Department of Communication and Cognition, Tilburg School of Humanities and Digital Sciences, Tilburg University, Tilburg, The Netherlands.
Amsterdam School for Communication Research, University of Amsterdam, Amsterdam, The Netherlands.
Nicotine Tob Res. 2023 Jun 9;25(7):1241-1250. doi: 10.1093/ntr/ntac281.
Conversational agents (CAs; computer programs that use artificial intelligence to simulate a conversation with users through natural language) have evolved considerably in recent years to support healthcare by providing autonomous, interactive, and accessible services, making them potentially useful for supporting smoking cessation. We performed a systematic review and meta-analysis to provide an overarching evaluation of their effectiveness and acceptability to inform future development and adoption.
PsycInfo, Web of Science, ACM Digital Library, IEEE Xplore, Medline, EMBASE, Communication and Mass Media Complete, and CINAHL Complete were searched for studies examining the use of CAs for smoking cessation. Data from eligible studies were extracted and used for random-effects meta-analyses.
The search yielded 1245 publications with 13 studies eligible for systematic review (total N = 8236) and six studies for random-effects meta-analyses. All studies reported positive effects on cessation-related outcomes. A meta-analysis with randomized controlled trials reporting on abstinence yielded a sample-weighted odds ratio of 1.66 (95% CI = 1.33% to 2.07%, p < .001), favoring CAs over comparison groups. A narrative synthesis of all included studies showed overall high acceptability, while some barriers were identified from user feedback. Overall, included studies were diverse in design with mixed quality, and evidence of publication bias was identified. A lack of theoretical foundations was noted, as well as a clear need for relational communication in future designs.
The effectiveness and acceptability of CAs for smoking cessation are promising. However, standardization of reporting and designing of the agents is warranted for a more comprehensive evaluation.
This is the first systematic review to provide insight into the use of CAs to support smoking cessation. Our findings demonstrated initial promise in the effectiveness and user acceptability of these agents. We also identified a lack of theoretical and methodological limitations to improve future study design and intervention delivery.
近年来，会话代理（CA；使用人工智能通过自然语言与用户进行模拟对话的计算机程序）在支持医疗保健方面取得了长足的发展，通过提供自主、互动和便捷的服务，使其在支持戒烟方面具有潜在的用途。我们进行了一项系统评价和荟萃分析，以全面评估它们的有效性和可接受性，为未来的开发和采用提供信息。
在 PsycInfo、Web of Science、ACM 数字图书馆、IEEE Xplore、Medline、EMBASE、Communication and Mass Media Complete 和 CINAHL Complete 中搜索了关于使用 CA 进行戒烟的研究。提取符合条件的研究的数据，并用于随机效应荟萃分析。
搜索结果产生了 1245 篇出版物，其中 13 项研究符合系统评价的条件（总 N=8236），6 项研究进行了随机效应荟萃分析。所有研究都报告了对戒烟相关结果的积极影响。对报告戒烟的随机对照试验进行的荟萃分析得出样本加权优势比为 1.66（95%CI=1.33%至 2.07%，p<0.001），支持 CA 优于对照组。对所有纳入研究的叙述性综合表明总体接受度较高，同时也从用户反馈中发现了一些障碍。总体而言，纳入的研究设计多样，质量参差不齐，并且存在发表偏倚的证据。还注意到缺乏理论基础，以及未来设计中明确需要关系交流。
CA 在戒烟方面的有效性和可接受性很有前途。然而，需要对报告和设计代理进行标准化，以进行更全面的评估。
这是第一篇系统评价，提供了关于使用 CA 支持戒烟的使用情况的见解。我们的研究结果表明，这些代理在有效性和用户可接受性方面具有初步的潜力。我们还发现，在理论和方法方面存在一些局限性，需要改进未来的研究设计和干预措施的实施。
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