Kerimoglu Yildiz Gizem, Turk Delibalta Rukiye, Coktay Zehra
Faculty of Health Sciences, Department of Pediatric Nursing, Hatay Mustafa Kemal University, Hatay, Türkiye.
Faculty of Health Sciences, Department of Women Health and Nursing, Kafkas University, Kars, Türkiye.
BMC Pregnancy Childbirth. 2025 May 30;25(1):631. doi: 10.1186/s12884-025-07753-3.
Artificial intelligence (AI) is increasingly used in healthcare interventions to provide accessible, continuous, and personalized patient support. This study investigates the impact of a mobile breastfeeding counseling application developed with artificial AI on mothers' breastfeeding self-efficacy, success, and anxiety levels.
A quasi-experimental design was employed, involving 60 mothers. Participants were divided into two groups: 30 mothers received AI-based counseling, and 30 mothers were provided a booklet. Data collection tools included a personal information form, Breastfeeding Charting System and Assessment Tool (LATCH), Postnatal Breastfeeding Self-Efficacy Scale, and Beck Anxiety Inventory. Data were collected from mothers who delivered at a state hospital's obstetrics and gynecology department and were followed for ten days postpartum (postpartum days 1, 3, 7, and 10).
No significant differences were found in the demographic characteristics of the two groups (p > 0.05). Statistically significant improvements were observed in breastfeeding self-efficacy over time for both groups (AI group: f = 36.356, p = 0.000; booklet group: f = 43.349, p = 0.000). At day 10, the AI group scored significantly higher than the booklet group (Z=-2.216, p = 0.027). For breastfeeding success, as measured by the LATCH tool, significant differences were also noted over time for both groups (AI group: f = 68.466, p = 0.000; booklet group: f = 68.088, p = 0.000). At day seven, the AI group outperformed the booklet group (Z=-2.995, p = 0.003). Anxiety levels showed no significant differences between groups.
AI-based breastfeeding counseling positively impacts breastfeeding self-efficacy and success. The findings highlight the potential of AI applications in healthcare. AI-based chatbots can serve as effective tools for breastfeeding education, offering accessible, personalized, and continuous support. The significant improvements in breastfeeding outcomes indicate that innovative AI-assisted interventions can effectively support mothers during the critical early postpartum period. This research demonstrates the feasibility of integrating AI technology into maternal care and serves as a foundation for future studies.
Not applicable.
人工智能（AI）在医疗保健干预中的应用越来越广泛，旨在为患者提供便捷、持续且个性化的支持。本研究调查了一款基于人工智能开发的移动母乳喂养咨询应用程序对母亲母乳喂养自我效能感、母乳喂养成功率及焦虑水平的影响。
采用准实验设计，纳入60名母亲。参与者被分为两组：30名母亲接受基于人工智能的咨询，30名母亲收到一本手册。数据收集工具包括个人信息表、母乳喂养记录系统及评估工具（LATCH）、产后母乳喂养自我效能量表和贝克焦虑量表。数据收集对象为在一家州立医院妇产科分娩且产后随访10天（产后第1、3、7和10天）的母亲。
两组的人口统计学特征无显著差异（p>0.05）。两组的母乳喂养自我效能感均随时间推移有统计学意义的显著改善（人工智能组：f = 36.356，p = 0.000；手册组：f = 43.349，p = 0.000）。在第10天，人工智能组的得分显著高于手册组（Z = -2.216，p = 0.027）。对于用LATCH工具衡量的母乳喂养成功率，两组随时间推移也有显著差异（人工智能组：f = 68.466，p = 0.000；手册组：f = 68.088，p = 0.000）。在第7天，人工智能组的表现优于手册组（Z = -2.995，p = 0.003）。两组的焦虑水平无显著差异。
基于人工智能的母乳喂养咨询对母乳喂养自我效能感和成功率有积极影响。研究结果凸显了人工智能应用在医疗保健中的潜力。基于人工智能的聊天机器人可作为母乳喂养教育的有效工具，提供便捷、个性化且持续的支持。母乳喂养结果的显著改善表明，创新的人工智能辅助干预措施可在产后关键早期有效支持母亲。本研究证明了将人工智能技术整合到孕产妇护理中的可行性，并为未来研究奠定了基础。
不适用。
