Which behaviour change techniques are associated with interventions that increase physical activity in pre-school children? A systematic review
Bibliographic record
Abstract
BACKGROUND: Insufficient physical activity (PA) is a significant risk factor that contributes to several health problems and there is a need to improve our understanding of how to increase PA, particularly among young children. This review (PROSPERO registration: CRD42022328841) investigated the relationship between behaviour change techniques (BCTs) and interventions that increased PA among pre-school children aged < 6 years old. METHODS: Systematic searches of six databases were undertaken from inception to July 2022, updated in December 2022, to locate studies that evaluated interventions and reported a positive change in PA levels in children aged < 6 years old. RESULTS: A total of 5,304 studies were screened, and 28 studies involving 10,605 subjects aged 2.5 to 5.9 years met the eligibility criteria. Each eligible study (n = 28) was independently appraised by two researchers using the Cochrane risk of bias tool. The BCT Taxonomy v1 and the Template for Intervention Description and Replication (TIDieR) guided the extraction and analysis of data, and this process led to the identification of 27 BCTs. CONCLUSIONS: Potentially promising BCTs for increasing PA among young children included 'shaping knowledge,' 'antecedents,' 'goals and planning,' and 'comparison of behaviour.' Future PA interventions that target young children should consider integrating these promising BCTs into their programmes. However, such consideration needs to be tempered by the fact that most of the reviewed studies were deemed to have a high or unclear risk of bias and/or were limited with respect to the populations that they targeted. Further research using rigorous methodologies is required to establish a higher standard that addresses the needs of young children who are expected to have insufficient levels of physical activity.
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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 | 0.016 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".