Title: Snack purchasing is healthier when the cognitive demands of choice are reduced:
a randomised controlled trial.
Authors: Julia L. Allan*1, Marie Johnston1 & Neil Campbell2
1 Health Psychology, Institute of Applied Health Sciences, University of Aberdeen
2 Academic Primary Care, Institute of Applied Health Sciences, University of Aberdeen
Corresponding author name, affiliation, postal address and email address:
*Dr Julia L. Allan,
Health Psychology,
Division of Applied Health Sciences,
Health Sciences Building,
University of Aberdeen
Foresterhill
Aberdeen
AB25 2ZD
+44 (0)1224 438103
j.allan@abdn.ac.uk
Keywords: executive function, point-of-purchase intervention, snacking, food choice,
choice architecture
1
Abstract
Objective: Individuals with inefficient executive (higher level cognitive) function have a
reduced ability to resist dietary temptation. The present study aimed to design and test
a theory-based point of purchase intervention for coffee shops that reduced the calorie
content of customers’ purchases by reducing the need for executive function at the
moment of choice.
Methods: Key facets of executive function were identified by a multi disciplinary group
and used to develop a point of purchase intervention (signage). This intervention was
evaluated in a randomised controlled trial in a public coffee shop on consumer
purchases of >20,000 snacks and drinks over 12 weeks. A sample of customers (n=128)
was recruited to complete an embedded cross-sectional study measuring executive
function strength, dietary intentions, typical purchases and purchases made after
exposure to the intervention.
Results: The proportion of snack purchases that were high in calorie reduced
significantly (t(10)=2.34, p=.04) in intervention weeks relative to control. High calorie
drink purchases were also lower in intervention than control weeks, however, this
difference was not significant (t(10)=1.56, p=.15). On average, customers purchased
items containing 66 calories < usual after exposure to the intervention. The magnitude
of the intervention’s positive effect on customer behaviour increased as executive
function strength decreased (Beta=.24, p=.03).
Conclusions: The calorie content of cafe purchases can be lowered by reducing the
cognitive demands of healthy food choice at the moment of purchase, especially in those
with poor executive function. Environmental changes like these have the potential to
help achieve population weight control.
2
Introduction
Energy dense foods (i.e. foods that contain many calories per portion) are linked to
obesity (Vernarelli, Mitchell, Rolls & Hartman, 2013) and may displace healthier foods
like fruits and vegetables from the core diet (Kraak, Story & Swinburn, 2013).
Unfortunately, reducing consumption of high calorie foods is difficult because humans
have a strong preference for them (Drewnowski & Almiron-Roig, 2010), and they are
readily available in the modern food environment (Swinburn, Sacks, Hall, McPherson,
Finegood, Moodie & Gortmaker, 2011).
Contemporary theories suggest that tempting foods in the environment can lead
directly to consumption through the triggering of automatic affective associations in
long term memory unless these associations are effortfully suppressed by higher level
cognitive or ‘executive functions’ (Hall & Fong, 2007; Strack & Deutsch, 2004; Hoffman,
Friese & Strack, 2009). These executive functions (EF) can be considered the mental
processes that underpin self-control (Friese, Hofmann & Wiers, 2011), and deficiencies
in EF have been linked to obesity in children and adults (Smith, Hay, Campbell & Troller,
2011; Reinert, Po’e & Barkin, 2013), a reduced ability to stick to dietary intentions
(Allan, Johnston & Campbell, 2011; Hall, Fong, Epp & Elias, 2008), higher consumption
of fatty foods (Hall, 2012), and opportunistic snacking while dieting (Allan, Johnston &
Campbell, 2010).
If inefficient executive function reduces dietary control, then interventions to
improve dietary control can either try to enhance individual executive function, or to
reduce the executive demands of healthy choices. While there is some evidence that
executive function can be improved through training and repeated practice (Jaeggi,
Buschkuehl, Jonides, & Perrig, 2008; Karbach & Kray, 2009; Minear & Shah, 2008;
3
Verbruggen & Logan, 2008), training procedures tend to be labour intensive and time
consuming, requiring sustained motivation. An alternative is to extrapolate from the
logic of neuro-rehabilitation: that is, where improvement of function is not possible or
practical, the environment should be adapted to reduce or negate the need for the
cognitive processes involved (Wilson, 2000). The present paper takes this latter
approach and aimed to develop and test an environmental-level, point of purchase
intervention designed to reduce the calorie content of consumers’ purchases by
reducing the need for executive function at the moment of choice.
