ArticlePDF AvailableLiterature Review

Abstract

Background: Reductions in eating rate are recommended to prevent and treat obesity; yet, the relation between eating rate and energy intake has not been systematically reviewed, with studies producing mixed results. Objective: Our main objective was to examine how experimentally manipulated differences in eating rate influence concurrent energy intake and subjective hunger ratings. Design: We systematically reviewed studies that experimentally manipulated eating rate and measured concurrent food intake, self-reported hunger, or both. We combined effect estimates from studies by using inverse variance meta-analysis, calculating the standardized mean difference (SMD) in food intake between fast and slow eating rate conditions. Results: Twenty-two studies were eligible for inclusion. Evidence indicated that a slower eating rate was associated with lower energy intake in comparison to a faster eating rate (random-effects SMD: 0.45; 95% CI: 0.25, 0.65; P < 0.0001). Subgroup analysis indicated that the effect was consistent regardless of the type of manipulation used to alter eating rate, although there was a large amount of heterogeneity between studies. There was no significant relation between eating rate and hunger at the end of the meal or up to 3.5 h later. Conclusions: Evidence to date supports the notion that eating rate affects energy intake. Research is needed to identify effective interventions to reduce eating rate that can be adopted in everyday life to help limit excess consumption.
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A systematic review and meta-analysis examining the effect of eating rate
on energy intake and hunger
Eric Robinson1, Eva Almiron-Roig2, Femke Rutters3,4, Cees de Graaf 5,6, Ciarán G. Forde7,
Catrin Tudur Smith8, Sarah J. Nolan8, Susan A. Jebb2,9
1Psychological Sciences, University of Liverpool, UK
2MRC Human Nutrition Research, Cambridge, UK
3Department of Epidemiology and Biostatistics, VUmc, Amsterdam, the Netherlands
4EMGO + Institute for health and care research, VUmc, Amsterdam, the Netherlands
5Division of Human Nutrition, Wageningen University, the Netherlands
6Top Institute Food and Nutrition, Wageningen, the Netherlands
7 Nestle Research Centre, Vers-Chez-Les-Blanc, Lausanne, Switzerland
8 Department of Biostatistics, University of Liverpool, UK
9 Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
ROBINSON, ALMIRON-ROIG, RUTTERS, DE GRAAF, FORDE, TUDUR SMITH,
NOLAN, JEBB
Correspondence: Dr Eric Robinson, Psychological Sciences, University of Liverpool,
Liverpool, L69 7ZA, Telephone: 0151 794 1187, E-mail: eric.robinson@liv.ac.uk
Funding: no external funding
Word count: 4, 719
Key words: eating rate, energy intake; hunger; oral exposure; chewing
Running head: Eating rate and energy intake
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ABBREVIATIONS
BMI = Body mass index
CCK = Cholecystokinin
CI = Confidence interval
GLP1 = glucagon-like peptide-1
PRISMA = Preferred Reporting Items for Systematic Reviews and Meta-Analyses
PYY = Peptide YY
SMD = Standardized mean difference
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ABSTRACT
Background: Reductions in eating rate are recommended to prevent and treat obesity, yet the
relationship between eating rate and energy intake has not been systematically reviewed, with
studies producing mixed results.
Objective: Our main objective was to examine how experimentally manipulated differences
in eating rate influence concurrent energy intake and subjective hunger ratings.
Design: We systematically reviewed studies that experimentally manipulated eating rate and
measured concurrent food intake and/or self-reported hunger. We combined effect estimates
from studies using Inverse Variance meta-analysis, calculating the standardized mean
difference (SMD) in food intake between fast and slow eating rate conditions.
Results: Twenty-two studies were eligible for inclusion. Evidence indicated that a slower
eating rate was associated with lower energy intake, in comparison to a faster eating rate
[Random effects SMD: 0.45, 95% CI: (0.25 to 0.65), p < 0.0001]. Sub-group analysis
indicated that the effect was consistent regardless of the type of manipulation used to alter
eating rate, although there was a large amount of heterogeneity between studies. There was
no significant relationship between eating rate and hunger at the end of the meal or up to 3.5
hours later.
Conclusions: Evidence to date supports the notion that eating rate affects energy intake.
Research is needed to identify effective interventions to reduce eating rate, which can be
adopted in everyday life to help limit excess consumption.
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INTRODUCTION
Observational studies report that overweight and obese individuals eat at a faster rate than
lean individuals (1, 2). There is also some evidence that eating rate is a heritable behavioural
phenotype (3) and thus may be a contributing factor to weight gain and obesity. This has
prompted clinical interventions to reduce eating rate in the dietary treatment of obesity (4)
and public health recommendations to slow eating rate to reduce the risk of weight gain (5).
However, observational studies are fraught with confounding factors. Moreover, differences
between overweight and lean participants have not been consistently observed when more
objective measures of eating rate have been used in laboratory settings (6, 7). Thus, from
these data it is unclear whether faster eating rates promote greater energy intake and weight
gain.
Experimental laboratory based studies that have directly manipulated eating rate offer the
opportunity for direct comparisons of the effect of different eating rates on appetite and
energy intake. However, data from these studies have also reported mixed results, with eating
rate not significantly influencing food intake across all studies (8-30). This may in part be due
to differences in the types of interventions used to change eating rate, which have included
verbal instructions (8), altering food texture (9) and computerized feedback to participants
(10). For example, Forde et al. (9) served participants either a hard (slower eating rate) or soft
textured version (faster eating rate) of one of two meal types and found that a slower eating
rate led to a reduction in energy intake, while, Andrade et al. (14) manipulated eating rate via
verbal instructions and found no effect on intake.
Here we conduct a systematic review and meta-analysis of the effect of eating rate on energy
intake from controlled laboratory experiments that have manipulated eating rate and observed
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its effects on concurrent energy intake and/or later self-reported hunger. Thus, our main
objective was to assess whether there is evidence supporting the proposition that eating rate
affects energy consumption. Our secondary aim was to examine whether the method chosen
to manipulate eating rate determines changes to energy intake, as this information has the
potential to inform behavioural therapies.
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METHOD
Eligibility criteria
Participants: We included experimental studies testing human participants (of any age) and
excluded studies that only included participants with a clinically defined eating disorder.
Intervention/studies: Experiments and studies were included if they examined and reported
the effect of manipulating eating rate on concurrent energy intake, with or without self-
reported hunger ratings measured after meal completion.
Comparator groups: To be eligible for inclusion, all experiments and studies were required to
include at least one condition, in which participants ate a meal at a statistically significant
slower rate than a different experimental condition. If a significant difference in eating rate
between two conditions was not clear or could not be ascertained by contacting the authors,
then the study was not deemed suitable for inclusion. The reason was that we were primarily
interested in whether successful manipulation of eating rate influences consumption. For
studies that had examined the effect of eating rate via manipulating food form (i.e. softening
the texture of a food to increase eating rate), we only included studies that made comparisons
between modified versions (for example, apple versus apple puree, see 9) of the exact same
food in which these manipulations produced different eating rates.
Outcome measure: Studies were required to measure food intake assessed as either energy
intake or quantity of food consumed with or without self-reported hunger at the end of the
meal, or hunger at a later time point.
Study design: Only studies with experimental designs were included, and both repeated-
measures/within-subjects (also referred to as ‘cross-over’ designs in some of the papers) and
between-subjects (independent groups) designs were suitable for inclusion. The repeated-
measures/within-subjects design studies involved a participant eating at both a fast and
slower rate during different experimental sessions (order of sessions counterbalanced). The
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between-subjects studies involved participants eating at either a fast or slower rate during a
session. We included studies reporting randomized and non-randomized designs.
Information sources and search strategy: For the formal search strategy, three electronic
databases were searched during March 2013: PsycINFO, Web of Knowledge and Medline.
Searches included a combination of key words relevant to eating rate, speed, energy intake
food intake, hunger and satiety. We limited searches to human subjects. For an example of
search strategy please see Supplemental Appendix 1. The formal electronic searches were
supplemented by a manual search of reference sections in articles identified by the electronic
search and other relevant sources. Between conducting the formal searches and submitting
the manuscript for publication, we also conducted supplementary electronic searches using
Web of Science, Journal Citation Report, BIOSIS Citation Index and Medline via Web of
Knowledge to ensure we were aware of all potential studies. The search process was guided
by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses: PRISMA (11).
Article selection and extraction of data
Two authors independently performed the searches (ER, EAR). Three authors were
responsible for the evaluation of articles for inclusion, with disagreements resolved by
discussion (ER, EAR, FR). A single author extracted data from the included studies (ER);
30% of these articles were checked independently by two different authors (EAR, FR) and
there were no disagreements. All authors were responsible for suggesting relevant additional
articles. We contacted corresponding authors with specific questions related to the design and
outcome of their trails, when this was not clear from their papers.
Data items extracted for individual studies
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Type of study: We classified Type 1 studies as those that manipulated eating rate and
examined concurrent energy intake and Type 2 studies as those in Type 1 but that also
measured self-reported hunger at the end of the meal. Type 3 studies had manipulated eating
rate whilst keeping energy intake constant by serving a fixed meal and measured self-
reported hunger at a later time.
Participants: We recorded participant sample size, age, body mass index (BMI), gender and
inclusion criteria were extracted.
Study design: Whether the study was a repeated measures/within subjects or between subjects
experiment was extracted.
Eating rate manipulation: We recorded the method of manipulation used and number of
participants using each method.
Food intake and eating rate measures: The food type that was eaten, testing times and the
method used to record eating rate were all extracted.
Hunger measure: Type of hunger measure (e.g. self-reported) and time when the measure
was taken were recorded.
Results: Eating rate (e.g. g/min), intake (g or kcal) and/or hunger scores were extracted. If
data relating to study results were not reported in the paper, authors were contacted.
Additional information: We extracted information on whether or not participants were
randomized to conditions, whether a cover story was used, if the researchers checked if
participants were aware of the aims of the study, and potential confounders, for example,
whether water intake was balanced across conditions.
Whenever the design of the study resulted in additional conditions that were not relevant to
our hypotheses (e.g. if one condition did not differ from another for eating rate), we only
extracted data about the relevant eating rate conditions.
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Quality assessment
As the studies reviewed had to be experiments (all of which had a follow up of close to
100%), the usual quality filters for randomized trials or observational epidemiologic studies
did not apply. However, we examined whether studies used designs in which participants
would be blind to the true purpose of the experiments, because this may have influenced
conscious decisions made about consumption. We further examined whether randomization
was used, whether participants reported awareness of the study aims and we identified any
potential confounding variables or methodological limitations in each study.
