Background
Behaviour change is key to improving healthcare and health outcomes. Behaviours may be those of healthcare workers, such as implementation of evidence-based practice, of patients, such as medication adherence, or of the general population, such as smoking cessation and increasing physical activity. Despite high-level recommendations to improve implementation of evidence-based practice [
1,
2] and a rapidly developing field of implementation science, implementation remains variable, with numerous organisational and individual factors influencing healthcare workers’ behaviour. These factors include the availability of evidence, its relevance to practice, the dissemination of evidence and guidelines, individual motivation, the ability to keep up with current changes, clarity of roles and practice, and the culture of specific healthcare practices [
3,
4].
Improving implementation of evidence-based practice by healthcare workers depends on changing multiple behaviours of multiple types of people (
e.g., health professionals, managers, administrators) [
5]. Changing behaviour is not easy, but is more effective if interventions are based on evidence-based principles of behaviour change [
6]. These principles form part of many theories of behaviour change, but are seldom drawn on in designing and evaluating implementation interventions. There is some evidence that behaviour change interventions informed by theory are more effective than those that are not [
7,
8]. Designing interventions on the basis of practitioner or researcher intuition rather than theory precludes the possibility of understanding the behaviour change processes that underlie effective interventions and of applying this knowledge to inform the design of future interventions. This is also the case where theory is cited but poorly applied to intervention development [
9].
In a review of 235 guideline development and implementation studies, only 22.5% were judged to have used theories of behaviour change, and 16.6% of studies using a single theory [
10]. A further 4.3% used only selected constructs from theories; across the majority of studies there was no clear rationale for theory use. While use of a single theory may be appropriate and lends itself to theory testing, in many cases the selection has not been justified and the theory is not tested [
9]. If theory selection is not informed by a comprehensive theoretical assessment of the implementation or other behavioural problem, there is a risk of missing relevant theoretical constructs or including irrelevant ones. A second problem in applying theory to intervention design stems from basing interventions on several theories with overlapping theoretical constructs [
11,
12]. This makes it difficult to identify the specific processes underlying successful behaviour change.
To overcome such problems, an integrative framework of theories of behaviour change was developed by 18 psychological theorists in collaboration with 16 health service researchers and 30 health psychologists [
13]. The aim of the Theoretical Domains Framework (TDF) was to simplify and integrate a plethora of behaviour change theories and make theory more accessible to, and usable by, other disciplines. The group identified 33 theories and 128 key theoretical constructs related to behaviour change and synthesised them into a single framework to assess implementation and other behavioural problems and inform intervention design. They used a six stage consensus approach: identifying theories and theoretical constructs relevant to behaviour change, where a theoretical construct was defined as ‘a concept specially devised to be part of a theory’ [
13]; simplifying these resulting constructs into overarching theoretical domains, where a theoretical domain was defined as ‘a group of related theoretical constructs’ [
13]; evaluating the importance of the theoretical domains; conducting an interdisciplinary evaluation and synthesis of the domains and constructs; validating the domain list; and piloting interview questions relevant to the constructs and domains. This resulted in 12 theoretical domains and exemplar questions for each to use in interviews or focus groups to provide a comprehensive theoretical assessment of implementation problems.
This framework has been used by research teams across several healthcare systems to explain implementation problems and inform implementation interventions. For example, in Australia it has been used to identify the barriers and enablers to the implementation of evidence-based guidelines for acute low back pain [
14,
15] and develop theory-informed behaviour change interventions [
16]. In the UK, examples include studies of the barriers and levers related to hand hygiene [
17]; the assessment of theoretical domains relevant to blood transfusion practice across different contexts including neonatal and adult intensive care units [
18,
19]; and identifying difficulties in implementing guidelines relating to schizophrenia [
20]. In Denmark, it has been used to understand behaviour in the implementation of tobacco use prevention and counselling guidelines amongst dental providers [
21]. Most of this research has used interviews and focus groups that are resource intensive; a questionnaire measure is currently being developed by the authors. This will facilitate research investigating prediction of implementation and other types of behaviour change.
