Digital health readiness among rural hypertensive patients: a latent profile analysis

Background: Driven 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. Methods: This 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. Results: 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. Conclusion: These 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.

Authors

Institutions

Publication Details

Journal
Frontiers in Public Health
Published
2026-04-15
DOI
https://doi.org/10.3389/fpubh.2026.1786213
Primary Topic
Mobile Health and mHealth Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Digital health readiness among rural hypertensive patients: a latent profile analysis

Yu Pu, Ershan Xu, Cuijuan Lin, Ying Xiong et al.
Frontiers in Public Health
Mobile Health and mHealth Applications
article

Digital health readiness among rural hypertensive patients: a latent profile analysis

Yu Pu, Ershan Xu, Cuijuan Lin, Ying Xiong, Yingzi Tang, Jiangnan He
article en

Abstract

Background: Driven 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. Methods: This 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. Results: 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. Conclusion: These 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.

Frontiers in Public HealthVol. 14
Hunan University (CN), Dongyang University (KR), Wenzhou Medical University (CN), Hunan Vocational Institute of Technology (CN), Dongyang People's Hospital (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 25%
Mobile Health and mHealth Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.