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Changing Behaviour to Improve Clinical Practice and Policy

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Changing Behaviour to Improve Clinical Practice and Policy

Changing Behaviour to Improve Clinical Practice and Policy
Susan Michie1, Lou Atkins1, Heather L. Gainforth1
1 Department of Clinical, Educational and Health Psychology, University College London,
WC1E 6BT, UK
1
Introduction
Evidence-based clinical practice and patient care is supported by guidelines providing
recommendations and standards, such as those produced by the National Institute for Health
and Care Excellence (NICE) in the UK. Despite this guidance, research suggests that many
health professionals do not follow them. For example, an examination of implementation of
clinical practice guidelines by physicians in the Netherlands found that on average 33% of
physicians were not following the guidelines in their practice (Grol, 2001) and a review of
academic literature reporting quality of care in the United States found that 20-25% of
patients may have received health care that was unnecessary or even harmful (Schuster,
McGlynn, & Brook, 1998). Given that evidence-based clinical practice will require health
professionals to change their behaviour, interventions drawing on the science of behaviour
change are needed promote evidence-based practice.
Developing, implementing and evaluating interventions to change established behaviour
patterns can be challenging. The same is true for one-off behaviours where one is working
against a strong psychological, social or environmental gradient. Interventions are often
developed without a systematic method and without drawing on the evidence and theories
produced by the behavioural and social sciences. This point was made by Martin Eccles,
Emeritus Professor of Clinical Effectiveness in the UK, in referring to a frequently used
principle of intervention design, the ‘ISLAGIATT’ principle. The letters stand for ‘It Seemed
Like A Good Idea At The Time’! This principle encapsulates an approach in which the
intervention strategy is arrived at before having conducted a thorough assessment of the
behavioural target(s) and an analysis of what it would take to achieve change in these and
how best to implement this change. Instead, personal experience, a favoured theory or a
cursory analysis may be used as the starting point for intervention design, compromising the
potential for the intervention to have the desired effect.
Evidence suggests that the most effective interventions to change behaviour are those
that simultaneously and consistently target population, community and individual levels
(National Institute for Health and Care Excellence, 2007). The UK’s comprehensive national
tobacco control policy is an excellent example of such an approach (HM Government, 2010).
Policy makers, health care providers, researchers and educators may not draw on best
evidence or use a systematic approach to developing interventions, such as that recommended
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by the UK’s MRC for several reasons (Craig et al., 2008). They may also lack the necessary
time, resources, knowledge and/or skills.
There are several frameworks to support a systematic approach to intervention
development, for example MINDSPACE (Institute for Government, 2010), an approach used
by UK government departments, and Intervention Mapping (Bartholomew, Parcel, Kok,
Gottlieb, & Fernandez, 2011), an approach developed in the Netherlands. A systematic
review of behaviour change frameworks in 2010 identified 19 and evaluated them in terms of
comprehensiveness, coherence and links to a model of behaviour.(Michie, van Stralen, &
West, 2011). Since no framework met all three criteria and since there was a degree of
overlap between the frameworks, they were synthesised into one framework with two levels,
one representing intervention functions and the other representing higher-order policy
categories. The resulting Behaviour Change Wheel (BCW) provides a systematic way of
characterising interventions that enables their outcomes to be linked to mechanisms of action,
and can help to diagnose why an intervention may have failed to achieve its desired goal. The
nine intervention functions and seven policy categories are linked to a model of behaviour at
the hub of the wheel, the Capability-Opportunity-Motivation Behaviour (COM-B) model.
This allows interventions to be designed from the starting point of a theoretical analysis of
the target behaviour in its context. The simplicity, comprehensiveness and practical nature of
the BCW has led to wide take up in academic, policy and intervention fields. For examples
of use, see the Behaviour Change Wheel Guide to Designing Behaviour Change Interventions
(Michie et al, 2014; www.behaviourchangewheel.com ).
In less than three years since the BCW was published, the paper has been accessed more
than 59,000 times, cited more than 140 times and widely used in training (see
www.ucl.ac.uk/behaviour-change). The BCW has been used to understand and change
clinical practice in a range of areas including: understanding general practitioners’ use of
different cardiovascular risk assessment strategies (Bonner et al., 2013) and providing
contraception to adolescents (Rubin, Davis, & McKee, 2013); improving paediatric services
in Kenyan hospitals (English, 2013); implementing evidence-based guidelines for pre-term
babies (Crowther et al., 2013); and for women with postnatal depression (Crowther et al.,
2013).
This chapter aims to summarise an approach to developing behaviour change
interventions based on conducting a behavioural diagnosis using COM-B and selecting
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intervention functions and policy categories using the BCW. It is aimed at policy makers,
health care providers, researchers, educators and all those wishing to systematically develop
behaviour change interventions to improve clinical practice. To achieve this aim, we first
present an overview of the BCW. Using extracts and examples from the recently published
book The Behaviour Change Wheel A Guide to Designing Interventions
(www.behaviourchangewheel.com) (Michie, Atkins, & West, 2014), we then present a step-by
step process of intervention design using the example of improving hand hygiene in UK
hospital staff to illustrate how the BCW has been applied to improve clinical practice.
The Behaviour Change Wheel
The BCW is a synthesis of 19 frameworks of behaviour change identified in a
systematic literature review (Michie et al., 2014; Michie, van Stralen, et al., 2011). As noted
earlier, none of these frameworks were found to be comprehensive. In addition, few of them
were conceptually coherent or clearly linked to a model of behaviour change. Some of the
frameworks assumed that behaviour was primarily driven by beliefs and perceptions, while
others placed greater emphasis on unconscious biases and yet others focused on the social
environment. Clearly, all of these are important and needed to be brought together in a
coherent manner. The BCW aimed to address these limitations by synthesising the common
features of the frameworks and linking them to a model of behaviour that was sufficiently
broad that it could be applied across behaviours and settings.
