Associations Between Active School Transport and Physical Activity, Body Composition, and Cardiovascular Fitness: A Systematic Review of 68 Studies
Bibliographic record
Abstract
BACKGROUND: The impact of active school transport (AST) on daily physical activity (PA) levels, body composition and cardiovascular fitness remains unclear. METHODS: A systematic review was conducted to examine differences in PA, body composition and cardiovascular fitness between active and passive travelers. The Medline, PubMed, Embase, PsycInfo, and ProQuest databases were searched and 10 key informants were consulted. Quality of evidence was assessed with GRADE and with the Effective Public Health Practice Project tool for quantitative studies. RESULTS: Sixty-eight different studies met the inclusion criteria. The majority of studies found that active school travelers were more active or that AST interventions lead to increases in PA, and the quality of evidence is moderate. There is conflicting, and therefore very low quality evidence, regarding the associations between AST and body composition indicators, and between walking to/from school and cardiovascular fitness; however, all studies with relevant measures found a positive association between cycling to/from school and cardiovascular fitness; this evidence is of moderate quality. CONCLUSION: These findings suggest that AST should be promoted to increase PA levels in children and adolescents and that cycling to/ from school is associated with increased cardiovascular fitness. Intervention studies are needed to increase the quality of evidence.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".