Free-sugar, total-sugar, fibre, and micronutrient intake within elite youth British soccer players: a nutritional transition from schoolboy to fulltime soccer player
Bibliographic record
Abstract
It is recommended that soccer players consume a high carbohydrate diet to augment performance. However, growing evidence suggests that there is a link between high free-sugar (FS) intake (>5% total energy intake; TEI) and metabolic diseases. Furthermore, foods that are often high in sugar, such as processed foods, are typically lacking in nutrient quality. We therefore analysed total-sugar, FS, dietary fibre, and micronutrient intake of players from an English Premier League academy under (U) 18 (n = 13), U15/16 (n = 25), and U13/14 (n = 21) using a 7-day food diary. Data were compared with current United Kingdom (UK) dietary reference value (DRV) for FS via a t test. The U13/14s (10% ± 18%) and U15/16s (11% ± 30%) both consumed higher amounts of FS in comparison with the UK DRV of 5% TEI (P < 0.01); conversely, the U18s did not exceed the DRV (5% ± 13%). Furthermore, FS intake of the U18s was significantly lower than the U13/14s and U15/16s (P < 0.01). Dietary fibre was below the DRV (25 g/day for U13/14 and U15/16s; 30 g/day for U18s) for all squads (19.0 ± 4.7, 19.6 ± 8.3, 17.1 ± 4.2 g/day, respectively), but not different between squads. Additionally, micronutrient reference intakes were generally met. In conclusion, we provide novel data on dietary sugar, fibre, and micronutrient intake within elite youth soccer players. We report an apparent "nutritional transition" from schoolboy to fulltime soccer player, with U18s showing a significantly lower intake of sugar in comparison with younger squads, and a similar intake of FS to the UK DRVs. Practitioners should target improving player education around sugar and fibre consumption.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".