ArticlePDF Available

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


Abstract and Figures

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.
Content may be subject to copyright.
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
1. Craig P, Dieppe P, Macintyre S, et al. Developing and evaluating
complex interventions: The new Medical Research Council guid-
ance. BMJ. 2008:337.
2. Michie S, Fixsen D, Grimshaw JM, Eccles MP. Specifying and
reporting complex behavior change interventions: The need for a
scientific method. Implement Sci. 2009;40:1-6.
3. Davidson KW, Goldstein M, Kaplan RM, et al. Evidence-based
behavioral medicine: What is it and how do we achieve it? Ann
Behav Med. 2003;26:161-171.
4. Boutron I, Moher D, Altman DG, et al. Extending the CONSORT
statement to randomized trials of non-pharmacologic treatment:
Explanation and elaboration. Ann Intern Med. 2008;148:295-309.
5. Michie S, Johnston M, Francis J, Hardeman W, Eccles M. From
theory to intervention: Mapping theoretically derived behavioral
determinants to behavior change techniques. Appl Psychol
6. Michie S, Hardeman W, Fanshawe T, Provost TA. Investigating
theoretical explanations for behavior change: The case study of
ProActive.Psychol Health. 2008;23:25-39.
7. Albarracin D, Gillette J, Earl AN, Glasman LR. A test of major
assumptions about behavior change: A comprehensive look at the
effects of passive and active HIV-prevention interventions since
the beginning of the epidemic. Psychol Bull. 2005;131:856-897.
8. Hardeman W, Griffin S, Johnston M, Kinmonth AL, Wareham NJ.
Interventions to prevent weight gain: A systematic review of
psychological models and behavior change methods. Int J Obesity.
9. Mischel W. Presidential address. Washington: Association for
Psychological Science Annual Convention; 2012.
10. West R, Walia A, Hyder N, Shahab L, Michie S. Behavior change
techniques used by the English Stop Smoking Services and their
associations with short-term quit outcomes. Nicotine Tob Res.
11. Abraham C, Michie S. A taxonomy of behavior change techniques
used in interventions. Health Psychol. 2008;27:379-387.
12. Michie S, Abraham C, Eccles MP, et al. Strengthening evaluation
and implementation by specifying components of behavior change
interventions: A study protocol. Implement Sci. 2011;6:10.
13. Michie S, Johnston M. Behavior change techniques. In: Gellman
MD, Turner JR, eds. Encyclopedia of behavioral medicine. New
York: Springer; 2011.
14. Michie S, Abraham C, Whittington C, McAteer J, Gupta S. Effec-
tive techniques in healthy eating and physical activity interven-
tions: A meta-regression. Health Psychol 2009;28:690-701.
15. Michie S, Hyder N, Walia A, West R. Development of a taxonomy
of behavior change techniques used in individual behavioral sup-
port for smoking cessation. Addict Behav. 2011;36:315-319.
16. Michie S, Whittington C, Hamoudi Z, et al. Identification of
behavior change techniques to reduce excessive alcohol consump-
tion. Addiction. 2012;107:1431-1440.
17. Abraham C, Good A, Warren MR, Huedo-Medina T, Johnson B.
Developing and testing a SHARP taxonomy of behavior change
techniques included in condom promotion interventions. Psychol
Health. 2011;26(Supplement 2):299.
18. Ivers N, Jamtvedt G, Flottorp S, Young JM, et al. Audit and
feedback: Effects on professional practice and patient outcomes.
Cochrane Database Syst Rev. 2012; (6): CD000259.
19. Araujo-Soares V, MacIntyre T, MacLennan G, Sniehotta FF. De-
velopment and exploratory cluster-randomized opportunistic trial
of a theory-based intervention to enhance physical activity among
adolescents. Psychol Health 2009;24:805-822.
20. Gardner B, Whittington C, McAteer J, Eccles MP, Michie S. Using
theory to synthesize evidence from behavior change interventions:
The example of audit and feedback. Soc Sci Med. 2010;70:1618-1625.
