Week 3 Combinations of Techniques That Effectively Change Health Behavior Discussion Brief Therapy and Solution-Focused Therapy: last names beginning with

Week 3 Combinations of Techniques That Effectively Change Health Behavior Discussion Brief Therapy and Solution-Focused Therapy: last names beginning with S through U

Begin by reviewing the case of Sammy listed below.

Sammy is a 33 years old, Hispanic female, who resides in southern California with her sister Glenda. Approximately six months ago, her doctor diagnosed her with Type 2 diabetes and hypertension. She was instructed to implement a new meal plan, exercise, and take prescribed medication two times a day. Unfortunately, Sammy had an adverse reaction to her medication and was not successful in implementing a consistent exercise regimen, due to her rigorous work schedule. Disappointed by this set-back, Sammy begins to isolate herself from loved ones and stays in bed all day for two weeks. Concerned for her physical health and emotional well-being, her sister called a local wellness center to set-up an appointment. When Sammy arrives at the center, she notices a list of health counselors who use different counseling approaches. Sammie is interested in knowing more, and asks you to explain your counseling approach.

Provide Sammy with a general overview of your assigned counseling approach, supporting your response with information from Meier and Davis (2018). Next, explain how this approach could help Sammy meet her targeted behavioral goals. Evaluate the cultural issues within this case, and describe how your counseling approach takes into account cultural factors. Post your response by Day 3 and respond to two of your classmates by Day 7. Include one of the course texts and one additional scholarly resource in your response. Use APA formant and organize your post using subheadings.

Guided Response: Respond to at least two of your peers who were assigned a different counseling approach than you, and evaluate the efficacy of the approach. Explain how a combination of techniques can effectively change the client’s health by referencing the Dusseldorp, van Genugten, van Buuren, Verheijden, and van Empelen (2014) article. Health Psychology
2014, Vol. 33, No. 12, 1530 –1540
© 2013 American Psychological Association
0278-6133/14/$12.00 http://dx.doi.org/10.1037/hea0000018
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Combinations of Techniques That Effectively Change Health Behavior:
Evidence From Meta-CART Analysis
Elise Dusseldorp
Lenneke van Genugten
Netherlands Organization for Applied Scientific Research
(TNO), Leiden, the Netherlands, and Katholieke Universiteit
Leuven
Netherlands Organization for Applied Scientific Research
(TNO), Leiden, the Netherlands, and University Medical Centre
Rotterdam
Stef van Buuren
Marieke W. Verheijden and Pepijn van Empelen
Netherlands Organization for Applied Scientific Research
(TNO), Leiden, the Netherlands, and Utrecht University
Netherlands Organization for Applied Scientific Research
(TNO), Leiden, the Netherlands
Objective: Many health-promoting interventions combine multiple behavior change techniques (BCTs)
to maximize effectiveness. Although, in theory, BCTs can amplify each other, the available metaanalyses have not been able to identify specific combinations of techniques that provide synergistic
effects. This study overcomes some of the shortcomings in the current methodology by applying
classification and regression trees (CART) to meta-analytic data in a special way, referred to as
Meta-CART. The aim was to identify particular combinations of BCTs that explain intervention success.
Method: A reanalysis of data from Michie, Abraham, Whittington, McAteer, and Gupta (2009) was
performed. These data included effect sizes from 122 interventions targeted at physical activity and
healthy eating, and the coding of the interventions into 26 BCTs. A CART analysis was performed using
the BCTs as predictors and treatment success (i.e., effect size) as outcome. A subgroup meta-analysis
using a mixed effects model was performed to compare the treatment effect in the subgroups found by
CART. Results: Meta-CART identified the following most effective combinations: Provide information
about behavior– health link with Prompt intention formation (mean effect size g៮ ⫽ 0.46), and Provide
information about behavior– health link with Provide information on consequences and Use of follow-up
prompts (g៮ ⫽ 0.44). Least effective interventions were those using Provide feedback on performance
without using Provide instruction (g៮ ⫽ 0.05). Conclusions: Specific combinations of BCTs increase the
likelihood of achieving change in health behavior, whereas other combinations decrease this likelihood.
Meta-CART successfully identified these combinations and thus provides a viable methodology in the
context of meta-analysis.
Keywords: intervention effectiveness, behavior change techniques, synergistic effects, classification and
regression trees, subgroup, meta-analysis
Interventions to change health-related behaviors often include
multiple behavior change techniques (BCTs) that are assumed to
interact or have a cumulative effect, with the aim of maximizing
the effectiveness of an intervention (Craig et al., 2008; Malotte et
al., 2000). Thus far, heterogeneity in the effectiveness of healthrelated interventions is observed, and it is expected that differences
in the techniques used by interventions may account for this
heterogeneity. BCTs, such as Prompt intention formation and
Provide feedback on performance, can be considered as the atomic
parts of an intervention. Interventions may differ greatly in number
and type of BCTs. A good theoretical understanding is needed
concerning when and how interventions cause changes in health-
This article was published Online First November 25, 2013.
