Effectiveness of Physical Activity Interventions for Preschoolers: A Meta-Analysis
Bibliographic record
Abstract
PURPOSE: The purpose of the meta-analysis was to examine the effectiveness of physical activity interventions on physical activity participation among preschoolers. A secondary purpose was to investigate the influence of several possible moderator variables (e.g., intervention length, location, leadership, type) on moderate-to-vigorous physical activity (MVPA). METHOD: Nine databases were systematically searched for physical activity interventions. Studies were included if they contained statistics necessary to compute an effect size (ES), were written in or translated into English, examined physical activity in preschoolers, incorporated a physical activity intervention, and targeted preschool-aged children. Fifteen studies satisfied these criteria. ESs were calculated using a random-effects model. RESULTS: Results indicated that overall, interventions had a small-to-moderate effect on general physical activity (Hedges g = 0.44, p < .05, n = 73 ESs) and a moderate effect on MVPA (Hedges g = 0.51, p < .05, n = 39 ESs). The greatest effects for MVPA were identified for interventions that were less than 4 weeks in duration, were offered in an early-learning environment, were led by teachers, involved outdoor activity, and incorporated unstructured activity. CONCLUSIONS: This meta-analysis provides an overview and synthesis of physical activity interventions and highlights effective strategies for future interventions aimed at increasing physical activity levels among preschoolers.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.035 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".