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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Dimple, Kumari
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Computer Science & IT Engineering

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Evaluation of midwifery educated mobile applications for labor guidance and a roadmap for future developers Devi, Seeta; Rahane, Swapnil Vitthal; Podder, Lily; X., Sangeetha; Dimple, Kumari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5268-5278

Abstract

The objective of the study was to review the midwifery guided mobile apps for labor advice, assessing features, functions, and content relevance. In February to March 2024, midwifery labor-guided applications were reviewed in mobile platforms such as the Google Play Store and Apple iTunes Store. We used multimodal evaluation tools, such as the mobile app rating scale (MARS), specific statements, and IQVIA ratings, to assess the quality of these applications. The study evaluated midwifery-guided applications, resulting in an average objective quality score of 3.96±0.96 out of 5. 'Safe delivery' scored the highest rating of 4.94, followed by 'Pregnancy mentor' (4.89), 'Hypno-birthing' (4.61), 'Obstetrics 6th edition' (4.68), and 'MSD manual guide to obstetrics' (4.56). Functionality received the highest score (4.16±0.865), followed by information (3.99±0.97), engagement (3.88±1.07), and aesthetics (3.82±0.28) areas. Subjective quality score was 3.6±1.18 out of 5 for an overall MARS score of 3.76±1.02. Most applications received favorable reviews, indicating good quality, and it is recommended that future app developers design applications that include comprehensive information on labor management.
Co-Authors Devi, Seeta Podder, Lily Rahane, Swapnil Vitthal X., Sangeetha