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Record W4411894464 · doi:10.1186/s12889-025-23421-9

Identifying behaviour change techniques in school-based childhood obesity prevention interventions: a secondary analysis of a systematic review

2025· review· en· W4411894464 on OpenAlexaff
Daniel C.W. Lee, Serene Yoong, Sam McCrabb, Brittany J. Johnson, Justin Presseau, Ashleigh Stuart, Kate O’Brien, Rebecca K Hodder

Bibliographic record

VenueBMC Public Health · 2025
Typereview
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersNational Health and Medical Research CouncilHospital Research Foundation
KeywordsMedicinePsychological interventionBiostatisticsChildhood obesityOverweightSystematic reviewBehavior change methodsIntervention (counseling)Randomized controlled trialPublic healthObesityPhysical therapyGerontologyMEDLINEPsychiatryNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Childhood overweight and obesity is increasingly prevalent, can persist into adulthood, and lead to lifelong negative health trajectories. Schools are a recommended setting for childhood obesity prevention interventions; however, these interventions are often complex and multicomponent. While previous reviews have demonstrated their effectiveness, they have not identified which behaviour change techniques (BCTs - active ingredients of an intervention) are most effective. OBJECTIVES: Describe BCTs used in healthy eating (HE) and physical activity (PA) intervention components of obesity prevention interventions supporting children aged 6-18 years; and explore which BCTs are associated with child weight. METHODS: A secondary analysis of school-based trials included in a 2022 update of a Cochrane systematic review was undertaken. The previous review included 195 randomised controlled trials of childhood obesity prevention interventions targeting HE and/or PA that assessed the body mass index of children aged 6-18 years. For this study, only trials delivered in schools that compared an intervention to a non-intervention control group and targeted HE, PA or both were eligible. Individual BCTs of each HE and PA intervention were coded according to the BCT taxonomy v1. Meta-regressions were conducted to determine the association between BCTs included in the trials and child weight. RESULTS: This secondary analysis included 124 eligible trials. Fifty-five of the 93 BCTs from 14 of the 16 BCT domains were identified across interventions. Interventions with a HE component that included BCTs from three domains (Goals and planning; Social support; Comparison of behaviour) were found to have a significant association with a positive effect on child weight, whereas there were no significant associations found for interventions with a PA component. CONCLUSION: School-based obesity prevention interventions with HE components that included BCTs within the Goals and planning, Social support, and Comparison of behaviour domains, such as Goal setting (outcome), Social support (unspecified) and Demonstration of the behaviour were associated with a positive effect on child weight and should be considered for prioritisation in future interventions. Further research is required to identify effective BCTs for PA intervention components, and for effective individual BCTs and combinations of BCTs for all obesity prevention interventions broadly. TRIAL REGISTRATION: CRD42022366743.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.045
metaresearch head score (Gemma)0.127
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.045
Threshold uncertainty score0.238

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0450.127
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0180.027
Bibliometrics0.0150.015
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.164
GPT teacher head0.448
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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