Energy expenditure and dietary intake during high-volume and low-volume training periods among male endurance athletes
Bibliographic record
Abstract
The primary purpose of this study was to examine dietary intake in endurance-trained athletes during a week of high-volume and a week of low-volume training while measuring exercise energy expenditure (EEE), resting metabolic rate (RMR), and nonexercise activity thermogenesis (NEAT). In addition, compliance with current American College of Sports Medicine/American Dietetic Association nutrition and performance recommendations for macronutrients was evaluated. Energy expenditure and dietary intake were measured in 15 male endurance athletes during 2 nonconsecutive weeks resembling a high-volume and a low-volume training period. Anthropometric measurements were taken and percentage body fat was determined at the beginning and end of each week of training. Total daily energy expenditure (TDEE) was calculated by summing RMR, NEAT, and EEE. Dietary intake was assessed with an online food-frequency questionnaire completed at the end of each week of data collection. Despite significant differences between TDEE and energy intake, no difference in body composition between the beginning and end of either week of training was observed, suggesting underreporting of caloric intake. Further, no changes in total caloric intake or macronutrient intake occurred even though TDEE increased significantly during the high-volume training. Reported carbohydrate intake (4.5 g·kg(-1)) and fiber intake (25 g·day(-1)) were below recommendations, whereas fat intake (1.3 g·kg(-1)) was slightly above recommendations. In summary, no short-term dietary adjustments occurred in response to differences in training regimen. Because these athletes were generally consuming a Western diet, they may have required some support to achieve desirable intakes for health and performance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".