Smart Normal Labor: Healthcare Providers' Experience With an AI-Based Mobile App

NCT07720531 | Recruiting

• 1 other identifier: Smart Normal Labor
• Study Type: interventional
• Target: 427
• Locations: 1 country
• Sites: 1

Brief Summary

Pregnancy and childbirth are uniquely important events in women's lives because they are accompanied by major physical, emotional, and psychological changes. Maternal satisfaction, emotional well-being, and perceptions of childbirth are strongly influenced by the quality of labor management. A woman's childbirth experience is shaped by multiple factors, including communication, autonomy, and active participation in the decision-making process. These factors are widely recognized as important indicators of the quality of maternity care. \[1\] Recent demographic changes and global population growth have placed increasing demands on healthcare systems, particularly maternal health services. High birth rates in some regions, combined with shortages of trained healthcare professionals, have created a need for scalable, adaptable, and innovative models of care. In response to these challenges, digital health technologies have emerged as promising tools to enhance the quality of maternity care and support both healthcare providers and pregnant women. \[2\]

Conditions

Normal Labor

Keywords

Normal Labor; Clinical Decision-Making in Labor; Artificial Intelligence

Outcome Measures

Primary Outcomes (1)

• Primary Outcome

  The time required for healthcare providers to make appropriate clinical decisions during the management of normal labor, measured using a structured clinical decision-making assessment tool.

  During labor management (from the onset of active labor until delivery, assessed up to 6-8 hours).

Secondary Outcomes (1)

• Secondary Outcome

  During labor management (from the onset of active labor until delivery, assessed up to 6-8 hours).

Study Arms (2)

AI-Based Smart Normal Labor Application

EXPERIMENTAL

Healthcare providers use the AI-based Smart Normal Labor Application during the management of normal labor to support clinical decision-making, labor monitoring, and timely identification of labor-related conditions.

Other: The intervention group

Routine Care Group

NO INTERVENTION

Healthcare providers provide routine normal labor management according to standard institutional protocols without using the AI-based Smart Normal Labor Application.

Interventions

The intervention group OTHER

participants who actively use the AI application during labor management,

AI-Based Smart Normal Labor Application

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• Participants must meet the following conditions to be included in the study:
• Healthcare providers (obstetricians and nurses) currently working in the Labor Kiosk, Obstetrics and Gynecology Department, or Outpatient Gynecology Clinics at Mansoura University Hospital.
• Direct involvement in the care and supervision of women in active labor.
• For the intervention group: previous exposure to and use of the AI-based smart normal labor application for a minimum defined period (e.g., 1 month).
• For the control group: no prior use of the AI-based application, following standard care practices.

You may not qualify if:

• Participants will be excluded if they:
• Are healthcare providers not directly involved in labor management (e.g., administrative staff or laboratory personnel).
• Have less than the minimum required clinical experience in labor management (e.g., \<6 months).
• Are on leave or unavailable during the study period.
• Decline to participate or do not provide informed consent.

Sponsors & Collaborators

• Delta University for Science and Technology: lead

Study Sites (1)

Basma wageah Basma

Al Mansurah, Dakahlia Governorate, 35712, Egypt

RECRUITING

Central Study Contacts

Basma W Basma

CONTACT

01552602703; Basma.wageh@deltauniv.edu.eg

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: SINGLE
• Who Masked: PARTICIPANT
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: lecturer

Model Details: quasi-experimental study

Study Record Dates

• First Submitted: April 4, 2026
• First Posted: July 22, 2026
• Study Start: May 6, 2026
• Primary Completion: July 21, 2026
• Study Completion: July 30, 2026
• Last Updated: July 22, 2026

Record last verified: 2026-01

Locations

EG (1)