Effectiveness of an AI-Enabled Mobile Intervention on Lifestyle Behaviors and Maternal-Infant Health

Официальное название

Effects of an AI-driven Mobile Health Management Intervention to Prevent Gestational Diabetes Mellitus in High-risk Pregnant Women: A Pragmatic Randomized Controlled Trial

Обзор

The goal of this pragmatic randomized controlled trial is to evaluate the effectiveness of an AI-enabled mobile health management application ("Better Pregnancy" app), grounded in the Theory of Planned Behavior, in preventing gestational diabetes mellitus (GDM) among pregnant women at high risk of GDM, and to assess its impact on maternal and infant health outcomes. Study Population: Eligible participants are pregnant women aged 18-49 years, with a singleton pregnancy, gestational age \<12 weeks at enrollment, and at least one GDM risk factor (age ≥35 years, pre-pregnancy BMI ≥24 kg/m², family history of diabetes, previous history of GDM, prior delivery of a macrosomic infant \[birth weight ≥4000 g\], or polycystic ovary syndrome). Participants must have no heart, liver, or kidney diseases, use an Android smartphone, and provide written informed consent. The main questions it aims to answer are: Can the AI-driven mobile lifestyle intervention reduce the incidence of GDM in high-risk pregnant women? Does the intervention improve lifestyle behaviors (diet, physical activity, sleep) and glycemic control (measured by continuous glucose monitoring) in participants? What is the adherence, acceptability, and cost-effectiveness of this intervention in real-world clinical settings? Comparison: Researchers will compare two groups of pregnant women: the intervention group (receiving the AI-enabled "Better Pregnancy" app plus routine antenatal care) and the control group (receiving routine antenatal care alone). Participants will: Enroll in the first trimester (\<12 weeks of gestation) and complete a baseline lifestyle and health status questionnaire In the intervention group: watch 7 cognitive-attitudinal videos within the first week, then complete 12 weeks of AI-driven personalized intervention (daily check-ins, receiving tailored recommendations on diet, physical activity, sleep, etc.) During the intervention period, participants in the intervention group will wear a continuous glucose monitoring device for 7 days to assess the impact of the intervention on glycemic fluctuations Complete the first follow-up lifestyle and health status assessment after the intervention (approximately in the second trimester) Undergo an oral glucose tolerance test during the second trimester (24-28 weeks) Complete the second follow-up lifestyle and health status assessment in the third trimester (≥28 weeks) Complete the third follow-up lifestyle and health status assessment at 42 days postpartum, along with collection of delivery outcomes and infant health information A subset of participants may voluntarily provide blood and stool samples for mechanistic studies

Вмешательства

• Поведенческое Multi-component lifestyle intervention
  Participants receive a 12-week AI-powered intervention via the "PregSelfCare" app plus routine antenatal care. The intervention includes: (1) watching 7 theory-based videos and completing an attitude questionnaire in week 1; (2) daily check-ins (mood, water, fruit, bowel movements, sunlight, weight) and receiving AI-driven personalized recommendations (diet photo feedback, step goals, sleep advice, emotion-relief videos, supplement reminders) during weeks 1-12, with content dynamically adjusted

Первичные конечные точки

• The prevalence of gestational diabetes mellitus [Срок оценки: 24 Weeks]

Вторичные конечные точки (1)

• Adverse pregnancy and adverse birth outcomes [Срок оценки: 42 days postpartum]

Критерии участия

Критерии включения

• Pregnant women aged ≥18 years
• Singleton pregnancy
• Gestational age <13+6 weeks at enrollment
• At least one of the following GDM risk factors:

• Age ≥35 years
• Pre-pregnancy BMI ≥24 kg/m²
• Family history of diabetes (first-degree relatives)
• Previous history of gestational diabetes mellitus
• Prior delivery of a macrosomic infant (birth weight ≥4000 g)
• Polycystic ovary syndrome
• Use of an Android smartphone
• No severe heart, liver, or kidney diseases
• Voluntary participation and signed informed consent

Критерии исключения

• Pre-pregnancy diagnosis of diabetes mellitus (type 1 or type 2)
• Severe mental illness
• Confirmed severe pregnancy complications (e.g., preeclampsia, placental abruption, etc.)
• Inability to complete questionnaires or intervention
• Participation in other interventional studies

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

Здоровые добровольцы: Да

Дизайн исследования

Распределение

Рандомизированное

Модель

Параллельные группы

Маскирование

Простое слепое

Основная цель

Профилактика

Центры проведения

Китай · 1 центр

• Anhui Medical University — Хэфэй

Публикации

• Duan B, Liu L, Ma C, Liu Z, Gou B, Liu W. Effects of mobile health management model on the prevention of gestational diabetes mellitus in pregnant women at risk of gestational diabetes: A randomized controlled trial. Int J Nurs Stud. 2026 Jan;173:105252. doi: 10.1016/j.ijnurstu.2025.105252. Epub 2025 Oct 16. PMID 41135300

Идентификаторы

NCT: NCT07582068 · 20260506

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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