Timing and pattern of postexercise protein ingestion affects whole-body protein balance in healthy children: a randomized trial
Bibliographic record
Abstract
The dose and timing of postexercise protein ingestion can influence whole-body protein balance (WBPB) in adults, although comparable data from children are scarce. This study investigated how protein intake (both amount and distribution) postexercise can affect WBPB in physically active children. Thirty-five children (26 males; 9–13 years old) underwent a 5-day adaptation diet, maintaining a protein intake of 0.95 g·kg−1·day−1. Participants consumed [15N]glycine (2 mg·kg−1) before performing 3 × 20 min of variable-intensity cycling, and whole-body protein kinetics were assessed over 6 and 24 h of recovery. Fifteen grams of protein was distributed across 2 isoenergetic carbohydrate-containing beverages (15 and 240 min postexercise) containing reciprocal amounts of protein (i.e., 0 + 15 g, 5 + 10 g, 10 + 5 g, and 15 + 0 g for Groups A–D, respectively). Over the 6 h that included the exercise bout and consumption of the first beverage at 15 min postexercise, WBPB (i.e., synthesis – breakdown) demonstrated a linear increase of 0.647 g·kg−1·day−1 per 1 g protein intake (P < 0.001). Over 24 h, robust regression revealed that WBPB was best modeled by a parabola (P < 0.05), suggesting that a maximum in WBPB was achieved between groups B and C. In conclusion, despite a dose response early in recovery, a periodized protein intake with multiple smaller doses after physical activity may be more beneficial than a single bolus dose in promoting daily WBPB in healthy active children.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".