Increased carbohydrate availability effects energy and nutrient periodisation of professional male athletes from the Australian Football League
Bibliographic record
Abstract
This research aimed to explore the effect of increased carbohydrate availability intervention on energy intake and distribution in professional Australian Football athletes. Six 24-h energy and macronutrient intakes were quantified (n = 19 males; age 24 ± 4 y, stature 187 ± 8 cm, mass 87 ± 9 kg) using photographic food diaries and Foodworks analyses. Energy expenditure was estimated for the same period using GeneActiv accelerometers. During 3 control days, athletes had ad libitum access to food, while the 3 intervention days increased carbohydrate availability, through greater prompting and access to carbohydrate foods. Daily energy intake was higher during intervention (185 ± 40 kJ/kg/d) compared with control (172 ± 31 kJ/kg/d; p < 0.05) but remained below estimated expenditure, and carbohydrate intake was also greater with intervention (5.0 ± 0.2 g/kg/d) than control (4.0 ± 0.2 g/kg/d; p < 0.05). Expenditure was highest during the morning, which coincided with lowest intake on all days, while the intervention was associated with greater carbohydrate intake in the morning (0.6 g/kg, p < 0.05) compared with control. Increasing availability of carbohydrate during high-load training generated a modest increase in carbohydrate and energy intake, and the intervention was most effective in improving carbohydrate intake during mornings. Novelty: Increased access and provision of carbohydrate foods increased carbohydrate consumption and energy intake on high training load days. Daily distribution of energy intake can be modified through actively promoting carbohydrate consumption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".