Digital Health Adoption, eHealth Literacy, and Trust in AI Among Generation Z University Students in Sri Lanka: An Empirical Study
Athukorala, S. C.
Show abstract
Background: Digital health technologies, spanning mobile applications, telemedicine, and AI-driven platforms, are rapidly reshaping healthcare delivery globally. Although Generation Z university students are classified as digital natives, empirical data evaluating their eHealth literacy, technology acceptance, and specific trust barriers in developing South Asian nations like Sri Lanka remain scarce. Objective: This study evaluated eHealth literacy, technology acceptance, online health information-seeking behaviors, and adoption barriers among Gen Z undergraduates in Sri Lanka, focusing on the interplay between eHealth literacy, AI trust, and digital care preferences. Methods: A cross-sectional survey (N = 172) was conducted among Sri Lankan university undergraduates utilizing adapted, validated instruments: the eHealth Literacy Scale (eHEALS) and the Technology Acceptance Model (TAM). Statistical analysis included scale reliability validation (Cronbach's alpha), descriptive profiling, Chi-Square ({chi}{superscript 2}) contingency tests, Pearson correlations, and Multiple Linear OLS Regression models. Results: Participants demonstrated high overall eHealth literacy (Mean = 3.84 {+/-} 0.58) and strong endorsement of digital health utility (Mean = 3.99 {+/-} 0.59). Online health searches were reported by 86.6% of respondents. AI tools (e.g., ChatGPT, Gemini) emerged as the second most frequent source for health queries (57.6%), surpassing YouTube (44.2%) and social media (26.2%), with medical students showing significantly higher AI utilization ({chi}{superscript 2} = 8.70, p = .003). In multiple regression analysis, digital platform preference over physical clinic visits (R{superscript 2} = .352, p < .001) was significantly predicted by Perceived Ease of Use ({beta} = 0.371, p = .001) and Trust in AI Recommendations ({beta} = 0.370, p < .001), whereas face-to-face consultation preference (76.7%) and personal data privacy risks (50.0%) remained predominant adoption barriers. Conclusion: Gen Z students in Sri Lanka exhibit high digital health readiness and substantial reliance on AI-driven information seeking. However, institutional deployment must address privacy concerns and integrate hybrid clinical workflows to bridge the gap between high perceived utility and physical consultation preferences.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Defining Destigmatizing Design Guidelines for Use in Sexual Health-Related Digital Technologies: A Delphi Study 96%
- Use of Generative AI for Health Among Urban Youth in Pakistan: A Mixed-Methods Study 95%
- Benefits and Challenges of Using Virtual Primary Care During the COVID-19 Pandemic: From Key Lessons to a Framework for Implementation 95%
Similar papers in this journal
- How the COVID-19 pandemic is favoring the adoption of digital technologies in healthcare: a literature review 96%
- Has the pandemic enhanced and sustained digital health-seeking behaviour? A big data interrupted time-series analysis of Google Trends 96%
- Improving Patient Engagement in Phase 2 Clinical Trials with a Trial-specific Patient Decision Aid (tPDA): A Development and Usability Study 95%
Similar papers in this journal
- Telemedicine Ready or Not? a cross-sectional assessment of telemedicine maturity of federally funded tertiary health institutions in Nigeria 94%
- Validating a Clinical Decision Support System for Palliative Care using healthcare professionals’ insights 94%
- Enhancing Uploads of Health Data in the Electronic Health Record - The Role of Framing and Length of Privacy Information 94%
Similar papers in this journal
- A Web-based, Mobile Responsive Application to Screen Healthcare Workers for COVID Symptoms: Descriptive Study 95%
- Is virtual care the new normal? Evidence supporting Covid-19’s durable transformation on healthcare delivery 95%
- Applications and barriers to use of an mHealth iPhone application for self-management of chronic recurrent medical conditions: A Pilot Study 94%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.