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The Behavior Change Technique Taxonomy (v1) of 93 Hierarchically Clustered Techniques: Building an International Consensus for the Reporting of Behavior Change Interventions


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Background: CONSORT guidelines call for precise reporting of behavior change interventions: we need rigorous methods of characterizing active content of interventions with precision and specificity. Objectives: The objective of this study is to develop an extensive, consensually agreed hierarchically structured taxonomy of techniques [behavior change techniques (BCTs)] used in behavior change interventions. Methods: In a Delphi-type exercise, 14 experts rated labels and definitions of 124 BCTs from six published classification systems. Another 18 experts grouped BCTs according to similarity of active ingredients in an open-sort task. Inter-rater agreement amongst six researchers coding 85 intervention descriptions by BCTs was assessed. Results: This resulted in 93 BCTs clustered into 16 groups. Of the 26 BCTs occurring at least five times, 23 had adjusted kappas of 0.60 or above. Conclusions: "BCT taxonomy v1," an extensive taxonomy of 93 consensually agreed, distinct BCTs, offers a step change as a method for specifying interventions, but we anticipate further development and evaluation based on international, interdisciplinary consensus.
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The Behavior Change Technique Taxonomy (v1)
of 93 Hierarchically Clustered Techniques: Building
an International Consensus for the Reporting
of Behavior Change Interventions
Susan Michie, DPhil, CPsychol &Michelle Richardson, PhD &Marie Johnston, PhD,
CPsychol &Charles Abraham, DPhil, CPsychol &Jill Francis, PhD, CPsychol &
Wendy Hardeman, PhD &Martin P. Eccles, MD &James Cane, PhD &
Caroline E. Wood, PhD
Published online: 20 March 2013
#The Society of Behavioral Medicine 2013
Background CONSORT guidelines call for precise
reporting of behavior change interventions: we need rigor-
ous methods of characterizing active content of interven-
tions with precision and specificity.
Objectives The objective of this study is to develop an
extensive, consensually agreed hierarchically structured tax-
onomy of techniques [behavior change techniques (BCTs)]
used in behavior change interventions.
Methods In a Delphi-type exercise, 14 experts rated la-
bels and definitions of 124 BCTs from six published
classification systems. Another 18 experts grouped BCTs
according to similarity of active ingredients in an open-
sort task. Inter-rater agreement amongst six researchers
coding 85 intervention descriptions by BCTs was
Results This resulted in 93 BCTs clustered into 16 groups.
Of the 26 BCTs occurring at least five times, 23 had adjust-
ed kappas of 0.60 or above.
Conclusions BCT taxonomy v1,an extensive taxonomy
of 93 consensually agreed, distinct BCTs, offers a step
change as a method for specifying interventions, but we
anticipate further development and evaluation based on
international, interdisciplinary consensus.
Electronic supplementary material The online version of this article
(doi:10.1007/s12160-013-9486-6) contains supplementary material,
which is available to authorized users.
S. Michie (*):M. Johnston :C. E. Wood
Centre for Outcomes Research Effectiveness,
Research Department of Clinical, Educational and Health Psychology,
University College London, 1-19 Torrington Place,
London WC1E 7HB, UK
M. Richardson :C. Abraham
University of Exeter Medical School, University of Exeter,
St Lukes Campus, Heavitree Road,
Exeter EX1 2LU, UK
M. Johnston
Aberdeen Health Psychology Group, University of Aberdeen,
Institute of Applied Sciences,
College of Life Sciences and Medicine,
2nd floor, Health Sciences Building, Foresterhill,
Aberdeen AB25 2ZD, UK
J. Francis
Division of Health Services Research & Management,
City University London,
C332 Tait Building, City University London, Northampton Square,
London EC1V 0HB, UK
W. Hardeman
Behavioural Science Group, The Primary Care Unit,
University of Cambridge,
Cambridge Institute of Public Health, Robinson Way,
Cambridge CB2 0SR, UK
M. P. Eccles
Institute of Health and Society, Newcastle University,
21 Claremont Place,
Newcastle upon Tyne NE2 4AA, UK
J. Cane
School of Psychology, University of Kent,
Keynes College, Canterbury,
Kent CT2 7NP, UK
ann. behav. med. (2013) 46:8195
DOI 10.1007/s12160-013-9486-6
Keywords Behavior change techniques .Taxonomy .
Behavior change interventions
Interventions to change behavior are typically complex, in-
volving many interacting components [1]. This makes them
challenging to replicate in research, to implement in practical
applications, and to synthesize in systematic literature re-
views. Complex interventions also present challenges for
identifying the active, effective components within them.
Replication, implementation, evidence synthesis, and identi-
fying active components are all necessary if we are to better
understand the effects and mechanisms of behavior change
interventions and to accumulate knowledge to inform the
development of more effective interventions. However, the
poor description of interventions in research protocols and
published reports presents a barrier to these essential scientific
and translational processes [2,3]. A well-specified interven-
tion is essential before evaluation of effectiveness is worth
undertaking: an under-specified intervention cannot be deliv-
ered with fidelity and, if evaluated, could not be replicated.
The CONSORT statement for randomized trials of non-
pharmacologicalinterventions recommends precise specifica-
tion of trial processes, including some details of the delivery of
interventions and description of the different components of
the interventions[4]. As currently constituted, CONSORT
gives no guidance as to what this description or components
should be. Intervention components have been identified by
Davidson et al. [3] as follows: who delivers the intervention, to
whom, how often, for how long, in what format, in what
context, and with what content. These are mainly procedures
for delivery (often referred to as modeof delivery), except
for the key intervention component, content,i.e., the active
ingredients that bring about behavior change (the whatrather
than the howof interventions).
The content, or active components of behavior change in-
terventions, is often described in intervention protocols and
published reports with different labels (e.g., self-monitoring
may be labeled daily diaries); the same labels may be applied
to different techniques (e.g., behavioral counselingmay
involve educating patientsor feedback, self-monitoring,
and reinforcement[5]). This may lead to uncertainty and
confusion. For example, behavioral medicine researchers and
practitioners have been found to report low confidence in their
ability to replicate highly effective behavioral interventions for
diabetes prevention [6]. The absence of standardized defini-
tions and labels for intervention components means that sys-
tematic reviewers develop their own systems for classifying
behavioral interventions and synthesizing study findings
[710]. This proliferation of systems is likely to lead to dupli-
cation of effort and undermines the potential to accumulate
evidence across reviews. It also points to the urgent need for
consensus. Consequently, the UK Medical Research Councils
(MRC) guidance [1] for developing and evaluating complex
interventions calls for improved methods of specifying and
reporting intervention content in order to address the problems
of lack of consistency and consensus.
A method recently developed for this purpose is the reliable
characterization of interventions in terms of behavior change
techniques (BCTs) [11]. By BCT, we mean an observable,
replicable, and irreducible component of an intervention
designed to alter or redirect causal processes that regulate
behavior; that is, a technique is proposed to be an active
ingredient(e.g., feedback, self-monitoring, and reinforcement)
[12,13]. BCTs can be used alone or in combination and in a
variety of formats. Identifying thepresenceofBCTsininter-
vention descriptions included in systematic reviews and nation-
al datasets of outcomes has allowed the identification of BCTs
associated with effective interventions. Effective BCTs have
been identified for interventions to increase physical activity
and healthy eating [14] and to support smoking cessation [10,
15], safe drinking [16], prevention of sexually transmitted in-
fections [7,17], and changing professional behavior [18].
