Evidence map›Paper›PMID 41573403›Full record

ArticleFrontiers in neurology2025

Protocol for a randomized controlled trial evaluating the artificial intelligence health education accurately linking system in patients with mild-to-moderate stroke.

Zhixia Liu, Yun-Hua Li, Yuan Fang, Huili Wang, Tao Wu, Shu Liu, Yanming Yang, Yangyang Qin, Xiaoge Tao, Jing Mao and 11 more

Abstract read
In one paragraph

Article in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Article
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

21 authors.

Zhixia Liu *Nursing Department, the First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China.
Yun-Hua Li *School of Education, Chengdu College of Arts and Sciences, Chengdu, China.
Yuan FangSchool of Nursing, Henan University of Science and Technology, Luoyang, China.
Huili WangThe Brain Disease Regional Diagnosis and Treatment Center, the First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China.
Tao WuThe Brain Disease Regional Diagnosis and Treatment Center, the First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China.
Shu LiuNursing Department, the First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China.
Yanming YangNursing Department, the First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China.
Yangyang QinNursing Department, the First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China.
Xiaoge TaoNursing Department, the First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China.
Jing MaoNursing Department, the First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China.
Lijun WangDepartment of Neurology, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China.
Xiangmei LiIntensive Care Unit, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China.
Xiaoyi WangDepartment of Neurology, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China.
Ruirui YangDepartment of Neurology, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China.
Yi LiuIntensive Care Unit, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China.
Mengke ChenIntensive Care Unit, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China.
Dandan ShiDepartment of Neurology, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China.
Nan LiSchool of Nursing, Henan University of Science and Technology, Luoyang, China.
Yajuan WangSchool of Nursing, Henan University of Science and Technology, Luoyang, China.
Yi Hu *School of Nursing, Shandong Second Medical University, Weifan, China.
Shumei Zhang *Nursing Department, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Stroke is a leading cause of death and disability worldwide. Although survival rates from mild-to-moderate stroke are high, long-term functional impairment remains common, requiring sustained self-management beyond traditional rehabilitation. Conventional models depend on institutional medical care, which not only drives up costs but also disrupts continuity of care. Meanwhile, psychological, risk-related, and behavioral factors are often overlooked. Advances in artificial intelligence (AI) and mobile health provide opportunities for individualized, long-term support. Based on this, we developed the AI Health Education Accurately Linking System (AI-HEALS) to evaluate its potential to improve physiological parameters, risk perception, and self-management in patients with mild-to-moderate stroke. Methods: This single-blind randomized controlled trial evaluates AI-HEALS, delivered via WeChat (China's most widely used social media app), to improve the monitoring of key physiological indicators in patients with mild-to-moderate stroke. Eligible participants are randomly allocated either standard care as a control or standard care plus a three-month regimen of AI-HEALS. It features an AI-powered interactive Q&A system that supports both voice and text communication, real-time monitoring of physiological and behavioral indicators, personalized health reminders, and specially designed educational content. These are all offered through the official WeChat account "Stroke Health Management Expert." The primary outcomes are changes in blood pressure, glucose, and blood lipids. Secondary outcomes include risk perception of recurrence of stroke, self-management behaviors, and psychological state of mind. Follow-up assessments are conducted at 3, 6, and 9 months after completion of the intervention to evaluate both short-term and sustained effects. Discussion: This protocol presents a new AI-mHealth approach to delivering stroke care. If proven feasible and effective, AI-HEALS could offer a scalable and sustainable model for improving long-term health outcomes, reducing the risk of recurrence, and optimizing the use of healthcare resources for stroke and other chronic conditions. Clinical trial registration: https://www.chictr.org.cn/showproj.html?proj=251515, Identifier, ChiCTR2500096422.

Indexed as

artificial intelligencelarge language modelmobile healthrandomized controlled trailstroke

Identifiers

PMID41573403
PMCPMC12819302

What OpenQuestion holds

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LicenceCC BY
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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 OpenQuestion graph.