Performance Effects of Carbohydrate Ingestion Between Bouts of Intense Aerobic Interval Exercise
Bibliographic record
Abstract
Background: Rest between training sessions can be short for athletes. In these situations, consuming carbohydrate (CHO) postexercise replenishes glycogen stores, which is important for recovery and subsequent performance. Purpose: This study tested whether CHO intake during a 2-hour rest between exercise bouts improved performance in the subsequent bout. Methods: In a randomized, single-blinded, crossover design, 10 recreationally active participants (23 [4] y, 70.8 [6.6] kg, 47.0 [5.4] mL·O2·min−1·kg·body·mass−1) arrived at the lab postprandial and completed 2 exercise bouts separated by a 2-hour rest. Bouts included 5 × 4-minute intervals at ∼80% peak oxygen consumption separated by 2 minutes at ∼40% peak oxygen consumption and ended with an endurance trial to voluntary exhaustion at ∼90% peak oxygen consumption. During intervals 1 and 4 in each bout, expired gases were collected and O2 deficit was estimated. Immediately following bout 1, either a CHO (1.2 g CHO·kg·body·mass−1) or placebo solution was consumed. Results: Endurance trial duration decreased in bout 2 versus 1 in both conditions (P < .01) but was ∼35% longer in bout 2 with CHO versus placebo (interaction, P = .03; post hoc, P = .03). Oxygen uptake increased during interval 4 versus 1 in both bouts (P < .01) but was unaffected by CHO (P ≥ .58). O2 deficit was unaffected by CHO (P = .93), bout, or interval (P ≥ .15). Perceived exertion was higher in bout 2 versus 1 (P < .001) and reduced in intervals 2 and 4 in CHO (P ≤ .01). Conclusions: When rest between training sessions is 2 hours, athletes may improve subsequent performance by consuming CHO during recovery.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".