Abraham and Michie [11] developed the first cross-behavior
BCT taxonomy, building on previous intervention content
analyses [7,8]. These authors demonstrated reliability in iden-
tifying 22 BCTs and 4 BCT packages across 221 intervention
descriptions in papers and manuals. This method has been
widely used internationally to report interventions, synthesize
evidence [14,1922], and design interventions [6,23]. It has
also enabled the specification of professional competences for
delivering BCTs [24,25] and as a basis for a national training
program (see Guidance has also been devel-
oped for incorporating BCTs in text-based interventions [26].
Although the subsequent development of classification sys-
tems of defined and reliably identifiable BCTs has been accom-
panied by a progressive increase in their comprehensiveness
and clarity, this work has been conducted by only a few
research groups. For this method to maximize scientific ad-
vance, collaborative work is needed to develop agreed labels
and definitions and reliable procedures for their identification
and application across behaviors, disciplines, and countries.
Previous classification systems have either been in the form
of an unstructured list or have been mapped to, or structured,
according to categories (e.g., theory [7,11] and theoretical
mechanism [24,25]) judged to be the most appropriate by the
authors. In addition, they have mainly been developed for
particular behavioral domains (e.g. physical activity, smoking,
or safer sex). A comprehensive taxonomy will encompass a
greater number of BCTs and therefore require structure to
facilitate recall and access to the BCTs and thus to increase
speed and accuracy of use. A true, i.e., hierarchically struc-
tured, taxonomy provides the advantage of making it more
coherent to, and useable by, those applying it [27]. As the
82 ann. behav. med. (2013) 46:8195
number of identified BCTs has increased, so also has the need
for such a structure, to improve the usability of the taxonomy.
Simple, reliable grouping structures have previously been
used by three groups of authors. Dixon and Johnston [25]
grouped BCTs according to routes to behavior change,”“moti-
vation,”“action,and prompts/cues;Michieetal.[15]grouped
according to functionin changing behavior, motivation,
self-regulation capacity/skills,”“adjuvant,and interaction;
and Abraham et al. [17] grouped according to change target,
that is, knowledge, awareness of own behavior, attitudes, social
norms, self-efficacy, intention formation, action control, behav-
ioral maintenance, and change facilitators. However, there is a
need for a basic method of grouping which does not depend on a
theoretical structure. We therefore adopted an empirical approach
to developing an international consensus of BCT groupings.
Potential Benefits
There are at least five potential benefits of developing a cross-
domain, internationally supported taxonomy. First, it will pro-
mote the accurate replication of interventions (and control
conditions in comparative effectiveness research), a key activ-
ity in accumulating scientific knowledge and investigating
generalizability across behaviors, populations, and settings.
Second, specifying intervention content by BCT will facilitate
faithful implementation of interventions found to be effective.
Third, systematic reviews will be able to use a reliable method
for extracting information about intervention content, thus
identifying and synthesizing discrete, replicable, potentially
active ingredients (or combinations of ingredients) associated
with effectiveness. Earlier BCT classification systems, com-
bined with the statistical technique of meta-regression, have
allowed reviewers to synthesize evidence from complex, het-
erogeneous interventions to identify effective component
BCTs [6,14,21,28,29]. Fourth, intervention development
will be able to draw on a comprehensive list of BCTs (rather
than relying on the limited set that can be brought to mind) to
design interventions, and it will be possible to report the
intervention content in well-defined and detailed ways. Fifth,
linking BCTs with theories of behavior change has allowed the
investigation of possible mechanisms of action [14,29,30].
The work reported here represents the first stages of a
program of work to develop an international taxonomic
classification system for BCTs, building on previous work.
The aims of the work reported in this paper are as follows:
1. To generate a taxonomy that
(a) Comprises an extensive hierarchical classification
of clearly labeled, well-defined BCTs with a con-
sensus that they are proposed active components of
behavior change interventions, that they are dis-
tinct (non-overlapping and non-redundant) and
precise, and that they can be used with confidence
to describe interventions;
(b) Has a breadth of international and disciplinary
2. To assess and report the reliability of using BCT labels
and definitions to code intervention descriptions
The work involved three main tasks. The first involved the
rigorous development of a list of distinct BCT labels and
definitions, using Delphi methods, with feedback from a
multidisciplinary International Advisory Board and mem-
bers of the study team. The inter-rater reliability of coding
intervention descriptions using the list of BCTs was then
assessed in two rounds of reliability testing. The third task
was the development of a hierarchical structure.
Participants were international behavior change experts (i.e.,
active in their field and engaged in investigating, designing,
and/or delivering behavior change interventions) who had
agreed to take part in one or more of the study phases,
members of the International Advisory Board, and the study
team (including a layperson). All Board members, as
leaders in their field, were eligible to take part as a behavior
change expert. However, in light of their advisory role
commitments, members were not routinely approached for
further participation unless it would help widen participation
in terms of country, discipline, and behavioral expertise.
For the Delphi exercise, 19 international behavior change
experts were invited to take part. Experts were identified
from a range of scientific networks on the basis of breadth of
knowledge of BCTs, experience of designing and/or deliv-
ering behavior change interventions, and of being able to
complete the study task in the allotted time. Recruitment
was by email, with an offer of an honorarium of £140
(approximately US$200) on completing the task. Of the 19
originally approached, 14 agreed to take part (response rate
of 74 %). Ten participants were female, with an age range of
37 to 62 years (M=50.57; SD=7.74). Expert participants
were from the UK (eight), Australia (two), Netherlands
(two), Canada (one), and New Zealand (one). Eleven were
psychologists (six health psychologists, one clinical psy-
chologist, three clinical and health psychologists, and one
educational psychologist), one a cognitive behavior thera-
pist, and two had backgrounds in health sciences or com-
munity health. Eleven were active practitioners in their
discipline. Eleven had research or professional doctorates,
ann. behav. med. (2013) 46:8195 83
and two had registered psychologist status. There was a
wide range of experience of using BCTs, with all having
used at least six BCTs, more than half having used more
than 30 BCTs, and four having used more than 50 BCTs for
intervention design, delivery, and training (see Electronic
Supplementary Materials Table 1for more information).
For the international feedback phase, 16 of the 30 Interna-
tional Advisory Board members (see:
in discussions to comment on a prototype BCT classification
system. Advisory Board members were identified by the study
team as being leaders in their field within the key domains of
interest (e.g., types of health-related behaviors, major disease
types, and disciplines such as behavioral medicine) following
consultation of websites, journals, and scientific and profes-
sional organizations. Advisory Board members were from the
USA, Canada, Australia, UK, the Netherlands, Finland, and
Germany. Feedback was also provided by members of the
study team, who had backgrounds in psychology and/or imple-
mentation science and a layperson with a BA (Hons) in
English but no background in psychology or behavior change.
Five members of the UK study team conducted the first
round of reliability testing and six the second round. Eigh-
teen of 19 participants approached from the pool of experts
in behavior change interventions completed the open-sort
grouping task. Eight were women, and ten were men with an
age range of 27 to 67 years (M=43.94); 16 were from the
UK, and two were from Australia.
Participants recruited for the Delphi exercise and open-sort
grouping task provided written consent and were assured that
their responses would remain confidential. All participants were
asked to provide demographic information (i.e., age, gender, and
nationality). Delphi exercise participants were also asked to
provide their professional background (i.e., qualifications, regis-
trations, job title, and area of work) and how many BCTs they
had used professionally in intervention design, face-to-face de-
livery, and training (reported in increments of 5 up to 50+).