Statistical analysis and subgroup analysis
We calculated a standardized mean difference (SMD) and standard error of the SMD between
experimental conditions for each study and synthesized individual study SMDs via
meta-analysis using the method of generic inverse variance implemented in Revman version
5.1 (31). We adopted SMD as our main outcomes of interest could be measured by different
outcome scales; e.g. energy intake could be measured in grams (g) or calories (kcal) and self-
reported hunger could be measured using different self-report methods (e.g. Visual Analogue
Scales, VAS). The SMD is a measure of effect size which accounts for variability from the
use of different outcome scales by estimating the difference between two experimental
conditions for an outcome variable (e.g. food intake) and dividing that difference by the
standard deviation of the outcome variable for the two experimental conditions. Pooled
Standardized mean differences and 95% confidence intervals were reported for each study
type (1, 2 and 3) separately.
All but two of the studies (9, 12) used repeated measures/within subjects designs in which the
same single group of participants ate at different eating speeds during multiple study sessions.
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We took account of the correlation between the experimental sessions due to the repeated
measures design in the calculation of the standard error of SMD. As studies did not report
values for the correlation between experimental sessions, we estimated these correlations for
study types 1, 2 and 3 using summary data made available from study authors and our
knowledge about repeated measures laboratory eating behaviour studies. Type 1 ρ = 0.75,
Type 2 ρ = 0.5, Type 3 ρ = 0.6); see Supplemental Appendix 2 for further details.
Amongst type 1 studies there were sufficient comparisons to test subgroup differences
between four different methods of manipulating eating rate; verbal instructions (e.g. 8),
computerized feedback (e.g. 13), food form manipulations; some studies manipulated the
texture of food so it would be eaten slower or faster (e.g. 9) and food delivery manipulations
(e.g. 12, in which food was consumed with a straw (faster) or spoon (slow)). We calculated
SMDs for each study type (1, 2 and 3) and examined differences in the four methods of
manipulating eating rate in type 1 studies formally using a chi-square test of subgroup
differences (see Supplemental Appendix 2 for further details). Pooled Standardized mean
differences and 95% confidence intervals were calculated for each subgroup within the type 1
studies separately.
We assessed heterogeneity with the I2 statistic (the percentage of variability between studies
which is due to statistical heterogeneity). See Supplemental Appendix 2 for I2 guidelines. As
there was evidence of moderate to considerable heterogeneity in the majority of analyses, we
used a random-effects model in all meta-analyses reported and also conducted sub-group
analyses to examine possible causes of this heterogeneity.
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Most studies included two conditions, so contributed one comparison to the main analyses.
Some studies included multiple conditions that significantly differed in eating rate. For
example, (13) included fast, slow and intermediate eating rate conditions. Thus, we
conducted separate meta-analyses, in order to compare the effect of fast vs. slow speeds, slow
vs. intermediate speeds and intermediate vs. fast speeds. In some instances within
subjects/repeated measures design studies contributed multiple comparisons to analyses (e.g.
the design of (20) resulted in conditions in which eating rate instructions and food form
manipulations were used across different sessions), so in these cases we adjusted the sample
size of each comparison accordingly.
In the main analyses, we classified the faster of the two eating rates as the ‘Fast’ condition
and the slower of the two as the ‘Slow’ condition without taking into account the numeric
values of the eating rates. As the recorded eating rates for the fast and slow conditions
(measured in g/min or kcal/min) and therefore the difference between the rates varied
between studies, we performed a meta-regression to examine the influence of the eating rate
on the pooled standardized mean differences (i.e. whether a larger difference in the ‘fast’ and
‘slow’ rates resulted in a larger difference in energy intake or hunger pooled across the
studies). For individual studies, either the percentage difference between eating rates was
reported or we were able to calculate the percentage difference from the numeric eating rates
reported. Meta-regression by percentage difference in eating rates was performed for type 1
and 2 studies with fast vs. slow eating rates only using statistical software Stata version 11.2
(see Supplemental materials 2). We did not perform Meta-regression studies due to the small
number of comparisons available for type 3 studies, for intermediate vs slow conditions and
for intermediate vs fast conditions.
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RESULTS
Study selection
Figure 1 shows details of the selection process. After removal of duplicates, the search
yielded seven hundred and thirty three publications. The titles and abstracts of these were
screened and this left seventy-six publications that were of likely interest. Of these, five were
conference papers and one was a dissertation. We searched for versions of these publications
in peer reviewed journals and as a result we identified three further peer-reviewed
publications. Thus, we fully text screened seventy-three publications of which fifteen
qualified for inclusion. Of the remaining fifty-eight publications that did not qualify for
inclusion; forty-one did not examine the effect of an experimental manipulation of eating
rate, eight did not examine eating rate, five sampled participants with eating disorders, on
closer inspection of data it was unclear whether two studies achieved a statistically significant
difference in eating rate between conditions, one examined eating rate but did not measure
food intake or hunger and one reported insufficient method information to qualify for
inclusion. We next identified any remaining publications through the authors’ collective
knowledge and by searching the reference sections of each eligible paper identified through
the search process. An additional seven eligible papers were identified, resulting in a total of
twenty-two studies. Some studies provided more than one unique comparison into each meta-
analysis (i.e. Martin (17) examined the effect of eating rate in men and women separately), so
rather than combine data for such occurrences, we entered each comparison separately.
Overview
Participant characteristics
See Table 1 (type 1 and type 2 studies) and Table 2 (type 3 studies) for detailed information
regarding each individual study, including sample sizes. Three studies sampled males only
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(16, 28, 30), six sampled females only (8, 13-15, 19, 22) and the remaining studies included
both sexes. Eleven studies reported a mean age between 18-25 years old (8, 10, 12-15, 22-25,
30), seven studies had a mean age above 25 years (9, 17, 20, 26-29), two studies sampled
adolescents (21, 29) and the remaining studies did not report mean age. Mean BMI of study
participants was in the healthy weight range according to WHO BMI guidelines (18.5-24.9
kg/m2) for fifteen studies (8-9, 12-16, 20-26, 30) and in seven studies mean BMI was > 24.9
kg/m2 (10, 17-19, 27-29). Only one study (16) did not report eligibility criteria for
participants, whilst common criteria were exclusion of participants using medication known
to interfere with appetite, history of an eating disorder and food allergies. All but two of the
studies (9, 12) used a within subjects design.
Type 1 studies: The effect of manipulating eating rate on concurrent energy intake
Figure 2 depicts the forest plots from studies examining the effect of fast vs. slow eating
rates on concurrent energy intake. Overall, a small to medium sized effect was observed,
which was statistically significant. In the fast eating rate conditions participants had a higher
energy intake than in the slow eating rate conditions (Random effects SMD: 0.45, 95% CI:
(0.25 to 0.65), p < 0.0001; I2 = 92%).
Three studies (13, 21-22) provided comparisons between fast, intermediate, and slow eating
rate conditions (forest plot figures not shown). A statistically significant effect was observed,
with participants in the fast condition tending to eat more than participants in the intermediate
conditions (Random effects SMD: 0.70, 95% CI: (0.53 to 0.88 (p < 0.0001; I2 = 35%).
Similarly, for the comparison between slow and intermediate eating rate conditions a large
effect was observed, whereby participants tended to eat less in the slow vs. intermediate
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condition, but this was not statistically significant at the 5% level (Random effects SMD:
1.30, 95% CI: (-0.07, 2.66), p = 0.06; I2 = 98%).
Subgroup analyses: Method of manipulating eating rate
Studies examining the effect of fast vs. slow eating rates could be classified into four main
sub-groups (Figure 2). Four studies examined the effect of providing participants with verbal
instructions (8, 14, 18, 20). These studies typically involved researchers instructing
participants to chew slowly and take their time whilst eating. Seven studies (9, 16, 19-20, 23-
25), examined the effect of manipulating food form. These studies typically provided
participants with soft (fast eating rate) or hard (slow eating rate) textured versions of the same
food. Six studies, (10, 13, 17, 21-22, 26) manipulated eating rate using computerized
feedback. Here intake was measured on a set of scales while subjects were eating and they
received instructions on a computer screen to modify eating rate. Two studies (12, 15)
manipulated eating rate via the method of food delivery. In one study (12) this involved
eating with a spoon (slow eating rate) rather than a straw (fast eating rate) and in the other
study participants ate from a container that refilled quickly or slowly (15). We did not find
any statistically significant evidence of a difference between methods used to manipulate
eating rate on concurrent energy intake (test of subgroup differences: x2 = 3.90, df = 3, p =
0.27, I2 = 23%).
Type 2 studies: Self-reported hunger after consuming meal
Of the twenty-one type 1 studies, eleven studies also measured and reported hunger at meal
completion (8-10, 12, 14, 17-18, 22-25). Examining the effect of fast vs. slow eating rates,
there was no statistically significant effect of eating rate on hunger (Random effects SMD:
0.04, 95% CI: (-0.09, 0.16), p = 0.54; I2 = 66%), as shown in Figure 3. One study (22) that
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contributed to the overall analysis of fast vs slow eating rates also included an intermediate
eating rate group. For this study there was no statistically significant difference between fast
vs. intermediate conditions (SMD: 0.08, 95% CI: (-0.21, 0.37), p = 0.59) or intermediate vs.
slow eating rates (SMD: 0.07, 95% CI: (-0.22, 0.36), p = 0.62).
The relationship between change to eating rate and change to food intake
For the twenty-three comparisons from type 1 studies, there was statistically significant
evidence of a larger standardized mean difference of energy intake between fast and slow
conditions as the percentage difference between the eating rates increased (regression
coefficient: 0.013, 95% CI (0.002, 0.025), p = 0.02), that is, an increase of 1% in eating rate
results in an increase of SMD of an average 0.012 (as shown in Figure 4).
The relationship between change to eating rate and change to hunger
For the thirteen comparisons from type 2 studies, we did not find statistically significant
evidence of a relationship between difference in percentage eating rate and standardized
mean difference of self-reported hunger (regression coefficient: 0.002, 95% CI (-0.005,
0.010), p = 0.527), as shown in Figure 5. However, we had fewer comparisons from type 2
studies than for type 1 studies and therefore less power to detect a relationship between
percentage difference between eating rates and self-reported hunger.