The BCW consists of three layers (Figure 1). The hub of the wheel identifies the
sources of the behaviour that could prove fruitful targets for intervention. It uses the COM-B
model for this. The COM-B model provides a simple framework for understanding
behaviour, in which ‘capability’ (physical and psychological), ‘opportunity’ (physical and
social) and ‘motivation’ (automatic and reflective) are conceptualised as three essential
conditions for behaviour (Michie et al., 2011). Surrounding the COM-B model is a layer of
nine intervention functions to choose from that can be used to address deficits in one or more
of capability, opportunity or motivation. Then the outer layer, the rim of the wheel, identifies
seven types of policy that one can use to deliver the intervention functions.
Figure 1. The Behaviour Change Wheel
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The COM-B model and BCW can be applied across levels from individuals to groups,
sub-populations and populations, and within different organisational structures and systems.
When applying it to groups and populations the components are construed in terms of
aggregate parameters (e.g. proportion of the target population drinking above recommended
limits, understanding the harmfulness of excessive consumption, wanting to reduce
population consumption etc.). When applying the model to organisations with an internal
structure in terms of communication and influence, these are captured in terms of physical
and social opportunity. However, for some behaviour change problems these can be further
elaborated to capture specific features of these parameters (e.g. patterns of diffusion of
influence).
Step-by-Step Method for Designing Behaviour Change Interventions
To aid intervention designers to use the BCW, we present a step-by-step method for
designing behaviour change interventions (Figure 2). The steps are outlined in three stages:
1) Understand the behaviour; 2) Identify intervention options; and 3) Identify content and
implementation options. Although the process is described in linear terms, it is clear that it
may involve cycling back and forth between steps as one discovers issues and obstacles.
Below we summarise each of these steps using the example of designing an intervention to
improve hand hygiene in UK hospital staff.
Figure 2. Step-by-Step Method for Designing Behaviour Change Interventions
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Stage 1: Understand the behaviour
Define the behaviour in behavioural terms. In order to understand a behaviour, one must
first define the behaviour in behavioural terms by being specific about i) the target individual,
group or population involved in the behaviour and ii) the behaviour itself. For example, a
hospital may aim to reduce infection rates within a ward. Infection rates are not a behaviour.
A number of different behaviours performed by a number of individuals may be relevant to
reducing infection rates, including hand-washing, effective use of protective clothing,
cleaning surfaces, maintaining appropriate isolation etc. To address the problem, one must
identify as specifically as possible 1) the behaviour or behaviours that need to be changed to
solve the problem; 2) the location(s) in which the behaviour occurs; and 3) the individual,
group or population involved.
Select a target behaviour. Behaviours do not exist in a vacuum but occur within the
context of other behaviours of the same or other individuals. These interact as a system.
When considering which behaviour(s) on which to intervene, designers should think about all
relevant behaviours performed by the target group or groups. For example, a hospital may
find that its infection rates are high because nurses are not keeping their hands disinfected.
This behaviour could be influenced by the behaviours of several others, including senior
doctors disinfecting or not disinfecting their hands, patients asking them whether they have
cleaned their hands, and the domestic staff ensuring that there is enough alcohol gel in the
dispensers.
To select a target behaviour, intervention designers will need to generate a ‘long list’ of all
potential behaviours that may be relevant to the problem to be addressed. To select
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behaviours to start with, it is helpful to consider the following criteria: the likely impact if the
behaviour were to be changed; how easy it is to change the behaviour; the centrality of the
behaviour in a system of behaviours and the potential for spillover effects; and ease of
measurement for evaluation. For example, amongst a long list of behaviours that could be
used to reduce infection rates in hospital wards, Fuller and colleagues (2012) chose the target
behaviour of ‘cleaning hands using alcohol gel’ to improve hand hygiene among health care
staff. This behaviour was chosen over other behaviours because: (i) its impact: on infection
control; (ii) likelihood of change: judged to be fairly easy to change given that it is a
behaviour performed mostly in public and could be influenced by social desirability effects,
and is underpinned by national guidelines and local protocols;(iii) spillover: more health
professionals washing their hands are likely to prompt colleagues and visitors to do so; and
(iv) ease of measurement: there are reliable observational measures (McAteer et al., 2008)
and proxy measures such as consumption of soap and alcohol gel.
Specify the target behaviour. Once a target behaviour is selected is must be specified in
detail, including a detailed description of the behaviour as well as 1) Who needs to perform
the behaviour?; 2) What does the person need to do differently to achieve the desired
change?; 3) When will they do it?; 4) Where will they do it?; 5) How often will they do it?; 6)
With whom will they do it?. Table 1 outlines how the behaviour of ‘cleaning hands using
alcohol gel’ was specified in our infection control example.
Table 1. Example of specifying the target behaviour
Target behaviour Cleaning hands using alcohol gel
Who needs to perform the behaviour? All hospital staff
What do they need to do differently to
achieve the desired change?
Clean hands using alcohol gel
When do they need to do it? During each shift
Where do they need to do it? On hospital premises
How often do they need to do it? At the start of each shift
After using the toilet
Before physical contact with patients
After physical contact with patients, visitors or staff
members
After contact with potentially contaminated materials
With whom do they need to do it? Alone
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Identify what needs to change. The final step of Stage 1 is to identify what needs to change
in the person and/or in the environment in order to achieve the desired change in behaviour.
