It is important to understand the conditions preceding behaviour psychologically and sociologically and to combine psychological ideas about the automatic and reflective systems with sociological ideas about social practice.

Background Despite advances in behavioral science, there is no widely shared understanding of the “mechanisms of action” (MoAs) through which individual behavior change techniques (BCTs) have their effects. Cumulative progress in the development, evaluation, and synthesis of behavioral interventions could be improved by identifying the MoAs through which BCTs are believed to bring about change. Purpose This study aimed to identify the links between BCTs and MoAs described by authors of a corpus of published literature. Methods Hypothesized links between BCTs and MoAs were extracted by two coders from 277 behavior change intervention articles. Binomial tests were conducted to provide an indication of the relative frequency of each link. Results Of 77 BCTs coded, 70 were linked to at least one MoA. Of 26 MoAs, all but one were linked to at least one BCT. We identified 2,636 BCT–MoA links in total (mean number of links per article = 9.56, SD = 13.80). The most frequently linked MoAs were “Beliefs about Capabilities” and “Intention.” Binomial test results identified up to five MoAs linked to each of the BCTs ( M = 1.71, range: 1–5) and up to eight BCTs for each of the MoAs ( M = 3.63, range: 1–8). Conclusions The BCT–MoA links described by intervention authors and identified in this extensive review present intervention developers and reviewers with a first level of systematically collated evidence. These findings provide a resource for the development of theory-based interventions, and for theoretical understanding of intervention evaluations. The extent to which these links are empirically supported requires systematic investigation.

Psychological understandings and individualistic theories of human behaviour and behaviour change have dominated both academic research and interventions at the 'coalface' of public health. Meanwhile, efforts to understand persistent inequalities in health point to structural factors, but fail to show exactly how these translate into the daily lives (and hence health) of different sectors of the population. In this paper, we suggest that social theories of practice provide an alternative paradigm to both approaches, informing significantly new ways of conceptualising and responding to some of the most pressing contemporary challenges in public health. We introduce and discuss the relevance of such an approach with reference to tobacco smoking, focusing on the life course of smoking as a practice, rather than on the characteristics of individual smokers or on broad social determinants of health. This move forces us to consider the material and symbolic elements of which smoking is comprised, and to follow the ways in which these elements have changed over time. Some of these developments have to do with the relation between smoking and other practices such as drinking alcohol, relaxing and socialising. We suggest that intervening in the future of smoking depends, in part, on understanding the nature of these alliances, and how sets of practices co-evolve. We conclude by reflecting on the implications of taking social practices as the central focus of public health policy, commenting on the benefits of such a paradigmatic turn, and on the challenges that this presents for established methods, policies and programmes.

Nudging has captured the imagination of the public, researchers, and policy makers as a way of changing human behaviour, with both the UK and US governments embracing it. Theresa Marteau and colleagues ask whether the concept stands up to scientific scrutiny

Background Understanding links between behaviour change techniques (BCTs) and mechanisms of action (the processes through which they affect behaviour) helps inform the systematic development of behaviour change interventions. Purpose This research aims to develop and test a methodology for linking BCTs to their mechanisms of action. Methods Study 1 (published explicit links): Hypothesised links between 93 BCTs (from the 93-item BCT taxonomy, BCTTv1) and mechanisms of action will be identified from published interventions and their frequency, explicitness and precision documented. Study 2 (expert-agreed explicit links): Behaviour change experts will identify links between 61 BCTs and 26 mechanisms of action in a formal consensus study. Study 3 (integrated matrix of explicit links): Agreement between studies 1 and 2 will be evaluated and a new group of experts will discuss discrepancies. An integrated matrix of BCT-mechanism of action links, annotated to indicate strength of evidence, will be generated. Study 4 (published implicit links): To determine whether groups of co-occurring BCTs can be linked to theories, we will identify groups of BCTs that are used together from the study 1 literature. A consensus exercise will be used to rate strength of links between groups of BCT and theories. Conclusions A formal methodology for linking BCTs to their hypothesised mechanisms of action can contribute to the development and evaluation of behaviour change interventions. This research is a step towards developing a behaviour change 'ontology', specifying relations between BCTs, mechanisms of action, modes of delivery, populations, settings and types of behaviour.

BackgroundThe idea that behaviour can be influenced at population level by altering the environments within which people make choices (choice architecture) has gained traction in policy circles. However, empirical evidence to support this idea is limited, especially its application to changing health behaviour. We propose an evidence-based definition and typology of choice architecture interventions that have been implemented within small-scale micro-environments and evaluated for their effects on four key sets of health behaviours: diet, physical activity, alcohol and tobacco use.DiscussionWe argue that the limitations of the evidence base are due not simply to an absence of evidence, but also to a prior lack of definitional and conceptual clarity concerning applications of choice architecture to public health intervention. This has hampered the potential for systematic assessment of existing evidence. By seeking to address this issue, we demonstrate how our definition and typology have enabled systematic identification and preliminary mapping of a large body of available evidence for the effects of choice architecture interventions. We discuss key implications for further primary research, evidence synthesis and conceptual development to support the design and evaluation of such interventions.SummaryThis conceptual groundwork provides a foundation for future research to investigate the effectiveness of choice architecture interventions within micro-environments for changing health behaviour. The approach we used may also serve as a template for mapping other under-explored fields of enquiry.

This is a repository copy of The TIPPME intervention typology for changing environments to change behaviour.

BackgroundBehaviour 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?’.MethodsThe 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.DiscussionThe 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.Electronic supplementary materialThe online version of this article (10.1186/s13012-017-0641-5) contains supplementary material, which is available to authorized users.