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Michie S, Wood CE, Johnston M, et al. Behaviour change techniques: the development and evaluation of a taxonomic method for reporting and describing behaviour change interventions (a suite of five studies involving consensus methods, randomised controlled trials and analysis of qualitative data). Southampton (UK): NIHR Journals Library; 2015 Nov. (Health Technology Assessment, No. 19.99.)

Cover of Behaviour change techniques: the development and evaluation of a taxonomic method for reporting and describing behaviour change interventions (a suite of five studies involving consensus methods, randomised controlled trials and analysis of qualitative data)

Behaviour change techniques: the development and evaluation of a taxonomic method for reporting and describing behaviour change interventions (a suite of five studies involving consensus methods, randomised controlled trials and analysis of qualitative data).

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Chapter 3Behaviour Change Technique Taxonomy version 1 with hierarchical structure (study 2)


Objectives: To enhance understanding and use of BCTs by investigating the hierarchical structure of BCTTv1.

Methods: Participants grouped BCTs according to similarity of active ingredients in an open-sort task. This structure was examined for higher-order groupings using a dendrogram derived from hierarchical cluster analysis. This ‘bottom-up’ sort method was compared with a theory-based ‘top-down’ method in which 18 experts sorted BCTs into 14 theoretical domains in a closed-sort task. Discriminant content validity (DCV) was used to identify groupings and chi-squared tests, and Pearson’s residual values were used to examine the overlap between groupings.

Results: Participants created an average of 15.11 groupings (SD = 6.11 groupings, range 5–24 groupings). BCTs relating to ‘Reward and Punishment’ and ‘Cues and Cue Responses’ were perceived as markedly different from other BCTs. Fifty nine of the BCTs were reliably allocated to 12 of the 14 theoretical domains; 47 were significant and 12 were of borderline significance. Two domains had no BCTs significantly assigned to them. An additional grouping of ‘No Domain’ was included to represent these cases. There was a significant association between the 16 ‘bottom-up’ groupings and the 13 ‘top-down’ groupings (χ2 = 437.80; p < 0.001). Thirty-six of the 208 ‘bottom-up’ × ‘top–down’ pairings (i.e. 16 × 13) showed greater overlap than expected by chance. However, only six combinations achieved satisfactory evidence of similarity.

Conclusions: The moderate overlap between the groupings indicates some tendency to implicitly conceptualise BCTs in terms of the same theoretical domains. Understanding the nature of the overlap will aid the conceptualisation of BCTs in terms of theory and application. Further research into different methods of developing a hierarchical taxonomic structure of BCTs for international, interdisciplinary work is now required.


Study 1 presented the synthesis of existing BCT taxonomies into a single comprehensive, cross-context, overarching BCT taxonomy: BCTTv1.8,17,40 Previous BCTs groupings have been based on judgements made by the study author.20,27,32,33 Given the 93 items of BCTTv1, it is necessary to group the BCTs to make the taxonomy more memorable and useable. To achieve this, we need an agreed method for identifying links between particular BCTs and theoretical constructs.

Two methods were investigated: (1) a ‘bottom-up’ linkage and (2) a ‘top-down’, theoretically guided linkage. The ‘bottom-up’ approach allows each respondent to propose linkages inductively and then identifies which linkages are common across respondents. It makes no assumptions about underlying theory and, therefore, the results should be accessible to users from diverse theoretical and disciplinary backgrounds. Nevertheless, it may reflect commonalities in theoretical approaches. The ‘top-down’ approach prompts each respondent to deduce theoretical linkages based on underlying theory. Common linkages across respondents are then identified.

Theories of behaviour change summarise what is known about the mechanisms of behaviour change and the conditions in which behaviour change is most likely to occur.8 The importance of understanding the theoretical underpinnings of BCTs has been highlighted in previous research.10,21,56,57 However, a recent meta-analysis found that BCIs are often not designed on a clear theoretical foundation; for example, only 22.5% of 235 implementation studies explicitly used theories of behaviour change58,59 and the majority of those doing so gave no clear explanation for why the selected theories had been used. Therefore, there is a clear need for improving methods for applying theory to intervention design to increase our understanding of how BCTs exert their influences. Grouping BCTs by theory would help guide understanding of the functional relationships between BCTs, the underlying mechanisms through which they exert their effects and the most effective ways in which BCTs can be applied.

