Evidence map›Paper›PMID 40246567›Full record

ArticleBMJ open2025

Factors influencing mobile health utilisation among patients with diabetes in Sichuan, China: a cross-sectional study based on Andersen's behavioural model.

Ting He, Xiao Ling Yang, Li Yuan, Rao Li, Jing Lv, Yi Wang

Abstract read
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Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Ting He *Department of Pancreatic Surgery, Sichuan University, West China Hospital, Chengdu, China.ORCID http://orcid.org/0000-0002-7386-1675
Xiao Ling Yang *Sichuan University, West China Hospital, School of Nursing, Chengdu, Sichuan, China.
Li YuanSichuan University, West China Hospital, School of Nursing, Chengdu, Sichuan, China 1409933235@qq.com.
Rao LiSichuan University, West China Hospital, School of Nursing, Chengdu, Sichuan, China.
Jing LvSichuan University, West China Hospital, School of Nursing, Chengdu, Sichuan, China.
Yi WangSichuan University, West China Hospital, School of Nursing, Chengdu, Sichuan, China.ORCID http://orcid.org/0000-0001-6169-6468

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe development of mobile health (mHealth) in China has tremendous potential, especially for diabetes, which is one of the major chronic diseases affecting hundreds of millions of people. However, research on the current use of mHealth by patients with diabetes and the factors influencing their decision-making is insufficient. Most existing studies have approached the subject from a technological perspective and often overlooked the identity of patients as users of mHealth services. Based on the Andersen behavioural model, this study aimed to investigate the factors affecting patients' adoption of mHealth, with a special emphasis on individual patient characteristics, and provided recommendations for the promotion of mHealth and the management of diabetes.

methodThis was a cross-sectional study. A convenience sample survey was conducted in one tertiary hospital and two community health service centres, and an anonymous self-administered questionnaire survey was conducted among patients with diabetes. Based on Andersen's behavioural model, the questionnaire divided the influencing factors into predisposing factors, enabling factors and need factors. Multivariate logistic regression analysis was used to explore the factors influencing the utilisation of mHealth.

resultsA total of 533 questionnaires were valid. In this study, 36.8% of patients with diabetes used mHealth services. Among the predisposing factors, having better education and mHealth knowledge were found to be facilitators of mHealth utilisation, and employment status was a factor associated with mHealth utilisation. Among the enabling factors, patients with internet access and living in urban areas were more likely to have access to mHealth, and higher health literacy positively influenced mHealth utilisation. Among the need factors, self-assessed health status was linked to mHealth utilisation, and diabetes duration had a negative impact on mobile health utilisation.

conclusionsThe rate of mobile health utilisation remained low. In the future, improvements can be made in multiple aspects, such as policy, promotion, infrastructure and health education, to advance the development of mobile health and the management and control of diabetes.

Indexed as

Diabetes MellitusPatient Acceptance of Health CareTelemedicineAdultAgedChinaCross-Sectional StudiesFemaleHumansLogistic ModelsMaleMiddle AgedSurveys and QuestionnairesYoung AdultBehaviorChronic DiseaseHealth informatics

Identifiers

PMID40246567
PMCPMC12007034

What Socratic holds

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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.