Mobile Prenatal Education and Its Impact on Reducing Adverse Pregnancy Outcomes: Retrospective Real-World Study.

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.

Ifood: Development and usability study of a social media-based applet for dietary monitoring.

Background: Dietary monitoring is critical to maintaining human health. Social media platforms are widely used for daily recording and communication for individuals' diets and activities. The textual content shared on social media offers valuable resources for dietary monitoring. Objective: This study aims to describe the development of iFood, an applet providing personal dietary monitoring based on social media content, and validate its usability, which will enable efficient personal dietary monitoring. Methods: The process of the development and validation of iFood is divided into four steps: Diet datasets construction, diet record and analysis, diet monitoring applet design, and diet monitoring applet usability assessment. The diet datasets were constructed with the data collected from Weibo, Meishijie, and diet guidelines, which will be used as the basic knowledge for further model training in the phase of diet record and analysis. Then, the friendly user interface was designed to link users with backend functions. Finally, the applet was deployed as a WeChat applet and 10 users from the Beijing Union Medical College have been recruited to validate the usability of iFood. Results: Three dietary datasets, including User Visual-Textual Dataset, Dietary Information Expansion Dataset, and Diet Recipe Dataset have been constructed. The performance of 4 models for recognizing diet and fusing unimodality data was 40.43%(dictionary-based model), 18.45%(rule-based model), 59.95%(Inception-ResNet-v2), and 51.38% (K-nearest neighbor), respectively. Furthermore, we have designed a user-friendly interface for the iFood applet and conducted a usability assessment, which resulted in an above-average usability score. Conclusions: iFood is effective for managing individual dietary behaviors through its seamless integration with social media data. This study suggests that future products could utilize social media data to promote healthy lifestyles.

Comparison of Mortality Predictive Models of Sepsis Patients Based on Machine Learning.

Objective To compare the performance of five machine learning models and SAPS II score in predicting the 30-day mortality amongst patients with sepsis. Methods The sepsis patient-related data were extracted from the MIMIC-IV database. Clinical features were generated and selected by mutual information and grid search. Logistic regression, Random forest, LightGBM, XGBoost, and other machine learning models were constructed to predict the mortality probability. Five measurements including accuracy, precision, recall, F1 score, and area under curve (AUC) were acquired for model evaluation. An external validation was implemented to avoid conclusion bias. Results LightGBM outperformed other methods, achieving the highest AUC (0.900), accuracy (0.808), and precision (0.559). All machine learning models performed better than SAPS II score (AUC=0.748). LightGBM achieved 0.883 in AUC in the external data validation. Conclusions The machine learning models are more effective in predicting the 30-day mortality of patients with sepsis than the traditional SAPS II score.