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Dietary Fuels in Athletic Performance
Abstract
Focusing on daily nutrition is important for athletes to perform and adapt optimally to exercise training. The major roles of an athlete's daily diet are to supply the substrates needed to cover the energy demands for exercise, to ensure quick recovery between exercise bouts, to optimize adaptations to exercise training, and to stay healthy. The major energy substrates for exercising skeletal muscles are carbohydrate and fat stores. Optimizing the timing and type of energy intake and the amount of dietary macronutrients is essential to ensure peak training and competition performance, and these strategies play important roles in modulating skeletal muscle adaptations to endurance and resistance training. In this review, recent advances in nutritional strategies designed to optimize exercise-induced adaptations in skeletal muscle are discussed, with an emphasis on mechanistic approaches, by describing the physiological mechanisms that provide the basis for different nutrition regimens.
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“Athletes’ nutrition choices influence sporting performance and recovery post-exercise.”
From Adolescent athletes have better general than sports nutrition knowledge and lack awareness of supplement recommendations: a systematic literature review (2024) · cites this paper as [2]
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“Carefully timed pre-, during and post-training foods and fluids ensure adequate energy availability during exercise and training, aid glycogen re-synthesis post-exercise and maximise training adaptations(1,2).”
From Adolescent athletes have better general than sports nutrition knowledge and lack awareness of supplement recommendations: a systematic literature review (2024) · cites this paper as [2]
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“Carefully timed pre-, during and post-training foods and fluids ensure adequate energy availability during exercise and training, aid glycogen re-synthesis post-exercise and maximise training adaptations(1,2).”
From Adolescent athletes have better general than sports nutrition knowledge and lack awareness of supplement recommendations: a systematic literature review (2024) · cites this paper as [2]
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“Carefully timed pre-, during and post-training foods and fluids ensure adequate energy availability during exercise and training, aid glycogen re-synthesis post-exercise and maximise training adaptations(1,2).”
From Adolescent athletes have better general than sports nutrition knowledge and lack awareness of supplement recommendations: a systematic literature review (2024) · cites this paper as [2]
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Cited by
Arazi, H., Eghbali, E., & Suzuki, K. (2021) · Nutrients · doi:10.3390/nu13030869 · Cited by 145
Hulland, S. C., Trakman, G. L., & Alcock, R. D. (2024) · British Journal of Nutrition · doi:10.1017/s0007114523002799 · Cited by 34
Irwin, N., Ó Catháin, C., Ward, C., & Kelly, D. T. (2026) · Journal of the International Society of Sports Nutrition · doi:10.1080/15502783.2026.2671431 · Cited by 4
Metadata
- Authors
- Fritzen, A. M., Lundsgaard, A.-M., & Kiens, B.
- Affiliations
- Section of Molecular Physiology, Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, 2200 Copenhagen, Denmark;, ,
- Venue
- Annual Review of Nutrition
- Publisher
- Annual Reviews
- Published
- 2019-08-21
- Volume
- 39
- Issue
- 1
- Pages
- 45-73
- ISSN
- 0199-9885
- 1545-4312
- Cited by
- 35
- References
- 72
- Language
- en
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.