In light of the 93 BCTs of BCTTv1 and the large number of behaviour change theories and component constructs,60 grouping by individual theoretical constructs is impractical. An alternative is to group by broader domains of theoretical constructs (e.g. knowledge, skills, etc.) as has been done in the theoretical domains framework (TDF).45,61 The TDF is an integrative framework of theoretical constructs of behaviour change that was originally developed by 18 psychological theorists in collaboration with 16 health service researchers and 30 health psychologists.45 It was developed to make theory more accessible to, and usable by, a range of disciplines and theoretical orientations. The first version of the TDF contains 12 theoretical domains synthesised from 128 theoretical constructs related to behaviour change; the validated version61 suggested minor modifications, with 14 domains.

The TDF has been used by research teams across many countries and health-care systems to investigate implementation problems and inform interventions to change professional practice.6272 The TDF was validated using two sort tasks,61 producing a refined TDF containing 87 theoretical constructs relevant to behaviour change categorised across 14 domains: knowledge, skills, social/professional role and identity, beliefs about capabilities, optimism, beliefs about consequences, reinforcement, intentions, goals, memory, attention and decision processes, environmental context and resources, social influences, and emotion and behavioural regulation. A previous study linking 35 BCTs to 11 theoretical domains from the original TDF showed good reliability across four researchers, with 71% agreement over the 385 possible links.43 Building on this work, we aim to link BCTs from the more comprehensive BCTTv1 to the refined TDF, using a larger number of experts in behaviour change.

Study 2 aimed to:

  • investigate the hierarchical structure of the groupings of the taxonomy, which were obtained from an inductive ‘bottom-up’ method, using quantitative clustering methods
  • identify to what extent the taxonomy can be reliably grouped using a deductive, ‘top-down’ theory-based method into the 14 theory-based domains of the revised TDF
  • examine similarities and differences in the groupings that emerged using these two methods of developing a hierarchical structure.


This study is also published as Cane et al.73 Some text has been reproduced from Cane et al.73 © 2014 The Authors. British Journal of Health Psychology published by John Wiley & Sons Ltd on behalf of the British Psychological Society. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.


For the ‘bottom-up’ method, participants were recruited from the pool of behaviour change experts used in study 1 (see Chapter 2) to take part in an online, open-sort grouping task. Eighteen of 19 participants approached from the pool of experts completed the task. Eight were women and 10 men, with an age range of 27–67 years (mean = 43.94 years); 16 were from the UK and two were from Australia.

For the ‘top-down’ method, 25 individuals were invited to take part in the closed-sort task. Participants were eligible to take part if they had (1) experience in designing interventions that specifically used BCTs, (2) experience in writing manuals or protocols of BCIs or (3) undertaken a narrative or systematic review of behaviour change literature. Participants were recruited via announcements through university networks and scientific societies’ mailing lists – the Society of Behavioural Medicine, the American Psychological Association Health Division and the Society for Academic Primary Care. Eighteen people (72%) met the eligibility criteria and all who were eligible consented to complete the task (Table 1 shows demographic information). There was no overlap in participants between the ‘bottom-up’ and ‘top-down’ sort tasks. The sample size for the closed-sort task was based on estimates given for content-validation exercises, with 2–24 participants being shown to be sufficient7477 and more than five participants reducing the influence of rater outliers.78



Demographic information for open and closed-sort tasks


Invitations included a brief overview of the study and participation consent form. Consenting participants were given detailed instructions on how to complete the task and were asked to provide demographic information (including age, sex and nationality) and to rate their expertise in behaviour change theory and in delivering BCIs on a 5-point scale (1 – ‘A great deal’, 2 – ‘quite a bit’, 3 – ‘some’, 4 – ‘a little’, 5 – ‘none’).

