validation of the effectiveness of a digital integrated healthcare platform utilizing an AI-based dietary management solution and a real-time continuous glucose monitoring system for diabetes management: a randomized controlled trial.
Trial report in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04161170 (Validation of Diabetes Management Effectiveness of Digital Integrated Healthcare Platform Utilizing AI-based Dietary Management Solution and Real-time Continuous Glucose Monitoring System), which is not on this map. Cited by 12 papers.
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.
Validation of Diabetes Management Effectiveness of Digital Integrated Healthcare Platform Utilizing AI-based Dietary Management Solution and Real-time Continuous Glucose Monitoring System
Who cites it
12 citing papers in PubMed, 27 citations in OpenAlex.
- Application of the integrated data platform combined with dietary management for adults with diabetes: A prospective randomized controlled trial.Journal of diabetes investigation · 2024Trial
- The application of AI-based interventions in diabetes personalized management: a systematic review and meta-analysis.Diabetology & metabolic syndrome · 2026Review
- Temporal gradient analysis of blood glucose responses to non-standard physical activity: a free-living study in type 1 diabetes.Frontiers in sports and active living · 2026Article
- Unraveling MASLD: The Role of Gut Microbiota, Dietary Modulation, and AI-Driven Lifestyle Interventions.Nutrients · 2025Review
- Overcoming barriers in continuous glucose monitoring: Challenges and future directions in diabetes management.Journal of diabetes investigation · 2025Review
- Exploring Psychosocial Burdens of Diabetes in Pregnancy and the Feasibility of Technology-Based Support: Qualitative Study.JMIR diabetes · 2025Article
- AI-Driven Management of Type 2 Diabetes in China: Opportunities and Challenges.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Article
- A Scoping Review of Artificial Intelligence-Based Health Education Interventions for Patients with Type 2 Diabetes.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Review
- Beyond the Pain Management Clinic: The Role of AI-Integrated Remote Patient Monitoring in Chronic Disease Management - A Narrative Review.Journal of pain research · 2024Review
- Optimizing type 2 diabetes management: AI-enhanced time series analysis of continuous glucose monitoring data for personalized dietary intervention.PeerJ. Computer science · 2024Article
- Physician-Authored Feedback in a Type 2 Diabetes Self-management App: Acceptability Study.JMIR formative research · 2022Article
- An Innovative Artificial Intelligence-Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study.Journal of medical Internet research · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundDespite the numerous healthcare smartphone applications for self-management of diabetes, patients often fail to use these applications consistently due to various limitations, including difficulty in inputting dietary information by text search and inconvenient and non-persistent self-glucose measurement by home glucometer. We plan to apply a digital integrated healthcare platform using an artificial intelligence (AI)-based dietary management solution and a continuous glucose monitoring system (CGMS) to overcome those limitations. Furthermore, medical staff will be performing monitoring and intervention to encourage continuous use of the program. The aim of this trial is to examine the efficacy of the program in patients with type 2 diabetes mellitus (T2DM) who have HbA1c 53-69 mmol/mol (7.0-8.5%) and body mass index (BMI) ≥ 23 mg/m
methodsThis is a 48-week, open-label, randomized, multicenter trial consisting of patients with type 2 diabetes. The patients will be randomly assigned to three groups: control group A will receive routine diabetes care; experimental group B will use the digital integrated healthcare platform by themselves without feedback; and experimental group C will use the digital integrated healthcare platform with continuous glucose monitoring and feedback from medical staff. There are five follow-up measures: baseline and post-intervention at weeks 12, 24, 36, and 48. The primary end point is change in HbA1c from baseline to six months after the intervention. DISCUSSION: This trial will verify the effectiveness of a digital integrated healthcare platform with an AI-driven dietary solution and a real-time CGMS in patients with T2DM.
trial registrationClinicaltrials.gov NCT04161170, registered on 08 November 2019. https://clinicaltrials.gov/ct2/show/NCT04161170?term=NCT04161170&draw=2&rank=1.
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What 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.