Correlates of domain-specific sedentary behaviors and objectively assessed sedentary time among elementary school children
Bibliographic record
Abstract
Abstract Understanding the correlates of sedentary behavior among children is essential in developing effective interventions to reduce sitting time in this vulnerable population. This study aimed to identify correlates of domain-specific sedentary behaviors and objectively assessed sedentary time among a sample of children in Japan. Data from 343 children (aged 6–12 years) living in Japan were used. Domain-specific sedentary behaviors were assessed using a questionnaire. Total sedentary time was estimated using hip-worn accelerometers. Twenty-two potential correlates across five categories (parental characteristics, household indoor environment, residential neighborhood environment, school environment, and school neighborhood environment) were included. Multivariable linear regression models were used to identify correlates of domain-specific sedentary behaviors and objectively assessed sedentary time. Eight correlates were significantly associated with children’s domain-specific sedentary behaviors: mother’s and father’s age, mother’s educational level, having a video/DVD recorder/player, having a video console, having a TV one’s own room, home’s Walk Score®, and pedestrian/cycling safety. No significant associations were found between potential correlates and accelerometer-based total sedentary time. These findings highlight that strategies to reduce children’s sedentary time should consider the context of these behaviors. For example, urban design attributes such as perceived pedestrian and cycling safety can be improved to reduce children’s car sitting time.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".