Open-sort task: the open-sort grouping task was delivered via an online computer program. Participants were asked to sort the list of BCTs into groupings (up to a maximum of 24) of their choice and label the groupings. Instructions guided the experts to ‘group together BCTs which have similar active ingredients i.e. by the mechanism of change, NOT the mode of delivery’.73

Closed-sort task: participants sorted BCTs into the 14 domains specified in the revised version of the TDF:61 knowledge, skills, social/professional role and identity, beliefs about capabilities, optimism, beliefs about consequences, reinforcement, intentions, goals, memory, attention and decision processes, environmental context and resources, social influences, and emotion and behavioural regulation. The closed-sort task was delivered via a Microsoft Word document 1997–2003 (Microsoft Corporation, Redmond, WA, USA), comprising labels and definitions of the 14 theoretical domains and of the 87 BCTs from BCTTv1, which were randomly ordered.40 The closed-sort task was conducted on an earlier version of the taxonomy containing 87 BCTs and the open-sort task an even earlier version containing 85 BCTs. Both were conducted while BCTTv1 was in development. Participants were required to indicate which domain was most relevant for each BCT and give a confidence rating for their allocation. Participants were asked to allocate each of the 87 BCTs to one or more of the 14 theoretical domain(s), giving a confidence rating for each allocation (from 1 – ‘not at all confident’ to 10 – ‘extremely confident’). After assigning all BCTs, participants were asked to review their BCT allocations and to revise any allocations if they wanted to. There was no time limit for the tasks and participants were debriefed about the study on completion.


To analyse open-sort data, a binary dissimilarity matrix containing all possible BCT × BCT combinations was produced for each participant, for whom a score of one indicated BCTs that were not sorted into the same grouping, and a score of 0 indicated items that were sorted into the same grouping. Individual matrices were aggregated to produce a single dissimilarity matrix, which could be used to identify the optimal grouping of BCTs using cluster analysis. Using hierarchical cluster analysis, the optimal number of groupings (2–20) were examined for suitability using measures of internal validity (Dunn’s Index) and stability (figure of merit).79 Bootstrap methods were used in conjunction with the hierarchical cluster analysis, whereby data were resampled 10,000 times, to identify which groupings were strongly supported by the data. The approximately unbiased p-values yielded by this method indicated the extent to which groupings were strongly supported by the data with higher approximately unbiased values (e.g. 95%) indicating stronger support for the grouping.80 The words and phrases used in the labels given by participants were analysed to identify any common themes and to help identify appropriate labels for the groupings. For each grouping, labels were created based on their content and, when applicable, based on the frequency of word labels given by participants. After the labels were assigned to relevant groupings, the fully labelled groupings with the word frequency analysis were sent out to a subset of five of the original participants for refinement.

To analyse closed-sort data, mean confidence ratings for each BCT × domain pairing were calculated and analysed using DCV methods.74,77 BCT × domain pairings that had no confidence rating from individual participants (i.e. BCT was not allocated to that domain by that participant) were scored zero and entered into the mean score for that pairing. A series of one-sample t-tests compared the mean confidence ratings for the assignment of BCTs to a value of zero. This established the extent to which BCTs were related to each domain. In cases for which no experts allocated a BCT to a specific domain (i.e. all scores for a BCT × domain pairing were zero) the BCT × domain pairings was excluded from t-test analyses.

The BCTs were considered to be reliably allocated to a domain if their mean confidence ratings were significantly greater than zero (p < 0.05) after Hochberg’s correction81 [applied using the p.adjust function in R version 3.0.1 (The R Foundation for Statistical Computing, Vienna, Austria)]. This was used to control for the family-wise error rate, given the large number of tests used, and provided a suitable criterion for inclusion and exclusion of BCTs to a particular domain, over and above the use of a subjective cut-off value. Hochberg’s correction also provides a conservative p-value that makes it less likely that a BCT × domain pairing achieving low confidence ratings across the majority of participants will achieve significance. The agreement of BCT allocation across participants was analysed using a two-way intraclass correlation coefficient (ICC) within each domain.

To identify any overlap between groupings, two types of comparisons were made between the ‘bottom-up’ groupings and the ‘top-down’ TDF derived groupings – comparison between the theoretically derived ‘top-down’ groupings and, (1) the higher order strategy groupings found in the ‘bottom-up’ sort task, and (2) the final groupings of the ‘bottom-up’ sort task. To test the possibility of overlap between groupings derived from using ‘bottom-up’ and ‘top-down’ methods, Pearson’s chi-squared test was adopted. To adjust for potential inaccuracy of the p-value estimation (resulting from the number of cells that had expected frequencies < 1), Monte Carlo simulation (using 2000 replications) was used. Pearson’s residual values [(observed – expected)/sqrt (expected)] were used to quantify the extent of overlap between individual BCTTv1 grouping × TDF domain pairings resulting from the ‘bottom-up’ and ‘top-down’ methods. Positive values indicate that the observed overlap in BCT assignment between the BCT taxonomy and TDF domain pairings is greater than expected by chance whereas negative values indicate that it is less than expected.