Type 3 studies: Later self-reported hunger after consuming a fixed meal
Four studies (27-30) examined self-reported hunger several hours after eating a fixed amount
of food at a fast or slow rate. In all studies we analysed the final time point available for
measured hunger, which was one hundred and eighty minutes from the start of eating for two
studies (27, 30) and two hundred and ten minutes for the other studies (28, 29). Comparing
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fast vs. slow eating rates, no significant effect on later hunger was observed (SMD: 0.48,
95% CI: (-0.17, 1.13), p = 0.15, I2 = 93%), as shown in Figure 6. One study (27) examined
the effect of a fast vs. intermediate eating rate (SMD: 0.0, 95% CI: (-0.40, 0.40), p = 0.99) as
well as the effect of an intermediate vs. slow eating rate (SMD: -0.05, 95% CI: - 0.45, 0.34),
p = 0.82) and there were no significant differences.
Heterogeneity across comparisons
We noted a large degree of heterogeneity in the majority of meta-analyses and therefore
presented results from random-effects models. Meta-regression for type 1 studies showed that
the percentage difference between eating rates was associated with energy intake, which is
likely to contribute to some of this heterogeneity. We found no statistically significant
evidence of difference between methods of manipulation of eating rate. For all study types,
we conducted further subgroup analyses to identify factors which may have contributed to the
heterogeneity. For example, variations across studies for participant sex (female only studies
vs. mixed gender studies), average weight status (studies in which mean BMI was within vs
above the healthy weight range) or meal type (e.g. was food intake measured during a main
meal or snack). However, we found no evidence to support any of these as significant
determinants.
Quality of evidence
Of the twenty-two studies, the majority reported that participants had been randomly assigned
to experimental conditions, although six (9, 10, 15-17, 19) did not report whether random
assignment was used. Seven of the studies reported the use of a cover story to disguise the
aims of the research and (18, 19, 22, 24-26, 30), whilst the remaining studies did not.
Andrade and colleagues have previously raised concerns about water intake confounding the
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influence of eating rate on food intake (8). Nineteen included studies either did not serve
water/ had similar water consumption across conditions. In one study it was unclear whether
intake was balanced across conditions (21), in another there was a significant difference
between conditions (14) and in one study (25) there was a non-significant difference (p =
0.09) in water intake between fast and slow eating conditions. Exclusion of these three
studies from the analysis (collectively) had no effect on the overall results.
Few other limitations were observed that would be likely to influence the observed results. In
one computerized instruction study participants in the slow and fast eating rate conditions
were served different sized portions of food (13). In one study participants were told to let go
and binge eat during the meal, although this instruction was used in both eating rate
conditions (15). In two of the food form/texture studies (24-25) there was a tendency for the
soft textured food (fast condition) to be liked more than the hard textured food (slow
condition). We examined whether removing these studies from our analyses (individually and
collectively) influenced the eating rate effect on concurrent energy consumption; the overall
effect remained significant and a similar sized effect was observed. Visual inspection of
funnel plots indicated there was no evidence of publication bias.
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DISCUSSION
Overview
Results of available study data included in this systematic review, suggest that slower eating
rates lead to a significant reduction in food intake. Altering eating rate was not shown to alter
subjective reports of hunger at meal end or 2-3.5 hours later. Analysis also showed that the
approach used to manipulate eating rate did not significantly influence whether a slower-
eating rate was associated with reductions to food intake. However, there was a large amount
of heterogeneity across study outcomes.
Mechanisms
Several experimental studies have suggested that the speed of eating and frequency of
chewing may influence various satiety hormones; altering levels of insulin, glucagon-like
peptide-1 (GLP-1), cholecystokinin (CCK), Peptide YY (PYY), pancreatic polypeptide and
triglycerides (26, 28, 29, 32). Additionally, eating rate could also influence food intake via
differences in stomach distension, with slow eating resulting in slower gastric emptying (33,
34). However, more work is needed to clarify the relative role of different satiety hormones,
as findings have been inconsistent to date (26-29).
A probable mechanism by which eating rate may affect intake is through the magnitude
(duration + intensity) of oral exposure to taste. A high eating rate is directly related to a lower
duration of sensory exposure per unit (gram or kcal) of food (35). Numerous studies have
shown that when eating rate is held constant, increasing oral sensory exposure leads to a
lower energy intake (36, 37). Another complementary potential mechanism is due to slower
eating being related to a higher number of sips, bites or chews per unit of food (35). The
results of two experimental studies suggest that increasing the number of bites/sips, while
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keeping eating rate constant, leads to lower energy intake (38, 39). In another recent study,
increasing the number of chews per unit of food also decreased ad libitum energy intake in
obese and normal weight subjects (40). It may be that some people have developed a learned
association between the number of sips/bites/chews and feelings of satiation that bring a meal
to an end. These suggestions are also in line with the finding from the present review that
altering eating rate influenced concurrent intake, but had little influence on hunger measured
later that day.
Implications for behavioral interventions
Our analyses indicate that reducing eating rate in individuals could be an important approach
implemented in order to help curtail energy consumption. In the present analysis, the
reduction in food intake observed as a result of interventions to slow eating rate was not
associated with an increase in hunger which decreases the risk of later energy compensation
(25). Ford and colleagues (4) have reported results from a childhood obesity trial that
suggests targeting eating rate may be beneficial for weight loss, confirming that modifying
eating rate could be an approach that contributes to weight loss or weight maintenance.
However further research is now needed.
How best to reduce eating rate now deserves attention both in a clinical and public health
context. Strategies that attempt to retrain and teach individuals to eat at a slower rate are one
option and computerized training has potential for individual interventions (4). At a
population level, altering food form may be a strategy worthy of further investigation. Other
manipulations to a food product to reduce eating rate may include combinations of textures
that require smaller bites, an increased number of chew cycles per bite and a longer oral
residence time. This approach could represent a more sustainable approach to alter eating
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rate than verbal or visual instructions because it can be less disruptive of habitual eating
patterns, without subsequent compensation (25).
Limitations and future research
Most studies used random assignment and we did not identify any major limitations.
Moreover, accounting for studies that did have some limitations did not change the pattern of
results observed. Few studies used detailed cover stories, so the observed effects could be
inflated by demand characteristics. For example, if participants believe that eating slowly
makes you feel fuller, this belief may have influenced their energy consumption (41). Some
studies manipulated eating rate via alterations to food form, which could result in changes to
food intake independent of eating rate. However, there was clear evidence from the other
study types that eating rate impacted on food intake. A limitation of the present work was that
reviewed studies predominantly examined energy consumption during an experimental
session, so we are not able to conclude whether habitually changing the speed at which
individuals eat would produce consistent and sustained reductions in energy intake in free-
living conditions. Although, as noted, a recent study did find that a slower eating rate was
associated with reduced food intake during that meal and compensation did not occur during
other meals that day (25). Caution is also needed in extrapolating these data to people who
are obese. The majority of studies reviewed sampled predominantly healthy weight young
adults, although in some studies the mean sample BMI was in the overweight BMI range.
Finally, there was a large amount of heterogeneity across studies for our main outcome
variables which formal analysis was not able to fully explain. This suggests that the effect of
manipulating eating rate on food intake is variable across studies. The methods used to
manipulate eating rate did not appear to explain this variability in the present analysis, so it
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may be that individual differences and usual eating rate moderate any experimental
manipulation. Future work teasing apart why this variability occurs is now needed.
Conclusions
There is evidence that manipulating eating rate affects energy consumption, although future
work will need to address the factors that determine the magnitude of this effect. Reducing
eating rate may be an effective intervention to decrease energy intake as part of behavioural
strategies to prevent and treat obesity.
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ACKNOWLEDGEMENTS
EAR was supported by the UK Medical Research Council Programme U105960389.
CONFLICTS OF INTEREST
All authors report there are no conflicts of interest relating to this work.
CONTRIBUTIONS
The authors’ responsibilities were as follows—ER and EAR were responsible for the
electronic literature search, ER, EAR and FR were responsible for data extraction. ER, SN
and CTS conducted the analyses. All authors designed the review protocol and participated in
writing the manuscript. All authors were also responsible for suggesting articles to include in
the review and for the final content of the manuscript.
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FIGURE HEADINGS
FIGURE 1. Search flowchart
FIGURE 2. Forest plot for studies examining effect of fast vs. slow eating rates on concurrent
intake (type 1 studies)
FIGURE 3. Forest plot for studies examining effect of fast vs. slow eating rates on hunger
after measuring concurrent intake (type 2 studies
FIGURE 4. Meta regression plot (Bubble plot) examining the change in the standardised
mean difference of fast vs slow on concurrent intake (type 1 studies) by the percentage
difference in fast and slow eating rates
FIGURE 5. Meta regression plot (Bubble plot) examining the change in the standardised
mean difference of fast vs slow on post meal hunger (type 2 studies) by the percentage
difference in fast and slow eating rates
FIGURE 6. Forest plot for studies examining effect of fast vs. slow eating rates whilst eating
a fixed portion of food on later hunger (type 3 studies)
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TABLE 1. Studies (type 1& 2) examining the effect of manipulating eating rate on food intake and/or hunger on meal completion.
Study and type Participant
information
Study design Eating rate
manipulation
Food intake and
eating rate
measures
Hunger
measure
Results
Means (SD in brackets)
Additional
information
Andrade 2008
(8)
Effect of verbal
eating rate
instructions on
intake and
hunger.
PPS recruited from
US university and
surrounding area.
Age: 22.9 yrs.
BMI: 22.1.
Gender: Female.
Eligibility criteria:
No allergies to test
foods, caffeine or
alcohol dependency,
adrenal or thyroid
disease, chronic
illnesses related to
weight loss, eating
disorders,
medications altering
appetite, BMI > 35,
no participants tested
during mid-follicular
phase.
Repeated
measures.
Two meals eaten
fast and slow,
separate sessions
3-7 days apart.
Fast condition
(N=30): PPS used a
large spoon and were
told to consume the
meal as fast as
possible without
pausing between
bites.
Slow condition
(N=30): PPS used a
small spoon and were
told to take small
bites, put the spoons
down between each
bite and chew each
bite for 20 to 30
seconds.
Researcher was
present and
monitored PPS
during eating.
Food: Pasta intake.
Time: Lunch,
following 4 hour
fast.
Meal initiation and
completion times
recorded by
researcher.
Type: Self-report
VAS.
Time: Upon
meal completion.
Fast condition:
Rate = 84.8 (36.3) kcal/min.
Intake = 645.7 (155.9) kcal.
Hunger = 14.3 (19.4).
Slow condition:
Rate = 21.0 (7.2) kcal/min.
Intake = 579.0 (154.7) kcal.
Hunger = 7.6 (8.7).
Randomization: Yes
Awareness: No
cover story or
demand awareness
checks reported.
