Post on 30-Jan-2018
A real-time examination of context effects on alcohol cognitions
Rebecca L. Monk and Derek Heim
of Edge Hill University, UK
Author Note
Rebecca Louise Monk and Derek Heim, Department of Psychology, Edge Hill
University, St. Helens Road, Ormskirk, Lancashire, L39 4QP, UK. Email:
monkre@edgehill.ac.uk; derek.heim@edgehill.ac.uk
Correspondence concerning this article should be addressed to Rebecca Monk,
Department of Psychology, Edge Hill University, St. Helens Road, Ormskirk,
Lancashire, L39 4QP, UK. Email: monkre@edgehill.ac.uk. Tel: +44 (0)1695 65 0940
Word count: 36953279
Running Head: Context effects on alcohol-related expectancies
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“A real-time examination of context effects on alcohol cognitions”
Background: This research used context aware experiential sampling to investigate
the effect of contexts on in vivo alcohol-related outcome expectancies. Method: A
time-stratified random sampling strategy was adopted in order to assess 72 students
and young professionals at 5-daily intervals over the course of a week using a
specifically designed smart-phone application. This application recorded
respondents' present situational and social contexts, alcohol consumption and
alcohol-related cognitions in real-time. Results: In-vivo social and environmental
contexts and current alcohol consumption accounted for a significant proportion of
variance in outcome expectancies. For instance, prompts which occurred whilst
participants were situated in a pub, bar or club and in a social group of friends were
associated with heightened outcome expectancies in comparison with other settings.
Conclusion: Alcohol-related expectancies do not appear to be static but instead
demonstrate variation across social and environmental contexts. Modern technology
can be usefully employed to provide a more ecologically valid means of measuring
such beliefs.
Key Words: Alcohol, Social cognition, Social cognition models, Context,
Expectancies, Smartphone technology, Real-time sampling
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Despite longstanding awareness that people's immediate environments mediate
behaviour (Bourdieu, 1977; Nyaronga, Greenfield, & McDaniel, 2009; Lott, 1996;
Rosnow & Rosenthal, 1989), most psychological theories of behaviour and cognitions
are formulated upon data which are obtained without sufficient consideration of
contextual influences (Biglan & Hayes, 1996; Biglan, 2001; Hayes, 2004). When
using social cognition models to explain alcohol consumption this negligence might
constitute a critical oversight in view of long-documented contextual influences on
alcohol behaviours (MacAndrew & Edgerton, 1969).
Research indicates that alcohol-related beliefs predict consumption and, resultantly,
interventions have been designed to target these beliefs to reduce drinking (c.f. Jones
et al., 2001). Specifically, outcome expectancies – people’s beliefs about the likely
consequences of drinking have been found to impact both the quantity and frequency
of alcohol consumption (c.f. Ham & Hope, 2003; Oei & Morawska, 2004; Reich,
Below, & Goldman, 2010). Specifically, high positive outcome expectancies appear
to be associated with recurrent drinking in greater quantities (c.f. for example
Andersson et al., 2012), whilst higher negative expectancies seem to be associated
with reduced consumption (c.f. for example Stacy, Widaman, & Marlatt, 1990). While
it has also been noted for some time that outcome expectancies may vary across
different contexts (Wall, Mckee, & Hinson, 2000), this body of research has tended
to rely on single occasion testing and on retrospective self-reports obtained within
laboratory settings or non-alcohol-related environments (e.g. lecture theatres) without
adequate consideration of possible contextual influences (Monk & Heim 2013a; in
press). Accordingly, studies have begun to address these limitations by utilising more
ecologically aware testing environments such as simulated bars (e.g., Larsen, Engels,
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Wiers, Granic, & Spijkerman, 2012) or wine tasting events (e.g., Kuendig &
Kuntsche, 2012), and recent findings suggest that social contexts and alcohol-related
environments are associated with increases in positive expectancies (Monk & Heim,
2013b; 2013c). While pointing to the importance of social and environmental contexts
in shaping alcohol-related beliefs, these studies have tended to test participants in
environments which, to a greater or lesser extent, are removed from real world
drinking contexts. The current study addresses this by using an experience sampling
method.