A prototype classification system was developed by the
study team based on all known published classifications of
BCTs following a literature review [27] (Step 1). An online
Delphi-type exercise (see Pill [31]) with two roundswas
used for initial evaluation and development of the classification
system. Participants worked independently and rated the pro-
totype BCT labels and definitions on a series of questions
designed to assess omission, overlap, and redundancy (Step
2). The results of Step 2 subsequently informed the develop-
ment of an improved BCT list, which was sent to the Delphi
participants for round 2. They were asked to rate BCTs for
clarity, precision, distinctiveness, and confidence of use (Step
3). The resulting list of BCTs was then scrutinized by the
Advisory Board, who submitted verbal and written feedback,
and was assessed by the lay and expert members of the study
team (Step 4). Following each of Steps 2, 3, and 4, the results
were synthesized by SM and MJ in preparation for the next
step. Using the developed BCT list, members of the study team
coded published descriptions of interventions, and inter-rater
agreement for the presence of each BCT was calculated (Step
5). An open-sort grouping task was then carried out to generate
reliable and stable groupings and create a hierarchical structure
within the taxonomy (Step 6).
Step 1: Developing the Prototype Classification System
The labels and definitions of distinct BCTs were extracted
from six BCT classification systems identified by a literature
search (relevant papers [11,1416,25,26]). For BCTs with
two or more labels (n=24) and/or definitions (n= 37), five
study team members rated their preferred labels and defini-
tions. Where there was complete or majority agreement, the
preferred label and/or definition was retained. Where there
was some, little, or no agreement, new labels and definitions
were developed by synthesizing the existing labels and
definitions across classification systems. Definition wording
was modified to include active verbs and to be non-
directional (i.e., applicable to both the adoption of a new
wanted behavior and the removal of an unwanted behavior).
Step 2: Delphi Exercise First Round
Participants were provided with the study definition of a
BCT [13], i.e., having the following characteristics: (a) aim
to change behavior, (b) are proposed active ingredientsof
interventions, (c) are the smallest components compatible
with retaining the proposed active ingredients, (d) can be
used alone or in combination with other BCTs, (e) are
observable and replicable, (f) can have a measurable effect
on a specified behavior/s, and (g) may or may not have an
established empirical evidence base. It was explained that
BCTs could be delivered by someone else or self-delivered.
The BCTs (labels and definitions) from Step 1 were
presented in a random order, and participants were asked
five questions about each of them:
1. Does the definition contain what you would consider to
be potentially active ingredients that could be tested em-
pirically? Participants were asked to respond to this ques-
tion using a five-point scale (definitely no,”“probably
no,”“not sure,”“probably yes,and definitely yes).
2. Please indicate whether you are satisfied that the BCT is
conceptually unique or whether you consider that it is
redundant or overlapping with other BCTs (with forced
choice as to whether it was conceptually unique, re-
dundant, or overlapping).
84 ann. behav. med. (2013) 46:8195
3. If participants indicated that the BCT was redundant,
they were asked to state why they had come to this
4. If they indicated that the BCT was overlapping,they
were asked to state (a) with which BCT(s) and (b)
whether they can be separated (yesor no).
5. If the BCTs were considered to be separate, participants
were asked how the label or definition could be
rephrased to reduce the amount of overlap or, if not
separate, which label and which definition was better.
Participants were given an opportunity to make com-
ments on the exercise and to detail any BCTs not included
on the list. They were asked, does the definition and/or
label contain unnecessary characteristics and/or omitted
characteristics?This question item was open-ended. The
exercise was designed to take 2 hour; follow-up reminders
were sent to participants after 2 weeks, and all responses
were submitted within 1 month of the initial request.
Frequencies, means, and/or modes of responses to ques-
tions (1) and (2) were considered for each BCT. Based on
the distribution of responses, BCTs for which (a) more than
a quarter of participants doubted that they contained active
ingredients and/or (b) more than a third considered them to
be overlapping or redundant were flagged as requiring
further consideration.These data, along with the responses
to questions (3) to (5), guided the rewording of BCT labels
and definitions and the identification of omitted BCTs. The
BCTs for re-consideration and the newly identified BCTs
were presented in the second Delphi exercise round.
Step 3: Delphi Exercise Second Round
The BCTs identified as requiring further consideration were
presented; the rest of the BCTs were included for reference
only, to assist judgments about distinctiveness. For each
BCT, participants were asked three questions and asked to
respond using a five-point scale (definitely no,”“probably
no,”“not sure,”“probably yes,and definitely yes):
1. If you were asked to describe a behavior change interven-
tion in terms of its component BCTs, would you think the
following BCT was (a) clear, (b) precise, or (c) distinct?
2. Would you feel confident in using this BCT to describe
the intervention?
3. Would you feel confident that two behavior change
researchers or practitioners would agree in identifying
this BCT?
If participants responded probably no,”“definitely no,
or not sureto any question, they were asked to state their
suggestions for improvement.
Frequencies, means, and/or modes were calculated for all
questions for each BCT. BCTs for which more than a quarter
of participants responded probably noor definitely no
or not sureto any question were flagged as needing to be
given special attention. Using information on the distribu-
tion of ratings, the modal scores, and suggestions for im-
provement, SM and MJ amended the wording of definitions
and labels. This included changes to make BCTs more
distinct from each other where this had been identified as
a problem and to standardize wording across BCTs. Where
it was not obvious how to amend the BCT from the second
round responses, other sources [32] were consulted for
definitions of particular words or descriptions of BCTs.
Step 4: Feedback from the International Advisory Board
Sixteen of the 30 members of the Advisory Board took part
in one of three 2-hour-long teleconferences to give advice to
the study team, and the BCT list was refined based on their
Step 5: Reliability Testing Round 1
Five members of the study team coded 45 intervention de-
scriptions. The descriptions were selected from Implementation
Science,BMC Public Health Services,andBMC Public Health
in 2009 and 2010 using quota sampling to ensure spread across
preventive, illness management, and health professional behav-
iors. The study team then discussed reasons for discrepancies in
round 1 and amended the BCT list as needed.
Step 6: Investigating Hierarchical Structure of the BCT List
An open-sort grouping task was delivered via an online
computer program. Participants were asked to sort the de-
veloped list of BCTs into groups (up to a maximum of 24) of
their choice and to label the groups. They were asked to
group together BCTs which have similar active ingredients,
i.e., by the mechanism of change, NOT the mode of deliv-
ery.BCTs were presented to participants in a random order,
and definitions for each BCT were made available.
For data analysis, a binary dissimilarity matrix containing all
possible BCT×BCT combinations was produced for each par-
ticipant, where a score of 1 indicated BCTs which were not
sorted into the same group and a score of 0 indicated items
which were sorted into the same group. 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 (HCA), the optimal
number of groupings (220) were examined for suitability using
measures of internal validity (Dunns index) and stability (figure
of merit, FOM) [33]. Bootstrap methods were used in conjunc-
tion with the HCA, whereby data were re-sampled 10,000 times,
to identify which groupings were strongly supported by the data.
The approximately unbiased (AU) pvalues yielded by this
ann. behav. med. (2013) 46:8195 85
method indicated the extent to which groupings were strongly
supported by the data with higher AU values (e.g., 95 %)
indicating stronger support for the grouping [34].