This article is one in a series of articles documenting the development and use of the TDF to advance the science of implementation research. To inform future use of the TDF, we conducted the current study to provide a more thorough test of the validity of the framework than was carried out in the original research. The overall objective of the study was to examine the content validity of the TDF. Specifically, we wanted to confirm the optimal domain structure (number of domains), domain content (component constructs in each domain), and domain labels (most appropriate names that best reflected the content of the validated domain structure). Card sorting methodology was used to conduct the validation of the TDF in this study. By building on the validation process undertaken by Michie
et al. [
13] the present study aimed to improve the empirical basis of this framework.
Materials
There were 112 unique constructs (see Additional file
1), after 12 duplicates from the original framework were removed (participants had the opportunity to sort each construct to multiple domains). Definitions for the domains and constructs were selected or constructed from dictionaries, (
e.g., American Psychological Association Dictionary of Psychology [
36]), and internet sources (
e.g.
http://www.oed.com). Each definition was evaluated by the authors of the original framework and definitions were agreed by consensus. The sort tasks were delivered via an online computer program with constructs displayed at the top of the computer screen. For the open sort task, 24 unlabelled boxes were displayed below the construct item window into which the participants could sort the constructs. Above each box a space was given so that labels and descriptions for each group created could be given. For the closed sort task, 12 labeled boxes were displayed, each described by a single domain label from the original framework. In both tasks, individual constructs could be assigned to multiple boxes and for every allocation a confidence rating was requested using a drop-down menu (from 1 – ‘not at all confident’ to 10 – ‘extremely confident’). Constructs were presented in random order that was determined by the online program for each participant. Definitions for each construct (open and closed sort tasks) and domain (for closed sort task only) were available when the participant hovered over the word with their mouse. Participants were asked, through open-ended questions, to record the length of time they had been involved in using behaviour change theories, the context in which they used them (
e.g., teaching, research, etc.) and their expertise in behaviour change theory and in using behaviour change interventions (1 – ‘A great deal’, 2 – ‘quite a lot’, 3 – ’some’, 4 – ‘a little’, 5 – ’none’).
Procedure
Invitations were emailed to potentially eligible participants giving a brief overview of the study and inquiring as to their expertise. If they considered themselves to have expertise in behaviour change theory and reported not knowing about the original framework, they were invited to participate and emailed the relevant web link to the task they were allocated to. Eligible participants were alternately allocated to an open or closed sort task based on the order in which they contacted the researchers. To avoid contamination of results across tasks, each participant was allocated to, and completed, only the closed sort task or the open sort task. For both tasks, an information screen gave a brief background to the study and asked for consent to take part. Participants were given detailed instructions on how to complete their task (see Additional file
2) before completing the sort task they were assigned to. There was no time limit. In both tasks, participants were asked to familiarise themselves with the construct definitions and, in the closed sort task only, the domain definitions. In the open sort task, participants were asked to sort the constructs into groups based on their semantic similarity using as many groups as they wanted to (up to 24) and were asked to provide a label for each group created. Participants could also provide a description for each group if they felt it was necessary. In the closed sort task, participants were asked to assign each construct to one or more of the 12 labelled domain boxes that they thought were most appropriate. Across both tasks, participants were asked to give confidence ratings for each assignment; if an item was not allocated to a domain it automatically received a confidence rating of 0. For both tasks, participants were made aware that they could allocate each construct to multiple groups. After assigning all constructs, participants were asked to review their construct allocations and to change any allocations if they wished to. On completion, participants were given further information about the project.
Data analysis
Data were collected using MySQL databases. For the open sort task, data were the construct-group allocations, confidence ratings, and group labels allocated by the participant. For the closed sort task data were the construct-group allocations and confidence ratings.
