Application of Chatbots to Help Patients Self-Manage Diabetes: Systematic Review and Meta-Analysis
Authors: Wu Yibo , Zhang Jinzi , Ge Pu , Duan Tingyu , Zhou Junyu , Wu Yiwei , Zhang Yuening , Liu Siyu , Liu Xinyi , Wan Erya , Sun Xinying 
Published: December 2024

Background
The number of people with diabetes is on the rise globally. Self-management and health education of patients are the keys to control diabetes. With the development of digital therapies and artificial intelligence, chatbots have the potential to provide health-related information and improve accessibility and effectiveness in the field of patient self-management.

Objective
This study systematically reviews the current research status and effectiveness of chatbots in the field of diabetes self-management to support the development of diabetes chatbots.

Methods
A systematic review and meta-analysis of chatbots that can help patients with diabetes with self-management was conducted. PubMed and Web of Science databases were searched using keywords around diabetes, chatbots, conversational agents, virtual assistants, and more. The search period was from the date of creation of the databases to January 1, 2023. Research articles in English that fit the study topic were selected, and articles that did not fit the study topic or were not available in full text were excluded.

Results
In total, 25 studies were included in the review. In terms of study type, all articles could be classified as systematic design studies (n=8, 32%), pilot studies (n=8, 32%), and intervention studies (n=9, 36%). Many articles adopted a nonrandomized controlled trial design in intervention studies (n=6, 24%), and there was only 1 (4%) randomized controlled trial. In terms of research strategy, all articles can be divided into quantitative studies (n=10, 40%), mixed studies (n=6, 24%), and qualitative studies (n=1, 4%). The evaluation criteria for chatbot effectiveness can be divided into technical performance evaluation, user experience evaluation, and user health evaluation. Most chatbots (n=17, 68%) provided education and management focused on patient diet, exercise, glucose monitoring, medications, and complications, and only a few studies (n=2, 8%) provided education on mental health. The meta-analysis found that the chatbot intervention was effective in lowering blood glucose (mean difference 0.30, 95% CI 0.04-0.55; P=.02) and had no significant effect in reducing weight (mean difference 1.41, 95% CI –2.29 to 5.11; P=.46) compared with the baseline.

Conclusions
Chatbots have potential for the development of self-management for people with diabetes. However, the evidence level of current research is low, and higher level research (such as randomized controlled trials) is needed to strengthen the evidence base. More use of mixed research in the research strategy is needed to fully use the strengths of both quantitative and qualitative research. Appropriate and innovative theoretical frameworks should be used in the research to provide theoretical support for the study. In addition, researchers should focus on the personalized and user-friendly interactive features of chatbots, as well as improvements in study design.