Designing digital conversational agents for youth with multiple mental health conditions: Insights on key features from a youth-engaged qualitative descriptive study

Digital Health, Journal Year: 2025, Volume and Issue: 11

Published: March 1, 2025

This qualitative study aims to examine the key features and design elements of a mental health digital conversational agent ("Digital Conversational Agent" or "DCA") for youth with multiple conditions. Twenty-eight participants aged 14 25 were recruited from Toronto Adolescent Youth (TAY) Cohort study. Data collected through focus groups guided by semi-structured interview guide. Focus group discussions audio-recorded, transcripts analyzed using codebook thematic analysis. engagement was integrated throughout Four themes generated data: (1) importance customizable flexible personalization; (2) confidentiality, privacy risk mitigation features; (3) need reliable, informative content that is user tested validated; (4) friendly human-like interaction style. The identified may enhance trust in DCAs support. Collaborating specialist industry partners underscored value co-designed approach preparing develop relevant, feasible, ethical DCAs.

Language: Английский

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Integrating Emotional AI into Mobile Apps with Smart Home Systems for Personalized Mental Wellness

Journal of Technology in Behavioral Science, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

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The Efficacy of Incorporating Artificial Intelligence (AI) Chatbots in Brief Gratitude and Self-Affirmation Interventions: Evidence from Two Exploratory Experiments

Computers in Human Behavior Artificial Humans, Journal Year: 2025, Volume and Issue: unknown, P. 100151 - 100151

Published: April 1, 2025

Language: Английский

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Mental health practitioners’ perceptions and adoption intentions of AI-enabled technologies: an international mixed-methods study

BMC Health Services Research, Journal Year: 2025, Volume and Issue: 25(1)

Published: April 16, 2025

As mental health disorders continue to surge, exceeding the capacity of available therapeutic resources, emergence technologies enabled by artificial intelligence (AI) offers promising solutions for supporting and delivering patient care. However, there is limited research on practitioners' understanding, familiarity, adoption intentions regarding these AI technologies. We, therefore, examined what extent characteristics are associated with their learning use in four application domains (diagnostics, treatment, feedback, practice management). These include medical readiness its subdimensions, anxiety technology self-efficacy, affinity interaction, professional identification. Mixed-methods data from N = 392 German US practitioners, encompassing psychotherapists (in training), psychiatrists, clinical psychologists, was analyzed. A deductive thematic approach employed evaluate understanding familiarity Additionally, structural equation modeling (SEM) used examine relationship between different Qualitative analysis unveiled a substantial gap applications healthcare among practitioners. While some practitioner were only specific areas (e.g., cognitive feedback tools), we found that intention, ethical knowledge, interaction relevant across all areas, underscoring relevance healthcare. In conclusion, this pre-registered study underscores importance recognizing interplay diverse factors training opportunities consequently, streamlined implementation AI-enabled

Language: Английский

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vFerryman: An Artificial Intelligence-Driven Personalized Companion Providing Calming Visuals and Social Interaction for Emotional Well-Being

Published: April 26, 2025

Language: Английский

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