11
British Journal of Health Psychology (2016), 21, 11–30
© 2015 The Authors. British Journal of Health Psychology published by
John Wiley & Sons Ltd on behalf of the British Psychological Society
www.wileyonlinelibrary.com
Is the intention–behaviour gap greater amongst
the more deprived? A meta-analysis of five studies
on physical activity, diet, and medication
adherence in smoking cessation
Milica Vasiljevic1, Yin-Lam Ng1, Simon J. Griffin1,2, Stephen Sutton1,3
and Theresa M. Marteau1*
1
Behaviour and Health Research Unit, University of Cambridge, UK
Primary Care Unit, Department of Public Health and Primary Care, University of
Cambridge, UK
3
Behavioural Science Group, University of Cambridge, UK
2
Objectives. Unhealthy behaviour is more common amongst the deprived, thereby
contributing to health inequalities. The evidence that the gap between intention and
behaviour is greater amongst the more deprived is limited and inconsistent. We tested
this hypothesis using objective and self-report measures of three behaviours, both
individual- and area-level indices of socio-economic status, and pooling data from five
studies.
Design. Secondary data analysis.
Methods. Multiple linear regressions and meta-analyses of data on physical activity, diet,
and medication adherence in smoking cessation from 2,511 participants.
Results. Across five studies, we found no evidence for an interaction between
deprivation and intention in predicting objective or self-report measures of behaviour.
Using objectively measured behaviour and area-level deprivation, meta-analyses
suggested that the gap between self-efficacy and behaviour was greater amongst the
more deprived (B = .17 [95% CI = 0.02, 0.31]).
Conclusions. We find no compelling evidence to support the hypothesis that the
intention–behaviour gap is greater amongst the more deprived.
Statement of contribution
What is already known on this subject?
Unhealthy behaviour is more common in those who are more deprived.
This may reflect a larger gap between intentions and behaviour amongst the more
deprived.
The limited evidence to date testing this hypothesis is mixed.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use,
distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
*Correspondence should be addressed to Theresa M. Marteau, Behaviour and Health Research Unit, Addenbrooke’s Hospital,
University of Cambridge, Forvie Site, Cambridge CB2 0SR, UK (email: tm388@cam.ac.uk).
DOI:10.1111/bjhp.12152
12
Milica Vasiljevic et al.
What does this study add?
In the most robust study to date, combining results from five trials, we found no evidence for this
explanation.
The gap between intentions and behaviour did not vary with deprivation for the following: diet,
physical activity, or medication adherence in smoking cessation.
We did, however, find a larger gap between perceived control over behaviour (self-efficacy) and
behaviour in those more deprived.
These findings add to existing evidence to suggest that higher rates of unhealthier behaviour in more
deprived groups may be reduced by the following:
○ Strengthening behavioural control mechanisms (such as executive function and non-conscious
processes) or
○ Behaviour change interventions that bypass behavioural control mechanisms.
Morbidity and mortality are socially patterned: Those who are least deprived, on average,
live longer and in better health (Marmot et al., 2010). Whilst life expectancy is increasing,
this increase has been greatest amongst the least deprived. One of the explanations for the
social patterning of health outcomes is the social patterning of health behaviours, in
particular smoking, alcohol consumption, poor diet, and physical inactivity (Laaksonen
et al., 2008; Lantz et al., 1998; Lynch, Kaplan, & Salonen, 1997; Martikainen, Brunner, &
Marmot, 2003; Stringhini et al., 2010; van Oort, van Lenthe, & Mackenbach, 2005). Given
the long-standing interest in the social patterning of health outcomes, the lack of data to
inform strategies for reducing health inequalities, particularly through changing healthrelated behaviour, is noteworthy.
In addition, there has been a general lack of progress in reducing health inequalities.
This is despite a clear policy focus on reducing health inequalities (Department of
Health, 1999; Judge, Platt, Costongs, & Jurczak, 2006; Marmot et al., 2010). Thus, there
is a need for more innovative research including a focus on the behavioural
contribution to health inequalities and the development of interventions that target
these directly.
Targeting behaviour to reduce health inequalities has been largely eschewed in the
mainstream health inequalities literature on the grounds that the social patterning of
behaviour reflects structural inequalities and social determinants and so should be
changed through the latter routes (e.g., Marmot, 2005). Yet, such a complex problem as
health inequalities will require multiple solutions, with no one approach necessarily
precluding any others.
Social patterning of the intention–behaviour gap
Unlike sociological and epidemiological studies, psychological research has for the most
part concentrated on health cognitions associated with people’s health behaviours, whilst
largely leaving structural inequalities out of the equation. Two aspects of health
cognitions most studied by psychologists are intention and self-efficacy as exemplified in
several seminal theories: Social cognitive theory (Bandura, 1998), protection motivation
(Maddux & Rogers, 1983), and the theory of planned behaviour (Ajzen, 1991). Intentions
have been defined as conscious decisions and motivations in the enactment of healthrelated behaviours (cf. Ajzen, 1991), and self-efficacy has been defined as perceived
confidence in the ability to perform health-related behaviours (cf. Bandura, 1998).
Importantly, many people fail to enact their intentions, thereby producing an intention–
behaviour gap (Orbell & Sheeran, 1998; Webb & Sheeran, 2006).
Moderating effect of SES
13
We are unaware of any reviews on the social patterning of the intention–behaviour
gap, or the self-efficacy–behaviour gap. Studies assessing the social patterning of
intention to consume a healthier diet find that children and adults from higher socioeconomic groups have greater intentions to eat more healthily which was associated
with consuming a healthier diet (Bere, van Lenthe, Klepp, & Brug, 2008; Leganger &
Kraft, 2003; Sandvik, Gjestad, Samdal, Brug, & Klepp, 2009). Similar findings were
reported for the social patterning of self-efficacy and diet (Ball et al., 2009). However, a
study by Godin, Amireault, et al. (2010) did not find any evidence of social patterning of
the intention–behaviour gap regarding fruit and vegetable consumption. Studies
investigating the social patterning of intention and self-efficacy in relation to physical
activity also report that participants from higher socio-economic groups report greater
intentions and self-efficacy to engage in physical activity, which were associated with
more physical activity compared to their counterparts from lower socio-economic
groups (Ball et al., 2007; Cerin & Leslie, 2008; De Cocker et al., 2012; Kamphuis et al.,
2008, 2009; Murray, Rodgers, & Fraser, 2012; Pan et al., 2009). However, the findings on
physical activity have also been inconsistent. For example, Godin, Sheeran, et al. (2010)
found that only education moderated the intention–physical activity relationship, with
no such effect being found for other socio-economic status (SES) indices including
income and social deprivation. A more recent investigation by Sch€
uz et al. (2012) found
no evidence of SES (expressed as district-level GDP) moderating the relationship
between intention and physical activity. To our knowledge, only one study has
examined the possible social patterning of self-efficacy on multiple behaviours: Diet,
physical activity, smoking, and alcohol, reporting that self-efficacy explains part of the
association between socio-economic status and the four types of behaviours (Grembowski et al., 1993).
