Supplementary file 1_Digital health readiness among rural hypertensive patients: a latent profile analysis.pdf

BackgroundDriven by the rapid advancements in big data and artificial intelligence technologies, digital health tools have become deeply integrated into healthcare systems, offering novel pathways for blood pressure control and management. However, rural patients with hypertension face greater obstacles in accessing and utilizing digital technologies due to disparities in healthcare resources, education levels, and digital infrastructure. This study aims to identify latent classes of digital health readiness among rural hypertensive patients and explore their predictors based on the Health Ecological Model using latent profile analysis.
MethodsThis cross-sectional study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines and collected data from 980 rural hypertensive patients across three townships in Hunan Province, China. Relevant factors were identified based on the Health Ecological Model. Research instruments included a General Information Questionnaire (23 items), the Social Support Rating Scale (10 items), the Cardiovascular Disease Risk Perception Assessment Tool (1 item), and the Digital Health Readiness Questionnaire (18 items). Latent profile analysis was employed to identify distinct subgroups of digital health readiness, and multivariate logistic regression analysis was used to determine predictive factors for each class.
ResultsLatent profile analysis revealed three distinct subtypes of digital health readiness among rural hypertensive patients: Low Digital Health Readiness group (n = 247, 25.2%), Moderate Digital Health Readiness group (n = 533, 54.4%), and High Digital Health Readiness group (n = 200, 20.4%). Multivariate logistic regression analysis demonstrated that age, duration of hypertension, number of chronic comorbidities, activities of daily living, regular exercise, awareness of “digital health,” cardiovascular disease risk perception, number of children, social support rating, educational level, employment status, physician recommendation to use information-based blood pressure management devices, and home wireless network coverage were significant factors influencing digital health readiness.
ConclusionThese findings underscore the characteristics associated with lower digital health readiness. Rural healthcare institutions should develop tailored interventions targeting the specific vulnerabilities of different hypertensive patient populations and strengthen social support systems to enhance digital health readiness among rural patients with hypertension.