Smart Normal LaborWomen PowerNCT07143903
Normal Delivery : Optimization of Women Power Using Artificial Intelligence
A study testing artificial application in people with Smart Normal Labor, run by Delta University for Science and Technology at 1 site in Egypt.
Trial 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… Read more about this study
- Who is it for?
- Ages 16 to 50 years, women only
- Conditions the study is for
- Smart Normal Labor
- What is being tested?
- artificial application
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 216 participants
- Where
- 1 siteEgypt
- Study dates
- 28 Aug 2025to 25 Oct 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 16 to 50 years
- Women only
- No healthy volunteers
- Child, Adult
Who may be able to join
- 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
Who cannot join
- Women with high-risk pregnancies or contraindications for normal labor.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Smart Normal Labor: Optimization of Women Power Using Artificial Intelligence
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.
Read more
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.
Read the 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
- Being tested
- artificial application
- Keywords
- Smart- Normal Labor-Women Power
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
artificial applicationOther
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 group | Role | What they receive |
|---|---|---|
| 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. Receives: 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 Receives: Other: artificial application |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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
Measured: 3 months
Secondary outcomes (3)
Fetal Birth weight outcome
Birth weight: Weight of the newborn at birth, measured in kilograms (kg). Higher values indicate a heavier newborn. Unit: kg. Time :at birth (delivery).
Measured: 3 months
Fetal Birth length outcome
Birth length: Length of the newborn at birth, measured in centimeters (cm). Higher values indicate a longer newborn. Unit: cm. Time frame: at birth.
Measured: 3 months
Fetal Apgar score outcome
Apgar score: Newborn's Apgar score at 5 minutes after birth (scale 0-10). Higher scores indicate better neonatal health/status.
Measured: 3 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Aug 2025
- Study start28 Aug 2025
- Primary completion20 Oct 2025
- Study completion25 Oct 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Smart Normal LaborStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 216 participants (actual)
Sponsor and investigators
- Lead sponsor
- Delta University for Science and Technology
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07143903
- Sponsor's protocol ID
- delta U
- Acronym
- Women Power
- Status last verified
- Nov 2024
- First submitted
- 29 Jan 2025
- Last updated
- 14 Aug 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
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.
PubMed 9638601
Study at a glance
From the registry record, last updated by the sponsor on 14 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Smart Normal Labor: Optimization of Women Power Using Artificial Intelligence
- ClinicalTrials.gov ID
- NCT07143903
- Acronym / sponsor ID
- Women Power / delta U
- Conditions
- Smart Normal Labor
- Keywords
- Smart- Normal Labor-Women Power
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 216 participants (actual)
- Lead sponsor
- Delta University for Science and TechnologyUniversity, hospital or other
- Study start
- 28 Aug 2025 (actual)
- Primary completion
- 20 Oct 2025 (actual)
- Study completion
- 25 Oct 2025 (actual)
- First posted
- 27 Aug 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Primary purpose
- Other
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| pregnant women experience using the artificial application during the first stage of labor . | Other | Other: artificial applicationthe 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. |
| the labor experience of pregnant women will be using the traditional methods | Other | Other: artificial applicationthe labor experience of pregnant women will be using the traditional methods labor and labor outcome |
Who may qualify
- Ages
- 16 to 50 years
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult
Key inclusion criteria (4 listed)
- 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
Key exclusion criteria (1 listed)
- Women with high-risk pregnancies or contraindications for normal labor.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| artificial applicationThe 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. | Other | 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 |
Primary outcome
Childbirth Experience Score
Time frame: 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
Time frame: 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
3 secondary outcomes
Fetal Birth weight outcome
Time frame: 3 months
Fetal Birth length outcome
Time frame: 3 months
Fetal Apgar score outcome
Time frame: 3 months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 14 Aug 2026.
- Background: PubMed 9638601Wijma 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.
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing artificial application in people with Smart Normal Labor, run by Delta University for Science and Technology at 1 site in Egypt.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 14 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Egypt. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Childbirth Experience Score (time frame: 3 months). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07143903, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 14 Aug 2026.