Exploring Smart Healthcare Technology Adoption Among Hong Kong Students: A Cross-Sectional Analysis.

Aims: This study investigates the adoption of smart healthcare technologies among students in Hong Kong using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. A cross-sectional survey of 105 secondary and tertiary students examined how Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC) influence Behavioral Intention (BI) toward technologies like wearables, telemedicine, and AI health apps.

Key Findings: Statistical analysis has evidenced that Facilitating Conditions (FC) emerged as the strongest predictor (β = 0.359, p < .001), followed by Social Influence (SI; β = 0.196) and Effort Expectancy (EE; β = 0.171). Privacy concerns (67% reluctance to share health data) and cultural preferences for in-person care (78% prioritized doctor visits) significantly hindered adoption. The model explained 65% of variance in BI (R² = 0.650), highlighting the importance of infrastructure and social networks.

Implications: The research findings have the implications that smart healthcare technology adoption should be promoted from the preventive healthcare perspective. It is recommended that (i) Privacy-assured designs and digital literacy education, (ii) Culturally tailored messaging leveraging family and physician endorsements, and (iii) Policy interventions to improve access and trust. These strategies can transform smart health technologies into preventive tools for community health, reducing long-term healthcare burdens. The findings contribute to UTAUT's adaptation in high-density, tech-savvy urban contexts and underscore the need to address contextual barriers beyond technological readiness.