ArticlePLOS digital health2025
Chatbot -assisted self-assessment (CASA): Co-designing an AI -powered behaviour change intervention for ethnic minorities.
Article in PLOS digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- An Evidence-Based AI Virtual Assistant for Young People With Attention Deficit Hyperactivity Disorder: Co-Design and Prototype Development.JMIR formative research · 2026Article
- Technology-mediated screening interviews for youth mental health: Content validation, randomized controlled trial, and expert evaluation.PLOS digital health · 2026Article
Corrections and comments
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Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe digitalisation of healthcare has provided new ways to address disparities in sexual health outcomes that particularly affect ethnic and sexual minorities. Conversational artificial intelligence (AI) chatbots can provide personalised health education and refer users for appropriate medical consultations. We aimed to explore design principles of a chatbot-assisted culturally sensitive self-assessment intervention based on the disclosure of health-related information.
methodsIn 2022, an online survey was conducted among an ethnically diverse UK sample (N = 1,287) to identify the level and type of health-related information disclosure to sexual health chatbots, and reactions to chatbots' risk appraisal. Follow-up interviews (N = 41) further explored perceptions of chatbot-led health assessment to identify aspects related to acceptability and utilisation. Datasets were analysed using one-way ANOVAs, linear regression, and thematic analysis.
resultsParticipants had neutral-to-positive attitudes towards chatbots and were comfortable disclosing demographic and sensitive health information. Chatbot awareness, previous experience and positive attitudes towards chatbots predicted information disclosure. Qualitatively, four main themes were identified: "Chatbot as an artificial health advisor", "Disclosing information to a chatbot", "Ways to facilitate trust and disclosure", and "Acting on self-assessment".
conclusionChatbots were acceptable for health self-assessment among this sample of ethnically diverse individuals. Most users reported being comfortable disclosing sensitive and personal information, but user anonymity is key to engagement with chatbots. As this technology becomes more advanced and widely available, chatbots could potentially become supplementary tools for health education and screening eligibility assessment. Future research is needed to establish their impact on screening uptake and access to health services among minoritised communities.
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Registered trials
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