How mental health status and attitudes toward mental health shape AI Acceptance in psychosocial care: a cross-sectional analysis
Abstract Introduction Artificial 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 fa...
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| Format: | Article |
| Language: | English |
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BMC
2025-06-01
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| Series: | BMC Psychology |
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| Online Access: | https://doi.org/10.1186/s40359-025-02954-z |
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| author | Birthe Fritz Lena Eppelmann Annika Edelmann Sonja Rohrmann Michèle Wessa |
| author_facet | Birthe Fritz Lena Eppelmann Annika Edelmann Sonja Rohrmann Michèle Wessa |
| author_sort | Birthe Fritz |
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| description | Abstract Introduction Artificial 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. Method In 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. Results Higher 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. |
| format | Article |
| id | doaj-art-9c159058fed64b828c006ebe477f2f6f |
| institution | Kabale University |
| issn | 2050-7283 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Psychology |
| spelling | doaj-art-9c159058fed64b828c006ebe477f2f6f2025-08-20T03:25:20ZengBMCBMC Psychology2050-72832025-06-0113112010.1186/s40359-025-02954-zHow mental health status and attitudes toward mental health shape AI Acceptance in psychosocial care: a cross-sectional analysisBirthe Fritz0Lena Eppelmann1Annika Edelmann2Sonja Rohrmann3Michèle Wessa4Department of Clinical Psychology and Neuropsychology, Johannes Gutenberg-University MainzLeibniz-Institute for Resilience Research (LIR) gGmbHLeibniz-Institute for Resilience Research (LIR) gGmbHDepartment of Differential Psychology and Psychological Assessment, Johann Wolfgang Goethe University, Institute for PsychologyLeibniz-Institute for Resilience Research (LIR) gGmbHAbstract Introduction Artificial 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. Method In 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. Results Higher 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.https://doi.org/10.1186/s40359-025-02954-zArtificial intelligencePsychological distressMobile applicationsMental healthHealth literacyPersonality |
| spellingShingle | Birthe Fritz Lena Eppelmann Annika Edelmann Sonja Rohrmann Michèle Wessa How mental health status and attitudes toward mental health shape AI Acceptance in psychosocial care: a cross-sectional analysis BMC Psychology Artificial intelligence Psychological distress Mobile applications Mental health Health literacy Personality |
| title | How mental health status and attitudes toward mental health shape AI Acceptance in psychosocial care: a cross-sectional analysis |
| title_full | How mental health status and attitudes toward mental health shape AI Acceptance in psychosocial care: a cross-sectional analysis |
| title_fullStr | How mental health status and attitudes toward mental health shape AI Acceptance in psychosocial care: a cross-sectional analysis |
| title_full_unstemmed | How mental health status and attitudes toward mental health shape AI Acceptance in psychosocial care: a cross-sectional analysis |
| title_short | How mental health status and attitudes toward mental health shape AI Acceptance in psychosocial care: a cross-sectional analysis |
| title_sort | how mental health status and attitudes toward mental health shape ai acceptance in psychosocial care a cross sectional analysis |
| topic | Artificial intelligence Psychological distress Mobile applications Mental health Health literacy Personality |
| url | https://doi.org/10.1186/s40359-025-02954-z |
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