Abstract

Digital wellness tools promise to scale lifestyle-medicine support, yet many applications achieve initial uptake but struggle with sustained engagement. A key reason is the persistent theory-to-design gap: behavioural and relational theories are frequently cited but rarely translated into concrete, user-facing system mechanisms. This TREO paper introduces a theory-driven, user-centred design (UCD) framework that translates theory into implementable design mechanisms, operationalising Self-Determination Theory (SDT) (Ryan & Deci, 2000) and the CARE (Compassion, Assistance, Respect, Empathy) framework into a coherent design logic for an AI-augmented progressive web application targeting preventive wellness. Using Design Science Research Methodology (DSRM) (Hevner et al., 2004) and co-design sessions with young adults and domain experts, we formalise a transparent and traceable Design Requirements → Design Principles → Design Features (DR–DP–DF) chain (Gregor & Hevner, 2013). Three core design requirements target sustained engagement: autonomy-supportive personalisation, competence reinforcement through feedback and guidance, and empathic interaction support via CARE-aligned, clinician-reviewed conversational responses. These are complemented by inclusive, low-effort interaction design considerations, including WCAG-compliant interfaces, clear navigation, and multilingual scaffolding. The framework is instantiated in a functional progressive web application built with React and Firebase, ensuring the design logic is grounded in practice rather than remaining purely conceptual. As a TREO submission, this study focuses on exposing and refining design logic prior to full empirical validation. An ongoing evaluation (Q2 2026) combines Task-Technology Fit (TTF) and UTAUT measures with interaction logs to assess usability, usefulness, satisfaction, and early retention. Beyond theory, the study provides actionable guidance by demonstrating a systematic and traceable translation of behavioural and relational theories into implementable features through a transparent DR–DP–DF mapping. This supports designers in operationalising autonomy, competence, and empathy in scalable, low-burden ways while ensuring accessibility. We seek feedback on the completeness of the DR–DP–DF mapping, the design of low-burden nudging, and opportunities for multi-site validation.

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