This step is critical to intervention design and is often overlooked. To identify what needs to
change, intervention developers should conduct a behavioural analysis which seeks to
understand the target behaviour in the context. A behavioural analysis can be conducted
using focus groups, questionnaires, observations and/or documentary analysis. The analysis
can be guided by the COM-B model (i.e. the hub of the BCW) and aims to identify which of
the COM-B components need to change in order for the behaviour to occur. To illustrate
how COM-B can be used to guide a behavioural analysis, Table 2 provides a behavioural
analysis and diagnosis of the target behaviour ‘cleaning hands using alcohol gel’.
Table 2. Example behavioural analysis and diagnosis.
Target Behaviour: Hospital staff to disinfect their hands using alcohol gel in identified high risk situations.
COM-B components What needs to happen for the target
behaviour to occur?
Is there a need for change?
Physical capability Have the physical skills to clean hands No change needed as hospital staff have these
skills
Psychological capability Know the correct technique to clean
hands
No change needed as knowledge of hand
cleaning techniques is sufficient
Know how to create ‘if-then’ rules to
prompt hand cleaning
Change needed as hospital staff do not
necessarily know how to create and routinely
apply if-then rules
Physical opportunity Have alcohol gel available No change needed as gel is available at each
bedside
Social opportunity See senior health professionals clean
their hands using alcohol gel
Change needed as staff do not always see seeing
senior health professionals cleaning their hands
using alcohol gel
Reflective motivation Hold beliefs that using alcohol gel more
frequently will reduce infection
transmission
No change needed as research literature shows
staff old these beliefs
Believing that consistent hand hygiene
will require improved cognitive and self-
regulation skills
Change needed as staff do not necessarily
recognise the value of these skills
Automatic motivation Have established routines and habits for
hand cleaning
Change needed to establish routine and habit
formation
Behavioural diagnosis
of the relevant COM-B
components:
Psychological capability, social opportunity, reflective and automatic motivation need to
change in order for the target behaviour.
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To conduct a more elaborate behavioural analysis than possible using either individual
theories or COM-B, the Theoretical Domains Framework can be used (TDF; Cane, O’Connor
& Michie, 2012). Developed using a consensus approach, the Theoretical Domains
Framework is a synthesis 128 explanatory constructs from 33 theories of behaviour change
into 14 theoretical domains and can be thought of as an elaboration of the COM-B model
which subdivides the components in a particular way (Cane, O’Connor, & Michie, 2012). A
description of how the COM-B model and domains of the Theoretical Domains Framework
are linked is provided in the book The Behaviour Change Wheel A Guide to Designing
Interventions (Michie et al., 2014).
Stage 2: Identify Intervention Options
Identify intervention functions. The Behaviour Change Wheel enables a comprehensive
approach to considering the range of interventions that can be used. We use the term
intervention ‘function’ rather than intervention ‘type’ or ‘category’ since the same
intervention may have more than one function. For example, a mass media campaign to
promote smoking cessation may be educational (providing new information on the harms of
smoking) and also persuasive (generating feelings of worry about the health harms of
smoking). Labels, definitions and examples of the nine intervention functions are given in
Table 3.
Table 3. BCW intervention functions
Intervention
function
Definition Example of intervention function
Education Increasing knowledge or understanding Providing information to promote healthy eating
Persuasion Using communication to induce
positive or negative feelings or
stimulate action
Using imagery to motivate increases in physical
activity
Incentivisation Creating an expectation of reward Using prize draws to induce attempts to stop
smoking
Coercion Creating an expectation of punishment
or cost
Raising the financial cost to reduce excessive
alcohol consumption
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Intervention
function
Definition Example of intervention function
Training Imparting skills Advanced driver training to increase safe driving
Restriction Using rules to reduce the opportunity
to engage in the target behaviour (or to
increase the target behaviour by
reducing the opportunity to engage in
competing behaviours)
Prohibiting sales of solvents to people under 18
to reduce use for intoxication
Environmental
restructuring
Changing the physical or social context Providing on-screen prompts for GPs to ask
about smoking behaviour
Modelling Providing an example for people to
aspire to or imitate
Using TV drama scenes involving safe-sex
practices to increase condom use
Enablement Increasing means/reducing barriers to
increase capability (beyond education
and training) or opportunity (beyond
environmental restructuring)
Behavioural support for smoking cessation,
medication for cognitive deficits, surgery to
reduce obesity, prostheses to promote physical
activity
The COM-B model identifies what needs to shift for the desired behaviour to be
achieved and therefore what to target in an intervention. The BCW identifies intervention
functions and supporting policies likely to be effective in bringing about change. The links
between COM-B and the intervention functions, identified by a group of experts in a
consensus exercise, are shown in Table 4. For each COM-B component identified as relevant
to bringing about the desired change in the target behaviour, specific intervention functions
are likely to be effective in bringing about that change (see Table 4). For example to promote
cleaning hands using alcohol gel, psychological capability, social opportunity, reflective and
automatic motivation were identified as needing to change. Table 4 illustrates that all
intervention functions are potentially relevant in bringing about this desired change.
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Table 4. Matrix of links between COM-B and intervention functions
Intervention functions
COM-B
components Education Persuasion Incentiv-
isation
Coercion Training Restriction Environmental
restructuring
Modelling Enablement
Physical
capability
Psychological
capability
Physical
opportunity
Social
opportunity
Automatic
motivation
Reflective
motivation
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Understanding the context within which interventions are to be delivered is key to
their successful delivery. To guide this process, dimensions to consider in selecting and
shaping interventions have been identified. Termed the APEASE criteria, they are:
Affordability, Practicality, Effectiveness and cost-effectiveness, Acceptability, Side-
effects/safety, and Equity (Michie et al., 2014). Prior to implementing an intervention,
intervention designers can use the APEASE criteria to determine which intervention
functions, policy categories, behaviour change techniques and modes of delivery are most
appropriate for their context and therefore most likely to be implemented and have an impact.