Methods
Intervention Development
The concept for the intervention was developed by a multi-disciplinary group
comprising health, cognitive and neuro-psychologists, nutritionists and health services
researchers with experience of behavioural interventions (n=8) who met on two
occasions to review existing evidence and theorise the intervention components. The
intervention was designed to target consumption of ‘speciality’ blended coffees and
energy dense snacks because consumption of these items contributes significantly to
weight gain (Nicklas, Baranowski, Cullen & Berenson, 2001; Shields, Corrales,
Metallinos-Katsaras, 2004), and consumption of high calorie items is associated with
individual differences in executive function (Allan et al, 2010; Hall, 2012).
The first meeting of the project group identified executive function processes which
were likely to be involved in healthy food choices and which were theoretically
amenable to intervention (Table 1). Based on this classification, the group developed
4
two point of purchase signs (one for snacks and one for drinks, Figure 1) for use in a
coffee shop, targeting each of the six executive functions identified.
[insert Table 1 and Figure 1 about here]
As signs were to be displayed alongside the food and drink items for sale, the signs
provided a visible memory prompt about dietary intentions at the moment of choice
(reducing the need for prospective memory). The wording followed the format of an
implementation intention (i.e. an ‘if-then’ plan; Gollwitzer, 1999) directing consumers
towards options that would help them to meet the goal of reducing calorie intake
(reducing the need for advance planning). As people display subtle attentional biases
to stimuli presented in the left visual field (Siman-Tov, Mendelsohn, Schonberg, et al,
2007) the signs were displayed at eye level with items arranged from least calorific on
the left to most calorific on the right (directing selective attention towards healthy
items). Arrows were used to highlight the locations of low calorie items as knowledge
of the spatial position of relevant information increases inhibition of information in
other locations (Yantis, 2000) (facilitating inhibition of high calorie items). Signs
displayed one single, calorie value for each whole snack/ drink (calculated from the
kcals per 100g) to enable easy comparison across items (reducing the need for flexible
evaluation). Similarly, the signs included information about every snack and drink
available so that all relevant information was presented simultaneously (reducing the
need for working memory updating).
The finished intervention was then coded
according to Michie et al’s (2013) Behaviour Change Technique Taxonomy as including
8 discrete behaviour change techniques: instruction on how to perform a behaviour
(BCT 4.1), salience of consequences (BCT 5.2), action planning (BCT 1.4), prompts/cues
5
(BCT 7.1), behaviour substitution (BCT 8.2), conserving mental resources (BCT 11.3),
distraction (BCT 12.4), and adding objects to the environment (BCT 12.5).
Intervention Evaluation
Design: Randomised controlled trial (RCT) to evaluate intervention effectiveness
(whether the intervention would reduce the proportion of high calorie items
purchased) plus an embedded cross-sectional study testing whether the intervention
had a larger beneficial effect on customers with weak executive function (as would be
expected if the intervention reduces the need for executive function at the moment of
choice). In the RCT, intervention signs were displayed in a public coffee shop for 6
randomly allocated weeks over a 12 week period, no signs were displayed on ‘control’
weeks and all purchases were recorded. A sample of customers making purchases
during intervention weeks was recruited to the embedded cross sectional study to
complete questionnaire measures of executive function strength, dietary intentions and
typical purchases. The study was carried out in accordance with institutional research
governance policies and was approved by the local research ethics committee.
Setting: A public coffee shop on a large UK academic hospital site was selected because
it sold a selection of high and low calorie snacks/drinks and could provide purchasing
data to the research team. Access was negotiated through the catering manager.
Randomisation: Sign display (or absence) was allocated by week. Sealed envelopes
containing the instructions ‘I’ (intervention) and ‘C’ (control) were selected at random
by an independent observer to determine the final allocation sequence (C-C-I-C-I-I-C-CI-C-I-I).
6
Participants: For the RCT, all coffee shop customers during the 12 week period were
participants (making >20,000 purchases). For the embedded cross-sectional study, a
sample of n=128 customers (97 female, mean age=23.3 years, mean years
education=15.8, 61% of 210 customers approached) were recruited at the point of
purchase during intervention weeks.
