Abstract
Background/Objectives: Digital health ecosystems increasingly integrate content, behavioral interventions, and commercial offerings across multiple platforms. While design consistency is established as critical for trust in commercial contexts, its associations with health behavior change and objective health outcomes remain underexplored. This study examined how cross-platform design consistency and aesthetic experience are associated with behavioral adoption through psychological pathways and investigated relationships between design-driven adoption and objective health outcomes. Methods: A convergent mixed-method design comprised five integrated studies: systematic content analysis of short-form videos (N = 200), expert evaluation and user testing (N = 33), a cross-sectional survey (N = 186), semi-structured interviews (N = 15), and a 3-month longitudinal health outcome analysis (N = 143). Structural equation modeling tested pathways from design features through psychological mediators and COM-B components (capability, opportunity, motivation) to behavioral adoption and health outcomes. Results: Design consistency was significantly associated with trust (β = 0.52), perceived value (β = 0.68), and reduced perceived risk (β = -0.41; all p < 0.001). Aesthetic experience predicted emotional resonance (β = 0.71, p < 0.001) and moderated design-trust associations. COM-B components mediated 75% of the intention-to-adoption pathway (total indirect effect = 0.51, p < 0.001). High-adoption users showed clinically meaningful improvements in weight (-2.8 kg, d = 0.89), HbA1c (-0.7%, d = 0.65), fasting glucose (-0.9 mmol/L, d = 0.72), and LDL-C (-0.4 mmol/L, d = 0.51) over three months. Conclusions: Within a single, influencer-centered Chinese digital health ecosystem, design consistency and aesthetic experience were significantly associated with trust, psychological readiness, and behavioral adoption. These findings are observational; randomized controlled trials and multi-site replication are required to establish causal mechanisms and assess generalizability.