Smart Normal Labor: Optimization of Women Power Using Artificial Intelligence
Trial ID: NCT07143903
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-08-28
Completion date: 2025-10-25
Sponsor: Delta University for Science and Technology
Conditions: Smart Normal Labor
Interventions: artificial application
Locations: Basma Wageah Mohamed Mohamed Elrefay
Countries: Egypt
Enrollment: 216
Age range: 16 Years - 50 Years
Sex: FEMALE

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.

Eligibility criteria:
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.

Outcomes:
Childbirth Experience Score; Delivery Expectation