From Needs Assessment to Pre-Trial Specification: Participatory Co-Design of a Generative AI Chatbot (CARES-MDD) for Persons Living with Depression in Hong Kong Psychiatric Care

Background/Objectives: The burden of major depressive disorder (MDD) calls for assessing the needs and design of innovative add-on interventions. Generative artificial-intelligence (AI) tools are increasingly used in everyday life. Evidence remains limited on how needs assessment and co-participatory design can inform AI chatbot trials for MDD. Methods: This study reports a mixed-methods design. Phase 1 comprised a needs assessment of 511 psychiatric outpatient attendees in Hong Kong. Phase 2 involved a participatory co-design and test-run experiences with 10 lived-experience experts with MDD and eight multidisciplinary clinical professionals. This process informed the development of CARES-MDD, a Hong Kong Cantonese, CBT-informed generative AI conversational-agent prototype intended to provide adjunctive support alongside usual psychiatric care. Results: In Phase 1, 66.2% reported using generic AI chatbots for emotional concerns; privacy, anticipated negative evaluation, and concern about burdening others were recurring themes in the qualitative data. Localized empathy, non-judgmental, and 24/7 accessibility were the main themes of a desired mental health generative AI chatbot. In Phase 2, CARES-MDD was co-designed and assessed against a 7-domain feedback rubric measuring perceived usefulness and acceptability. Overall, CARES-MDD received favorable feedback rubric ratings. Content analysis identified positive and negative experiences, which established parameters for future trial methodology. Conclusions: This study identifies needs and yields the co-design of the CARES-MDD purposed as adjunct support for MDD in psychiatric care in Hong Kong, providing the parameters to inform future controlled trials.