Cycling Time Trial Performance 4 Hours After Glycogen-Lowering Exercise Is Similarly Enhanced by Recovery Nondairy Chocolate Beverages Versus Chocolate Milk
Bibliographic record
Abstract
Postexercise chocolate milk ingestion has been shown to enhance both glycogen resynthesis and subsequent exercise performance. To assess whether nondairy chocolate beverage ingestion post-glycogen-lowering exercise can enhance 20-km cycling time trial performance 4 hr later, eight healthy trained male cyclists (21.8 ± 2.3y, VO2max = 61.2 ± 1.4 ml·kg-1·min-1; M ± SD) completed a series of intense cycling intervals designed to lower muscle glycogen (Jentjens & Jeukendrup, 2003) followed by 4 hr of recovery and a subsequent 20-km cycling time trial. During the first 2 hr of recovery, participants ingested chocolate dairy milk (DAIRYCHOC), chocolate soy beverage (SOYCHOC), chocolate hemp beverage (HEMPCHOC), low-fat dairy milk (MILK), or a low-energy artificially sweetened, flavored beverage (PLACEBO) at 30-min intervals in a double-blind, counterbalanced repeated-measures design. All drinks, except the PLACEBO (247 kJ) were isoenergetic (2,107 kJ), and all chocolate-flavored drinks provided 1-g CHO·kg body mass-1·h-1. Fluid intake across treatments was equalized (2,262 ± 148 ml) by ingesting appropriate quantities of water based on drink intake. The CHO:PRO ratio was 4:1, 1.5:1, 4:1, and 6:1 for DAIRYCHOC, MILK, SOYCHOC, and HEMPCHOC, respectively. One-way analysis of variance with repeated measures showed time trial performance (DAIRYCHOC = 34.58 ± 2.5 min, SOYCHOC = 34.83 ± 2.2 min, HEMPCHOC = 34.88 ± 1.1 min, MILK = 34.47 ± 1.7 min) was enhanced similarly vs PLACEBO (37.85 ± 2.1) for all treatments (p = .019) These data suggest that postexercise macronutrient and total energy intake are more important for same-day 20-km cycling time trial performance after glycogen-lowering exercise than protein type or protein-to-carbohydrate ratio.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".