Table 5. The APEASE criteria for designing and evaluating interventions
Criterion Description
Affordability Interventions often have an implicit or explicit budget. It does not matter
how effective, or even cost-effective it may be if it cannot be afforded.
An intervention is affordable if within an acceptable budget it can be
delivered to, or accessed by, all those for whom it would be relevant or
of benefit.
Practicability An intervention is practicable to the extent that it can be delivered as
designed through the means intended to the target population. For
example, an intervention may be effective when delivered by highly
selected and trained staff and extensive resources but in routine clinical
practice this may not be achievable.
Effectiveness and cost-
effectiveness
Effectiveness refers to the effect size of the intervention in relation to
the desired objectives in a real world context. It is distinct from efficacy
which refers to the effect size of the intervention when delivered under
optimal conditions in comparative evaluations. Cost-effectiveness refers
to the ratio of effect (in a way that has to be defined, and taking account
of differences in timescale between intervention delivery and
intervention effect) to cost. If two interventions are equally effective
then clearly the most cost-effective should be chosen. If one is more
effective but less cost-effective than another, other issues such as
affordability, come to the forefront of the decision making process.
Acceptability Acceptability refers to the extent to which an intervention is judged to
be appropriate by relevant stakeholders (public, professional and
political). Acceptability may differ for different stakeholders. For
example, the general public may favour an intervention that restricts
marketing of alcohol or tobacco but politicians considering legislation
on this may take a different view. Interventions that appear to limit
agency on the part of the target group are often only considered
acceptable for more serious problems (Nuffield Council on Bioethics,
2007).
Side-effects/safety An intervention may be effective and practicable, but have unwanted
side-effects or unintended consequences. These need to be considered
when deciding whether or not to proceed.
Equity An important consideration is the extent to which an intervention may
reduce or increase the disparities in standard of living, wellbeing or
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health between different sectors of society.
Continuing with the example of promoting cleaning hands using alcohol gel in UK hospital
staff, Table 6 illustrates application of the APEASE criteria to identify appropriate
intervention functions for the intervention. On the basis of this process, intervention functions
incentivisation and enablement were selected as most appropriate.
Table 6. Identifying appropriate intervention functions for an intervention to promote cleaning hands using
alcohol gel in UK hospital staff
Candidate intervention
functions
Does the intervention function meet the APEASE criteria (affordability,
practicability, effectiveness/cost-effectiveness, acceptability, side-
effects/safety, equity) in the context of cleaning hands using alcohol gel?
Education Not practicable as there is not enough time to take staff off wards to educate them
Persuasion Unlikely to be effective as most staff intend to clean their hands this intervention
function is unlikely to add value
Incentivisation Yes
Coercion Not acceptable to staff
Training Not practicable as there is not enough time to take staff off wards to train them
Restriction Not practicable as there are no options to restrict in this context
Environmental
restructuring
Not practicable to restructure the environment so senior doctors are seen more
frequently cleaning their hands to bring about change in social opportunity
Modelling Not practicable to deliver in this context
Enablement Yes
Selected intervention
functions:
Incentivisation and enablement.
Identify policy categories. Policies are decisions made by authorities that support the
delivery of intervention functions. The way in which intervention functions can be supported
by policy categories is illustrated in relation to the goal of reducing the rate at which GPs
prescribe antibiotics for sore throats. For the sake of argument, assume a COM-B analysis
suggests that increasing GPs’ capability to resist what they perceive as pressure from patients
to issue a prescription is an important target. This points to intervention function of education
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and training. This can be supported through policies such as issuing guidelines and through
communication and marketing, for example through articles in professional magazines or
through websites. Yet another approach might be to provide a service in which trained
facilitators run workshops to demonstrate what is required, develop ‘scripts’ and provide
opportunities for practice and feedback. Policy categories in the BCW, including labels,
definitions and examples, are provided in Table 7 and linkages between intervention
functions and policy categories are tabulated in Table 8.
Table 7. BCW policy categories
Policy Category Definition Example
Communication/
marketing
Using print, electronic, telephonic or
broadcast media
Conducting mass media campaigns
Guidelines Creating documents that recommend or
mandate practice. This includes all
changes to service provision
Producing and disseminating treatment protocols
Fiscal measures Using the tax system to reduce or increase
the financial cost
Increasing duty or increasing anti-smuggling
activities
Regulation Establishing rules or principles of
behaviour or practice
Establishing voluntary agreements on advertising
Legislation Making or changing laws Prohibiting sale or use
Environmental/social
planning
Designing and/or controlling the physical
or social environment
Using town planning
Service provision Delivering a service Establishing support services in workplaces,
communities etc.
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Table 8. Matrix of links between intervention functions and policy categories
Intervention functions
Education Persuasion Incentivisation Coercion Training Restriction Environ.
restructuring
Modelling Enablement
Policy
Categories
Communication/
marketing
Guidelines
Fiscal measures
Regulation
Legislation
Environ./ Social
planning
Service provision
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As with selecting intervention functions, designers will need to decide which of the
potential policy categories suggested in the matrix are most appropriate for their intervention.
Table 9 shows how the APEASE criteria have been applied to select the most appropriate
policy categories to deliver the intervention functions of incentivisation and enablement.
