JMIR diabetes · 2026
Obstacles and Enablers Related to Gestational Diabetes Self-Management: Systematic Review Using the Socio-Eecological Model.
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Luk Bronya H K, Ma Polly H X, Chung Rosenna Wai Ming, Zhang Wendy W, Lam William Wing-Kuen
Аннотация
BACKGROUND: Gestational diabetes mellitus (GDM) requires effective self-management to mitigate associated health risks. A comprehensive understanding of the multilevel factors influencing adherence is essential to designing effective, holistic support strategies.
OBJECTIVE: This systematic review aimed to synthesize the obstacles and enablers related to GDM self-management across all 5 levels of the socioecological model (SEM).
METHODS: A systematic search was conducted across 6 databases (MEDLINE, PsycINFO, CINAHL, PubMed, Cochrane Library, and Web of Science) for literature published from January 2010 to October 2025. Thirty studies (24 qualitative, 4 quantitative, and 2 mixed methods) were included. Data on obstacles and enablers were extracted and synthesized using the SEM as an analytical framework.
RESULTS: The analysis identified 11 key factors across the intrapersonal (eg, knowledge and emotional response), interpersonal (eg, functional support network), organizational (eg, health care access and workplace demands), community (eg, food environment and digital information landscape), and policy levels (eg, funding and economic support). The synthesis reveals how these factors interact across levels, creating systemic challenges such as fragmentation of support and information inequity.
CONCLUSIONS: Successful GDM self-management support requires integrated, multilevel strategies that address factors ranging from the individual level to the policy level. This review provides an evidence-based SEM framework to inform the development of comprehensive support interventions, including those leveraging digital health platforms, to improve maternal and neonatal outcomes.
OBJECTIVE: This systematic review aimed to synthesize the obstacles and enablers related to GDM self-management across all 5 levels of the socioecological model (SEM).
METHODS: A systematic search was conducted across 6 databases (MEDLINE, PsycINFO, CINAHL, PubMed, Cochrane Library, and Web of Science) for literature published from January 2010 to October 2025. Thirty studies (24 qualitative, 4 quantitative, and 2 mixed methods) were included. Data on obstacles and enablers were extracted and synthesized using the SEM as an analytical framework.
RESULTS: The analysis identified 11 key factors across the intrapersonal (eg, knowledge and emotional response), interpersonal (eg, functional support network), organizational (eg, health care access and workplace demands), community (eg, food environment and digital information landscape), and policy levels (eg, funding and economic support). The synthesis reveals how these factors interact across levels, creating systemic challenges such as fragmentation of support and information inequity.
CONCLUSIONS: Successful GDM self-management support requires integrated, multilevel strategies that address factors ranging from the individual level to the policy level. This review provides an evidence-based SEM framework to inform the development of comprehensive support interventions, including those leveraging digital health platforms, to improve maternal and neonatal outcomes.