21. Michie S, Jochelson K, Markham WA, Bridle C. Low-income
groups and behavior change interventions: A review of interven-
tion content, effectiveness and theoretical frameworks. J
Epidemiol Community Health 2009;63:610-622.
22. Quinn F. On integrating biomedical and behavioral approaches to
activity limitation with chronic pain: Testing integrated models be-
tween and within persons. Aberdeen: University of Aberdeen; 2010.
23. Cahill K, Moher M, Lancaster T. Workplace interventions for
smoking cessation. Cochrane Database Syst Rev. 2008; (4):
24. Michie S, Churchill S, West R. Identifying evidence-based com-
petences required to deliver behavioral support for smoking ces-
sation. Ann Behav Med. 2011;41:59-70.
25. Dixon D, Johnston M. Health behavior change competency frame-
work: Competences to deliver interventions to change lifestyle be-
haviors that affect health. Edinburgh: Scottish Government; 2012.
26. Abraham C. Mapping change mechanisms and behaviour change
techniques: A systematic approach to promoting behaviour change
through text. In: Abraham C, Kools M, eds. Writing Health Com-
munication: An Evidence-Based Guide for Professionals. London:
SAGE Publications; 2011.
27. Stavri Z, Michie S. Classification systems in behavioral science:
Current systems and lessons from the natural, medical and social
sciences. Health Psychol Rev. 2012;6:113-140.
28. de Bruin M, Viechtbauer W, Hospers HJ, Schaalma HP, Kok G.
Standard care quality determines treatment outcomes in control
groups of HAART-adherence intervention studies: Implications for
the interpretation and comparison of intervention effects. Health
Psychol. 2009;28:668-674.
29. Dombrowski SU, Sniehotta FF, Avenell A, et al. Identifying active
ingredients in complex behavioral interventions for obese adults with
obesity-related co-morbidities or additional risk factors for co-
morbidities: A systematic review. Health Psychol Rev 2012;6:7-32.
94 ann. behav. med. (2013) 46:8195
30. Michie S, Johnston M. Theories and techniques of behavior
change: Developing a cumulative science of behavior change.
Health Psychol Rev 2012;6:1-6.
31. Pill J. The Delphi method: Substance context, a critique and the
annotated bibliography. Socioecon Planning Sci. 1991;5:57-71.
32. Vandenbos GR. APA dictionary of psychology. Washington, DC:
American Psychological Association; 2006.
33. Brock G, Pihur V, Datta S, Datta S. Package clvalid: Validation
of clustering results. J Statistical Software. 2008;25:1-22.
34. Suzuki R, Shimodaira H. Pvclust: An R package for assessing the
uncertainty in hierarchical clustering. Bioinformatics. 2006;22:1540-
35. Byrt T, Bishop J, Carlin JB. Bias, prevalence and kappa. J Clin
Epidemiol. 1993;46:423-429.
36. Lantz CA, Nebenzahl E. Behavior and interpretation of the kappa
statistic: Resolution of the two paradoxes. J Clin Epidemiol.
37. Landis JR, Koch GG. Measurement of observer agreement for
categorical data. Biometrics. 1977;33:159-174.
38. Michie S, Johnston M. Changing clinical behavior by making
guidelines specific. BMJ. 2004;328:343-345.
39. American Psychiatric Association. Diagnostic and Statistical Man-
ual of Mental Disorders. 4th ed. Text Revision. Washington, DC:
American Psychiatric Association; 2000.
40. World Health Organisation. ICD-10 international statistical clas-
sification of diseases and related health problems. Geneva, Swit-
zerland: Illu; 1992.
41. Miller GA. The magical number seven, plus or minus two: Some
limits on our capacity for processing information. Essential
Sources in the Scientific Study of Consciousness. Cambridge: A
Bradford Book; 2003:357-372.
42. Baddeley A. Short-term memory for word sequences as a function
of acoustic, semantic and formal similarity. Q J Exp Psychol.
43. Polyn SM, Erlikhman G, Kahana MJ. Semantic cuing and the scale
insensitivity of recency and contiguity. J Exp Psychol Learn Mem
Cogn. 2011;37:766-775.
