Objective: This review provides the first meta-analysis of the impact of physically active lessons on lesson-time and overall physical activity (PA), as well as health, cognition and educational outcomes.Design: Systematic review and meta-analysis of controlled studies. Six meta-analyses pooled effects on lesson-time PA, overall PA, in-class educational and overall educational outcomes, cognition and health outcomes. Meta-analyses were conducted using the metafor package in R. Risk of bias was assessed using the Cochrane tool for risk of bias.Data sources: PubMed, Embase, PsycINFO, ERIC and Web of Science, grey literature and reference lists were searched in December 2017 and April 2019.Studies eligibility criteria: Physically active lessons compared with a control group in a randomised or non-randomised design, within single component interventions in general school populations.Results: 42 studies (39 in preschool or elementary school settings, 27 randomised controlled trials) were eligible to be included in the systematic review and 37 of them were included across the six meta-analyses (n=12 663). Physically active lessons were found to produce large, significant increases in lesson-time PA (d=2.33; 95% CI 1.42 to 3.25:k=16) and small, increases on overall PA (d=0.32; 95% CI 0.18 to 0.46:k=8), large, improvement in lesson-time educational outcomes (d=0.81; 95% CI 0.47 to 1.14:k=7) and a small improvement in overall educational outcomes (d=0.36; 95% CI 0.09 to 0.63:k=25). No effects were seen on cognitive (k=3) or health outcomes (k=3). 25/42 studies had high risk of bias in at least two domains.Conclusion: In elementary and preschool settings, when physically active lessons were added into the curriculum they had positive impact on both physical activity and educational outcomes. These findings support policy initiatives encouraging the incorporation of physically active lessons into teaching in elementary and preschool setting.Trial registration number: CRD42017076933.

Behaviour change is key to addressing both the challenges facing human health and wellbeing and to promoting the uptake of research findings in health policy and practice. We need to make better use of the vast amount of accumulating evidence from behaviour change intervention (BCI) evaluations and promote the uptake of that evidence into a wide range of contexts. The scale and complexity of the task of synthesising and interpreting this evidence, and increasing evidence timeliness and accessibility, will require increased computer support. The Human Behaviour-Change Project (HBCP) will use Artificial Intelligence and Machine Learning to (i) develop and evaluate a ‘Knowledge System’ that automatically extracts, synthesises and interprets findings from BCI evaluation reports to generate new insights about behaviour change and improve prediction of intervention effectiveness and (ii) allow users, such as practitioners, policy makers and researchers, to easily and efficiently query the system to get answers to variants of the question ‘What works, compared with what, how well, with what exposure, with what behaviours (for how long), for whom, in what settings and why?’. The HBCP will: a) develop an ontology of BCI evaluations and their reports linking effect sizes for given target behaviours with intervention content and delivery and mechanisms of action, as moderated by exposure, populations and settings; b) develop and train an automated feature extraction system to annotate BCI evaluation reports using this ontology; c) develop and train machine learning and reasoning algorithms to use the annotated BCI evaluation reports to predict effect sizes for particular combinations of behaviours, interventions, populations and settings; d) build user and machine interfaces for interrogating and updating the knowledge base; and e) evaluate all the above in terms of performance and utility. The HBCP aims to revolutionise our ability to synthesise, interpret and deliver evidence on behaviour change interventions that is up-to-date and tailored to user need and context. This will enhance the usefulness, and support the implementation of, that evidence.

Background: Behaviour change interventions (BCI), their contexts and evaluation methods are heterogeneous, making it difficult to synthesise evidence and make recommendations for real-world policy and practice. Ontologies provide a means for addressing this. They represent knowledge formally as entities and relationships using a common language able to cross disciplinary boundaries and topic domains. This paper reports the development of the upper level of the Behaviour Change Intervention Ontology (BCIO), which provides a systematic way to characterise BCIs, their contexts and their evaluations. Methods: Development took place in four steps. (1) Entities and relationships were identified by behavioural and social science experts, based on their knowledge of evidence and theory, and their practical experience of behaviour change interventions and evaluations. (2) The outputs of the first step were critically examined by a wider group of experts, including the study ontology expert and those experienced in annotating relevant literature using the initial ontology entities. The outputs of the second step were tested by (3) feedback from three external international experts in ontologies and (4) application of the prototype upper-level BCIO to annotating published reports; this informed the final development of the upper-level BCIO. Results: The final upper-level BCIO specifies 42 entities, including the BCI scenario, elaborated across 21 entities and 7 relationship types, and the BCI evaluation study comprising 10 entities and 9 relationship types. BCI scenario entities include the behaviour change intervention (content and delivery), outcome behaviour, mechanism of action, and its context, which includes population and setting. These entities have corresponding entities relating to the planning and reporting of interventions and their evaluations. Conclusions: The upper level of the BCIO provides a comprehensive and systematic framework for representing BCIs, their contexts and their evaluations.