South Africa faces a dual burden of non-communicable diseases (NCDs) and communicable diseases, exacerbated by the high consumption of processed foods. The Eat Better South Africa (EBSA) program implements community-based low-carbohydrate, high-fat (LCHF) interventions to address these issues. This study evaluated the impact of EBSA's 6-week program on the metabolic health and well-being of 32 women from underserved communities. It assessed outcomes before, immediately after, and six months post-intervention (n = 21). Quantitative findings showed significant improvements in key health markers. After six months, participants experienced an average weight loss of 5.6 kg (+- 5.5), a BMI reduction of 2 kg/m2, and a waist circumference decrease of 6.6 cm. Blood pressure dropped by 10.7 mmHg on average, and fasting glucose levels decreased significantly. Reductions were also observed in triglycerides and HbA1c, indicating better glycemic control. Liver function markers (GGT, ALT) and inflammation markers (CRP) improved as well. Qualitative analysis highlighted several key themes: participants were motivated by a desire to improve their health and lose weight but faced challenges such as social pressures, community violence, and scepticism about the diet's affordability and sustainability. Despite these barriers, positive experiences like increased energy and better hunger control were reported. Participants expressed the need for ongoing support to maintain these changes, both from the program and from their community. These findings suggest that LCHF diets can effectively manage metabolic conditions, but long-term adherence is challenged by socio-economic factors. The study highlights the importance of community-based interventions and highlights the need for further research to develop sustainable health strategies in low-income settings.

Qualitative data analysis software (QDAS) packages that support live data extraction are a relatively recent innovation. Little has been written concerning the research implications of differences in such QDAS packages’ functionalities, and how such disparities might contribute to contrasting analytical opportunities. Consequently, early-stage researchers may experience difficulties in choosing an apt QDAS for Twitter analysis. In response to both methodological gaps, this paper presents a software comparison across the four QDAS tools that support live Twitter data imports, namely, ATLAS.ti™, NVivo™, MAXQDA™ and QDA Miner™. The authors’ QDAS features checklist for these tools spotlights many differences in their functionalities. These disparities were tested through data imports and thematic coding that was derived from the same queries and codebook. The authors’ resultant QDAS experiences were compared during the first activity of a broad qualitative analysis process, ‘organising data’. Notwithstanding large difference in QDAS pricing, it was surprising how much the tools varied for aspects of qualitative research organisation. Notably, the quantum of data extracted for the same query differed, largely due to contrasts in the types and amount of data that the four QDAS could extract. Variations in how each supported visual organisation also shaped researchers’ opportunities for becoming familiar with Twitter users and their tweet content. Such disparities suggest that choosing a suitable QDAS for organising live Twitter data must dovetail with a researcher’s focus: ATLAS.ti accommodates scholars focused on wrangling unstructured data for personal meaning-making, while MAXQDA suits the mixed-methods researcher. QDA Miner’s easy-to-learn user interface suits a highly efficient implementation of methods, whilst NVivo supports relatively rapid analysis of tweet content. Such findings may help guide Twitter social science researchers and others in QDAS tool selection. Future research can explore disparities in other qualitative research phases, or contrast data extraction routes for a variety of microblogging services.