Taken together, these studies provide an inconsistent pattern of results with, for
example, some finding associations with social patterning only amongst women (Pan
et al., 2009), and some only for leisure-time activity (Ball et al., 2007). The self-report
nature of the measures used to measure behaviour is another weakness of all these studies,
not least because social desirability bias may be socially patterned. Furthermore, only a
handful of these papers actually carried out a formal analysis of the moderating effect of
SES on the intention–behaviour and self-efficacy–behaviour gap (Godin, Amireault, et al.,
2010; Godin, Sheeran, et al., 2010; Pan et al., 2009; Sandvik et al., 2009; Sch€
uz et al.,
2012) whilst using widely heterogeneous indices of SES thus precluding any possibility of
systematic synthesis of these effects.
A recent analysis of data sets concerning three behaviours – smoking in children,
breastfeeding in first-time mothers, and physical activity in a workforce – reported that the
intention–behaviour gap was narrower amongst those who were least socially deprived
(Conner et al., 2013). That is, those who were least socially deprived were more likely to
act in line with their intentions, compared to those who were more deprived.
Surprisingly, the studies found no evidence for such interaction between deprivation
and self-efficacy. This finding conflicts with the evidence reviewed above showing that
self-efficacy, a correlate of self-control and more broadly executive function, is related to a
range of health behaviours as well as being socially patterned. It should be noted,
however, that all three sets of behaviours were assessed by self-report, and assessment of
SES was by area of residence in one of the studies, occupation in another, and receipt
of free school lunches in the third study. A more robust test using objective measures of
behaviour and consistent measures of SES is needed to elucidate the nature of the
association between deprivation and the intention–behaviour gap.
14
Milica Vasiljevic et al.
In summary, uncertainty remains about the extent to which the intention–behaviour
gap is moderated by socio-economic status. Significant moderation by socio-economic
status could inform the design and targeting of interventions to reduce the inequality
arising from the intention–behaviour gap, and caution against interventions that increase
a socially moderated intention–behaviour gap.
The present research
This study comprises a series of analyses using existing data sets with robust measures to
examine the extent to which SES modifies the relationship between intention and selfefficacy for different health behaviours.
Study aims:
1. To estimate the extent to which the intention–behaviour relationship is moderated
by SES in each of three sets of behaviour: Physical activity, diet, and medication
adherence in smoking cessation.
2. To test the extent to which any associations vary with (i) how the target behaviour is
measured (self-report vs. objectively measured) and (ii) the index of socio-economic
status that is used (individual vs. area level).
3. To consider possible explanations for any observed attenuation of the intention–
behaviour relationship in low-SES groups.
Methods
We analysed five data sets, each of which included the following variables:
1. Socio-economic status, measured using individual- and area-level indices.
2. Intention to perform a behaviour.
3. Self-efficacy to perform a behaviour.
4. Behaviour, measured using objective and/or self-report measures.
The data sets available to us included randomized controlled trials of individual-level
interventions aimed at changing one or more of three sets of behaviour (physical activity,
diet, and medication adherence in smoking cessation). All studies were conducted in
England between 2001 and 2012 and included data on 2,511 participants. The main
results or protocol papers of these five data sets are published (Godino et al., 2012; Griffin
et al., 2011; Kinmonth et al., 2008; Marteau, Aveyard, et al., 2012; Watkinson, van
Sluijs, Sutton, Marteau, & Griffin, 2010). The analyses reported in this paper are novel and
have not been reported elsewhere.
Overview of included data sets and measures
Socio-economic status was expressed in terms of individual- and area-level measures. Both
measures were the same across all five studies. Age of leaving full-time education served as
a measure of individual-level SES (see Chappell, Ota, Berryman, Elo, & Preston, 1996;
Liberatos, Link, & Kelsey, 1988). This ordinal measure was coded into a binary categorical
variable: 1 = full-time education finished at or below 18 years of age; 2 = full-time
education finished after 18 years of age, following Conner et al. (2013).
Postcode information was used to obtain an Index of Multiple Deprivation for
each participant, serving as the measure of area-level SES. Following Conner et al. (2013),
Moderating effect of SES
15
this ordinal variable was dichotomized as follows: 1 = low SES (high deprivation);
2 = high SES (low deprivation). Below follow the summaries of the individual studies and
measures used.
Medication (nicotine replacement therapy) adherence in smoking cessation – Marteau
2012
Participants comprised 633 smokers wanting to quit (54% women; Mage = 47.2 years)
(see Marteau, Aveyard, et al., 2012). They were recruited from general practices and were
randomized to receive information that their nicotine replacement therapy (NRT) dose
was determined according to their genotype or alternatively their phenotype.
Measures
Intention to adhere to NRT use was measured by two items with good internal reliability
(r = .69; a = .81) that were averaged into a single index: ‘Do you intend to use all your
NRT every day in the first 4 weeks of your quit attempt?’ (1 = definitely do not,
7 = definitely do); ‘How likely is it that you will use all your NRT every day in the first
4 weeks of your quit attempt?’ (1 = not at all likely, 7 = extremely likely).
Self-efficacy to use prescribed NRT was measured by two items that were averaged
together (r = .57; a = .72): ‘I am confident I can use all my NRT every day in the first
4 weeks of my quit attempt’ (1 = strongly disagree, 7 = strongly agree); ‘How much
control do you have over using all NRT every day in the first 4 weeks of quit attempt?’
(1 = no control, 7 = complete control).
Objective measure of NRT adherence was obtained by a research nurse counting the
pills consumed by each participants over 28 days following the smoking quit date
(expressed as a continuous variable ranging from 0% = no NRT consumed, 100% = all
NRT consumed).
Physical activity: Data set 1 and diet – Griffin 2011
Participants in this trial comprised 478 patients (38% women; Mage = 59.7 years) recently
diagnosed with type 2 diabetes. They were randomized to receive either intensive medical
treatment alone or intensive treatment coupled with facilitator-led, theory-based
individual behaviour change intervention aimed at teaching patients key skills to facilitate
change and maintenance of key behaviours (see Griffin et al., 2011).
