An mHealth Intervention to Reduce Gestational Obesity (mami-educ): Protocol for a Randomized Controlled Trial.

BACKGROUND: The World Federation of Obesity warns that the main health problem of the next decade will be childhood obesity. It is known that factors such as gestational obesity produce profound effects on fetal programming and are strong predictors of overweight and obesity in children. Therefore, establishing healthy eating behaviors during pregnancy is the key to the primary prevention of the intergenerational transmission of obesity. Mobile health (mHealth) programs are potentially more effective than face-to-face interventions, especially during a public health emergency such as the COVID-19 outbreak. OBJECTIVE: This study aims to evaluate the effectiveness of an mHealth intervention to reduce excessive weight gain in pregnant women who attend family health care centers. METHODS: The design of the intervention corresponds to a classic randomized clinical trial. The participants are pregnant women in the first trimester of pregnancy who live in urban and semiurban areas. Before starting the intervention, a survey will be applied to identify the barriers and facilitators perceived by pregnant women to adopt healthy eating behaviors. The dietary intake will be estimated in the same way. The intervention will last for 12 weeks and consists of sending messages through a multimedia messaging service with food education, addressing the 3 domains of learning (cognitive, affective, and psychomotor). Descriptive statistics will be used to analyze the demographic, socioeconomic, and obstetric characteristics of the respondents. The analysis strategy follows the intention-to-treat principle. Logistic regression analysis will be used to compare the intervention with routine care on maternal pregnancy outcome and perinatal outcome. RESULTS: The recruitment of study participants began in May 2022 and will end in May 2023. Results include the effectiveness of the intervention in reducing the incidence of excessive gestational weight gain. We also will examine the maternal-fetal outcome as well as the barriers and facilitators that influence the weight gain of pregnant women. CONCLUSIONS: Data from this effectiveness trial will determine whether mami-educ successfully reduces rates of excessive weight gain during pregnancy. If successful, the findings of this study will generate knowledge to design and implement personalized prevention strategies for gestational obesity that can be included in routine primary care. TRIAL REGISTRATION: ClinicalTrials.gov NCT05114174; https://clinicaltrials.gov/ct2/show/NCT05114174. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/44456.

Predicting Cardiovascular Rehabilitation of Patients with Coronary Artery Disease Using Transfer Feature Learning.

Cardiovascular diseases represent the leading cause of death worldwide. Thus, cardiovascular rehabilitation programs are crucial to mitigate the deaths caused by this condition each year, mainly in patients with coronary artery disease. COVID-19 was not only a challenge in this area but also an opportunity to open remote or hybrid versions of these programs, potentially reducing the number of patients who leave rehabilitation programs due to geographical/time barriers. This paper presents a method for building a cardiovascular rehabilitation prediction model using retrospective and prospective data with different features using stacked machine learning, transfer feature learning, and the joint distribution adaptation tool to address this problem. We illustrate the method over a Chilean rehabilitation center, where the prediction performance results obtained for 10-fold cross-validation achieved error levels with an NMSE of 0.03±0.013 and an R2 of 63±19%, where the best-achieved performance was an error level with a normalized mean squared error of 0.008 and an R2 up to 92%. The results are encouraging for remote cardiovascular rehabilitation programs because these models could support the prioritization of remote patients needing more help to succeed in the current rehabilitation phase.

Impact of Remote Monitoring Technologies for Assisting Patients With Gestational Diabetes Mellitus: A Systematic Review.

Introduction: In Chile, 1 in 8 pregnant women of middle socioeconomic level has gestational diabetes mellitus (GDM), and in general, 5-10% of women with GDM develop type 2 diabetes after giving birth. Recently, various technological tools have emerged to assist patients with GDM to meet glycemic goals and facilitate constant glucose monitoring, making these tasks more straightforward and comfortable. Objective: To evaluate the impact of remote monitoring technologies in assisting patients with GDM to achieve glycemic goals, and know the respective advantages and disadvantages when it comes to reducing risk during pregnancy, both for the mother and her child. Methods: A total of 188 articles were obtained with the keywords "gestational diabetes mellitus," "GDM," "gestational diabetes," added to the evaluation levels associated with "glucose level," "glycemia," "glycemic index," "blood sugar," and the technological proposal to evaluate with "glucometerm" "mobile application," "mobile applications," "technological tools," "telemedicine," "technovigilance," "wearable" published during the period 2016-2021, excluding postpartum studies, from three scientific databases: PUBMED, Scopus and Web of Science. These were managed in the Mendeley platform and classified using the PRISMA method. Results: A total of 28 articles were selected after elimination according to inclusion and exclusion criteria. The main measurement was glycemia and 4 medical devices were found (glucometer: conventional, with an infrared port, with Bluetooth, Smart type and continuous glucose monitor), which together with digital technology allow specific functions through 2 identified digital platforms (mobile applications and online systems). In four articles, the postprandial glucose was lower in the Tele-GDM groups than in the control group. Benefits such as improved glycemic control, increased satisfaction and acceptability, maternal confidence, decreased gestational weight gain, knowledge of GDM, and other relevant aspects were observed. There were also positive comments regarding the optimization of the medical team's time. Conclusion: The present review offers the opportunity to know about the respective advantages and disadvantages of remote monitoring technologies when it comes to reducing risk during pregnancy. GDM centered technology may help to evaluate outcomes and tailor personalized solutions to contribute to women's health. More studies are needed to know the impact on a healthcare system.

Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review.

Introduction: Artificial intelligence is widely used in medical field, and machine learning has been increasingly used in health care, prediction, and diagnosis and as a method of determining priority. Machine learning methods have been features of several tools in the fields of obstetrics and childcare. This present review aims to summarize the machine learning techniques to predict perinatal complications. Objective: To identify the applicability and performance of machine learning methods used to identify pregnancy complications. Methods: A total of 98 articles were obtained with the keywords "machine learning," "deep learning," "artificial intelligence," and accordingly as they related to perinatal complications ("complications in pregnancy," "pregnancy complications") from three scientific databases: PubMed, Scopus, and Web of Science. These were managed on the Mendeley platform and classified using the PRISMA method. Results: A total of 31 articles were selected after elimination according to inclusion and exclusion criteria. The features used to predict perinatal complications were primarily electronic medical records (48%), medical images (29%), and biological markers (19%), while 4% were based on other types of features, such as sensors and fetal heart rate. The main perinatal complications considered in the application of machine learning thus far are pre-eclampsia and prematurity. In the 31 studies, a total of sixteen complications were predicted. The main precision metric used is the AUC. The machine learning methods with the best results were the prediction of prematurity from medical images using the support vector machine technique, with an accuracy of 95.7%, and the prediction of neonatal mortality with the XGBoost technique, with 99.7% accuracy. Conclusion: It is important to continue promoting this area of research and promote solutions with multicenter clinical applicability through machine learning to reduce perinatal complications. This systematic review contributes significantly to the specialized literature on artificial intelligence and women's health.