ArticlePDF Available

Abstract and Figures

The literature on whether varying plate size has an effect on consumption is mixed and contradictory. This meta-analysis of 56 studies from 20 papers shows that varying the size of the container holding food (e.g., plate or bowl) has a substantial effect on amount self-served and/or consumed (Cohen’s d=.43). More generally, we found a doubling of plate size increased the amount self-served or amount consumed by 41%. Our analysis resolves the various contradictions of past reviews: we found that the plate size-effect had a substantial effect on amount served (d=.51), and on amount consumed when the portion was self-served (d=.70) or manipulated along with (confounded with) plate size (d=.48). However, plate size had no effect on amount consumed when the portion size was held constant (d=.03). Overall, plate size had a stronger effect when participants were unaware that they were participating in a food study (d=.76).
Content may be subject to copyright.
Electronic copy available at: http://ssrn.com/abstract=2706338
1
Whether smaller plates reduce consumption depends on who’s
serving and who’s looking: a meta-analysis
Stephen S. Holden, Natalina Zlatevska, Chris Dubelaar
Abstract
The literature on whether varying plate size has an effect on consumption is mixed and
contradictory. This meta-analysis of 56 studies from 20 papers shows that varying the size of
the container holding food (e.g., plate or bowl) has a substantial effect on amount self-served
and/or consumed (Cohen’s d=.43). More generally, we found a doubling of plate size
increased the amount self-served or amount consumed by 41%. Our analysis resolves the
various contradictions of past reviews: we found that the plate size-effect had a substantial
effect on amount served (d=.51), and on amount consumed when the portion was self-served
(d=.70) or manipulated along with (confounded with) plate size (d=.48). However, plate size
had no effect on amount consumed when the portion size was held constant (d=.03). Overall,
plate size had a stronger effect when participants were unaware that they were participating in
a food study (d=.76).
Electronic copy available at: http://ssrn.com/abstract=2706338
2
Introduction
Does plate size affect how much we eat? The Small Plate Movement
(www.smallplatemovement.org) is founded on the premise that smaller plates lead us to eat
less, but the evidence on the effect of plate-size
1
is greatly disputed. Some researchers report
that smaller plates reduce consumption (Van Kleef et al. 2012; Wansink and Van Ittersum
2013; Wansink and Kim 2005; Wansink et al. 2008). But many others report finding no effect
(Libotte et al. 2014; Rolls et al. 2004; Rolls et al. 2007; Shah et al. 2011; Yip et al. 2013).
Some even report negative effects (e.g., Robinson et al 2015c).
Four recent reviews do little to clarify the effects of plate (or bowl or other food-
container) size on consumption. In a qualitative review, Casazza et al. (2014) concluded that
plate size was a “robust driver of self-served portion sizes.” T. Robinson and Matheson
(2015) in another qualitative review concluded that “smaller diameter plates [and] bowls”
reduce consumption while acknowledging that there were some studies that showed no effect.
In the only meta-analysis (quantitative review), E. Robinson et al. (2014b) concluded that
“evidence to date does not show that dishware size has a consistent effect on food intake.
Finally, Libotte et al. (2014) in another qualitative review noted that despite widespread
recommendations to use plate size to control portions, “the evidence to support this is
contradictory”.
In short, the effects of plate size are unclear. We therefore sought to answer the
question of whether smaller plates reduce consumption and given the equivocal results, to
determine the conditions under which plate size affects consumption.
We first distinguished between two distinct dependent variables: the amount of food
self-served vs the amount consumed. Robinson et al’s (2014b) meta-analysis focused on
1
We are using the term “plate size” generically here. It therefore includes plates, bowls and other food-
containers such as packages.
3
“formally measured or recorded food intake while Casazza et al (2014) focused on self-
served portions. In general, the amount self-served is presumed to mediate the amount
consumed (see Figure 1), an assumption confirmed in a number of studies that examine both
dependent variables (e.g., Koh and Pliner 2009; Van Kleef et al. 2012). Wansink and Johnson
(2015) and Robinson et al. (2015b) report that studies measuring both show that the amount
consumed is 85-90% of the amount self-served. However, the effect of plate size on amount
consumed might be different if portions are not self-served. We therefore examined both
dependent variables.
Figure 1: Plate size effects
In terms of independent variables, we first distinguished between manipulations of
area (i.e., plate-size in its strict sense) and volumes (as in bowls and packages). Some studies
manipulate the plate-size in two dimensions (i.e., expanding the diameter of a round plate
increases its area), others manipulate three dimensions (i.e., increasing the volume).
Importantly, both Robinson et al. (2014b) and Libotte et al. (2014) have observed that
manipulations of area appeared to have no effect while manipulations of volume did. This
result seems surprising as a doubling of area would be more noticeable than a doubling of
volume. Research by Chandon and Ordabeyeva (2009, Ordabeyeva & Chandon 2013)
supports this notion showing that consumers appear to be more sensitive to a manipulation in
one dimension, and less sensitive to manipulations in three dimensions. We therefore sought
to re-examine this unexpected result.
Serving plate
Consumption
plate
Amount
Consumed
Amount
Self-served
4
We also distinguish between whether it was the consumption plate or the serving plate
that was manipulated, a variable Libotte et al. (2014) referred to as “food-serving mode”.
Conceptually, consumption and serving plates could independently and even interactively
affect both amount of food self-served and amount consumed (see Figure 1). While
consumption and serving plate manipulations could be crossed, no one has done so to our
knowledge. We anticipated that manipulations of the serving-plate size and the consumption-
plate size would show a positive effect.
We also examined two further variables that have been little examined in the past.
The first is to break down those studies examining amount consumed by how the portion size
was manipulated: was the portion size fixed (the same for both plate sizes), self-served, or
varied (and therefore confounded) with plate size. Research has shown that portion size has a
strong effect on consumption (doubling of a portion leads to 35% greater consumption on
average) (Zlatevska et al. 2014). We therefore sought to examine whether plate size had an
effect independent of portion size.
The second is whether the plate-size effect is mitigated in studies where people are
aware that they are participating in a food study. A number of recent studies suggest that
awareness may mitigate various food consumption effects in general (Robinson et al. 2014a),
and more specifically, plate-size effects (Libotte et al 2014), portion-size effects (Zlatevska et
al 2014), and partitioning effects (Holden & Zlatevska 2015).
Finally, we also extended previous analyses by addressing the multi-dimensionality
and scalability of plate size by developing an elasticity measure of the effect of plate size on
consumption.
5
Method
We have followed PRISMA principles (Preferred Reporting Items for Systematic
Reviews and Meta-Analyses) as the basis for reporting our method and our results (Beller et
al. 2013). The interventions we examined were manipulations of plate size (defined broadly
as plate, bowl, and other food container), the outcomes we examined were amount self-served
and amount consumed. Further details on the interventions, outcomes, and studies that were
included in our analysis are provided in the following.
Interventions
Our primary interest was in examining the effect of plate size manipulations, so
studies needed to have at least two levels of plate size to be included in the analysis
i
. Some
studies included three or more levels, e.g., “small,” “medium” and “large” (Rolls et al. 2004;
Rolls et al. 2007). In these cases, the comparison of small versus medium was entered
into the meta-analysis as one study, and “medium” vs “large” as another. We note that
Robinson et al. (2014b) did this but also included an additional study comparing “small” vs
“large”. We did not as it creates a problem of “double-counting” the effects. It also creates
one effect that is a function of a manipulation that is equal to the sum of two other
manipulations. This highlights that the plate size effect is scalable and we might expect a
larger change from a larger change in plate size. This is an issue that we address later.
In order to try and separate some of the conflicting findings regarding plate size, we
distinguish between various types of interventions. First, and in line with Robinson et al.
(2014b), we distinguish between manipulation of area whether reported as diameter (e.g.,
Rolls et al. 2007) or area (e.g., Koh and Pliner 2009), and volume whether reported as bowl-
size (Ahn et al. 2010) or package size (Wansink and Kim 2005). Second and in line with an
observation by Libotte et al. (2014), we distinguished between whether the plate being
6
manipulated was the serving plate (e.g., Wansink 1996) or the consumption plate (e.g., Rolls
et al. 2007).
Outcomes
Amount of food consumed was recorded in a wide variety of ways: grams (Marchiori
et al. 2012), ounces (Wansink et al. 2006), kilojoules (Shah et al. 2011), calories (Di Santis et
al. 2013) and “percentage of plate surface” (Wansink and Van Ittersum 2013). To avoid any
confusion with energy intake (kilojoules), we only included amount consumed for foods that
were homogenous in terms of energy density. In such cases, whether a study measured
weight consumed or energy consumed was of no consequence
2
.