Measures
Physical activity. Intention to be more physically active was measured by two items
that were averaged together into a single index (r = .80; a = .89): ‘I intend to be more
physically active in the next 12 months’; ‘It is likely that I will be more physically active in
the next 12 months’ (both anchored on a 5-point scale ranging from 1 = strongly
disagree, 5 = strongly agree).
An index of self-efficacy to engage in physical activity was made by averaging two
items together (r = .65; a = .78): ‘I am confident that I could be more physically active in
the next 12 months, if I wanted to’; ‘It would be difficult for me to be more physically
active in the next 12 months even if I wanted to’ (reverse scored) (both anchored on a
5-point scale ranging from 1 = strongly disagree, 5 = strongly agree).
16
Milica Vasiljevic et al.
Objective measure of physical activity was obtained by an ActiHeart device
(CamNtech, Cambridge, UK; see Brage, Brage, Franks, Ekelund, & Wareham, 2005)
recording participants’ activity via individually calibrated heart rate and movement
sensing. Physical activity energy expenditure was captured at 12 months post-baseline
and expressed as a continuous variable summarized in kJ/kg/day. Self-report measure of
physical activity consisted of a validated questionnaire EPAQ2 (Wareham & Rennie,
1998) measured at 12 months post-baseline (expressed as a continuous variable denoting
weekly total activity energy expenditure discounting resting).
Diet. Intention to consume a lower fat diet was measured by two items that were
combined in a composite measure (r = .8; a = .89): ‘I intend to eat a lower fat diet in the
next 12 months’; ‘It is likely that I will eat a lower fat diet in the next 12 months’ (both
anchored on a 5-point scale ranging from 1 = strongly disagree, 5 = strongly agree). Selfefficacy to consume a lower fat diet was measured by two items averaged together
(r = .67; a = .8): ‘I am confident that I could eat a lower fat diet in the next 12 months, if I
wanted to’; ‘It would be difficult for me to eat a lower fat diet in the next 12 months even if
I wanted to’ (reverse scored) (anchored from 1 = strongly disagree to 5 = strongly
agree).
Self-report measure of consumption of fat as a percentage of energy consisted of a
validated food frequency questionnaire (FFQ; McKeown et al., 2001) calculated as a delta
change score between participants’ measures obtained at baseline and after 12 months.
This study did not use an objective measure of dietary fat.
Physical activity data set 2 – Kinmonth 2008
Three hundred and sixty-five sedentary adults (62% women; Mage = 40.4 years) with
parental history of type 2 diabetes participated in the ProActive trial. Participants were
randomized to receive a brief advice leaflet (control group), theory-based facilitator-led
behaviour change programme delivered in participants’ homes, or delivered via the
telephone (Kinmonth et al., 2008).
Measures
Intention to be more physically active was measured by two items that were averaged
together into a single index (r = .63; a = .77). Self-efficacy to be more physically active
was measured by two items averaged in a single index (r = .39; a = .54). These items
were identical to the ones used in the trial by Griffin et al. (2011) described above.
Objective measure of physical activity was captured by the daytime physical
activity ratio (dayPAR), which is the ratio of daytime energy expenditure to resting
energy expenditure measured using heart rate monitoring with individual calibration
for the heart rate–energy expenditure relationship (Polar monitor, Kempele,
Finland). Our measure was obtained by calculating a delta change score between
the measures gathered at baseline and after 1 year on participants’ energy
expenditure levels. Self-report measure of physical activity consisted of a validated
questionnaire EPAQ2 (Wareham & Rennie, 1998) calculated as a delta change score
between participants’ measures obtained at baseline and after 1 year (expressed as a
continuous variable denoting weekly total activity energy expenditure discounting
resting).
Moderating effect of SES
17
Physical activity data set 3 – Watkinson 2010
Four hundred and sixty-six healthy participants (57% women; Mage = 47.2 years)
recruited from a population-based observational study were randomized to either a control
group (no feedback) or to one of three types of personalized physical activity feedback
groups (‘simple’, ‘visualized’, or ‘contextualized’) (see Watkinson et al., 2010).
Measures
Intention to be more physically active was measured by a single item: ‘I intend to be more
physically active in the next 2 months’ (1 = strongly disagree, 5 = strongly agree). Selfefficacy to engage in more physical activity was measured by two items identical to the
ones used in Griffin et al. (2011) and Kinmonth et al. (2008) (r = .52; a = .67).
Objective measure of physical activity was obtained by an ActiHeart device
(CamNtech; see Brage et al., 2005) recording participants’ activity via individually
calibrated heart rate and movement sensing (expressed as a continuous variable in kJ/kg/
day, in this case calculated as a delta change score between baseline and 2-month followup). Self-report measure of physical activity was obtained via a validated Recent Physical
Activity Questionnaire (RPAQ; Besson, Brage, Jakes, Ekelund, & Wareham, 2010) that
probes participants for their physical activity levels at work, travel, recreation, and
domestic life (expressed as a continuous delta change score between baseline and
2-month follow-up).
Physical activity data set 4 and diet – Godino 2012
Five hundred and sixty-nine participants (53% women; Mage = 47.1 years) recruited from
a population-based observational study were randomized to standard lifestyle advice
alone (control group), or in combination with a genetic or a phenotypic estimate of their
lifetime risk of developing type 2 diabetes (see Godino et al., 2012).
Measures
Physical activity. Intention to be more physically active was measured by a single item:
‘I intend to be more physically active in the next 8 weeks’ (1 = strongly disagree,
5 = strongly agree). Self-efficacy to engage in more physical activity was measured by a
single item: ‘I am confident I could be more physically active if I wanted to’ (1 = strongly
disagree, 5 = strongly agree).
Objective measure of physical activity was obtained by an ActiHeart device
(CamNtech; see Brage et al., 2005) recording participants’ activity via individually
calibrated heart rate and movement sensing (expressed as a continuous variable in kJ/kg/
day, calculated as a delta change score between baseline and follow-up). There was no selfreport measure of physical activity in this study.
Diet. Intention to consume more fruit and vegetables was measured by a single item: ‘I
intend to consume five servings of fruit and vegetables each day over the next 8 weeks’
(1 = extremely unlikely, 5 = extremely likely). Self-efficacy to consume more fruit and
vegetables was measured by a single item: ‘I feel confident in my ability to consume five
servings of fruit and vegetables each day over the next 8 weeks’ (1 = strongly disagree,
5 = strongly agree).
18
Milica Vasiljevic et al.
