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Mobile Video Education and Machine Learning-Based Risk Prediction in Neonatal Seizure Management

NCT07695623 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 60

Last updated 2026-07-10

No results posted yet for this study

Summary

This mixed-methods study will develop a machine learning-based model to identify infants at high risk of seizure recurrence after discharge from the neonatal intensive care unit. Based on qualitative interviews with mothers, a mobile video-supported education program will be developed. In the randomized controlled phase, mothers of high-risk infants will be assigned to intervention and control groups to evaluate the effect of the education on knowledge and anxiety levels during follow-up.

Conditions

  • Seizure Recurrence

Interventions

BEHAVIORAL

Mobile Video-Supported Education

The mobile video-supported education program will be provided to mothers of high-risk infants in addition to standard discharge education. The program will include video-based educational content on neonatal seizure recognition, safety precautions, antiseizure medication management, emergency response, and follow-up care after discharge. The education content will be developed based on qualitative interviews with mothers and will aim to improve maternal knowledge and reduce anxiety related to neonatal seizure management.

Sponsors & Collaborators

  • Istanbul Saglik Bilimleri University

    lead OTHER

Principal Investigators

  • Eda Aktaş · University of Health Sciences Turkey, Hamidiye Faculty of Nursing

Study Design

Allocation
RANDOMIZED
Purpose
SUPPORTIVE_CARE
Masking
NONE
Model
PARALLEL

Eligibility

Min Age
18 Years
Sex
FEMALE
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-07-04
Primary Completion
2028-12-31
Completion
2028-12-31

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07695623 on ClinicalTrials.gov