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Data-Driven Optimization of Carbohydrate Intake for Enhanced Performance and Recovery in Sports Dance

Yuan Sun

International Journal of Healthcare Information Systems and Informatics2026https://doi.org/10.4018/ijhisi.400753article
ABDC C
Weight
0.50

What the paper says

Sports dance demands both high metabolic efficiency and precise nutritional support because of its combination of physical exertion and artistic performance. The author examined energy metabolism patterns in sports dance athletes and proposes a data-driven model to optimize carbohydrate intake on the basis of individual needs. Through experimental tracking and multidimensional analysis, the author evaluated the impact of dynamic dietary adjustments on energy utilization and recovery. The results demonstrated that the optimized carbohydrate intake model significantly enhanced metabolic efficiency, lactate clearance, and perceived performance, outperforming traditional intake strategies. By incorporating real-time feedback and adaptive regulation, this approach transcends static guidelines, better addressing athletes' fluctuating energy demands. The findings provide a scientific foundation for personalized nutrition in sports dance, highlighting the potential of data-informed dietary strategies to enhance both performance and recovery.

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https://doi.org/https://doi.org/10.4018/ijhisi.400753

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@article{yuan2026,
  title        = {{Data-Driven Optimization of Carbohydrate Intake for Enhanced Performance and Recovery in Sports Dance}},
  author       = {Yuan Sun},
  journal      = {International Journal of Healthcare Information Systems and Informatics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.4018/ijhisi.400753},
}

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Data-Driven Optimization of Carbohydrate Intake for Enhanced Performance and Recovery in Sports Dance

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.