Participants for the closed-sort task reported moderately high levels of expertise in behaviour change theory (mean = 3.17, SD = 0.71) and in delivering BCIs (mean = 2.17, SD = 1.38) as measured on 5-point scales (scores are reversed so a higher score indicates more experience). This was not significantly different from the level of expertise reported by participants in the open-sort task: behaviour change theory, mean = 3.00, SD = 0.88, t(34) = 0.64; p > 0.10; BCIs, mean = 2.42, SD = 0.96, t(34) = 0.63; p > 0.10. Although the age of participants did not differ significantly between the two sort tasks [t(34) = 0.77; p > 0.10] the number of female and male participants did [χ2(1) = 4.33; p < 0.05] as did the country of residence (χ2 = 20.76; p < 0.001) (Monte-Carlo simulation using 2000 replicates was used to compute the p-value given that a number of the expected cell values were < 1). This was an artefact of the selection process as there was no duplication of participants across the two sort tasks.

Developing a basic hierarchical structure within Behaviour Change Technique Taxonomy version 1 using an open-sort task (‘bottom-up’ method)

The BCTs were grouped using an inductive ‘bottom-up’ method based on the similarity of their active ingredients. This process yielded 16 distinct sets of BCTs, as follows (with number of BCTs in parentheses): scheduled consequences (10), reward/threat (7), repetition/replacement (7), antecedents (4), associations (8), covert learning (3), natural consequences (6), feedback and monitoring (5), goals and planning (9), social support (3), comparison of behaviour (3), self-belief (4), comparison of outcomes (3), identity (5), shaping knowledge (4) and adjunctive (4). The hierarchical structure is illustrated using a dendrogram (see Michie et al.40). The distance between the groupings at each split is indicated by the ‘height’ on the y-axis of the dendrogram, with greater height values indicating greater distance and less similarity between the groupings, and lower height values indicating less distance and greater similarity between the groupings.

Within the reported 16-grouping open-sort solution of the taxonomy, there are six points at which groupings of BCTs split into groupings containing similar BCTs (creating seven split groupings, i.e. higher order strategy groupings). These groupings themselves contain more subtle distinct groupings as detailed in BCTTv1. The first split is at ‘split 1′ (height = 31.78), for which the body of BCTs split into two groupings, the grouping to the left containing the groupings of ‘scheduled consequences’ and ‘reward and threat’ that involve BCTs relating to the anticipation of a direct reward or punishment (e.g. social reward, negative reinforcement, extinction). The next split, ‘split 2’ (height = 14.16) reveals three groupings to the left of the remaining BCTs: ‘repetition and substitution’, ‘antecedents’ and ‘associations’ comprising BCTs relating to cues and cue responses. From split 3 onwards, the distance between the groupings is markedly smaller (height < 10), indicating that the groupings formed are less distinct from each other. At split 3 (height = 9.56) BCTs from the groupings ‘covert learning’ and ‘natural consequences’ are separated off from the remaining groupings. At split 4 (height = 7.69), the split includes the groupings ‘feedback and monitoring’, and ‘goals and planning’ and BCTs relating to goals, planning and feedback. At split 5 (height = 5.55), the split includes the groupings ‘social support’ and ‘comparison of behaviour’ and BCTs related to social factors. The final split occurs at split 6 (height = 4.18), where the groupings ‘self-belief’, ‘comparison of outcome’ and ‘identity’ (BCTs relating to the self and identity) are separated from the groupings of ‘shaping knowledge’ and ‘regulation’ (BCTs relating to knowledge and regulation).

Identifying whether or not behaviour change techniques can reliably be linked to theoretical domains using a closed-sort task (‘top-down’, theoretically driven method)

All TDF domains had BCTs allocated to them in the closed-sort task, with the number of BCTs allocated ranging from 15 for social/professional role and identity to 68 for behavioural regulation (Table 2). This allocation was reliable for 12 of the 14 domains, that is BCTs were allocated consistently with high confidence across experts, leading to p < 0.05 (see Table 2 for frequencies and see Table 4 for confidence ratings, ICC values and related p-values).



