Effects of Early Nursing Monitoring on Pregnancy Outcomes of Pregnant Women with Gestational Diabetes Mellitus under Internet of Things

Abstract

To analyze the effect of early nursing intervention based on fetal heart signal extraction algorithm and Internet of Things (IoT) wireless communication technology on the adverse pregnancy outcomes of pregnant women with gestational diabetes mellitus (GDM) and newborns, 88 pregnant women diagnosed with GDM who underwent the 75 g glucose tolerance test at 24-28 gestational weeks in the hospital were selected as the research objects. According to the different intervention methods, the patients were divided into 44 cases of the experimental group (nursing intervention based on maternal and infant monitoring system) and 44 cases of the control group (outpatient follow-up intervention). The results showed that the compliance score and diet compliance rate of patients in the experimental group were signally higher than those in the control group at 1 and 3 months after intervention ( $P<0.05$ ). The levels of fasting blood glucose (FBG), blood glucose 2 hours after the meal, and hemoglobin A1c (HbA1c) in the experimental group were lower than those in the control group at 1 and 3 months after intervention ( $P<0.05$ ). The number of giant babies, hypoglycemia, hyperbilirubinemia, fetal distress, premature delivery, and birth weight in the experimental group was all lower than those in the control group, while the Apgar scores were higher than that in the control group ( $P<0.05$ ). To sum up, the intervention based on the intelligent maternal and infant monitoring system could timely help pregnant women adjust their diet structure and optimize the management of blood glucose and blood lipids, thus effectively improving the adverse pregnancy outcome and maintaining the health of pregnant women and newborns.