We also included studies that reported amount self-served even though they were
excluded in some previous reviews
3
. We note that some five studies measured both amount
self-served and amount consumed, and are therefore reported for each outcome (Di Santis et
al. 2013; Koh and Pliner 2009; Van Ittersum and Wansink 2013; Van Kleef et al. 2012;
Wansink et al. 2014).
We excluded all studies that measured perceptions and judgments such as serving a
portion to match a target (Bryant and Dundes 2005; McClain et al. 2014; Penaforte et al.
2014; Van Ittersum and Wansink 2012). Despite their promise, we also excluded studies that
measured body weight change (Ahn et al. 2010; Hanks et al. 2013; Pedersen et al. 2007)
4
and
food wastage (Kallbekken and Sælen 2013) as the outcomes.
2
Studies measuring the consumption of non-food items such as bleach and detergent were excluded
(Wansink 1996, Study 5). A six-week pilot study showing a decline in consumption by Robinson and Matheson
(2015) also had to be excluded due to insufficient data.
3
We note that while some previous reviewers focused on amount consumed (Libotte et al. 2014;
Robinson et al. 2014b), they did appear to include some studies measuring amount self-served. (e.g., Van
Ittersum and Wansink 2013; Wansink et al. 2006).
4
Ahn et al’s data on actual consumption were included.
7
Participants and Study Designs
We included both within-subject (e.g., Rolls et al. 2007) and between-subject
experimental designs (e.g., Wansink and Van Ittersum 2013), field and laboratory based
experiments, with both random (e.g., Van Kleef et al. 2012) and non-random assignment of
subjects to conditions (e.g., Koh and Pliner 2009).
Search Strategy
Studies relevant for the meta-analysis were initially identified through a search of
ABI/Inform, ProQuest Digital Dissertations, Business Source Premier, Web of Science,
PsychInfo, SCOPUS, Google Scholar and other databases using the following keywords:
portion size, plate size, package size, bowl size, dishware, and container size. We also
manually searched through the following relevant journals and conference proceedings where
papers on portion size, plate or container size have been previously published: Journal of
Marketing, Journal of Marketing Research, Journal of Consumer Research, Journal of
Consumer Psychology, Journal of Public Policy and Marketing, Obesity Reviews, Annual
Review of Nutrition, American Journal of Clinical Nutrition, Body and Society, British
Journal of Sociology, Social Science and Medicine, Appetite, Journal of Obesity Research,
Advances in Consumer Research, American Marketing Association Proceedings, and the
Obesity Society Abstract Supplements. The references in papers found in our search were
also examined to identify further studies. We also acknowledge and thank the reviewers and
editors for identifying some papers that were in press or otherwise missed through the above
process.
Data extraction
We recorded data from each study that would allow for the calculation of a
standardized mean difference (Cohen’s d) (see Figures 2a & b). To enable the later
8
calculation of an elasticity coefficient, we also recorded the amount self-served and/or
consumed from the “small” and “large” plate size condition, and the size of the plate if, and
as, reported by the researchers themselves.
Finally, we coded for two further variables of interest. First, we coded for whether or
not portion-size was self-served, manipulated along with (i.e., confounded with) plate size, or
held constant across plate size conditions. Second we coded for whether people were aware
that they were participating in a food study”. For participants to be unaware, the research
would generally feature a non-food cover-story and consumption was measured covertly.
While it is difficult for a within-subjects design to disguise the fact that food is the focus of
the study as noted by Van Kleef et al. (2012), within-subject designs with children were in
some instances, included (e.g., Van Ittersum and Wansink 2013; Wansink et al. 2014 Study
2). These studies featured a field-setting such as a school or summer-camp and the food
manipulations were incorporated into regular meals without notice to the children, so we
judged the participants were unaware.
Results
Fifty six studies reported in 20 papers representing over 3507 subjects were included
in our meta-analysis (see Figures 2a & b for details of the included studies and their
respective effect-sizes, see Web Appendix for full details of all studies). Using Cohen’s d, a
measure of standardized mean differences, calculated under a random effects model, we
found the overall effect of plate size across the 56 studies was d=.43 (95% confidence
intervals +/-.11) which would be described conventionally as a medium effect-size (Cohen
1988).
To address the file-drawer problem, (Rosenthal 1979), we calculated the fail-safe N to
be 2828. This is “the number of [null effect] studies that would need to be added to a meta-
9
analysis to reduce an overall statistically significant observed result to non-significance”
(Rosenberg 2005, p.464). A visual observation of the funnel plot for the k=56 studies shows
some asymmetry with some studies with larger standard errors being over-represented. The
first possible interpretation is that this shows publication bias. However, another possibility is
that given the scalability of plate size, the variation in standard errors may represent different
strength manipulations of plate size (Sterne et al. 2011). That a large change in plate size
might result in a larger effect is something we will capture later by calculating the plate size
elasticity of consumption. In any case, standard meta-analytic reporting gives less weight to
studies with higher standard errors and smaller ns.
The heterogeneity of studies was considerable and the effect-size varied from study to
study as indicated by Q=212.3 which was much greater than the degrees of freedom (df=55).
The I
2
index, indicating the percentage of variation in the meta-analysis that was attributable
to study heterogeneity, was a substantial 74% (Higgins and Thompson 2002; Huedo-Medina
et al. 2006).
10
Figure 2a: Effect of plate size on amount consumed
11
Figure 2b: Effect of plate size on amount self-served
The overall d=.43 shows that plate size had a positive effect on amount consumed
and/or self-served. Due to the high degree of study heterogeneity, the fact that five studies
provided measures of both amount self-served and amount consumed (and are therefore
12
double-counted), and the existence of reviews reporting no plate size effect at least in terms
of those studies which manipulate only area as opposed to volume (Libotte et al. 2014;
Robinson et al. 2014b), we proceeded by breaking down the effect.
We first examined plate size effects broken down by the outcome measured (amount
consumed vs amount self-served), dimensions manipulated (area vs volume), and type of
plate (consumption vs serving plate) (see Figure 3). This analysis enabled us to understand
more clearly the conflicting views of whether plate size has an effect, but we caution against
interpreting this analysis as if the variables are fully crossed in an experimental sense. As
may be seen, some cells in this analysis had no or few observations: we found no studies
manipulating the area of a serving plate, and only one study that examined the effect of a
volume manipulation on amount consumed (Van Kleef et al. 2012). Even for those cells with
more observations, the studies contained in each cell differed from studies in other cells in
many ways beyond those variables used to create the subgroups.
13
Figure 3: Effects of plate size - summary
The chart is a forest plot displaying the average effect sizes as a standardized mean difference
or Cohen’s d (displayed in the box) and their respective 95% confidence intervals shown with
bars. The number of studies on which the effect-size estimate was based is shown as k in
parentheses.
As shown in Figure 3, plate sizes had moderate to strong effects on amounts self-
served and consumed across most cells (d=.33 up to d=1.15). There was one notable
exception: the manipulation of area of consumption plates had a small (and non-significant)
effect on amount consumed (d
A
=.06 +/-.20). This single cell where the area of a consumption
plate was manipulated appears to account for the conflicting conclusions seen in the
literature. The two reviews which focused on amounts consumed (top half of Figure 3)
concluded that manipulations of area (“plates”) had no effect relative to manipulations of
AMOUNT SELF-SERVED:
AMOUNT CONSUMED:
14
volume (“bowls”) (Libotte et al. 2014; Robinson et al. 2014b). However, our analyses show
that area and volume had an approximately equal effect on amount self-served (d
area
=.49 +/-
.30 vs d
volume
=.52 +/-.19) (bottom half of Figure 3). The two other reviews focused on amount
self-served and accordingly concluded that plate size had a clear positive effect (Casazza et
al. 2014; Robinson and Matheson 2015).
While the presentation in Figure 3 may encourage us to think that the anomalous
effect is driven by something special about the interaction of area and consumption plate, we
again highlight that it would be a mistake to think of the elements in this cell as having been
randomly drawn from the population of all possible studies. This is perhaps highlighted by
the fact that five of the 10 studies in this cell are from one paper (Rolls et al 2007), and that
there are no observations of the effect of manipulating the serving-plate area on amount
consumed.
We then proceeded by examining the independent effects of five different variables
on the plate size effect: (1) outcome variable (amount consumed vs amount self-served), (2)
dimensions manipulated (area or volume), (3) type of plate (consumption vs serving), (4)
whether subjects were aware that they were participating in a food study or not, and (5) how
portion size was manipulated within plate size for amount consumed. The results are
presented in Figure 4
15
Figure 4: Effect of plate size - analysis by subgroups
* This analysis examines the effect of plate size on amount consumed only (k=27).