Elise Dusseldorp, Expertise Group Life Style, Netherlands Organization
for Applied Scientific Research (TNO), Leiden, the Netherlands, and
Department of Psychology, Katholieke Universiteit Leuven, Leuven, Belgium; Lenneke van Genugten, Expertise Group Life Style, Netherlands
Organization for Applied Scientific Research (TNO), and Department of
Public Health, Erasmus, University Medical Centre Rotterdam, the Netherlands; Stef van Buuren, Expertise Group Life Style, Netherlands Organization for Applied Scientific Research (TNO), and Department of Methodology and Statistics, Faculty of Social and Behavioral Sciences, Utrecht
University, Utrecht, the Netherlands; Marieke W. Verheijden and Pepijn
van Empelen, Expertise Group Life Style, Netherlands Organization for
Applied Scientific Research (TNO).
The research reported in this paper was supported by a grant from
“Platform Sport, Physical Activity and Education” [Platform Sport, Bewegen en Onderwijs], project Effectiveness of Interventions, the Netherlands,
and supported by the Fund for Scientific Research – Flanders (project
G.0546.09), Belgium. The authors gratefully acknowledge Susan Michie,
Charles Abraham, Craig Whittington, and colleagues for making available
their meta-analytic data for reanalysis, and Theo Paulussen, Paula van
Dommelen, and two anonymous reviewers for helpful comments on a
previous version of this article.
Correspondence concerning this article should be addressed to Elise
Dusseldorp, TNO: Expertise Group Life Style, P. O. Box 2215, 2301 CE
Leiden, the Netherlands. E-mail: elise.dusseldorp@tno.nl
1530
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
EFFECTIVE COMBINATIONS OF BEHAVIOR CHANGE TECHNIQUES
related behavior (Abraham & Michie, 2008; Bartholomew, Parcel,
Kok, Gottlieb, & Fernández, 2011; Craig et al., 2008). This knowledge is essential for understanding how to design complex interventions to induce change. In addition, there is a growing need for
an evidence-based instrument that can be used to evaluate and
qualify the potential of existing interventions (e.g., Brug et al.,
2010; Craig et al., 2008).
Various meta-analyses have shown that interventions targeted
on health behavior change may be effective (Kroeze, Werkman, &
Brug, 2006; Shahab & McEwen, 2009). These studies have difficulties in identifying which particular BCTs are responsible for
heterogeneity in effectiveness of interventions. An exception is the
study by Albarracin et al. (2005), which made an attempt to
identify theoretically derived BCTs within HIV prevention programs and showed that some technique types were more likely to
effectively change behavior than others, increasing our understanding of why variation in intervention effectiveness exists. In
general, however, comparisons between BCTs in intervention effectiveness studies and meta-analyses have been hampered by the
lack of a systematic framework for identifying BCTs within interventions.
Recent developments of taxonomies of BCTs provide frameworks that can be used to classify interventions in a systematic
way. As such, they provide the possibility to systematically evaluate theory-based BCTs within complex interventions (Abraham
& Michie, 2008). A BCT taxonomy clarifies differences and
similarities in content of interventions targeting similar behaviors
in similar settings. It provides a detailed definition of each BCT,
including essential elements. In the taxonomy of Abraham and
Michie (2008), for example, Prompt intention formation is defined
as “encouraging the person to decide to act or set a general goal”
(see Table 1 for an overview of the taxonomy).
Some meta-analyses that have used a BCT taxonomy have
succeeded in identifying BCTs that influence the effectiveness of
interventions. Webb, Joseph, Yardley, and Michie (2010) examined the effectiveness of Internet-based interventions using a taxonomy adapted from Hardeman, Griffin, Johnston, Kinmonth, and
Wareham (2000). They found that the two BCTs that were associated with the greatest changes in behavior were Stress management and General communication skills training. Moreover, they
found that intervention effectiveness was larger when more BCTs
were included. Michie, Abraham, Whittington, McAteer, and
Gupta (2009) examined the effectiveness of physical activity (PA)
and healthy eating (HE) interventions using the taxonomy of 26
BCTs from Abraham and Michie (2008). They showed that interventions were most likely to be effective when Self-monitoring
was used as a technique, or when Self-monitoring plus an additional self-regulation technique were used. Using the same taxonomy, Dombrowski et al. (2012) identified several BCTs (including
Self-monitoring) with greater probability of intervention success
on weight and kilocalorie consumption. In addition, Dombrowski
et al. (2012) identified several BCTs that hampered intervention
success, including Provide general information and Provide information on consequences. De Bruin, Viechtbauer, Hospers,
Schaalma, and Kok (2009) also used the same taxonomy to code
standard care in control groups of studies evaluating the effectiveness of interventions for HIV. They showed that the control groups
differed greatly in number and type of BCTs. Finally, refined
taxonomies have been used to successfully identify BCTs that
1531
increase effectiveness for reducing excessive alcohol consumption
(e.g., Michie et al., 2012).
