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PE-CARE: An Artificial Intelligence (AI)-Based Mobile Health Application to Improve Maternal Knowledge of Early Preeclampsia Detection – A Quasi-Experimental Study
Background: Preeclampsia remains a leading cause of maternal mortality
worldwide, yet awareness and early detection remain limited in low- and
middle-income countries. While artificial intelligence (AI)-based applications
have been increasingly utilized in hospital settings, their adoption in
Indonesian primary care remains minimal. Objective: This study aimed to
evaluate the effectiveness of an AI-based mobile health application (PECARE) in improving maternal knowledge on early detection of preeclampsia.
Methods: A quasi-experimental pretest–posttest control group design was
conducted at Puskesmas Parongpong, West Bandung Regency, from February
to March 2025. A total of 100 pregnant women (≤20 weeks gestation) were
recruited using purposive sampling and assigned equally to the intervention
(n=50) and control (n=50) groups. The intervention group used the PE-CARE
application for 14 days, while the control group received conventional health
education. Knowledge was assessed using a validated 15-item questionnaire.
Data were analyzed using paired and independent t-tests, complemented by
effect size (Cohen’s d) and 95% confidence intervals. Results: Knowledge
scores improved significantly in both groups, with a larger gain in the
intervention group (mean difference 28.1; Cohen’s d=3.79, 95% CI 25.7–30.5,
p
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