Self-report measure of fruit and vegetables consumption consisted of a validated
questionnaire FFQ (McKeown et al., 2001) calculated as a delta change score between
participants’ measures obtained at baseline and at follow-up. This study did not use an
objective measure of consumption of fruit and vegetables.
Analyses
Regression analyses
First, we examined the distribution and intercorrelation of measures (see Tables 1–5).
Second, using the same strategy as Conner et al. (2013), we conducted a series of multiple
linear regression analyses for each target behaviour in each study in turn. Before analyses,
we mean-centred the indices of intention and self-efficacy and standardized the final
scores of the behaviours in studies where the use of delta change scores was not possible.
At Step 1, we entered the demographic variables: Age, gender, and SES. At Step 2, we
added the variables of intention and self-efficacy. In the third and final step, we added the
interaction terms between SES and the mean-centred indices of intention or self-efficacy.
Meta-analyses
For the second part of this paper, we pooled the results of our models and examined them
as part of meta-analyses to provide a more robust test of the hypothesis that SES moderates
the intention–behaviour gap, thus providing more precise estimates of effect sizes (see
Cumming, 2014, for discussion and recommendation of this procedure). We conducted
separate meta-analyses for objective and self-report measures of behaviours and different
types of SES index (individual vs. area level).
Final measurements and delta scores
In our studies, measures of intention and behaviour were well aligned. In other words, in
some of our studies participants indicated their intentions to change a particular
behaviour, whereas in some studies participants indicated their intentions to simply
Table 1. Means, standard deviations, and intercorrelations (medication adherence in smoking cessation –
Marteau 2012)
1
1. Gender
2. Age
3. Ethnicity
4. Individual-level SES
5. Area-level SES
6. Self-efficacy
7. Intention
8. Objective NRT on day 28
Mean
SD
2
.08*
–
–
47.23
13.31
3
.07
.11**
1.18
0.65
4
5
6
.04
.08*
.05
.09*
.08
.04
.04
.01
.09*
.01
.04
.07
0.16
0.37
0.16
0.36
5.95
1.14
Note. NRT = nicotine replacement therapy; SES = socio-economic status.
Significance denoted as *p < .05; **p < .01; ***p < .001.
7
.11**
.12**
.04
.05
.01
.52***
6.31
1.02
8
.02
.08*
.06
.12***
.00
.01
.03
66.04
37.74
–
–
59.67
7.50
.04
2
1.04
0.28
.10*
.07
3
4
0.18
0.38
.08
.13**
.24***
Note. SES = socio-economic status.
Significance denoted as *p < .05; **p < .01; ***p < .001.
1. Gender
2. Age
3. Ethnicity
4. Individual-level SES
5. Area-level SES
6. Self-efficacy (PA)
7. Self-efficacy (diet)
8. Intention (PA)
9. Intention (Diet)
10. Objective (PA)
11. Subjective (PA)
12. Subjective delta (diet)
Mean
SD
1
0.80
0.40
.03
.01
.04
.11*
5
3.75
0.86
.10
.24***
.07
.18***
.01
6
3.73
0.81
.00
.1**5
.09
.01
.00
.42***
7
Table 2. Means, standard deviations, and intercorrelations (physical activity, diet – Griffin 2011)
3.75
0.78
.08
.18***
.08
.08
.05
.67***
.39***
8
3.77
0.94
.03
.11*
.07
.01
.01
.32***
.70***
.49***
9
34.49
17.08
.25***
.35***
.01
.02
.01
.18***
.13**
.09
.10*
10
87.58
53.11
.14**
.18***
.07
.04
.04
.14***
.11*
.19***
.16**
.30***
11
0.78
5.90
.04
.05
.06
.00
.10
.05
.13**
.09
.15**
.00
.14**
12
Moderating effect of SES
19
20
Milica Vasiljevic et al.
Table 3. Means, standard deviations, and intercorrelations (physical activity – Kinmonth 2008)
1
1. Gender
2. Age
3. Individual-level SES
4. Area-level SES
5. Self-efficacy
6. Intention
7. Objective PA delta
8. Subjective PA delta
Mean
SD
–
–
2
3
4
5
.02
.13*
.06
.04
.00
.07
.11*
.06
.09
.08
40.42
5.96
0.24
0.43
0.92
0.27
6
3.84
0.59
.02
.12*
.01
.00
.46***
3.72
0.63
7
8
.02
.06
.00
.03
.03
.03
.06
.02
.10
.12*
.04
.11
.03
0.11
0.60
16.99
50.52
Note. SES = socio-economic status.
Significance denoted as *p < .05; **p < .01; ***p < .001.
Table 4. Means, standard deviations, and intercorrelations (physical activity – Watkinson 2010)
1
1. Gender
2. Age
3. Ethnicity
4. Individual-level SES
5. Area-level SES
6. Self-efficacy
7. Intention
8. Objective PA delta
9. Subjective PA delta
Mean
SD
–
–
2
3
4
.08
.07
.03
.03
.01*
.01*
47.19
6.79
1.01
0.12
0.27
0.45
5
.02
.01
.05
.15**
0.60
0.49
6
.04
.09*
.03
.02
.01
3.49
0.77
7
.06
.11*
.05
.06
.08
.39***
3.34
0.87
8
.10*
.04
.01
.10*
.10
.15**
.11*
0.39
12.19
9
.05
.00
.04
.04
.01
.08
.07
.04
24.19
58.01
Note. SES = socio-economic status.
Significance denoted as *p < .05; **p < .01; ***p < .001.
engage in certain behaviours. Therefore, some of our included studies used changes from
baseline and some used final measurements alone. Measures of the actual change were
then derived by calculating before-and-after differences in the behaviour of interest.
Change scores provide a more powerful test by eliminating undesirable betweenparticipant variability. Furthermore, changes from baseline are addressing exactly the
same underlying moderation effects as analyses based on final measurements. Change
scores were used when possible to increase precision and those studies were
appropriately given higher weights in the meta-analyses. However, as can be seen from
the study-level analyses (see Appendix S1), no systematic differences emerged between
studies using change scores and final outcome measures.
Random-effects meta-analysis
Regression models were fitted individually to the data sets for self-report and objective
measures of behaviour. The outcomes of interest in our meta-analyses were the
Moderating effect of SES
21
Table 5. Means, standard deviations, and intercorrelations (physical activity, diet – Godino 2012)
1
2
1. Gender
.02
2. Age
3. Individual-level SES
4. Area-level SES
5. Self-efficacy (PA)
6. Intention (PA)
7. Self-efficacy (diet)
8. Intention (diet)
9. Objective delta (PA)
10. Subjective delta (diet)
Mean
– 47.09
SD
– 7.34
3
.10*
.12**
0.44
0.50
4
.13**
.10*
.19***
0.59
0.49
5
6
7
8
9
.06
.04
.05
.01
.12
.04
.01
.02
.32
.08
.13
.02
.02
.28
.12
.11
.16
.03
.06
.20
.10
.76
.01
.03
.01
.04
.05
.07
.05
.04
3.87
0.77
3.40
0.86
3.89
0.96
10
.00
.08
.06
.01
.01
.02
.00
.03
.03
3.84 1.40
6.29
0.97 15.23 285.47
Note. SES = socio-economic status.