Table 9. Identifying appropriate policy categories for an intervention to promote cleaning hands using
alcohol gel in UK hospital staff
Intervention
function
COM-B component Potentially useful policy
categories
Does the policy category meet the
APEASE criteria (affordability,
practicability, effectiveness/cost-
effectiveness, acceptability, side-
effects/safety, equity) in the context
of cleaning hands using alcohol
gel?
Incentivisation
Reflective motivation
Automatic motivation
Communication/marketing Yes
Guidelines Unlikely to be effective (add value)
as guidelines around hand hygiene
already exist
Fiscal measures Not relevant in the hospital context
Regulation Possible in the long term but not
present
Legislation Not practicable in the hospital
context
Service provision Yes
Enablement
Psychological
capability
Social opportunity
Automatic motivation
Guidelines As above
Fiscal measures As above
Regulation As above
Legislation As above
Environmental/social planning Not practicable in this context
Service provision Yes
Policy category selected: Communication/marketing and service provision
Stage 3: Identify Content and Implementation Options
Having identified the intervention functions and policy categories, the next step is to
identify specific behaviour change techniques and mode/s of delivery are likely to be most
feasible in the local context (again, consideration of APEASE criteria relevant to inform this).
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Identify behaviour change techniques. A behaviour change technique is an active
component of an intervention designed to change behaviour. Several taxonomies of behaviour
change techniques relating to specific behavioural domains have been published (Abraham,
Good, Huedo-Medina, Warren, & Johnson, 2012; Abraham & Michie, 2008; Michie,
Ashford, et al., 2011; Michie, Hyder, Walia, & West, 2011; Michie et al., 2012) and most
recently a hierarchically-structured taxonomy consisting 93 distinctive, non-overlapping
behaviour change techniques clustered into 16 groupings has been published, the Behaviour
Change Techniques Taxonomy (BCTT) v1 (Michie et al., 2013).
Behaviour change techniques linking BCT may have several intervention functions
and any one intervention function may be delivered by a variety of behaviour change
techniques. The book The Behaviour Change Wheel A Guide to Designing Interventions
(Michie et al., 2014), provides guidance for linking behaviour change techniques to
intervention functions. In designing an intervention, a ‘long list’ of behaviour change
techniques can be drawn up. These can be narrowed down to the ones that are most likely to
be appropriate for the situation, using the APEASE criteria outlined above (Michie, Atkins, &
West, 2014). A prototype intervention strategy for the infection control example is shown in
Table 10. This strategy illustrates how the behaviour change techniques ‘feedback on
behaviour’, ‘non-specific reward’, ‘goal setting (behaviour)’ and ‘action planning’ could be
delivered through changes to service provision in order to enable and incentivise staff to
clean their hands using alcohol gel.
Table 10. Identifying appropriate behaviour change techniques for an intervention to promote cleaning hands
using alcohol gel in UK hospital staff
Intervention
functions
COM-B components
served by intervention
functions
Policy categories
through which BCTs
can be delivered
Intervention strategy
BCTs delivered in an extract from the
Feedback Intervention Trial
(Fuller et al. 2012)
Incentivisation Reflective motivation
Automatic motivation
Service provision The intervention was delivered by a
‘ward co-ordinator’ who observed hand
hygiene practices of staff individually
and in groups.
Following observation, staff received
feedback individually and in group
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Intervention
functions
COM-B components
served by intervention
functions
Policy categories
through which BCTs
can be delivered
Intervention strategy
BCTs delivered in an extract from the
Feedback Intervention Trial
(Fuller et al. 2012)
meetings on the percentage of times the
behaviour was appropriately
performed. (BCT - feedback on
behaviour). In cases of 100%
compliance with hand hygiene practice
staff received a certificate and feedback
at their annual appraisal (BCT non-
specific reward).
Where staff members were observed
not cleaning their hands, a goal was set
to clean their hands in identified high
risk situations (BCT – goal setting
behaviour) and an action plan formed
to support achieving the goal (BCT
action planning).
Enablement
Psychological capability
Social opportunity
Automatic motivation
Identify mode of delivery. The final step to intervention development requires
designers consider the most appropriate mode or modes for delivering the intervention given
the population group and setting. Again, the APEASE criteria are likely to be helpful here. In
our infection control example, the intervention was delivered face-to-face by a ward co-
ordinator both individually and in groups (Fuller et al., 2012).
Final Statements
To change clinical practice, sometimes we will have access to ‘off the shelf’ interventions
that have been found to work for similar problems in similar situations. But more often we
will need to design the intervention for our particular circumstances. Using COM-B and the
BCW to inform intervention development offers a systematic way of starting with making a
behavioural diagnosis of what needs to change and then linking that diagnosis to intervention
functions, policy categories and behaviour change techniques to bring about change. The
BCW is not a ‘magic bullet’ that can pre-determine what works in which contexts but
provides theoretically-based guidance and a structured method to facilitate the process of
development and the opportunity to learn from the results.
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Supplementary resource (1)

... Detailed program planning methods exist for traditional health promotion programs. Systematic methods such as Intervention Mapping and the Behaviour Change Wheel (BCW) emphasize using behavior change theories and techniques to link modifiable determinants of behavior to health-related behaviors and outcomes [10][11][12]. However, these approaches do not readily extend to the intricacies of game design. ...
... Thus, it is especially important to ensure that key scientific and game design principles are adequately integrated early in design. The BCW specifies commonly accepted BCTs and how to select them for program design [11,12]. We present a step-by-step program planning model adapting the BCW [11,12] to the development of Challenges for Healthy Aging: Leveraging Limits for Engaging Networked Game-Based Exercise (CHALLENGE). ...
