ArticleBMC psychology2025
How mental health status and attitudes toward mental health shape AI Acceptance in psychosocial care: a cross-sectional analysis.
Article in BMC psychology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Exploring Students' Perceptions and Usage of Artificial Intelligence in Supporting Mental Health: A Preliminary Study in Higher Education in Qatar.Healthcare (Basel, Switzerland) · 2026Article
- Anxiety and Depression Associated With the Dependent Use of Generative AI in Medical Students: Cross-Sectional Study.JMIR formative research · 2026Article
- Article
- Platform shift in mental health support among undergraduates: from campus counselors to GenAI-based consultation.Frontiers in psychiatry · 2026Article
- From promise to practice: artificial intelligence in mental health care in the MENA region.Frontiers in psychiatry · 2026Review
- Influencing public acceptance of artificial intelligence (AI) in healthcare delivery.Frontiers in digital health · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionArtificial Intelligence (AI) has become part of our everyday lives and is also increasingly applied in psychosocial healthcare as it can enhance it, make it more accessible, and reduce barriers for help seeking. User behaviour and readiness for AI can be predicted by various factors, such as perceived usefulness (PU) of AI, personality traits and mental health-related variables. Investigating these factors is essential for understanding user acceptance and the future use of AI tools in mental health. This study examines the individual factors that influence the PU of AI in mental health care. In addition, it examines how PU of AI affects the use of mental health apps. For ethical and practical reasons, these apps were considered independently of their AI integration, aiming to support the development of AI-driven mental health applications.
methodIn a German-speaking convenience sample N = 302 participants socio-demographic information, personality factors, mental health status, mental health literacy, and various aspects concerning the integration of AI into psychosocial care (PU, AI awareness, digital skills, app use in general) were assessed. Two linear, stepwise regression analyses were conducted, with PU of AI and the participants' use of mental health apps in general as dependent variables, respectively, and the above-mentioned variables as predictors. Profession, gender, own experience with mental impairments, AI awareness and digital skills were included as covariates. Finally, we performed two moderation analyses to investigate mental health problems and psychological distress as moderators for the relationship between PU and frequency of mental health-related app use-irrespective of AI integration-with working field and digital capabilities as covariates.
resultsHigher openness, pessimism and conscientiousness predicted lower PU, whereas higher agreeableness, lower levels of stigma and social distance predicted higher PU. The covariates psychological/ pedagogical training, digital capabilities and experience had a significant influence on PU. Higher frequency of app use in general was predicted by better digital capabilities, higher psychological distress, and more help seeking behaviour. The relationship between PU and the overall use of mental health apps was moderated by psychological distress but not by mental health problems. DISCUSSION: Our study identified individual factors influencing PU for integrating AI into psychosocial care and the frequency of using mental health apps-irrespective of AI integration-and thereby underlines the necessity to tailor AI interventions in psychosocial care to individual needs, personality, and abilities of users to enhance their acceptance and effectiveness.
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