Artificial Intelligence–Based Chatbots for Promoting Health Behavioral Changes: Systematic Review

聊天机器人 心理信息 计算机科学 可用性 行为改变 电子健康 系统回顾 心理干预 远程医疗 数字健康 个性化 梅德林 医学 应用心理学 人工智能 万维网 心理学 医疗保健 人机交互 护理部 经济 法学 病理 经济增长 政治学
作者
Abhishek Aggarwal,Cheuk Chi Tam,Dezhi Wu,Xiaoming Li,Shan Qiao
出处
期刊:Journal of Medical Internet Research [JMIR Publications]
卷期号:25: e40789-e40789 被引量:311
标识
DOI:10.2196/40789
摘要

Background Artificial intelligence (AI)–based chatbots can offer personalized, engaging, and on-demand health promotion interventions. Objective The aim of this systematic review was to evaluate the feasibility, efficacy, and intervention characteristics of AI chatbots for promoting health behavior change. Methods A comprehensive search was conducted in 7 bibliographic databases (PubMed, IEEE Xplore, ACM Digital Library, PsycINFO, Web of Science, Embase, and JMIR publications) for empirical articles published from 1980 to 2022 that evaluated the feasibility or efficacy of AI chatbots for behavior change. The screening, extraction, and analysis of the identified articles were performed by following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Results Of the 15 included studies, several demonstrated the high efficacy of AI chatbots in promoting healthy lifestyles (n=6, 40%), smoking cessation (n=4, 27%), treatment or medication adherence (n=2, 13%), and reduction in substance misuse (n=1, 7%). However, there were mixed results regarding feasibility, acceptability, and usability. Selected behavior change theories and expert consultation were used to develop the behavior change strategies of AI chatbots, including goal setting, monitoring, real-time reinforcement or feedback, and on-demand support. Real-time user-chatbot interaction data, such as user preferences and behavioral performance, were collected on the chatbot platform to identify ways of providing personalized services. The AI chatbots demonstrated potential for scalability by deployment through accessible devices and platforms (eg, smartphones and Facebook Messenger). The participants also reported that AI chatbots offered a nonjudgmental space for communicating sensitive information. However, the reported results need to be interpreted with caution because of the moderate to high risk of internal validity, insufficient description of AI techniques, and limitation for generalizability. Conclusions AI chatbots have demonstrated the efficacy of health behavior change interventions among large and diverse populations; however, future studies need to adopt robust randomized control trials to establish definitive conclusions.
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