In exercise science, the crossover effect denotes that fat oxidation is the primary fuel at rest and during low-intensity exercise with a shift towards an increased reliance on carbohydrate oxidation at moderate to high exercise intensities. This model makes four predictions: First, >50% of energy comes from carbohydrate oxidation at ≥60% of maximum oxygen consumption (VO2max), termed the crossover point. Second, each individual has a maximum fat oxidation capacity (FATMAX) at an exercise intensity lower than the crossover point. FATMAX values are typically 0.3–0.6 g/min. Third, fat oxidation is minimized during exercise ≥85%VO2max, making carbohydrates the predominant energetic substrate during high-intensity exercise, especially at >85%VO2max. Fourth, high-carbohydrate low-fat (HCLF) diets will produce superior exercise performances via maximizing pre-exercise storage of this predominant exercise substrate. In a series of recent publications evaluating the metabolic and performance effects of low-carbohydrate high-fat (LCHF/ketogenic) diet adaptations during exercise of different intensities, we provide findings that challenge this model and these four predictions. First, we show that adaptation to the LCHF diet shifts the crossover point to a higher %VO2max (>80%VO2max) than previously reported. Second, substantially higher FATMAX values (>1.5 g/min) can be measured in athletes adapted to the LCHF diet. Third, endurance athletes exercising at >85%VO2max, whilst performing 6 × 800 m running intervals, measured the highest rates of fat oxidation yet reported in humans. Peak fat oxidation rates measured at 86.4 ± 6.2%VO2max were 1.58 ± 0.33 g/min with 30% of subjects achieving >1.85 g/min. These studies challenge the prevailing doctrine that carbohydrates are the predominant oxidized fuel during high-intensity exercise. We recently found that 30% of middle-aged competitive athletes presented with pre-diabetic glycemic values while on an HCLF diet, which was reversed on LCHF. We speculate that these rapid changes between diet, insulin, glucose homeostasis, and fat oxidation might be linked by diet-induced changes in mitochondrial function and insulin action. Together, we demonstrate evidence that challenges the current crossover concept and demonstrate evidence that a LCHF diet may also reverse features of pre-diabetes and future metabolic disease risk, demonstrating the impact of dietary choice has extended beyond physical performance even in athletic populations.

Eat Better South Africa (EBSA) is an organization that provides low-carbohydrate, high-fat (LCHF) nutrition and health education programs for women from under-resourced South African communities. Community assessments are essential to explore participants' potential facilitators and challenges of adhering to new dietary behaviours and should be implemented before any dietary interventions. This study is a qualitative community assessment to enable the EBSA program to better meet potential participants' needs and explore their willingness to enrol in the EBSA program. Sixty women from two communities in the Western Cape were interviewed through six focus group discussions. A thematic analysis was conducted using NVivo 12 software, and four themes were developed around the women's (1) role within the households; (2) dietary behaviour; (3) health perceptions; and (4) willingness to participate in an LCHF program. Women mentioned that they were responsible for cooking and shopping for their households. They expressed their understanding of healthy and unhealthy behaviours and their dietary patterns. Some women showed concerns about LCHF diets, but others wanted to learn more due to their knowledge of other people's positive experiences with the diet. There was a general desire to become healthy. However, the women anticipated dietary behaviour change to be challenging. Those challenges mostly revolved around their socioeconomic environments. The findings are intended to inform EBSA (or other nutrition interventions) on what to consider when implementing their interventions in these communities.

The World Health Organization (WHO) has established a public research agenda to address infodemics. In these, ‘an overflow of information of varying quality surges across digital and physical environments’. The WHO’s expert panel has raised concerns that this can result in negative health behaviours and erosion of trust in health authorities and public health responses. In sponsoring this agenda, the WHO positioned itself as a custodian that can flag illegitimate narratives (misinformation), the spread of which can potentially result in societal harm. Such ‘post-truth’ moments are rife with the coronavirus disease 2019 (COVID-19) public health emergency. It provides an opportunity for researchers to analyse divisions in knowledge labour, which can help explain when ‘post-truth’ moments arrive. The first COVID-19 example for this division foregrounds the development of knowledge in an academic context. Added to this is the infodemic or disinfodemic research agenda and personal health responsibility, whose academic contributors are similar. In contrast, the division of labour for messenger ribonucleic acid (mRNA) vaccine research foregrounds the role of vaccine manufacturing pharmaceutical companies in driving and promoting related knowledge production. Transdiciplinary Contribution: This analysis focuses on intergroup contradictions between the interests of agencies and their contrasting goals and across different types of knowledge division. Many intergroup contradictions exist, and a few intergroup examples are also described. An overarching contradiction was identified where rushed guidance based on weak evidence from international health organisations may well perpetuate negative health and other societal outcomes rather than ameliorate them.