Open sort
To examine the optimal clustering of constructs (step one: identify domains), the open sort data were first organised into a dissimilarity matrix for each participant. Construct pairs, consisting of all possible construct-by-construct combinations, were assigned 0 if they were placed in the same group and 1 if they were placed in a different group. Agreement across these individual matrices was assessed using Mantel Correlations and Kendall's Coefficient of Concordance,
W[
37] using CADM.global and CADM.post from the ‘ape’ package [
38] in the R statistics program [
39]. Mantel Correlations determine the extent to which an individual participants’ matrix correlates with other participants’ matrices and were used to identify any potential outlying sort patterns that should be excluded from subsequent analysis. An individual’s matrix is considered to be an outlier when it negatively correlates with the other participants’ matrices [
40]. Kendall’s Coefficient of Concordance provides an indication of the overall concordance across all participants’ sort patterns, Kendall’s
W ranges from 1 to 0 [
37], where 1 equals complete agreement in sorting patterns and 0 equals no agreement across sorting patterns. To identify the clusters formed through these sorting patterns, means were calculated for each construct pairing across individual matrices to form a single, aggregated dissimilarity matrix. Fuzzy Cluster Analysis of this matrix, using the FANNY algorithm [
34,
41] in the R statistics program, led to a membership value assigned to each construct-cluster pairing. These membership values, converted into percentages, serve as an indication of the extent to which a construct belongs to a particular cluster. Values near 100% indicate a high probability of association with a cluster and values near 0% indicate a low probability of association. Using these values, construct membership to multiple domains can be assessed (
e.g., construct x might have 53% membership to cluster y and 47% membership to cluster z).
Constructs were then allocated to the cluster with which it has the highest membership value (known as a ‘hard’ cluster solution and comparable to outputs of the k-means and k-medoid cluster methods). The fit of constructs within the clusters was calculated by silhouette values (
s(i)) [
42]. Silhouette values are calculated for each construct and range from +1, indicating strong association with a cluster and distance from neighboring clusters, through 0, indicating no distinct association with clusters, to −1, indicating that a construct is probably assigned to the wrong cluster and should be considered as belonging to the neighbouring cluster [
42]. The average silhouette values (ave
s(i)) across construct items within a cluster indicates how well a cluster is defined, and the overall average of silhouette values across clusters can be used to compare cluster solutions of different sizes.
The optimal outcome of the cluster analysis is to achieve the highest average silhouette value with the fewest clusters. It has been argued that average cluster silhouette values greater than 0.70 indicate a strong structure, whilst average silhouette values below 0.50 indicate weak structures and silhouette values <0.25 indicate that there is little evidence for any reliable structure [
34]. Informed by these cutoff values, we considered that a construct with a silhouette value <0.25 in relation to a cluster did not belong to that cluster.
In addition to identifying the optimal domain structure, the open sort results were used to identify the extent to which the clusters replicated the construct allocation in the original framework when domain labels were not provided (step two: establish domain content). Congruence was quantified as the percentage of constructs from the original framework domain remaining in a cluster solution (e.g., if domain m contained constructs x, y, and z and the cluster contained only x and z, then congruence was 67%). If the structure of the domains identified in the Fuzzy Cluster Analysis was considerably different from that of the original framework, confidence ratings would be used for secondary analysis to infer construct allocation to the new domains formed.
The group labels given by participants in the open sort task were organised according to their similarity and the frequency that they occurred across participants noted. Those labels that occurred frequently and were related to the content of the newly-formed domains were used to inform newly-formed domain labels (step three: finalise domain labels).
Closed sort
To identify pre-existing domains that might also be considered for inclusion in the framework (step one: identify domains), the strength and agreement of construct allocations to pre-existing domains from the closed sort task were examined. Confidence ratings for each construct x domain pairing, excluding those that had no confidence ratings, were applied to a table. To examine the agreement of these construct x domain ratings and construct assignment across participants, two-way intraclass correlation coefficient (ICC) measures of consistency were used within each domain [
43]. In line with previous research we classified ICC values <0.21 as indicating poor agreement, values between 0.21 to 0.40 as fair agreement, values between 0.41 to 0.60 as moderate agreement, and values of ≥0.61 as good to excellent agreement [
44]. ICC values were used as an indication of the agreement in assignments and ratings across participants, but were not used to influence the final domain content.
To identify the strength of construct assignment to particular domains, DCV methods were used with one-sample t-tests on the participants’ confidence ratings against the value zero. A construct was considered as belonging to a domain if its mean confidence rating across participants was significantly greater than zero (
p < 0.05) following the adoption of Hochberg’s correction [
45] (see [
29,
35] for similar methods). Hochberg’s correction was used to control for the family-wise error rate given the number of tests used. Whilst this approach may not be considered a conventional use of one-sample t-tests, it provides a suitable criterion for inclusion and exclusion of constructs to a particular domain over and above the use of a subjective cut-off value. To ensure that domains with highly-rated, relevant constructs assigned to them were considered for inclusion in the framework, domains containing two or more constructs with ratings significantly greater than zero were considered. These constructs were also used to inform construct allocation to pre-existing domains (step two: establish domain content). The allocation of constructs to domains in the closed sort task was compared with construct allocation in the original framework to identify the extent of congruence between assigned constructs when domain labels were available. Here congruence was quantified as the percentage of constructs from the original framework domain that were also in that domain within this study.
