Normal Delivery : Optimization of Women Power Using Artificial Intelligence

NCT07143903 | Completed

• 1 other identifier: delta U
• Study Type: interventional
• Target: 216
• Locations: 1 country
• Sites: 1

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.

Conditions

Smart Normal Labor

Keywords

Smart- Normal Labor-Women Power

Outcome Measures

Primary Outcomes (2)

• Childbirth Experience Score

  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.

  3 months

• Delivery Expectation

  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

  3 months

Secondary Outcomes (3)

• Fetal Birth weight outcome

  3 months

• Fetal Birth length outcome

  3 months

• Fetal Apgar score outcome

  3 months

Study Arms (2)

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

OTHER

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.

Other: artificial application

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

OTHER

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

Other: artificial application

Interventions

artificial application 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.

pregnant women experience using the artificial application during the first stage of labor .; the labor experience of pregnant women will be using the traditional methods

Eligibility Criteria

• Age: 16 Years - 50 Years
• Sex: female
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64)

You may qualify if:

• 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

You may not qualify if:

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

Sponsors & Collaborators

• Delta University for Science and Technology: lead

Study Sites (1)

Basma Wageah Mohamed Mohamed Elrefay

Al Mansurah, Dakhlyia, 35111, Egypt

Location

Related Publications (1)

• 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.

  PMID: 9638601 BACKGROUND

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: NONE
• Purpose: OTHER
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 29, 2025
• First Posted: August 27, 2025
• Study Start: August 28, 2024
• Primary Completion: October 20, 2025
• Study Completion: October 25, 2025
• Last Updated: November 20, 2025

Record last verified: 2024-11

Locations

EG (1)