Objectively Measured Sedentary Levels and Bouts by Day Type in Australian Young Children
Bibliographic record
Abstract
BACKGROUND: This study aimed to understand whether a higher number of sedentary bouts (SED bouts) and higher levels of sedentary time (SED time) occur according to different day types (childcare days, nonchildcare weekdays, and weekends) in Australian toddlers (1-2.99 y) and preschoolers (3-5.99 y). METHODS: The SED time and bouts were assessed using ActiGraph GT3X+ accelerometers. The sample was composed of 264 toddlers and 343 preschoolers. The SED bouts and time differences were calculated using linear mixed models. RESULTS: The toddlers' percentage of SED time was higher on nonchildcare days compared with childcare days (mean difference [MD] = 2.3; 95% confidence interval, 0.7 to 3.9). The toddlers had a higher number of 1- to 4-minute SED bouts on nonchildcare days compared with childcare days. The preschoolers presented higher percentages of SED time during nonchildcare days (MD = 3.1; 95% confidence interval, 1.6 to 4.5) and weekends (MD = 1.9; 95% confidence interval, 0.4 to 3.4) compared with childcare days. The preschoolers presented a higher number of SED bouts (1-4, 5-9, 10-19, and 20-30 min) during nonchildcare days and weekends compared with childcare days. No SED times or bout differences were found between nonchildcare days and weekends, neither SED bouts >30 minutes on toddlers nor on preschoolers. CONCLUSION: The SED time and bouts seem to be lower during childcare periods, which means that interventions to reduce sedentary time should consider targeting nonchildcare days and weekends.
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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 | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".