Ethical approval
The study was approved by University College London’s Psychology Department Ethics Committee [STF/2007/003], and each participant gave full informed consent prior to participating.
Discussion
This validation study, using open and closed sort tasks, has shown good support for the basic structure of the TDF and led to refinements producing 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’, ‘Emotions’, and ‘Behavioural Regulation’. There are three key advantages of this framework. First, there is comprehensive coverage of possible influences on behavior. Second, there is clarity about each kind of influence, as a result of each domain being specified by component constructs. Third, the framework makes links between theories of behaviour change and techniques of behaviour change to address implementation problems. The framework can be applied by gathering either qualitative data (interviews or focus groups) or quantitative data (e.g., by questionnaires). The findings have strengthened the evidence for the structure and content of the domains, increasing confidence in the usefulness of the TDF as an approach to assessing implementation and other behaviour problems, and laying the foundation for theoretically informed interventions.
To the authors’ knowledge, Fuzzy Cluster Analysis and Discriminant Content Validity have not been used in combination to determine the validity of a framework structure. By combining these methods, we have investigated the validity of the original framework both when the original domain labels were, and were not, presented. The results from both the open and closed sort tasks generally replicated the original framework, which adds confidence to the validity of the framework’s structure.
The study findings pointed to some changes in the framework, which had good face validity. First, there was a separation and clarification of a number of existing domains. The separation of ‘Motivation and Goals’ into two domains of ‘Intentions’ and ‘Goals’ was indicated by both the closed and open sort task results and was particularly apparent in the labels provided by the participants, with labels relating to ‘intentions’ and ‘goals’ amongst the most frequently used. The APA dictionary of psychology defines a goal as ‘the end state toward which a human or non-human animal is striving: the purpose of an activity or endeavour.’ [
36] and defines intention as ‘a conscious decision to perform a behaviour; a resolve to act in a certain way or an impulse for purposeful action. In experiments, intention is often equated with goals defined by the task instructions.’ [
36]. Therefore ‘Goals’ tends to refer to an end state that can be seen as a preferred outcome, whereas ‘Intentions’ is concerned with the resolve to initiate or terminate a behaviour. The separation of ‘Beliefs about Consequences’ into two domains, one retaining the original name and one termed ‘Reinforcement’, made psychological sense. The former refers to beliefs whereas the latter refers to constructs of associative learning. There was also a separation within the ‘Beliefs about Capabilities’ domain with a separate ‘Optimism’ domain being formed. This separation makes psychological sense in that the constructs in the optimism cluster concern general disposition rather than specific capabilities required to achieve an outcome. The domain ‘Behavioural Regulation’ is clearer in the refined framework where it refers to self-regulatory processes rather than including a mixture of self-regulation and goal-related constructs, as was the case in the original TDF.
Second, the ‘Nature of the Behaviours’ domain was dropped in the new framework, because its original component constructs were not assigned to the domain in the closed sort, and there was no cluster representing the ‘Nature of the Behaviours’ in the open sort. This strengthens the coherence of the new TDF because the domain did not sit easily in the original TDF. It was defined as the ‘Essential characteristics of the behaviour’, had constructs relating to habit and experiences/past behaviours, and constituted an outcome, or dependent variable, rather than an independent variable. Whilst understanding the nature of behaviours is absolutely key to analyzing implementation and other behavioural problems, analysing the nature of behaviour is a different task than analysing influences on behaviour. A complementary theoretical approach to analyzing behaviour as a basis for intervention design has been recently developed, as part of the ‘Behaviour Change Wheel’ [
46]. Previous studies that have adopted the TDF framework have seldom used the ‘Nature of the Behaviours’ domain [
17]. Furthermore, where the domain has been used, in relation to changing transfusion practice, it was noted that when participants were asked questions relating to the ‘Nature of the Behaviours’ domain they often repeated answers that were previously given in response to questions relating to the ‘Behavioural Regulation’ domain [
19], therefore making responses in respect to ‘Nature of the Behaviours’ redundant. This along with empirical evidence shown in the present study shows a clear indication that the ‘Nature of the Behaviours’ domain should be considered differently to the components of the TDF.
