Evidence map›Paper›PMID 42748086›Full record

ArticlePLOS digital health2026

DEPP: A diabetes exercise prescription protocol knowledge base.

Ting Bao, Xingyun Liu, Shumin Ren, Ke Zhang, Jiale Du, Danting Li, Bingqing Liu, Jinhua Feng, Rongrong Wu, Erman Wu and 8 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PLOS digital health, 2026. 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

18 authors.

Ting BaoHealth Management Center, General Practice Medical Center and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
Xingyun LiuHealth Management Center, General Practice Medical Center and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.ORCID https://orcid.org/0000-0002-9295-2767
Shumin RenHealth Management Center, General Practice Medical Center and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
Ke ZhangHealth Management Center, General Practice Medical Center and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
Jiale DuHealth Management Center, General Practice Medical Center and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
Danting LiHealth Management Center, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China.
Bingqing LiuDepartment of Epidemiology and Health Statistics, West China School of Public Health, Sichuan University, Chengdu, China.
Jinhua FengHealth Management Center, General Practice Medical Center and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
Rongrong WuHealth Management Center, General Practice Medical Center and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
Erman WuHealth Management Center, General Practice Medical Center and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
Chaoying ZhanHealth Management Center, General Practice Medical Center and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
Min JiangHealth Management Center, General Practice Medical Center and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
Li ShenInstitute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsink, Finland.
Cheng BiHealth Management Center, General Practice Medical Center and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
Yingbo ZhangHealth Management Center, General Practice Medical Center and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
Sheyu LiDepartment of Endocrinology and Metabolism, Division of Guideline and Rapid Recommendation, Cochrane China Centre, MAGIC China Centre, Chinese Evidence-Based Medicine Centre, West China Hospital, Sichuan University, Chengdu, China.
Juan M RusoSoft Matter and Molecular Biophysics Group, Department of Applied Physics and Institute of Materials (iMATUS), University of Santiago de Compostela, Santiago de Compostela, Spain.
Bairong ShenHealth Management Center, General Practice Medical Center and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.ORCID https://orcid.org/0000-0003-2899-1531

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Exercise plays a critical role in diabetes management by improving glycemic control and reducing cardiovascular risk. However, personalizing exercise prescriptions to individual characteristics and preferences is challenging, especially given the global shortage of exercise professionals, and current approaches often rely on general guidelines that lack support for individualized, evidence-based planning. To address this gap, we developed the Diabetes Exercise Prescription Protocol (DEPP) knowledge base, a structured evidence resource to assist healthcare experts in formulating personalized exercise prescriptions. DEPP was constructed by manually extracting and annotating data from 529 English-language studies retrieved from PubMed, resulting in a curated set of 766 exercise prescription protocols. The system uses a browser/server architecture implemented with Flask, SQLite, and Waitress. The knowledge base enables personalized retrieval and comparison of evidence-based protocols using a novel "FITT-VP-WC" framework, which extends the established FITT-VP principles by explicitly integrating warnings and contraindications as core safety components. Usability and clinical utility were evaluated with 12 sports physicians and 16 students (SUS/NPS) and in a vignette-based comparison with 20 clinicians (DEPP vs. no tool). The overall SUS score was 80.18 (physicians: 83.13, students: 77.97, p > 0.05) and NPS was 28.6% (physicians: 50.0%, students: 12.5%, p > 0.05), indicating good acceptance. In the comparative study, DEPP-assisted prescriptions significantly outperformed those without the tool across three standardized patient cases (all p < 0.001), confirming its practical benefit. The knowledge base is freely accessible at http://depp.sysbio.org.cn. This resource offers a new tool for evidence-based, personalized exercise prescription in diabetes care with positive user feedback.

Identifiers

PMID42748086
PMCPMC13581003

What OpenQuestion holds

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Registered trials

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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.