DEPP: A diabetes exercise prescription protocol knowledge base
8 affiliations
- 1Health 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
- 2Department of Computer Science and Information Technology, University of A Coruña, Coruña, Spain
- 3Health Management Center, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- 4Department of Epidemiology and Health Statistics, West China School of Public Health, Sichuan University, Chengdu, China
- 5Department of Biliary Surgery, West China Hospital of Sichuan University/West China School of Nursing, Sichuan University, Chengdu, China
- 6Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsink, Finland
- 7Department 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
- 8Soft Matter and Molecular Biophysics Group, Department of Applied Physics and Institute of Materials (iMATUS), University of Santiago de Compostela, Santiago de Compostela, Spain
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. Author summary: Regular physical activity is just as critical as medication or diet for managing diabetes, yet many patients struggle to get safe, personalized exercise guidance. The reality is that most clinical guidelines offer one-size-fits-all advice, and access to trained exercise specialists is limited. This leaves healthcare providers with little support when tailoring physical activity plans for individuals with varying complications, fitness levels, and health risks. To tackle this challenge, we built the Diabetes Exercise Prescription Protocol (DEPP), a free, online knowledge base designed to put evidence-based exercise recommendations directly into clinicians’ hands. Our team analyzed 529 scientific studies published over nearly four decades (1986–2025) and curated 766 specific exercise protocols. Unlike standard guides, DEPP introduces a practical framework that not only covers exercise type, intensity, and duration, but also highlights critical safety warnings and contraindications, helping doctors avoid potentially dangerous advice. We tested DEPP with real users, including sports physicians and medical students—and found that it scored 80.18 out of 100 on the System Usability Scale, placing it in the “excellent” range for ease of use. Its Net Promoter Score of 28.6% further indicated that users would generally recommend it to peers. More importantly, when we asked 20 clinicians to prescribe exercise plans both with and without DEPP, the protocols generated using our platform were significantly better in quality, proving that it offers real, practical value over clinical intuition alone. By making four decades of scientific evidence freely accessible and searchable, DEPP aims to empower healthcare professionals worldwide to deliver safer, more effective exercise guidance. We invite clinicians, educators, and researchers to explore the platform and put this resource to use at http://depp.sysbio.org.cn.
Related
- Effectiveness of a Physical Exercise Prescription Program in Primary Care to Improve Physical Fitness and Quality of Life in Adults Aged 65 Years and Older in a Rural Basic Health Area-Study Protocol for a Randomized Controlled Trial (PREFIS-AP).
- Effects and mechanisms of exercise timing on cognitive function in older type 2 diabetes mellitus patients with mild cognitive impairment: A magnetic resonance imaging-based randomized controlled trial protocol
- Development and preliminary psychometric evaluation of a modified Chinese instrument for self-rated mastery of diabetes exercise knowledge (DEKS-C)
- Walking for Chronic Pain: More Than an Exercise Prescription.
- Heart Rate Variability in Cardiometabolic Disease: From Molecular Mechanisms to Personalized Exercise Prescription
BibTeX
@article{bao2026depp,
title = {{DEPP: A diabetes exercise prescription protocol knowledge base}},
author = {Ting Bao and Xingyun Liu and Shumin Ren and Ke Zhang and Jiale Du and Danting Li and Bingqing Liu and Jinhua Feng and Rongrong Wu and Erman Wu and Chaoying Zhan and Min Jiang and Li Shen and Cheng Bi and Yingbo Zhang and Sheyu Li and Juan M. Ruso and Bairong Shen},
journal = {PLOS Digital Health},
year = {2026},
month = sep,
doi = {10.1371/journal.pdig.0001728},
url = {https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001728},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - DEPP: A diabetes exercise prescription protocol knowledge base
AU - Ting Bao
AU - Xingyun Liu
AU - Shumin Ren
AU - Ke Zhang
AU - Jiale Du
AU - Danting Li
AU - Bingqing Liu
AU - Jinhua Feng
AU - Rongrong Wu
AU - Erman Wu
AU - Chaoying Zhan
AU - Min Jiang
AU - Li Shen
AU - Cheng Bi
AU - Yingbo Zhang
AU - Sheyu Li
AU - Juan M. Ruso
AU - Bairong Shen
PY - 2026
DA - 2026/09/16
JO - PLOS Digital Health
DO - 10.1371/journal.pdig.0001728
UR - https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001728
AB - 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. Author summary: Regular physical activity is just as critical as medication or diet for managing diabetes, yet many patients struggle to get safe, personalized exercise guidance. The reality is that most clinical guidelines offer one-size-fits-all advice, and access to trained exercise specialists is limited. This leaves healthcare providers with little support when tailoring physical activity plans for individuals with varying complications, fitness levels, and health risks. To tackle this challenge, we built the Diabetes Exercise Prescription Protocol (DEPP), a free, online knowledge base designed to put evidence-based exercise recommendations directly into clinicians’ hands. Our team analyzed 529 scientific studies published over nearly four decades (1986–2025) and curated 766 specific exercise protocols. Unlike standard guides, DEPP introduces a practical framework that not only covers exercise type, intensity, and duration, but also highlights critical safety warnings and contraindications, helping doctors avoid potentially dangerous advice. We tested DEPP with real users, including sports physicians and medical students—and found that it scored 80.18 out of 100 on the System Usability Scale, placing it in the “excellent” range for ease of use. Its Net Promoter Score of 28.6% further indicated that users would generally recommend it to peers. More importantly, when we asked 20 clinicians to prescribe exercise plans both with and without DEPP, the protocols generated using our platform were significantly better in quality, proving that it offers real, practical value over clinical intuition alone. By making four decades of scientific evidence freely accessible and searchable, DEPP aims to empower healthcare professionals worldwide to deliver safer, more effective exercise guidance. We invite clinicians, educators, and researchers to explore the platform and put this resource to use at http://depp.sysbio.org.cn.
SN - 2767-3170
ER -