Association Between Accelerometer-Measured Physical Activity Intensities and Sedentary Time in 8- to 10-Year-Old Children
Bibliographic record
Abstract
This study examines the association between objectively-measured physical activity (PA) intensities and sedentary behavior (SED) in a cohort of 532 children aged 8-10 y. PA and SED were assessed by accelerometer over 7-days. Television and computer/video-game use were self-reported. Associations between PA intensities and SED variables were assessed by Spearman correlations and adjusted multiple linear regression. Higher mean daily moderate-to-vigorous and vigorous PA (MVPA, VPA) were negatively associated with mean daily SED (r = -0.47 and -0.37; p < .001), and positively associated with mean daily total PA (r = .58 and 0.46; p < .001). MVPA was also positively associated with light PA (LPA; r = .26, p < .001). MVPA and VPA were not significantly associated with TV, computer/video or total screen time; accelerometer SED was only weakly associated with specific SED behaviors. On average, for each additional 10 min daily MVPA, children accumulated >14 min less SED, and for each additional 5 min VPA, 11 min less SED. Thus, over the course of a week, higher mean daily MVPA may displace SED time and is associated with higher total PA over and above the additional MVPA, due to concomitant higher levels of LPA. Public health strategies should target both MVPA and SED to improve overall PA and health in 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".