Injury aetiology models that have evolved over the previous two decades highlight a number of factors which contribute to the causal mechanisms for athletic injuries. These models highlight the pathway to injury, including (1) internal risk factors (eg, age, neuromuscular control) which predispose athletes to injury, (2) exposure to external risk factors (eg, playing surface, equipment), and finally (3) an inciting event, wherein biomechanical breakdown and injury occurs. The most recent aetiological model proposed in 2007 was the first to detail the dynamic nature of injury risk, whereby participation may or may not result in injury, and participation itself alters injury risk through adaptation. However, although training and competition workloads are strongly associated with injury, existing aetiology models neither include them nor provide an explanation for how workloads alter injury risk. Therefore, we propose an updated injury aetiology model which includes the effects of workloads. Within this model, internal risk factors are differentiated into modifiable and non-modifiable factors, and workloads contribute to injury in three ways: (1) exposure to external risk factors and potential inciting events, (2) fatigue, or negative physiological effects, and (3) fitness, or positive physiological adaptations. Exposure is determined solely by total load, while positive and negative adaptations are controlled both by total workloads, as well as changes in load (eg, the acute:chronic workload ratio). Finally, we describe how this model explains the load-injury relationships for total workloads, acute:chronic workload ratios and the training load-injury paradox.

Low-carbohydrate high-fat (LCHF) diets are a highly contentious current topic in nutrition. This narrative review aims to provide clinicians with a broad overview of the effects of LCHF diets on body weight, glycaemic control and cardiovascular risk factors while addressing some common concerns and misconceptions. Blood total cholesterol and LDL-cholesterol concentrations show a variable, highly individual response to LCHF diets, and should be monitored in patients adhering to this diet. In contrast, available evidence from clinical and preclinical studies indicates that LCHF diets consistently improve all other markers of cardiovascular risk—lowering elevated blood glucose, insulin, triglyceride, ApoB and saturated fat (especially palmitoleic acid) concentrations, reducing small dense LDL particle numbers, glycated haemoglobin (HbA1c) levels, blood pressure and body weight while increasing low HDL-cholesterol concentrations and reversing non-alcoholic fatty liver disease (NAFLD). This particular combination of favourable modifications to all these risk factors is a benefit unique to LCHF diets. These effects are likely due in part to reduced hunger and decreased ad libitum calorie intake common to low-carbohydrate diets, allied to a reduction in hyperinsulinaemia, and reversal of NAFLD. Although LCHF diets may not be suitable for everyone, available evidence shows this eating plan to be a safe and efficacious dietary option to be considered. LCHF diets may also be particularly beneficial in patients with atherogenic dyslipidaemia, insulin resistance, and the frequently associated NAFLD.

Maximising participation in preseason training may protect elite rugby league players against in-season injury.

Training load monitoring is a core aspect of modern-day sport science practice. Collecting, cleaning, analysing, interpreting, and disseminating load data is usually undertaken with a view to improve player performance and/or manage injury risk. To target these outcomes, practitioners attempt to optimise load at different stages throughout the training process, like adjusting individual sessions, planning day-to-day, periodising the season, and managing athletes with a long-term view. With greater investment in training load monitoring comes greater expectations, as stakeholders count on practitioners to transform data into informed, meaningful decisions. In this editorial we highlight how training load monitoring has many potential applications and cannot be simply reduced to one metric and/or calculation. With experience across a variety of sporting backgrounds, this editorial details the challenges and contextual factors that must be considered when interpreting such data. It further demonstrates the need for those working with athletes to develop strong communication channels with all stakeholders in the decision-making process. Importantly, this editorial highlights the complexity associated with using training load for managing injury risk and explores the potential for framing training load with a performance and training progression mindset.