Exploring Users′ Acceptance and Engagement With Mental Health Chatbots: Insights From an Integrative Review

Background The potential of well‐being chatbots as supportive tools in mental healthcare is increasingly recognised. Nevertheless, user acceptance remains low, highlighting the need to understand the factors influencing adoption and engagement. Objective: The purpose of this study is to identify the factors influencing user acceptance and engagement with chatbots and to generate insights that can inform the design and implementation of more effective chatbot interventions for mental well‐being. Methods: Following the PRISMA 2020 guidelines, an integrative review was conducted in June 2024. Literature searches were carried out in Web of Science, Scopus, PubMed, IEEE Xplore and ScienceDirect to identify peer‐reviewed articles published between January 2010 and May 2024. Thematic analysis was employed using an inductive approach. Results: From a total of 1232 papers identified, 20 studies met the inclusion criteria. The findings developed three themes, which involve technological factor, user factor and environmental factor. Different subtopics within the technological aspects have different effects on user behaviour. Technological limitations and excessive anthropomorphism have emerged as key barriers to user–chatbot interaction; empathy, interactivity, user‐centred design, ease of use, personalisation, usability and stability were found to promote user engagement. From the user perspective, barriers included lack of motivation, low trust, privacy concerns and effort expectations, while facilitators encompassed enjoyment, positive attitudes, learning opportunities and emotion. Environmental factors could influence user adoption behaviour, for instance, advertising and social influences. Conclusions: Multiple interdisciplinary factors have been found to influence user engagement with chatbots for mental well‐being. This will contribute to refining extant theories and fostering interdisciplinary collaboration. Moreover, this study provides a valuable source of instruction for designers and developers of health chatbots.