Measures -Main RCT: Purchasing Behaviour- All items purchased during the 12 week
measurement period (n=20,516) were recorded automatically by the electronic cash
register in the host site. Sales summaries obtained from this cash register were passed
to the research team by the coffee shop manager at the end of each week.
Measures - Embedded Cross-sectional study: Self-report questionnaire asking forDemographic information: free report of age, gender and years of formal education.
Current purchases and typical purchases - assessed with the items “what did you buy at
this coffee shop today?” and ““on an average trip to this coffee shop (or similar), what
do you typically buy?” Self-reports of current purchases were verified by a researcher
stationed at the point of purchase. The calorie content of current and typical purchases
was calculated using product information/nutritional composition tables. Executive
function: EF was assessed using the Dysexecutive Questionnaire (DEX; Wilson,
Alderman, Burgess, Emslie, & Evans, 1996), a 20 item self-report measure which asks
respondents how frequently behaviours indicative of executive dysfunction occur
(never, occasionally, sometimes, fairly often, very often). Scores range from 0-80, with
higher scores indicative of weak executive function. DEX scores correlate significantly
with performance on objective tests of executive function in both clinical (Burgess,
Alderman, Evans, Emslie & Wilson, 1998) and non-clinical populations (Chan, 2001)
and have good reliability (Bodenburg & Dopslaff, 2008). In the current study, α=.93.
7
Dietary intentions were assessed with four items (α=.94); “Right now…..; (1) “I intend
to reduce my calorie intake”, (2) “I intend to avoid high calorie foods”, (3) I am watching
what I eat”, and (4) “I am trying to lose weight”. Each item was scored on a five point
scale (0-4) from “strongly disagree” to “strongly agree” with higher scores indicating
stronger intentions to reduce intake.
Procedure: The study ran for 12 weeks with signs being put up / taken down at 7.30am
on Monday mornings. Signs were displayed at eye level in the midst of the snack
selection and at the drink order point, and were checked daily to ensure that they had
not been moved or obscured. The manager of the coffee shop provided electronically
collected sales data at the end of each week. During two randomly selected intervention
weeks, a researcher spent 2 hours per day (10am-12noon, 20 hours in total) asking
customers whether they would be willing to participate in a short study about
consumer behaviour. 128 customers complete the embedded cross sectional study.
Analysis: Data were analysed using SPSS v20. All products displayed on the left hand
side of the intervention signs (i.e. all products with a calorie content <the median calorie
content of all available snacks/drinks) were coded as ‘low calorie’, and the remaining
products as ‘high calorie’. For the main RCT analyses, the % of all purchases which were
high calorie was calculated for each individual week and these independent data points
analysed using a standard t-test (see Campbell, Mollison, Steen, Grimshaw & Eccles,
2000). For the embedded cross-sectional study, multiple regression was used to predict
reductions in calorie intake (calculated as differences between typical and current
purchases) from executive function strength after controlling for age, gender, education
and dietary intentions.
8
Results
Intervention Effects: Product Purchases
As summarised in Figure 2, the proportion of snack purchases that were high in calorie
was significantly lower (t(10)=2.34, p=.04, d=1.3) in intervention weeks relative to
control. The proportion of high calorie drink purchases was not significantly different
(t(10)=1.56, p=.15).
[insert Figure 2 about here]
Intervention Effects: Underlying Mechanisms (embedded cross-sectional study)
The sample of 128 customers reported mean intentions to reduce calories of 2.74 ( out
of 4, SD=1.03), and scored an average of 31.72 on the DEX (out of 80, SD=15.21). Only
74 participants (58%) reported current intentions to reduce calorie intake (i.e. reported
agreement with the dietary intention items). Independent samples t-tests revealed that
intenders did not significantly differ from non-intenders in terms of age, education, or
the calorie content of either typical or current snack and drink purchases (p>.10).