The words and phrases used in the labels given by participants
were analyzed 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, where applicable,
based on the frequency of word labels given by participants.
After the labels were assigned to relevant groupings, the fully
labeled groups with the word frequency analysis were sent out to
a subset of five of the original participants for refinement.
Step 7: Reliability Testing Round 2
An additional member of the study team was recruited for
the second round of reliability testing. The team coded 40
intervention descriptions using the amended list. The six
members each coded 914 intervention descriptions.
For both rounds, each intervention description was coded
independently by two team members, and inter-rater agree-
ment by BCT was assessed using kappas adjusted for prev-
alence and bias effects [35,36]. Conventionally, a kappa of
<0.60 is considered poor to fair agreement, 0.610.80
strong, and more than 0.80 near complete agreement [37].
The more frequent the BCTs, the greater the confidence that
the kappa is a useful indicator of reliability of judging the
BCT to be present. We therefore only report the kappa
scores for BCTs which were observed at least five times
by either coder in the 40 intervention descriptions.
Step 8: Feedback from Study Team Members
The BCT definitions were checked to ensure that they
contained an active verb specifying the action required to
deliver the intervention [38].The laymember of the study
team (FR) read through the list to ensure syntactic consistency
and general comprehensibility to those outside the field of
behavioral science. Subsequently, the study team members
made a final check of the resulting BCT labels and definitions.
Full details of the procedure are available in Electronic
Supplementary Materials Table 2.
The evolution of the taxonomy at the different steps of the
procedure is summarized in Electronic Supplementary Ma-
terials Table 2.
Step 1: Developing the Prototype Classification System
Of the 124 BCTS in the prototype classification system, 32
were removed: five composite BCTs and 26 BCTs overlapping
with others were rated to have better definitions. One additional
BCT was identified, given a label and definition informed by
other sources and then added to the system. This produced a list
of 94 BCTs.
Step 2: Delphi Exercise First Round
The means, modes, and frequencies of responses to the
Delphi exercise first round questions are shown in Table 1.
On the basis of these scores, 21 BCTs were judged to be
satisfactoryand 73 requiring further consideration.Of
the 73 reconsidered BCTs, four were removed, four were
divided, and one BCT was added (see Electronic Supple-
mentary Materials Table 2for more details of changes at
each step), giving 70 BCTs. During this process, one reason
for overlap became evident: there was a hierarchical struc-
ture meaning that deleting overlapping BCTs would end up
with only the superordinate BCT and a loss of specific
variation (for example, adopting the higher order BCT
consequenceswould have deleted reward).
Step 3: Delphi Exercise Second Round
The means, modes, and frequencies of responses to the five
Delphi exercise second round questions are shown in Ta-
ble 2. On the basis of these scores, 38 BCTs were judged to
be satisfactoryand 32 requiring further consideration.
Of the reconsidered BCTs, seven labels and 35 definitions
were amended, and seven BCTs were removed (see Elec-
tronic Supplementary Materials Table 2for more details),
giving 63 BCTs. Together with the 21 BCTs judged to be
satisfactoryin the first round, there were 84 BCTs at the
end of the Delphi exercise. Some further standardization of
wording across all BCTs was made by study team members
(e.g., specifying unwantedor wantedbehaviors rather
than the more generic targetbehaviors and ensuring that
all definitions included active verbs).
Step 4: Feedback from the International Advisory Board
The Advisory Board members made two general recommenda-
tions: first, to make the taxonomy more usable by empirically
grouping the BCTs, and secondly, to consider publishing a
sequence of versions of the taxonomy (with each version clearly
labeled) that would achieve a balance between stability/usability
and change/evolution. Feedback from members led to the addi-
tion of two and the removal of four BCTs. Further refinement of
labels and definitions resulted in a list of 82 BCTs.
Step 5 and 7: Reliability Testing Round 1 and 2
Inter-rater agreement for BCTs is shown in Table 3. For the
first round of reliability testing, 22 BCTs were observed five
86 ann. behav. med. (2013) 46:8195
or more times and therefore could be assessed. Adjusted
kappa scores ranged from 0.38 to 0.85, with three scores
below 0.60. Results from the first round of reliability testing
led to the addition of five and the removal of one BCT
resulting in a list of 86 BCTs.
For the second round of reliability testing, 15 BCTs were
observed five or more times. Adjusted kappa scores ranged
from 0.60 to 0.90. In all, 26 BCTs were tested for reliability,
23 of which achieved kappa scores of 0.60 or above and met
our criteria of a BCT (see Table 3).
Step 6: Investigating Hierarchical Structure of the BCT List
Participants created an average of 15.11 groups (SD=6.11;
range, 524 groups). Measures of internal validity indicated
that the maximum internal validity Dunn index value (.57) was
for a 16-cluster solution using hierarchical cluster analysis (see
Fig. 1), with no increase in internal validity on subsequent
cluster solutions (>16). Similarly, FOM values showed greater
stability in the 16-cluster solution compared to the 215 cluster
solutions, and there was negligible increase in stability over
cluster solutions 1720. Therefore, hierarchical clustering
methods identified the 16-cluster solution as the optimal solu-
tion. The frequency of the words and phrases used in the labels
given by participants is shown in Table 4. On the basis of
participant responses, the groups were assigned the following
labels (number of component BCTs in brackets): reinforcement
(10), reward and threat (7), repetition and substitution (7),
antecedents (4), associative learning (8), covert learning (3),
consequences (6), feedback and monitoring (5), goals and
planning (9), social support (3), comparison of behavior (3),
self-belief (4), comparison of outcomes (3), identity (5), shap-
ing knowledge (4), and regulation (4). Three of these labels
were modified to facilitate comprehensibility across disciplines:
reinforcementwas changed to scheduled consequences,
and associative learningwas changed to associations.
Consequenceswas then changed to natural consequences
to distinguish it from scheduled consequences.
The final results of the cluster analysis are shown in Table 5.
Seven of the 16 clusters (clusters 3, 4, 5, 8, 10, 15, and 16)
showed AU values greater than 95 %, indicating that these
groupings were strongly supported by the data. Clusters 1, 2,
9, 12, and 13 had AU values between 90 % and 95 %, and
clusters 6, 7, 11, and 14 had AU values less than 90 %; these
were 73 %, 85 %, 83 %, and 86 %, respectively. The standard
errors (SE) of AU values for all clusters were less than 0.009.
