Motor Skills of Children and Adolescents With Obesity and Severe Obesity—A CIRCUIT Study
Bibliographic record
Abstract
Häcker, A-L, Bigras, J-L, Henderson, M, Barnett, TA, and Mathieu, M-E. Motor skills of children and adolescents with obesity and severe obesity-a CIRCUIT study. J Strength Cond Res 34(12): 3577-3586, 2020-During childhood, excessive weight is negatively associated with the development of motor skills, with overweight children or children with obesity having poorer motor skills compared with children with normal weight. The objectives of the current study are to identify the differences in motor skills between children and adolescents with obesity and severe obesity and the extent of this difference. To do so, we examined cross-sectionally 165 subjects. Physical fitness was analyzed in both subjects with obesity (>97th to 99.9th body mass index [BMI] percentile) and severe obesity (>99.9th BMI percentile) using 8 standardized tests: sit-and-reach, grip force, sit-ups, push-ups, balance, hand-eye coordination, standing long jump and 5-m shuttle run. Poorer performance were observed in subjects with severe obesity in sit-ups (children: 59%; 18.6 ± 17.0 vs. 29.5 ± 23.2 percentile value, p = 0.008), balance (adolescent: 59%; 12.1 ± 12.2 vs. 19.3 ± 13.9 seconds, p = 0.034), and in the 5-m shuttle run (children: 49%; 14.0 ± 13.9 vs. 20.8 ± 19.4 percentile value, p = 0.046; adolescents: 11%; 13.2 ± 2.2 vs. 11.8 ± 1.6 seconds, p = 0.008) compared with obese counterparts. In conclusion, although physical performance was found to be similar between the different obesity levels for most tests, youth with severe obesity demonstrated impairments ranging from 11 to 59% in specific tests.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".