Significance denoted as *p < .05; **p < .01; ***p < .001.
unstandardized regression coefficients estimating the interactions between individual- or
area-level SES, and intention or self-efficacy: Individual-level SES 9 intention, individuallevel SES 9 self-efficacy, area-level SES 9 intention, area-level SES 9 self-efficacy. Random-effects meta-analyses were conducted across studies for each of these four
interaction terms separately for self-report and objective measures of behaviour,
generating eight meta-analyses in total.
Heterogeneity
The chi-squared test for heterogeneity showed no significant results. As the chi-squared
test of heterogeneity has low power in a meta-analysis when studies are few in number as
in our case, a p-value of .10, rather than the conventional level of .05, was used, thus
providing a more conservative test.
Results
Individual analyses of studies
In the regression analyses, we found no support for the hypothesized moderation of the
intention–behaviour gap by socio-economic status. In particular, none of the interactions
involving SES was statistically significant.1 A detailed breakdown of these analyses can be
seen in the Appendix S1 accompanying this paper.
Meta-analyses
Meta-analyses for both objective and self-report measures of behaviour in each of the five
studies, using both individual- and area-level measures of SES, did not show significant
moderation between SES and intention on behaviour. At a meta-level, using objectively
measured behaviour only and SES as an area-level variable, meta-analyses revealed a
1
All five studies included in our analyses were randomized controlled trials. Therefore, we also carried out our analyses by
controlling for randomization condition which did not change our findings.
22
Milica Vasiljevic et al.
significant interaction between self-efficacy and SES (B = .17 [95% CI = 0.02, 0.31]). This
was the only statistically significant effect. Breaking down the significant interaction into
simple main effects showed that the health behaviours of individuals living in more
deprived areas did not change with their level of self-efficacy, whereas individuals living in
less deprived areas engaged in more healthy behaviours when they had higher self-efficacy
(Figures 1 and 2). Interestingly, albeit not significant, the interaction between area-level
SES and intention using objectively measured behaviour reveals a pattern of results that is
different from the findings reported by Conner et al. (2013). The meta-analyses did not
provide an indication of any other trends in the data.
Discussion
This paper examined the hypothesized moderation by SES of the relationship between
intention and behaviour, and self-efficacy and behaviour. Across five studies, using both
multiple linear regression analyses and meta-analyses on both objective and self-report
measures of behaviour, and using both individual- and area-level measures of SES, we
found no significant interactions between SES and intention on behaviour. Using
objectively measured behaviour and area-level deprivation, meta-analyses suggested that
the gap between self-efficacy and behaviour was greater amongst the more deprived. No
other effect was significant.
The strongest evidence to date for the moderation by SES of the intention–behaviour
gap presented analyses from three studies all of which used self-report measures of
behaviour (Conner et al., 2013). SES did not moderate the relationship between selfefficacy and behaviour. By contrast, we did not replicate these findings for self-reported
health behaviours in any of the five studies in which we followed Conner’s data analytic
approach. We did, however, replicate the lack of moderation of SES for the relationship
between self-efficacy and behaviour. By pooling our data into meta-analyses for a more
robust test of the hypotheses and using objective measures of behaviour, we again failed to
replicate the intention–behaviour moderation by SES. We did, however, find that SES
moderated the link between self-efficacy and behaviour when SES was measured as an
area-level variable.
Study
Moderation effect [95% CI]
Participants
Study
0.05 [ –0.40 , 0.51 ]
–0.05 [ –0.27 , 0.17 ]
–0.20 [ –0.91 , 0.52 ]
Physical activity
Griffin 2011
478
Watkinson 2010 544
Kinmonth 2008 365
Diet
Griffin 2011
Godino 2012
–0.41 [ –0.87 , 0.05 ]
0.03 [ –0.26 , 0.32 ]
Diet
Griffin 2011
Godino 2012
–0.06 [ –0.21 , 0.09 ]
RE model summary
478
569
RE model summary
–1.00
0.00
Study
Study
Physical activity
Griffin 2011
478
Watkinson 2010 544
Kinmonth 2008 365
Diet
Griffin 2011
Godino 2012
0.26 [ –0.15 , 0.67 ]
–0.08 [ –0.34 , 0.18 ]
Diet
Griffin 2011
Godino 2012
0.00 [ –0.15 , 0.15 ]
–1.00
0.00
1.00
Individual−level SES x intention effect size
1.00
Moderation effect [95% CI]
Participants
0.12 [ –0.29 , 0.53 ]
–0.04 [ –0.29 , 0.21 ]
–0.11 [ –0.62 , 0.39 ]
RE model summary
0.00 [ –0.19 , 0.19 ]
0.00
Area−level SES x self-efficacy effect size
Physical activity
Griffin 2011
478
Watkinson 2010 544
Kinmonth 2008 365
478
569
0.42 [ 0.00 , 0.85 ]
0.04 [ –0.27 , 0.34 ]
–1.00
Moderation effect [95% CI]
Participants
–0.20 [ –0.61 , 0.20 ]
–0.10 [ –0.36 , 0.15 ]
–0.11 [ –0.83 , 0.61 ]
478
569
1.00
Area−level SES x intention effect size
Moderation effect [95% CI]
Participants
Physical activity
Griffin 2011
478
Watkinson 2010 544
Kinmonth 2008 365
–0.18 [ –0.55 , 0.20 ]
–0.02 [ –0.28 , 0.24 ]
0.37 [ –0.14 , 0.88 ]
0.01 [ –0.34 , 0.37 ]
0.01 [ –0.26 , 0.28 ]
478
569
RE model summary
0.00 [ –0.14 , 0.15 ]
–1.00
0.00
1.00
Individual−level SES x self-efficacy effect size
Figure 1. Forest plots of the interactions between individual- (bottom) and area-level (top) socioeconomic status (SES) with intention (left) and self-efficacy (right) on self-reported behaviours.
