Motivated Non-Use in AI-Based Mental Health Interventions: Why Engagement Fails Even When Motivation Is Present

Digital mental health interventions (DMHIs), including AI-enabled applications, chatbots, and digital cognitive behavioural therapy platforms, have expanded rapidly as scalable approaches to improving access to mental health care. Despite demonstrated efficacy under controlled conditions, real-world impact remains constrained by persistent engagement failure and early discontinuation. A substantial proportion of users disengage shortly after adoption, even when they report valuing the intervention and intending to use it. Existing explanations of engagement failure have largely focused on motivation, usability, or perceived usefulness; however, these frameworks provide limited insight into why motivated users often fail to initiate or re-initiate engagement. Drawing on a narrative review of the digital mental health literature, this paper argues that engagement breakdown frequently occurs at the level of action initiation rather than intention formation. To address this gap, a structural readiness lens is proposed, emphasising activation dynamics, ignition thresholds, and the conditions under which engagement becomes possible in real time. From this perspective, non-use is conceptualised as a readiness mismatch rather than a deficit of motivation or interest. The paper outlines implications for readiness-aware and ethically responsible AI design, highlights relevance for clinical interpretation and mental health policy, and identifies directions for future research. By reframing engagement failure through the lens of structural readiness, this work contributes conceptual clarity to a longstanding problem and suggests human-centred pathways for improving engagement with AI-based mental health interventions.