Across the whole sample, current purchases had significantly fewer calories (M= 66kcals, SD=116) than self-reported ‘typical’ purchases (t(127)= -5.86, p<.01, d= -1.1). To
determine whether this calorie reduction in purchasing behaviour was related to the
executive function strength of customers, regression analyses were conducted (see
Table 2). Age gender and years of education were entered into block 1 of a hierarchical
multiple regression model (R2= .11, F(3,94)=3.84, p=.01), followed by intentions about
calorie reduction in block 2 (R2Change=.004, FChange(1, 93)=0.39, p=.53) and finally executive
function strength (DEX score) in block 3 (R2Change= .04, FChange(1, 92)=4.70, p<.03). The
final model (Table 2) accounted for 16% of the variance in calorie reduction, and model
9
coefficients revealed that customers with weaker executive functioning (i.e. high DEX
scores) showed a larger reduction from typical calorie intake (i.e. negative calorie
change score) than customers with stronger executive function; mean calorie reduction
was -25kcals for the strongest, and -107kcals for the weakest DEX quartiles.
Additionally, customers with lower levels of formal education showed a larger
reduction from typical calorie intake, independently of the executive control effect.
Discussion
The present paper reports the development and evaluation of an environmental, pointof-purchase intervention designed to help people make lower calorie food purchases by
reducing the need for executive function at the moment of choice. Purchases of high
calorie snacks (as a % of total purchases) were significantly lower during intervention
(41%) than control (45%) weeks, a difference equating to >300 fewer high calorie
snack purchases a week. Purchases of high calorie drinks were also lower in
intervention (46%) than control (50%) weeks, but this difference was not statistically
significant. Within a sample of customers exposed to the intervention, individuals with
weak executive function reduced the calorie content of their purchases more than
individuals with strong executive function, suggesting that the intervention was indeed
reducing the demands on executive function at the moment of choice. Overall,
purchases of drinks and snacks after exposure to the intervention contained 66kcals
fewer than typical purchases.
Although the effects reported appear modest, they have the potential to produce
meaningful benefits at the population level. The intervention objectively reduced high
10
calorie snack purchasing by more than 1SD and subjectively by 66kcals on a single café
visit. Small changes in snacking will benefit health if consistently enacted over time.
Many studies suggest that reducing energy intake by as little as 100kcals a day is
sufficient to prevent weight gain (Hill, Wyatt, Reed & Peters, 2003; Rodearmel, Wyatt,
Stroebele, Smith, Ogden & Hill, 2007; Stroebele, de Castro, Stuht, Catenacci, Wyatt & Hill,
2009). If the present reduction in calorie intake (66kcals) was achieved on a daily basis,
normal body weight individuals could lose (or not gain) 2kg over a year. Such small
changes are a viable approach to population level behaviour change as smaller, less
dramatic changes in behaviour are likely to be more acceptable to, manageable for, and
easily implemented by individuals interested in weight loss (Stroebele et al, 2009).
The present study aimed to facilitate healthy choices by targeting the cognitive
processes involved in self-control and behavioural regulation and is, to our knowledge,
the first point of purchase intervention to use theoretical models of executive function
to change health behaviour. The approach was based largely on ideas from neurorehabilitation where rather than trying to improve cognitive function, environmental
prompts are used strategically to compensate for cognitive inefficiencies by providing
external structure that allows individuals to operate successfully even with reduced
capacity. As self-control (and underlying executive function) is effortful and becomes
impaired when resources are depleted (Hagger, Wood, Stiff, & Chatzisarantis, 2010;
Muraven & Baumeister, 2000), interventions which reduce the need for executive
function during decision making should be beneficial for all decision makers, not just
those with initially weak executive function. However, as demonstrated here, the
results would be expected to be larger in those with weak executive function, as well as
those with less education.
11
The present approach has three strengths worth noting. Firstly, it is cheap,
simple and requires no effort from individuals who come into contact with it. Secondly
the intervention was designed to shift sales from one product to another rather than to
reduce purchases per se, i.e. relative rather than absolute behaviour change to enhance
acceptability to host sites. Thirdly, as people coming into contact with the intervention
could simultaneously hold many different and potentially incompatible goals (e.g. eat
healthily; eat something tasty; choose something quick, spend less than X; etc
Baumeister, Heatherton & Tice, 1994; Trope & Fishbach, 2000), the intervention was
designed to be used flexibly. For example, if a dieting customer preferred chocolate to
fruit, the intervention could be used to find the lowest calorie chocolate bar ‘at a glance’.
