Based on the multi-theory model perspective, what are the influencing factors of health behavior change among community-dwelling elderly patients with type 2 diabetes in China? A qualitative study.

Abstract

Background: The high prevalence of chronic diseases in the elderly, especially type 2 diabetes, poses a major challenge to the global health system. In China, elderly patients with type 2 diabetes mainly rely on family and community for long-term management. In view of the importance of health behavior change in improving the health of patients with chronic diseases, the multi-theory model (MTM), as the fourth generation of theoretical model in the field of health behavior change, provides a new perspective for promoting patients' behavior change in chronic disease management and has been widely used in many health fields. However, from the perspective of research methods, the application of multi-theory model in qualitative research is less, accounting for only 6 % of the total research. In terms of research objects, there is no research applied to diabetes patients. Therefore, this study adopts qualitative research methods and takes MTM theory as the guiding framework to deeply analyze the factors of health behavior change in elderly patients with type 2 diabetes in the community. This paper aims to provide a basis for the development of targeted intervention strategies, explore and optimize MTM constructs, provide a reference for future empirical research, and promote a better understanding and application of MTM.

Methods: This study used semi-structured interviews and MTM theory as the guiding framework to deeply analyze the factors of health behavior change in elderly patients with type 2 diabetes in the community. Thematic analysis and topic modeling (python machine learning) were used to analyze the interview data simultaneously. By comparing the results of thematic analysis and topic modeling, the key factors for health behavior change with community elderly patients with type 2 diabetes were identified.

Results: This study combined thematic analysis with machine learning and provided a comprehensive and nuanced picture of the key factors for health behavior change among older people with type 2 diabetes in the community. Thematic analysis yielded eight key factors and 19 influencing factors, and python topic modeling yielded eight key factors and eight influencing factors. By comparing the similarities and differences between the results of thematic analysis and python topic modeling, this study finally determined 9 key factors and 20 influencing factors of health behavior change in elderly patients with type 2 diabetes in the community, including The Science of Dietary Rationing and Exercise, Internal self-confidence, Convenience and accessibility of fitness facilities, etc. CONCLUSIONS: In this study, two analytical methods (thematic analysis and topic modeling) were used to analyze the interview results. Nine different key factors and 20 influencing factors were finally identified. In the future, targeted interventions can be carried out based on these related factors to more accurately and efficiently promote the health behavior management of elderly patients with type 2 diabetes in the community and improve the health status of elderly patients with diabetes.