Evidence map›Paper›PMID 41127607›Full record

ArticleFrontiers in public health2025

Modeling the path to digital health intention: the mediating role of system expectation and health beliefs.

Bowen Pu, Kuoliang Huang

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. 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

2 authors.

Bowen PuDepartment of Industrial Design, Design Academy, Sichuan Fine Arts Institute, Chongqing, China.
Kuoliang HuangDepartment of Industrial Design, Design Academy, Sichuan Fine Arts Institute, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: While mobile health (mHealth) offers a seemingly scalable solution to the persistent challenge of chronic disease prevention, its real-world public health impact has arguably been blunted by a single, stubborn issue: low user adherence. The difficulty, in our view, stems from a tendency in the existing literature to treat technology and user psychology as separate domains. This creates what we call a theoretical "black box" between the features of a digital intervention and the behavioral outcomes it is meant to produce. Without a clearer picture of what happens inside this box, efforts to create truly data-driven and effective population-level interventions remain somewhat handicapped. Methods: A self-administered online survey using Wenjuanxing (wjx.cn) was undertaken in a cross-sectional design. Chinese adults (≥18 years) with pre-existing exposure to or intention to use digital health were the target population; a non-probability, voluntary sampling frame yielded 620 usable surveys after screening for quality. The psychometrics were tested, and screening of common-method bias (full-collinearity VIF) preceded testing of structural paths and serial mediation from persuasive features (functional/experiential) to system expectations and through to health beliefs to intention using PLS-SEM. Results: The data showed that Persuasive Experiential Support (PES) was a key antecedent for Integrated System Expectation (ISE), which in turn stood out as the strongest predictor of Persuasive Health Belief (PHB). Interestingly, we also uncovered a substantial measurement overlap between our PHB construct and Behavioral Intention (BI)-a finding that points toward a potential "belief-intention fusion" process in these kinds of highly persuasive digital environments. Conclusion: Taken together, these results seem to advocate for what might be called an "experience-first, function-as-assist" design philosophy for mHealth interventions targeting chronic disease at scale. In other words, prioritizing an engaging user experience looks to be a critical precondition for building the system trust needed to actually foster health beliefs and drive intentions. Perhaps more importantly, our unexpected finding regarding belief-intention fusion opens up a new, testable research agenda-one that explores how real-time digital interactions might be fundamentally reshaping the cognitive pathways of decision-making. This is a crucial question for the next generation of AI-driven, population-level health promotion tools.

Indexed as

Health Belief ModelIntentionTelemedicineAdolescentAdultAgedChinaCross-Sectional StudiesDigital HealthFemaleHumansMaleMiddle AgedPsychometricsSurveys and QuestionnairesYoung Adultbehavioral intentionchronic disease preventionhealth belief modelmobile health (mHealth)persuasive systems designPLS-SEMpopulation healthuser adherence

Identifiers

PMID41127607
PMCPMC12537790

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.