Developing an evidence-based online method of linking behaviour change techniques and theoretical mechanisms of action: a multiple methods study

Background: Many global health challenges may be targeted by changing people’s behaviour. Behaviours including cigarette smoking, physical inactivity and alcohol misuse, as well as certain dietary behaviours, contribute to deaths and disability by increasing the risk of cancers, cardiovascular diseases and diabetes. Interventions have been designed to change these health behaviours with a view to reducing these health risks. However, the effectiveness of these interventions has been quite variable and further information is needed to enhance their success. More information is needed about the specific processes that underlie the effectiveness of intervention strategies. Aim: Researchers have developed a taxonomy of 93 behaviour change techniques (i.e. the active components of an intervention that bring about behavioural change), but little is known regarding their potential mechanisms of action (i.e. the processes through which a behaviour change technique affects behaviour).We therefore aimed to examine links between behaviour change techniques and mechanisms of action. Method: First, we conducted a literature synthesis study of 277 behaviour change intervention studies, from which we extracted information on links, described by authors, between behaviour change techniques and mechanisms of action, and identified an average of 10 links per intervention report. Second, behaviour change experts (n = 105) were engaged in a three-round consensus study in which they discussed and rated their confidence in the presence/absence of ‘links’ and ‘non-links’ between commonly used behaviour change techniques (n = 61) and a set of mechanisms of action (n = 26). Ninety links and 460 ‘non-links’ reached the pre-set threshold of 80% agreement. To enhance the validity of these results, a third study was conducted that triangulated the findings of the first two studies. Discrepancies and uncertainties between the studies were included in a reconciliation consensus study with a new group of experts (n = 25). The final results identified 92 definite behaviour change technique–mechanism of action links and 465 definite non-links. In a fourth study, we examined whether or not groups of behaviour change techniques used together frequently across interventions revealed shared theoretical underpinnings. We found that experts agreed on the underlying theory for three groups of behaviour change techniques. Results: Our results are potentially useful to policy-makers and practitioners in selecting behaviour change techniques to include in behaviour change interventions. However, our data do not demonstrate that the behaviour change techniques are effective in targeting the mechanism of action; rather, the links identified may be the ‘best bets’ for interventions that are effective in changing mechanisms of action, and the non-links are unlikely to be effective. Researchers examining effectiveness of interventions in either primary studies or evidence syntheses may consider these links for further investigation. Conclusion: To make our results usable by researchers, practitioners and policy-makers, they are available in an online interactive tool, which enables discussion and collaboration (https://theoryandtechniquetool. humanbehaviourchange.org/); accessed 1 March 2020. This work, building on previous work to develop the behaviour change technique taxonomy, is part of an ongoing programme of work: the Human Behaviour Change Project (www.humanbehaviourchange.org/; accessed 1 March 2020). Funding: This project was funded by the Medical Research Council via its Methodology Panel: ‘Developing methodology for designing and evaluating theory-based complex interventions: an ontology for linking behaviour change techniques to theory’ (reference MR/L011115/1). ABSTRACT NIHR Journals Library www.journalslibrary.nihr.ac.uk viMR

An ontology-based modelling system (OBMS) for representing behaviour change theories applied to 76 theories

Background: To efficiently search, compare, test and integrate behaviour change theories, they need to be specified in a way that is clear, consistent and computable. An ontology-based modelling system (OBMS) has previously been shown to be able to represent five commonly used theories in this way. We aimed to assess whether the OBMS could be applied more widely and to create a database of behaviour change theories, their constructs and propositions. Methods: We labelled the constructs within 71 theories and used the OBMS to represent the relationships between the constructs. Diagrams of each theory were sent to authors or experts for feedback and amendment. The 71 finalised diagrams plus the five previously generated diagrams were used to create a searchable database of 76 theories in the form of construct-relationship-construct triples. We conducted a set of illustrative analyses to characterise theories in the database. Results: All 71 theories could be satisfactorily represented using this system. In total, 35 (49%) were finalised with no or very minor amendment. The remaining 36 (51%) were finalised after changes to the constructs (seven theories), relationships between constructs (15 theories) or both (14 theories) following author/expert feedback. The mean number of constructs per theory was 20 (min. = 6, max. = 72), with the mean number of triples per theory 31 (min. = 7, max. = 89). Fourteen distinct relationship types were used, of which the most commonly used was 'influences', followed by 'part of'. Conclusions: The OBMS can represent a wide array of behavioural theories in a precise, computable format. This system should provide a basis for better integration and synthesis of theories than has hitherto been possible.info:eu-repo/semantics/publishedVersio

Do Combinations of Behavior Change Techniques That Occur Frequently in Interventions Reflect Underlying Theory?

