Eating Patterns and Composition of Meals and Snacks in Elite Canadian Athletes
Bibliographic record
Abstract
The purpose of this study was to determine the meal- and snack-eating frequency and the nutritional composition of each eating occasion of Canadian high-performance athletes during training. Athletes from 8 Canadian Sport Centres prospectively completed 3-d dietary records including all food, fluid, and supplements consumed. The time of consumption and whether the consumption was a meal or snack were also identified. The dietary records were analyzed for energy (kcal) and macronutrient intake (carbohydrate, protein, and fat) and compared based on gender, age, meal vs. snack, and training vs. rest days. Three hundred twenty-four athletic subjects (64% female and 36% male) completed the study. On average, the athletes ate 4.8 ± 0.8 times daily. Nearly all athletes consumed 3 daily meals of breakfast (98.9%), lunch (97.9%), and dinner (98.7%), with few having snacks: 57%, 71.6%, and 58.1% of athletes consumed an a.m., p.m., and evening snack, respectively. Training-day meal frequency did not differ from that during rest days; however, fewer snacks were consumed on rest days. A.m. and p.m. snacks were consumed significantly more often on training days than rest days. Overall, snacks contributed 24.3% of total daily energy intake. Few dietary variations were discovered between genders, while the youngest athletes (<18 yr) ate less often, especially their morning snack, than the older athletes. In conclusion, Canadian high-performance athletes self-adjusted their energy intakes on training vs. rest days primarily by snacking less and reducing their carbohydrate and protein intakes on rest days, yet they consistently ate regular meals.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".