The increasing accessibility of advanced mobile devices (Katz & Aakus, 2002) has
facilitated the regular, day-to-day assessment of individuals in naturally diverging
contexts and has opened the field for Ecological Momentary Assessment (EMA) or
Experience Sampling (Collins, Lapp, Emmons, & Isaac, 1990; Collins et al., 1998;
Courvoisier, Eid, Lischetzke, & Schreiber, 2010; Killingsworth & Gilbert, 2010;
Kuntsche & Robert, 2009). The present research used smartphone technology to
enable participants to provide real-time in vivo reports with a particular focus on
alcohol-related expectancies. In line with previous research (Monk & Heim, 2013a;
2013b; Wall et al., 2000; 2001; Wiers et al., 2003), it was predicted that there would
be an increase in alcohol-related expectancies when assessment occurred within
alcohol-related environments and in the presence of a social group (in comparison
with assessments that take place in alcohol neutral environments and in solitary
contexts).
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Method
Design
A within participant design was utilised to investigate the effect of environmental and
social contexts on participant real-time responses to alcohol expectancy questions.
Participants
72 participants comprising students (n = 43) and young professionals (n = 29) who
were aged 18-34 years (M = 21.73, S.D = 3.64) were recruited for this study from
universities and businesses in the UK (North West). The majority of the sample were
White British (88.9%) and 69% of this sample were female. Baseline average AUDIT
scores were 9.02 (2.07) in the student sample and 8.72 (1.28) in the business sample.
Measures
Demographic information and reports regarding personal alcohol consumption
(AUDIT-C) were recorded at participants’ initial briefings. These were anonymously
combined with participants’ individual responses using a unique numeric identifier.
The smart-phone application ascertained participants’ environment (home,
work/lecture, bar/pub/club, restaurant, sporting event, party or other) and social
contexts (alone, with one friend, with two or more fiends, with family, work
colleagues or other), whether they were drinking or had had a drink (yes or no), and if
so what they had been drinking (quantity). Furthermore, all participants answered a
random selection of items taken from the 34-item Alcohol Outcomes Expectancy
Questionnaire (Leigh & Stacy, 1993) which covers a range of outcomes, including
social, sexual and emotional outcomes. However, pilot studies (n = 42) which trialled
the administration of full and abridged versions of this questionnaire revealed that
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participants were less likely to respond when all items were included. Furthermore, if
all of the 34 items had been available for random allocation, analyses would be
limited as any variation observed between contexts may have been the result of
variation in the expectancy measure presented (e.g. social vs. sexual expectancy
items). Resultantly, it was only the six social items that were part of the question pool
(three positive and three negative). In each response session, two positive and two
negative expectancy items were randomly selected from the question pool and
separate average scores for positive and negative expectancies were subsequently
calculated, giving a standardised maximum and minimum score of 1-6.
Equipment
A web based smart-phone application designed specifically for this research enabled
participants to respond to questioning via the use of their own mobile phone – when
prompted by automated SMS messages. The application was a website built using
HTML and JavaScript (JavaScript's jQuery mobile library) and answers were tracked
and stored using Google Analytics. The survey was designed to work on mobile
phones and native mobile browsers and was web-standards compliant. Each response
session was individually tracked and involved a personally interactive user experience
using tree based logic. For example, only those who responded that they consumed
alcohol were asked about what they had consumed. Participants’ response
mechanisms were also interactive, determined by the users’ smart-phone - for
example, Iphone or Android users could indicate their response by pressing or
‘dragging’ the onscreen response items whilst those without touch screen technology
responded in a fashion compatible with their phone (e.g., ‘scroll and click’). The
questions were randomly selected from the database of questions using a computer-
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generated randomisation code. The application was designed to make the user
interface as intuitive/user friendly as possible and, in accordance with
recommendations (c.f. Palmblad & Tiplady, 2004), there no default answers set..
