ArticleBMC public health2024
mhealth-based interventions to improving liver cancer screening among high-risk populations: a study protocol for a randomized controlled trial.
Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Digital solutions, real-world challenges: lessons from mHealth trials in oncology.Frontiers in digital health · 2025Trial
- Role of hepatotropic viruses in promoting hepatocellular carcinoma-current knowledge and recent advances.Medical oncology (Northwood, London, England) · 2025Review
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.
Funding
Abstract
backgroundLiver cancer (LC) screening, such as AFP test and abdominal ultrasound, is an effective way to prevent LC, one of the most common cancers worldwide. Despite the proven screening benefits, screening participation among high-risk populations for LC remains low. This suggests that targeted, systematic, and effective interventions should be provided to improve knowledge and awareness related to LC screening, enhance screening intentions, and thereby promote screening behaviors. Telephone is people's main medium of daily communication and mHealth-based programs offer a potential and effective solution for promoting health behaviors. The purpose of this study is to develop and implement a mHealth (WeChat app) based intervention guided by Fogg's Behavior Model (FBM) to augment the knowledge of LC prevention among people at risk of LC and enhance their motivation for screening, and to validate its effectiveness in improving LC screening.
methodsWe propose a two-arm, single-blind randomized controlled trial with 82 at-risk individuals of LC, delivering a 6-month mHealth-based intervention program with optional health counseling. Recruitment will be through tertiary hospitals and community organizations in 4 districts in Heng Yang. In total, 82 individuals at high risk for HCC will be randomized 1:1 to intervention or control (usual care) groups. The intervention group will receive intervention, whose contents are based on the FBM model, via multiple forms of media including PowerPoint presentation, multimedia video, health information booklet and screening message, which is delivered in the WeChat Applet. Control dyads will be provided with usual health education. Outcomes will be assessed at baseline and post-intervention. DISCUSSION: The findings of this study will provide evidence of the benefits of utilizing mHealth-based approaches in intervention development to enhance the effectiveness of screening adherence for high-risk people of LC. Further, the findings would provide reference to the potential incorporation of the targeted intervention in local community organizations.
trial registrationChinese Clinical Trial Registry (ChiCTR2400080530) Date registered: 31/1/2024.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.