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.

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 $0.03\pm 0.013$ and an $R^2$ of $63\pm 19\%$, where the best-achieved performance was an error level with a normalized mean squared error of 0.008 and an $R^2$ 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.

Background: Preeclampsia remains a leading cause of maternal morbidity worldwide. There is a critical need for predictive systems that not only perform accurately but also provide interpretable insights for clinical decision-making. This work introduces SK-MOEFS, an explainable framework based on fuzzy logic and multi-objective evolutionary optimization, designed to classify preeclampsia risk while generating clinically interpretable rules. Methods: The model integrates fuzzy decision trees with a genetic algorithm to identify a compact and relevant set of rules, optimized for both accuracy and interpretability. The system was trained and evaluated on third-trimester pregnancy data from a publicly available, multi-ethnic cohort comprising 574 individuals. All processes, including preprocessing, training, and evaluation, were conducted using open-source tools, ensuring reproducibility. Results: SK-MOEFS achieved 91% classification accuracy, an AUC of 0.89, and a recall of 0.88—outperforming other standard interpretable models while maintaining high transparency. The model emphasizes minimizing false negatives, which is critical in clinical risk stratification for preeclampsia. Conclusions: Beyond predictive performance, SK-MOEFS offers a rule translation and defuzzification layer that outputs probabilistic interpretations in natural language, enhancing its suitability for clinical use. This framework provides an effective bridge between algorithmic inference and human clinical judgment, supporting transparent and reliable decision-making in maternal care.

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

Background: Metabolic syndrome (MetS) is a major risk factor for type 2 diabetes and cardiovascular disease. In recent years, telemedicine and digital health interventions have emerged as promising strategies to support lifestyle modification and the long-term management of cardiometabolic conditions. However, their clinical effectiveness in MetS remains heterogeneously reported. Methods: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed, Scopus, and Web of Science were searched for studies published between 2019 and 2024. Eligible studies included adults with MetS or its components and evaluated telemedicine or technology-enabled interventions, including mobile health (mhealth) applications, remote monitoring, wearable devices, or telecommunication-based care. Outcomes of interest included waist circumference (WC), glycemic parameters, blood pressure, and lipid profile. Results: Twenty seven studies met inclusion criteria. Most interventions incorporated telemedicine components such as remote coaching, digital feedback, and continuous monitoring, frequently delivered through mobile platforms and wearable technologies. These interventions consistently resulted in reductions in WC, while modest but recurrent improvements were observed in glycemic control and blood pressure. Effects on lipid parameters were more variable, with more frequent improvements in high-density lipoprotein cholesterol than in low-density lipoprotein cholesterol. Higher intervention intensity and user engagement were associated with greater clinical benefits. Conclusions: Telemedicine and digital health interventions represent effective adjuncts to conventional lifestyle management of MetS, particularly for central obesity and glycemic outcomes. Their ability to deliver scalable, personalized, and remotely supported care highlights their potential role in cardiometabolic prevention and management. Further research is needed to standardize intervention components and optimize long-term effectiveness.

Food-based dietary guidelines (FBDGs) are key public health instruments aimed at promoting healthy dietary patterns. However, dairy-products recommendations vary substantially across countries, reflecting not only scientific evidence but also socioeconomic conditions, institutional capacity, and food system characteristics. The extent to which these differences are structured linguistically across income levels has not been systematically quantified. The present study aims to explore the use of advanced NLP techniques to characterize semantic differences and similarities in dairy-products dietary messages within FBDGs from countries with different levels of socioeconomic development. We conducted a comparative analysis of dairy-products recommendations extracted from national FBDGs officially recognised by the Food and Agriculture Organization, covering 98 countries. Using advanced natural language processing techniques, including lexical frequency analysis, co-occurrence networks, and latent topic modelling, we examined semantic patterns in recommendation statements and justificatory texts. Countries were stratified according to World Bank income group classifications. Across all income groups, “milk” emerged as the central lexical anchor of dairy recommendations. However, high-income countries demonstrated greater lexical diversity and semantic complexity, incorporating differentiated references to product types, fat content, and fermentation (e.g., yogurt, cheese, low-fat). In contrast, low- and lower-middle-income countries presented more general and nutritionally basic messaging, primarily focused on consumption adequacy and child nutrition. Justification texts consistently contained higher nutrient-related terminology density than recommendation statements. Dairy-products dietary messaging in national FBDGs shows consistent descriptive differences across income groups. These semantic disparities likely reflect contextual differences in institutional capacity, epidemiological priorities, and food system infrastructure. The findings may be particularly relevant for the Americas, where high-, upper-middle-, and lower-middle-income countries coexist within a region undergoing rapid nutrition transitions and facing the persistent triple burden of malnutrition. In this context, PAHO/WHO may play an important role in supporting greater harmonisation of dietary guidance across diverse socioeconomic settings. NLP-based approaches offer scalable tools for monitoring global nutrition policy discourse and supporting evidence-informed policy development. Research supported by the Vice-Presidency of Research and Doctoral Studies of Universidad San Sebastián, Grant USS-FIN-26-APCS-01; Institutional collaboration provided by the Scientific Committee of Dairy Products of the Chilean Dairy Consortium (Consorcio Lechero) through the "Gracias a la Leche" program.