Procedure
Following ethical approval, participants were recruited and given a demonstration of
the response mechanism on their personal mobile phone. In accordance with similar
EMA procedures (Csikszentmihalyi & Larson, 1992; Wichers et al., 2007) and
recommendations by Larson and Delespaul (1992), a time-stratified random sampling
strategy was adopted (c.f. Moberly & Watkins, 2008). Pilot questionnaire data
examining perceptions of online vs. real-time assessments (Response N = 108)
indicated that respondents preferred SMS reminders and that five daily prompts were
deemed the most acceptable number of daily participation requests. Therefore, the
volunteers received five randomly allocated SMS participation prompts every day for
one week. No two prompts could occur within 15 minutes (ibid) or outside 0800 -
2300 hours. Each day of participation was divided into five equal three hour periods
and one prompt was randomly sent within each period (e.g., once between 0800 and
1100, once between 11 and 1400 and so on). The exact time a participant was
prompted at was determined using a random number generator - each 3 hour section
was split into 15 minute blocks and the generator selected the time that the prompt
would be sent, making response sessions unpredictable Upon receiving the prompts,
participants activated the Application by clicking on a link provided in the SMS. The
questions provided were randomly selected from the question database in order to
prevent the order effects (Csikszentmihalyi & Larson, 1992).
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Average completion time was recorded at 2 minutes 27 seconds and the overall study
retention rate was 84.7%. Only relatively few participants completely stopped
responding and dropped out (n = 8). Furthermore, respondents were removed from
the sample (n = 3) where the response rate was below 40 percent, based on previous
research which indicates that low response rates on substance-use-related assessments
have low reliability (Shiffman, 2009).
Over the course of the week, there was the potential for participants to respond to 35
prompted sessions (5 per day for a week). There was no substantial increase in the
number of missed response sessions as interaction with the application increased,
suggesting that order effects were limited by the use of this technology. The average
percentage of failed responses (sessions which were not completed following a
prompt) was 20% per participant, with the 0800-1100 time-slot eliciting the highest
number of late or failed responses. The average percentage of late responses (> 15
minutes post prompt) was 5% per participant and these late responses were excluded
from subsequent analyses in order to ensure that the results could reasonably be
asserted to be a representative account of the specific time as opposed to a
retrospective report (Delespaul, 1995). The study therefore had an average overall
valid response rate of 75% per participant (26 out of a total possible 35 prompts
responded to).
Analytic Strategy
Multilevel modelling (MLM) is a method of statistical analyses which is capable of
advanced portioning of variance (Tabachnick & Fidell, 2001). MLM was used as this
technique can incorporate the natural complex (and related) nature of the data (Heck,
Thomas, & Tabata, 2010) and look for explained and unexplained variance both
between and within groups (see Goldstein, 2011). MLM is also able to deal with
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missing data which are to be expected in experiential sampling (Tabachnick & Fidell,
2001). In the present study variances in outcome expectancies (the dependent
variable) were modelled at a number of levels: Prompts were nested within days
which were nested within participants. However, given that data were not recorded at
the day level (e.g. day, weather etc), it was decided that this level did not warrant
inclusion within the statistical modelling. Indeed, the day of the week in which
participants began the research was not consistent in this study (participants chose
their most personally convenient starting point). This meant that no specific predictors
required modelling at this level and the lack of information at this level may have
unduly reduced the overall explanatory power of the model. A series of 2 level
random intercept multilevel models (prompts within participants) were therefore fitted
– one for each of the alcohol-related cognitions (positive and negative outcome
expectancies). MLM therefore allowed analysis of variance at the prompt level
(context factors) and the person level (individual differences). The resultant
hierarchical random intercept multilevel model was fitted with predictor variables
which were justified by correlational analyses (see Table 1). Preliminary analyses
revealed no evidence of multicollinearity, residuals were normally distributed and
scatterplots indicated that the assumption of linearity and homoscedasticity were met.