... The BCW specifies commonly accepted BCTs and how to select them for program design [11,12]. We present a step-by-step program planning model adapting the BCW [11,12] to the development of Challenges for Healthy Aging: Leveraging Limits for Engaging Networked Game-Based Exercise (CHALLENGE). CHALLENGE is a social media game, delivered through Facebook and centered on increasing physical activity in insufficiently active older women. ...
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Full-text available
BACKGROUND Games for health are a promising approach to health promotion. Their success depends on achieving both experiential (game) and instrumental (health) objectives. There is little to guide game for health (G4H) designers in integrating the science of behavior change with the art of game design. OBJECTIVE The aim of this study is to extend the Behaviour Change Wheel program planning model to develop Challenges for Healthy Aging: Leveraging Limits for Engaging Networked Game-Based Exercise (CHALLENGE), a G4H centered on increasing physical activity in insufficiently active older women. METHODS We present and apply the G4H Mechanics, Experiences, and Change (MECHA) process, which supplements the Behaviour Change Wheel program planning model. The additional steps are centered on identifying target G4H player experiences and corresponding game mechanics to help game designers integrate design elements and G4H objectives into behavioral interventions. RESULTS We identified a target behavior of increasing moderate-intensity walking among insufficiently active older women and key psychosocial determinants of this behavior from self-determination theory (eg, autonomy). We used MECHA to map these constructs to intervention functions (eg, persuasion) and G4H target player experiences (eg, captivation). Next, we identified behavior change techniques (eg, framing or reframing) and specific game mechanics (eg, transforming) to help realize intervention functions and elicit targeted player experiences. CONCLUSIONS MECHA can help researchers map specific linkages between distal intervention objectives and more proximal game design mechanics in games for health. This can facilitate G4H program planning, evaluation, and clearer scientific communication.
... We selected BCW intervention functions following the affordability, practicability, effectiveness and cost-effectiveness, acceptability, side effects and safety, and equity (APEASE) criteria [57]. The criteria include whether the intervention is within an acceptable budget, whether it can be delivered as designed, whether it delivers desirable outcomes in practice, whether the benefit-cost ratio is favorable, whether relevant stakeholders consider it as appropriate, whether the risk-benefit ratio is favorable, and whether it narrows or widens disparities between different societal groups [57]. ...
... We selected BCW intervention functions following the affordability, practicability, effectiveness and cost-effectiveness, acceptability, side effects and safety, and equity (APEASE) criteria [57]. The criteria include whether the intervention is within an acceptable budget, whether it can be delivered as designed, whether it delivers desirable outcomes in practice, whether the benefit-cost ratio is favorable, whether relevant stakeholders consider it as appropriate, whether the risk-benefit ratio is favorable, and whether it narrows or widens disparities between different societal groups [57]. We selected 4 intervention functions: education (ie, "increasing knowledge or understanding"), persuasion (ie, "using communication to induce positive or negative feelings or stimulate action"), incentivization (ie, "creating an expectation of reward"), and enablement (ie, "increasing means/reducing barriers to increase capability [beyond education and training] or opportunity [beyond environmental restructuring]") from the BCW [57]. ...
... The criteria include whether the intervention is within an acceptable budget, whether it can be delivered as designed, whether it delivers desirable outcomes in practice, whether the benefit-cost ratio is favorable, whether relevant stakeholders consider it as appropriate, whether the risk-benefit ratio is favorable, and whether it narrows or widens disparities between different societal groups [57]. We selected 4 intervention functions: education (ie, "increasing knowledge or understanding"), persuasion (ie, "using communication to induce positive or negative feelings or stimulate action"), incentivization (ie, "creating an expectation of reward"), and enablement (ie, "increasing means/reducing barriers to increase capability [beyond education and training] or opportunity [beyond environmental restructuring]") from the BCW [57]. Furthermore, we picked 2 policy categories: communication and marketing, and service provision. ...
Article
Full-text available
Background: Cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM) are posing a huge burden on health care systems worldwide. Mobile apps can deliver behavior change interventions for chronic disease prevention on a large scale, but current evidence for their effectiveness is limited. Objective: This paper reported on the development and user testing of a mobile app that aims at increasing risk awareness and engaging users in behavior change. It would form part of an intervention for primary prevention of CVD and T2DM. Methods: The theoretical framework of the app design was based on the Behaviour Change Wheel, combined with the capability, opportunity, and motivation for behavior change system and the behavior change techniques from the Behavior Change Technique Taxonomy (version 1). In addition, evidence from scientific literature has guided the development process. The prototype was tested for user-friendliness via an iterative approach. We conducted semistructured interviews with individuals in the target populations, which included the System Usability Scale. We transcribed and analyzed the interviews using descriptive statistics for the System Usability Scale and thematic analysis to identify app features that improved utility and usability. Results: The target population was Australians aged ≥45 years. The app included 4 core modules (risk score, goal setting, health measures, and education). In these modules, users learned about their risk for CVD and T2DM; set goals for smoking, alcohol consumption, diet, and physical activity; and tracked them. In total, we included 12 behavior change techniques. We conducted 2 rounds of usability testing, each involving 5 participants. The average age of the participants was 58 (SD 8) years. Totally, 60% (6/10) of the participants owned iPhone Operating System phones, and 40% (4/10) of them owned Android phones. In the first round, we identified a technical issue that prevented 30% (3/10) of the participants from completing the registration process. Among the 70% (7/10) of participants who were able to complete the registration process, 71% (5/7) rated the app above average, based on the System Usability Scale. During the interviews, we identified some issues related to functionality, content, and language and clarity. We used the participants’ feedback to improve these aspects. Conclusions: We developed the app using behavior change theory and scientific evidence. The user testing allowed us to identify and remove technical errors and integrate additional functions into the app, which the participants had requested. Next, we will evaluate the feasibility of the revised version of the app developed through this design process and usability testing.
