Evidence map›Paper›PMID 41343777›Full record

ArticleJMIR research protocols2025

Efficacy of FiberMore, an AI-Based mHealth Intervention to Increase Dietary Fiber Intake Among Type 2 Diabetes Patients: Protocol for a Pilot Randomized Controlled Trial.

Wei Thing Sze, Kayo Waki, Daniel Lane, Kyohei Hasegawa, Ryohei Nakada, Shuya Iwata, Yuexiang Ji, Akihiro Isogawa, Tomohisa Aoyama, Kana Miyake and 11 more

Abstract readClinical Trial Protocol
In one paragraph

Article in JMIR research protocols, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Wei Thing SzeDepartment of Clinical Information Engineering, Graduate School of Medicine, The University of Tokyo, 7 Chome-3-1 Hongo, Bunkyo City, Tokyo, 113-8654, Japan, 81-3-58411892.ORCID 0000-0003-1054-6886
Kayo WakiDepartment of Clinical Information Engineering, Graduate School of Medicine, The University of Tokyo, 7 Chome-3-1 Hongo, Bunkyo City, Tokyo, 113-8654, Japan, 81-3-58411892.ORCID 0000-0003-0046-2523
Daniel LaneDepartment of Planning, Information and Management, The University of Tokyo Hospital, Tokyo, Japan.ORCID 0009-0004-4638-465X
Kyohei HasegawaDepartment of Planning, Information and Management, The University of Tokyo Hospital, Tokyo, Japan.ORCID 0009-0006-1033-6490
Ryohei NakadaDepartment of Planning, Information and Management, The University of Tokyo Hospital, Tokyo, Japan.ORCID 0009-0002-8430-875X
Shuya IwataDepartment of Planning, Information and Management, The University of Tokyo Hospital, Tokyo, Japan.ORCID 0009-0004-5662-2316
Yuexiang JiDepartment of Planning, Information and Management, The University of Tokyo Hospital, Tokyo, Japan.ORCID 0009-0006-5173-5877
Akihiro IsogawaDivision of Diabetes, Mitsui Memorial Hospital, Tokyo, Japan.ORCID 0000-0003-1355-5825
Tomohisa AoyamaDepartment of Diabetes and Metabolic Diseases, The University of Tokyo, 7 Chome-3-1 Hongo, Bunkyo City, Tokyo, 113-8654, Japan, 81 03-3812-2111.ORCID 0009-0000-6234-4472
Kana MiyakeDepartment of Diabetes and Metabolic Diseases, The University of Tokyo, 7 Chome-3-1 Hongo, Bunkyo City, Tokyo, 113-8654, Japan, 81 03-3812-2111.ORCID 0000-0003-2631-7084
Yuri KadowakiDepartment of Diabetes and Metabolic Diseases, The University of Tokyo, 7 Chome-3-1 Hongo, Bunkyo City, Tokyo, 113-8654, Japan, 81 03-3812-2111.ORCID 0009-0008-7453-1775
Tomoya KawaguchiDepartment of Diabetes and Metabolic Diseases, The University of Tokyo, 7 Chome-3-1 Hongo, Bunkyo City, Tokyo, 113-8654, Japan, 81 03-3812-2111.ORCID 0009-0006-6217-7057
Yoshinori MatsuoDepartment of Diabetes and Metabolic Diseases, The University of Tokyo, 7 Chome-3-1 Hongo, Bunkyo City, Tokyo, 113-8654, Japan, 81 03-3812-2111.ORCID 0000-0002-9252-8042
Kengo MiyoshiDepartment of Diabetes and Metabolic Diseases, The University of Tokyo, 7 Chome-3-1 Hongo, Bunkyo City, Tokyo, 113-8654, Japan, 81 03-3812-2111.ORCID 0009-0001-1735-2075
Nagisa IshibashiDepartment of Diabetes and Metabolic Diseases, The University of Tokyo, 7 Chome-3-1 Hongo, Bunkyo City, Tokyo, 113-8654, Japan, 81 03-3812-2111.ORCID 0009-0002-9523-8873
Gotaro TodaDepartment of Diabetes and Metabolic Diseases, The University of Tokyo, 7 Chome-3-1 Hongo, Bunkyo City, Tokyo, 113-8654, Japan, 81 03-3812-2111.ORCID 0000-0003-3194-4533
Saori KamedaDepartment of Diabetes and Metabolic Diseases, The University of Tokyo, 7 Chome-3-1 Hongo, Bunkyo City, Tokyo, 113-8654, Japan, 81 03-3812-2111.ORCID 0009-0005-1026-2391
Masaki IgarashiDepartment of Diabetes and Metabolic Diseases, The University of Tokyo, 7 Chome-3-1 Hongo, Bunkyo City, Tokyo, 113-8654, Japan, 81 03-3812-2111.ORCID 0000-0002-3331-3877
Masaki TanakaDepartment of Diabetes and Metabolic Diseases, The University of Tokyo, 7 Chome-3-1 Hongo, Bunkyo City, Tokyo, 113-8654, Japan, 81 03-3812-2111.ORCID 0009-0005-9344-1044
Toshimasa YamauchiDepartment of Diabetes and Metabolic Diseases, The University of Tokyo, 7 Chome-3-1 Hongo, Bunkyo City, Tokyo, 113-8654, Japan, 81 03-3812-2111.ORCID 0000-0003-4827-6404
Masaomi NangakuDivision of Nephrology and Endocrinology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.ORCID 0000-0001-7401-2934