44. Tulving E, Pearlsto Z. Availability versus accessibility of informa-
tion in memory for words. J Verb Learn Verb Behav. 1966;5:381-
45. Michie S, Johnston M, Abraham C, et al. Making psychological
theory useful for implementing evidence based practice: A con-
sensus approach. Qual Saf Health Care. 2005;14:26-33.
Intervention mapping: A protocol for applying health psychol-
ogy theory to prevention programmes. J Health Psychol.
47. Michie S, van Stralen MM, West R. The behavior change wheel: A
new method for characterising and designing behavior change
interventions. Implement Sci. 2011;6:42.
48. Kolehmainen N, Francis JJ. Specifying content and mechanisms of
change in interventions to change professionalspractice: An il-
lustration from the Good Goals study in occupational therapy.
Implement Sci. 2012;7:100.
ann. behav. med. (2013) 46:8195 95
... These included: author and country; participant numbers and characteristics; study design; treatment intervention parameters including duration, frequency, and follow up. The synthesis of findings from key papers in the area (25)(26)(27)(28)(29) were used to create a framework to guide data extraction and included: theoretical underpinning for the intervention; instruction on how to perform the PA or exercise; recording and tracking of PA or exercise; the use of goal setting; the use of action and coping planning; the type, use, and delivery of feedback and monitoring; the use and delivery of prompts; the use of any additional online PA or exercise resources; the use of PA or exercise testimonials; and the use of gamification. PA&E related outcome measures and results at the end of the intervention, and at follow up, if reported, were also recorded. ...
... The most common intervention strategies and features used were instructions on how to perform the PA or exercise, goal setting, and the use of feedback and monitoring. These align with the behavior change techniques (BCT) taxonomy clusters proposed by Michie and colleagues (27). The self-guided interventions with the larger effect sizes employed strategies from at least three of these clusters (39,42,46,(53)(54)(55). ...
Full-text available
Objective The aim of this systematic review was to determine the effectiveness of self-guided digital physical activity (PA) and exercise interventions to improve physical activity and exercise (PA&E) outcomes for people living with chronic health conditions. Digital health interventions, especially those with minimal human contact, may offer a sustainable solution to accessing ongoing services and support for this population.MethodsA comprehensive and systematic search was conducted up to December 2021, through seven databases, for randomized trials that evaluated the effect of self-guided web- or internet-based PA interventions on physical activity or exercise outcomes. Included studies had to have interventions with minimal human contact and interaction with participants needed to be automatically generated. All studies were screened for eligibility and relevant data were extracted. Two independent reviewers assessed the risk of bias using the Cochrane risk of bias tool. Standardized mean differences and 95% confidence intervals (CI) were calculated. PA data were pooled, and forest plots were generated.ResultsSixteen studies met the eligibility criteria and included a total of 2,439 participants. There was wide variation in health conditions and intervention characteristics in mode and parameters of delivery, and in the application of theory and behavioral strategies. Self-reported PA in the intervention group was greater than controls at the end of the intervention [standardized mean difference (SMD) 0.2, 95% CI = 0.1, 0.3] and at follow up (SMD 0.3, 95% CI 0.2–0.5). The difference in objectively measured PA was small and non-significant (SMD 0.3, 95% CI −0.2 to 0.9). All interventions included behavioral strategies and ten of the sixteen were underpinned by theory.Conclusions Self-guided digital PA&E interventions provided a positive effect on PA immediately after the intervention. An unexpected and positive finding was a sustained increase in PA at follow-up, particularly for interventions where the behavioral strategies were underpinned by a theoretical framework. Interventions with minimal contact have the potential to support sustained PA engagement at least as well as interventions with supervision.Systematic Review Registration, identifier: CRD42019132464.
... Motivational interviewing is a person-centered approach used in behavioral interventions, which comprises behavior change techniques (BCTs) [37] and relational techniques [36]. Motivational interviewing has also been associated with the self-determination theory (SDT) [38]. ...
