Evidence map›Paper›PMID 41952892›Full record

ArticleFrontiers in medicine2026

Study protocol: Artificial Intelligence Health Education Accurately Linking System for enhancing self-management in chronic obstructive pulmonary disease patients via WeChat.

Ju Wu, Yun-Hua Li, Tingting He, Yan Guo, Jing Zhong, Lifen Wang, Zhen Xiao, Ping Wang, Ping Li, Xia Shu and 5 more

Abstract read
In one paragraph

Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

Ju Wu *Department of Respiratory and Critical Care Medicine, Zigong First People's Hospital, Zigong, China.
Yun-Hua Li *School of Education, Chengdu College of Arts and Sciences, Chengdu, China.
Tingting HeDepartment of Respiratory and Critical Care Medicine, Zigong First People's Hospital, Zigong, China.
Yan GuoDepartment of Respiratory and Critical Care Medicine, Zigong First People's Hospital, Zigong, China.
Jing ZhongDepartment of Respiratory and Critical Care Medicine, Zigong First People's Hospital, Zigong, China.
Lifen WangDepartment of Respiratory and Critical Care Medicine, Zigong First People's Hospital, Zigong, China.
Zhen XiaoDepartment of Respiratory and Critical Care Medicine, Zigong First People's Hospital, Zigong, China.
Ping WangDepartment of Respiratory and Critical Care Medicine, Zigong First People's Hospital, Zigong, China.
Ping LiDepartment of Respiratory and Critical Care Medicine, Zigong First People's Hospital, Zigong, China.
Xia ShuDepartment of Respiratory and Critical Care Medicine, Zigong First People's Hospital, Zigong, China.
Qiuhong GuoDepartment of Respiratory and Critical Care Medicine, Zigong First People's Hospital, Zigong, China.
Qing YuanDepartment of Respiratory and Critical Care Medicine, Zigong First People's Hospital, Zigong, China.
Lijuan QiuDepartment of Respiratory and Critical Care Medicine, Zigong First People's Hospital, Zigong, China.
Ni Yang *School of Nursing, Henan University of Science and Technology, Luoyang, China.
Liping Liao *Department of Respiratory and Critical Care Medicine, Zigong First People's Hospital, Zigong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Chronic obstructive pulmonary disease (COPD) is a widespread chronic illness that affects millions of people around the world. Its management not only necessitates medical intervention but also long-term patient self-management. Most traditional self-management education fails to address individualized needs and ongoing support. With improvements in information technology, especially in the application of artificial intelligence (AI) for health management, new avenues have emerged that can improve COPD patients' self-management capacity, ultimately leading to better disease control and improved quality of life. Thus, we have developed an AI Health Education Accurately Linking System (AI-HEALS) to examine if AI-HEALS-based intervention is effective in enhancing COPD patients' ability for self-management, ultimately leading to better disease management and improved quality of life. Methods and analysis: This randomized controlled trial (RCT) aims to evaluate the efficacy of AI-HEALS intervention implemented through WeChat platform in improving self-management ability of COPD patients. Eligible COPD patients meeting the inclusion criteria will be randomly assigned to either an intervention group or a control group. The control group will receive standard treatment, while the intervention group will receive the AI-HEALS program in addition to standard treatment. The AI-HEALS system is an AI driven question and answer platform with integrated voice interaction capabilities, designed to facilitate easier interaction between patients and the system. It provides real-time physiological monitoring, medication and behavior reminders, and personalized health education through the "COPD Health Management Expert" WeChat platform. The system is supported by a self-developed knowledge base based on established clinical guidelines and evidence for COPD, ensuring the accuracy and reliability of the information provided. Outcome measurements will be conducted at multiple time points, including baseline (upon enrollment), hospital discharge, the end of the intervention, and at 3, 6, and 9 months after the completion of the intervention. Ethics and dissemination: Ethical approval for the study was obtained from the Medical Ethics Committee of Zigong First People's Hospital [Ethics No. Ethics (Research) 2024, Issue 152, 31/10/2024]. The clinical trial is registered under ChiCTR2400092829, with registration completed on 25/11/2024. Study Protocol Registration: [https://www.chictr.org.cn/bin/home], identifier [ChiCTR2400092829].

Indexed as

artificial intelligencechronic diseasechronic obstructive pulmonary diseaselarge language modelmobile healthRCT

Identifiers

PMID41952892
PMCPMC13055525

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.