J Korean Diabetes.  2024 Sep;25(3):135-144. 10.4093/jkd.2024.25.3.135.

Utilization of Smart Healthcare for Gestational Diabetes Mellitus Management

Affiliations
  • 1Center for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Seoul, Korea
  • 2Department of Regulatory Science, Kyung Hee University, Seoul, Korea
  • 3Department of Endocrinology and Metabolism, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, Korea
  • 4Department of Pediatrics, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, Korea

Abstract

Gestational diabetes mellitus (GDM) poses significant health risks to both mothers and newborns, requiring rigorous self-management and frequent medical consultations. Advances in information and communications technology (ICT) have shown promising results in reducing the number of in-person visits for GDM management. ICT enhances patient self-care engagement, with some studies reporting reductions in average blood glucose and HbA1c levels. ICT for GDM management has demonstrated benefits such as fewer in-person visits, improved adherence to self-monitoring of blood glucose, increased global user satisfaction, and maintenance of blood glucose control and perinatal outcomes. Common barriers to ICT for GDM include technological literacy, inadequate education, limited technical support, the additional burden of non-customized applications, and restricted interoperability. Further research is needed on the impact of technology on GDM management to optimize digital health solutions.

Keyword

Diabetes, gestational; Digital health; Information technology

Figure

  • Fig. 1. Follow-up and care of pregnant women with diabetes. Adapted from the article of Moon et al. [11] (Diabetes Metab J 2024;48:546-708) under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC 4.0) license. GDM, gestational diabetes mellitus.

  • Fig. 2. Random effects meta-analysis of the mean difference in fasting plasma glucose (mmol/L), 2-hour postprandial glucose (mmol/L), and HbA1c (%), comparing digital health or routine care [21]. Adapted from the article of Leblalta et al. [21] (PLOS Digit Health 2022;1:e0000015) under the terms of the Creative Commons Attribution (CC BY 4.0) license. SD, standard deviation; IV, inverse variance; CI, confidence interval; m-Health, mobile health; HbA1c, glycated hemoglobin.


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