Search

Additional file 3. Resources for IINDIAGO intervention.

Additional file 4. Outline of training for lay counsellors.

Additional file 2. IINDIAGO matrix: Expanded COM-B: TDF domains, theoretical constructs and relevance to GDM women (identified barriers & enablers): Self-care.

Additional file 1. IINDIAGO matrix: Expanded COM-B: TDF domains, theoretical constructs and relevance to GDM women (identified barriers & enablers): Physical Activity.

Overweight and obesity increase the risk of a range of poor physiological and psychosocial health outcomes. Previous work with well-defined cohorts has explored the determinants of obesity and employed various methods and measures; however, less is known on the broader societal drivers, beyond individual-level influences, using a systems framework with adolescents. The aim of this study was to explore the drivers of obesity from adolescents’ perspectives using a systems approach through group model building in four South African schools. Group model building was used to generate 4 causal loop diagrams with 62 adolescents aged 16–18 years. These maps were merged into one final map, and the main themes were identified: (i) physical activity and social media use; (ii) physical activity, health-related morbidity, and socio-economic status; (iii) accessibility of unhealthy food and energy intake/body weight; (iv) psychological distress, body weight, and weight-related bullying; and (v) parental involvement and unhealthy food intake. Our study identified meaningful policy-relevant insights into the drivers of adolescent obesity, as described by the young people themselves in a South African context. This approach, both the process of construction and the final visualization, provides a basis for taking a novel approach to prevention and intervention recommendations for adolescent obesity.

Women with gestational diabetes mellitus (GDM) have increased risk of adverse pregnancy outcomes such as delivering Large-for-gestational-age babies, preeclampsia or birth trauma, as well as increased risk of developing type 2 diabetes (T2DM) later in life. Lifestyle management through a healthy diet and physical activity both during and after a GDM pregnancy is the first line treatment option in GDM management and for delaying the onset of T2DM. The research for this Master thesis had two main aims: firstly, to investigate the dietary intake and beliefs related to dietary intake of pregnant women with GDM in Cape Town, and whether they adhere to established dietary recommendations and secondly, to investigate the change in dietary intake, physical activity and associated factors as well as beliefs related to these lifestyle behaviours in women with GDM from pregnancy to a postpartum follow-up assessment. Methods: For the first aim a cross-sectional study was conducted on 239 pregnant women with GDM in Cape Town and for the second aim, 98 women were followed-up 3 to 15 months postpartum. Assessments included: a quantified Food Frequency Questionnaire (qFFQ), General Practice Physical Activity Questionnaire (GPPAQ) and beliefs relating to specific dietary components were assessed using the Theory of Planned Behaviour (TPB). Results: At baseline, the majority of the sample had inadequate intakes of vitamin D (87.4%), folate (96.5%) and iron (91.3%), and the dietary intake of these women was not optimal and fell short in meeting several nutritional guidelines for pregnant women with hyperglycaemia. At follow-up, the dietary changes made during pregnancy were not maintained postpartum. Fruit and vegetable intake (F&V) fell short of the recommended 400g intake at both baseline and follow-up. The intake of carbohydrates, added sugar, table sugar, sugar sweetened beverages (SSBs), pulses and energy-dense foods increased significantly from pregnancy to postpartum. In conclusion, women with prior GDM fail to maintain the dietary changes made during pregnancy. These women being at risk for the development of T2DM would benefit from interventions supporting behaviour change towards a healthier lifestyle in pregnancy and continued in the postpartum period.

Includes abstract., Includes bibliographical references., A food composition database needs to be reflective of the commonly consumed foods eaten by the population, in order for it to be comprehensive. The South African Food Data System (SAFOODS) is one of a few food composition databases used amongst nutrition researchers and academia nationally for dietary intake analysis of South Africans. The SAFOODS comprises of 37% truly analysed South African nutrient values, which provides for an improved analysis when using this food composition database to analyse dietary intake data of South Africans. Indigenous vegetables and traditional recipe foods are limited within the current SAFOODS, resulting in nutrition researchers making assumptions when coding dietary records comprising of these foods eaten in selected study areas. The aim of this cross-sectional descriptive study was to collect and evaluate the different assumptions made by researchers when analysing food intake data inclusive of indigenous vegetables and traditional recipe foods, when utilising the South African food composition database for dietary analysis. In addition, it aims to record how these assumptions could possibly over or under report on actual dietary intake. Forty (40) nutrition researchers, actively engaged in dietary intake studies across the nine provinces of South Africa, were conveniently selected for this study. These researchers were all linked to an academic institution and consisted of dietitians and nutritionists. A questionnaire was completed, assumptions recorded by the nutrition researchers for indigenous vegetables and traditional recipe foods not found within SAFOODS. The study investigator further entered assumptions reported by participants of the study into a sample menu dataset. Information received from participants‟ were compared to a reference meal analysis report and analysed results compared for energy and macronutrients (carbohydrate, fat, protein) and micronutrients (vitamin A and C, iron, magnesium and sodium).