The MLM was designed to portion variance in outcome expectancies and the
predicted variance from the null and fitted models were compared in each case. Table
1 outlines the correlational analyses and the findings of these analyses were used to
inform the subsequent MLMs. Any variable which significantly correlated with at
least one of the dependent variables was included.
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Results
Full random intercept MLMs were calculated, one with positive expectancies as the
dependent variable and another for negative expectancies. Predictor variables were
imputed at both levels (as specified in Table 1): Prompt level variables (j social
context, environmental context, alcohol consumption - yes or no, and number of
drinks), and individual level predictors (ij age, gender, ethnicity, student/professional
status and raw (as opposed to therapeutic categories) AUDIT scores were used for
analyses. In all analyses, binary variables (Gender, 1 = female; Student/Professional
status, 1 = student ; Ethnicity, 1 = white British; Alcohol Consumption, 1 = yes) were
dummy coded (for a more easily interpretable outcome), and the two categorical
predictors (environmental and social context) were dummy coded using Home and
Alone conditions as the respective reference categories (k-1), and the remaining
variable were left as continuous variables (Positive expectancies, Negative
expectancies, Age, AUDIT, Number of drinks)..
INSERT TABLE 1 HERE
How much variance in positive and negative outcome expectancies is found and can
be subsequently explained at the individual level (variance between participants) and
the group level (prompt level, variance between prompts/within participants)?
Empty models (also known as the variance component models - models without
imputed predictor variables) indicated that there was a significant proportion of
variance (ICC = 95.55%) to be explained at the prompt (μ0j = 3.68, p < .001) and the
individual level (ICC = 4.41%, μ0ij = .17, p < .01). The same was also true of
negative expectancies, with 46.36% (μ0j = .61, p < .001) and 19.74% (μ0ij = .15, p
< .01) of unexplained variability being identified at the prompt and the individual
levels respectively. 2* log likelihood statistics (using chi square) and ICC calculations
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revealed that the full positive expectancy model resulted in a significant reduction of
unexplained variance (χ² (30, n = 61) = 978.06, p < .001), explaining 36.7%.and
35.3% of the identified variability in positive expectancies at the prompt and
individual levels respectively. The negative expectancy model also produced
significant reduction in the amount of unexplained variance (χ² = (9, n = 61) = 575.88,
p < .001), with 22.95% and 15.38% of variance in negative expectancies being
explained at the prompt and individual levels respectively.
Which predictors are significant predictors of variance in expectancies?
No single individual level predictor was significant within the MLM model of
negative expectancies. However, for positive expectancies, the only individual level
predictor that was significant was student/professional status (β0ij = -.23, p < .01),
such that being a young professional was associated with reduced positive
expectancies to a significant degree, whilst being a university student was associated
with an increase in positive expectancies. At the prompt level, having consumed
alcohol within the last hour of prompting was a significant predictor of both increased
positive (β0j = -.82, p < .001) and negative expectancies (β0j = -.51, p < .001) whilst
number of drinks was not a significant predictor of positive expectancies but it did
negatively predict variance in negative expectancies (β0j = -.09, p < .001). This
suggests that any level of alcohol consumption may increase both positive and
negative expectancies. Nonetheless,, whilst the number of drinks did not appear to
alter positive beliefs (they remained heightened during consumption), negative beliefs
began to decrease as alcohol consumption increased.
Both prompt level categorical predictor variables (social and environmental context)
were also significant predictors of positive and negative outcome expectancies.
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Specifically, responses whilst situated within alcohol-related contexts including bars
(β0j = -.52, p < .05), parties (β0j = -.91, p < .01) and sporting events (β0j = - .79, p
< .05) were associated with increased positive expectancies. Similarly, negative
expectancies were significantly predicted by being in a bar/pub/club (β0j = -.25, p
< .01), although sporting and party venues did not account for significant variance.
