The Influence of Post-exercise Macronutrient Intake on Energy Balance and Protein Metabolism in Active Females Participating in Endurance Training
Bibliographic record
Abstract
The purpose of this investigation was to determine the influence of post-exercise macronutrient intake on weight loss, protein metabolism, and endurance exercise performance during a period of increased training volume. Ten healthy young female endurance athletes performed 4 60-min bouts of cycle ergometry at approximately 65% of VO2peak on 4 days (day 1, 3, 4, and 6) during 2 separate 1-week periods. On day 7, participants performed a ride to exhaustion at approximately 75% of VO2peak. One of the 7-day periods served as a control condition, where a placebo beverage was consumed following the exercise bouts on days 1, 3, 4, and 6 (CON). During the other 7-day protocol (POST), participants consumed a predefined formula beverage with added carbohydrate following the exercise bouts on days 1, 3, 4, and 6. Energy intake and macronutrient proportions were the same between the 2 trials; the only difference was the timing at which the macronutrients were consumed. Calculated fat oxidation was greater during exercise on day 6 during POST as compared to CON (p < .05). Glucose and insulin concentrations were significantly higher (p < .05) following exercise during POST as compared to CON. There was a trend (p = .06) for nitrogen balance to be greater on days 5 and 6 with POST as compared to CON. Time to exhaustion during exercise on day 7 was longer during POST as compared to CON (p < .05). POST resulted in a maintenance of body weight during the 7-day protocol, while there was a significant (p < .05) reduction with CON. It was concluded that post-exercise macronutrient intake following endurance exercise can attenuate reductions in body weight and improve nitrogen balance during 7 days of increased energy expenditure. Importantly, post-exercise supplementation improved time to exhaustion during a subsequent bout of endurance exercise.
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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 | 0.000 | 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".