Step 8: Feedback from Study Team Members
Feedback from study team members led to the addition of
three BCTs, the division of one BCT, and further refinement
of labels and definitions. This resulted in a taxonomy of 93
Table 1 BCTs judged to be satisfactory and those requiring further consideration in Delphi exercise round 1 (step 2): means, modes, and frequencies of responses to questions
Question Satisfactory (BCTs=21) Reconsider (BCTs=73)
Range of means Range of
Frequency of
modes, % (N)
Range of means Range of
Frequency of
modes, % (N)
(1) Does the definition contain what you would consider
to be potentially active ingredients that could be tested
empirically?: 1 definitely yes,2probably yes,
3not sure,4probably no,and 5 definitely no
1.21 (SD=0.47) to 1.93
(SD= 0.92)
0.43 to
1= 71 % (15) 1.29 (SD= 0.47) to 4.79
(SD= 0.43)
0.43 to
1= 44 % (32)
2= 29 % (6) 2= 51 % (37)
3= 0 % (0) 3= 4 % (3)
4= 0 % (0) 4= 1 % (1)
5= 0 % (0) 5= 0 % (0)
(2) Please indicate whether you are satisfied that it is
conceptually unique or whether you consider that it is
redundant or overlapping with other BCTs (1 conceptually
unique; 2 redundant; 3 overlapping)
N/A N/A 1= 100 % (21) N/A N/A 1= 60 % (44)
2= 0 % (0)
3= 40 % (29)
ann. behav. med. (2013) 46:8195 87
Table 2 BCTs judged to be satisfactory and those requiring further consideration in Delphi exercise round 2 (step 3): means, modes, and frequencies of responses to questions
Question Satisfactory (BCTs=38) Reconsider (BCTs=32)
Range of means Range of
Frequency of modes, %
Range of means Range of
Frequency of modes, %
(1) If you were asked to describe a behavior
change intervention in terms of its component
BCTs, would you think the following BCT was
(a) Clear? 1.07 (0.27) to 1.79
0.27 to 1.15 1= 95 % (36) 1.13 (0.36) to 3.36
0.36 to 1.76 1=56 % (18)
2= 5 % (2) 2= 38 % (12)
3= 0 % (0) 3= 3 % (1)
4= 0 % (0) 4= 3 % (1)
5= 0 % (0) 5= 0 % (0)
(b) Precise? 1.07 (0.27) to 2.14
0.27 to 1.29 1= 87 % (33) 1.14 (0.36) to 3.07
0.36 to 1.33 1=56 % (18)
2= 13 % (5) 2= 41 % (13)
3= 0 % (0) 3= 0 % (0)
4= 0 % (0) 4= 3 % (1)
5= 0 % (0) 5= 0 % (0)
(c) Distinct? 1.14 (0.36) to 2.14
0.36 to 1.38 1= 89 % (34) 1.64 (0.93) to 3.21
0.78 to 1.66 1=59 % (19)
2= 11 % (4) 2= 31 % (10)
3= 0 % (0) 3= 9 % (3)
4= 0 % (0) 4= 0 % (0)
5= 0 % (0) 5= 0 % (0)
(2) Confidence in identifying BCT: would you
feel confident in using this BCT
to describe the intervention? (1 definitely yes,
2probably yes,3not sure,4probably no,
and 5 definitely no)
1.21 (0.43) to 1.93
0.43 to 1.12 1= 87 % (33) 1.14 (0.36) to 3.07
0.36 to 1.46 1=47 % (15)
2= 13 % (5) 2= 47 % (15)
3= 0 % (0) 3= 3 % (1)
4= 0 % (0) 4= 3 % (1)
5= 0 % (0) 5= 0 % (0)
(3) Confidence in others identifying BCT:
would you feel confident that two behavior
change researchers or practitioners would agree
in identifying this BCT? (1 definitely yes,2
probably yes,3not sure,4probably no,and 5
definitely no)
1.21 (0.43) to 2.14
0.43 to 1.20 1= 76 % (29) 1.36 (0.63) to 3.29
0.51 to 1.46 1=46 % (15)
2= 24 % (9) 2= 41 % (13)
3= 0 % (0) 3= 6 % (2)
4= 0 % (0) 4= 6 % (2)
5= 0 % (0) 5= 0 % (0)
88 ann. behav. med. (2013) 46:8195
An extensive hierarchically organized taxonomy of 93 dis-
tinct BCTs has been developed in a series of consensus
exercises involving 54 experts in delivering and/or design-
ing behavior change interventions. These experts were
drawn from a variety of disciplines including psychology,
behavioral medicine, and health promotion and from seven
countries. The resulting BCTs therefore have relevance
among experts from varied behavioral domains, disciplines,
and countries and potential relevance to the populations
from which they were drawn. The extent to which we can
generalize our findings across behaviors, disciplines, and
countries is an important question for future research. Build-
ing on a preliminary list generated from six published BCT
classification systems, BCTs were added, divided, and re-
moved, and their labels and definitions refined to capture the
smallest components compatible with retaining the pro-
posed active ingredients with the minimum of overlap. This
resulted in 93 clearly defined, non-redundant BCTs,
grouped into 16 clusters, for use in specifying the detailed
content of a wide range of behavior change interventions. Of
the 26 BCTs which could be assessed for inter-rater reliabil-
ity, 23 had kappa scores of 0.60 or above and met our
definition of a reliable BCT. BCT Taxonomy v1 is the first
consensus-based, cross-domain taxonomy of distinct BCTs
to be published, with reliability data for the most frequent
BCTs. The process of building a shareable consensus lan-
guage and methodology is necessarily collaborative and will
be an ongoing cumulative and iterative process, involving
an international network of advisors and collaborators (see
The methodologies used here represent an attempt to get
a basic version of a taxonomy, a foundation on which to
build future improvements. Like other classificatory sys-
tems, e.g., Linnaeusclassification of plants, or even sys-
tems based on consensus such as DSM [39]orICD[40], we
anticipate and plan to continue to work on improvements.
There is no agreed methodology for this work, and there are
limitations to the methods we have used. The purpose of the
Delphi exercise was to develop a prototype taxonomy on
which to build. It was one of a series of exercises adapted to
develop the taxonomy. Our Delphi-type methods involved
14 individuals, an appropriate number for these methods
[31], but a number that makes the choice of participants
important. We attempted to ensure adequate coverage of
behavior change experts (see Electronic Supplementary Ma-
terials Table 1). While we had some diversity of expertise,
Table 3 Inter-rater agreement for each BCT: adjusted kappas for two rounds of reliability testing
Round 1 and 2 Adjusted
Round 1 only Adjusted
Round 2 only Adjusted
Pharmacological support
0.87, 0.85 Non-specific encouragement
0.82 Social comparison [6.2] 0.90
Self-monitoring of behavior
0.82, 0.75 Review of outcome goal [1.7] 0.78 Material reward [10.2 and 10.10] 0.85
Restructuring of the physical
environment [30]
0.82, 0.85 Discrepancy between current behavior
0.73 Incentive [10.1] 0.80
Social support (practical) [3.2] 0.78, 0.70 Self-monitoring of outcome of behavior
0.73 Monitoring outcome of behavior by others
without feedback [2.5]
Behavioral practice/rehearsal
0.78, 0.70 Health consequences [5.1] 0.69
Problem solving/coping
planning [1.2]
0.73, 0.75 Feedback on behavior [2.2] 0.69
Persuasive argument [9.1] 0.73, 0.60 Action planning (including
implementation intentions) [1.4]
Review behavior goal(s) [1.5] 0.69, 0.75 Social support (general) [3.1] 0.60
Goal setting (outcome) [1.3] 0.64, 0.85 Goal setting behavior [1.1] 0.56
Prompts/cues [7.1] 0.42, 0.70 Tailored personalized message
Demonstration of the behavior
0.87, 0.75 Instruction on how to perform the
behavior [4.1]
Some BCT labels differ as a result of the consensus exercises; number in brackets indicates related BCT in Electronic Supplementary Materials
Table 3
Reliability shown for BCTs observed at least five times
BCT not in Taxonomy v1
ann. behav. med. (2013) 46:8195 89
we acknowledge the predominance of European experts
from a psychological background within our sample. At
various stages, we made arbitrary decisions such as the
cut-offs for amending BCT labels and descriptions and the
minimum frequency of occurrence of BCTs for reporting
reliability. In the absence of agreed standards for such
Fig. 1 Results of hierarchical
cluster analysis (step 6):
dendrogram for 85 behavior
change techniques (BCTs)
partitioned across 16 clusters
90 ann. behav. med. (2013) 46:8195
decisions, we were guided by the urgent need to develop an
initial taxonomy which was fit for purpose and would there-
fore form a basis for future development. Our amendments
of the BCT labels and definitions also depend on the
expertise available, and we therefore based our amendments
on a wide range of inputs: the data we collected from Delphi
participants and coders, expert modification, international
advice, and lay user improvements.
