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Obstacles and Enablers to Gestational Diabetes Self-Management: A Systematic Review Using the Socio-Ecological Model to Guide Digital Health Solutions
ABSTRACT
Background:
Gestational diabetes mellitus (GDM) requires effective self-management to mitigate associated health risks. While digital health tools hold promise for supporting self-management, a comprehensive understanding of the multi-level factors influencing adherence is needed to design effective interventions
Objective:
This systematic review aimed to synthesize the barriers to and facilitators of GDM self-management across the Socio-Ecological Model (SEM), with the explicit goal of informing the design of future digital health interventions
Methods:
A systematic search was conducted across six databases (MEDLINE, PsycINFO, CINAHL, PubMed, Cochrane Library, Web of Science) for literature published from 2010 to October 2025. Thirty studies (24 qualitative, 4 quantitative, 2 mixed-methods) were included. Data on barriers and facilitators were extracted and synthesized using the Socio-Ecological Model (SEM) as an analytical framework
Results:
The analysis identified 11 key factors across five SEM levels. Findings highlight critical intervention points for digital solutions: at the intrapersonal level (knowledge, emotional response); interpersonal level (functional support network); organizational level (healthcare access, workplace demands); community level (digital peer-support, food environment); and policy level (funding for digital health). The synthesis demonstrates how a digital platform could integrate strategies across these levels to provide holistic support
Conclusions:
Successful GDM self-management support requires addressing multi-level factors. This review provides an evidence-based framework for developing digital health interventions. The findings propose that a multi-level, digitally-enabled approach is essential to overcome existing barriers and leverage enablers, ultimately improving maternal and neonatal outcomes Clinical Trial: The systematic review protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO, registration number CRD42023491588.
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.