Of the five variables examined, the effect of plate size remains fairly consistent
whether amount self-served or amount consumed is measured, and whether area or volume,
and whether serving or consumption plate is manipulated. However, the effect of plate-size is
considerably greater under specific conditions as shown in the bottom two panels of Figure 4.
OUTCOME
DIMENSIONS
PLATE-TYPE
FOOD STUDY AWARENESS
PORTION SIZE & AMOUNT CONSUMED*
16
Specifically, the plate-size effect was greater when consumers were unaware that they were
participating in a food study (d
unaware
=.76 vs d
aware
=.31). We note that not only was the effect
larger, but it was statistically significant the 95% confidence intervals for each estimate do
not overlap. Furthermore, examining the effect of plate size on amount consumed, the effect
was stronger if the consumers self-served their portions, or the portion sizes were confounded
with the plate size. There is an important implication here plate size does not appear to have
an effect on amount consumed if the portion size remains fixed across plate sizes. So while
the effect of plate size on amount consumed and amount self-served does not appear to be
very different on average, the results suggest that the effect of plate size on amount consumed
is to a large degree, mediated by a portion-size effect. In the case of self-service, the larger
plates apparently encourage larger portions, and so the consumer eats more.
We note that there are insufficient observations to say whether the effect of
unawareness is crossed with these other variables. Of the 10 studies in which portion-size
was fixed, all comprised participants who were aware they were participating in a food study.
Further research is required to establish whether or not plate size might have an effect where
the portion is fixed if participants are unaware.
A limitation of this subgroup analysis is that it ignores possible interactions of the
identified variables with one another although, as noted, the lack of observations and lack of
random sampling constrain any effort to conduct such an analysis. However, the problem
does highlight that the anomalous cell seen in Figure 3 where the plate size effect has no
effect, may reflect the influence of variables other than those identified. For instance, in this
anomalous cell, nine of the 10 studies were with subjects who were aware they were
participating in a food which tends to show a smaller effect as seen in Figure 4. Moreover,
there was one study out of 10 in this cell that showed a strong plate-size effect (Wansink &
17
Van Ittersum, 2013, Study 2 see Figure 2a). The subjects in this study self-served their
portions and were unaware they were participating in a food study.
Scaled effect size
To complete our analysis, we developed a scalable measure of the plate size effect to
address the problem that Cohen’s d is difficult to interpret. Cohen’s d (and related
standardized mean difference measures) report the effect of “control versus treatment or in
our case, large versus small plate size, but nothing beyond this (Chernev et al. 2010). No
allowance is made for the fact that some researchers increased the plate size from the small to
large condition by 200% (Marchiori et al. 2012) while others increased it by just 30% (Rolls
et al. 2004). In other words, effect size as measured by Cohen’s d cannot capture a plate-size
effect which changes as a function of the change in plate size.
In an effort to address this problem, we calculated percentage change in plate size and
the resulting percentage change in consumption to allow for the effect-size to be expressed as
a plate size elasticity of consumption. The percentage change in consumption was calculated
as follows:
(1)
C / C
S
C = change in consumption (amount eaten from larger plate amount eaten
from smaller plate)
C
S
= consumption from smaller plate size
We then calculated the same change parameter for plate size although we note that it
was necessarily conditioned on the dimensions that were manipulated. For those studies
reporting plate size as a diameter, we first converted this into an area ((dia/2)^2*π) and then
expressed change in area for all observations as follows.
(2) A / A
S
A = change in area = (larger area smaller area)
18
A
S
= smaller area
Change in volume was measured in a similar way although we note that studies
typically report change by capacity of the container (e.g., 100g package or 2L bowl), and
almost never included information about the actual physical dimensions.
(3) V / V
S
V = change in volume = (larger volume smaller volume)
V
S
= smaller volume
We then regressed change in consumption (Equation 1) on the change in plate-size
(be it area as in Equation 2 or volume as in Equation 3) with no constant and with each study
weighted by the meta-analytic weights generated under a random effects model
5
. The
coefficient generated represents an elasticity measure which can be interpreted as the
percentage change in consumption for a doubling (100%) increase in plate size.
Looking across all 56 studies combined (including five studies that provided measures
of both amounts self-served and consumed), doubling the plate size led to increases in
consumption of 41% on average (p<.001, k=56). Alternatively stated, halving the plate size
led to a 29% reduction in amounts self-served/consumed on average. This scalable measure
might offer some promise for exploring the conditions under which plate size effect varies,
but requires more observations (studies) in order to be stable.
Discussion
The results from both the meta-analysis and the elasticity analysis show that plate size
has a considerable effect overall on amount self-served and consumed. While two previous
5
The regressions were modelled without a constant because a zero percent change in plate size has a
zero percent change in consumption. In any case, and following Eisenhauer (2003), we note that when included,
the constant was not significant, and the coefficient for change in plate size was little changed. While the
presented regressions were weighted, unweighted regressions returned virtually identical results, consistent with
the random-effects model weights being more “balanced” than under fixed-effects (Borenstein et al. 2009).
19
reviews concluded that plate size had no reliable effect on amount consumed (Libotte et al.
2014; Robinson et al. 2014b), our analysis found that there was a substantial plate size effect
on amount consumed, but only if the consumer self-served their portions, or portion size was
varied in line with plate-size. Plate size had no effect on average in situations where portion
sizes were held constant across plate sizes.
Our analyses have also suggested that a major driver of the effect is whether subjects
are aware that they are participating in a food study or not. If participants were unaware that
they were participating in a food study, the effect of manipulating plate-size was substantially
(and significantly) larger (d
unaware
=.76 vs d
aware
=.31), a finding in line with a suggestion by
Libotte et al. (2014). This result is consistent with other recent reports suggesting that
important demand effects operate in food studies. Zlatevska and colleagues (Zlatevska et al.
2014, Holden & Zlatevska 2015) have showed that the effects of portions and partitions
respectively were reduced when research participants were participating in a study where
food was the focus. Robinson and colleagues (Robinson 2015, Robinson et al. 2014a, 2015a)
have conducted a meta-analysis and empirical studies showing that when subjects know they
are being observed, their consumption is reduced.
Overall, our analysis supports the notion that plate size positively influences
consumption when portions are self-served or varied in line with plate size, and if consumers
are unaware that their consumption is being monitored. But significant areas remain to be
explored more fully. Our research highlights that more attention needs to be directed to the
distinction between amount consumed and amount self-served, and in particular, the way in
which self-served portions may effectively mediate the observed plate size effect.
While some previous reviewers have suggested that bowls or manipulations of
volume, have a greater effect than manipulation of area (Robinson et al. 2014, Libotte et al.
2014), we consider this unlikely. Our results show that there is little distinction between
20
manipulating a plate in two dimensions (area) or three dimensions (volume as in a bowl).
Moreover, if there were to be a difference, we would expect two-dimensional changes to have
a greater effect than three-dimensional changes in view of the compelling evidence that the
perceived size and perceived change in size in containers is quite distinct from the actual
change (Chandon and Ordabayeva 2009; Ordabayeva and Chandon 2013). Changes in one
dimension are generally perceived as bigger than an equivalent change in three dimensions
(Chandon and Ordabayeva 2009; Raghubir and Krishna 1999). In any case, very few of the
studies included in our analysis gave any consideration to the perceived change. With regard
to volume in particular, we found virtually no studies that reported the dimensions of the
container. This made it impossible to code those studies manipulating volume for the number
of dimensions changed. Given the promising opportunities offered through the manipulation
of one vs all three dimensions as highlighted by Chandon and Ordabeyeva (2009), we think
that it would be helpful if at the very least, future studies examining the effect of plate size,
and especially volume, report the three dimensions of the container of both the smaller and
larger plate.
In terms of future directions and implications, we think that further exploration of the
awareness by participants of their participation in a food study deserves more attention. In
their qualitative review, Libotte et al. (2014) noted that in all the studies they reviewed where
they found a plate size effect, “participants were distracted from food-consumption or
serving” and “no distraction factors were present in the studies that did not find a significant
effect of plate size.They confirmed this in their own study of plate size in a setting using a
“fake-food buffet”: their study produced no effect. In our review, we report no effect of plate
size on amount consumed if the portion size was held constant but there is an important
caveat: all studies contributing to this result featured participants who were aware they were
21
in a food study. Therefore, we need studies with unaware participants investigating whether
or not plate size has an effect when portion sizes are held constant.