The study also had several limitations, most notably that; the embedded crosssectional study only sampled a small percentage of consumers who made purchases in
the intervention weeks of the larger RCT, intentions were measured after food
purchases had been made, reports of ‘typical’ purchases could not be objectively
verified, the measure of executive function was a self-report questionnaire rather than a
more objective test (due to the constraints of the test environment), and it was not
possible to ascertain how many customers throughout the RCT noticed the intervention
signs. In addition, the study setting was a coffee shop used primarily by healthcare
workers / university staff. Customers in this setting may be more educated, more
affluent, or more interested in health relevant information than a representative sample
of the general population.
In conclusion, the present results demonstrate that frameworks from cognitive
neuropsychology can be usefully applied to the design and delivery of interventions in
12
the health behaviour setting. In future, it will be important to determine whether
beneficial effects are maintained over time and across different contexts.
Acknowledgements: The study was funded by the Scottish Government’s Chief
Scientist’s Office (Award CZF/1/37 to JA).
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17
Executive function
process
Implications for healthy food
choice
Intervention strategy
Cognitive flexibility (the
ability to think flexibly,
generate alternatives,
solve problems)
Available options must be
flexibly evaluated to locate an
option that meets all relevant
selection criteria (e.g. tasty,
healthy, low cost, etc)
Make information about all key
decision criteria available in a
format that facilitates easy
comparison and evaluation
Selective attention (the
ability to focus attention
on goal relevant stimuli)
Attention must be directed
Make healthy options more
towards healthy options so they salient, direct attention towards
can be selected
healthy options
Inhibition (the ability to
direct attention away from
goal-irrelevant stimuli,
and to suppress habitual or
prepotent responses)
Attention must be directed
Make unhealthy options less
away from unhealthy items to salient, direct attention away
minimise automatically elicited from unhealthy options
affective associations
Planning (the ability to
work out sequences of
action required to achieve
a future goal)
Planning food choices in
advance (e.g. making action
plans) helps people choose
foods that are in line with
dietary goals
Current dietary intentions must
be recalled during food choice
Provide information about how
to achieve goals at the moment
of choice to negate the need for
advance planning
Food choices should be
updated as new information
emerges (e.g. when other
options on a menu are noticed,
or when questions about
portion size are encountered)
Present all options and all
relevant information
simultaneously
Prospective memory (the
ability to remember to
enact actions at future
points in time)
Updating (the ability to
update information
currently in working
memory with new and
relevant information as it
emerges)
Prompt recall of dietary
intentions at moment of choice
Table 1: Executive function processes, involvement in food choice and strategies for
intervention
18
Std
Beta t
p
R
R2
F
p
Err
____________________________________________________________________________________________________________________
1
.331 .109 3.843 .012
Step
Variable
B
(constant)
Age
Gender
Education
-130.778
-2.812
-41.284
12.804
80.218
2.236
26.558
4.329
-1.630
-.146 -1.258
-.153 -1.555
.340 2.958
.106
.212
.123
.004**
2
.336
(constant)
Age
Gender
Education
Intention to reduce calories
-116.950
-2.683
-33.343
12.190
-7.563
83.459
2.253
29.513
4.452
12.089
-1.401
-.139 -1.191
-.123 -1.130
.324 2.738
-.069 -.626
.113
2.962 .024
.164
.237
.261
.007**
.533
3
.395 .156 3.404 .007
(constant)
-84.507
83.201
-1.016 .312
Age
-2.868
2.211
-.148 -1.297 .198
Gender
-18.830
29.706
-.070 -.634 .528
Education
11.141
4.393
.296 2.536 .013*
Intention to reduce calories
-.733
12.266
-.007 -.060 .952
Executive function strength
-1.747
.805
-.235 -2.169 .033*
____________________________________________________________________________________________________________________
Note: *p<.01, **p<.05
Table 2: Multiple hierarchical regression predicting reduction (from typical) calorie content of purchases from executive function strength,
intentions and demographic factors
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Figure 1: Point of purchase intervention signs targeting snack and hot drink consumption. ©
University of Aberdeen, 2011. Note: Snack sign used full colour pictures of each item which
are not fully reproduced here for copyright reasons.
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Figure 2: Proportion of high calorie purchases in intervention and control weeks (as a % of
total drinks/snacks sold)
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