Evidence mapPaperPMID 41808920Full record

ReviewJournal of multidisciplinary healthcare2026

Smart Community Hypertension Management: A Narrative Review of Promises and Challenges.

Manhong Yang, Long Wu, Yuzhen Zeng, Chengxia Zhao, Huiling Ye, Jianfei Wang, Ruotong Hao

Abstract readReview
In one paragraph

Review in Journal of multidisciplinary healthcare, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Manhong Yang *Hengqin Hongqi Community Health Service Center, Hengqin Hospital, The First Affiliated Hospital of Guangzhou Medical University, Zhuhai, People's Republic of China.ORCID 0009-0009-8398-0684
Long Wu *Hengqin Hongqi Community Health Service Center, Hengqin Hospital, The First Affiliated Hospital of Guangzhou Medical University, Zhuhai, People's Republic of China.
Yuzhen Zeng *Hengqin Hongqi Community Health Service Center, Hengqin Hospital, The First Affiliated Hospital of Guangzhou Medical University, Zhuhai, People's Republic of China.
Chengxia ZhaoHengqin Hongqi Community Health Service Center, Hengqin Hospital, The First Affiliated Hospital of Guangzhou Medical University, Zhuhai, People's Republic of China.
Huiling YeDepartment of General Practice, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China.
Jianfei WangHengqin Hongqi Community Health Service Center, Hengqin Hospital, The First Affiliated Hospital of Guangzhou Medical University, Zhuhai, People's Republic of China.
Ruotong HaoHengqin Hongqi Community Health Service Center, Hengqin Hospital, The First Affiliated Hospital of Guangzhou Medical University, Zhuhai, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hypertension control remains a significant challenge in primary care worldwide. Conventional community management, which relies on manual follow-ups and scattered records, often leads to inefficiencies and inequities. The emergence of mobile health, Internet of Things devices, and artificial intelligence offers transformative solutions. This narrative review synthesizes recent evidence on smart hypertension management in community settings. Here, we identified three dominant management approaches. One approach uses mobile health systems to support patient self-management. Another employs remote monitoring platforms to enable timely clinical interventions. A third applies artificial intelligence tools to automate follow-up processes. Evidence indicates these smart models can lead to clinically meaningful improvements. For instance, remote monitoring interventions have been associated with improved blood pressure control rates, in some cases by 15-25 percentage points. They also optimize resource allocation, particularly for underserved groups. However, key challenges persist, including the digital divide and data security concerns. This review synthesizes evidence demonstrating that smart management models can yield measurable, quantitative improvements in blood pressure control and resource efficiency. Future efforts must therefore be guided by this evidence, prioritizing large-scale trials to strengthen the findings and developing supportive policies on equity and data governance to ensure these solutions are effective, equitable, and secure.

Indexed as

community health servicesdigital healthhypertensionprimary health caresmart management

Identifiers

PMID41808920
PMCPMC12970008

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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 Socratic graph.