Data-Driven Decision Support Systems in Elite Sports Nutrition: Current Applications and Future Directions

Elite sport is increasingly supported by wearable technologies, biosensors, and athlete monitoring systems, yet sports nutrition decision-making often remains dependent on generalized guidelines and practitioner experience. This narrative review examined how multimodal athlete data can be translated into actionable nutritional decisions through data-driven decision support systems. Behavioral, physiological, external load, and performance data were discussed within a hierarchical framework progressing from descriptive monitoring to diagnostic, predictive, and prescriptive decision-making. Key applications included energy availability risk profiling, fueling–recovery coupling, and dynamic macronutrient periodization. Although these systems may improve the precision and contextual relevance of nutrition support, implementation remains limited by data quality, model generalizability, interpretability, and workflow integration. Data-driven decision support systems represented a promising pathway toward more individualized and effective sports nutrition in elite sport.