ArticleDiabetes therapy : research, treatment and education of diabetes and related disorders2019
Study Protocol for the Effects of Artificial Intelligence (AI)-Supported Automated Nutritional Intervention on Glycemic Control in Patients with Type 2 Diabetes Mellitus.
Article in Diabetes therapy : research, treatment and education of diabetes and related disorders, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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
10 citing papers in PubMed, 30 citations in OpenAlex.
- Feasibility and Acceptability of a Web-Based Dietary Intervention with Text Messages for Colorectal Cancer: A Randomized Pilot Trial.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2020Trial
- Validity of an AI-Assisted Dietary Recording Application for Family-Based Nutritional Management in Young Patients with Anorexia Nervosa.Nutrients · 2026Article
- Article
- Impact of machine learning on dietary and exercise behaviors in type 2 diabetes self-management: a systematic literature review.PeerJ. Computer science · 2025Article
- The usefulness of an application-supported nutritional intervention on non-high-density lipoprotein cholesterol in people with a risk of lifestyle-related diseases.PLOS digital health · 2024Article
- Innovative strategies to predict and prevent the risk for malnutrition in child, adolescent, and young adult cancer survivors.Frontiers in nutrition · 2023Review
- Development and validation pathways of artificial intelligence tools evaluated in randomised clinical trials.BMJ health & care informatics · 2021Article
- Artificial Intelligence in Nutrients Science Research: A Review.Nutrients · 2021Review
- Advancing Artificial Intelligence in Health Settings Outside the Hospital and Clinic.NAM perspectives · 2020Review
- Application of Artificial Intelligence in Diabetes Education and Management: Present Status and Promising Prospect.Frontiers in public health · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
introductionNutritional intervention is effective in improving glycemic control in patients with type 2 diabetes but requires large inputs of manpower. Recent improvements in photo analysis technology facilitated by artificial intelligence (AI) and remote communication technologies have enabled automated evaluations of nutrient intakes. AI- and mobile-supported nutritional intervention is expected to be an alternative approach to conventional in-person nutritional intervention, but with less human resources, although supporting evidence is not yet complete. The aim of this study is to test the hypothesis that AI-supported nutritional intervention is as efficacious as the in-person, face-to-face method in terms of improving glycemic control in patients with type 2 diabetes.
methodsThis is a multicenter, unblinded, parallel, randomized controlled study comparing the efficacy of AI-supported automated nutrition therapy with that of conventional human nutrition therapy in patients with type 2 diabetes. Patients with type 2 diabetes mainly controlled with diet are to be recruited and randomly assigned to AI-supported nutrition therapy (n = 50) and to human nutrition therapy (n = 50). Asken, a mobile application whose nutritional evaluation has been already validated to that by the classical method of weighted dietary records, has been specially modified for this study so that it follows the recommendations of Japan Diabetes Society (total energy restriction with proportion of carbohydrates to fat to protein of 50-60, 20, and 20-30%, respectively). PLANNED OUTCOMES: The primary outcome is the change in glycated hemoglobin levels from baseline to 12 months, and this outcome is to be compared between the two groups. The secondary outcomes are changes in fasting plasma glucose, plasma lipid profile, body weight, body mass index, waist circumference, blood pressures, and urinary albumin excretion. The results of this randomized controlled trial will fill the gap between the demand for support of AI in nutritional interventions and the scientific evidence on its efficacy.
trial registrationUMIN000032231.
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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 OpenQuestion graph.