Total number of BCT allocations per domain in the closed-sort ‘top-down’ task



Taxonomy grouping (‘bottom-up’) and TDF domain (‘top-down’) combinations achieving positive Pearson’s residual values for similarities in the assignment of BCTs

Within these domains, 59 (68%) of the BCTs were considered to be reliably allocated, with a further 12 (14%) BCTs having borderline statistical significance (p > 0.05 but p < 0.1) and six being allocated to multiple domains (Table 3). The domains, in order of number of BCT allocations obtaining statistical or marginal statistical significance, were (numbers of BCTs in brackets): reinforcement (17), beliefs about consequences (10), social influences (10), goals (6), environmental context and resources (6), skills (5), emotion (5), knowledge (4), beliefs about capabilities (2), intentions (2), optimism (1) and behavioural regulation (1). Two domains, ‘social/professional role and identity’ and ‘memory, attention and decision processes’ had no BCTs significantly assigned to them. This indicates that, although both of these domains had BCTs allocated to them during the sort process (15 and 49, respectively), experts did not consistently allocate or rate highly any of the BCTs to these two domains.



Assignment of BCTs to the TDF domains in the closed-sort ‘top-down’ task

Of the 24 most commonly occurring BCTs (see Michie et al.40), 18 (75%) were reliably linked to seven of the theory domains, with a further two (8%) obtaining borderline statistical significance. These domains were (with number of BCTs in brackets): goals (5), social influences (4), environmental context and resources (3), knowledge (2), reinforcement (2), skills (1), and behavioural regulation (1). The following commonly identified BCTs were not linked to any of the theoretical domains: problem-solving, credible source, discrepancy between current behaviour, self-monitoring of outcome of behaviour, monitoring of outcome behaviour by others without feedback and pharmacological support.

Identifying overlap between the ‘bottom-up’ and ‘top-down’ groupings

The chi-squared analyses used for the grouping comparisons did not allow us to include domains in which no BCTs were assigned (i.e. not linked to domains through the DCV process); therefore, the domains of memory, attention and decision processes, and social/professional identity were excluded from these analyses. An additional grouping of ‘No domain’ was included in the ‘top-down’ groupings and represented cases for which BCTs included in BCTTv1 were not assigned to any TDF domain. Therefore, the chi-squared analysis was conducted first on 91 (7 × 13) possible pairings for the seven higher-order ‘bottom-up’ sorting strategy groupings and the 13 ‘top-down’ groupings, and second on 208 (16 × 13) possible pairings derived from the original 16 BCTTv1 ‘bottom-up’ groupings and the 13 ‘top-down’ groupings.

Comparison of the BCT groupings derived from the higher-order ‘bottom-up’ sorting strategies, shown in the dendrogram and the ‘top-down’ TDF derived groupings (see Table 3) revealed a significant association (χ2 = 236.13; p < 0.001). Figure 2 shows the level of overlap between each of the grouping × TDF pairings within each cell (Pearson’s residual value range –2.10 to 6.61).

FIGURE 2. Pearson’s residual values for the association between BCT allocation to ‘top-down’ theoretical domain groupings and the ‘bottom-up’ higher-order hierarchical split groupings.


Pearson’s residual values for the association between BCT allocation to ‘top-down’ theoretical domain groupings and the ‘bottom-up’ higher-order hierarchical split groupings. Note: ‘No domain’ indicates (more...)

Twenty one of the 91 ‘bottom-up’ and ‘top-down’ TDF domain combinations showed a greater than expected overlap with positive Pearson’s residual values (Table 4). Only two combinations achieved Pearson’s residual values > 5, which were: ‘grouping 1′ with ‘reinforcement’ (Pearson’s residual value = 6.61) and ‘grouping 5′ with ‘social influences’ (Pearson’s residual value = 5.04).

There was also an association between the 16 ‘bottom-up’ groupings and the 13 ‘top-down’ groupings (χ2 = 437.80; p < 0.001). Figure 3 shows the level of overlap between structures; Pearson’s residual values range from –1.72 to 6.66. Thirty six of the 208 combinations showed greater than expected overlap, achieving positive Pearson’s residual values (see Table 4). Six combinations achieved Pearson’s residual values > 5 indicating a comparatively high level of overlap and these combinations were ‘repetition and substitution’ and ‘skills’ (Pearson’s residual value = 6.66), ‘goals and planning’ and ‘goals’ (Pearson’s residual value = 6.41), ‘covert learning’ and ‘beliefs about consequences’ (Pearson’s residual value = 5.76), ‘self-belief’ and ‘beliefs about capabilities’ (Pearson’s residual value = 5.70), ‘scheduled consequences’ and ‘reinforcement’ (Pearson’s residual value = 5.22), and ‘antecedents’ and ‘environmental context and resources’ (Pearson’s residual value = 5.20).

