Abstract

Gestational diabetes mellitus (GDM) affects 6 to 15 percent of pregnancies globally and disproportionately impacts 
low-income populations, where access to traditional healthcare resources remains limited. Technology-enabled self
management interventions have emerged as potentially scalable tools to support glycemic control, dietary 
adherence, and perinatal outcomes in resource-constrained settings. This review critically evaluated the biochemical 
rationale, implementation characteristics, clinical efficacy, and contextual barriers of technology-enabled self
management interventions for GDM in low-income populations. A comprehensive literature search identified peer
reviewed studies examining mobile health applications, text messaging platforms, telemedicine, and wearable 
devices for GDM self-management among economically disadvantaged women, with emphasis on glycemic 
outcomes and maternal-fetal health indicators. Technology-enabled interventions demonstrated modest 
improvements in fasting plasma glucose (mean reduction 4 to 8 mg/dL), hemoglobin A1c (0.2 to 0.4 percent 
decrease), and self-monitoring adherence rates (15 to 30 percent improvement) compared to standard care. However, 
efficacy is substantially moderated by digital literacy, smartphone ownership, reliable internet connectivity, and 
culturally adapted content delivery. Implementation barriers included limited baseline technology access, inadequate 
integration with existing prenatal care systems, and insufficient attention to socioeconomic determinants that 
compound GDM risk. Evidence quality remained heterogeneous, with most studies showing a moderate risk of bias 
and inadequate long-term follow-up. While technology-enabled self-management interventions offer promise for 
GDM management in low-income settings, current evidence revealed significant implementation gaps and modest 
clinical effect sizes that necessitated context-specific adaptation, enhanced digital infrastructure investment, and 
integration with comprehensive prenatal care models.