Remote Maternal Glucose Data Sharing With Care Teams: Transforming Diabetes Management in Pregnancy

Abstract

Remote maternal glucose data sharing with care teams represents a paradigm shift in the management of diabetes during pregnancy. Leveraging digital health technologies, this approach enables continuous glucose monitoring (CGM) data to be transmitted in real-time to multidisciplinary healthcare teams, facilitating timely intervention, optimizing glycemic control, and potentially improving maternal and fetal outcomes. This review synthesizes current evidence, explores underlying mechanisms, and provides clinically relevant insights into the impact, challenges, and future directions of remote glucose data sharing for pregnant individuals with diabetes.

Introduction

Diabetes complicates an increasing number of pregnancies worldwide, with both pregestational and gestational diabetes mellitus (GDM) associated with significant maternal and fetal morbidity. Traditional management relies on periodic self-monitoring of blood glucose (SMBG) and in-person consultations, which may be insufficient for optimal glycemic control. The advent of remote glucose data sharing enabled by CGM devices and telemedicine platforms has emerged as a transformative strategy, allowing real-time, bidirectional communication between patients and care teams. This article reviews the clinical implications and operationalization of remote glucose data sharing in maternal care, focusing on scientific evidence, practical applications, and guideline-based recommendations.

Epidemiology / Disease Burden

Globally, the prevalence of diabetes in pregnancy is rising, affecting approximately 16% of live births, with GDM accounting for the majority. Poorly controlled diabetes during pregnancy is linked with increased risks of macrosomia, preterm birth, preeclampsia, and neonatal hypoglycemia. The burden is further amplified by disparities in access to diabetes education and specialized care, particularly in underserved populations. Remote glucose data sharing offers a scalable solution to bridge these gaps and mitigate the disease burden by enabling more frequent monitoring and rapid clinical response.

Pathophysiology

Pregnancy induces complex metabolic adaptations that increase insulin resistance, particularly in the second and third trimesters. In women with underlying insulin secretory defects or pre-existing diabetes, this physiologic stress unmasks or exacerbates hyperglycemia. Persistent maternal hyperglycemia directly affects fetal development, increasing the risk of congenital anomalies, excessive fetal growth, and long-term metabolic sequelae. Real-time glucose data sharing provides a mechanism for proactive management, facilitating dynamic insulin adjustments and dietary interventions tailored to the evolving metabolic demands of pregnancy.

Risk Factors

Key risk factors for diabetes in pregnancy include advanced maternal age, obesity, family history of diabetes, prior GDM or macrosomic infant, polycystic ovary syndrome, and certain ethnic backgrounds. Social determinants such as limited access to healthcare and diabetes education further compound risk. Notably, the risk of adverse outcomes increases with suboptimal glycemic control, underscoring the importance of timely data-driven interventions, which remote sharing platforms can enhance by overcoming geographical and logistical barriers.

Clinical Features

Clinical manifestations of diabetes in pregnancy range from asymptomatic hyperglycemia detected via screening to overt symptoms such as polyuria, polydipsia, and fatigue. Adverse perinatal outcomes include fetal macrosomia, birth trauma, neonatal hypoglycemia, and increased cesarean delivery rates. Continuous and remotely monitored glucose data can help detect subtle glycemic trends and excursions, supporting more nuanced clinical decision-making and reducing the risk of both hyper- and hypoglycemic episodes.

Diagnosis

Diagnosis of GDM or pregestational diabetes relies on standardized oral glucose tolerance tests (OGTT) and fasting plasma glucose criteria, as per guidelines from the American Diabetes Association (ADA) and International Association of Diabetes and Pregnancy Study Groups (IADPSG). Remote monitoring post-diagnosis, using CGM and integrated data platforms, allows for detailed glycemic profiling, identification of patterns such as nocturnal hypoglycemia, and assessment of time-in-range metrics increasingly recognized as predictors of pregnancy outcomes.

Treatment & Management

Optimal management of diabetes during pregnancy requires meticulous glycemic control, often involving dietary modification, physical activity, and pharmacotherapy (insulin being the mainstay). Remote data sharing enhances traditional management by enabling: (1) real-time feedback and insulin titration, (2) timely intervention for out-of-range values, (3) improved adherence through direct patient-provider engagement, and (4) reduction in unnecessary clinic visits. Multidisciplinary care, including endocrinologists, obstetricians, diabetes educators, and dietitians, is facilitated by shared digital platforms, promoting coordinated decision-making.

Recent Advances / Emerging Therapies

The integration of CGM with cloud-based platforms and mobile health applications has revolutionized remote maternal glucose monitoring. Recent studies demonstrate that remote sharing of CGM data results in lower mean glucose levels, increased time-in-range, and reduced episodes of severe hyper- and hypoglycemia in pregnant women with diabetes. Emerging therapies include automated insulin delivery systems (closed-loop), decision-support tools using artificial intelligence, and personalized alerts for patients and providers. These advances offer the potential for more precise, individualized care, though their long-term impact on perinatal outcomes requires further investigation.

Guideline Recommendations

Current guidelines from the ADA, American College of Obstetricians and Gynecologists (ACOG), and international bodies support the use of CGM in pregnancy, particularly for those with type 1 diabetes or challenging glycemic control. Increasingly, guidelines recognize the value of telemedicine and remote data sharing, recommending their adoption where feasible to enhance monitoring and facilitate multidisciplinary care. However, implementation should consider patient preferences, digital literacy, data privacy, and equitable access.

Conclusion

Remote maternal glucose data sharing with care teams is redefining diabetes management in pregnancy by enabling personalized, timely, and collaborative care. It addresses key challenges of traditional management pathways, harnessing technology to improve glycemic outcomes and reduce perinatal risk. While evidence supports its efficacy and safety, ongoing research is needed to optimize integration, ensure equity, and assess long-term maternal and child health outcomes. As digital health continues to evolve, remote glucose data sharing is poised to become a cornerstone of modern obstetric diabetes care.