Confounders: Water
intake not
controlled for and
significantly higher
(p<0.05) in slow
eating condition.
Forde 2013 (9)
Effect of
manipulating
eating rate via
food form on
intake and
PPS recruited from
local are of Swiss
research facility.
Age: 44.8 yrs.
BMI: 22.6.
Between
subjects.
2x2. Two
factors:
Food texture
(hard or soft) &
Participants ate the
same meal either with
an intense flavour (A)
or normal flavoured
(B) sauce, which was
either mashed (fast
condition) or hard
Food: Meat, potato
and vegetable dish
intake.
Time: Lunch, with
no fasting
instructions.
Type: Self-report
VAS.
Time: Upon
meal completion.
Fast condition (intense):
Rate = 55.3(3.5) grams/min.
Intake = 606.5 (28.4) grams.
Hunger = 20.1 (18.6).
Fast condition (normal):
Rate = 58.8(3.5) grams/min.
Randomization:
Not reported.
Awareness: No
cover story or
demand awareness
checks reported.
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704
705
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hunger.
Gender: Males and
females.
Eligibility criteria:
BMI between 19-25
only, not currently
dieting, regular
consumers of hot
lunchtime meals
only, no past history
of eating disorders,
no food allergies, no
dislikes towards food
served at lunch, no
vegetarians, no
pregnancy, current
breast feeders or
having recently
participated in a
similar study.
Taste (intense or
normal).
(slow condition).
Intense soft (N=39)
Intense hard (N=41)
Normal soft (N=37)
Normal hard (N=40)
Meal initiation and
completion times
recorded by
researcher.
Intake = 569.2 (29.9) grams.
Hunger = 27.5 (23.3).
Slow condition (intense):
Rate = 45.0(3.4) grams/min.
Intake = 569.3 (28.6) grams.
Hunger = 21.3 (22.1).
Slow condition (normal):
Rate = 49.4(3.6) grams/min.
Intake = 582.9 (30.4) grams.
Hunger = 15.8 (13.6).
Comparison A =
Intense flavour.
Comparison B =
normal flavour.
Confounders: Water
intake controlled
for. Similar liking
of food in different
conditions.
Scisco 2011 (10)
Effect of
computerized
eating rate
instructions on
intake and
hunger.
US university
students (21 healthy
weight, 9 overweight
or obese).
Age: 19.7 yrs.
BMI: 25.0.
Gender: Male and
female.
Eligibility criteria:
No food allergies,
regularly ate
breakfast, no self-
Repeated
measures.
PPS ate at a
normal rate (rate)
and a slowed rate
of eating, with
each session no
more than 7 days
after the previous
laboratory
session.
Fast condition
(N=30): PPS
provided with
computer feedback
regarding their
normal eating rate.
Slow condition
(N=30): Participants
provided with
computer feedback
instructing them to
eat at a 50% reduced
rate than their normal
rate.
Food: Waffle
intake.
Time: Breakfast
after overnight fast.
Computerized
equipment
measured eating
time.
Type: Self-report
VAS.
Time: Upon
meal completion.
Fast condition :
Rate: 3.6 (1.0) bites/min.
Intake: 428.2 (201.4) kcal.
Hunger: 8.4 (7.2).
Slow condition :
Rate: 2.0 (0.6) bites/min.
Intake: 357.8 (176.8) kcal.
Hunger: 12.1 (9.7).
Randomization: Not
reported.
Awareness: No
cover story or
demand awareness
checks reported.
Confounders: Water
intake controlled
for.
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reported history of an
eating disorder.
Sample stratified for
BMI population
statistics.
Hogenkamp
2010 (12)
Effect of
manipulating
eating rate via
food delivery on
intake and
hunger.
Young Dutch adults.
Age: 22.0 yrs.
BMI: 21.6.
Gender: Males and
females.
Eligibility criteria:
Had to like yogurt,
eat breakfast
regularly, low dietary
restraint, normal
appetite, no recent
weight loss or gain in
past 2 months,
allergies, no
gastrointestinal or
endocrine disorders.
Between
subjects.
Within a larger
study design,
PPS consumed a
yoghurt
breakfast 10
times with a
straw (fast) or
spoon (slow).
Fast condition
(N=16): PPS ate
liquid yoghurt out of
a bottle with a straw.
Slow condition
(N=13): PPS ate
liquid yoghurt out of
a bowl with a spoon.
Food: Yoghurt
intake.
Time: Breakfast,
following
overnight fast.
PPS recorded own
eating time with a
stopwatch.
Type: Self-report
VAS.
Time: Upon
meal completion.
Fast condition:
Rate = 132 (83) grams/min.
Intake = 575 (260) grams.
Hunger = 23.1 (20.2).
Slow condition:
Rate = 106 (53) grams/min.
Intake = 475 (192) grams.
Hunger = 24.5 (19.9).
Randomization:
Yes.
Awareness: No
cover story or
demand awareness
checks reported.
Confounders: Water
intake controlled
for. Participants
were exposed to
foods 10 times,
rather than a single
exposure.
Ioakimidis 2009
(13)
Effect of
computerized
eating rate
instructions on
intake.
Swedish university
students, all healthy
weight.
Age: 19.8 yrs.
BMI: 21.7.
Gender: Female.
Eligibility criteria:
Repeated
measures.
Three meals
served on
separate days,
with 7-20 days in
between.
Intermediate
meal, fast meal
Fast condition
(N=16): Eating rate
increased 40% from
usual rate, by PPS
following computer
instructions.
Slow condition
(N=16): Eating rate
decreased 30% from
usual rate, by
Food: ‘Standard
Swedish meal’
intake.
Time: Lunch,
following fasting
since breakfast.
Eating time and
rate recorded via
Mandometer.
No hunger
measure.
.
Fast condition:
Rate: 58.6(13.1) grams/min.
Intake: 390.2 (76.3) grams.
Slow condition:
Rate: 18.7 (4.2) grams/min.
Intake: 237.1 (46.5) grams
Intermediate condition:
Rate: 29.0 (9.9) grams/min.
Intake: 310.2 (107.6) grams.
Randomization:
Yes.
Awareness: No
cover story or
demand awareness
checks reported.
Confounders: Water
intake controlled
for. The amount of
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non-smoking, no
vegetarians, no food
allergies, no medical
problems, not
currently taking
medication, no
history of anxiety or
eating disorders.
Screened to be ‘linear
eaters’.
or slow meal. following computer
instructions.
Control condition
(N=16): Eating rate
ate at usual rate
following computer
instructions.
Comparison A =
Fast condition vs.
intermediate condition.
Comparison B =
Fast condition vs. slow
condition.
Comparison C =
Intermediate condition vs.
slow condition.
starting food on
plates of different
eating rate
conditions differed.
Andrade 2012
(14)
Effect of verbal
eating rate
instructions on
intake and
hunger.
Females recruited
from US university
and surrounding area.
Age: 22.7 yrs.
BMI: 22.4.
Gender: Female.
Eligibility criteria:
Non-smoking, pre-
menopausal, BMI 19-
30, no allergies to
foods, caffeine or
alcohol dependency,
adrenal or thyroid
disease, chronic
illnesses likely to
cause weight change,
eating disorders,
medications altering
appetite, dieting.
Repeated
measures.
Two meals eaten
fast and slow on
test days 3-7
days apart.
Fast condition
(N=30): PPS used a
large spoon and were
told to consume the
meal as fast as
possible without
pausing between
bites.
Slow condition
(N=30): PPS used a
small spoon and were
told to take small
bites, put the spoons
down between each
bite and chew each
bite for 20 to 30
seconds.
Researchers
monitored PPS
during eating.
Food: Pasta intake.
Time: Lunch,
following 4 hour
fast.
Meal initiation and
completion times
recorded by
researcher. Total
kcal consumed
divided by time
taken to consume
meal.
Type: Self-report
VAS.
Time: Upon
meal completion.
Fast condition:
Rate = 94.0 (30.6) kcal/min.
Intake = 707.9 (142.4) kcal.
Hunger = 5.1 (5.5).
Slow condition:
Rate: 29.0 (10.4) kcal/min.
Intake: 694.0 (178.6) kcal.
Hunger = 4.7 (3.3).
Randomization:
Yes.
Awareness: No
cover story or
demand awareness
checks reported.
Confounders: Water
intake controlled
for.
65
66
34
Kissileff 2008
(15)
Effect of
manipulating
eating rate via
food delivery on
intake.
US women.
Age: 25.07 yrs.
BMI: 21.5.
Gender: Female.
Eligibility criteria:
No history of eating
disorders or
significant
psychiatric disorders,
not attempting to lose
weight, not taking
medications other
than oral
contraceptives.
Repeated
measures.
Two yoghurt
meals served fast
and slow, on
non-consecutive
days.
Fast condition
(N=14): Yoghurt
delivered into
refilling cup at fast
rate.
Slow condition
(N=14): Yoghurt
delivered into
refilling cup at slow
rate.
Food: Strawberry
yoghurt intake.
Time: Afternoon
session after fixed
breakfast and 5 ½
hour fast.
PPS observed on
CCTV.
No hunger
measure.
Fast condition:
Rate: 146.1(19.1)
grams/min.
Intake: 824.0 (398.4) grams.
Slow condition:
Rate: 72.1 (19.1)
grams/min.
Intake: 655.3 (398.4) grams.
Randomization: Not
reported.
Awareness: No
cover story or
demand awareness
checks reported.
Confounders: Water
intake controlled
for.
PPS instructed to
‘binge’ during meal.
Kissileff 1980
(16)
Effect of
manipulating
eating rate via
food form on
intake.
US men.
Age: not reported.
BMI: 22.7.
Gender: Male.
Eligibility criteria:
non reported.
Repeated
measures.
Meals served as:
hard (slow) and
soft (fast) after
either 3 (A) or 6
hours (B) of food
deprivation,
served on
separate days
between 1-4 days
apart.
Fast condition
(N=16): Blended
food.
Slow condition
(N=16) Non-blended
food.
Additional factor of
deprivation; 3 vs. 6
hours.
PPS observed on
CCTV.
Food: Yoghurt,
fruit and nuts snack
intake.
Time: Afternoon, 3
or 6 hours after
standardized
breakfast.
PPS observed on
CCTV.
No hunger
measure.
Fast condition (3h):
Rate: 108.7(49.8)
grams/min.
Intake: 706.8 (601.4) grams.
Fast condition (6h):
Rate: 106.1(56.5)
grams/min.
Intake: 848.4 (420.3) grams.
Slow condition (3h):
Rate: 73.8 (20.9)
grams/min.
