Associations of objectively measured physical activity and sleep in preschoolers aged 3 to 6 years
Bibliographic record
Abstract
Research has demonstrated various negative effects of poor sleep on overall health in children. Engaging in physical activity during the day is often recommended to help children sleep better. Limited research has examined this recommendation for preschool children, although physical activity is generally supported as a healthy sleep practice. When measuring physical activity and sleep, objective measures (e.g., accelerometers) are recommended as opposed to subjective measures (e.g., parental reports). The purpose of the current study was to examine the relationship between objectively measured sleep (key variables included sleep efficiency, sleep onset latency, and sleep duration) and physical activity (operationalized as mean daily total physical activity) among preschool-aged children in Nova Scotia, Canada. Children (n = 29) wore a wrist accelerometer to objectively measure sleep and wore an accelerometer on their waist to measure physical activity for nine consecutive days. Overall, linear regression analyses demonstrate physical activity as a predictor of sleep efficiency but not total sleep time or sleep onset latency among preschool-aged children. Future research should examine the causal relationships between sleep efficiency and physical activity by conducting interventions to increase physical activity and determining the impact on sleep efficiency.
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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 | 0.000 |
| 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".