Normal Delivery : Optimization of Women Power Using Artificial Intelligence

NCT ID: NCT07143903

Last Updated: 2025-11-20

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 216 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2024-08-28
• Study Completion Date: 2025-10-25

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.

Conditions

Smart Normal Labor

Study Design

• Allocation Method: NON_RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: OTHER
• Blinding Strategy: NONE

Study Groups

pregnant women experience using the artificial application during the first stage of labor .

the labor experience for pregnant women will be using the artificial application during the first stage of labor .artificial application will expected not only enhances the labor experience for women but also contributes to the overall improvement of maternal healthcare systems, addressing both individual and systemic challenges which create an intuitive AI-driven mobile application that assists in monitoring the first stage of labor.

Group Type: OTHER

artificial application

Intervention Type: OTHER

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.

the labor experience of pregnant women will be using the traditional methods

the labor experience of pregnant women will be using the traditional methods labor and labor outcome

Group Type: OTHER

artificial application

Intervention Type: OTHER

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.

Interventions

artificial application

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.

Intervention Type: OTHER

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.

• Minimum Eligible Age: 16 Years
• Maximum Eligible Age: 50 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: No

Sponsors

Delta University for Science and Technology

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Basma Wageah Mohamed Mohamed Elrefay

Al Mansurah, Dakhlyia, Egypt

Countries

Egypt

References

Wijma K, Wijma B, Zar M. Psychometric aspects of the W-DEQ; a new questionnaire for the measurement of fear of childbirth. J Psychosom Obstet Gynaecol. 1998 Jun;19(2):84-97. doi: 10.3109/01674829809048501.

Reference Type: BACKGROUND

PMID: 9638601 (View on PubMed)

Other Identifiers

delta U

Identifier Type: -

Identifier Source: org_study_id