In view of the potential importance of subject awareness, we add our voice to
Robinson’s (2015) call “to lie more to participants in eating behaviour experiments.”
Importantly, we highlight that the blinding required is more than simply blinding to the
manipulation but also blinding to the measurement. That is, the individual needs to be
“double-blinded”: unaware of the plate size manipulation and unaware even that they are
participating in a food-consumption experiment.
However we also hasten to add that blinding in the classic social experimentation
sense overlooks another promising possibility related to the human adaptation to novel
stimuli (Berlyne 1971). A novel stimulus attracts attention initially, becomes familiar, and
ultimately, is forgotten. This may explain how some longer-term trials with smaller dishware
have reported success in reducing consumption (Ahn et al 2010, Robinson and Matheson
2015) and weight (Hanks et al. 2013; Pedersen et al. 2007). Ensuring participants are blind to
the manipulation or the measurement in such trials is likely to be virtually impossible. We
speculate that awareness of the changed dishware attenuates over time. The result therefore,
consistent with the notion that the effect works best when the participant is blind to the
intervention. The difference is that through adaptation, the consumer becomes blind to long-
term manipulations of plate size.
In summary, smaller plates will reduce the amount self-served to a plate. Smaller
plates also reduce the amount consumed if the consumers are self-serving to those smaller
plates or portion size is manipulated in line with the plate size. However, simply reducing
plate size and holding portion size constant appears to have no effect but this needs further
investigation with unaware consumers. The plate size effect is observed to be larger if the
consumers do not believe they are being watched. So, the widespread, long-term use of
22
smaller plate sizes may help reduce consumption and perhaps obesity in precisely the same
way that we have become blind to how large plate sizes have become. Continual use of
smaller plate sizes may be both habit-forming and good for your health.
The Larger Theme: Small steps towards overcoming obesity
Obesity is growing (Flegal et al. 2002, 1998; Young and Nestle 2002), and concern
about obesity is growing at a proportional rate. Despite enormous amounts of research and
attention directed to the obesity problem, there appears to be no simple solutions. Perhaps this
is an important finding: there is no large simple solution, rather obesity can only be
successfully tackled by a series of small steps.
Our research suggests that substituting small for larger plates is one such small step
that will help, especially if consumers are self-serving to the plate, and if the change to
smaller plates is not signalled to the consumer. This research fits within a larger field of small
ways in which the amount we consume can be potentially limited by portions served such as:
smaller plates (shown in this paper), smaller “food units” (Davis et al. 2016), smaller portions
(Zlatevska et al 2014), partitioned portions (Holden & Zlatevska 2015), or conversely,
encouraging people to eat less by making portions appear larger (e.g., McClain et al. 2014;
Wansink and Van Ittersum 2003, 2006).
At a broader level, the efforts towards downsizing consumed portion sizes all fit
within a much larger field of small steps used to modify the consumption environment so as
to nudge people towards healthier and less wasteful consumption behaviours (e.g., Block,
Williamson and Keller 2016; Szocs and Biswas 2016).
So there are solutions: they are small, but also numerous. The greatest challenge
perhaps, is how to implement these ideas. In this regard, we see four different stakeholders
23
we need to address: public health policy-makers, food marketers, consumers and food
researchers.
The first stage in encouraging these small steps towards a solution is through the
public health policy path. But it is difficult to imagine how these small steps might be
implemented by the public health authorities, especially given evidence that consumers can
react negatively to heavy-handed approaches (Pham et al. 2016). The best public health
approach would appear to be to provide information and ideas for implementing small
changes, and leaving the changing to others.
A second step is for marketers to actively engage in encouraging healthier consumer
habits. While some may view marketer involvement with some caution, they do in fact have
the capacity and in many instances, the interest to help nudge consumers in the right
direction. In this regard then, we might encourage marketers to implement ideas such as
making healthy the default option (e.g., Peters et al 2016).
Ultimately, change has to be implemented at the consumer level, and for this to work,
a more consumer-empowered, bottom-up approach is probably needed. In this consumer-
centric version, consumers are encouraged (by distributed information) to make informed
decisions to help themselves. It is noted that in this regard, the very public outcry about the
growth in obesity may be viewed as a positive. Due to this media attention, many consumers
are searching for solutions, even if small and subtle. The Small Plate Movement
(www.smallplatemovement.org) is an excellent example of a consumer-focused intervention
that encourages better habits for better living.
Finally, food researchers can help, and our research suggests directions for future
efforts. While food researchers are typically united in seeking solutions for obesity, a search
for strong, simple solutions can potentially hide smaller, subtle solutions. Our research was
motivated by the considerable confusion about whether small plates work to reduce
24
consumption or not. Many had studied the effect of small plate sizes in various settings
generating a wide range of results, positive, neutral and even negative. Even review articles
have disagreed about whether there is an effect or not (Casazza et al. 2014; Libotte et al.
2014; E. Robinson et al. 2014b; T. Robinson and Matheson 2015). Our research resolved this
confusion: we show that the effect of smaller plates on amount self-served is substantial but
the effect of smaller plates on amount consumed holds only under specific conditions,
notably where consumers self-serve their portions, or when portion size is reduced along with
plate size. More research on the small steps that can be taken, and the conditions under which
they do and do not operate is to be encouraged.
Our research also revealed another sometimes entrenched practice in food science that
we believe needs shifting: we found the overall effect of plate sizes was stronger if study
participants were unaware that their consumption is being monitored. Our paper adds to a
growing body of research suggesting that the effects of portion sizes and partitioning are
mitigated when subjects are aware they are in a food study (Holden & Zlatevska 2015;
Zlatevska et al. 2014). This fits with broader reviews showing that aware participants tend to
modify or constrain their consumption (Robinson et al. 2014a; Robinson et al.; 2015a).
Importantly then, the small and subtle steps that can be used to modify consumption might be
missed in studies where participants are aware they are in a food study.
The obesity problem can be resolved through a series of small steps. Our research
offers some clarification around one such small step. Use small plates, especially if you are
self-serving your food to your plate. It will encourage you to self-serve and eat less. What
about the problem that if someone installs smaller plates in their household, everyone will
know, that is, be aware of the change? Our research does show that smaller plates work best
if people are unaware that their consumption is being monitored. Fortunately, while humans
tend to notice novel stimuli, the flipside is that they tend to adapt to and overlook familiar
25
stimuli. So, even if the change to small plates in a household is a conscious decision, over
time, the members of the household are likely to become unaware of the change, and we
might expect to see behavior change accordingly. There is already some evidence to suggest
that this is the case with individuals adopting smaller dishware showing a loss of weight over
time (Hanks et al. 2013; Pedersen et al. 2007).
The problem of obesity is growing. Smaller plates are one of a range of small steps
that can help reduce the amount self-served, and so the amount consumed.
26
References
Ahn, Hee Jung, Kyung Ah Han, Hwi Ryun Kwon, Kyung Wan Min (2010), “Small rice
bowl-based meal plan was Effective at Reducing Dietary Intake, Body Weight, and
Blood Glucose Levels in Korean Women with Type 2 Diabetes Mellitus,” Korean
Diabetes Journal, 34 (6), 340-349.
Beller, Elaine M., Paul P. Glasziou, Douglas G. Altman, Sally Hopewell, Hilda Bastian, Iain
Chalmers, Peter C. Gøtzsche, Toby Lasserson, and David Tovey (2013), "PRISMA for
abstracts: Reporting systematic reviews in journal and conference abstracts," PLOS
Medicine, 10 (4), 1-8.
Berlyne, D. E. (1971), "Novelty and attention: Controls for retinal adaptation and for
stimulus-response specificity," Psychonomic Science, 25 (6), 349-351.
Block, Lauren, Sara Williamson, Punam Keller (2016), “Of waste and waists: The effect of
plate material on food consumption and waste,” Journal of the Association for Consumer
Research.
Borenstein Michael, Larry V. Hedges, Julian P.T. Higgins, Hannah R. Rothstein (2009),
Introduction to Meta-Analysis. John Wiley & Sons, Ltd, Chichester, UK
Bryant, Rachel, and Lauren Dundes (2005), "Portion distortion: A study of college students,"
Journal of Consumer Affairs, 39 (2), 399-408.
Casazza, Krista, Andrew Brown, Arne Astrup, Fredrik Bertz, Charles Baum, Michelle Bohan
Brown, John Dawson, Nefertiti Durant, Gareth Dutton, David A. Fields, Kevin R.