Compared with many of the previous taxonomies
which are more accurately described as nomenclatures,
BCT Taxonomy v1 is not only a list of reliable, distinct
BCTs but it also has a hierarchical structure. Such a structure
has been shown to improve processing of large quantities of
information by organizing it into chunks[41] that com-
pensates for human memory limitations. In turn, this enables
the user to attend to and recall the full range of BCTs
available when reporting and designing interventions.
Use of an open-sort grouping task is an improvement on
previous efforts to develop hierarchical structure in that it
allowed for the individual groupings defined by participants
to hold equal weight within the final solution, rather than
using consensus approaches amongst small groups of par-
ticipants. Second, 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 in the
long-term, particularly when the semantic category is cued
[4244]. The hierarchical structure shown in the dendro-
gram (see Fig. 1) gives an indication of the distance between
clusters of BCTs and can be used as a starting point to
compare the conceptual similarities and differences between
BCTs. Sixteen clusters are too many for easy recall, and a
higher-level cluster would be desirable. A simpler, higher-
level structure of grouping BCTs has been used by Dixon
and Johnston [25] and Michie et al. [24]. However, such a
structure was not apparent in our data and points to the need
for further research to refine the hierarchical structure of this
Other advantages of v1 are that it is relevant to a wide
range of behavior rather than being restricted to a single
behavioral domain; it provides examples of how the BCTs
can be implemented and gives users enough detail to
operationalize BCTs.
The results indicated that using the taxonomy to code
intervention descriptions was generally reliable for those
BCTs occurring relatively frequently. However, it was not
possible to assess reliability for the 62 BCTs occurring with
low frequency in the 85 coded intervention descriptions. Of
the BCTs which could be assessed, three had kappa scores
below 0.60 [instruction on how to perform the behavior,
tailored personal message,and goal setting (behavior)].
Exploring reasons for discrepancies between coders may
help to identify where further refinement of BCT
labels/definitions and training may be required. For exam-
ple, users reported difficulties distinguishing between goal
setting (behavior)(i.e., when goal is unspecified, the most
general BCT in the grouping should be coded) and other
Table 4 Hierarchical structure labeling (step 6): frequency of words
from labels given by participants (conjunctions removed)
Rank Word/phrase Frequency
1 Behavior/behavioral 24
2 Monitoring 10
2 Emotional/emotion/emotions/emotional
3 Environment/environmental 9
4 Consequences 8
4 Self-efficacy 8
5 Feedback 7
5 Motivation 7
5 Reinforcement/reinforcing 7
6 Change 6
6 Conditioning 6
6 Identity 6
6 Planning 6
7 Antecedents 5
7 Goal-setting 5
7 Information 5
7 Learning 5
7 Manipulate 5
7 Other 5
7 Persuasion 5
7 Punishment 5
7 Self-regulation 5
7 Social 5
7 Social-support 5
8 Cognitions 4
8 Goals 4
8 Outcome expectancies 4
9 Resources 4
9 Restructuring 4
9 Reward 4
10 Commitment 3
10 Contingencies 3
10 Cues 3
10 Factors 3
10 Increase 3
10 Influence 3
10 Knowledge 3
10 Modeling 3
10 Physical 3
10 Practice 3
10 Prompts 3
Table shows most frequently used words ranked from 1 to 10
ann. behav. med. (2013) 46:8195 91
Table 5 Results of hierarchical cluster analysis of behavior change
techniques (step 6): grouping within the 16 cluster solution, approxi-
mately unbiased pvalues (AU), and standard errors
Cluster label and component BCTs AU, %
(1) Scheduled consequences 91 (.004)
Punishment [14.2]
Response cost [14.1]
Chaining [14.5]
Extinction [14.3]
Discrimination training [14.6]
Shaping [14.4]
Negative reinforcement [14.10]
Counter-conditioning [14.7]
Thinning [14.9]
Differential reinforcement [14.8]
(2) Reward and threat 90 (.005)
Social reward [10.4]
Material reward [10.2]
Self-reward [10.9]
Non-specific reward [10.3]
Threat [10.11]
Anticipation of future rewards or removal of
punishment [14.10]
Incentive [10.1]
(3) Repetition and substitution 97 (.002)
Behavior substitution [8.2]
Habit reversal [8.4]
Habit formation [8.3]
Graded tasks [8.7]
Overcorrection [8.5]
Behavioral rehearsal/practice [8.1]
Generalization of a target behavior [8.6]
(4) Antecedents 96 (.002)
Restructuring the physical environment [12.1]
Restructuring the social environment [12.2]
Avoidance/changing exposure to cues for the behavior
Distraction [12.4]
(5) Associations 97 (.002)
Discriminative (learned) cue [7.2]
Time out [7.4]
Escape learning [7.5]
Satiation [7.6]
Exposure [7.7]
Classical conditioning [7.8]
Fading [7.3]
Prompts/cues [7.1]
(6) Covert learning 73 (.008)
Vicarious reinforcement [16.3]
Covert sensitization [16.1]
Covert conditioning [16.2]
(7) Natural consequences 85 (.006)
Table 5 (continued)
Cluster label and component BCTs AU, %
Health consequences [5.1]
Social and environmental consequences [5.3]
Salience of consequences [5.2]
Emotional consequences [5.6]
Self-assessment of affective consequences [5.4]
Anticipated regret [5.5]
(8) Feedback and monitoring 97 (.002)
Feedback on behavior [2.2]
Biofeedback [2.6]
Other(s) monitoring with awareness [2.1 and 2.5]
Self-monitoring of outcome of behavior [2.4]
Self-monitoring of behavior [2.3]
(9) Goals and planning 90 (.002)
Action planning (including implementation intentions)
Problem solving/coping planning [1.2]
Commitment [1.9]
Goal setting (outcome) [1.3]
Behavioral contract [1.8]
Discrepancy between current behavior and goal
standard [1.6]
Goal setting (behavior) [1.1]
Review behavior goal(s) [1.5]
Review of outcome goal(s) [1.7]
(10) Social support 100 (.001)
Social support (practical) [3.2]
Social support (general) [3.1]
Social support (emotional) [3.3]
(11) Comparison of behavior 83 (.006)
Modeling of the behavior [6.1]
Information about othersapproval [6.3]
Social comparison [6.2]
(12) Self-belief 92 (.005)
Mental rehearsal of successful performance [15.2]
Self-talk [15.4]
Focus on past success [15.3]
Verbal persuasion to boost self-efficacy [15.1]
(13) Comparison of outcomes 90 (.005)
Persuasive argument [9.1]
Pros and cons [9.2]
Comparative imagining of future outcomes [9.3]
(14) Identity 86 (.006)
Identification of self as role model [13.1]
Self-affirmation [13.4]
Identity associated with changed behavior [13.5]
Reframing [13.2]
Cognitive dissonance [13.3]
(15) Shaping knowledge 95 (.003)
Reattribution [4.3]
Antecedents [4.2]
92 ann. behav. med. (2013) 46:8195
goal-related BCTs, and between instruction on how to
perform the behaviorand demonstration of the behavior.
