ROLE OF CONTINUOUS PHYSICAL ACTIVITY MONITORING IN THE PREDICTION OF GESTATIONAL DIABETES: OPPORTUNITIES OF WEARABLE DEVICES

Abstract

Gestational diabetes mellitus (GDM) remains one of the most pressing issues in modern obstetrics, affecting up to 15-20% of pregnancies worldwide. One of the key modifiable causes of GDM development is hypothermia. Traditional methods for assessing physical activity based on patient self-reports often suffer from subjectivity and low accuracy. This article examines the potential for using wearable devices (accelerometers and smartwatches) to objectively and continuously collect data on the motor activity of pregnant women. Special attention is paid to integrating this data into predictive models based on machine learning. The analysis of "digital biomarkers" allows for the early detection of carbohydrate metabolism disorders, opening up new opportunities for personalized prevention and timely lifestyle adjustment.