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

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

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Gestational Diabetes Mellitus (GDM)
• Intervention / Treatment: Behavioral: Digital Health Lifestyle Intervention

Detailed Description

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.

• Study Type: Interventional
• Enrollment (Estimated): 1200
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Jue Liu, PhD; Phone Number: +86 13426455743; Email: jueliu@bjmu.edu.cn

Study Locations

• China
  • Beijing Municipality
    • Beijing, Beijing Municipality, China, 10010
      • Beijing Tongzhou Maternal and Child Health Hospital
      • Contact: Tao Su, PhD; Phone Number: +86 18605021872; Email: 18605021872@163.com
  • Shandong
    • Weifang, Shandong, China, 261042
      • Weifang Maternal and Child Health Hospital
      • Contact: Xiansheng Ding; Phone Number: +86 0536-5919002; Email: 2411210109@stu.pku.edu.cn

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• 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

Exclusion Criteria:

• • 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

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Prevention
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: None (Open Label)

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Digital Health Intervention; Participants in this arm will receive a digital health intervention in addition to routine antenatal care. The intervention includes online health education, individualized lifestyle guidance, self-management tools, health information delivery, and interactive consultation through a digital platform. The intervention focuses on gestational weight management, diet, physical activity, sleep, and prevention of gestational diabetes mellitus.	Behavioral: 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 risk.
No Intervention: Usual Care; Participants in this arm will receive routine antenatal care and standard pregnancy health education provided by the hospital, without additional digital health intervention.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Incidence of Gestational Diabetes Mellitus	Gestational diabetes mellitus diagnosed during pregnancy according to routine oral glucose tolerance testing and the IADPSG/WHO diagnostic criteria used at participating hospitals.	At 24 to 28 weeks of gestation

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Gestational Weight Gain	Total gestational weight gain during pregnancy based on routine antenatal records.	From enrollment to delivery
Cesarean Delivery	Mode of delivery recorded from hospital medical records.	At delivery
Macrosomia	Occurrence of macrosomia recorded in delivery records.	At delivery

Collaborators and Investigators

• Sponsor: Peking University
• Collaborators: National Natural Science Foundation of China

Publications and helpful links

General Publications

• 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.
• 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, Kinnunen TI, Kothari A, Teede H, Poston L, Betran AP, Moss N, Iliodromiti S, Austin F, Roberts T, Zamora J, Riley RD, Thangaratinam S; i-WIP Collaborative Group. Effects of lifestyle interventions in pregnancy on gestational diabetes: individual participant data and network meta-analysis. BMJ. 2026 Jan 6;392:e084159. doi: 10.1136/bmj-2025-084159.

Study record dates

Study Major Dates

• Study Start (Estimated): June 1, 2026
• Primary Completion (Estimated): June 1, 2028
• Study Completion (Estimated): December 1, 2028

Study Registration Dates

• First Submitted: March 24, 2026
• First Submitted That Met QC Criteria: March 24, 2026
• First Posted (Actual): March 30, 2026

Study Record Updates

• Last Update Posted (Actual): March 30, 2026
• Last Update Submitted That Met QC Criteria: March 24, 2026
• Last Verified: March 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Urogenital Diseases; Endocrine System Diseases; Female Urogenital Diseases and Pregnancy Complications; Metabolic Diseases; Pregnancy Complications; Glucose Metabolism Disorders; Diabetes Mellitus; Nutritional and Metabolic Diseases; Diabetes, Gestational
• Other Study ID Numbers: PKUGDM-2026

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: Individual participant data will not be made publicly available because the study involves sensitive personal and health information from pregnant women, and data sharing is restricted by ethical approval, informed consent, and institutional data protection policies.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No