Objectively assessed school-based intervention to reduce children’s sedentary time: a systematic review
Bibliographic record
Abstract
Childhood is a period characterized by a constant increase in sedentary time (ST) but also provides a great window of opportunity for children to learn how to limit ST. As a result, school-based interventions aimed at reducing and preventing children's ST are increasingly becoming more widespread. A previous review found that school-based multi-component interventions appeared to be more effective in comparison to those based on a single component. However, this conclusion was based on just 11 studies published before 2016 and needs to be verified due to the currently growing number of studies on this topic. The objective of this systematic review is to update the evaluation of the effectiveness of school-based interventions published since the previous review according to the PRISMA guidelines. Studies published between August 2015 and August 2023 containing objective measures of ST were analyzed. Of the 18 studies identified, 11 (61.1%) reported significant positive results. Multi-component studies were slightly more effective than their single-component equivalent (63.7% vs. 57.1%). The components that proved the most effective of the multi-component studies were the implementation of sit-to-stand desks (100%), and teachers' training (77.8%). The combination of these two components is the most promising method to limit ST in the school context. Future research should determine how sit-to-stand desks can be introduced into the class environment and how courses can be adapted to this material.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".