Normal Delivery : Optimization of Women Power Using Artificial Intelligence

Not Applicable

Recruiting

• Conditions: Smart Normal Labor

• Registration Number: NCT07143903
• Lead Sponsor: Delta University for Science and Technology

Brief Summary

As the global population continues to rise, the demand for efficient and effective maternal healthcare solutions becomes increasingly urgent. According to the United Nations, the world population is projected to reach approximately 9.7 billion by 2050, with a significant increase in the number of pregnancies and births. This demographic shift underscores the necessity for innovative healthcare technologies that can address the unique challenges faced by expectant mothers during childbirth.

The first stage of labor, which involves the onset of contractions and the gradual dilation of the cervix, is a critical period that requires careful monitoring and support. Many women experience anxiety and uncertainty during this time, often exacerbated by a lack of accessible information about labor progression. A lack of information and support during this pivotal time can lead to stress, impacting both maternal well-being and the overall labor experience. To address these challenges, the integration of artificial intelligence (AI) and mobile health technologies offers a transformative opportunity to empower women. Traditional methods of labor monitoring can be resource-intensive and may not provide the real-time insights that mothers need to make informed decisions about their care.

In this context, the integration of artificial intelligence (AI) and mobile health technologies presents a transformative opportunity. By developing a mobile application specifically designed to monitor the first stage of labor, we can empower expectant mothers with real-time data and personalized guidance. This application aims to track contractions, analyze symptoms, and provide educational resources, ultimately enhancing the labor experience for women .Furthermore, the application will not only serve individual users but also support healthcare providers by offering valuable insights into patient progress. With data-driven analytics, practitioners can make more informed decisions, allocate resources more efficiently, and improve overall care delivery.

This proposal outlines the development and evaluation of an AI-powered labor monitoring application that addresses the challenges posed by a growing population and increasing childbirth rates. By focusing on validity and reliability in our methodology, this project aims to contribute to the evolving field of digital health, promoting better outcomes for mothers and their newborns in an increasingly complex healthcare landscape.

By developing a mobile application specifically designed to monitor the first stage of labor, we aim to equip expectant mothers with real-time data and personalized guidance. This application will track contractions, analyze symptoms, and provide educational resources tailored to individual needs. By empowering women with knowledge and insights about their labor progression, the app will foster confidence and enable informed decision-making regarding their care. Furthermore, the application will facilitate communication between expectant mothers and healthcare providers, ensuring that women receive timely support and intervention when necessary. By utilizing predictive analytics, the app can alert users and healthcare professionals to concerning patterns, thus improving responsiveness and care outcomes.

Detailed Description

The conceptual framework for this study proposes that an AI-powered labor monitoring application, designed to track contractions, analyze symptoms, provide educational resources, and utilize predictive analytics, will enhance maternal experience by increasing perceived control, reducing anxiety, and boosting confidence during the first stage of labor. These improvements in maternal experience are expected to mediate positive outcomes, including higher maternal satisfaction, lower stress levels, and timely interventions when necessary, ultimately leading to better fetal outcomes, such as higher Apgar scores and reduced NICU admissions. The framework hypothesizes that expectant mothers using the AI application will have a more empowered labor experience compared to those receiving traditional monitoring, with measurable benefits for both mothers and their newborns.

Study Timeline

• Status Verified: 2024-11
• Study First Submitted: 2025-01-29
• Study First Submitted QC: 2025-08-23
• Study First Posted: 2025-08-27
• Study Start: 2025-08-28
• Last Update Submitted: 2025-08-28
• Last Update Posted: 2025-09-05
• Primary Completion: 2025-11-11
• Study Completion: 2025-12-30

Recruitment & Eligibility

• Status: RECRUITING
• Sex: Female
• Target Recruitment: 323

Inclusion Criteria

Women aged 18 years or older

Currently in the third trimester of pregnancy

Planning to deliver at the participating healthcare facility

Anticipating a normal labor without medical interventions

Exclusion Criteria

Women with high-risk pregnancies or contraindications for normal labor.

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Primary Outcome Measures

Name	Time	Method
Childbirth Experience Score	3 months	The Questionnaire for Assessing the Childbirth Experience (QACE) . It originally contained 25 items, created through literature review, expert input, and validation on a cohort of first-time mothers. Factor analyses showed good reliability (Cronbach's alpha between 0.70-0.85).ong form (25 items): evaluates each dimension separately.; Short form (13 items): structured into four domains (relationship with staff, first moments with baby, postpartum emotions, emotional state), which can be combined into a total childbirth experience score.
Delivery Expectation	3 months	After delivery, the participants will be asked to complete the validated post-labor questionnaire (Wijma Delivery Experience Questionnaire, W-DEQ version B) before discharge from the hospital. The survey measures the woman's experience of childbirth, and like the pre-labor questionnaire. The survey measures a woman's prenatal perception and expectation of childbirth. Higher total scores indicate a greater fear of childbirth . During induction and delivery, the personnel involved had no information about the women's W-DEQ A score.; Scoring system The W-DEQ B consists of 33 items; each scored from 0 to 5. A high total score indicates a negative experience of childbirth

Secondary Outcome Measures

Name	Time	Method
Fetal Apgar score outcome	3 months	Apgar score: Newborn's Apgar score at 5 minutes after birth (scale 0-10). Higher scores indicate better neonatal health/status.
Fetal Birth weight outcome	3 months	Birth weight: Weight of the newborn at birth, measured in kilograms (kg). Higher values indicate a heavier newborn. Unit: kg. Time :at birth (delivery).
Fetal Birth length outcome	3 months	Birth length: Length of the newborn at birth, measured in centimeters (cm). Higher values indicate a longer newborn. Unit: cm. Time frame: at birth.

Trial Locations

Locations (1)

Basma Wageah Mohamed Mohamed Elrefay; 🇪🇬 Al Mansurah, Dakhlyia, Egypt