Perspectives of community-dwelling older adults with chronic diseases on Baduanjin practice: A qualitative study

by Haixu Ji, Wei Li, Jinhua Zhang, Xuyan Liu, Jing Wang, Guanglei Dong

Background

With the accelerating aging of China’s population, Baduanjin has been promoted as a community-based exercise to enhance public health, particularly among older adults with chronic diseases. As a traditional Chinese exercise with a long history and profound cultural connotations, Baduanjin has attracted a large number of practitioners. However, the factors underlying the sustained practice of Baduanjin remain insufficiently explored.

Objective

This study aims to explore the factors underlying the persistence of older adults with chronic diseases in practicing Baduanjin.

Methods

A qualitative research approach was adopted in this study. 25 practitioners participated in semi‑structured face‑to‑face interviews. Thematic analysis was employed to analyze the data and generate core themes.

Results

The factors driving community-dwelling older adults with chronic diseases to persist in practicing Baduanjin were analyzed across five dimensions: perceived safety and learning-practice ease, improvements in physical health, promotion of mental well-being, enhancement of social functioning, and appreciation of traditional culture.

Conclusion

Community-dwelling older adults with chronic diseases maintain long-term Baduanjin practice not only due to its perceived safety and ease of learning and practice, but also because it embodies the essence of traditional Chinese culture. Moreover, regular practice contributes to improved physical health, promoted mental well-being, and enhanced social functioning in this population. Accordingly, Baduanjin shows considerable potential as a community-based exercise intervention to support health promotion among community-dwelling older adults with chronic diseases.

The application of large language models in bariatric surgery: A scoping review

by Ningjing Guo, Xuyan Li, Xiaoxue Li, Congmin Kang, Xiaoyan Gong, Xinyu Ji, Jie Zheng

Background

Exploratory applications of large language models within the specialized field of metabolic and bariatric surgery have begun to emerge. Nevertheless, existing research remains fragmented, lacking comprehensive integration.

Objective

To conduct a scoping review of studies on the application of large language models in the field of metabolic and bariatric surgery, aiming to provide a reference for clinical practice and future research.

Methods

This scoping review adhered to the Joanna Briggs Institute methodological framework and followed the preferred reporting items for systematic reviews and meta-Analyses extension for scoping reviews (PRISMA-ScR) guidelines.PubMed, Web of Science, The Cochrane Library, Embase, CINAHL, CNKI, Wanfang, and VIP databases were searched for relevant studies, with the search timeframe from database inception to November 2025. The included literature was summarized and analyzed.

Results

A total of 21 English-language studies were included. LLMs were primarily applied in scenarios such as patient education and information consultation, clinical decision support, and professional knowledge assessment. While LLMs performed well in information-provision tasks, they showed low consistency with expert opinions in complex clinical tasks such as individualized surgical recommendations. Performance varied across different models, with GPT-4 generally demonstrating superior performance, and domain-specific models showing professional potential. Current research still faces challenges regarding information accuracy, readability, and clinical applicability.

Conclusion

Large language models hold auxiliary potential in the field of metabolic and bariatric surgery, particularly for knowledge dissemination and patient education. However, their reliability in complex clinical decision-making remains limited. Future efforts should focus on conducting high-quality studies, advancing model specialization and standardized evaluation, and exploring safe and effective human-AI collaboration models.