In designing interventions, the TDF fits well with the Behaviour Change Wheel (BCW) [
46] referred to above. The BCW characterises the target behavior in terms of Capability, Opportunity and Motivation (the COM-B system in the Behaviour Change Wheel), with Capability divided into psychological and physical capability, Opportunity divided into social and physical opportunity and Motivation divided into reflective and automatic motivation. The domains from the refined framework have been independently mapped onto the COM-B segments by three experts in behavior change, with 100% agreement (Table
3). Use of the COM-B may help identify the TDF domains that are likely to be important in changing behaviour. By starting with a behavioural analysis such as this, intervention designers can be selective about the domains they investigate to inform the nature of the intervention.
Table 3
Mapping of the Behaviour Change Wheel’s COM-B system to the TDF Domains
Capability | Psychological | Knowledge |
| Skills |
| Memory, Attention and Decision Processes |
| Behavioural Regulation |
| Physical | Skills |
Opportunity | Social | Social Influences |
Physical | Environmental Context and Resources |
Motivation | Reflective | Social/Professional Role & Identity |
| Beliefs about Capabilities |
| Optimism |
| Beliefs about Consequences |
| Intentions |
| Goals |
| Automatic | Social/Professional Role & Identity |
| | Optimism |
| | Reinforcement |
| | Emotion |
Research using the TDF has identified lack of knowledge as a potential barrier to a number of professional health behaviours, including hand hygiene [
17], changing transfusion practice [
19], and the adoption of tobacco use cessation counseling in dental practices [
21]. However, for most health-related behaviours that are the target of theoretically-based behaviour change interventions (
e.g., smoking, healthy eating, physical activity), knowledge is not an important source of variance [
47‐
52]. This may be why participants did not identify a separate domain for knowledge, but that it has been identified as an important influence on some health professional behaviours. We therefore recommend that knowledge be assessed along with the other TDF domains.
Of the original 112 unique constructs in the TDF, 34 have been removed. They appear to be a mixture of rather vague constructs (
e.g., Mindsets), very general constructs (
e.g., Review), ambiguous constructs (
e.g., Commitment), and infrequently used constructs in behaviour change theory (
e.g., Generating alternatives). Because constructs that are ‘poorly defined’, ‘undifferentiated’, and ‘imprecisely partitioned’ have previously been found to influence the content validity of assessment instruments [
53], their exclusion from the refined framework seems warranted. The remaining constructs stand as a more defined, focused set of constructs that are more relevant to behaviour change theory and more precisely partitioned into domains. Within these remaining constructs, there are also a number of constructs that appear in more than one domain. Such allocations indicate the relevance of constructs across different domain contexts. For example, ‘Action Planning’ appears in both the ‘Goals’ domain and the ‘Behavioural Regulation’ domain and can be considered as being influential in achieving a particular goal (
e.g. I plan to achieve goal x through specific actions) and also in regulating behaviour (
e.g. in a certain situation I plan to behave in a particular way).
Two domains showed weak clustering: ‘Environmental Context and Resources’ and ‘Behavioural Regulation’. However, these domains, alongside the domain of ‘Knowledge’, were comprised of constructs consistently assigned to them when the original domain labels were presented in the closed sort task. This suggests that people are clear about the constructs within these domains when the domain labels are present. A second limitation is that the refined framework is limited to the constructs identified in the original framework. Whilst the current range of component constructs is quite extensive, it does not cover all theories of behaviour change [
54], and future research is likely to identify others that are important to behaviour change. Just as the current framework is an advance on the 2005 version, so future work is likely to improve it further. The issue of how to evaluate appropriateness and quality of theories in given contexts is an under-researched area, but one that is beginning to be addressed [
54].
Competing interests
SM and DOC are both Associate Editors of Implementation Science.
Authors’ contributions
JC conducted preparation of materials, data collection, data analysis, and drafted the manuscript. DOC and SM commented on and aided in the drafting of the manuscript. All authors read and approved the final manuscript.