Moderating effect of SES
Study
Moderation effect [95% CI]
Participants
Physical activity
Griffin 2011
Watkinson 2010
Kinmonth 2008
Godino 2012
478
544
365
569
Smoking
Marteau 2012
633
–0.13 [ –0.55 , 0.30 ]
–0.20 [ –0.46 , 0.06 ]
–0.47 [ –1.28 , 0.35 ]
–0.18 [ –0.41 , 0.05 ]
0.09 [ –0.19 , 0.37 ]
–0.12 [ –0.26 , 0.01 ]
RE model summary
–1.50
–0.50
0.50
Study
478
544
365
569
0.28 [ –0.11 , 0.67 ]
0.21 [ –0.10 , 0.52 ]
0.69 [ –0.08 , 1.47 ]
0.17 [ –0.08 , 0.43 ]
Smoking
Marteau 2012
633
0.02 [ –0.24 , 0.29 ]
Moderation effect [95% CI]
Participants
0.17 [ 0.02 , 0.31 ]
RE model summary
–1.50
Area−level SES x intention effect size
Study
Moderation effect [95% CI]
Participants
Physical activity
Griffin 2011
Watkinson 2010
Kinmonth 2008
Godino 2012
1.50
–0.50
0.50
1.50
Area−level SES x self−efficacy effect size
Study
Moderation effect [95% CI]
Participants
Physical activity
Griffin 2011
Watkinson 2010
Kinmonth 2008
Godino 2012
478
544
365
569
0.15 [ –0.24 , 0.54 ]
–0.14 [ –0.43 , 0.15 ]
–0.28 [ –0.75 , 0.20 ]
0.06 [ –0.16 , 0.27 ]
Physical activity
Griffin 2011
Watkinson 2010
Kinmonth 2008
Godino 2012
478
544
365
569
–0.06 [ –0.42 , 0.31 ]
0.20 [ –0.11 , 0.51 ]
0.35 [ –0.13 , 0.82 ]
–0.16 [ –0.39 , 0.08 ]
Smoking
Marteau 2012
633
–0.01 [ –0.26 , 0.23 ]
Smoking
Marteau 2012
633
–0.12 [ –0.37 , 0.12 ]
–0.01 [ –0.14 , 0.11 ]
RE model summary
RE model summary
–1.50
–0.50
0.50
1.50
Individual−level SES x intention effect size
23
–0.01 [ −0.18 , 0.16 ]
–1.50
–0.50
0.50
1.50
Individual−level SES x self−efficacy effect size
Figure 2. Forest plots of the interactions between individual- (bottom) and area-level (top) socioeconomic status (SES) with intention (left) and self-efficacy (right) on objectively measured behaviours.
We consider five possible explanations for our non-replication of Conner et al.’s
(2013) findings, relating to differences in samples, measures, types of behaviours
studied, data analysis, and data interpretation. Although there were some differences in
populations sampled, measures used, types of behaviour studied, and data analytic
approach, there were also sufficient similarities in all these domains to suggest that
they are unlikely explanations for the non-replication. In addition, we note that the
strength of associations between SES and intention was similarly small and not
significant in all three of Conner’s data sets and all five data sets included in the current
analysis. We therefore turn to discuss differences in data interpretation that may have
led to our non-replication.
Statistical power
Considering the small effect sizes of the interactions between intention and SES on
behaviour, it is important to consider the statistical power for detecting these small effect
sizes. Our studies (combined n = 2,511) had more statistical power for detecting these
interactions than did Conner et al. (combined n = 1,537) and furthermore, we pooled
our studies into a meta-analysis, thereby allowing us to provide a more robust test of the
moderation hypothesis. This way we were able to address a common limitation of field
experiments, where tests for moderations are often underpowered (cf. McClelland &
Judd, 1993). That we did not find the interactions reported by Conner in the analyses of
the individual studies and meta-analyses points to the need for caution in the
interpretation of SES as a potential moderator of the intention–behaviour gap.
Effect sizes
Our analyses highlight the need to consider effect sizes in research rather than to rely on
traditional significance tests (cf. Ioannidis, 2005). An examination of Conner et al.’s
effects reveals small effects that may contribute to the non-replicability of the SES
moderation of the intention–behaviour gap. Our study points to the need to re-evaluate
the importance of intention in the social patterning of health behaviours. Considering the
24
Milica Vasiljevic et al.
small effects obtained by Conner, other variables beyond intention are needed to elucidate
the socio-economic patterning of health behaviours.
Moreover, whilst Conner et al. reported medium to large effects between intention
and behaviour, and self-efficacy and behaviour, we found weak and inconsistent
relationships between intention and behaviour, and self-efficacy and behaviour in our
studies. Our findings are compatible with the meta-analysis by Webb and Sheeran (2006),
which concluded that the link between intention and behaviour is much weaker than
previously assumed (cf. Armitage & Conner, 2001). The authors found that interventions
that had a large effect on individuals’ intentions only produced a small change in
behaviour that can be attributed to the intentional control of behaviour. They went on to
suggest that as the interventions may have activated behaviour-consistent goals outside of
participants’ conscious awareness, future research on behaviour change should explore
non-conscious processes.
Executive function is one such set of pertinent non-conscious processes (Marteau &
Hall, 2013). Executive function is a theorized control network linked to the prefrontal
cortex that regulates behaviour, comprising three core cognitive functions: Response
inhibition (including self-control), working memory, and attention (Diamond, 2013).
Importantly, it engenders the ability to persist with a goal in unsupportive environments.
Several studies to date have investigated aspects of executive functioning as modifiers of the
well-established gap between behavioural intentions and actual behaviour. For example,
differences in executive functioning were used to explain variations in maintaining a
healthy lifestyle amongst undergraduates (including daily fruit and vegetable consumption,
engaging in recommended levels of physical activity, sleeping at least 8 hr per night,
consuming breakfast, moderating the consumption of alcohol, and avoiding smoking)
(Booker & Mullan, 2013; for a review see Vainik, Dagher, Dube, & Fellows, 2013).
Importantly, executive function (particularly response inhibition) was significantly
predictive of engagement with healthier behaviours amongst those students living in
environments unsupportive of healthier behaviours (Booker & Mullan, 2013). Growing
evidence also shows that executive function in childhood and adulthood is associated with
socio-economic status at birth (Moffitt et al., 2011; Raver, Blair, & Willoughby, 2013). In our
sample, the relationship between health behaviour and self-efficacy (the ability to control
behaviour and a correlate of executive function) was moderated by SES. This gap was
greatest amongst those living in areas of high deprivation, further highlighting the
importance of the environment as a non-conscious behavioural cue, the need to explore the
role of executive function in the enactment of health behaviours, and how this may be
modified by SES. Future studies investigating this could usefully integrate more direct
measures of executive function, such as the Stroop, Stop Signal, and Go/No Go tasks (for a
discussion of different executive function measures see Diamond, 2013).