Being at a friend or family member’s house was also a significant predictor of
increased positive (β0j = -1.10, p < .001) and negative expectancies (β0j = -.67, p
< .001). Being at work was also a significant predictor of positive (β0j = .61, p < .01)
and negative expectancies (β0j = -.28, p < .05). Here, being outside of work was
associated with an increase in positive expectancies, and a decrease in negative
expectancies. Being at home during responses was the reference category for both
expectancy types and this context also appeared to be associated with decreased
positive and negative expectancies..
The social context sub-categories also varied to a statistically significant degree.
Prompts that occurred whilst participants were with 1 friend (β0j = -1.78, p < .001:
β0j = -.74, p < .001), 2 or more friends (β0j = -1.75, p < .001: β0j = -.84, p < .001) or
family members (β0j = -1.10, p < .001: β0j = -.79, p < .001) were significant
predictors associated with increases in positive and negative expectancies
respectively. However, being with work colleagues predicted significant decreases in
positive expectancies (β0j = .72, p < .05) and increases in negative expectancies (β0j
= -.43, p < .001). Being alone during responses was the reference category for both
expectancies categories, meaning that this context also appears to be associated with
decreased expectancies. The ‘other’ response for social context was also a significant
predictor of positive expectancies (β0j = 2.44, p < .01) but the large standard error
here (.92) suggests a high degree of variability in participants’ responses in this
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category, perhaps due to the diversity of contexts captured by this response. Any
attempt to interpret this finding without any further contextual information would
therefore be unwise.
Discussion
With the aim of conducting an ecologically valid assessment of the impact social and
environmental contexts have on outcome expectancies, this study used smart-phone
technology to conduct context aware experiential sampling. Social and environmental
contexts, specifically, being in a pub, bar or club, were significant predictors of both
increased positive and negative outcome expectancies. The same pattern was observed
for social contexts including being with a friend, with two or more friends and with
family members. Being at work or at home, and being with work colleagues or alone
was associated with a reverse pattern of results, whereby these contexts were
associated with decreased expectancies. In accordance with previous lab (e.g., Wall et
al., 2000; 2001) and field research (e.g., LaBrie et al., 2011), these findings provide
real-time support for the assertion that alcohol-related environmental contexts are
associated with changes in cognition – specifically, changes in the anticipated
consequences of alcohol consumption. It was particularly interesting to note that,
against expectations, negative as well as positive expectancies increased in alcohol-
related environments and in social group contexts. In studies of problem and non-
problem drinkers, alcohol-related cues (their favourite alcoholic drink) have been
shown to create both positive and negative expectations and physiological arousal
(Cooney, Gillespie, Baker, & Kaplan, 1987). These results suggest that in vivo
contextual cues can trigger both positive and negative beliefs (c.f. Wall et al., 2000;
Wiers et al., 2003) and underline the current findings that both positive and negative
expectancies increased when participants were in social groups and alcohol-related
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environments. The importance of the relationship between social and environmental
contexts in and the decision to drink or exercise restraint is also affirmed by the
current findings (Andersson et al., 2013; Lau-Barraco & Linden, 2014). It has been
suggested that interventions need to be able to target the context-dependent nature of
substance use and associated beliefs in order to be successful (Biglan & Hayes, 1996;
Davies, 1997). The current research may therefore offer insights towards the
improvement of therapeutic practice, by increasing our ability to target the contextual-
varying beliefs which are associated with alcohol consumption. Any level of alcohol
consumption alcohol within the last hour was also associated with increases in both
positive and negative expectancies respectively. However, number of drinks was only
a significant predicator of decreased negative expectancies. Therefore, whilst positive
expectancies appear to remain heightened regardless of the level of alcohol consumed,
greater levels of consumption may be associated with subsequent decreases in
negative beliefs. This suggests that real-time alcohol consumption is associated with a
reduction of the invivo cognitions which are related to restraint (c.f. Baldwin, Oei, &
Young, 1993). Conversely, consumption appears to increase the positive beliefs which
are associated with drinking (c.f. Reich et al., 2010).
