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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