Fontaine, David Levitsky, Tapan Mehta, Nir Menachemi, Pk Newby, Russell Pate,
Hollie Raynor, Barbara J. Rolls, Bisakha Sen, Daniel L. Smith, Diana Thomas, Brian
Wansink, David B. Allison, and Steven Heymsfield (2014), "Weighing the evidence of
common beliefs in obesity research," Critical Reviews in Food Science and Nutrition,
27
Chandon, Pierre, and Nailya Ordabayeva (2009), "Supersize in one dimension, downsize in
three dimensions: Effects of spatial dimensionality on size perceptions and preferences,"
Journal of Marketing Research, 46 (December), 739-53.
Chang, Un-Jae, Hyung-Joo Suh, Sun O. Yang, Yang H. Hong, Suk K. Young, Jin M. Kim,
and Eun Y. Jung (2012), "Distinct foods with smaller unit would be an effective
approach to achieve sustainable weight loss," Eating Behaviors, 13 (1), 74-77.
Chernev, Alexander, Ulf Bockenholt, and Joseph Goodman (2010), "Commentary on
Scheibehenne, Greifeneder, and Todd : Choice overload: Is there anything to it?"
Journal of Consumer Research, 37 (3), 426-8.
Cohen, Jacob (1988), Statistical power analysis for the behavioral sciences, 2nd edn.
Hillsdale, NJ: Lawrence Erlbaum Associates.
Davis, Brennan, Collin R. Payne, and My Bui (2016), “ Table diameter changes the effect of
unit size on food choice”, Journal of the Association for Consumer Research.
Di Santis, Katherine I., Leann L. Birch, Adam Davey, Elena L. Serrano, Jun Zhang, Yasmeen
Bruton, and Jennifer O. Fisher (2013), "Plate size and children’s appetite: Effects of
larger dishware on self-served portions and intake," Pediatrics, 131 (5), e1451-8.
Eisenhauer, Joseph G. (2003), “Regression through the origin,” Teaching Statistics, 25 (3),
76-80.
Flegal, Katherine M., Margaret D. Carroll, Robert J. Kuczmarski, Clifford L. Johnson (1998)
“Overweight and obesity in the United States: prevalence and trends, 1960-1994,”
International Journal of Obesity and Related Metabolic Disorders, 22 (1), 39-47.
Flegal, Katherine M., Margaret D. Carroll, Cynthia L. Ogden, and Clifford L. Johnson (2002)
“Prevalence and trends in obesity among US adults, 1999-2000,”JAMA: The Journal of
the American Medical Association, 288 (14), 1723-1727.
28
Hanks, Andrew, K. Kaipainen, and Brian Wansink (2013), "The Syracuse plate: Reducing
BMI by introducing smaller plates in households," Journal of Nutrition Education and
Behavior, 45 (4), S41.
Higgins, Julian P. T., and Simon G. Thompson (2002), "Quantifying heterogeneity in a meta
analysis," Statistics in Medicine, 21 (11), 1539-58.
Holden, Stephen S., and Natalina Zlatevska (2015), "The partitioning paradox: The big bite
around small packages," International Journal of Research in Marketing, 32 (2), 230-
233.
Huedo-Medina, Tania B., Julio Sánchez-Meca, Fulgencio Marín-Martínez, and Juan Botella
(2006), "Assessing heterogeneity in meta-analysis: Q statistic or I² index?"
Psychological Methods, 11 (2), 193-206.
Kallbekken, Steffen, and Håkon Sælen (2013), "Nudging' hotel guests to reduce food waste
as a win-win environmental measure," Economics Letters, 119, 325-7.
Koh, Jiaqi, and Patricia Pliner (2009), "The effects of degree of acquaintance, plate size, and
sharing on food intake," Appetite, 52 (3), 595-602.
Libotte, E., Michael Siegrist, and T. Bucher (2014), "The influence of plate size on meal
composition. literature review and experiment," Appetite, 82, 91-96.
Marchiori, David, Olivier Corneille, and Olivier Klein (2012), "Container size influences
snack food intake independently of portion size," Appetite, 58 (3), 814-7.
McClain, A. D., Wouter van den Bos, Donna Matheson, Manisha Desai, Samuel M.
McClure, and Thomas N. Robinson (2014), "Visual illusions and plate design: The
effects of plate rim widths and rim coloring on perceived food portion size,"
International Journal of Obesity, 38 (5), 657-662.
Ordabayeva, Nailya, and Pierre Chandon (2013), "Predicting and managing consumers'
package size impressions," Journal of Marketing, 77 (5), 123-37.
29
Pedersen, Sue D., Jian Kang, and Gregory A. Kline (2007), "Portion control plate for weight
loss in obese patients with type 2 diabetes mellitus," Archives of Internal Medicine, 167
(June), 1277-1283.
Penaforte, F. R. O., C. C. Japur, R. W. Diez-Garcia, J. C. Hernandez, I. Palmma-Linares, and
P. G. Chiarello (2014), "Plate size does not affect perception of food portion size,"
Journal of Human Nutrition and Dietetics, 27, 214-9.
Pham, Nguyen, Naomi Mandel, and Andrea C. Morales (2016), “Messages from the food
police: how food-related warnings backfire among dieters,” Journal of the Association
for Consumer Research, 1 (1), forthcoming
Raghubir, Priya, and Aradhna Krishna (1999), "Vital dimensions in volume perception: Can
the eye fool the stomach?" Journal of Marketing Research, 36 (3), 313-26.
Robinson, Eric (2015), "I'm watching you. Why we might need to lie more to participants in
eating behaviour experiments," Appetite, 83, 343.
Robinson, Eric, Inge Kersbergen, Jeffrey M. Brunstrom, and Matt Field (2014a), "I'm
watching you. Awareness that food consumption is being monitored is a demand
characteristic in eating-behaviour experiments," Appetite, 83, 19-25.
Robinson, Eric, Charlotte Hardman, Jason Halford, and Andrew Jones (2015a) “Eating under
observation: a systematic review and meta-analysis of the effect that heightened
awareness of observation has on laboratory measured energy intake,” American Journal
of Clinical Nutrition.
Robinson, Eric, Sarah Nolan, Catrin Tudur-Smith, Emma J. Boyland, Jo A. Harrold, and
Jason C. G. Halford (2015b), "The not so clean plate club: Food self-served won’t
always result in food eaten," International Journal of Obesity, 39, 376.
Robinson, Eric, Sarah Nolan, Catrin TudurSmith, Emma J. Boyland, Jo A. Harrold,
Charlotte A. Hardman, and Jason C. G. Halford (2014b), "Will smaller plates lead to
30
smaller waists? A systematic review and metaanalysis of the effect that experimental
manipulation of dishware size has on energy consumption," Obesity Reviews, 15 (10),
812-21.
Robinson, Eric, Florence Sheen, Jo Harrold, Emma Boyland, Jason Halford, and Una Masic
(2015c) Dishware size and snack food intake in a between-subjects laboratory
experiment,” Public Health Nutrition, May (21), 1-5
Robinson, Thomas N., and Donna M. Matheson (2015), "Environmental strategies for portion
control in children," Appetite, 88 (May), 33-38.
Rolls, Barbara J., Liane S. Roe, Kitti H. Halverson, and Jennifer S. Meengs (2007), "Using A
smaller plate did not reduce energy intake at meals," Appetite, 49 (3), 652-60.
Rolls, Barbara J., Liane S. Roe, Tanja V. E. Kral, Jennifer S. Meengs, and Denise E. Wall
(2004), "Increasing the portion size of a packaged snack increases energy intake in men
and women," Appetite, 42 (1), 63-9.
Rosenberg, Michael S. (2005), "The filedrawer problem revisited: A general weighted
method for calculating failsafe numbers in metaanalysis," Evolution, 59 (2), 464-8.
Rosenthal, Robert (1979), "The 'file drawer problem' and the tolerance for null results,"
Psychological Bulletin, 86 (3), 638-41.
Shah, Meena, Rebecca Schroeder, Walker Winn, and Beverley Adams-Huet (2011), "A pilot
study to investigate the effect of plate size on meal energy intake in normal weight and
overweight/obese women," Journal of Human Nutrition and Dietetics, 24 (6), 612-5.
Sharp, David, and Jeffery Sobal (2012), "Using plate mapping to examine sensitivity to plate
size in food portions and meal composition among college students," Appetite, 59 (3),
639-645.