... The COM-B model and the BCW have been applied in several settings, from understanding behavior change by individuals, to groups, sub-populations, and populations, and within different organizations and systems [26]. For instance, Barker et al. propose a successful application of the COM-B model and the BCW to develop an intervention to promote regular, long-term use of hearing aids by adults with acquired hearing loss [13]. ...
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Full-text available
Implementing software security practices is a critical concern in modern software development. Industry practitioners, security tool providers, and researchers have provided standard security guidelines and sophisticated security development tools to ensure a secure software development pipeline. But despite these efforts, there continues to be an increase in the number of vulnerabilities that can be exploited by malicious hackers. There is thus an urgent need to understand why developers still introduce security vulnerabilities into their applications and to understand what can be done to motivate them to write more secure code. To understand and address this problem further, we propose DASP, a framework for diagnosing and driving the adoption of software security practices among developers. DASP was conceived by combining behavioral science theories to shape a cross-sectional interview study with 28 software practitioners. Our interviews lead to a framework that consists of a comprehensive set of 33 drivers grouped into 7 higher-level categories that represent what needs to happen or change so that the adoption of software security practices occurs. Using the DASP framework, organizations can design interventions suitable for developers' specific development contexts that will motivate them to write more secure code.
... The BCW can guide intervention design and it has been used extensively in the health sciences [49]. The BCW can also help 'diagnose' the reasons an intervention may not have been successful in changing behaviour [48,50,51]. Central to the use of the BCW is a comprehensive analysis and understanding of the target population and the target-group specific determinants of behaviours. ...
Article
We are dependent on our oceans for economic, health and social benefits; however, demands on our oceans are escalating, and the state of the oceans is deteriorating. Only 2% of countries are on track to achieve the desired outcomes for the sustainable development goal (SDG 14) for the oceans by 2030, and the changes needed to prevent further degradation, or limit the impact of existing degradation, are not being undertaken fast enough. This paper uses a socio-ecological lens to explore the nature of actors and behaviours for change at the local, community, state, national and international levels, and introduces the need for technology, information- and knowledge-sharing, and policy as interconnected mediators, that work both in concert, and independently, to address the ‘super wicked’ problem of ocean health and to promote resilience. We recommend the need to develop transformational teams and leaders, as well as transformative policies within a holistic and integrated system to ensure ocean health initiatives are greater than the sum of their parts and are actual, realistic, achievable and evidence-informed pathways to change. This article is part of the theme issue ‘Nurturing resilient marine ecosystems’.
... Scales guided by theory have the added benefit of an empirical foundation to link behavioural change techniques to address barriers based on an understanding of their psychological underpinnings [19]. Developing interventions is difficult, but choosing evidence based methods are preferred over the all too common ISLAGIATT principle, "It seemed like a good idea at the time" [20]. Interventions developed to improve access to care that systematically address the known psychological, social or environmental barrier factors are poised to have a higher probability of success [21]. ...
Article
Full-text available
Background Military health care providers often under access both physical and mental health care, yet research has predominantly focused on barriers to mental health care. This study explored a comprehensive set of barriers using hypothetical scenarios to quantify barrier impact on access to both mental and physical health care. Methods Canadian military health services personnel ( N = 1033) completed one of two electronic surveys (assessing either physical health or other mental health barriers) that captured participant’s demographics, health, endorsement of barriers, intent to seek care, and whether the respondent would access care in different health scenarios (pneumonia, back injury, depression and post-traumatic stress disorder). Logistic regression was used to calculate odds of not accessing care (versus accessing care) for each of the four health scenarios. Results All barrier factors independently predicted increased odds of not accessing care for all four scenarios. When entered into an adjusted model none of the barrier factors significantly predicted accessing care in the physical health scenarios. Staffing and workload resources and Treatment preferences (e.g., self-treat) were significant predictors of accessing care in the mental health scenarios. Weak general intentions to access care was the strongest predictors of not accessing care across all four scenarios. Conclusions The impact of barriers on hypothetical care-seeking behaviour differs depending on the context for which one is accessing care, with access to resources and preference to self-treat driving mental health care seeking. Intent appears to be the most impactful predictor of accessing care potentially mediating the effect of other barrier types on care seeking.
... Considering vaccine hesitancy is a behavior in a complex network of related behaviors, changing that one behavior is likely to influence the other components of the system and reconfigure the whole network. In this case, a systematic framework considering a wide range of options would aid the development and implementation of any intervention [52]. Furthermore, although behavior change models such as HBM and TPB have been used to inform the interventions and may have some effect, they may suit only one specific circumstance and have limited value. ...
Article
Full-text available
It is widely acknowledged that vaccine hesitancy is a multifaceted problem that cannot be addressed by a single strategy. Behavior change theories and social media tools may together help to guide the design of interventions aimed at improving vaccination uptake. This systematic review aims to identify the breadth and effectiveness of such theories and tools. The systematic review search was performed in PubMed, Scopus, ACM, Cochrane Library, ProQuest, and Web of Science databases for studies between January 2011 and January 2021 that applied social media tools to increase vaccine confidence or improve vaccination uptake. The literature search yielded a total of 3,065 publications. Twenty articles met the eligibility criteria, 12 of which were theory-based interventions. The result shows that the Health Belief Model was the most frequently deployed theory, and the most common social media tool was educational posts, followed by dialogue-based groups, interactive websites, and personal reminders. Theory-based interventions were generally more measurable and comparable and had more evidence to trigger the positive behavior change. Fifteen studies reported the effectiveness in knowledge gain, intention increase, or behavior change. Educational messages were proved to be effective in increasing knowledge but less helpful in triggering behavior change. Dialogue-based social media intervention performed well in improving people’s intention to vaccinate. Interventions informed by behavior change theory and delivered via social media platforms offer an important opportunity for addressing vaccine hesitancy. This review highlights the need to use a multitheory framework and tailoring social media interventions to the specific circumstances and needs of the target audience in future interventions. The results and insights gained from this review will be of assistance to future studies.
