Effectiveness and Acceptability of Conversational Agents for Smoking Cessation: A Systematic Review and Meta-analysis.

INTRODUCTION: 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. AIMS AND METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. IMPLICATIONS: 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.

Effectiveness and acceptability of conversational agents for sexual health promotion: a systematic review and meta-analysis.

Digital health interventions for sexual health promotion have evolved considerably alongside innovations in technology. Despite these efforts, studies have shown that they do not consistently result in the desired sexual health outcomes. This could be attributed to low levels of user engagement, which can hinder digital health intervention effectiveness, as users do not engage with the system enough to be exposed to the intervention components. It has been suggested that conversational agents (automated two-way communication systems e.g. Alexa) have the potential to overcome the limitations of prior systems and promote user engagement through the increased interactivity offered by bidirectional, natural language-based interactions. The present review, therefore, provides an overview of the effectiveness and user acceptability of conversational agents for sexual health promotion. A systematic search of seven databases provided 4534 records, and after screening, 31 articles were included in this review. A narrative synthesis of results was conducted for effectiveness and acceptability outcomes, with the former supplemented by a meta-analysis conducted on a subset of studies. Findings provide preliminary support for the effectiveness of conversational agents for promoting sexual health, particularly treatment adherence. These conversational agents were found to be easy to use and useful, and importantly, resulted in high levels of satisfaction, use and intentions to reuse, whereas user evaluations regarding the quality of information left room for improvement. The results can inform subsequent efforts to design and evaluate these interventions, and offer insight into additional user experience constructs identified outside of current technology acceptance models, which can be incorporated into future theoretical developments.