Intake: 621.9 (353.6) grams.
Slow condition (6h):
Rate: 67.8 (28.3)
grams/min.
Intake: 894.9 (542.5) grams.
Randomization: Not
reported.
Awareness: No
cover story or
demand awareness
checks reported.
Confounders: Water
intake controlled
for.
67
68
35
Comparison A = 3h fast
Comparison B = 6h fast
Martin 2007
(17)
Effect of
computerized
eating rate
instructions on
intake and
hunger.
Overweight and
Obese US adults.
Age: 30.7 yrs.
BMI: 30.1.
Gender: Male and
female.
Eligibility criteria:
18-65 yrs old, BMI
25-35, no smokers,
no chronic disease,
no medications
affecting body weight
or apptite, allergy or
dislike of test food,
no irregular
menstrual cycles or
triphasic oral
contraceptive
medication.
Repeated
measures.
Two meals eaten
at normal rate
and quickly, with
sessions one
week apart.
Additional factor
of gender.
Fast condition (N=48,
26F, 22M): PPS ate
at normal eating rate,
guided by
computerized
instructions.
Slow condition
(N=48, 26F, 22M):
PPS ate at 50% of
normal eating rate,
guided by
computerized
instructions.
Eating rate controlled
via computer.
Food: Chicken
intake.
Time: Lunch meal,
after twelve hour
fast.
Eating rate timed
via computer.
Type: Self-report
VAS.
Time: Before
and upon meal
completion.
Fast condition (women):
Rate: Baseline.
Intake: 588 (212) grams.
Hunger: 6.2 (12.2).
Fast condition (men):
Rate: Baseline.
Intake: 1020 (248) grams.
Hunger: 2.1 (3.6)
Slow condition (women):
Rate: 40% slower than
baseline
Intake: 585 (216) grams.
Hunger: 7.2 (14.3)
Slow condition (men):
Rate: 40% slower than
baseline.
Intake: 918 (225) grams.
Hunger: 3.1 (5.6).
Comparison A = women
Comparison B = men
Randomization: Not
reported.
Awareness: No
cover story or
demand awareness
checks reported.
Confounders: Water
intake controlled
for.
Smit 2011
(18)
Effect of verbal
eating rate
instructions on
intake and
hunger.
UK university staff
and students (6
healthy weight, 5
obese).
Age: 20-50 yrs.
BMI: 27.0.
Repeated
measures.
PPS ate at a slow
rate (35 chews of
each mouthful)
and a fast rate
(10 chews per
mouthful),
Fast condition
(N=11): PPS were
told to take 2 bites of
pasta on a fork at a
time and swallow
properly and to chew
each mouthful 10
times.
Food: Pasta dish
intake.
Time: Lunch meal.
Eating rate
measured by
electromyography.
Type: Self-report
hunger VAS.
Time: Upon
meal completion.
Fast condition :
Rate: 23.7 (2.7) grams /min.
Intake: 358 (63.02) grams.
Hunger: 10.4 (9.0).
Slow condition :
Rate: 11.1 (1.7) g/min.
Intake: 313 (56.38) grams.
Hunger: 9.6 (8.2).
Randomization:
Yes.
Awareness: Cover
story used, no
demand awareness
checks reported.
Confounders: Water
69
70
36
Gender: Male and
female.
Eligibility criteria:
20–50 years of age
and generally
healthy, fluent
English speaking, full
dental record, not
habitually skipping
any meals, BMI =
18.5–25 or 30–
40, not actively
dieting, not
exercising any more
than moderate
physical activity, not
suffering from sleep
complaints, food
allergies or food
intolerances.
during two
sessions that
were 2-7 days
apart.
Slow condition
(N=11): as above, but
PPS told to chew
each mouthful 35
times.
intake controlled
for.
Spiegel 1993
(19)
Effect of
manipulating
eating rate via
food form on
intake.
Participants from US
university (9 healthy
weight, 9 obese).
Age: 18-46 yrs.
BMI: 26.9.
Gender: Female.
Eligibility criteria:
No eating disorders, a
history of drug use,
food allergies, or
endocrine disorders
that might influence
Repeated
measures.
Large (fast
eating rate) and
small (slow
eating rate) sized
sandwiches
consumed during
two different
non-consecutive
day sessions.
Fast condition
(N=18): Participants
were told to eat as
much of the food as
they liked; large 15g
sized sandwiches.
Slow condition
(N=18): as above, but
with small 5g sized
sandwiches.
Food: Sandwich
intake.
Time: Lunch meal,
after a morning
fast.
Eating rate
measured by
electromyography.
Type: Self-report
hunger VAS.
Time: Upon
meal completion
Fast condition :
Rate: 19.5 (1.6) grams/min
Intake: 268.3 (20.1) grams.
Hunger: Not reported.
Slow condition :
Rate: 16.3 (1.4) grams/min.
Intake: 271.9 (21.4) grams.
Hunger: Not reported.
Randomization: Not
reported.
Awareness: Cover
story used, no
demand awareness
checks reported.
Confounders: Water
intake controlled
for.
71
72
37
food intake. Subjects
were carefully
interviewed to
include only those
who had maintained a
stable weight (& no
more than 2.5 kg)
without dieting for
the past year and who
were currently not
dieting. They had to
be regular lunch
eaters and they had to
agree to eat freely in
the laboratory meals.
Weijzen 2008
(20)
Effect of
manipulating
eating rate via
food form on
intake.
&
Effect of verbal
eating rate
instructions on
intake.
Dutch adults.
Age: 28.4 yrs.
BMI: 22.3.
Gender: Male and
female.
Eligibility criteria: no
weight change of
more than 5 kg
during the last 6
months, no
gastrointestinal
illness or illness
of the thyroid gland,
diabetes, allergy for
any of the products
used in the study,
pregnancy, lactation,
the use of medication
Repeated
measures.
16g (fast eating
rate) and 1.5g
(slow eating rate)
sized candy bars
eaten on non-
consecutive
days.
Additional factor
of verbal
instructions to
eat slowly vs. no
instructions.
Food form
Fast condition
(N=59): Participants
were told to eat as
much of the food as
they liked (large 16g
bar of candy).
Slow condition
(N=59): as above, but
with small 1.5g bars.
Instructions
Fast condition
(N=59): PPS were
instructed to consume
the foods until they
would feel
pleasantly satiated.
Food: Candy
intake.
Time: Mid-
morning or
afternoon snack,
following a one
hour fast.
Total eating time
measured by PPS
using stopwatch.
Type: Self-report
hunger VAS.
Time: Upon
meal completion
No instructions
Rate: 9.5 (3.1) grams/min.
Fast condition (16g bars) :
Intake: 43.6 (30.0) grams
Hunger: Not reported.
Slow condition (1.5g bars) :
Rate: 8.0 (3.1) grams/min.
Intake: 38.4 (29.2) grams
Hunger: Not reported.
Instructions
Fast condition (16g bars) :
Rate: 8.9 (4.6) grams/min.
Intake: 39.7 (26.9) grams.
Hunger: Not reported.
Slow condition (1.5g bars):
Rate: 6.6(2.3) grams/min.
Intake: 39.8 (28.4) grams.
Hunger: Not reported.
Randomization: Yes
Awareness: PPS
were unaware of the
aims of the study,
but no demand
awareness checks
reported.
Confounders: Water
intake controlled
for.
73
74
38
with a possible effect
on taste and/or
appetite, high dietary
restraint.
Slow condition
(n=59): PPS
instructed to chew the
snacks properly, to
swallow each bite
before taking the
next one, and to
consume the snacks
until they would feel
pleasantly satiated.
Comparison A = 16g vs.
1.5g bars with no
instructions.
Comparison B = 16g vs.
1.5g bars with instructions
Comparison C =
Instructions vs. no
instructions for 1.5g bars.
15g bars, effect of
instructions vs. no
instructions = no significant
difference in eating rate.
Zandian 2012
(21)
Effect of
computerized
eating rate
instructions on
intake.
Swedish school
children.
Age: 13yrs.
BMI: 20.
Gender: Male and
female.
Eligibility criteria:
Healthy and no eating
disorder symptoms.
Repeated
Measures.
3 sessions,
involving lunch
consumed at
normal rate, fast
rate and slowed
rate, a week
apart.
Fast condition
(N=30): PPS ate at a
30% quicker rate than
baseline meal.
Slow condition
(N=30): PPS ate at a
30% slower rate than
baseline meal.
Intermediate
condition (N=30):
baseline meal
consumed with no
eating rate
instructions.
PPS ate using a
Mandometer.
Food: Meal of
chicken, rice and
vegetables.
Time: Lunch.
Eating rate
controlled and
measured using
Mandometer,
No hunger
measure.
Fast condition :
Rate: 30% quicker than at
baseline.
Intake: 303 (87.8) grams.
Slow condition :
Rate: 30 % slower than at
baseline.
Intake: 285.6 (58.8) grams.
Intermediate condition :
Rate: Baseline rate.
Intake: 271.4 (44.8) grams.
Comparison A =
Fast condition vs.
intermediate condition.
Comparison B =
Fast condition vs. slow
condition.
Randomization: Yes
Awareness: No
cover story or
demand awareness
checks reported.
Confounders: PPS
appeared to be able
to drink and no
information
available regarding
fluid intake.
75
76
39
Comparison C =
Intermediate condition vs.
slow condition.
Zandian 2009
(22)
Effect of
computerized
eating rate
instructions on
intake and
hunger.
Healthy weight PPS
recruited from
Swedish university
Age: 21.2.
BMI: 22.2.
Gender: Females.
Eligibility criteria:
18–25 years old,
BMI of 19–25, of
good health, non-
smokers, no food
allergies , no history
of eating, disorders or
use medication
known to affect food
intake. Athletes and
pregnant and
lactating women
were excluded.
Repeated
measures.
PPS consumed a
meal at normal
eating rate
(intermediate)
and slow and fast
eating rates,
during sessions
one week apart.
Fast condition
(N=47): PPS ate
using a Mandometer,
which provided
instructions resulting
in a 40% reduction in
eating rate from
control meal eating
rate.
Slow condition
(N=47): as above, but
instructions resulting
in a 30% reduction in
eating rate from
control meal eating
rate.
Intermediate
condition (N=47):
PPS ate as above but
with no instructions.
Food: Meal of
chicken, rice and
vegetables.
Time: Lunch time,
after fasting from
breakfast.
Eating rate
measured using
software.
Type: Self-report
hunger VAS.
Time: Upon
meal completion.
Fast condition :
Intake: 305 (97) grams
Hunger: 2.7 (1.3)
Rate: 40% fast than normal.
