Efficacy of a Physical Activity e-Learning Course Delivered to Early Childhood Educators on Preschoolers’ Physical Activity and Sedentary Behaviors: A Cluster Randomized Controlled Trial
Bibliographic record
Abstract
PURPOSE: This study examined the effectiveness of an early childhood educator (ECE)-focused physical activity e-Learning course on children's physical activity and sedentary time in childcare. METHODS: A cluster randomized controlled trial was conducted in 12 childcare centers in London, Ontario, Canada. A total of 145 preschoolers and 42 ECEs participated in this study. ECEs in the intervention condition completed a 5-hour e-Learning course related to physical activity. Outcomes were preschoolers' minutes of moderate- to vigorous-intensity physical activity, light-intensity physical activity, and sedentary time assessed using accelerometers. RESULTS: The intervention did not have a significant effect on moderate- to vigorous-intensity physical activity (d < 0.01, P = .984), light-intensity physical activity (d = -0.17, P = .386), or sedentary time (d = 0.07, P = .717) from baseline to postintervention. There was also no significant intervention effect on moderate- to vigorous-intensity physical activity (d = 0.27, P = .260), light-intensity physical activity (d = -0.08, P = .740), or sedentary time (d = -0.15, P = .520) from baseline to follow-up. CONCLUSIONS: Providing ECEs with online training in physical activity through an e-Learning course may not be sufficient to increase physical activity levels among young children in their care. It may be essential to deliver multicomponent interventions to increase preschoolers' engagement in physical activity in childcare.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".