... Clinicians were trained in motivational interviewing [36] before the start of the study. A motivational interviewing guide (discussion plan) based on BCTs [37] and motivational techniques [36] was conceived by the research team and provided to the clinicians as a support tool that can help them choose strategies adapted to the client's needs. ...
Full-text available
Background: Exergames are increasingly being used among survivors of stroke with chronic upper extremity (UE) sequelae to continue exercising at home after discharge and maintain activity levels. The use of virtual reality exergames combined with a telerehabilitation app (VirTele) may be an interesting alternative to rehabilitate the UE sequelae in survivors of chronic stroke while allowing for ongoing monitoring with a clinician.
... Engage care partner and PARTICIPANT (with hygienist present) to revise SMART behaviour goals and/or develop new SMART goals for sustaining newly learnt behaviours for tailored oral self-care.*Retrieved from Michie et al.28 ...
To cite: Wu B, Plassman BL, Poole P, et al. Study protocol for a randomised controlled trial of a care partner assisted intervention to improve oral health of individuals with mild dementia. BMJ Open 2022;12:e057099. Abstract Introduction Individuals with mild dementia are at high risk of poor oral health outcomes. To address this issue, we describe an intervention to teach care partners skills to guide individuals with mild dementia in proper oral hygiene techniques and provide reminders to practice oral hygiene care. By providing support to perform these tasks successfully, we aim to delay oral health decline among this vulnerable population. Methods and analysis This multisite study is a three-arm randomised controlled trial. The primary objective is to evaluate the efficacy of an intervention to improve oral hygiene outcomes by promoting positive oral hygiene behaviours and skills among individuals with mild dementia. Care partners’ behaviour factors, such as oral care self-efficacy and implementation of the care plan, serve as mediators of the intervention. Participant–care partner dyads will be randomly assigned to either Treatment Group 1, Treatment Group 2 or the Control Group. All groups will receive an educational booklet. Treatment Group 1 and Treatment Group 2 will receive a smart electronic toothbrush. Treatment Group 2 (the intervention group) will also receive an oral hygiene care skill assessment, personalised oral hygiene instruction and treatment plan; and care partners will receive in-home and telephone coaching on behaviour change. Oral health outcomes will be compared across the three groups. The duration of the active intervention is 3 months, with an additional 3-month maintenance phase. Data collection will involve three home visits: baseline, 3 months and 6 months. The study enrollment started in November 2021, and the data collection will end in Spring 2024. Ethics and dissemination The study has been approved by the Institutional Review Board of the NYU Grossman School of Medicine and Duke University, and is registered at A Data Safety Monitoring Board has been constituted. The study findings will be disseminated via peer-reviewed publications, conference presentations and social media. Trial registration number NCT04390750.
Background: Professional caregivers are important in the daily support of lifestyle change for adults with mild intellectual disabilities; however, little is known about which behaviour change techniques (BCTs) are actually used. This study aims to gain insight in their use for lifestyle behaviour change using video observations. Methods: Professional caregivers (N = 14) were observed in daily work supporting adults with mild intellectual disabilities. Videos were analysed using the Coventry Aberdeen London Refined (CALO-RE-NL) taxonomy and BCTs utilised were coded. Results: Twenty one out of 40 BCTs were used by professional caregivers. The BCTs 'Information about others' approval', 'Identification as role model', 'Rewards on successful behaviour', 'Review behavioural goals' and 'Instructions on how to perform the behaviour' were most employed. Conclusion: Professional caregivers used BCTs to support healthier lifestyle behaviour of adults with mild intellectual disabilities. However, most promising of them as defined previous by professionals were rarely used by professional caregivers.
