Background: Pregnancy is a pivotal phase in a woman’s life, demanding special attention to ensure maternal and fetal health. Prenatal education plays a vital role in promoting healthy pregnancies and reducing adverse outcomes for pregnant women. Mobile prenatal education programs have gained traction due to their accessibility and timeliness, especially in light of finite health care resources and the constraints imposed by the COVID-19 pandemic. Objective: This study aims to develop and evaluate the effectiveness of a mobile-based prenatal education program in improving pregnancy outcomes. Methods: We developed a mobile-based prenatal education curriculum in collaboration with a multidisciplinary maternal care team from Peking Union Medical College Hospital (PUMCH) in Beijing, China. Data were retrospectively collected from 1941 pregnant women who had registered for the PUMCH mobile prenatal education program and subsequently delivered at PUMCH between May 2021 and August 2022. The study compared pregnancy outcomes between the completing group, which were pregnant women who had completed at least 1 course, and the noncompleting group. We also analyzed differences among course topics within the completing group and assessed course topic popularity among pregnant women. Results: The PUMCH mobile prenatal education curriculum consists of 436 courses across 9 topics. Out of the participants, a total of 1521 did not complete any courses, while 420 completed at least 1 course. Compared with the noncompleting group, pregnant women who completed courses exhibited a significant reduction in the risk of gestational diabetes mellitus, induced abortion, postpartum infection, fetal intrauterine distress, and neonatal malformation. Among those in the completing group, a total of 86% (361/420) started course completion during the first and second trimesters. Furthermore, completing courses related to topics of pregnancy psychology and pregnancy nutrition was associated with reduced risks of premature rupture of membranes and small for gestational age infants, respectively. Pregnancy psychology and postpartum recovery were the preferred topics among pregnant women. Conclusions: The study demonstrates the potential of mobile-based prenatal education programs in improving pregnancy outcomes and supporting health care providers in delivering effective prenatal education. The rise of mobile prenatal education presents an opportunity to improve maternal and child health outcomes. Further research and broader implementation of such programs are warranted to continually improve maternal and child health.

Objective: Gestational diabetes mellitus (GDM) is one of the most common pregnancy complications. Electronic health records (EHRs) promise GDM risk prediction, but missing data poses a challenge to developing reliable and generalizable risk prediction models. This study aims to address the problem of missing EHR data in GDM prediction before 12 weeks gestation. Methods: A total of 5066 women with singleton pregnancies, aged 18 to 50, were included in this retrospective study. This study evaluated 6 imputation methods, combined with 4 classification machine learning models. The evaluation encompassed downstream predictive performance, robustness to variable missingness, ability to restore original data distribution, and influence on feature selection based on 10-fold cross-validation. Results: Our findings revealed a significant improvement in model performance with imputation. When using the top 30 features, logistic regression (LR) with multivariate imputation by chained equations using classification and regression trees (mice) achieved the highest area under the receiver operating characteristic curve of 0.6899, compared to 0.6336 for the LR model without imputation. Mice also led to the best average performance across prediction models and yielded the most accurate restoration of the original data distribution. LR models trained on data imputed by mice remained the most robust across varying levels of missingness. The classification algorithm primarily accounted for differences in predictive performance. In addition, we identified 18 key features for early GDM prediction in the Chinese population. Conclusion: This study demonstrates the critical role of imputation in improving the performance and fairness of GDM prediction models. The findings provide practical guidance for integrating imputation into clinical machine learning pipelines.

In this article, we report a case of spontaneous ruptured pyomyoma during pregnancy with successful delivery after myomectomy. A 35-year-old pregnant female (27 weeks of gestation) presented with lower abdominal pain. The patient had a history of uterine fibroids. Ultrasound, computed tomography, and magnetic resonance imaging confirmed a spontaneous ruptured pyomyoma (maximum diameter: 12.6 cm). Myomectomy was performed, and her abdominal distension and pain were significantly improved within 1 day; the condition of the fetus was normal. One month after surgery, severe tenderness was detected in the lower right side of the uterus. Considering the higher risk of uterine rupture and associated complications, a cesarean section was performed. The mother and neonate were discharged 3 and 42 days after delivery, respectively, in good condition. Myomectomy for spontaneous ruptured pyomyoma during pregnancy may be feasible and extend gestational age to improve outcomes for the mother and neonate.