Slow condition :
Intake: 258g (42).
Hunger: 2.5 (1.5).
Rate: 30% slower than
normal.
Intermediate condition:
Intake: 291g (64).
Hunger: 2.6 (1.2).
Rate: Normal rate.
Comparison A =
Fast condition vs.
intermediate condition.
Comparison B =
Fast condition vs. slow
condition.
Comparison C =
Intermediate condition vs.
slow condition.
Randomization:
Yes.
Awareness: Cover
story used, no
demand awareness
checks reported.
Confounders: Water
intake controlled
for.
Zijlstra 2010
(23)
Effect of
manipulating
eating rate via
Healthy weight
Dutch adults from
university and local
community.
Age: 24 yrs.
Repeated
measures.
PPS ate a soft
(fast condition)
and hard (slow
Fast condition
(N=106): PPS ate a
soft luncheon meat.
Slow condition
(N=106): PPS ate a
Food: Luncheon
meat intake.
Time: Evening
snack, after
receiving preload
Type: Self-report
hunger VAS.
Time: Upon
meal completion
Fast condition :
Rate: 25 (13) grams/min.
Intake: 157 (125) grams
Hunger: 35.0 (27.0)
Slow condition :
Randomization:
Yes.
Awareness: No
cover story or
demand awareness
77
78
40
food form on
intake and
hunger.
BMI: 21.8.
Gender: Male and
female.
Eligibility criteria: no
restrained eaters, no
pps with a lack of
appetite for any
reason, following an
energy restricted diet
during the last 2
months, change in
body weight >5 kg
during the last 2
months, healthy
weight range,
stomach or bowel
diseases, diabetes,
thyroid disease or any
other endocrine
disorder, having
difficulties with
eating or swallowing,
hypersensitivity
to the test product,
being vegetarian or
vegan or participation
in previous studies.
condition)
portion of
luncheon meat,
on separate days
in a cinema.
hard version of the
same luncheon meat.
of wheat buns, with
preload amount
calculated
according to
individual energy
needs.
In a separate final
session eating rate
was examined. PPS
recorded their own
start and finish
time of eating.
Rate: 21 (10) grams/min.
Intake: 148 (121) grams.
Hunger: 35.0 (27.0).
checks reported.
Confounders: Water
intake controlled
for, food liking
controlled for.
Zijlstra 2008
(24)
Study 2
Effect of
manipulating
Healthy weight
Dutch adults from
university and local
community.
Age: 24 yrs.
Repeated
measures.
As part of a
larger design,
PPS ate a liquid
(fast condition)
Fast condition
(N=49): PPS ate
liquid chocolate
custard drank from a
straw connected to a
pump.
Food: Chocolate
custard.
Time: Evening
meal, after
receiving preload
of pizza, with
Type: Self-report
hunger VAS.
Time: Upon
meal completion.
Fast condition :
Rate: 89.5(50.1) grams/min.
Intake: 319 (176) grams.
Hunger: 3.5 (1.9).
Slow condition :
Rate: 56.7(20.2) grams/min.
Intake: 226 (122) grams.
Randomization:
Yes.
Awareness: Cover
story used, but no
demand awareness
checks reported.
79
80
41
eating rate via
food form on
intake and
hunger.
BMI: 22.2.
Gender: Male and
female.
Eligibility crtieria:
had to be healthy,
aged 18–50 years,
BMI 18.5–30.0, had
To like chocolate
flavoured dairy
products. Exclusion
criteria were
restrained eating, lack
of appetite for any
(unknown) reason,
following an energy
restricted diet during
the last 2 months,
change in weight of
45 kg during the last
2 months, stomach or
bowel diseases,
diabetes, thyroid
disease or any other
endocrine disorder or
hypersensitivity for
milk components.
and semi-solid
(slow condition)
chocolate
custard, on
separate days.
Slow condition
(N=49): PPS ate as
above, but it was a
semi-solid chocolate
custard.
preload amount
calculated
according to
individual energy
needs.
Hunger: 3.1 (1.7)
. Confounders: Water
intake controlled
for, food liking
differences between
conditions (liquid
tended to be liked
more than semi-
solid).
Bolhuis 2013
(25)
Effect of
manipulating
eating rate via
food form on
intake and
hunger.
Chinese nationals.
Age: 24.2 yrs.
BMI: 21.2.
Gender: Male and
female.
Repeated
measures.
On two separate
days, PPS ate a
soft (fast) and
hard (slow)
textured meal.
Fast condition
(N=50): PPS
consumed a lunch
meal that had a soft
texture.
Slow condition
(N=50): PPS
consumed a lunch
Food: Hamburger,
rice and salad
intake.
Time: Lunchtime,
after fast from
breakfast.
PPS were video
Type: Self-report
hunger VAS.
Time: Upon
meal completion.
Fast condition :
Rate: 37 (11) grams/min.
Intake: 3082 (866.1) kj.
Hunger: 12.0 (14).
Slow condition :
Rate: 25 (7) grams/min.
Intake: 2694.2 (1025.2) kj.
Hunger: 13.0 (14).
Randomization:
Yes.
Awareness: Cover
story used, but no
demand awareness
checks reported.
Confounders:
81
82
42
Eligibility criteria:
not be vegetarian,
following an energy-
restricted diet during
the last two months,
gained or lost > 5 kg
weight during the last
year, lack of appetite,
have food allergies/
intolerances or
difficulties with
eating or swallowing.
meal that had a hard
texture.
recorded to
calculate eating
rates.
Significant
differences between
sensory
characteristics of
food, including
slow condition food
being rated as
slightly lower in
pleasantness. Trend
for water intake to
be higher in slow
condition.
Karl 2013 (26)
Effect of
computerized
eating rate
instructions on
intake.
Non-obese healthy
men and women from
a US army centre and
surrounding areas.
Age: 30 yrs.
BMI: 24.
Gender: Male and
female
Eligibility criteria:
no previous diagnosis
with any disease
known to affect
metabolism or
current use of
medications affecting
metabolism and/or
appetite, 2.2 kg body
mass change during
the 3-month
preceding study
participation, recent
Repeated
measures.
On four separate
days, PPS ate a
high energy
dense (HED)
breakfast or a
low energy dense
(LED) breakfast
quickly or
slowly.
Fast condition
(N=20): PPS ate
using a Mandometer,
which provided
instructions.
Slow condition
(N=20): PPS ate
using a Mandometer,
which provided
instructions.
Food: Oatmeal
entrée served as
HED OR LED.
Time: Breakfast.
Eating rate
measured using
software.
Type: No hunger
measure.
Time: Upon
meal completion
LED fast condition:
Intake: 697kcal (336).
Rate: 80g/min.
LED slow condition:
Intake: 601kcal (283).
Rate: 20g/min.
HED fast condition:
Intake: 999kcal (459).
Rate: 80g/min.
HED slow condition:
Intake: 733kcal (419).
Rate: 20g/min.
Comparison A =
LED fast condition vs. slow
condition.
Comparison B =
HED fast condition vs. slow
condition.
Randomization:
Yes.
Awareness: Cover
story used and
effective demand
awareness checks
reported.
Confounders: Water
intake controlled
for.
83
84
43
pregnancy, allergies
to or dislike of the
test foods, and a
score of on the Eating
Attitudes Test.
Reference number in brackets, N = refers to number of participants, PPS = participants, VAS = visual analogue scale, numerical values are
means unless otherwise stated. HED & LED = high and low energy density. BMI = body mass index.
TABLE 2. Studies examining the effect of manipulating eating rate (for a fixed meal size) on hunger later in the day (type 3 studies).
Study and type Participant
information
Study design Eating rate
manipulation
Food intake and
eating rate
measures
Hunger
measure
Results
Means (SD in brackets)
Additional
information
Karl 2011
(27)
Effect of
computerized
eating rate
instructions on
hunger.
Non-obese (10) and
obese men (15) from
a US army centre and
surrounding areas.
Age: 30.2 yrs.
BMI: 27.3.
Gender: Males and
females.
Eligibility criteria:
18-55 yrs old, no
previous diagnosis
with any disease
affecting
metabolism, chronic
use of
Repeated
measures.
Three meals
served on non-
consecutive days
during 2 week
period.
Meal duration set
at 7, 14 or 28
minutes to alter
eating rate using
a Mandometer.
Fast condition
(N=25): Eating rate
increased by setting
meal time at 28
minutes.
Slow condition
(N=25): Eating rate
decreased by setting
meal time at 7
minutes.
Intermediate
condition (N=25):
Eating rate set at
intermediate level by
setting meal time at
14 minutes.
Food: Fixed meal
of corned beef
hash.
Time: Morning
meal after
overnight fast.
Eating time and
rate recorded using
a Mandometer.
Type: Self-
report VAS.
Time: 180 mins
from start of
eating.
Fast condition:
Rate: 92.7kcal/min.
Hunger: 5.0 (30.6).
Slow condition:
Rate: 23.1kcal/min.
Hunger: 6.1 (28.8).
Intermediate condition:
Rate: 46.4kcal/min.
Hunger: 5.0 (29.5).
Comparison A =
Fast condition vs.
intermediate condition.
Comparison B =
Fast condition vs. slow
condition.
Randomization:
Yes.
Awareness: No
cover story or
demand awareness
checks reported.
Confounders: Water
intake controlled
for.
85
86
706
707
708
709
710
711
712
44
medications affecting
metabolism and/or
appetite, ≥2.2 kg
weight change during
the 3 months
preceding
participation,
pregnancy, allergies
to or stated dislike of
the test foods, and
clinically diagnosed
eating disorder or a
high score on the
Eating Attitudes Test.
Comparison C =
Intermediate condition vs.
slow condition.
Kokkinos 2010
(28)
Effect of
manipulating
eating rate via
food delivery on
hunger.
Greek male
volunteers.
Age: 29.7 yrs.
BMI: 26.1.
Gender: Male.
Eligibility criteria:
Healthy, not taking
medication.
Repeated
measures:
Two servings of
ice cream eaten
within 5 or 30
minutes, eaten
on separate days
at least 1 week
apart.
Fast condition
(N=17): PPS
instructed to consume
ice cream within 5
minutes.
Slow condition
(N=17) PPS served
ice cream
intermittently across
30 minutes.
Food: Fixed
serving of ice
cream.
Time: Morning
meal consumed
after overnight fast.
Timed by
researcher.
Type: Self-report
VAS.
Time: Upon
meal completion.
Hunger: 210
mins from start
of eating.
Fast condition:
Rate: 60ml/min.
Hunger: 41.1 (19.0).
