PEP-DSS-DBC1.0: A Personalized Exercise Prescription Decision Support System for Diabetes Care (Preprint)
Authors: Bao Ting , Liu Xingyun , Ren Shumin , Zhang Ke , Du Jiale , Li Danting , Liu Bingqing , Feng Jinhua , Wu Rongrong , Wu Erman , Zhan Chaoying , Jiang Min , Shen Li , Bi Cheng , Zhang Yingbo , Li Sheyu , Ruso Juan M. , Shen Bairong 
Published: December 2024

BACKGROUND

Exercise plays a critical role in managing diabetes, improving glycemic control, and reducing the risk of cardiovascular complications. However, personalizing exercise prescriptions (PEPs) to address individual characteristics and preferences is challenging, particularly in an environment with a global shortage of exercise professionals. While digital tools hold promise for improving personalized care, many existing systems rely on generalized guidelines or expert consensus, lacking the ability to provide highly individualized and evidence-based recommendations. To address this gap, we developed PEP-DSS-DBC1.0, a knowledge-based digital decision support system for generating tailored PEPs in diabetes care.

OBJECTIVE

The aim of this study was to create a digital, knowledge-based decision support tool for personalized exercise prescriptions that incorporates individual characteristics and preferences. PEP-DSS-DBC1.0 was designed to enhance the precision and usability of PEPs for people with diabetes while ensuring alignment with evidence-based principles.

METHODS

The development of PEP-DSS-DBC1.0 was informed by an extensive review of the literature. A working group, including experts in exercise medicine, diabetes care, and informatics, was formed to establish a decision support framework. The PubMed database served as the primary resource for knowledge extraction, covering studies published between January 1975 and May 2023. Data from 938 studies were annotated, resulting in 1,171 exercise protocols, 10,316,497 patient records, and 2,904 additional entries, totaling 13,263 data points. The web-based tool was built using a browser/server architecture and the WIMA (Windows, IIS, MySQL, ASP.NET) web stack. Validation and comparative analysis of the tool were performed against ChatGPT4o and sports physicians, using multiple metrics and two rating scales: the System Usability Scale (SUS) and the Net Promoter Score (NPS).

RESULTS

PEP-DSS-DBC1.0 provides personalized exercise prescriptions based on the newly developed “FITT-VP-WC” principles, which include frequency, intensity, time, type, volume, progression, warning, and contraindication. The tool demonstrated superior performance compared to ChatGPT4o and sports physicians, achieving a total performance score of 100% compared to 48% for ChatGPT4o and 12% for sports physicians (χ2=853.3, P<.001). Usability assessments showed that the average SUS score was 77.25 ± 6.06, indicating above-average usability, while the NPS score was 68.75%, reflecting high user loyalty and satisfaction. The tool is freely accessible at http://PEP-DSS-DBC.sysbio.org.cn/.

CONCLUSIONS

PEP-DSS-DBC1.0 represents a significant advancement in diabetes care, providing a knowledge-based system for generating highly individualized exercise prescriptions. While the tool has demonstrated superior performance and usability, future randomized controlled trials are essential to further validate its clinical utility and broader application in diabetes management.