Tailored nutrition strategies for Paralympic athletes: addressing unique energy, nutrients, and hydration needs to enhance performance and health
Bibliographic record
Abstract
The achievements of Olympic athletes are often highlighted, whereas the successes of Paralympians are frequently overlooked. Paralympic athletes with disabilities face unique nutritional challenges due to variations in energy expenditure and nutrient requirements associated with their specific disabilities and sports. Hydration is critical, particularly for athletes with spinal cord injuries, who may struggle with body temperature regulation. Despite the importance of nutrition in enhancing athletic performance, there is a lack of research focused on the nutritional requirements of Paralympic athletes. This scholarly review provides a comprehensive overview of the energy, macronutrient, and micronutrient requirements, including those for minerals and vitamins, supplements, and fluid intake, of Paralympic athletes aged 18 and older. This literature search was conducted via the Scopus and Google Scholar databases and focused on English-language original articles published between 1990 and 2024. This review included 56 studies. These findings highlight the necessity for tailored nutritional planning to support the performance and health of Paralympic athletes. Close monitoring of individual intake is essential to adjust for fluctuations in macronutrients, micronutrient supplements, and fluid intake. Ongoing research is vital for developing effective nutritional strategies that accommodate the diverse needs of these athletes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".