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@ARTICLE{Fritz:301909,
      author       = {B. Fritz and L. Eppelmann and A. Edelmann and S. Rohrmann
                      and M. Wessa$^*$},
      title        = {{H}ow mental health status and attitudes toward mental
                      health shape {AI} {A}cceptance in psychosocial care: a
                      cross-sectional analysis.},
      journal      = {BMC Psychology},
      volume       = {13},
      number       = {1},
      issn         = {2050-7283},
      address      = {London},
      publisher    = {BioMed Central},
      reportid     = {DKFZ-2025-01179},
      pages        = {617},
      year         = {2025},
      note         = {#LA:C160#},
      abstract     = {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.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.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.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.},
      keywords     = {Humans / Female / Male / Cross-Sectional Studies / Adult /
                      Artificial Intelligence / Middle Aged / Patient Acceptance
                      of Health Care: psychology / Mental Health / Young Adult /
                      Mental Disorders: therapy / Mental Disorders: psychology /
                      Aged / Mobile Applications / Personality / Germany /
                      Artificial intelligence (Other) / Health literacy (Other) /
                      Mental health (Other) / Mobile applications (Other) /
                      Personality (Other) / Psychological distress (Other)},
      cin          = {C160},
      ddc          = {150},
      cid          = {I:(DE-He78)C160-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40481588},
      pmc          = {pmc:PMC12143098},
      doi          = {10.1186/s40359-025-02954-z},
      url          = {https://inrepo02.dkfz.de/record/301909},
}