Artificial Intelligence in Sports Nutrition: Toward Personalized Dietary Strategies for Athletic Performance in Medan City, Indonesia

Abstract

Objectives: This study aimed to explore the application of artificial intelligence (AI) in developing personalized dietary strategies to enhance athletic performance among athletes in Medan City, Indonesia.

Materials and Methods: This study used a mixed-method approach combining experimental and survey designs. A total of 45 athletes from various sports disciplines participated in an eight-week AI-based nutrition intervention. The AI-based nutrition system analyzed individual characteristics, including body composition, training load, and dietary habits, to generate personalized nutrition plans. Data were collected using VO₂max performance tests, body composition analysis, and athlete feedback questionnaires. Quantitative data were analyzed to determine changes in endurance and body composition, while survey data were used to describe athletes’ adherence and satisfaction with the AI-based dietary recommendations.

Results: The results showed significant improvements in endurance performance after the intervention, as indicated by increased VO₂max scores (p < 0.05). The intervention also contributed to more optimized body composition and better adherence to dietary plans. In addition, athlete feedback showed higher satisfaction with the nutrition program because the recommendations were individualized according to training load, physical condition, and dietary habits.

Conclusions: AI-based personalized dietary strategies are an effective and innovative approach to supporting athletic performance. By providing individualized nutrition recommendations, AI systems can improve endurance, support body composition management, and increase dietary adherence among athletes. These findings provide practical implications for coaches, athletes, and sports nutrition practitioners in developing technology-based nutrition programs.