Sterne, Jonathan A. C., Alex J. Sutton, John P. A. Ioannidis, Norma Terrin, David R. Jones,
Joseph Lau, James Carpenter, Gerta Rücker, Roger M. Harbord, Christopher H. Schmid,
31
Jennifer Tetzlaff, Jonathan J. Deeks, Jaime Peters, Petra Macaskill, Guido Schwarzer,
Sue Duval, Douglas G. Altman, David Moher, and Julian P. T. Higgins (2011),
"Recommendations for examining and interpreting funnel plot asymmetry in meta-
analyses of randomised controlled trials," British Medical Journal, 343 (July), d4002.
Szocs, Courtney and Dipayan Biswas (2016), “Healthier with a spoon? The effects of mode
of consumption on food perceptions and consumption decisions,” Journal of the
Association for Consumer Research.
Van Ittersum, Koert, and Brian Wansink (2012), "Plate size and color suggestibility: The
delboeuf illusion’s bias on serving and eating behavior," Journal of Consumer Research,
39 (2), 215-28.
Van Ittersum, Koert, and Brian Wansink (2013), "Extraverted children are more biased by
bowl sizes than introverts," PloS One, 8 (10), e78224.
Van Kleef, Ellen, Mitsuru Shimizu, and Brian Wansink (2012), "Serving bowl selection
biases the amount of food served," Journal of Nutrition Education and Behavior, 44
(January), 66-70.
Wansink, Brian (1996), "Can package size accelerate usage volume?" Journal of Marketing,
60 (3), 1-14.
Wansink, Brian (2006) Mindless Eating: Why we eat more than we think, New York, NY:
Bantam Dell.
Wansink, Brian, and Matthew M. Cheney (2005), "Super bowls: Serving bowl size and food
consumption," JAMA: The Journal of the American Medical Association, 293 (14),
1727.
Wansink, Brian and Koert Van Ittersum (2003), “Bottoms up! The influence of elongation on
pouring and consumption volume.” Journal of Consumer Research, 30 (December), 455-463.
32
Wansink, Brian and Koert Van Ittersum (2006), “The visual illusions of food: Why plates,
bowls, and spoons can bias consumption volume.” The Journal of the Federation of
American Societies for Experimental Biology, 20: A618
Wansink, Brian, and Koert Van Ittersum (2013), "Portion size me: Plate-size induced
consumption norms and win-win solutions for reducing food intake and waste," Journal
of Experimental Psychology: Applied, 19 (4), 320-32.
Wansink, Brian, Koert Van Ittersum, and James E. Painter (2006), "Ice cream illusions.
bowls, spoons, and self-served portion sizes," American Journal of Preventative
Medicine, 31 (3), 240-3.
Wansink, Brian, Koert Van Ittersum, and Collin R. Payne (2014), "Larger bowl size increases
the amount of cereal children request, consume, and waste," The Journal of Pediatrics,
164 (2), 323-6.
Wansink, Brian, and K.A. Johnson (2015), "The clean plate club: About 92% of self-served
food is eaten." International Journal of Obesity, 29 (2), 371-374.
Wansink, Brian, and Junyong Kim (2005), "Bad popcorn in big buckets: Portion size can
influence intake as much as taste," Journal of Nutrition Education and Behavior, 37 (5),
242-5.
Wansink, Brian, Collin Payne, and Carolina Werle (2008), "Consequences of belonging to
the 'clean plate club'," Archives of Pediatric and Adolescent Medicine, 162 (10), 994-5.
Yip, Wilson, Katy R. Wiessing, Stephanie Budgett, and Sally D. Poppitt (2013), "Using a
smaller dining plate does not suppress food intake from a buffet lunch meal in
overweight, unrestrained women," Appetite, 69, 102-7.
Young, Lisa R. and Marion Nestle (2002), “The Contribution of Expanding Portion Sizes to
the US Obesity Epidemic,” American Journal of Public Health, 92 (2), 24649.
33
Zlatevska, Natalina, Chris Dubelaar, and Stephen S. Holden (2014), "Sizing up the effect of
portion size on consumption: A meta-analytic review," Journal of Marketing, 78 (3),
140-54.
i
We excluded studies that manipulated the size and number of containers simultaneously. Specifically,
Wansink and Cheney (2005) which manipulated both the size and the number of serving bowls (2x4L vs 4x2L)
was excluded even though it was included in other reviews (Libotte et al. 2014; Robinson and Matheson 2015).
The problem is that the size manipulation is confounded with the number manipulation, a feature of
“partitioning” studies which can have paradoxical and contradictory effects (Zlatevska et al. 2014, Holden &
Zlatevska 2015).

Supplementary resource (1)

... Evidence is inconclusive as to whether the size of tableware impacts on the amount of food consumed. One systematic review found no consistent effect of larger tableware on consumption [6], a Cochrane review found a small to medium effect [7], while the most recent meta-analysis [8] found a substantial effect. These meta-analyses specified different inclusion criteria for the consumption outcomes which may contribute to the observed differences in effect sizes. ...
... These meta-analyses specified different inclusion criteria for the consumption outcomes which may contribute to the observed differences in effect sizes. Importantly, in Holden et al. [8], the overall effect was explained by a large effect of plate size on consumption when food was self-served, with a minimal effect when portion size was held constant, i.e. when people were given pre-served portions. Given selection (including serving on to a plate) of food is a necessary precursor to consumption, some studies have examined this outcome separately to consumption, with the aforementioned Cochrane review by Hollands et al. [7] finding a medium-sized effect of plate size on selection. ...
... In terms of plate size, the finding that larger plates led to larger servings-suggesting approximately linear differences between the three plate sizes-is in line with previous meta-analyses that show moderate to large effects of plate size on selection and consumption [7,8]. However, the effect size lies outside the confidence intervals reported in another meta-analysis [6], as well as our previous study in a naturalistic eating laboratory [11], both of which showed no clear effect on selection or consumption. ...
Article
Full-text available
Background The physical properties of tableware could influence selection and consumption of food and alcohol. There is considerable uncertainty, however, around the potential effects of different sizes and shapes of tableware on how much food and alcohol people self-serve. These studies aimed to estimate the impact of: 1. Plate size and shape on amount of food self-served; 2.Wine glass and bottle size on amount of wine self-poured. Methods 140 adults participated in two laboratory studies—each using randomised within-subjects factorial designs—where they self-served food (Study 1) and wine (Study 2): Study 1: 3 plate sizes (small; medium; large) × 2 plate shapes (circular; square). Study 2: 3 wine glass sizes (small; medium; large) × 2 wine bottle sizes (75 cl; 50 cl). Results Study 1: There was a main effect of plate size: less was self-served on small (76 g less, p < 0.001) and medium (41 g less, p < 0.001) plates, compared to large plates. There was no evidence for a main effect of plate shape (p = 0.46) or a size and shape interaction (p = 0.47). Study 2: There was a main effect of glass size: less was self-served in small (34 ml less, p < 0.001) and medium (17 ml less, p < 0.001) glasses, compared to large glasses. There was no evidence of a main effect of bottle size (p = 0.20) or a glass and bottle size interaction (p = 0.18). Conclusions Smaller tableware (i.e. plates and wine glasses) decreases the amount of food and wine self-served in an initial serving. Future studies are required to generate estimates on selection and consumption in real world settings when numerous servings are possible. Protocol registration information: OSF (https://osf.io/dj3c6/) and ISRCTN (https://doi.org/10.1186/ISRCTN66774780).
... Portion control tools may help to modulate these factors by promoting meal planning and "correcting" misperception of inappropriate portion sizes at the time of serving [21,22]. However, not all commercially available portion control tools have been demonstrated to be scientifically valid, and much controversy exists over the real impact of reduced size tableware, including for solid food [23][24][25][26][27] and drinks [28][29][30][31]. ...
... Additional weight management strategies such as dietetic counselling were included in some studies also, making it difficult to determine the effect of PS tools per se. Two other previous MA specifically looking at portion control tools [23,24] focused only on a type of tableware (i.e., small vs. large plates and bowls) and are now slightly outdated (2014,2016). These MA however showed important influences of study design not necessarily accounted for in earlier analyses (i.e., who serves the food and what the volunteers know about the study); therefore, new reviews need to integrate these factors as covariates. ...
... For studies reporting specific macronutrients or foods, we analysed the impact of the tool on selected and/or consumed amounts for vegetables, protein, carbohydrates and fat. Based on previous reviews suggesting a potential role for specific covariates [24], we also extracted data on subject awareness of the study purpose, strategy used (only the tool vs. dietitian/other strategy involved) and format of administration (self-served vs. fixed portion size). ...