When interventions use multiple BCTs, several situations may
occur: (a) the effects of the BCTs are additive, (b) the effects of the
BCTs cancel out, or (c) the effects of the BCTs amplify. This latter
effect is the focus of our study. The amplification of effects implies
that a combination of BCTs has a synergistic effect, which is also
called an interaction effect. A synergistic effect occurs if the
combination of two or more BCTs has a more potent effect than
would be expected by their additive effect. For instance, from
literature on fear appeals, it has been suggested that Fear arousal
as a strategy can only be effective when also Skill information is
provided (Rogers, 1995; Ruiter, Abraham, & Kok, 2001). Thus,
Fear arousal and Skill information do not enhance success when
applied separately, but their combined use can be quite effective
(Peters, Ruiter, & Kok, 2013). Similarly, Implementation intentions have been suggested to be effective only when people are
sufficiently motivated to engage in specific behavior (Sheeran,
Webb, & Gollwitzer, 2005). Thus, Implementation intentions have
to be combined with a motivation-enhancing technique to achieve
success. Generally, it is expected that BCTs have synergistic
effects (Malotte et al., 2000; Michie et al., 2009; Rothman, Baldwin, & Hertel, 2004), and it is considered to be important to gain
an understanding on which combinations of BCTs matter (Dixon
& Johnston, 2010; Michie et al., 2009). In addition, insight into the
combination of techniques is essential with regard to the development of interventions. The synergistic effects of BCTs, however,
generally cannot be examined by means of meta-analysis due to
the lack of power in meta-regression to identify interaction effects
(e.g., Michie et al., 2009). As such, only univariate or, to a lesser
extent, additive effects have been examined.
In the present study, the use of classification and regression trees
(CART; Breiman, Friedman, Olshen, & Stone, 1984) has been
proposed to identify synergistic effects. CART is especially suitable for data with many predictor variables that could interact.
CART has been used in the field of health psychology and medical
sciences, for example, to examine which combination of factors
can predict cancer (van Dijk, Steyerberg, Stenning, & Habbema,
2004), or to stratify patients on disease severity (Trujillano, Badia,
Serviá, March, & Rodriguez-Pozo, 2009). As far as known, CART
has never been used in the field of meta-analysis, except for a
small study by Dusseldorp (2001). The aim of the present study
was to gain a further understanding of synergistic effects of BCTs
by applying CART in a special way to meta-analytic data. This
novel approach will be referred to as Meta-CART. The main
objective was to examine which combinations of BCTs explain
intervention success.
Method
Data from the 101 studies that were included in the metaanalysis of Michie et al. (2009) were used. The studies were
published between 1990 and 2008 in peer-reviewed journals written in English. The study effect size data and the scores on the
taxonomy of 26 BCTs were obtained from the authors of the
meta-analysis.
DUSSELDORP ET AL.
1532
Table 1
Overview of Behavior Change Techniques Grouped Into Three Categories: Motivational Enhancing, Planning and Preparation, and
Goal Striving and Persistence
Technique
Category 1: Motivation enhancing
1. Provide information about behavior–health link
2. Provide information on consequences
3. Provide information about others’ approvala
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
4. Prompt intention formation
25. Motivational interviewing
Category 2: Planning and preparation
5. Prompt barrier identification
7. Set graded tasks
8. Provide instruction
9. Model or demonstrate the behavior
10. Prompt specific goal setting
16. Agree on behavior contract
19. Provide opportunities for social comparison
20. Plan social support or social change
21. Prompt identification as a role modela
26. Time management
Category 3: Goal striving and persistence
6. Provide general encouragement
13. Provide feedback on performance
15. Teach to use prompts or cues
17.
11.
12.
14.