FIGURE 3. Pearson’s residual values for the association between BCT allocation in ‘top-down’ theoretical domain groupings and ‘bottom-up’ groupings.


Pearson’s residual values for the association between BCT allocation in ‘top-down’ theoretical domain groupings and ‘bottom-up’ groupings. Note: ‘No domain’ indicates BCTs within a ‘bottom-up’ (more...)


Examination of the hierarchical structure of BCTTv1 uncovered a ‘higher-order’ grouping strategy taken by the behaviour change experts in the ‘bottom-up’ task and the dendrogram indicates that some groupings of BCTs within the 16-grouping solution can be considered as more clearly distinct from others. The grouping of BCTs in the ‘top-down’ sort task has helped illuminate relationships between particular BCTs and theoretical domains and could aid the selection of BCTs in the construction of theory-based interventions. There was a moderate overlap between the 16 BCT groupings derived from the ‘bottom-up’ inductive approach and the 12 groupings from the ‘top-down’ theoretically driven approach, indicating some common conceptualisation of BCTs across these two approaches. These findings may help to further our understanding of the relationships between BCTs and enable researchers to use common BCT grouping labels to discuss individual, or groupings of, BCTs in behaviour change research.

The grouping methods employed in the ‘bottom-up’ and ‘top-down’ sort tasks improve on previous attempts to group BCTs using consensus approaches. First, use of an open-sort grouping task allowed for the individual groupings of BCTs defined by participants to hold equal weight within the final solution and be aggregated using empirical techniques (hierarchical cluster analysis in the ‘bottom-up’ sort task and DCV methods in the ‘top-down’ sort task). As a result, the groupings reported here are potentially more robust than those derived using consensus methods among a few people.43

A second advance was that a comprehensive, cross-behavioural domain taxonomy of BCTs was used rather than BCTs relevant for a single behavioural domain (e.g. road safety,82 smoking cessation,32 weight management83). Third, the BCTs were grouped according to the perceived active ingredients underlying BCTs, rather than by categorisations that may not have reflected how people think about BCTs.

In addition to providing 16 groupings, the ‘bottom-up’ open-sort task yielded systematic empirical estimates of how distinct the groupings are. Examination of this hierarchical structure revealed that BCTs related to reward and threat, and those related to cues and cue responses, were conceptualised quite distinctly from the other BCTs. The least distinct groupings (i.e. ‘social support’, ‘comparison of behaviour’, ‘self-belief’, ‘comparison of outcome’ and ‘identity’) comprised BCTs relating to social support, social comparisons, and self and identity, suggesting that there is less clarity about the BCTs within these theoretical domains. Four further groupings of BCTs (‘covert learning’ and ‘natural consequences’, and ‘feedback and monitoring’ and ‘goals and planning’) lay between these most distinct and least distinct groupings. BCTs in distinct groupings are clearly perceived to share a common mode of action in changing behaviour whereas BCTs in less distinct groupings may be viewed as having less distinct or more than one mode of action.

This difference in distinctiveness not only has implications for understanding how BCTs are conceptualised by behaviour change experts but also has implications for the practical use of BCTTv1 in behaviour change research. For example, the groupings increase the practical use of BCTs by aiding recall. Distinct sets of individual items with semantic similarity can be more easily recalled than a single list of individual items both in the short term and long term, particularly when the semantic category is cued.8486 This is especially useful when quick reference to BCTs is necessary, for instance when coding descriptions of interventions or in choosing BCTs to develop or report a BCI. Therefore, in those cases where the groupings are less distinct, adopting additional strategies to aid recall the groupings may be of particular advantage.

