背景:基于人工智能(AI)的聊天机器人可以提供个性化,引人入胜,和按需健康促进干预措施。
目的:本系统评价的目的是评估可行性,功效,以及人工智能聊天机器人促进健康行为改变的干预特征。
方法:在7个书目数据库中进行了全面搜索(PubMed,IEEEXplore,ACM数字图书馆,PsycINFO,WebofScience,Embase,和JMIR出版物),用于1980年至2022年发表的经验文章,这些文章评估了AI聊天机器人对行为改变的可行性或功效。筛选,提取,根据PRISMA(系统评价和荟萃分析的首选报告项目)指南对已确定的文章进行分析。
结果:在纳入的15项研究中,一些人证明了人工智能聊天机器人在促进健康生活方式方面的高效(n=6,40%),戒烟(n=4,27%),治疗或药物依从性(n=2,13%),并减少物质滥用(n=1,7%)。然而,关于可行性的结果好坏参半,可接受性,和可用性。选择行为改变理论和专家咨询用于开发AI聊天机器人的行为改变策略,包括目标设定,监测,实时强化或反馈,和按需支持。实时用户聊天机器人交互数据,例如用户偏好和行为表现,在聊天机器人平台上收集,以确定提供个性化服务的方式。通过可访问的设备和平台进行部署,AI聊天机器人展示了可扩展性的潜力(例如,智能手机和FacebookMessenger)。参与者还报告说,人工智能聊天机器人为传达敏感信息提供了一个非判断性的空间。然而,报告的结果需要谨慎解释,因为内部有效性的风险中等到较高,对人工智能技术的描述不足,和通用性的限制。
结论:人工智能聊天机器人已经证明了健康行为改变干预措施在大量和多样化人群中的有效性;然而,未来的研究需要采用稳健的随机对照试验来建立明确的结论.
Artificial intelligence (AI)-based chatbots can offer personalized, engaging, and on-demand health promotion interventions.
The aim of this systematic
review was to evaluate the feasibility, efficacy, and intervention characteristics of AI chatbots for promoting health behavior change.
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.
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.
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.