Slow condition:
Rate: 10ml/min.
Hunger: 39.9 (23.7).
Randomization:
Yes.
Awareness: No
cover story or
demand awareness
checks reported.
Confounders: Water
intake controlled
for.
Rigamonti 2013
(29)
Effect of
manipulating
eating rate via
food delivery on
hunger.
Obese Italian
adolescents and
adults.
Age: 16.7 & 30.1 yrs.
BMI: 37.2 & 44.1.
Gender: Male and
female.
Repeated
measures: PPS
ate fast and slow
with sessions at
least 2 weeks
apart.
Additional factor
of adult or
adolescent.
Fast condition
(N=18): Participants
instructed to consume
ice cream within 5
minutes.
Slow condition
(N=18) Participants
served ice cream
intermittently across
Food: Fixed
serving of ice
cream.
Time: Morning
meal consumed
after overnight fast.
Timed by
researcher.
Type: Self-report
VAS.
Time: Upon
meal completion.
Hunger: 210mins
from start of
eating.
Fast condition (adolescent):
Rate: 102 grams/min.
Hunger: 43.3 (13.5).
Slow condition (adolescent):
Eating rate: 17 grams/min.
Hunger: 41.7 (12.5).
Fast condition (adults):
Rate: 102 grams/min.
Randomization:
Yes.
Awareness: No
cover story or
demand awareness
checks reported.
Confounders: Water
intake controlled
87
88
45
Eligibility criteria: no
previous diagnosis of
any disease affecting
the endocrine system
and metabolism
(apart from obesity),
no chronic use of
medications
(including oral
contraceptives)
affecting metabolism
and/or appetite, 5.0
kg weight change
during the 3 months
preceding
study participation,
pregnancy, allergies
to or stated dislike of
the components of
the test meal,
clinically diagnosed
eating disorder or a
score of 20 on the
Eating Attitudes Test.
30 minutes.
Food serving timed
by researcher.
Hunger: 58.3 (11.1).
Slow condition (adults):
Rate: 17 grams/min.
Hunger: 39.4 (11.4).
Comparison A = adolescent.
Comparison B = adult.
for.
Zhu 2013
(30)
Effect of verbal
eating rate
instructions on
hunger.
US university
students, staff and
local community.
Age: 24 yrs.
BMI: 24.8.
Gender: Males.
Eligibility: males
aged 18–40 yrs, BMI
Repeated
measures.
PPS consumed
pizza quickly (15
chews per
mouthful) or
slowly (40 chew
per mouthful),
during separate
sessions, at least
1 week apart.
Fast condition
(N=21): Participants
were instructed to
consume pizza and
chew each portion 15
times.
Slow condition
(N=21): Participants
were instructed to
consume pizza and
chew each portion 40
Food: Fixed
serving of pizza cut
into portions.
Time: Breakfast,
after overnight fast.
Researcher
recorded eating
duration.
Type: Self-report
hunger VAS.
Time: 180
minutes from
start of eating.
Fast condition :
Rate: 61.3 (4.6) kcal/min.
Hunger: 52.2 (23.1).
Slow condition :
Rate: 24.5 (4.6) kcal/min.
Hunger: 46.9 (24.7).
Randomization:
Yes.
Awareness: Cover
story used, no
demand awareness
checks reported.
Confounders: Water
intake controlled
for.
89
90
46
20·0–29·9, full set of
natural teeth and a
willingness to eat the
test foods. PPS
excluded if had
presence or history of
gastrointestinal
disease, had presence
of other chronic or
acute diseases,
currently using
medication that
affects appetite, were
restrained eaters and
had allergy or
intolerance or dislike
towards the test
foods.
times.
Researcher monitored
PPS during eating.
Reference number in brackets, N = refers to number of participants, PPS = participants, VAS = visual analogue scale, numerical values are
means unless otherwise stated.
91
92
713
714
715
716
717
... Early childhood obesity is concerning not only because it accompanies risk factors for chronic health outcomes such as diabetes, respiratory disease, hypertension, and cardiovascular disease, but also due to its impact on these health outcomes into adulthood (Cote et al., 2013;Daniels, 2006;Flynn, 2013). Investigations of modifiable behavioral markers of obesity risk have identified bite speed as one appetitive trait associated with subsequent obesity (Button et al., 2021;Fogel et al., 2017;Mattfeld et al., 2017;Ohkuma et al., 2015;Robinson et al., 2014). Additionally, child characteristics such as temperament that are known to underlie behavioral phenotypes of impulsivity and regulation have been shown to be associated with different aspects of eating behavior including food and satiety responsiveness (Pieper & Laugero, 2013;Tan & Holub, 2011), emotional overeating (Messerli-Burgy et al., 2018), and bite speed (Button et al., 2021). ...
... In acknowledgment of the link between overall eating speed and adiposity, there have been efforts to test the benefits of slower eating behaviors in adolescents and adults, which have been effective in regulating both the total amount of energy consumed and hormones that are responsible for satiety responses (Andrade et al., 2008;Galhardo et al., 2012;Robinson et al., 2014). Since faster eating speed may impact time spent to process internal satiety cues, it is plausible that the amount of consumption is not regulated in accordance with the physical needs of hunger (Hughes & Frazier-Wood, 2016;Llewellyn et al., 2008). ...
... Because individual differences in temperament remain relatively stable over time (Kopala-Sibley et al., 2018), early temperamental characteristics reflecting behavioral regulation that persist into adulthood may be an important factor to consider in the development of children's bite speed. In older children and adults, faster eating speed has been associated with more calories consumed, poor appetite regulation, and weight status, but less research has been conducted with very young children (Robinson et al., 2014). Considering faster bite speed as one behavioral marker associated with obesity, the findings from our study suggest the importance of identifying modifiable aspects of eating patterns that emerge at an early age and that may persist over time (Kral et al., 2018). ...
Article
Eating behaviors are shaped at an early age, persist into adulthood, and are implicated in the development of physical health outcomes, including obesity. Faster bite speed has been identified as an obesogenic eating behavior, prompting researchers to examine child and family factors associated with children's variability in bite speed. Child temperament, involving phenotypes of reactivity and regulation, and distractions in family food contexts are fruitful areas of investigation, but few studies have examined the interplay among these factors and their associations with bite speed. To address the gap in the literature, we examined relations between early child temperament, family mealtime distractions, and children's observed bite speed. Caregiver report of child temperament at 3 months was measured using the Infant Behavior Questionnaire Very Short Form - Revised. Child mealtime distractions and bite speed were assessed using family mealtime videos that were collected during home visits when children were 18–24 months of age (n = 109). Results revealed that children who were reported to be higher on orienting/regulation at 3 months, and who were more distracted during mealtimes at 18–24 months, had relatively slower bite speeds. No significant interactions were found. The findings from this correlational study inform further investigations into the implications of early temperament and food contexts for the development of eating behaviors implicated in obesity risk.
... * Christian Berón, cberon@gmail.com 2 Investigadora independiente. 3 Investigadora independiente. 4 Investigadora independiente. 5 Universidad de Iowa, Iowa, Estados Unidos de América. 6 Universidad de Antioquia, Medellín, Colombia. ...
... Se ha informado sobre dietas con alto contenido de nutrientes favorecedores de la presencia de obesidad y las ENT, y con bajo contenido en nutrientes protectores de la salud. Además, se han establecido relaciones entre el consumo elevado de productos ultraprocesados con una mayor incidencia de hipertensión arterial, dislipemias, diabetes y cáncer (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15). ...
Article
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Objetivo. Estimar el efecto que representa en la calidad de la dieta de la primera infancia uruguaya —niños de 2 a 4 años— el consumo de productos que contienen cantidades excesivas de nutrientes críticos asociados a las enfermedades no transmisibles (ENT) (azúcares libres, grasas totales, grasas saturadas y sodio), según el modelo de perfil de nutrientes de la Organización Panamericana de la Salud (OPS). Métodos. Se utilizó un recordatorio de ingesta de alimentos durante 24 horas en una muestra representativa de 401 participantes de la Encuesta de Nutrición, Desarrollo Infantil y Salud del año 2018. Se usó la clasificación de alimentos propuesta por el sistema NOVA para categorizarlos según su naturaleza y los procesos industriales a los que son sometidos. A partir de esto se lo analizó con base en el modelo de perfil de nutrientes de la OPS, lo cual permitió identificar los productos con contenido excesivo de estos nutrientes. Resultados. El 50 % de los niños consumieron tres o más productos con exceso de alguno de los nutrientes vinculados con las ENT. Aproximadamente 9 de cada 10 niños consumen productos con exceso de al menos uno de los nutrientes críticos estudiados. Conclusión. Las dietas que no contienen productos ultraprocesados y procesados con exceso de azúcares libres, grasas totales, grasas saturadas y sodio fueron la mejor opción para los niños de 2 a 4 años. La ingesta de productos con exceso de nutrientes críticos según la OPS (y cada gramo adicional consumido de tales productos) empeora la calidad de la dieta de manera significativa, e impide que se cumpla con las recomendaciones de la Organización Mundial de la Salud.
... By contrast, fast eating speed is related to obesity, T2DM, and NAFLD (14)(15)(16). The reasons for this may be as follows: slow eating speed leads to lower energy intake (43), but fast eating speed is related to increased energy intake (14,43,44). Furthermore, fast speed eating is associated with obesity, reducing energy consumption after meals, phosphorylation of Akt because of postprandial hyperglycemia and hyperinsulinemia (16). ...
... By contrast, fast eating speed is related to obesity, T2DM, and NAFLD (14)(15)(16). The reasons for this may be as follows: slow eating speed leads to lower energy intake (43), but fast eating speed is related to increased energy intake (14,43,44). Furthermore, fast speed eating is associated with obesity, reducing energy consumption after meals, phosphorylation of Akt because of postprandial hyperglycemia and hyperinsulinemia (16). ...