Article
Full-text available
Portion control utensils and reduced size tableware amongst other tools, have the potential to guide portion size intake but their effectiveness remains controversial. This review evaluated the breadth and effectiveness of existing portion control tools on learning/awareness of appropriate portion sizes (PS), PS choice, and PS consumption. Additional outcomes were energy intake and weight loss. Published records between 2006–2020 (n = 1241) were identified from PubMed and WoS, and 36 publications comparing the impact of portion control tools on awareness (n = 7 studies), selection/choice (n = 14), intake plus related measures (n = 21) and weight status (n = 9) were analyzed. Non-tableware tools included cooking utensils, educational aids and computerized applications. Tableware included mostly reduced-size and portion control/calibrated crockery/cutlery. Overall, 55% of studies reported a significant impact of using a tool (typically smaller bowl, fork or glass; or calibrated plate). A meta-analysis of 28 articles confirmed an overall effect of tool on food intake (d = –0.22; 95%CI: –0.38, –0.06; 21 comparisons), mostly driven by combinations of reduced-size bowls and spoons decreasing serving sizes (d = –0.48; 95%CI: –0.72, –0.24; 8 comparisons) and consumed amounts/energy (d = –0.22; 95%CI: –0.39, –0.05, 9 comparisons), but not by reduced-size plates (d = –0.03; 95%CI: –0.12, 0.06, 7 comparisons). Portion control tools marginally induced weight loss (d = –0.20; 95%CI: –0.37, –0.03; 9 comparisons), especially driven by calibrated tableware. No impact was detected on PS awareness; however, few studies quantified this outcome. Specific portion control tools may be helpful as potentially effective instruments for inclusion as part of weight loss interventions. Reduced size plates per se may not be as effective as previously suggested.
... First, estimates of the magnitude of the effect of four front-of-pack nutrition labels on the nutritional quality of the basket of foods purchased were 17 times smaller in a large randomized controlled trial conducted in supermarkets (Dubois et al. 2021) than in a high-quality incentive-compatible lab study using the same dependent variable (Crosetto et al. 2020). Second, meta-analyses of lab studies support the effectiveness of using smaller plates as a means of reducing consumption when people serve themselves (Holden, Zlatevska, and Dubelaar 2016;Hollands et al. 2015). However, a recent study using procedures more closely mapping onto "free-living eating" found no effect of smaller plates on how much people serve themselves and how much they eat (Kosīte et al. 2019). ...
... Yet the nature of these elements can have a significant effect. For instance, plate size may influence how much people serve themselves, affecting amounts consumed in some lab settings (Holden et al. 2016), while plate material (disposable vs. reusable) affects how much is consumed or thrown away (Williamson, Block, and Keller 2016). Even utensil size influences how much people eat (Geier, Rozin, and Doros 2006;Hollands et al. 2015). ...
Article
Full-text available
Food consumption and its physiological, psychological, and social antecedents and outcomes have received considerable attention in research across many disciplines, including consumer research. Although researchers use various methods to examine food decision-making, many insights generated stem from observing eating choices in tightly controlled lab settings. Although much insight can be gained through such studies (or “lab eating”), it is apparent that many factors differ between such settings and everyday consumption (or “free-living eating”). This article highlights key differences between “lab eating” and “free-living eating,” discusses ways in which such differences matter, and provides recommendations for researchers regarding how and when to narrow the gap between them, including by enriching lab studies in ways inspired by free-living eating. Besides suggesting how researchers can conduct studies offering a deeper understanding of eating patterns, we also highlight practical implications for improving food consumption for consumers, marketers, and policymakers.
... Third, other reviews have included studies from stimulated or laboratory settings (Bauer and Reisch [12]; Cadario and Chandon [15]; Escaron, Meinen [25]; Glanz, Bader [26]; Hartmann-Boyce, Bianchi [30] and Liberato, Bailie [31]. As previous studies have shown, people may act differently when they know that they are being monitored in laboratory settings, we see the need to evaluate interventions effect for only real-life settings [15,30,32].This is especially important for the evaluation of pricing interventions [33]. Few previous reviews have focused exclusively on the grocery store setting, instead included studies performed also in work cafeterias, school cafeterias and corner stores [15,26,31]. ...
Article
Full-text available
Grocery stores are important settings to promote healthier food and beverage choices. The present paper aims at reviewing the effectiveness of different types of in-store interventions and how they impact sales of different product category in real grocery stores. Systematic search was conducted in six databases. In-store interventions were categorized according to the framework by Kraak et al. (2017) into one or more of eight interventions (e.g., place, profile, portion, pricing, promotion, healthy default picks, prompting and proximity). This systematic theme-based review follows the preferred reporting items for systematic reviews and meta-analyses (PRISMA) data screening and selection. Thirty-six studies were included in the qualitative synthesis and 30 studies were included in the meta-analysis, representing 72 combinations of in-store interventions. The analysis demonstrates that interventions overall had small significant effect size (ES) using Cohen's d on food purchase behavior (d = 0.17, 95% CI [0.04, 0.09]), with largest ES for pricing (d = 0.21) and targeting fruits and vegetables (d = 0.28). Analysis of ES of in-store interventions show that pricing, and pricing combined with promotion and prompting, effectively impacted purchase behavior. Interventions significantly impacted both sales of healthy and unhealthy products and significantly increased sales of fruits and vegetables, healthy beverage and total volume of healthy products. Results should however be interpreted with some caution, given the relatively low quality of overall evidence and low number of studies and observations for some types of intervention. Further research exploring impact on different in-store interventions and targeting especially unhealthy products are needed.
Article
While the growing global obesity crisis in humans has attracted a great deal of attention from the media and healthcare professionals alike, the rapid increase in weight problems reported amongst pets is now attracting widespread recognition too. In humans, the emerging science of gastrophysics offers a number of concrete suggestions as to how people can be nudged into eating less by means of the enhanced multisensory design of both foods and the environments in which they choose to eat. In this narrative review, the potential relevance of gastrophysics to helping tackle the growing problem of overweight and obese domestic dogs is reviewed. This involves discussion of both the important similarities and difference in the way in which people and their pets perceive food, and the likely role of various product-extrinsic factors on consumption in the two cases. Nevertheless, despite the differences, a number of suggestions for future research are forwarded that may help to address the growing problem of overweight pets, and the behaviours that give rise to it.
Article
Individuals vary in the extent to which they engage in holistic and analytic information processing styles. Holistic processing involves focusing on the interconnectivity and relatedness of items being evaluated, while analytic processing involves focusing on items being judged as discrete elements and independent of context. We examined the contribution of these basic processing styles to the dishware size effect, which proposes that food consumption patterns may be influenced by the size of the dishware (i.e., larger plates increase the amount of food consumed). We observed that participants self-served and consumed more food when using and eating from a larger plate (LP) compared with a smaller plate (SP) (p≤0.01). Importantly, participants who reported greater levels of holistic information processing related to attitudes towards contradictions and attention allocation exhibited smaller variations in portions of food self-served and consumed based on the dishware size used (SP vs. LP). These findings suggest that the susceptibility of individuals to the dishware size effect may be associated with an individual's dispositional tendency to process information in a holistic (vs. analytic) manner.
Chapter
While hunger and micronutrient deficiencies remain a persistent problem affecting millions worldwide, obesity has also become a global epidemic. Despite an on-going debate regarding whether the substantial public expenditures on medical and health measures resulting from poor diets warrant policy interventions, the United States and Europe have seen a dramatic rise in policies designed to influence consumer diets over the last two decades. We provide a summary of the underlying arguments for addressing this issue via policy and complement this with a review of the existing literature on a variety of demand-side oriented policies, including nutritional labeling policies, fiscal policies, child-focused policies, and place-based policies. We point out how results are often contradictory and inconclusive partially due to the approach that was taken by the researcher in the analysis of the policy. This issue of robust findings leads us to a discussion of different data and methods typically used in economics to make inferences about the efficacy of policies. We follow-up on this by providing insights from the behavioral economics literature, which offers policy solutions that have the potential to complement more traditional policy designs. In a last step we provide an outlook and recommendations for future research directions.
Article
Food serving sizes are on the rise and this increase is one factor contributing to both obesity and food waste. Hence, reducing serving size is a potentially effective strategy for lessening overconsumption and food waste—but it carries the risk that consumers may perceive the smaller serving size as too small, lowering satisfaction. This research examines the role of serving size, unit size, and self-serving on the amount of food served, consumed, and wasted, with the main objective of reducing both overconsumption and food waste while maintaining consumer satisfaction. Across four experiments, we demonstrate that consumers who are served food in smaller units consume less but waste more, while consumers who serve themselves food in smaller units consume less and waste less. When self-serving food in smaller units, consumers benefit from pause moments providing decision-making opportunities that draw attention to the serving decision, as reflected in longer serving times and greater overestimation of the served amount of food. Consequently, consumers presented with smaller unit sizes serve themselves less food—resulting in decreased consumption and waste, without lessening consumer satisfaction. These findings offer a wide range of win-win implications that are of relevance to consumers as well as to managers of restaurants, food services, and health professionals.
