ArticleJMIR human factors2024
A New Research Model for Artificial Intelligence-Based Well-Being Chatbot Engagement: Survey Study.
Article in JMIR human factors, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Therapeutic Interaction Features of AI Chatbots in Depression Interventions: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Evaluation of an Artificial Intelligence Conversational Chatbot to Enhance HIV Preexposure Prophylaxis Uptake: Development and Usability Internal Testing.Journal of medical Internet research · 2026Article
- Feasibility of a generative AI chatbot to support breastfeeding in the Brazilian Unified National Health System.Cadernos de saude publica · 2026Article
- Perceived control and immersion in AI chatbot interaction: a psychological distance perspective.Frontiers in psychology · 2026Article
- The conflict between need and fear: how privacy concerns moderate the influence of depression on university students' acceptance of AI music therapy.Frontiers in psychology · 2026Article
- AI chatbots as 'pocket doctors': intimate health support for young women in Lebanon.BMC public health · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI)-based chatbots have emerged as potential tools to assist individuals in reducing anxiety and supporting well-being.
objectiveThis study aimed to identify the factors that impact individuals' intention to engage and their engagement behavior with AI-based well-being chatbots by using a novel research model to enhance service levels, thereby improving user experience and mental health intervention effectiveness.
methodsWe conducted a web-based questionnaire survey of adult users of well-being chatbots in China via social media. Our survey collected demographic data, as well as a range of measures to assess relevant theoretical factors. Finally, 256 valid responses were obtained. The newly applied model was validated through the partial least squares structural equation modeling approach.
resultsThe model explained 62.8% (R
conclusionsThe new extended model provides a theoretical basis for studying users' AI-based chatbot engagement behavior. This study highlights practical points for developers of AI-based well-being chatbots. It also highlights the importance of AI-based well-being chatbots to create an emotional connection with the users.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.