Background: Maternal health during the perinatal period is a global public health priority. While antenatal education is widely implemented, conventional lecture-based models often fail to achieve sustained behavior change. Innovative approaches that integrate experiential learning with digital support may enhance maternal knowledge, self-management, and pregnancy outcomes. Objective: The aim of this study is to evaluate the feasibility and preliminary effectiveness of a combined experiential class and online logging intervention for pregnant women in China and to explore the mechanisms underpinning its impact on health practices and service experiences. Methods: A mixed methods design was used in a district-level maternal and child health hospital in Beijing. In the quantitative arm, 40 women (intervention group, n=20; control group, n=20) were enrolled in a quasi-experimental comparison. Outcomes included knowledge-attitude-practice indicators, service satisfaction, and clinical birth outcomes. Given the limited sample size, a qualitative arm was conducted to complement statistical findings: semistructured interviews with 20 women (10 per group) were analyzed thematically. Quantitative and qualitative results were integrated during interpretation to provide a comprehensive evaluation. Results: Compared with the experiential class alone, the combined intervention was associated with higher knowledge scores (mean difference 1.6 points, 95% CI 0.8-2.4), stronger adherence to recommended health practices (composite adherence score difference 1.0, 95% CI 0.4-1.6), and higher overall service satisfaction (mean difference 0.6, 95% CI 0.2-1.0). Across multiple domains, a higher proportion of participants in the intervention group met dietary, exercise, and supplementation recommendations. Clinical outcome differences were exploratory, as the study was not powered for these end points. Qualitative analysis revealed 3 mechanisms, such as empowerment and self-efficacy, practice and persistence, and systemic/environmental support, through which the intervention influenced experiences and practices. Conclusions: The experiential class plus online logging model is feasible and acceptable in a real-world antenatal setting. Although limited by a small sample size, findings suggest that the intervention improves maternal knowledge, health practices, and service experiences and may inform future adequately powered trials to evaluate pregnancy outcomes. Qualitative insights highlight mechanisms of health practice change and provide contextual depth, underscoring the value of mixed methods designs in maternal health research.

BACKGROUND Maternal health during the perinatal period is a global public health priority. While antenatal education is widely implemented, conventional lecture-based models often fail to achieve sustained behaviour change. Innovative approaches that integrate experiential learning with digital support may enhance maternal knowledge, self-management, and pregnancy outcomes. OBJECTIVE To evaluate the feasibility and preliminary effectiveness of a combined experiential class and online logging intervention for pregnant women in China, and to explore the mechanisms underpinning its impact on health behaviours and service experiences. METHODS A mixed-methods design was employed in a district-level maternal and child health hospital in Beijing. In the quantitative arm, 40 women (intervention group, n=20; control group, n=20) were enrolled in a quasi-experimental comparison. Outcomes included knowledge-attitude-practice (KAP) indicators, service satisfaction, and clinical birth outcomes. Given the limited sample size, a qualitative arm was conducted to complement statistical findings: semi-structured interviews with 20 women (10 per group) were analyzed thematically. Quantitative and qualitative results were integrated during interpretation to provide a comprehensive evaluation. RESULTS Compared with experiential class alone, the combined intervention significantly improved maternal knowledge, healthy behaviour adherence, and satisfaction, with favourable but non-significant trends in clinical outcomes. Qualitative analysis revealed three mechanisms-empowerment and self-efficacy, practice and persistence, and systemic/environmental support-through which the intervention influenced experiences and behaviours. CONCLUSIONS The experiential class plus online logging model is feasible and acceptable in a real-world antenatal setting. While limited by small sample size, findings suggest the intervention improves maternal knowledge, behaviours, and service experiences, with potential to optimize pregnancy outcomes. Qualitative insights highlight mechanisms of behavioural change and provide contextual depth, underscoring the value of mixed-methods designs in maternal health research.