This study aimed to provide insights into Australian women’s experiences in gaining nutrition information during pregnancy. Individual semistructured telephone interviews were conducted with 17 pregnant (across all trimesters) and 9 postpartum women in five Australian states. Data were transcribed and analysed using inductive thematic analysis. Women valued nutrition information, actively sought it, and passively received it mainly from three sources: healthcare providers (HCPs), media, and their social networks. Women reported HCPs as highest for reliability but they had limited time and indifferent approaches. Various media were easily and most frequently accessed but were less reliable. Social networks were considered to be the least reliable and least accessed. Women reported becoming overwhelmed and confused. This in turn influenced their decisions (pragmatic/rational) and their eating behaviours (“overdo it,” “loosen it,” “ignore it,” and “positive response”). Individual and environmental barriers impacted their application of knowledge to dietary practice. Women wanted more constructive and interactive engagement with their HCPs. This study identified the need to establish and maintain mutually respectful environments where women feel able to raise issues with their HCPs throughout their pregnancies and where they are confident that the information they receive will be accurate and meet their needs.

Background: The Australian Guide to Healthy Eating (AGHE) for pregnancy provides a number of food- and nutrition-related recommendations to assist pregnant women in optimizing their dietary behavior. However, there are limited data demonstrating pregnant women’s knowledge of the AGHE recommendations. This study investigated Australian pregnant women’s knowledge of the AGHE and related dietary recommendations for maintaining a healthy pregnancy. The variations in nutrition knowledge were compared with demographic characteristics. Methods: A cross-sectional study assessed eight different nutrition knowledge domains and the demographic characteristics of pregnant women. Four hundred women across Australia completed a multidimensional online survey based on validated and existing measures. Results: More than half of the pregnant women surveyed (65%) were not familiar with the AGHE recommendations. The basic recommendations to eat more fruit, vegetables, bread, and cereals but less meat were poorly understood. An in-depth investigation of knowledge of nutrition information revealed misconceptions in a range of areas, including standard serving size, nutrients content of certain foods, energy density of fat, and the importance of key nutrients in pregnancy. Univariate analysis revealed significant demographic variation in nutrition knowledge scores. Multiple regression analysis confirmed the significant independent effects on respondents’ nutrition knowledge score (P<0.000) of the education level, income, age, stage of pregnancy, language, and having a health/nutrition qualification. The model indicated that independent variables explained 33% (adjusted R²) of the variance found between respondents’ knowledge scores.
Conclusion: Australian pregnant women’s knowledge regarding AGHE for pregnancy and other key dietary recommendations is poor and varies significantly with their demographic profile. The setting of dietary guidelines is not sufficient to ensure improvement in their nutrition knowledge. It is essential that women receive support to achieve optimal and healthy diets during pregnancy.

Background: Maternal depression and anxiety are significant public health concerns that play an important role in the health and well-being of mothers and children. The COVID-19 pandemic, the consequential lockdowns and related safety restrictions worldwide negatively affected the mental health of pregnant and postpartum women. Methods: This regional study aimed to develop a machine learning (ML) model for the prediction of maternal depression and anxiety. The study used a dataset collected from five Arab countries during the COVID-19 pandemic between July to December 2020. The population sample included 3569 women (1939 pregnant and 1630 postpartum) from five countries (Jordan, Palestine, Lebanon, Saudi Arabia, and Bahrain). The performance of seven machine learning algorithms was assessed for the prediction of depression and anxiety symptoms. Results: The Gradient Boosting (GB) and Random Forest (RF) models outperformed other studied ML algorithms with accuracy values of 83.3% and 83.2% for depression, respectively, and values of 82.9% and 81.3% for anxiety, respectively. The Mathew’s Correlation Coefficient was evaluated for the ML models; the Naïve Bayes (NB) and GB models presented the highest performance measures (0.63 and 0.59) for depression and (0.74 and 0.73) for anxiety, respectively. The features’ importance ranking was evaluated, the results showed that stress during pregnancy, family support, financial issues, income, and social support were the most significant values in predicting anxiety and depression. Conclusion: Overall, the study evidenced the power of ML models in predicting maternal depression and anxiety and proved to be an efficient tool for identifying and predicting the associated risk factors that influence maternal mental health. The deployment of machine learning models for screening and early detection of depression and anxiety among pregnant and postpartum women might facilitate the development of health prevention and intervention programs that will enhance maternal and child health in low- and middle-income countries.

Background: With increasing global rates of overweight, obesity and non-communicable diseases (NCDs) along with undernutrition and micronutrient deficiencies, the Eastern Mediterranean Region (EMR) is no exception. This review focuses on specific nutrition parameters among under five years children, namely ever breastfed, exclusive breastfeeding, mixed milk feeding, continued breastfeeding, bottle feeding, introduction of solid, semi-solid, or soft foods and malnutrition. Methodology: PubMed, Google Scholar, United Nations International Children’s Emergency Fund (UNICEF) databases, World Health Organization (WHO) databases, the World Bank databases and the Global Nutrition Report databases were explored between 10 January and 6 June 2022, to review the nutrition situation among under five years children in the EMR. Results: The regional average prevalence of ever breastfed, exclusive breastfeeding, mixed milk feeding, continued breastfeeding, bottle feeding, introduction of solid, semi-solid, or soft foods was estimated at 84.3%, 30.9%, 42.9%, 41.5%, 32.1% and 69.3%, respectively. Iran, Iraq, Libya and Palestine have seen a decline over time in the prevalence of exclusive breastfeeding. Lebanon, Egypt, Kuwait and Saudi Arabia reported early introduction of infant formula. Moreover, Lebanon, Pakistan, Saudi Arabia and United Arab Emirates were seen to introduce food early to the child, at between 4–6 months of age. The estimated weighted regional averages for stunting, wasting and underweight were 20.3%, 8.9% and 13.1%, respectively. Of concern is the increasing prevalence of stunting in Libya. As for overweight and obesity, the average prevalence was reported to be 8.9% and 3%, respectively. Lebanon, Libya, Kuwait and Palestine showed an increased trend throughout this time. Conclusions: In this review, the suboptimal infant and young child feeding patterns and the twofold incidence of malnutrition in the EMR are highlighted and we urge the prioritizing of measures to improve children’s nutrition.

Background. Excessive gestational weight gain (GWG) can negatively impact on maternal and foetal health. Guidelines based on Institute of Medicine (IOM) encourage managing GWG by following healthy eating recommendations and increasing physical activity. This study investigated pregnant women’s knowledge of their optimal GWG and recommended dietary approaches for GWG management.Method. English-speaking pregnant women were recruited from five hospitals in New South Wales (Australia) and an online link. Prepregnancy Body Mass Index (BMI) was calculated from self-reported height and prepregnancy weight. Participants identified their recommended GWG. A survey assessed practical dietary knowledge and asked about broad dietary recommendations to prevent excessive GWG. Chi square and logistic regression analyses were used.Results.N=326pregnant women completed the surveys; 49% entered pregnancy overweight (25.2%) or obese (23.6%); and knowledge of recommended GWG was lacking. Prepregnancy BMI was a significant predictor of GWG recommendation knowledge (P<0.000). Pregnant women were highly knowledgeable about broad dietary recommendations but had poor knowledge of detailed recommendations.Conclusions. Limited knowledge of IOM’s GWG guidelines and of specific dietary recommendations for pregnancy should be addressed by health care providers and education initiatives to assist the high number of women who enter pregnancy overweight or obese.