Compliance with the 24-hour movement behavior guidelines and the impact of sleep methods among toddler, preschool, and school-aged children enrolled in the Guelph Family Health Study
Bibliographic record
Abstract
Canadian movement guidelines focused on physical activity (PA), sleep, and screen time support childhood development and reduce the risk of chronic disease. Accelerometers are often used to capture these behaviors; however, they are limited in their ability to record daytime sleep due to potential misclassification. OBJECTIVES: The objectives of this study were to 1) determine the prevalence of children enrolled in the Guelph Family Health Study who met the guidelines and to 2) compare the impact of different sleep measurement methods. DESIGN/METHODS: Toddlers (1.5-<3 years; n = 128; valid data for all movement behaviors, n = 70), preschoolers (3-<5 years; n = 143; valid data for all movement behaviors, n = 104), and school-aged (5-<6 years; n = 49; valid data for all movement behaviors, n = 31) children were included. Screen time and sleep habits were obtained through parental report and published normative data. PA and sleep were recorded using accelerometers (wGT3X-BT ActiGraph; right hip). RESULTS: It was found that 66 % of toddler, 44 % of preschool, and 63 % of school-aged children met the screen time guidelines. Further, 63 % of toddler, 98 % of preschooler, and 80 % of school-aged children met PA guidelines. Sleep guideline compliance ranged from 3 % to 83 % in toddler, 27 % to 92 % in preschooler, and 32 % to 90 % in school-aged children. These proportions were found to be significantly different (Cochran's Q and McNemar's tests). CONCLUSIONS: Nearly all children met PA guidelines. In contrast, less than half to two-thirds met screen time guidelines. Compliance with sleep guidelines varied substantially with measurement method, highlighting the need for standardization.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".