In considering reasons for discrepancies, we agreed that
tailored personal messagewas a mode of delivery rather
than a BCT and therefore removed it from the taxonomy.
Since high reliability depends on both the content of the
taxonomy and the training of the user to use it, we are
currently evaluating methods of BCT user training and
conducting more detailed analyses of reliability of applica-
tion of the v1 classification system.
Future Developments
This is a fast-moving field: the first reliable taxonomy of BCTs
was published only 4 years before the current one [11]; while
widely cited and influential, this taxonomyincluded only 22
BCTs and 4 BCT packages so limiting the intervention content
that could be classified. We anticipate that further refinement
and development of BCT Taxonomy v1 will occur as a result
of its use and feedback from primary researchers, systematic
reviewers, and practitioners (e.g., the BCT, increase positive
emotionsappended as a footnote to Electronic Supplementary
Materials Table 3has been identified and will be included in
future revisions of the taxonomy). In order to continue the
development of the taxonomy and to further improve the
accuracy and reliability of its use, training courses and work-
shops involving researchers and practitioners from five coun-
tries, with varying scientific and professional backgrounds and
level of expertise, are being coordinated internationally. This
will facilitate the comparison of reliability across different
populations (e.g., disciplinary background, behavior, and con-
tinent). A web-based users resource, including the most recent
version of the taxonomy, guidance on its use, and a discussion
board for questions, comments, and feedback, has been devel-
oped to facilitate collaboration and synthesis of feedback (see
Research is needed to link BCTs to theories of behavior
change, for both designing and evaluating theory-based in-
terventions. Preliminary attempts have been made to link
BCTs to domains of theoretical constructs [17,45], and this
is part of an ongoing program of research. Guidance on
developing interventions informed by considering theoreti-
cal determinants of behavior can be found in Kok et al. [46]
and used in combination with the taxonomy. Work has also
begun to link BCTs to a framework of behavior change
interventions designed for use by policymakers, organiza-
tional change consultants, and systems scientists [47]. While
some of the BCTs such as those dealing with incentives or
environmental changes might be used in large-scale inter-
ventions, including health policy interventions, many would
only be appropriate for smaller scale, personally delivered
interventions. The current BCT Taxonomy v1 is a method-
ological tool for specifying intervention content and does
not, itself, make links with theory.
The aim is to produce a consensual coreBCT
Taxonomy that may be extended and/or modified
according to context, e.g., target behavior, country, spe-
cific setting. The BCT Taxonomy project will encourage
authors to report how they have amended the core
taxonomy so that other researchers can identify the links
between the version used and the core taxonomy. Future
work that increases the diversity of expertise and the
geographical and cultural contexts in which BCTs are
used would help to elucidate the extent to which BCT
Taxonomy v1 is relevant across contexts, countries, and
cultures and the extent to which specific adaptations
will be needed. To date, the taxonomy and coded in-
terventions have predominantly focused on interventions
delivered to the individuals whose behavior change is
targeted. Further work needs to be done to extend it to
the BCTs relevant to community and population-level
interventions [47].
BCT Taxonomy v1 thus lays the foundation for the
reliable and systematic specification of behavior change
interventions. This significantly increases the possibility of
identifying the active ingredients within interventions com-
ponents and the conditions under which they are effective,
and of replicating and implementing effective interventions,
thus advancing the science of behavior change. Historically,
it has often been concluded that how BCTs are delivered
may have as great or larger impact on outcomes as the
techniques themselves [48]. Dimensions of behavior
change interventions other than content, such as mode
and context of delivery [5], and competence of those
delivering the intervention [24,25] would thus also
benefit from being specified by detailed taxonomies.
Elucidation of how content, mode, and context of de-
livery interact in their impact on outcomes is a key
research goal for the field of behavioral science.
Table 5 (continued)
Cluster label and component BCTs AU, %
Behavioral experiments [4.4]
Instruction on how to perform a behavior [4.1]
(16) Regulation 98 (.001)
Regulate negative emotions [11.2]
Conserving mental resources [11.3]
Pharmacological support [11.1]
Paradoxical instructions [11.4]
Some BCT labels differ as a result of the consensus exercises; number
in brackets indicates related BCT in Electronic Supplementary Mate-
rials Table 3
AU adjusted unbiased pvalue, SE standard error of AU
ann. behav. med. (2013) 46:8195 93
In summary, the work reported in this paper is foundational
for our long-term goals of developing a comprehensive, hier-
archical, reliable, and generalizable BCT Taxonomy as a
method for specifying, evaluating, and implementing behav-
ior change interventions that can be applied to many different
types of intervention, including organizational and communi-
ty interventions, and that has multidisciplinary and interna-
tional acceptance and use. The work reported here is a step
toward the objective of developing agreed methods that per-
mit and facilitate the aims of CONSORT and UK MRC
guidance of precise reporting of complex behavioral interven-
tions. The next steps underway are to test the reliability and
usability of BCT Taxonomy v1 across different behaviors and
populations and to set up a system for its continuous devel-
opment guided by an international, multidisciplinary team.
Acknowledgments The present work carried out as part of the BCT
Taxonomy project was funded by the Medical Research Council. We
are grateful to the very helpful input from Felicity Roberts, Members of
the BCT Taxonomy project International Advisory Board (IAB), and
expert coders.
Conflicts of Interest The authors have no conflicts of interest to
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... The output of the ToC process was mapped against the COM-B model and using the Behaviour Change Wheel, relevant intervention functions were identified [21]. Specific behaviour change techniques (BCT's) were then chosen from the BCT Taxonomy [24] and linked to these intervention functions, targeting potential mechanisms of change to include and evaluate within the proposed feasibility study (Fig. 1). ...
... These intervention functions then identified 21 BCT's to be included in the design of the feasibility study protocol. These BCT's are detailed in Table 2 and listed according to their corresponding number in the BCT Taxonomy [24]. ...
... Whilst no studies in haemophilia to date have used this approach of mapping COM-B to qualitative findings, it has been identified as having potential importance in designing meaningful exercise based interventions for those with living with multi-morbidity [40]. A clearly documented development process is important to add to a growing body of knowledge about the explicit methods used in developing interventions [41], benefiting an understanding of the implementation as well as being able to examine the generalisability of the techniques used [24]. ...
Full-text available
Background Improved approaches for chronic pain management are a clinical and research priority for people with haemophilia (PWH). Involving people with lived experience in the design of a complex rehabilitation intervention strengthens the credibility and plausibility of the intervention, particularly in relation to rare disorders. Here we describe using a ‘Theory of Change’ (ToC) dialogue-based stakeholder process to create a programme theory for a telerehabilitation intervention. Methods An online workshop was convened and stakeholders received a briefing document in advance. Five stakeholders took part (3 PWH and 2 physiotherapists). At the workshop the group first agreed the overall aim of the intervention. Discussions then identified the resources, activities, barriers and enablers needed to achieve this outcome. All discussions were recorded and annotated by the workshop moderator. Behaviour change techniques were mapped for inclusion in the theory. Results A programme theory and narrative report were produced. All stakeholders reviewed these for clarity and to ensure a true reflection of the workshop discussions. Agreement was based on how meaningful, well-defined, do-able, plausible, credible, and testable each component was. Stakeholders highlighted the importance of issues unique to PWH. Key components included the need for physiotherapists to be knowledgeable of the condition, a range of exercises that were inclusive of all abilities, and the need for people to feel safe and supported whilst taking part. Conclusions Co-developed theory based approaches to intervention design offer an inclusive and transparent way to develop novel and meaningful interventions for people with complex health conditions. The ToC is wholly transparent in its design and content. Together with the identified behaviour change techniques, the theory informs the protocol for a feasibility study evaluating a telerehabilitation intervention. Importantly, it allows the opportunity to revise, adapt and improve the programme theory for further implementation and evaluation.
