Trial reportJMIR mHealth and uHealth2024
Development of a Health Behavioral Digital Intervention for Patients With Hypertension Based on an Intelligent Health Promotion System and WeChat: Randomized Controlled Trial.
Trial report in JMIR mHealth and uHealth, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 4 syntheses or guidelines pooled it, 27 citations in OpenAlex.
- Assessing Community-Based interventions effectiveness on hypertension prevention and control: A systematic review and Meta-Analysis.BMC public health · 2025Pooled it
- Development and evaluation of online medication support tools for older people: a systematic review.Age and ageing · 2025Pooled it
- Pooled it
- Effectiveness of telemedicine interventions on blood pressure control and self-management efficacy in hypertensive patients: a systematic review and meta-analysis.Frontiers in public health · 2025Pooled it
- Effect of a Mobile Health Intervention in the Management of Hypertension: Open-Label Cluster-Randomized Trial.JMIR mHealth and uHealth · 2025Trial
- Effects of a health education intervention based on the behavior change wheel on fear of hypoglycemia behavior in type 2 diabetes mellitus patients: a randomized controlled pilot trial.Acta diabetologica · 2025Trial
- 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.Frontiers in digital health · 2025Trial
- Application of AI in Hypertension Health Education: Scoping Review.Journal of medical Internet research · 2026Article
- Effect of Health Literacy on use of a Virtual Coach to Promote Home Blood Pressure Monitoring.Journal of clinical hypertension (Greenwich, Conn.) · 2026Article
- A Thematic Analysis of Sleep Behavior Self-Regulation in Young Adults with Type 1 Diabetes.Diabetology · 2026Article
- Precision cardiovascular medicine with big data and AI.NPJ digital medicine · 2026Review
- Targeting gut-liver-kidney axis: microbiota-derived metabolites and therapeutic implications.Cell communication and signaling : CCS · 2026Review
- Lifestyle Interventions and Pharmacological Adherence in the Management of Hypertension Among Community-Dwelling Older Adults: A Systematic Review.Nursing research and practice · 2026Review
- Digital Health Delivery Models for Hypertension Self-Care in Asia: A Systematic Review.Journal of multidisciplinary healthcare · 2026Review
- Impact of E-Coach chronic disease management model combined with the WeChat platform on self-management ability in hypertension patients.Frontiers in cardiovascular medicine · 2026Article
- Effectiveness of a Chatbot-Based Hypertension Management Program on Selected Outcomes Among Community-Dwelling Older Adults: A Quasi-Experimental Study.Journal of multidisciplinary healthcare · 2026Article
- Systematic review of the impact of digital health technologies on blood pressure control and treatment adherence in young and middle-aged hypertensive patients.Frontiers in cardiovascular medicine · 2026Review
- Application of digital health technologies in hypertension self-management: a narrative review.Frontiers in public health · 2026Review
- Network meta analysis of contributions by different healthcare practitioners in digital self care for hypertension.NPJ digital medicine · 2025Review
- Effects of a narrative-based psychoeducational intervention on medication adherence in individuals with schizophrenia: a multicentre, parallel-group randomised controlled trial.EClinicalMedicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe effectiveness of timely medication, physical activity (PA), a healthy diet, and blood pressure (BP) monitoring for promoting health outcomes and behavioral changes among patients with hypertension is supported by a substantial amount of literature, with "adherence" playing a pivotal role. Nevertheless, there is a lack of consistent evidence regarding whether digital interventions can improve adherence to healthy behaviors among individuals with hypertension.
objectiveThe aim was to develop a health behavioral digital intervention for hypertensive patients (HBDIHP) based on an intelligent health promotion system and WeChat following the behavior change wheel (BCW) theory and digital micro-intervention care (DMIC) model and assess its efficacy in controlling BP and improving healthy behavior adherence.
methodsA 2-arm, randomized trial design was used. We randomly assigned 68 individuals aged >60 years with hypertension in a 1:1 ratio to either the control or experimental group. The digital intervention was established through the following steps: (1) developing digital health education materials focused on adherence to exercise prescriptions, Dietary Approaches to Stop Hypertension (DASH), prescribed medication, and monitoring of BP; (2) using the BCW theory to select behavior change techniques; (3) constructing the intervention's logic following the guidelines of the DMIC model; (4) creating an intervention manual including the aforementioned elements. Prior to the experiment, participants underwent physical examinations at the community health service center's intelligent health cabin and received intelligent personalized health recommendations. The experimental group underwent a 12-week behavior intervention via WeChat, while the control group received routine health education and a self-management manual. The primary outcomes included BP and adherence indicators. Data analysis was performed using SPSS, with independent sample t tests, chi-square tests, paired t tests, and McNemar tests. A P value <.05 was considered statistically significant.
resultsThe final analysis included 54 participants with a mean age of 67.24 (SD 4.19) years (n=23 experimental group, n=31 control group). The experimental group had improvements in systolic BP (-7.36 mm Hg, P=.002), exercise time (856.35 metabolic equivalent [MET]-min/week, P<.001), medication adherence (0.56, P=.001), BP monitoring frequency (P=.02), and learning performance (3.23, P<.001). Both groups experienced weight reduction (experimental: 1.2 kg, P=.002; control: 1.11 kg, P=.009) after the intervention. The diet types and quantities for both groups (P<.001) as well as the subendocardial viability ratio (0.16, P=.01) showed significant improvement. However, there were no statistically significant changes in other health outcomes.
conclusionsThe observations suggest our program may have enhanced specific health outcomes and adherence to health behaviors in older adults with hypertension. However, a longer-term, larger-scale trial is necessary to validate the effectiveness.
trial registrationChinese Clinical Trial Registry ChiCTR2200062643; https://www.chictr.org.cn/showprojEN.html?proj=172782. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/46883.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.