Full-text available
Introduction Physical inactivity and excessive sedentary behaviours are major preventable causes in both the development and the treatment of obesity and type 2 diabetes mellitus (T2DM). Nevertheless, current programmes struggle to engage and sustain physical activity (PA) of patients over long periods of time. To overcome these limitations, the Digital Intervention Promoting Physical Activity among Obese people randomised controlled trial (RCT) aims to evaluate the effectiveness of a group-based digital intervention grounded on gamification strategies, enhanced by social features and informed by the tenets of the self-determination theory and the social identity approach. Methods and analysis This trial is a two-arm parallel RCT testing the effectiveness of the Kiplin digital intervention on obese and patients with T2DM in comparison to the usual supervised PA programme of the University Hospital of Clermont-Ferrand, France. A total of 50 patients will be randomised to one of the two interventions and will follow a 3-month programme with a 6-month follow-up postintervention. The primary outcome of the study is the daily step count change between the baseline assessment and the end of the intervention. Accelerometer data, self-reported PA, body composition and physical capacities will also be evaluated. To advance our understanding of complex interventions like gamified and group-based ones, we will explore several psychological mediators relative to motivation, enjoyment, in-group identification or perceived weight stigma. Finally, to assess a potential superior economic efficiency compared with the current treatment, we will conduct a cost–utility analysis between the two conditions. A mixed-model approach will be used to analyse the change in outcomes over time. Ethics and dissemination The research protocol has been reviewed and approved by the Local Human Protection Committee (CPP Ile de France XI, No 21 004-65219). Results will inform the Kiplin app development, be published in scientific journals and disseminated in international conferences. Trial registration number NCT04887077 .
Full-text available
Background Whether behaviour change interventions are effective for the maintenance of older migrants’ health and well-being is uncertain. A systematic review was conducted to assess evidence for the capacity of behaviour change techniques (BCTs) to promote the health and well-being of older migrants. Methods Electronic databases (Cochrane CENTRAL, Embase, Ovid MEDLINE and Web of Science) were searched systematically to identify relevant randomised controlled trials, pre–post studies and quasi-experimental studies published before March 2021. Additional articles were identified through citation tracking. Studies examining BCTs used to promote the health and/or well-being of older migrants were eligible. Two independent reviewers used the Behaviour Change Technique Taxonomy version 1 to extract data on BCTs. Data on intervention functions (IFs) and cultural adaption strategies were also extracted. Intervention contents (BCTs, IFs, culture adaption strategies) were compared across effective and ineffective interventions according to health and well-being outcome clusters (anthropometrics, health behaviour, physical functioning, mental health and cognitive functioning, social functioning and generic health and well-being). Results Forty-three studies (23 randomised controlled trials, 13 pre–post studies and 7 quasi-experimental studies) reporting on 39 interventions met the inclusion criteria. Thirteen BCTs were identified as promising for at least one outcome cluster: goal-setting (behaviour), problem-solving, behavioural contract, self-monitoring of behaviour, social support (unspecified), instruction on how to perform the behaviour, information about health consequences, information about social and environmental consequences, demonstration of the behaviour, social comparison, behavioural practice/rehearsal, generalisation of a target behaviour and addition of objects to the environment. Three BCTs (instruction on how to perform the behaviour, demonstration of the behaviour, and social comparison) and two IFs (modelling and training) were identified as promising for all outcome clusters. Conclusions Thirteen distinct BCTs are promising for use in future interventions to optimise health and well-being among older migrants. Future research should focus on the effectiveness of these BCTs (combinations) in various contexts and among different subgroups of older migrants, as well as the mechanisms through which they act. Given the scarcity of interventions in which cultural adaption has been taken into account, future behavioural change interventions should consider cultural appropriateness for various older migrant (sub)groups. Trial registration PROSPERO CRD42018112859 .
Health apps are supposed to support fighting sedentary lifestyles and, consequently, a variety of chronic diseases. For promoting physical activity in a sustained manner, these apps and corresponding research draw upon a variety of behavior change techniques and visualizations. To provide a structured overview of recent approaches and identify research gaps, we conducted a systematic literature review of empirical research works on app-based approaches for promoting everyday physical activity. In the 42 relevant studies identified, we thoroughly analyzed the applied behavior change techniques and in-app visualization types. We found a recent emphasis on feedback and monitoring as well as goal setting techniques, while the application of others such as informing about health consequences or shaping the user’s knowledge are applied only in rare cases. The range of visualization types is limited. Traditional charts and gamified illustrations turned out to be predominant. However, empirical research on alternative approaches such as innovative chart visualizations is scarce.
