JMIR Hum Factors. 2025 Aug 12. doi: 10.2196/76377. Online ahead of print.
ABSTRACT
BACKGROUND: Prevalence rates for anxiety and depression are increasing globally, outpacing the capacity of traditional mental health services. Digital Mental Health Interventions (DMHIs) offer a cost-effective solution, but user engagement is poor. Integrating AI-powered conversational agents could enhance engagement and the user experience, though AI technology is rapidly evolving, and the acceptability of these solutions remains uncertain.
OBJECTIVE: This study aims to understand the acceptability, engagement and usability of a conversational agent-led DMHI with human support for generalized anxiety by exploring patient expectations and experiences using mixed methods.
METHODS: Participants (N=299) were offered a DMHI for up to 9 weeks and completed self-report validated measures of engagement (User Engagement Scale, UES, N=190), usability (System Usability Scale, SUS, N=203) and acceptability (Service User Technology Acceptability Questionnaire, SUTAQ, N=203) post-intervention. To explore participants’ expectations and experiences with the digital program, a sub-sample of participants completed qualitative semi-structured interviews before the intervention (N=21) and after the intervention (N=16), analyzed using inductive Thematic Analysis.
RESULTS: Participants found the digital program engaging (mean UES total score = 3.7, 95%CI [3.5,3.8]), rewarding (mean UES rewarding subscale = 4.1; 95%CI [4.0-4.2]) and easy to use (SUS total score = 78.6, 95%CI [76.5, 80.7]). Participants were satisfied with the program and found it increased access to and enhanced their care (mean SUTAQ subscales = 4.3-4.9, 95% CI [4.1-5.1]). Insights from both pre and post-intervention qualitative interviews highlighted five themes representing user needs important for the acceptability of this digital program: 1) mental health support that is accessible, in terms of availability and emotional approachability (“Accessible Care”); 2) practical and effective solutions leading to tangible mental health improvements (“Effective Solutions”); 3) a personalized and tailored experience (“Personal Experience”); 4) being guided with a clear structure yet control over their journey (“Guided but in Control”); 5) fostering a sense of support facilitated by humans (“Feeling Supported”). Overall, the DMHI met participant expectations, except for theme 3 as participants wanted more personalization and felt frustrated when the conversational agent misunderstood them.
CONCLUSIONS: Incorporating factors important for patient acceptability into DMHIs is essential to maximize their global impact on mental healthcare. This study provides quantitative and qualitative evidence for the acceptability of a structured, conversational agent-driven digital program with human support for adults with generalized anxiety. Findings emphasize the role of design, clinical and implementation factors in enhancing engagement, highlighting opportunities for continued optimization and innovation. Scalable models with stratified human support and the safe integration of generative AI are poised to transform patient experience and enhance the real-world impact of conversational agent-led DMHIs.
PMID:40824528 | DOI:10.2196/76377
Recent Comments