Mechanical efficiency during a cycling test is not lower in children with excess body weight and low aerobic fitness
Bibliographic record
Abstract
OBJECTIVE: The aims of this study were to assess the association between (i) body weight status and mechanical efficiency (ME); and (ii) ME and aerobic fitness in children aged 8-10 years. DESIGN AND METHODS: The sample included 464 prepubertal children (258 boys). A total of 288 were normal-weight (NW); 84 overweight (OW); and 92 obese (OB). Subjects performed an incremental maximal cycling test with indirect calorimetry. MEcrude (%) was calculated for the first five stages of the protocol (25, 50, 75, 100, and 125 W) as follows: work produced, in watts total energy consumption, in watts(-1) · 100(-1). For MEnet, resting energy consumption was subtracted from total energy consumption. Energy consumption was calculated as follows: (4.94 · respiratory exchange ratio + 16.04) · VO2, in ml · min(-1) · 60(-1). RESULTS: MEcrude was significantly higher in NW compared to OW and OB children and in OW compared to OB children at 25, 50, 75, 100, and 125 W. In contrast, MEnet did not differ significantly among NW, OW, and OB children. No statistically significant association was found between crude or net ME and peak oxygen consumption (VO2 peak; in ml · kg(-1) · min(-1)); therefore, the ability to transfer chemical energy to mechanical work is maintained in children aged 8-10 years old regardless of body weight status and aerobic fitness. Moreover, higher values of MEcrude during exercise are explained by elevated oxygen consumption at rest and not by energy consumed during physical activity. CONCLUSIONS: These results highlight that prepubertal children are equally efficient since they are able to perform a physical task such as cycling using the same proportion of energy regardless of their body weight status.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".