Personalized Recommendation Method of ''Carbohydrate-Protein'' Supplement Based on Machine Learning and Enumeration Method

Carbohydrate-protein supplement (CPS) intake is a well-established strategy for enhancing athletic performance, promoting glycogen replenishment, maintaining a positive nitrogen balance, and minimizing muscle damage in endurance athletes. Current CPS intake recommendations often rely solely on weight, lacking personalization. This study aimed to develop a machine learning-based personalized CPS intake recommendation system for endurance sports enthusiasts. We recruited 171 participants and collected 45 indicators from 12 diverse aspects, including lifestyle, psychological state, sleep quality, demographics, anthropometrics, physical activity levels, exercise capacity, blood parameters, central nervous system parameters, cardiovascular metrics, meal timings, and beverage composition. Additionally, we assessed each subject's performance in the Jensen Kurt's 60-minute rowing ergometer distance race. Utilizing back propagation (BP) neural networks, we employed 5-fold cross-validation to optimize the learning rate and identify the relationship between the 45 indicators and the 1-hour rowing distance. Based on this optimized learning rate, we trained a well-fitted model on the training dataset. We further employed an enumeration method to tailor the CPS intake protocol for each individual. Our results demonstrate the feasibility and potential of using machine learning to deliver personalized CPS intake recommendations. Future work will focus on expanding the dataset's dimensions to iterate, update, and enhance the model's robustness.

Automated summary

This study aimed to develop a personalized carbohydrate-protein supplement (CPS) intake recommendation system for endurance sports enthusiasts. By utilizing machine learning, this system can tailor CPS intake protocols for each individual, showing great potential.