BACKGROUND: Behavioral interventions typically include multiple behavior change techniques (BCTs). The theory informing the selection of BCTs for an intervention may be stated explicitly or remain unreported, thus impeding the identification of links between theory and behavior change outcomes. PURPOSE: This study aimed to identify groups of BCTs commonly occurring together in behavior change interventions and examine whether behavior change theories underlying these groups could be identified. METHODS: The study involved three phases: (a) a factor analysis to identify groups of co-occurring BCTs from 277 behavior change intervention reports; (b) examining expert consensus (n = 25) about links between BCT groups and behavioral theories; (c) a comparison of the expert-linked theories with theories explicitly mentioned by authors of the 277 intervention reports. RESULTS: Five groups of co-occurring BCTs (range: 3-13 BCTs per group) were identified through factor analysis. Experts agreed on five links (≥80% of experts), comprising three BCT groups and five behavior change theories. Four of the five BCT group-theory links agreed by experts were also stated by study authors in intervention reports using similar groups of BCTs. CONCLUSIONS: It is possible to identify groups of BCTs frequently used together in interventions. Experts made shared inferences about behavior change theory underlying these BCT groups, suggesting that it may be possible to propose a theoretical basis for interventions where authors do not explicitly put forward a theory. These results advance our understanding of theory use in multicomponent interventions and build the evidence base for further understanding theory-based intervention development and evaluation

Development of an online tool for linking behavior change techniques and mechanisms of action based on triangulation of findings from literature synthesis and expert consensus.

Researchers, practitioners, and policymakers develop interventions to change behavior based on their understanding of how behavior change techniques (BCTs) impact the determinants of behavior. A transparent, systematic, and accessible method of linking BCTs with the processes through which they change behavior (i.e., their mechanisms of action [MoAs]) would advance the understanding of intervention effects and improve theory and intervention development. The purpose of this study is to triangulate evidence for hypothesized BCT-MoA links obtained in two previous studies and present the results in an interactive, online tool. Two previous studies generated evidence on links between 56 BCTs and 26 MoAs based on their frequency in literature synthesis and on expert consensus. Concordance between the findings of the two studies was examined using multilevel modeling. Uncertainties and differences between the two studies were reconciled by 16 behavior change experts using consensus development methods. The resulting evidence was used to generate an online tool. The two studies showed concordance for 25 of the 26 MoAs and agreement for 37 links and for 460 "nonlinks." A further 55 links were resolved by consensus (total of 92 [37 + 55] hypothesized BCT-MoA links). Full data on 1,456 possible links was incorporated into the online interactive Theory and Technique Tool (https://theoryandtechniquetool.humanbehaviourchange.org/). This triangulation of two distinct sources of evidence provides guidance on how BCTs may affect the mechanisms that change behavior and is available as a resource for behavior change intervention designers, researchers and theorists, supporting intervention design, research synthesis, and collaborative research.MR

Do combinations of behaviour change techniques that occur frequently in interventions reflect underlying theory?

Background: Behavioural interventions typically include multiple behaviour change techniques (BCTs). The theory informing the selection of BCTs for an intervention may be stated explicitly, or remain unreported, thus impeding the identification of links between theory and behaviour change outcomes. Purpose: This study aimed to identify groups of BCTs commonly occurring together in behaviour change interventions and examine whether behaviour change theories underlying these groups could be identified. Methods: The study involved three phases: 1) a factor analysis to identify groups of co-occurring BCTs from 277 behaviour change intervention reports; 2) an expert consensus exercise (n=25) to examine links between BCT groups and behavioral theories; 3) a comparison of the expert-linked theories with theories explicitly mentioned by authors of the 277 intervention reports. Results: Five groups of co-occurring BCTs (range: 3-13 BCTs per group) were identified through factor analysis. Experts agreed on five links (≥80% of experts), comprising three BCT groups and five behaviour change theories. Four of the five BCT Group-theory links agreed by experts were also stated by study authors in intervention reports using similar groups of BCTs. Conclusions: It is possible to identify groups of BCTs frequently used together in interventions. Experts made shared inferences about behaviour change theory underlying these BCT groups, suggesting it may be possible to propose a theoretical basis for interventions where authors do not explicitly put forward a theory. These results advance our understanding of theory use in multi-component interventions and build the evidence base for further understanding theory-based intervention development and evaluation

Development of an online tool for linking behavior change techniques and mechanisms of action based on triangulation of findings from literature synthesis and expert consensus

Background: Researchers, practitioners and policymakers develop interventions to change behavior based on their understanding of how behavior change techniques (BCTs) impact the determinants of behavior. A transparent, systematic and accessible method of linking BCTs with the processes through which they change behavior (i.e. their mechanisms of action (MoAs)) would advance understanding of intervention effects, and improve theory and intervention development. Purpose: To triangulate evidence for hypothesized BCT-MoA links obtained in two previous studies and present the results in an interactive, online tool. Methods: Two previous studies generated evidence on links between 56 BCTs and 26 MoAs based on their frequency in literature synthesis and on expert consensus. Concordance between the findings of the two studies was examined using multilevel modelling. Uncertainties and differences between the two studies were reconciled by 16 behavior change experts using consensus development methods. The resulting evidence was used to generate an online tool. Results: The two studies showed concordance for 25 of the 26 MoAs and agreement for 37 links and for 460 ‘non-links’. A further 55 links were resolved by consensus (total of 92 (37+55) hypothesized BCT-MoA links). Full data on 1456 possible links was incorporated into the online interactive Theory and Technique Tool (url link removed for anonymity peer-review). Conclusions: This triangulation of two distinct sources of evidence provides guidance on how BCTs may affect the mechanisms that change behavior and is available as a resource for behavior change intervention designers, researchers and theorists, supporting intervention design, research synthesis, and collaborative researc

An ontology-based modelling system (OBMS) for representing behaviour change theories

The ontology-based modelling system (OBMS) is a formal system for representing theories of behaviour change. A searchable database of 76 theories specified using this system is available at humanbehaviourchange.org/theory-databas