Whilst AUDIT scores did correlate with positive expectancies, being a university
student was the only demographic variable which, on its own, was a significant
predictor of increased positive outcome expectancies. Therefore, whilst the majority
of expectancy research relies on student samples, using a non-student sample with a
comparable age may produce different results (lower average expectancy scores).
Indeed, age was a not a significant predictor in the study which may suggest that there
are aspects of the student experience which create deviations in expectancies in
comparison to those of the same age who are not students. This pattern of results is in
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line with suggestions that there is a ‘culture of drinking’ at University which
moderates students’’ expectancies (Borsari & Carey, 2001). Future research may
therefore benefit from greater inclusion of non-student participants.
As responses that did not occur within 15 minutes of the participation prompt were
discarded, the current findings can be reasonably believed to be representative of real-
time cognitions. This removes the problems noted in previous EMA research (c.f.
Kuntesche & Labhart, 2012) where a lack of signal or power may delay prompts, thus
increasing the reliance on the participant’s memory and potentially limiting response
reliability. Nevertheless, it remains possible that a lack of signal or power of
respondents’ mobiles may have resulted in some data loss in the current research,
although the high response rate for this study suggests that this is likely to have been
minimal. It must also be noted that whilst the participation window of 0800-2300 was
selected in order to maximise responses, future research may be improved by
exploring the feasibility of responses beyond 2300. This would allow assessments of
late night/early morning drinking practices and may further elucidate complex
cognitive processes. Furthermore, it should be noted that participants’ intoxication
levels may have impaired/hindered responses (cf. Fromme, Katz, & D’Amico, 1997;
Hindmarch, Kerr, & Sherwood, 1991 LaBrie et al., 2011). While such effects may
mirror real-life situations, a degree of caution is nonetheless advised when
considering the current findings.
In conclusion the present research confirms concerns about the abundant previous
research which is conducted with participants who are assessed alone, in non alcohol-
related environments and are sober during the completion of their questionnaires. In
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particular, the results of the current investigation indicate that responses which were
recorded in solitary contexts and when in alcohol-neutral environments (such as at
work or at home) were associated with lower expectancies. As specified, alcohol
consumption was also associated with changes in responses. These results therefore
suggest that previous research in this field may have captured responses which do not
necessarily equate to cognitions in real-life situations. Here, the use of smart-phone
technology to conduct real-time, context aware experiential sampling appears to offer
a viable solution to this issue. Findings from this research may also provide a
promising avenue to pursue for the development of context-sensitive interventions.
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Table 1
Bivariate correlations between mean alcohol-related cognitions and all predicator variables.^
1 2 3 4 5 6 7 8 9 101. Positive Expect. -2. Negative Expect. .71** -3. EnvironmentalContext
.67** .48** -
4. Social Context .59** .50** .59** -5. Student/ Young Professional (YP = 0)
-.09** -.10** .09** .17* -
6.Ethnic (Non White British = 0 )
.02 -.04* .04* .05* .10** -
7.Gender (Male = 0) .09** .04 .01 .01 -.07** .49** -8. Age -.04 .08** .05* .14** .70** .27** -.22** -9. AUDIT .00 .04* .05* -.02 .00 .12** -.04 -.23** -10.Consumed Alcohol (No= 0)
.50** .26** .66** .31** .09 .37** .22** .06** .01
11. Number drinks consumed
.50** .28** .63** .63** .30** .07** .04 .03 .04 .04
** p < .01 * p < .05
^It may be noted that a number of these correlations are significant but are not sufficient to be deemed strong (r = .07). However, these weak effects may be an issue of sample size, whereby the ability to detect effects is only improved when sample sizes are increased (Cohen, 1992).
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