Evidence map›Paper›PMID 40075400›Full record

ArticleBMC nursing2025

A qualitative study on strategies to enhance diabetes Internet-based nursing clinic services from the perspective of patients in China.

Hongying Zheng, Dongtong Tong, Jing Feng, Fang Su, Yiqing He, Lunfang Xie

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Article in BMC nursing, 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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5 · Who and what money

Authors and funding

6 authors.

Hongying Zheng *The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Dongtong Tong *The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Jing Feng *School of Nursing, Anhui Medical University, Hefei, China.
Fang SuSchool of Nursing, Anhui Medical University, Hefei, China.
Yiqing HeSchool of Nursing, Anhui Medical University, Hefei, China.
Lunfang XieSchool of Nursing, The First Affiliated Hospital, Anhui Medical University, Hefei, China. 527548725@qq.com.

Funding

Anhui Medical University School of Nursing Graduate Youth Program Cultivation Project hlqm2022003'Anhui Zhongji Guoyi Medical Technology Co.,Ltd' Open Program of Hospital Management Institute, Anhui Medical University 2024gykjgx12
6 · The paper itself

Abstract

backgroundChina has the highest number of diabetics globally, necessitating refined disease management strategies. Diabetes Internet-based nursing clinic services (INCS) can significantly improve health behaviors, blood glucose levels, and help prevent complications. The services mainly rely on the Internet platform for nurses' door-to-door service abroad, while China's "Internet + Nursing Service" combines online and offline modes. Nonetheless, patient receptivity to the services is still ambiguous. This study aims to explore diabetics' experiences of INCS, identify key factors affecting their participation, and suggest strategies to enhance service quality.

methodsFrom May to December 2022, 14 diabetics who had used INCS were selected through purposive sampling and maximum differentiation sampling from a hospital in Hefei, China. A semi-structured interview was conducted based on the Health Information Technology Acceptance Model (HITAM) to develop the interview outline. Interviews were transcribed verbatim within 48 h and analyzed using Nvivo 12.0 software. Directed content analysis examined factors affecting diabetic patients' use of the services, while traditional content analysis explored patient experiences and suggestions.

resultsThe four themes of diabetic patients' experiences of INCS were content and effectiveness of Internet-based nursing clinic services, nurses' service attitude and level, timeliness of services, and service platform design. Factors influencing service adoption behavior include five themes: health status, perceived usefulness, perceived ease of use, subjective norm, and social influences, and three sub-themes, namely Internet development trends, epidemic of COVID-19, and the difficulty of accessing medical care offline. Patients' suggestions for the service included two themes: enriching the service channels, improving the platform's function, and fixing the consultation time to improve service efficiency. CLINICAL TRIAL NUMBER: Not applicable.

conclusionsEleven (78.6%) of the people with diabetes generally had a positive experience with INCS, recognizing its assistance, though they were also worried about the effects of diagnosis and treatment. Medical institutions should promote INCS and highlight the role of medical staff to improve patients' awareness of the service's benefits. They should encourage patient utilization of service by improving the platform's functionality and enhancing age-friendly design.

Indexed as

Diabetes mellitusInternet-based nursing clinic servicesQualitative studyService experience

Identifiers

PMID40075400
PMCPMC11900392

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LicenceCC BY-NC-ND
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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 OpenQuestion graph.