Digital Health Intervention for Early Identification and Prevention of Gestational Diabetes Mellitus

Официальное название

A Multicenter Randomized Controlled Trial of a Digital Health Intervention for Risk Identification, Intelligent Early Warning, and Prevention of Gestational Diabetes Mellitus Based on Multi-Source Data Integration

Обзор

This study aims to develop and evaluate an early risk identification and digital health intervention strategy for gestational diabetes mellitus (GDM) among pregnant women in China. Gestational diabetes mellitus is a common pregnancy complication associated with adverse maternal and neonatal outcomes, including excessive gestational weight gain, macrosomia, cesarean delivery, and increased long-term risk of metabolic disorders in both mothers and offspring. The study includes two components. First, retrospective multi-source clinical data from maternal health records will be used to develop and validate a risk prediction model for early identification of pregnant women at high risk of GDM. Second, pregnant women identified as high risk in early pregnancy will be enrolled in a multicenter randomized controlled trial and assigned to either a digital health intervention group or a usual care group. The intervention includes online health education, individualized lifestyle guidance, behavioral self-management tools, and interactive consultation through a digital platform. The primary outcome is the incidence of GDM diagnosed during pregnancy. Secondary outcomes include gestational weight gain, cesarean delivery, macrosomia, and other maternal and neonatal outcomes. This study is expected to provide evidence for improving early risk assessment, intelligent warning, and prevention strategies for GDM in the context of maternal health management in China.

Подробное описание

Gestational diabetes mellitus (GDM) has become an increasingly important public health problem among pregnant women in China. GDM is associated with short-term adverse pregnancy outcomes and long-term health risks for both mothers and their offspring. Current routine screening for GDM is mainly based on oral glucose tolerance testing performed at 24 to 28 weeks of gestation, which may miss the optimal window for earlier risk identification and preventive intervention.

This study is designed to improve early identification and prevention of GDM through multi-source data integration and digital health intervention. The study is conducted in the context of maternal health management in China and includes a retrospective model development phase and a prospective randomized controlled trial phase.

In the model development phase, retrospective clinical data from maternal health information systems, including demographic characteristics, obstetric history, physical examination records, laboratory indicators, and pregnancy follow-up data, will be used to identify predictors of GDM and to construct an early risk prediction model. Statistical and machine learning methods, including random forest and other predictive modeling approaches, will be used to evaluate model performance and optimize discrimination, calibration, robustness, and interpretability.

In the intervention phase, pregnant women in early pregnancy who are identified as being at high risk for GDM will be recruited from participating maternal and child health hospitals and randomly assigned in a 1:1 ratio to either a digital health intervention group or a usual care group. Participants in the intervention group will receive additional support through a digital platform, including structured health education, individualized recommendations on diet, physical activity, gestational weight management, and sleep, as well as online consultation and self-management tools. Participants in the control group will receive routine antenatal care and standard health education materials.

Follow-up will be conducted during pregnancy and at delivery. The primary outcome is the incidence of GDM diagnosed according to routine clinical criteria during pregnancy. Secondary outcomes include gestational weight gain, cesarean delivery, macrosomia, and selected maternal and neonatal outcomes. Safety monitoring will focus on possible adverse events related to lifestyle intervention, such as hypoglycemic symptoms or other discomforts.

The findings of this study may help optimize strategies for early risk assessment, intelligent warning, and intervention for GDM, and may contribute to maternal and child health policy and practice in China.

Вмешательства

• Поведенческое Digital Health Lifestyle Intervention
  Participants receive a digital health lifestyle intervention in addition to routine antenatal care. The intervention is delivered through a digital platform and includes online health education, individualized lifestyle guidance, self-management tools, health information delivery, and interactive consultation. The intervention focuses on gestational weight management, diet, physical activity, sleep, and early prevention of gestational diabetes mellitus among pregnant women identified as high ris

Первичные конечные точки

• Incidence of Gestational Diabetes Mellitus [Срок оценки: At 24 to 28 weeks of gestation]

Вторичные конечные точки (3)

• Gestational Weight Gain [Срок оценки: From enrollment to delivery]
• Cesarean Delivery [Срок оценки: At delivery]
• Macrosomia [Срок оценки: At delivery]

Критерии участия

Критерии включения

• Pregnant women in early pregnancy (within 13 weeks and 6 days of gestation)
• Planned delivery at a participating study hospital
• Age 18 years or older
• Singleton pregnancy
• Identified as high risk for gestational diabetes mellitus by the study risk assessment model
• Willing and able to provide informed consent

Критерии исключения

• • Pre-pregnancy diabetes mellitus or overt diabetes diagnosed at the first antenatal visit (fasting blood glucose greater than or equal to 7.0 mmol/L)

• Pre-existing hypertension, autoimmune disease, major infection, or severe liver or kidney disease
• Use of medications that affect glucose metabolism, such as corticosteroids or metformin
• Severe psychiatric disorders
• Inability or unwillingness to comply with study procedures

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Распределение

Рандомизированное

Модель

Параллельные группы

Маскирование

Открытое

Основная цель

Профилактика

Центры проведения

Китай · 2 центра

• Beijing Tongzhou Maternal and Child Health Hospital — Пекин
• Weifang Maternal and Child Health Hospital — Weifang

Публикации

• Artzi NS, Shilo S, Hadar E, Rossman H, Barbash-Hazan S, Ben-Haroush A, Balicer RD, Feldman B, Wiznitzer A, Segal E. Prediction of gestational diabetes based on nationwide electronic health records. Nat Med. 2020 Jan;26(1):71-76. doi: 10.1038/s41591-019-0724-8. Epub 2020 Jan 13. PMID 31932807
• Allotey J, Coomar D, Ensor J, Ruiz-Calvo G, Boath A, Ogwulu CO, Monahan M, Kabeya V, Zheng M, McNeill R, Meacham H, Mahmoud G, Simpson SA, Hitman GA, Nirantharakumar K, Heslehurst N, Pelaez M, Tonstad S, Yeo S, Cecatti JG, Facchinetti F, Motahari-Tabari NS, Renault KM, Guelfi KJ, Jensen DM, Harrison C, Khomami MB, Calle-Pascual AL, McAuliffe FM, Hauner H, Barakat R, Geiker NRW, Vinter CA, Phelan S PMID 41494781

Идентификаторы

NCT: NCT07499622 · PKUGDM-2026

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

Открыть это исследование на ClinicalTrials.gov ↗