Environmental Influences on Preschoolers' Physical Activity Levels in Various Early-Learning Facilities
Bibliographic record
Abstract
PURPOSE: This study aimed to: (a) compare the physical activity (PA) levels (i.e., moderate-to-vigorous PA [MVPA] and total PA [TPA]) of preschoolers in 3 different early-learning environments (center-based childcare, home-based childcare, and full-day kindergarten [FDK]); and (b) assess which characteristics (e.g., play equipment, policies, etc.) of these settings influenced preschoolers' PA. METHOD: Twenty-seven facilities (9 centers, 10 homes, and 8 FDK) participated in this study. Participants (aged 2.5-5 years; n = 297) were fitted with Actical™ accelerometers for 5 consecutive days during childcare/school hours to assess their PA. The Environment and Policy Assessment and Observation (EPAO) tool was used to objectively examine the PA environment of all participating facilities. Finally, demographic questionnaires were administered to preschoolers' parents/guardians. RESULTS: Preschoolers in FDK accumulated significantly more MVPA (p < .05; 3.33 min/hr) than those in center- (1.58 min/hr) and home-based (1.75 min/hr) childcare, and they accumulated significantly more TPA (p < .05; 20.31 min/hr) than those in center-based childcare (18.36 min/hr). For FDK, the Active Opportunities, Sedentary Opportunities, Sedentary Environment, and Fixed Play Environment subscales of the EPAO significantly impacted both MVPA and TPA. For center-based childcare, only the Sedentary Environment subscale was found to impact MVPA and TPA. No subscales influenced children's MVPA or TPA in home-based childcare. CONCLUSIONS: This research underscores the need to encourage/support preschoolers' active behaviors in early-learning settings, particularly for those in center- and home-based childcare. Furthermore, this article highlights environmental and staff characteristics on which future PA programming should focus.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".