Over the past decade, sports nutrition has quietly become one of the most technologically driven areas of performance support. Dietitians and athletes now interact with artificial intelligence (AI) every day, often without realising it: readiness scores pushed to their phones upon waking, automated messages interpreting training data after a ride or run, and wearable-generated summaries telling athletes whether they recovered “well” or “poorly.” This steady infiltration of A

Artificial intelligence (AI) has rapidly become one of the most frequently referenced concepts in high-performance sport. It is discussed in recruitment, in training planning, in tactical decision-making, and increasingly in sports nutrition. Yet although the term is used widely, the understanding of what AI is, how it works and what the underlying mechanisms are, is often limited. To practitioners sitting in the applied performance space (coaches, sport scientists, nutrition

Vitamin D remains a recurring topic in sport because low vitamin D status is common in athletic populations, particularly during winter at higher latitudes, and because vitamin D has well-established roles in calcium–phosphate homeostasis and skeletal health. Interest has expanded beyond bone, driven by mechanistic evidence that vitamin D receptors are expressed in multiple tissues (including skeletal muscle and immune cells) and by observational reports linking low vitamin D