Nutrition Assessment of Junior Elite Canadian Female Soccer Athletes
Bibliographic record
Abstract
The adolescent female soccer athlete is faced with the complex challenge of consuming adequate nutrition in order to fuel performance as well as growth and development. There are limited nutritional status data available for this large athlete population and it has yet to be well described. PURPOSE: This study provides a comprehensive assessment of nutrition status for 34 junior elite female soccer athletes (15.7 ± 0.7 years). METHODS: Using a cross sectional, descriptive design, athletes completed anthropometric assessment, four-day estimated food records and hematological analysis (complete blood count, iron status markers, prealbumin and 25-hydroxyvitamin D). Means and standard deviations were determined and Pearson's product moment correlation analysis was used to quantify relationships between vitamin D intake and serum 25-hydroxyvitamin D; iron intake and serum ferritin; protein intake and serum prealbumin; and energy intake and serum prealbumin. RESULTS: Mean sum of skinfolds (7 sites) was 103.1 ± 35.2mm (20.2 ± 5.4% body fat). Mean energy intake was 2079 ± 460kcal/day while estimated energy expenditure was higher at 2546 ± 190kcal/day. The average intake for macronutrients met current population recommendations, however when compared to sport nutrition guidelines, 51.5% of athletes consumed less than 5g/kg carbohydrate and 27.3% consumed less than 1.2g/kg protein. Dietary intake of several micronutrients was below DRI recommendations including that for pantothenic acid, vitamin D, folate, vitamin E, and calcium. When compared to recommendations for athletic populations, 89.3% and 50.0% had suboptimal serum ferritin and 25-hydroxyvitamin D stores respectively, although values were within normal clinical ranges. No significant correlations were found between dietary intake and serum measures (p ≥ 0.05). CONCLUSION: Players were not in energy balance, and many failed to meet carbohydrate and micronutrient requirements. When compared to recommendations for athletic populations, athletes may be at risk for iron depletion and suboptimal vitamin D status. More research is needed to confirm and support these findings and understand of the unique nutrition needs of this population. Supported by Dairyland Graduate Nutrition Scholarship and the GSSI Graduate Student Research Award.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".