Aims: Digital health interventions (DHIs) use different strategies to deliver behavior change techniques (BCTs). There is a lack of understanding on how BCTs can be strategized in DHIs to optimize users’ experience and effectiveness of the intervention. This review aimed to explore how behavior change techniques are strategized/operationalized in DHIs de-facto. Method: Thirty-five studies were included in the review. Data related to behavior change strategies were extracted and coded using the taxonomy of behavior change techniques. Results: Overall, 125 strategies were extracted from studies and coded into 33 BCTs. Most of the studies were focused on physical activity and healthy food consumption. ‘Prompts and cues’ (17/35 studies), ‘social support (unspecified technique)’ (15/35 studies), ‘goal setting (behavior technique)’ (11/35 studies), and ‘self-monitoring of behavior’ (10/35 studies) were the most frequently used behavior change techniques. ‘Prompts and cues’ was mostly strategized by sending reminders via text messages/email, mobile applications, or other digital systems. ‘Social support’ such as encouragement or counseling was strategized by online support groups using social networking websites, text-message platforms, and counselors’ phone calls. ‘Goal setting (behavior technique)’ was strategized via in-app calculators to set goals, build-in app features, and digital coach/virtual agents. ‘Self-monitoring of behavior’ was mainly strategized by transferring data in the mobile application (by users) and activity trackers. Conclusion: It is important to consider theories/frameworks of behavior change while selecting, strategizing, and reporting BCTs to produce effective and sustainable results. Furthermore, innovative ways of strategizing various BCTs are needed to be implemented in DHI.
Embracing the Bayesian approach, we aimed to synthesise evidence regarding barriers and enablers to physical activity in adults with heart failure (HF) to inform behaviour change intervention. This approach helps estimate and quantify the uncertainty in the evidence and facilitates the synthesis of qualitative and quantitative studies. Qualitative evidence was annotated using the Theoretical Domains Framework and represented as a prior distribution using an expert elicitation task. The maximum a posteriori probability (MAP) for the probability distribution for the log OR was used to estimate the relationship between physical activity and each determinant according to qualitative, quantitative, and qualitative and quantitative evidence combined. The probability distribution dispersion (SD) was used to evaluate uncertainty in the evidence. Three qualitative and 16 quantitative studies were included (N = 2739). High pro-b-type natriuretic peptide (MAP = -1.16; 95%CrI: [-1.21; -1.11]) and self-reported symptoms (MAP = - 0.48; 95%CrI: [ -0.40; -0.55]) were suggested as barriers to physical activity with low uncertainty (SD = 0.18 and 0.19, respectively). Modifiable barriers were symptom distress (MAP = -0.46; 95%CrI: [-0.68; -0.24], SD = 0.36), and negative attitude (MAP = -0.40; 95%CrI: [-0.49; -0.31], SD = 0.26). Modifiable enablers were social support (MAP = 0.56; 95%CrI: [0.48; 0.63], SD = 0.26), self-efficacy (MAP = 0.43; 95%CrI: [0.32; 0.54], SD = 0.37), positive physical activity attitude (MAP = 0.92; 95%CrI: [0.77; 1.06], SD = 0.36).
Full-text available
Background: Specifying individual behaviour change techniques (BCTs) is crucial for better development and evaluation of behaviour change interventions. Classification of BCTs will help this process and can be informed by classification systems in the natural, medical and social sciences. Method: A search of the classification literature in the natural, medical and social sciences produced a framework within which to consider a systematic search of classification systems of BCTs in the behaviour change literature. Results: Six distinct types of classification system from other scientific disciplines were identified: nomenclatures, ordered sets, hierarchical, matrices, faceted and social categorisations. Eight classification systems of BCTs were identified, none of which had a formal, hierarchical structure. Most were developed for specific behaviours, although one was general. Discussion: Developing a hierarchical structure, similar to those used in other scientific disciplines, would enable better communication and understanding of BCTs and inform the development and evaluation of interventions. Hierarchical structured classification systems contain many of the characteristics most desirable in a classification of BCTs.
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.