... To code and compare the techniques used in different interventions, the Behaviour Change Technique (BCT) Taxonomy v1 will be used (Michie et al., 2013). ...
... BCTs are components of interventions that influence the processes resulting in behavioural change (Michie et al., 2011). The BCT Taxonomy is a cross-domain and cross-discipline classification of techniques used in behaviour change interventions, with 93 BCTs organised into 16 groups (Michie et al., 2013). This taxonomy enables the reporting and classification of intervention content from various contexts and has previously been used in screening recruitment studies to analyse the individual components of behaviour change strategies (Acharya et al., 2021). ...
Background Lung cancer is the leading cause of cancer-related mortality worldwide. Despite this, the uptake of lung cancer screening (LCS) using low-dose CT is substantially low in comparison to established cancer screening programmes. Additionally, those at higher risk of the disease are the least likely to participate in screening, including current smokers and those experiencing socioeconomic deprivation. General practice (which may be referred to as primary care or family medicine depending on location) plays a vital role in screening through the identification of eligible individuals, overcoming participation barriers, and facilitating shared decision-making. Given the low rates of participation, it is important to understand which, if any, strategies from general practice could improve the effectiveness of a national programme. Objectives To assess and quantify the effects of strategies implemented in general practice to increase participation in LCS. Method A systematic review and meta-analysis, where possible, will be conducted following PRISMA reporting guidelines. Searches of PubMed, Embase, CINAHL, Cochrane Library, Web of Science,, and the WHO International Clinical Trials Registry Platform will be conducted. All randomised trials, non-randomised studies, and quantitative descriptive studies that report recruitment strategies based in general practice and LCS outcomes will be eligible. Screening and data extraction will be conducted independently by two reviewers. The risk of bias and overall certainty of findings will be assessed using the MMAT and GRADE tools, respectively. The Template for Intervention Description and Replication (TIDieR) checklist will be used for data extraction and the Behavioural Change Techniques (BCT) Taxonomy for data analysis of the components of interventions. Conclusion This review will provide data on the most effective general practice-based recruitment strategies aimed at improving LCS participation. Understanding the most effective and equitable strategies is important in the development of successful LCS and ensuring individuals at the greatest risk can participate.
... They concluded that the literature is limited in content, timing, and intensity of the interventions. Recent studies have discussed how voice therapy interventions fail to use specific terminology for similar interventions and have proposed theoretical frameworks such as the Rehabilitation Treatment Specification System [17] or the Behaviour Change Technique Taxonomy [18]. ...
Full-text available
Benign vocal fold lesions (BVFLs) are acquired structural anomalies of the vocal folds, and these are primarily a result of vocal abuse or phonotrauma. Phonotraumatic lesions are not generally regarded as recurrent, provided that appropriate behavioral changes are made after resolution or surgical removal. Voice therapy plays a crucial role in this aspect. The aim of this article is to propose a structured pre- and post-operative voice therapy program for patients undergoing surgical intervention for BVFLs. Voice therapy post-surgery has been proven to reduce the rate of recurrence in BVFLs. Having a standard treatment protocol is a useful tool for the therapist, particularly one without extensive voice training.
Full-text available
Background Patients who self-harm may consult with primary care nurses, who have a safeguarding responsibility to recognise and respond to self-harm. However, the responses of nursing staff to self-harm are poorly understood, and opportunities to identify self-harm and signpost towards treatment may be missed. It is unclear how to support nursing staff to implement national guidelines. Aims Among primary care nursing staff to: [1] Examine reported barriers and enablers to nurses’ use of, and adherence to, national guidance for self-harm; and [2] Recommend potential intervention strategies to improve implementation of the NICE guidelines. Methods Twelve telephone interviews partly structured around the capabilities, opportunities and motivations model of behaviour change (COM-B) were conducted with primary care nurses in the United Kingdom. The Theoretical Domains Framework was used as an analytical framework, while the Behaviour Change Wheel was used to identify exemplar behaviour change techniques and intervention functions. Results Nursing staff identified a need to learn more about risk factors (knowledge), and strategies to initiate sensitive conversations about self-harm (cognitive and interpersonal skills) to support their professional competencies (professional role and identity). Prompts may support recall of the guidance and support a patient centred approach to self-harm within practices (memory, attention, and decision making). GPs, and other practice nurses offer guidance and support (social influences), which helps nurses to navigate referrals and restricted appointment lengths (environmental context and influences). Conclusions Two converging sets of themes relating to information delivery and resource availability need to be targeted. Nine groups of behaviour change techniques, and five intervention functions offer candidate solutions for future intervention design. Key targets for change include practical training to redress conversational skill gaps about self-harm, the integration of national guidance with local resources and practice-level protocols to support decision-making, and creating opportunities for team-based mentoring.
Conference Paper
In many behaviour change interventions, computer-tailored health information has proven to be more effective than general health information. However, the majority of these studies have only achieved small effect sizes and the effectiveness of computer-tailored health communication (CTHC) remains inconsistent across different populations and behaviours. Since most CTHC studies measure a behaviour difference (e.g., steps per day) or biological difference (e.g., blood pressure), it is challenging to determine whether the intervention’s success is due to the quality of message tailoring or other factors (e.g., user interface design). This paper presents a study that assesses the performance of various algorithms for tailoring health information. These algorithms include a rule-based approach, based on behaviour change theories and machine learning algorithms. Despite limited data, the evaluated algorithms significantly outperform random message selection, achieving a 1.7-fold increase in precision for predicting participants’ preferred messages, and a 1.38-fold improvement in overall accuracy for anticipating participants’ preferences.
Adequate reporting of randomized, controlled trials (RCTs) is necessary to allow accurate critical appraisal of the validity and applicability of the results. The CONSORT (Consolidated Standards of Reporting Trials) Statement, a 22-item checklist and flow diagram, is intended to address this problem by improving the reporting of RCTs. However, some specific issues that apply to trials of nonpharmacologic treatments (for example, surgery, technical interventions, devices, rehabilitation, psychotherapy, and behavioral intervention) are not specifically addressed in the CONSORT Statement. Furthermore, considerable evidence suggests that the reporting of nonpharmacologic trials still needs improvement. Therefore, the CONSORT group developed an extension of the CONSORT Statement for trials assessing nonpharmacologic treatments. A consensus meeting of 33 experts was organized in Paris, France, in February 2006, to develop an extension of the CONSORT Statement for trials of nonpharmacologic treatments. The participants extended 11 items from the CONSORT Statement, added 1 item, and developed a modified flow diagram. To allow adequate understanding and implementation of the CONSORT extension, the CONSORT group developed this elaboration and explanation document from a review of the literature to provide examples of adequate reporting. This extension, in conjunction with the main CONSORT Statement and other CONSORT extensions, should help to improve the reporting of RCTs performed in this field.