Priors
Another pertinent point for discussion concerns the prior expectation, based on
evidence, that SES moderates the relationship between intention and behaviour. Many of
the papers reporting social patterning in the execution of different health behaviours did
not formally test for the moderating effects of SES on the relationship between intention
and self-efficacy with behaviour (Ball et al., 2007, 2009; Bere et al., 2008; Cerin & Leslie,
2008; De Cocker et al., 2012; Kamphuis et al., 2008, 2009; Leganger & Kraft, 2003;
Murray et al., 2012). Furthermore, the few papers that did test for such moderating effects
have been inconsistent, reporting no significant effects of moderation by SES on intention
Moderating effect of SES
25
to be more physically active (Sch€
uz et al., 2012), or to eat more fruits and vegetables
(Godin, Amireault, et al., 2010), with Godin, Sheeran, et al. (2010) finding that only
education moderated the intention–physical activity relationship, with no such effect
being found for other SES indices including income and social deprivation. Therefore, the
priors taken together in conjunction with ours and Conner et al.’s findings suggest that
the relationship between SES and the intention–behaviour gap is more complex than
previously thought and requires further empirical investigation. In a similar vein, Godin’s
(2013) invited commentary on the Conner et al. (2013) paper argued that the moderation
of the intention–behaviour gap by SES requires further examination. Godin surmised that
the inconsistent effects may arise from differences in the cultural context in which a study
was conducted, the characteristics of the sample under study, the nature of the behaviour
studied as well as the type of SES index used (individual or area level). Our analyses hint
that the type of SES index used may affect the results of such analyses. Future studies
should use different operationalizations of SES, utilizing both area- and individual-level
indices of SES, to capture different facets of relative deprivation. Moreover, our findings
highlight the need to replicate these analyses with more varied health behaviours
(including more episodic behaviours).
The paucity of studies formally testing the moderating role of SES of the intention–
behaviour gap, combined with the heterogeneity in the measurement of SES, precludes us
from conducting a systematic review on the topic. However, prior evidence and our
robust combined analyses of data sets that had homogeneous indices of SES (both
individual and area level) suggest that thus far there is no evidence of a larger intention–
behaviour gap amongst the more deprived.
Strengths and limitations of the present research and recommendations for further
empirical investigations
A notable strength of our study is that it is the first to use objectively measured health
behaviours to examine the social patterning of the gap between intention and behaviour,
as well as between self-efficacy and behaviour. Furthermore, the studies included in the
current analyses used both individual- and area-level measures of SES, uniformly measured
across all five studies, allowing us to examine the varying effects of SES as a function of
how it is measured. Our finding of a significant moderation of the relationship between
self-efficacy and behaviour only when SES was captured as an area-level variable suggests
that SES may have different effects on health behaviours depending on how it is defined.
Prior studies have demonstrated a relationship between area-level deprivation with
physical activity and diet, via decreased access to neighbourhood opportunities to be
more physically active (Panter, Jones, & Hillsdon, 2008) and via increased access to
unhealthy food outlets (Burgoine, Forouhi, Griffin, Wareham, & Monsivais, 2014). Thus, it
is plausible that area-level deprivation interacts with the particular types of behaviour we
examined that depend on environmental influences. Measures of individual-level SES may
perhaps interact with behaviours whose execution does not depend on the wider
environment, such as daily brushing of teeth and condom wearing. The differential effects
of area versus individual level of SES warrant further empirical investigation.
Adding further strength to our studies are the well-developed and matched measures
of intention and self-efficacy. Our studies for the most part used multiple indices to
measure intention and self-efficacy which had good interitem reliabilities. This contrasts
with Conner et al.’s (2013) studies that used single-item measures of intentions in two of
the three included studies. Furthermore, all our studies used intention and self-efficacy
26
Milica Vasiljevic et al.
indices that were carefully time-matched with the behaviour measures (for more details
see Methods section).
One of the limitations of our studies is that we only looked at three health behaviours.
As we used strict criteria in selecting studies that measured behaviours objectively and/or
subjectively, captured SES at both individual and area level, and had well-matched and
reliable indices of intention and self-efficacy, we limited our analyses to the five studies
which measured physical activity, diet, and medication adherence in smoking cessation.
All three health behaviours we examined are habitual; therefore, a question remains
whether the same pattern of results would emerge for episodic health behaviours, such as
screening and vaccination. Previous research has suggested that the impact of intentions
may be diminished in habitual health behaviours (Ouellette & Wood, 1998; Webb &
Sheeran, 2006). Therefore, future work should aim to further investigate the relationship
between SES, intention, self-efficacy, and health behaviours, using different examples of
health behaviours, including episodic health behaviours.
Finally, we carried out multiple statistical analyses on our dependent variables of
interest, thus potentially inflating the familywise type I error rate. Therefore, our only
significant effect observed may be due to chance and type I error. The robustness of our
conclusions therefore depends on future replications.
Implications for future research and policy
There are two major implications for future research and policy arising from our analyses:
Interventions targeting the intention–behaviour gap may not be effective to counter SES
related health disparities. The effect sizes observed in previous studies were small and the
effects did not replicate in the present research using both subjective and objective
measures. This underlines the importance of replication studies and considering effect
sizes as a criterion to evaluate research outcomes, in addition to statistical significance
(Cohen, 1994).
At present, there is no compelling evidence for a socio-economic patterning of the
intention–behaviour gap. This finding, combined with evidence that intentions are weak
predictors of behaviour regardless of SES, adds further weight to the scientific case for
targeting non-conscious processes to change behaviour across all social groups (Marteau,
Hollands, & Fletcher, 2012; Webb & Sheeran, 2006).
Acknowledgements
We would like to thank Stephen Sharp for his comments regarding our data analytic approach.
The study was funded by the Department of Health Policy Research Programme (Policy
Research Unit in Behaviour and Health [PR-UN-0409-10109]). The Department of Health had
no role in the study design, data collection, analysis, or interpretation. The research was
conducted independently of the funders, and the views expressed in this paper are those of the
authors and not necessarily those of the Department of Health in England. The final version of
the report and ultimate decision to submit for publication was determined by the authors.
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Supporting Information
The following supporting information may be found in the online edition of the article:
Appendix S1. Individual regression analyses for the five data sets.
