Evidence map›Paper›PMID 41146235›Full record

ArticleBMC health services research2025

From awareness to adoption: a panoramic perspective on the utilization of Internet Medical Services among Chinese patients with chronic disease.

Rui Qiu, Rui Song, Xiaoyi Wu, Jiahui Feng, Yingyue Yang, Yixin Pan, Xiaoying Lin

Abstract read
In one paragraph

Article in BMC health services research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
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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

7 authors.

Rui Qiu *The Second School of Clinical Medicine, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China.ORCID http://orcid.org/0009-0004-5592-2544
Rui Song *The Second School of Clinical Medicine, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China.ORCID http://orcid.org/0009-0005-0925-7616
Xiaoyi Wu *Department of Neurology, The Second Qilu Hospital of Shandong University, Jinan, 250033, China.ORCID http://orcid.org/0009-0009-8575-6627
Jiahui FengDepartment of Neurology, The Second Qilu Hospital of Shandong University, Jinan, 250033, China.ORCID http://orcid.org/0009-0003-4422-708X
Yingyue YangDepartment of Geriatrics, The Second Qilu Hospital of Shandong University, Jinan, 250033, China.ORCID http://orcid.org/0009-0004-7313-5288
Yixin PanDepartment of Neurology, The Second Qilu Hospital of Shandong University, Jinan, 250033, China.ORCID http://orcid.org/0009-0008-0738-5763
Xiaoying LinDepartment of Neurology, The Second Qilu Hospital of Shandong University, Jinan, 250033, China. xiaoyinglin@sdu.edu.cn.ORCID http://orcid.org/0009-0008-0194-8895

Funding

2024 Shandong Province Postgraduate Education and Teaching Reform Research Project XYJG2024010
6 · The paper itself

Abstract

backgroundChronic diseases pose substantial healthcare burdens globally, notably in aging nations like China. Internet Medical Services (IMS) demonstrate significant potential to mitigate healthcare challenges in chronic disease management through optimized resource allocation and enhanced remote care capabilities. However, persistent adoption disparities and the "high demand-low penetration" paradox highlight persistent barriers stemming from the digital divide. This study aims to investigate factors influencing IMS utilization among chronic disease patients, examining their effects across specific IMS domains and acceptance pathways, thereby offering new insights for optimizing chronic disease management.

methodsThis study extended the Technology Acceptance Model (TAM) by integrating eHealth literacy and Technology anxiety to evaluate the utilization of IMS among 520 patients with chronic diseases in Jinan, China. IMS was categorized by functional domains (Information Access, Convenience Services, Online Health) and utilization stages (Awareness, Want, Adoption). The dual-method analysis: Awareness-Want-Adoption Gap (AWAG) matrix for service-specific disparity mapping and Structural Equation Modeling (SEM) to quantify perceptual drivers, providing a panoramic perspective to deconstruct the complex utilization.

resultsInformation Access IMS showed the highest acceptance, while Online Health exhibited severe Want-to-Adoption collapse (71.43% gap). Affluent patients demonstrated paradoxical rejection of Online Health despite high Awareness. SEM confirmed Perceived Usefulness (β = 0.338-0.423, P < 0.001) and eHealth literacy (β = 0.184-0.395, P < 0.001) are significant and direct drivers of IMS utilization, with stage-specificity observed across the utilization process. Matrix analysis identified critical barriers for vulnerable subgroups: rural residents, elders (≥ 70 years), and low-education (≤ 9 years) patients.

conclusionsIMS adoption is governed by multidimensional determinants beyond access, including cognitive, socio-economic, and other factors. Counterintuitive patterns (e.g., affluent patients' rejection of Online Health) necessitate tiered interventions, such as eHealth literacy programs for vulnerable groups, service standardization to mitigate distrust, and regulatory frameworks to ensure data security. This study's dual-method framework (matrix analysis and SEM) critically delineated barrier typologies through staged decomposition, establishing an evidence-based scaffold for optimizing digital health equity.

Indexed as

InternetPatient Acceptance of Health CareTelemedicineAdultAgedAwarenessChinaChronic DiseaseEast Asian PeopleFemaleHealth LiteracyHumansMaleMiddle AgedChronic disease managementeHealth literacyInternet Medical ServicesMatrix analysisStructural equation modelTechnology acceptance model

Identifiers

PMID41146235
PMCPMC12560369

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.