Acute post-exercise energy and macronutrient intake in lean and obese youth: a systematic review and meta-analysis
Bibliographic record
Abstract
Aim: This review aims to determine if acute exercise affects subsequent energy and macronutrients intake in obese and non-obese children and adolescents. Methods: Databases were searched between January 2015 and December 2015 for studies reporting energy and/or macronutrients intake immediately after an acute exercise and control condition, in children and adolescents. From the initial 118 references found, 14 were included for subsequent analysis after screening representing 31 acute exercise conditions that varied in intensity, duration and modality. Results: One study found increased energy intake after exercise, seven decreased and 23 revealed no change. The meta-analysis revealed a significant effect of acute exercise on intake in obese but not in lean youth by a mean difference of −0.430 (95% confidence interval=−0.703 to −0.157, P=0.002) displaying low heterogeneity (I2=0.000; Q=5.875; df=9, P=0.752). The analysis showed that intense exercise only reduces intake in obese children (no intensity effect in lean). Unchanged macronutrients intake was reported in nine studies as opposed to three which found modified lipids, protein and/or carbohydrate intake. Conclusion: Although acute exercise does not affect energy intake in lean, it appears to reduced food intake in obese youth when intense, without altering the macronutrients composition of the meal.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.023 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".