DEPP: A diabetes exercise prescription protocol knowledge base.

Authors

Ting Bao, Xingyun Liu, Shumin Ren, Ke Zhang, Jiale Du, Danting Li, Bingqing Liu, Jinhua Feng, Rongrong Wu, Erman Wu, Chaoying Zhan, Min Jiang, Li Shen, Cheng Bi, Yingbo Zhang, Sheyu Li, Juan M Ruso, Bairong Shen

Published in

PLOS digital health. Volume 5. Issue 9. Pages e0001728. Epub Sep 16, 2026.

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.

PMID:
42748086
Bibliographic data and abstract were imported from PubMed on 17 Sep 2026.

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