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: A high intake of dietary fiber has been shown to improve glycemic control and decrease hyperinsulinemia in people living with type 2 diabetes (T2D). T2D patients in Japan consume less than the recommended amount of fiber. Based on findings from a formative study, we developed an artificial intelligence (AI)-powered mobile health (mHealth) intervention, FiberMore, that uses the theory of planned behavior to help T2D patients increase their dietary fiber intake by enhancing their perceived behavioral control and attitude toward fiber consumption. Objective: We aimed to assess the efficacy of FiberMore in improving the dietary fiber intake of T2D patients by conducting a pilot randomized controlled trial. In addition, we want to explore the efficacy of FiberMore in reducing HbA1c of T2D patients via improvement in dietary fiber intake. Methods: This is a randomized, single-blinded, multicenter study targeting 80 T2D patients from 3 institutions in Japan with a 2-week run-in, a 12-week intervention, and a 12-week observation. The intervention group is given access to FiberMore throughout the 12-week intervention period. A core feature of FiberMore is AI-powered meal photo logging using a fine-tuned GPT-4o (OpenAI) model, which analyzes the nutrient content of meals and delivers personalized, real-time feedback on fiber content. In addition, FiberMore provides personalized fiber goal setting and supports participants in identifying barriers to increasing fiber intake, along with corresponding coping strategies (labeled as "solutions" to the participant), through an AI chatbot. The AI chatbot also assesses participants' emotional attitudes toward eating more fiber and delivers relevant educational content on dietary fiber. The control group receives a sham intervention focused on salt reduction, consisting of educational content delivered at 3 time points during the intervention period and records their daily efforts in salt reduction in a diary. The 12-week intervention period will be followed by a 12-week observational period to investigate the sustainability of the intervention's effects. The primary outcome is between-group difference in the change of dietary fiber intake at 12 weeks. The secondary outcomes include HbA1c, other clinical measures, measurements of behavior changes, and assessment of participants' satisfaction and perceived usefulness of the intervention. Results: Recruitment began on February 12, 2025, and ended on September 1, 2025. We anticipate that the intervention period will conclude in December 2025 and the observation period will conclude in March 2026. As of September 22, 2025, a total of 72 participants have been officially enrolled and randomized. Unlabelled: There are currently no mHealth dietary interventions that specifically focus on increasing fiber intake in Japan, highlighting the novelty of this intervention. This trial will generate important evidence on the efficacy, feasibility, and safety of an AI-based mHealth intervention for enhancing dietary fiber intake and glycemic control in free-living individuals with T2D. Furthermore, as a pilot study, it will offer valuable insights into the development of AI as a promising tool for accurate, low-burden dietary assessment.

Indexed as

Artificial IntelligenceDiabetes Mellitus, Type 2Dietary FiberAgedFemaleGlycated HemoglobinHumansJapanMaleMiddle AgedPilot ProjectsRandomized Controlled Trials as TopicSingle-Blind MethodTelemedicineDietary FiberGlycated Hemoglobinartificial intelligencebehavior changedietary fiberdigital therapeuticsrandomized controlled trialtheory of planned behaviortype 2 diabetes

Identifiers

PMID41343777
PMCPMC12677880

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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.