Evidence map›Paper›PMID 41200539›Full record

ArticleDigital health

HealthLINE: A messaging chatbot for supporting chronic disease self-management.

Yi-Fan Wang, Mei-Hua Hsu, Max Yue-Feng Wang

RetractedAbstract readRetracted Publication
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

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

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Yi-Fan WangInstitute of Information and Decision Sciences, National Taipei University of Business, Taipei City.
Mei-Hua HsuCenter for General Education, Chang Gung University of Science and Technology, Taoyuan City.ORCID https://orcid.org/0000-0003-2689-9774
Max Yue-Feng WangDepartment of Marketing, The Pennsylvania State University, State College, University Park, PA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Chronic diseases such as hypertension and diabetes require ongoing lifestyle management, but traditional health education methods are limited by accessibility and resource constraints. Digital health tools, particularly chatbots embedded in widely used messaging platforms like LINE, offer scalable and continuous support for self-management. Objective: This study aimed to design, implement, and evaluate Methods: An 8-week action research study was conducted with 40 adults diagnosed with hypertension or type 2 diabetes. Participants used Results: Participants interacted with the chatbot an average of 4.2 times per day. System acceptance was high, with perceived usefulness ( Conclusions: The

Indexed as

behavior changedigital healthhealth promotionLINE chatbotmobile intervention

Identifiers

PMID41200539
PMCPMC12586868

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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.