A randomized controlled trial on the application of a chronic disease management platform based on digital health technology combined with an innovative model of intelligent management for hypertension in patients with hypertension.

Authors

Keli Liu, Benshan Niu, Xiaoyi Zhang, Lingyuan Zhang, Yuexia Gao, Juying Lu

Published in

Frontiers in digital health. Volume 7. Pages 1678235. Epub Jan 26, 2026.

Abstract

Digital health technology (DHT)-based chronic disease management platforms combined with smart hypertension models may improve patient self-management.
To compare the effect of Nantong University Affiliated Hospital's DHT platform combined with an intelligent hypertension management model (providing education, follow-up, evaluation) vs. traditional offline management on patients' systolic blood pressure (SBP).
This was a two-arm, parallel-group randomized controlled trial conducted from July 2023 to March 2025. Participants were adults (≥18 years) with hypertension and uncontrolled blood pressure.
Participants were randomly assigned using computer-generated sequences to an integrated digital health platform with intelligent hypertension management (intervention, n = 285) or to traditional offline management (control, n = 285).
Primary outcome: SBP at 12 months. Secondary outcomes: Diastolic blood pressure (DBP), BMI, biochemical/metabolic parameters (e.g., cholesterol, glucose, creatinine), and healthcare costs.
547 participants completed the study (Intervention: n = 273; Control: n = 274). The intervention group achieved a greater reduction in SBP at 12 months (adjusted between-group difference: -3.14 mmHg, 95% CI: -5.24 to -1.03, P = 0.004). Subgroup analysis revealed significant heterogeneity by baseline SBP (interaction P < 0.001). For participants with baseline SBP below the median (<146 mmHg), the intervention group achieved a significantly larger SBP reduction (between-group difference: -6.79 mmHg, 95% CI: -9.62 to -3.96). It is expected that a decrease of 5 mmHg can reduce the risk of cardiovascular events by about 10%.

PMID:
41669722
Bibliographic data and abstract were imported from PubMed on 11 Feb 2026.

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