Evidence map›Paper›PMID 41214540›Full record

ArticleBMC pregnancy and childbirth2025

Understanding gestational diabetes in Hong Kong: women's needs, self-management challenges, and the potential of digital health solutions in culturally sensitive care.

Bronya Hi Kwan Luk, Polly Haixia Ma, William Wing Kuen Lam, Agnes Yuen Kwan Lai, Rosenna Wai Ming Chung

Abstract read
In one paragraph

Article in BMC pregnancy and childbirth, 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

What it found

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Bronya Hi Kwan LukSchool of Nursing and Health Sciences, Hong Kong Metropolitan University, Homantin, Kowloon, Hong Kong, China. bluk@hkmu.edu.hk.
Polly Haixia MaSchool of Nursing and Health Sciences, Hong Kong Metropolitan University, Homantin, Kowloon, Hong Kong, China.
William Wing Kuen LamSchool of Nursing and Health Sciences, Hong Kong Metropolitan University, Homantin, Kowloon, Hong Kong, China.
Agnes Yuen Kwan LaiSchool of Nursing and Health Sciences, Hong Kong Metropolitan University, Homantin, Kowloon, Hong Kong, China.
Rosenna Wai Ming ChungSchool of Nursing and Health Sciences, Hong Kong Metropolitan University, Homantin, Kowloon, Hong Kong, China.

Funding

Hong Kong Metropolitan University Research and Development Fund R6608
6 · The paper itself

Abstract

backgroundGestational diabetes mellitus (GDM), defined as glucose intolerance first recognised during pregnancy, poses serious health risks to mothers and newborns. The alarmingly high GDM prevalence in Hong Kong (25.9%), which is double the global average, necessitates urgent examination of context-specific management challenges. This qualitative study explored the experiences of Hong Kong Chinese women with GDM, investigating their perceived needs, self-management barriers, and recommendations for culturally appropriate healthcare interventions.

methodsSemi-structured interviews were conducted with 16 women diagnosed with GDM across Hong Kong. Participants included both pregnant women and those within 12 months postpartum, all of whom were managing their condition in a community setting. The interview guide was developed using the Health Belief Model (HBM) as a framework. Thematic analysis was then used inductively to identify key patterns in participants' self-management experiences and healthcare needs related to GDM.

resultsAnalysis confirmed the relevance of all core HBM constructs (perceived susceptibility/severity, barriers, benefits, cues to action, and self-efficacy), revealing their unique manifestations in this context. Key barriers included cultural conflicts in diet, time and fatigue limiting exercise, and insufficient healthcare support. Motivators included protection of the baby's health and cues from healthcare providers and peers. Women expressed a strong preference for culturally tailored digital health tools to bridge these gaps.

conclusionThere is an urgent need for integrated, family-centered digital health solutions that offer culturally adapted dietary advice, trimester-specific guidance, and embedded psychological support. Hong Kong's sociocultural context provides an ideal setting for developing such interventions, with lessons applicable to other populations navigating tensions between tradition and modern healthcare.

Indexed as

Culturally Competent CareDiabetes, GestationalSelf-ManagementAdultDigital HealthFemaleHealth Knowledge, Attitudes, PracticeHealth Services Needs and DemandHong KongHumansPregnancyQualitative ResearchCulturally adapted interventions.Digital healthGestational diabetes mellitusHealth belief modelQualitative researchSelf-managementWomen’s experiences

Identifiers

PMID41214540
PMCPMC12604194

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.