Prompt practice
Prompt review of behavioral goals
Prompt self-monitoring of behavior
Provide contingent rewards
18. Use follow-up prompts
22. Prompt self-talka
23. Relapse prevention (relapse prevention therapy)
24. Stress management (stress theories)a
Definition
#
General information about behavior risk, for example, susceptibility to poor health
outcomes or mortality risk in relation to the behavior
Information about the benefits and costs of action or inaction, focusing on what
will happen if the person does or does not perform the behavior
Information about what others think about the person’s behavior and whether
others will approve or disapprove of any proposed behavior change
Encouraging the person to decide to act or set a general goal, for example, to
make a behavior resolution, such as “I will take more exercise next week”
Prompting the person to provide self-motivating statements and evaluations of
their own behavior to minimize resistance to change
37
Identify barriers to performing the behavior and plan ways of overcoming them
Set easy tasks, and increase difficulty until target behavior is performed
Telling the person how to perform a behavior and/or preparatory behaviors
An expert shows the person how to correctly perform a behavior, for example, in
class or on video
Involves detailed planning of what the person will do, including a definition of
the behavior specifying frequency, intensity, or duration, and specification of at
least one context, that is, where, when, how, or with whom
Agreement (e.g., signing) of a contract specifying behavior to be performed so
that there is a written record of the person’s resolution witnessed by another
person
Facilitate observation of nonexpert others’ performance, for example, in a group,
class, or using video or case study
Prompting consideration of how others could change their behavior to offer the
person help or (instrumental) social support, including “buddy” systems and/or
providing social support
Indicating how the person may be an example to others and influence their
behavior or provide an opportunity for the person to set a good example
Helping the person make time for the behavior (e.g., to fit it into a daily
schedule)
45
17
72
11
Praising or rewarding the person for effort or performance without this being
contingent on specified behaviors or standards of performance
Providing data about recorded behavior or evaluating performance in relation to a
set standard or others’ performance, i.e., the person received feedback on their
behavior
Teach the person to identify environmental cues that can be used to remind them
to perform a behavior, including times of day or elements of contexts
Prompt the person to rehearse and repeat the behavior or preparatory behaviors
Review and/or reconsideration of previously set goals or intentions
The person is asked to keep a record of specified behavior(s) (e.g., in a diary)
Praise, encouragement, or material rewards that are explicitly linked to the
achievement of specified behaviors
Contacting the person again after the main part of the intervention is complete
Encourage use of self-instruction and self-encouragement (aloud or silently) to
support action
Following initial change, help identify situations likely to result in readopting risk
behaviors or failure to maintain new behaviors, and help the person plan to
avoid or manage these situations
May involve a variety of specific techniques (e.g., progressive relaxation) that do
not target the behavior but seek to reduce anxiety and stress
36
64
0
74
17
27
12
20
34
2
7
61
20
11
19
46
30
34
4
23
4
Note. Techniques are numbered according to the taxonomy of Abraham and Michie (2008). The last column displays the number (#) of interventions that
included a technique.
a
Techniques 3, 21, 22, and 24 were not included in the analyses because these techniques were observed in fewer than five studies.
Characteristics of Interventions Used in Included Studies
The included studies reported on interventions targeted at adults
(18 years and older) to increase their levels of PA or HE. In
addition, the studies used an experimental or quasi-experimental
design, and applied cognitive or BCTs. Examples of interventions
were PA interventions (e.g., Harland et al., 1999), nutrition education (Oenema, Tan, & Brug, 2005), and interactive computertailored interventions for PA and HE (Vandelanotte, De Bourdeaudhuij, Sallis, Spittaels, & Brug, 2005). The target population in
the studies varied from the general population to patients at spe-
EFFECTIVE COMBINATIONS OF BEHAVIOR CHANGE TECHNIQUES
cific risk (e.g., at risk of cardiovascular disease). In order to
evaluate the intervention effect, all studies compared an intervention condition with a control or standard-care condition. The 101
studies reported on the effects of a total of 122 interventions, of
which 69 were targeted at PA and 53 were targeted at HE. In this
study, the interventions have been taken as the analytic level.
Following Michie et al. (2009), the PA and HE interventions were
considered together, because Michie et al. showed that PA and HE
interventions had similar mean effect sizes (i.e., 0.32 and 0.31,
respectively).
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Outcome Measures
All studies used objective or validated self-reported outcome
measures (Michie et al., 2009). In cases in which multiple outcome
measures were reported for one evaluation, the following decisions
were made: (a) for PA evaluations, the first of the following
sequence was selected: exercise level, energy expenditure, percent
active, body mass index; and (b) for HE evaluations, the first of the
following sequence was selected: diet score, food intake, fat intake, fruit and vegetables/fiber, fruit or vegetables/fiber. Study
effect sizes were computed as the standardized mean difference,
with a correction for small sample size, Hedges’s g (Hedges,
1981). For the purpose of this study, the d…
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