Article
Given the prevalence of brand extensions in the market it is important to consider extensions’ potentially harmful effects on the parent brand, i.e., negative feedback effects. This paper integrates experimental research on negative feedback effects using a meta‐analytic framework. The results support previous findings for extension evaluations, parent brand breadth, parent brand image fit and consumer task motivation on the occurrence of negative feedback effects. However, four moderator variables found in earlier work are not significant: accessibility of extension information, parent brand awareness, branding strategy, and participant type. Mixed findings related to extension fit, valence of extension information and parent brand quality are clarified, indicating that extension fit and valence of information appear to drive negative feedback while parent brand quality does not. Four methodological factors have significant effects: within‐subject dependent variable designs, parent brand product class, type of brand, and whether the extension was evaluated, suggesting that the effects may be, in part, an artifact of background factors. The results provide insights into when brands seem vulnerable to negative feedback effects, while simultaneously identifying common market scenarios under which brands appear less susceptible. Finally, a post hoc model points to involvement and level of processing as two key constructs that may underlie the effects of moderators.
Article
Background With increasing pressure on the Earth’s finite resources, there is significant demand for environmentally sustainable practices in foodservice. A shift to sustainable foodservice operations can decrease its environmental impact and may align with consumer expectations. Objective This systematic review explored consumer expectations (attitudes pre-intervention) and responses (behaviour, cognitive attitudes and affective attitudes post-intervention) towards environmentally sustainable initiatives of foodservice operations. Methods A systematic search following PRISMA guidelines was conducted across MEDLINE, EMABASE, CINAHL, and Web of Science databases. English and full text research articles published up to November 2019 were identified. Consumers’ expectations and responses to interventions were extracted. The quality of the studies was assessed using the Mixed Methods Appraisal Tool (MMAT). Results Thirty-four studies were included and given the heterogeneity of the studies; results were synthesized narratively. The main outcomes analyzed included changes in behaviour and attitudes (cognitive and affective) including knowledge and satisfaction. Intervention strategies were interpreted and categorized into three groups: food waste reduction, single-use item and packaging waste reduction, and initiatives related to menu, messaging and labelling. Most studies resulted in significant pro-environmental changes towards decreasing food waste, decreasing single use-item and packaging waste, and engaging consumers in sustainable eating. Conclusions There are a range of successful environmentally sustainable strategies that when implemented by foodservices can have a mostly positive impact on consumer attitudes and responses. However, positive consumer attitudes did not always translate to changes in behavior. Foodservices should carefully consider implementing interventions which support changes in consumer behavior.
Article
Full-text available
This research shows when and how food-related warnings can backfire by putting consumers in a state of reactance. Across three studies, we demonstrate that dieters (but not nondieters) who see a one-sided message fo-cusing on the negative aspects of unhealthy food (vs. a one-sided positive or neutral message) increase their desire for and consumption of unhealthy foods. In contrast, dieters who see a two-sided message (focusing on both the negative and positive aspects of unhealthy food) are more likely to comply with the message, thereby choosing fewer unhealthy foods. Our research suggests that negatively worded food warnings (such as public service announcements) are unlikely to work—nondieters ignore them, and dieters do the opposite. Although preliminary, our findings also suggest that two-sided messages may offer a better solution.
Article
Full-text available
A retrospective study of kids' meals purchased at Walt Disney World was conducted to determine acceptance rates for healthy sides and beverages. Purchase data from all 145 Walt Disney World restaurants were analyzed using a log-linear model and a Poisson regression. Across all restaurants, 47.9% and 66.3% of guests accepted healthy default sides and beverages, respectively. Acceptance rates of sides and beverages were higher at quick-service restaurants (49.4% and 67.8%, respectively) compared to table-service restaurants (40.3% and 45.6%, respectively). The healthy defaults reduced calories (21.4%), fat (43.9%), and sodium (43.4%) for kids' meal sides and beverages. This study contributes by examining the use of kids' meal healthy defaults in quick-service and table-service restaurant formats at the world's largest theme park, a previously unstudied setting, and by providing the largest ever healthy default data set. The results suggest that healthy defaults can shift food and beverage selection patterns toward healthier options.
Article
Full-text available
Laboratory paradigms are commonly used to study human energy intake. However, the extent to which participants believe their eating behavior is being measured may affect energy intake and is a methodologic factor that has received little consideration. Our main objective was to examine available evidence for the effect that heightened awareness of observation has on energy intake in a laboratory setting. We systematically reviewed laboratory studies that allowed for experimental examination of the effect that heightened awareness of observation has on energy intake. From these experimental studies we combined effect estimates using inverse variance meta-analysis, calculating the standardized mean difference (SMD) in energy intake between heightened-awareness and control conditions and qualitatively synthesized potential moderators of this effect. Nine studies, providing 22 comparisons, were eligible for inclusion. These studies largely sampled young women and examined the energy intake of energy-dense snack foods. Evidence indicated that heightened awareness of observation was associated with reduced energy intake when compared with the control condition (random-effects SMD: 0.45; 95% CI: 0.25, 0.66; P < 0.0001). We found little evidence that the type of experimental manipulation used to heighten awareness moderated the overall effect. The available evidence to date suggests that heightened awareness of observation reduces energy intake in a laboratory setting. These findings suggest that laboratory studies should attempt to minimize the degree to which participants are aware that their eating behavior is being measured. © 2015 American Society for Nutrition.
Article
Full-text available
The use of smaller dishware as a way of reducing food consumption has intuitive appeal and is recommended to the general public. Recent experimental studies have failed to find an effect of plate size on food intake, although the methods used across studies have varied. The aim of the present study was to examine the effect that bowl size had on snack food consumption in a 'typical' snacking context (snacking while watching television). Between-subjects. Laboratory experiment. Sixty-one adult participants served themselves and ate popcorn while watching television. Participants were randomly assigned to serve themselves with and eat from either a small or a large bowl. The use of a smaller bowl size did not reduce food consumption. Unexpectedly, participants in the small bowl condition tended to consume more popcorn (34·0 g) than participants in the large bowl condition (24·9 g; 37 % increase, d=0·5), although the statistical significance of this difference depended on whether analyses were adjusted to account for participant characteristics (e.g. gender) associated with food intake (P=0·02) or not (P=0·07). Counter to widely held belief, the use of a smaller bowl did not reduce snack food intake. Public health recommendations advising the use of smaller dishware to reduce food consumption are premature, as this strategy may not be effective.
Article
Two lab studies and three field studies indicate that plate disposability influences the amount of food wasted. The first lab study indicates that more food served on disposable (i.e., paper) plates is wasted than when the same food is served on permanent (i.e., hard plastic) plates. Study 2 employs an implicit association test (IAT) to confirm that disposable plates are strongly associated with behavior to stop eating while, simultaneously, permanent plates are more strongly associated with a keep eating behavior. Three field studies (3A, 3B, and 3C) indicate that this effect maintains when instead of being served a fixed quantity of food, participants select the amount and type of food.
Article
People compensate for small food-unit sizes by eating more units compared to regular-sized units, but the aggregate of calories people consume of smaller versus regular units is still less because each unit consumed increases perceptions of overindulgence and impulsivity. This suggests that if perceptions of a food unit’s smallness could be disrupted, people may not need to compensate, resulting in a further reduction in aggregate food chosen and consumed. In a lab and field experiment, people took the fewest calories when presented with smaller versus regular-sized pizza slices (i.e., from the same pizza pie diameter) placed on a larger table that distracted their attention away from the smallness of the pizza slices. We show that unit-size effects can be altered by food frame-size mechanisms like table diameter.
Article
Forks and spoons are present at nearly every meal in Western societies, and many foods can be appropriately consumed with either type of cutlery. We focus on foods that can be appropriately consumed with either a fork or a spoon and examine how eating with one piece of cutlery (vs. the other) influences consumers’ calorie estimates and consumption decisions. Holding bite size constant, we find that eating with a spoon (vs. a fork) leads consumers to estimate the number of calories in the food as being lower and also desire a greater volume of the food. The effect of cutlery on calories is attenuated when consumers focus on the oral sensations they experience while eating, as well as when foods do not adhere to the cutlery surface. Overall, our findings suggest that eating with a fork might be one way to encourage healthful consumption.