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Running head: SMOKING CESSATION
SMOKING CESSATION EVIDANCE TABLE
MSN5300 Advanced Nursing Inquiry and Evidence-Based Practice
Ricardo Guzman, Marina Fuller,
Aniel Louis Jean, Felix Sanchez,
Jackson Etienne, Karine Suarez,
Yonaicris Plasencia, Eduardo Frei,
Mirtha Hernandez. Samuel Dinamarca,
Yenis Carrera,Yunior Zaldivar.
Miami Regional University
Dr. Bernadette Dike
October 2, 2020
1
SMOKING CESSATION
2
EVIDENCE TABLE
Question: In smoking population, does behavioral therapy in combination with pharmacology
medication would be better than Pharmacology medication alone to help people in
Smoking cessation?
Article Author
#
& Date
Evidence
Sample Size Study findings that
Type
Limitations
help answer the
Raiting
question
1
Ebbert
et
Quantitative
al. Experimental
(2017)
Evidence
Level Quality
270
The combination of
Bupropion is
randomized
four sessions of in-
no more
cigarette
person behavioral
effective than
smokers
counseling and
NRT
bupropion
sustained release in
the Step Care
group, increased
prolonged smoking
I
A
III
A
There is no
biochemical
verification of
the study
abstinence
significantly.
Stepped care
approach is costeffective.
2
Patnode
et
Non-
al. Experimental
(2015)
Systematic
reviews
638
Behavioral
Review of
abstracts
interventions
reviews
and
114 increased smoking
full-text
cessation at month
reviews
6 or more.
approach.
Methods and
quality of the
SMOKING CESSATION
3
Six reviews served
included
as primary reviews
reviews that
on the effectiveness synthesized
of NRT, bupropion
the bodies of
or varenicline.
evidence.
40 trials found a
The
statistically
limitations of
significant benefit
the primary
of combined
studies
pharmacotherapy
themselves.
(primarily NRT or
bupropion) and
behavioral
interventions on
smoking cessation
3
Ghamri
Non-
(2018)
experimental
Systematic
reviews
4 databases
A combination of
The
NRT´s was more
limitations of
effective than
the primary
monotherapy.
studies
Interventions that
combined
pharmacotherapy
and behavioral
support increased
the success of
smoking cessation
compared with
minimal
themselves.
III
A
SMOKING CESSATION
4
intervention or
usual care
4
Çelik & Non-
Smokers
Sevi
with at least treatments were
limitations of
18 years old. more efficient
the primary
Experimental
(2020).
Systematic
reviews
20 studies
CBT-based
when applied as
studies
individual
themselves.
Groups of 5- therapies.
6
The
III
A
I
A
Quality and
Effectiveness gets
methods of
higher when
the articles
medication and
analyzed
NRT are also
combined with
CBT.
5
Kamile
et
Experimental
al. Quantitive
(2017)
3322 people
The rates of
It was not
smoking cessation
studied the
in the cases using
combination
varenicline and
of NRT and
behavioral therapy
behavioral
were significantly
therapy
higher compared to
the cases using
bupropion and
behavioral therapy.
Varenicline as a
smoking cessation
drug is better
tolerated than other
medications and it
SMOKING CESSATION
5
seems to be more
effective
SMOKING CESSATION
6
References
Çelik, Z. H., & Sevi, O. M. (2020). Effectiveness of Cognitive Behavioral Therapy for Smoking
Cessation: A Systematic Review. Current Approaches in Psychiatry / Psikiyatride
Guncel Yaklasimlar, 12(1), 54–71. https://doiorg.udlap.idm.oclc.org/10.18863/pgy.534638
Ebbert, J., Little, M., Klesges, R., Bursac, Z., Johnson, K., Thomas, F., Vander Weg, M. (2017).
Step Care treatment for smoking cessation. Published by Oxford University Press. Health
Education Research. Vol 32, No.1. Pages 1-11 doi:10.1093/her/cyw051
Ghamri, R. (2018). Identification of the most effective pharmaceutical products for smoking
cessation: A literature review. Journal of Substance Use.VOL. 23, NO. 6, 670–674
https://doi.org/10.1080/14659891.2018.1489010
Kamile Marakoğlu, Nisa Çetin Kargın, Rahime Merve Uçar, & Muhammet Kızmaz. (2017).
Evaluation of pharmacologic therapies accompanied by behavioural therapy on smoking
cessation success: a prospective cohort study in Turkey. Psychiatry and Clinical
Psychopharmacology, 27(3), 221–227. https://doiorg.udlap.idm.oclc.org/10.1080/24750573.2017.1342751
Patnode, C., Henderson, J., Thompson, J., Senger, C., Fortman, S., Whitlock, E. (2015).
Behavioral Counseling and Pharmacotherapy Interventions for
TobaccoCessationinAdults, Including PregnantWomen: A Review of Reviews for the
U.S. Preventive Services Task Force. Annals of Internal Medicine. Vol. 163 No.8.
Group Name:
4
Date: 9/17/2020
PICO Worksheet
Original
Revised
Define your Question using PICO
P
Describe your patient, population or problem (age, sex, race, past medical history, etc.), the disease,
or main topic of your question.
Smoking population.
I
What intervention or action are you considering treatment, diagnostic test, etc. Is there a specific issue
you’d like to investigate?
Combination of Behavioral therapy and
Pharmacology Treatment
C
Are you trying to compare or decide between two
options - drugs, a drug and no medication or placebo, or
two diagnostic tests? (Note: Your clinical question does
not always need a specific comparison.)
Pharmacology Medication alone.
O
M
e
t
h
o
d
o
l
o
g
y
Which research method (quantitative
or qualitative) should be used to
answer the study question?
Quantitative.
If experimental, what research
design should be used to compare
the intervention group with the
comparison (or control) group?
o
o
o
o
o
o
o
randomized controlled trial
cohort study
case controlled studies
case reports
systematic reviews
meta analysis
editorials/expert opinions
What is the outcome you’d like to achieve? What are you trying to do for the patient? Relieve or
eliminate the symptoms? Reduce the number of adverse events? Improve function or test scores?
Smoking cessation.
State your Question resulting from PICO:
In smoking population, does behavioral therapy in combination with
pharmacology medication would be better than Pharmacology medication
alone to help people in Smoking cessation?
List keywords from your PICO question that can be
used for your search.
Behavioral therapy, pharmacology medication,
smoking cessation, smoking population.
Use the databases page from the Library website for
suggestions.
List other criteria –gender, age, year of publication,
or language to be used to limit your search.
List the databases you plan to search: i.e., CINAHL.
MRU Virtual library.
Articles,text and magazines from 2015 to 2020
Rev. May 2020
MSN5300
KmR
Purchase answer to see full
attachment