Chapter
The onset of COVID-19, coupled with substantially heightened financial pressures on households, has led to a renewed interest in the topic of financial literacy. While it is difficult to arrive at a single and precise definition of financial literacy, it has been popularly used to refer to a combination of financial knowledge, attitude, abilities, and behaviors. This chapter reviews the current literature on financial literacy, focusing specifically on how the concept of financial literacy has evolved over the decades. The advent of large-scale and fine-grained data on the efficacy and correlates of financial literacy training has now made it possible to develop data-informed and technology-enabled frameworks for financial literacy enhancement. We highlight some key learnings from implementing national-level financial education programs in Singapore. Early insights reveal the growing importance of financial confidence as a driver for behavioral outcomes among learners, as well as population-level heterogeneities involved in financial knowledge, confidence, and behavior. We draw on these insights to recommend to policymakers and educators how to design and derive value from similar financial literacy enhancement programs worldwide.KeywordsFinancial literacyFinancial knowledgeFinancial confidenceLearner analyticsPsychological profiling
Article
Background Older adult women are at risk for negative health outcomes that engaging in sustained physical activity can help prevent. However, promoting long-term maintenance of physical activity in this population has proven to be a challenge. Increasing autonomous motivations (ie, intrinsic, integrated, and identified regulations) for physical activity may facilitate enduring behavior change. Digitally delivered games for health that take a celebratory technology approach, that is, using technology to create new ways to experience valued behaviors and express valued beliefs, may be a useful way to target autonomous motivations for physical activity. Formative research with the target population is needed to design compelling intervention content. Objective The objective of this study is to investigate older adult women’s reactions to and thoughts about a photography-based, social media walking game targeting autonomous motivations for physical activity. Methods During an individual semistructured interview, a moderator solicited feedback from 20 older adult women (age range 65-74 years) as part of formative research to develop a social media game featuring weekly walking challenges. The challenges were designed to target autonomous motivations for physical activity. Interviews were audio-recorded and transcribed verbatim. Two reviewers conducted thematic content analysis on interview transcripts. Results We identified 3 overarching themes in qualitative data analysis. These reflected the playful experiences, value, and acceptability associated with the intervention challenges. Generally, participants understood what the challenges were asking them to do, proffered appropriate example responses, and indicated that the challenges would be enjoyable. Participants reported that the intervention content afforded many and varied playful experiences (eg, competition, discovery, exploration, expression, fellowship, humor, nurture, sensation). Further, participants indicated that the intervention increased their motivation for physical activity, occasioned meaningful shifts in perspective, increased their knowledge of various topics of interest, provided an opportunity to create valued connection with others, and provided health-related benefits. Participants suggested the intervention emphasize local history, nature, and cultural events. Conclusions The photography-based, social media walking game with relatively simple game mechanics was well received and judged to be apt to bring about a wide variety of emotive experiences. A clear, geographically specific identity emerged as a key driver of interest for intervention content. Taking a celebratory technology approach holds promise for targeting autonomous motivations for physical activity in older adult women.
Article
Full-text available
Despite a wealth of behavior change theories and techniques available, designers often struggle to apply theory in the design of behavior change technologies. We present the Behavior Change Design (BCD) cards, a design support tool that makes behavioral science theory accessible to interaction designers during design meetings. Grounded on two theoretical frameworks of behavior change, the BCD cards attempt to map 34 behavior change techniques to five stages of behavior change, thus assisting designers in selecting appropriate techniques for given behavioral objectives. We present the design of the BCD cards along with the results of two formative and one summative study that aimed at informing the design of the cards and assessing their impact on the design process.
Article
Background Games for health are a promising approach to health promotion. Their success depends on achieving both experiential (game) and instrumental (health) objectives. There is little to guide game for health (G4H) designers in integrating the science of behavior change with the art of game design. Objective The aim of this study is to extend the Behaviour Change Wheel program planning model to develop Challenges for Healthy Aging: Leveraging Limits for Engaging Networked Game-Based Exercise (CHALLENGE), a G4H centered on increasing physical activity in insufficiently active older women. Methods We present and apply the G4H Mechanics, Experiences, and Change (MECHA) process, which supplements the Behaviour Change Wheel program planning model. The additional steps are centered on identifying target G4H player experiences and corresponding game mechanics to help game designers integrate design elements and G4H objectives into behavioral interventions. Results We identified a target behavior of increasing moderate-intensity walking among insufficiently active older women and key psychosocial determinants of this behavior from self-determination theory (eg, autonomy). We used MECHA to map these constructs to intervention functions (eg, persuasion) and G4H target player experiences (eg, captivation). Next, we identified behavior change techniques (eg, framing or reframing) and specific game mechanics (eg, transforming) to help realize intervention functions and elicit targeted player experiences. Conclusions MECHA can help researchers map specific linkages between distal intervention objectives and more proximal game design mechanics in games for health. This can facilitate G4H program planning, evaluation, and clearer scientific communication.
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