Abstract
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 behaviors (eg, change of BMI and stage of change). To evaluate the implementation process of the intervention, we summarized users' interaction with NLP-chatbots and their feelings (eg, satisfaction) about NLP-chatbot use. Additionally, we assessed the risk of bias of studies using the RoB 2.0 (Risk of Bias; The Cochrane Collaboration) tools. RESULTS: We finally included 7 RCTs. Concerning dietary and physical activity behaviors, the effectiveness of NLP chatbot-based interventions was inconsistent among adults, while no evidence of effect was observed among children. Concerning tobacco smoking behaviors, the included studies showed consistent evidence of improving this behavior among adults. Regarding the risk of bias of the changes in diet, physical activity, and tobacco smoking behaviors, 2 of 3, 2 of 4, and 1 of 2 studies had a high risk of bias, respectively, while the remaining had a low risk of bias. Concerning the interactions with NLP-chatbots, studies showed an overall high percentage of general interaction between users and NLP-chatbots, but not a satisfactorily high percentage of interactions specific to health behaviors. Concerning feelings about NLP-chatbot use, users showed a positive impression of NLP-chatbot use, feeling it was useful, credible, and financially feasible. CONCLUSIONS: NLP chatbot-based interventions were beneficial for adults' tobacco smoking behaviors, but no such evidence was found on diet or physical activity behaviors among adults or children. More RCTs with larger samples and lower risk of bias are urgently needed to enhance our findings in the future.