Nighttime feeding likely alters morning metabolism but not exercise performance in female athletes
Bibliographic record
Abstract
The timing of morning endurance competition may limit proper pre-race fueling and resulting performance. A nighttime, pre-sleep nutritional strategy could be an alternative method to target the metabolic and hydrating needs of the early morning athlete without compromising sleep or gastrointestinal comfort during exercise. Therefore, the purpose of this investigation was to examine the acute effects of pre-sleep chocolate milk (CM) ingestion on next-morning running performance, metabolism, and hydration status. Twelve competitive female runners and triathletes (age, 30 ± 7 years; peak oxygen consumption, 53 ± 4 mL·kg(-1)·min(-1)) randomly ingested either pre-sleep CM or non-nutritive placebo (PL) ∼30 min before sleep and 7-9 h before a morning exercise trial. Resting metabolic rate (RMR) was assessed prior to exercise. The exercise trial included a warm-up, three 5-min incremental workloads at 55%, 65%, and 75% peak oxygen consumption, and a 10-km treadmill time trial (TT). Physiological responses were assessed prior, during (incremental and TT), and postexercise. Paired t tests and magnitude-based inferences were used to determine treatment differences. TT performances were not different ("most likely trivial" improvement with CM) between conditions (PL: 52.8 ± 8.4 min vs CM: 52.8 ± 8.0 min). RMR was "likely" increased (4.8%) and total carbohydrate oxidation (g·min(-1)) during exercise was "possibly" or likely increased (18.8%, 10.1%, 9.1% for stage 1-3, respectively) with CM versus PL. There were no consistent changes to hydration indices. In conclusion, pre-sleep CM may alter next-morning resting and exercise metabolism to favor carbohydrate oxidation, but effects did not translate to 10-km running performance improvements.
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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 | 0.000 | 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) | 0.002 | 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".