Thresholds of physical activity associated with obesity by level of sedentary behaviour in children
Bibliographic record
Abstract
Summary Background It is unknown whether moderate‐to‐vigorous physical activity (MVPA) thresholds for obesity should be adapted depending on level of sedentary behaviour in children. Objective The objective of the study is to determine the MVPA thresholds that best discriminate between obese and non‐obese children, by level of screen time and total sedentary time in 12 countries. Methods This multinational, cross‐sectional study included 6522 children 9–11 years of age. MVPA and sedentary time were assessed using waist‐worn accelerometry, while screen time was self‐reported. Obesity was defined according to the World Health Organization reference data. Results Receiver operating characteristic curve analyses showed that the best thresholds of MVPA to predict obesity ranged from 53.8 to 73.9 min d−1 in boys and from 41.7 to 58.7 min d−1 in girls, depending on the level of screen time. The MVPA cut‐offs to predict obesity ranged from 37.9 to 75.9 min d−1 in boys and from 32.5 to 62.7 min d−1 in girls, depending on the level of sedentary behaviour. The areas under the curve ranged from 0.57 to 0.73 (‘fail’ to ‘fair’ accuracy), and most sensitivity and specificity values were below 85%, similar to MVPA alone. Country‐specific analyses provided similar findings. Conclusions The addition of sedentary behaviour levels to MVPA did not result in a better predictive ability to classify children as obese/non‐obese compared with MVPA alone.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".