Natural Language Processing Chatbot–Based Interventions for Improvement of Diet, Physical Activity, and Tobacco Smoking Behaviors: Systematic Review

作者：Jing Chen, Run-Ze Hu, Yu-Xuan Zhuang, J. W. Zhang, Rui Shan, Yang Yang, Zheng Liu · 发表于：JMIR mhealth and uhealth · 年份：2025 · DOI：10.2196/66403 · 被引用次数：7 · 研究领域：Digital Mental Health Interventions、Mobile Health and mHealth Applications、AI in Service Interactions

Background: The rapid development of artificial intelligence technology has enabled chatbots to increasingly promote health-related behaviors, addressing the high demand for human resources in traditional interventions. Several systematic reviews have been conducted in this area. However, the existing reviews have not focused on the rigorously designed randomized trials of the state-of-the-art chatbots (interacting with users through unconstrained natural language), thus calling for an updated review. Objective: We aimed to explore the effects of natural language processing (NLP) chatbot-based interventions on improving diet, physical activity, and tobacco smoking behaviors in the general population and to evaluate the chatbot use behaviors during the implementation process. Methods: We comprehensively searched 12 databases or registers for eligible studies published from January 1, 2010, until July 16, 2024, and obtained a total of 6301 studies. We included randomized controlled trials (RCTs) that used NLP-chatbots to promote diet, physical activity, or tobacco smoking behaviors among adults or children. Due to considerable heterogeneity across the included studies, we adopted the synthesis without meta-analysis guidelines and summarized the effectiveness of NLP chatbot-based interventions. We used the new evidence-mapping method (bubble plot) to visualize the results. We also described the results related to the changes in diet, physical activity, or tobacco smoking behavio...