Assessing Awareness, Want, and Adoption of Internet Medical Services Among Chronic Disease Patients in China:A Structural Equation Model and Matrix Analysis

Background Internet Medical Services (IMS) hold substantial potential to address healthcare challenges arising from demographic shifts, such as aging populations, and the evolving disease spectrum, marked by the rising prevalence of chronic conditions. However, their practical impact has yet to fully meet these expectations. This study seeks to investigate the factors influencing the adoption and utilization of IMS among chronic disease patients, focusing on their effects across specific IMS domains and acceptance processes, to provide a fresh perspective on enhancing chronic disease management. Methods We extended the Technology Acceptance Model (TAM) with the Information-Motivation-Behavioral Skills (IMB) framework, incorporating eHealth literacy, patient activation, and demographics (age, education duration, income level). A cross-sectional survey of 520 chronic disease patients in Jinan, China, was analyzed using Structural Equation Modeling (SEM) and matrix analysis to evaluate adoption patterns and influencing factors. Results Information IMS showed high acceptance with minimal disparities, while Diagnose IMS exhibited low uptake and significant gaps, particularly among older, less-educated, rural, and multimorbid patients. Notably, higher-income patients displayed lower acceptance and utilization across all IMS categories, and patient activation, expected to enhance adoption, unexpectedly hindered IMS use. SEM confirmed Perceived Usefulness and education duration as positive drivers of all adoption stages, with eHealth literacy boosting Adoption, and age exerting a negative effect. Conclusions This trailblazing model elucidates IMS adoption complexities, revealing counterintuitive barriers like income and patient activation. It underscores the need for targeted interventions to enhance eHealth literacy and service quality, providing a robust framework for optimizing IMS deployment and advancing digital health strategies for chronic disease care.