The ‘top-down’ mapping of BCTs to theoretical domains advances the limited consensus methods used by Michie et al.43 by using an improved BCT taxonomy, an empirically validated TDF and a larger number of respondents. In this ‘top-down’ task, 59 out of 87 BCTs were reliably allocated to one or more of the TDF domains with a further 12 BCTs having borderline statistical significance. Thirty-seven BCTs were allocated to three domains that also had high confidence ratings and ICCs: ‘beliefs about consequences’, ‘reinforcement’ and ‘social influences’. This suggests that these are the theoretical domains for which there is the greatest number of agreed methods for bringing about change. Other domains also showed high agreement but had fewer associated BCTs – ‘behavioural regulation’ had only one assigned BCT but achieved good agreement, while ‘goals’ had five BCTs assigned with good agreement. In designing interventions, it may be more important to have a few agreed BCTs than to have a large choice of BCTs available to target change in a given theoretical determinant of behaviour. Further evidence is required to ensure that these ‘agreed’ BCTs do in fact achieve behaviour change by changing the proposed theoretical domain. For the two theoretical domains for which no BCTs were reliably assigned, there would appear to be no shared, or recognised, way of changing them.

Most of the commonly used BCTs were associated with a theoretical domain. Of the 24 most frequently identified BCTs in Michie et al.,40 18 were clearly grouped into one of the 14 domains and the remaining seven BCTs were not reliably allocated to any domain even though they could be identified reliably in the intervention descriptions. This finding suggests that these BCTs may have evolved from several different behavioural domains, theoretical approaches or disciplines and, therefore, may be less associated with a particular theoretical domain.

Comparison of open and closed-sort tasks

Six of the open-sort tasks groupings, ‘repetition and substitution, ‘goals and planning’, ‘covert learning’, ‘self-belief’, ‘scheduled consequences’ and ‘antecedents’, showed a high level of overlap with the BCTs assigned to the equivalent TDF domains, suggesting that experts may have sorted BCTs by theoretical constructs or domains (implicitly or explicitly) across both tasks. This is supported by the fact that both groups of experts reported high levels of expertise in relation to behaviour change theory. For the higher-order groupings created by the top-down task, there were only two similarly strong overlaps with the BCTs allocated to the equivalent TDF domains indicating that the relationship between higher-order sorting strategies and theoretically derived groupings is not strong. It would appear that the lower-level groupings are more in line with the TDF domains than the empirically higher-order groupings, suggesting that the higher-level grouping shared by respondents does not align as well with the theoretical domains. It may be that the higher-order groupings of BCTs depended on considerations other than theory, for example target populations or behaviours.

The next step for this line of research is to evaluate the extent to which these groupings facilitate the usability of the taxonomy, and to do this for larger sample sizes and a greater disciplinary and geographical spread. It may be that different groupings may be useful for different tasks (e.g. identifying BCTs in reports of interventions vs. designing interventions) and/or be beneficial to different users in different contexts. It may be that for those applying BCTs to designing or specifying interventions without reference to theory, the open-sort groupings may be of more benefit as all of the BCTs were grouped. On the other hand, the closed-sort grouping of BCTs is likely to be more useful for those who are seeking a theoretical base for coding and designing interventions. Further work will be necessary to investigate the replicability and utility of these groupings, as well as their theoretical basis. As more evidence is gained from the application of BCTTv1, it may be that the BCT groupings will be modified to incorporate links between BCTs that are commonly used together in research practice and/or to reflect the ‘common mechanisms of action.’

The hierarchical structure and grouping of BCTs within the taxonomy has practical use in that it is predicted to increase the speed by which BCTs can be recalled by users. It also has theoretical interest in that links between BCTs and theory can be used to inform the design and evaluation of BCIs. Although BCTTv1 represents an advance in methods for specifying BCIs, reliable and valid application of BCTTv1 will require skills and, therefore, training. To investigate how best to train the skills of using BCTTv1, two programmes of user training were developed (face-to-face workshops and distance group tutorials). The next study reports the development and evaluation of these training programmes.

Copyright © Queen’s Printer and Controller of HMSO 2015. This work was produced by Michie et al. under the terms of a commissioning contract issued by the Secretary of State for Health. This issue may be freely reproduced for the purposes of private research and study and extracts (or indeed, the full report) may be included in professional journals provided that suitable acknowledgement is made and the reproduction is not associated with any form of advertising. Applications for commercial reproduction should be addressed to: NIHR Journals Library, National Institute for Health Research, Evaluation, Trials and Studies Coordinating Centre, Alpha House, University of Southampton Science Park, Southampton SO16 7NS, UK.

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