{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T17:28:19Z","timestamp":1776706099405,"version":"3.51.2"},"reference-count":88,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T00:00:00Z","timestamp":1754956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Maternal health in Lebanon faces significant challenges, particularly among disadvantaged populations, due to limited access to antenatal care (ANC) and a strained healthcare system. While mHealth interventions have improved maternal outcomes globally, few engage healthcare providers (HCPs) or incorporate advanced tools like artificial intelligence (AI) and gamification. This study evaluated the effectiveness of an AI-based, gamified mHealth intervention, Gamification and AI and mHealth Network for Maternal Health Improvement (GAIN MHI), on ANC utilization and maternal and neonatal outcomes in Lebanon.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods and materials<\/jats:title><jats:p>The intervention included two arms: one targeting pregnant women and their spouses without HCP engagement and another involving HCPs. A post-intervention analysis was conducted with 2,880 pregnant women divided into three groups: control (<jats:italic>n<\/jats:italic>\u2009=\u20091,315), non-HCP intervention (<jats:italic>n<\/jats:italic>\u2009=\u2009668), and HCP intervention (<jats:italic>n<\/jats:italic>\u2009=\u2009897). Intervention components included AI-driven, gamified HCP professional development via the GAIN MHI app, weekly WhatsApp-based educational messages, and ANC visit reminders. Data on healthcare access (ANC visits, supplement intake, ultrasounds, and lab tests) and outcomes (term delivery, maternal\/neonatal complications) were analyzed using logistic regression to calculate adjusted odds ratios (OR).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The HCP arm significantly improved healthcare access, with higher odds of attending \u22654 ANC visits (OR\u2009=\u20091.968, 95% CI: 1.575\u20132.459), completing \u22652 ultrasounds (OR\u2009=\u20093.026, 95% CI: 2.301\u20133.981), lab test completion (OR\u2009=\u20092.828, 95% CI: 1.894\u20134.221), and supplement intake (OR\u2009=\u20091.467, 95% CI: 1.221\u20131.762). Term deliveries were more likely in the HCP arm (OR\u2009=\u20091.360, 95% CI: 1.011\u20131.289), and neonatal morbidity decreased by 52.15% (OR\u2009=\u20091.521, 95% CI: 1.127\u20132.051). No improvements were seen in abortion rates, and normal deliveries decreased across intervention arms. Significant baseline demographic differences, including nationality and chronic disease prevalence, were observed between groups.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>Integrating HCPs into an mHealth intervention significantly enhanced ANC uptake and maternal and neonatal outcomes in disadvantaged populations in Lebanon. These findings underscore the importance of combining digital tools with clinical support to address systemic barriers and improve maternal health in resource-limited settings. Future interventions should address delivery practices and broader social determinants of health to achieve sustainable impacts.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fdgth.2025.1574946","type":"journal-article","created":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T12:37:47Z","timestamp":1755002267000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Evaluating the impact of engaging healthcare providers in an AI-based gamified mHealth intervention for improving maternal health outcomes among disadvantaged pregnant women in Lebanon"],"prefix":"10.3389","volume":"7","author":[{"given":"Shadi","family":"Saleh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nour","family":"El 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