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Full-text available
Background and Aims Maintenance of muscle mass is important for sarcopenia prevention. However, the effect of eating speed, especially fast, normal, or slow speed, on muscle mass changes remains unclear. Therefore, the purpose of this prospective study was to investigate the effect of eating speed on muscle mass changes in patients with type 2 diabetes (T2DM). Methods This study included 284 patients with T2DM. Based on a self–reported questionnaire, participants were classified into three groups: fast–, normal–, and slow–speed eating. Muscle mass was assessed using a multifrequency impedance body composition analyzer, and skeletal muscle mass (SMI) decrease (kg/m ² /year) was defined as [baseline SMI (kg/m ² )–follow–up SMI (kg/m ² )] ÷ follow–up duration (year). The rate of SMI decrease (%) was defined as [SMI decrease (kg/m ² /year) ÷ baseline SMI (kg/m ² )] × 100. Results The proportions of patients with fast–, normal–, and slow–speed eating were, respectively, 50.5%, 42.9%, and 6.6% among those aged <65 years and 40.4%, 38.3%, and 21.3% among those aged ≥65 years. In patients aged ≥65 years, the rate of SMI decrease in the normal (0.85 [95% confidence interval, CI: −0.66 to 2.35]) and slow (0.93 [95% CI −0.61 to 2.46]) speed eating groups was higher than that in the fast speed eating group (−1.08 [95% CI −2.52 to 0.36]). On the contrary, there was no difference in the rate of SMI decrease among the groups in patients aged <65 years. Compared with slow speed eating, the adjusted odds ratios of incident muscle loss [defined as rate of SMI decrease (%) ≥0.5%] due to fast– and normal–speed eating were 0.42 (95% CI 0.18 to 0.98) and 0.82 (95% CI 0.36 to 2.03), respectively. Conclusion Slow–speed eating is associated with a higher risk of muscle mass loss in older patients with T2DM.
... The evidence above best supported the findings of current study in which greater perceived satiety was observed in respondents who consumed more viscous formulated MMT peel powder. Next, a slower eating rate attributed to the presence of dietary fiber was also associated with a lower energy intake as reported by a systematic review [84]. ...
Article
Full-text available
Background Melon Manis Terengganu (MMT) peel has a high dietary fiber content, but there is no data examining its health benefits in adults at risk of type 2 diabetes. The objective of the study was to evaluate whether consumption of MMT peel powder improves glycemic response, satiety, and food intake in adults at risk of type 2 diabetes. Methods An open-label, randomized, placebo-controlled, crossover design trial was conducted among adults ( n = 30, ages 18–59 y) at risk of type 2 diabetes. They consumed Formulation 3 (formulated MMT peel powder) [A] and control (glucose) [B] with study breakfast based on randomly assigned treatment sequences (AB, BA) established by Research Randomizer ( www.randomizer.org ). Capillary blood glucose and perceived satiety were determined at baseline (0 min), 30, 60, 90 and 120 min, followed by a post-intervention food intake measurement. Results The repeated measures analysis of variance (ANOVA) revealed significant time (F = 84.37, p < 0.001, η p ² = 0.744), condition (F = 22.89, p < 0.001, η p ² = 0.441), and time*condition effects (F = 24.40, p < 0.001, η p ² = 0.457) in blood glucose levels. Respondents ( n = 30) who consumed Formulation 3 also had a significantly lower blood glucose 2-hour incremental area under the curve (iAUC) of 134.65 ± 44.51 mmol/L*min and maximum concentration (CMax) of 7.20 (7.10, 8.20) mmol/L with relative reduction of 26.8 and 13.3% respectively, when compared with control ( p < 0.001). Besides, significantly greater perceived satiety, lower energy and fat intake as well as higher dietary fiber intake were also observed in the intervention group compared with the placebo group ( p < 0.05). There were no marked side effects associated with the ingestion of the test products. Conclusions Short-term consumption of formulated MMT peel powder may improve glycemic response, increase perceived satiety and reduce food intake in adults at risk of type 2 diabetes with the potential to be utilized as a functional beverage. Medium-to long-term clinical trial is warranted to determine whether taking this formulated MMT peel powder on a daily basis has an influence on health outcomes. Trial registration ClinicalTrials.gov Identifier: NCT05298111. Registered 28/03/2022.
... Also, because the overall enjoyment derived from eating a food is influenced by the last bites, a small portion can be more enjoyable than a larger portion whose total enjoyment has been diminished by low-pleasure final bites (Garbinsky, Morewedge, & Shiv, 2014;Rode, Rozin, & Durlach, 2007;Rozin & Rozin, 2018;Schwartz, et al., 2020). Hence by making the sensory experience of eating more salient, sensory focus leads people to savor (and enjoy) their food more, which makes them satiate faster and eat less (Galak, Kruger, & Loewenstein, 2013;Robinson, et al., 2014;Rozin, Kabnick, Pete, Fischler, & Shields, 2003). In addition, sensory focus helps people better anticipate that smaller portions are more enjoyable than they would otherwise think, leading them to choose smaller portions (Cornil & Chandon, 2016a). ...
Article
Full-text available
Emerging research has shown that sensory-based interventions (e.g., inviting people to mindfully focus on the multisensory aspects of eating) can be a viable alternative to nutrition-based interventions (e.g., nutrition labeling) to encourage moderate eating. We contribute to this literature in two ways. First, we propose a novel and simple sensory-based intervention to increase the appeal of moderate food portions in commercial settings, epicurean labeling, which consists in emphasizing the aesthetic, multisensory properties of the food when describing it on menus or packages. Second, we show theory-relevant cross-cultural differences in the effectiveness of this intervention between the United States and France, two food cultures at the opposite ends of the hedonic-utilitarian food attitude spectrum. We report the results of a multi-day field experiment at a French cafeteria showing that epicurean labeling, unlike nutrition labeling, reduces intake while increasing the perceived monetary value of the meal thanks to higher savoring. We then show in a matched cross-national online experiment that epicurean labeling is more effective in France than in the United States. We provide additional evidence of this cross-cultural variation in a study of 9154 food products sold in supermarkets in both countries. We find that epicurean labeling is more prevalent, but also more likely to be associated with smaller portions in France than in the United States. While sensory-based interventions are a promising alternative to nutrition-based interventions, it is necessary to develop business-friendly interventions that can be implemented in everyday life, as well as to consider cultural factors that can modulate their effectiveness.
... Dado que se ha planteado que una VC más rápida puede conllevar a la obesidad (Otsuka et al., 2006), se han realizado intervenciones en personas con IMC de normopeso y obesidad para aumentar el número de masticaciones y comer a una velocidad más lenta, con la finalidad de disminuir la ingesta de alimentos (calorías consumidas por día), facilitar la saciedad, disminuir el hambre y evitar el riesgo de aumentar de peso. Sin embargo, los resultados reportados no han sido favorables para personas con obesidad (Andrade et al., 2008;Robinson et al., 2014;Shah et al., 2013;Tanihara et al., 2008;Zhu & Hollis, 2013). ...
Article
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Obesity has become one of the main public health problems in Mexico. Its study involves the analysis of eating behavior (EB) and some of its parameters, such as eating speed (ES). The objective of this study was to compare the ES, size and number of bites, number and pattern of chewing performed by normal-weight (n = 5) and overweight-obese (n = 4) individuals. Using a single-assessment quasi-experimental design, participants were videotaped while eating a slice of pizza (90 grams). Significant differences were found in bite size (Z = 2.357, p = 0.016) and number of bites (Z = -2.357, p = 0.016), with a small effect size in both parameters (r = 0.29), indicating that overweight-obese individuals have a larger bite size and take a smaller number of bites. Mexico has an obesogenic environment and a high prevalence of chronic degenerative diseases, which share EB as one of the main causes of their genesis; continuing with the study of ES and associated parameters will allow us to lay the foundations for the design of interventions for the prevention of overweight-obesity.
... Nevertheless, the intake of snacks must be in line with the nutritional needs and specificities of each individual [7]. In addition, spending more time on meals has been associated with lower energy intake [8]. Regarding commensality and, more precisely, eating meals with the family, it has been associated with healthier eating habits [1]. ...
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Background: The use of validated and reliable methods and instruments is necessary to study dietary practices and nutritional status due to their direct impacts on population health. Objective: The aim is the validity and reliability of the conceptual and methodological framework of research on factors associated with dietary practices and nutritional status (FADPNS), carried out on adult population of the Rabat-Salé-Kenitra region in Morocco. Material and methods: First, we developed a conceptual and methodological framework for research on FADPNS, which aimed to study dietary practices, nutritional status, and the factors associated with them in an adult Moroccan population. Then, we studied the validity and reliability of this framework in three phases. Phase 1 focused on the validation of the content of the conceptual and methodological framework, Phase 2 focused on the study by an expert committee of the internal consistency validity (ICV) of the questionnaires used in this research , and Phase 3 consisted of the study of the reliability of the items questionnaires by the test of Cronbach Alpha. Results: Thus, the validated content of the conceptual framework of research on FADPNS includes socio-demographic, socio-economic, and socio-cultural characteristics; health status; physical activity, places of food purchase; food preparation, taking of meals, family commensality; social representations of good dietary practices; food consumption; and nutritional status. The questionnaires used in this research received an ICV score of 85%. The reliability test of the questionnaires showed a Cronbach Alpha value ≥ 0.5, which turned out to vary from "moderate" to "excellent". Conclusion: This work enabled the validation of the conceptual framework and the methodology of the study of the factors associated with dietary practices and nutritional status in the RSK region.
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Background A fast rate of eating is associated with a higher risk for obesity but existing studies are limited by reliance on self-report and the consistency of eating rate has not been examined across all meals in a day. The goal of the current analysis was to examine associations between meal duration, rate of eating, and body mass index (BMI) and to assess the variance of meal duration and eating rate across different meals during the day. Methods Using an observational cross-sectional study design, non-smoking participants aged 18–45 years ( N = 29) consumed all meals (breakfast, lunch, and dinner) on a single day in a pseudo free-living environment. Participants were allowed to choose any food and beverages from a University food court and consume their desired amount with no time restrictions. Weighed food records and a log of meal start and end times, to calculate duration, were obtained by a trained research assistant. Spearman's correlations and multiple linear regressions examined associations between BMI and meal duration and rate of eating. Results Participants were 65% male and 48% white. A shorter meal duration was associated with a higher BMI at breakfast but not lunch or dinner, after adjusting for age and sex ( p = 0.03). Faster rate of eating was associated with higher BMI across all meals ( p = 0.04) and higher energy intake for all meals ( p < 0.001). Intra-individual rates of eating were not significantly different across breakfast, lunch, and dinner ( p = 0.96). Conclusion Shorter beakfast and a faster rate of eating across all meals were associated with higher BMI in a pseudo free-living environment. An individual's rate of eating is constant over all meals in a day. These data support weight reduction interventions focusing on the rate of eating at all meals throughout the day and provide evidence for specifically directing attention to breakfast eating behaviors.
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The ASN Board of Directors appointed the Nutrition Research Task Force to develop a report on scientific methods used in nutrition science to advance discovery, interpretation, and application of knowledge in the field. The genesis of this report was growing concern about the tone of discourse among nutrition professionals and the implications of acrimony on the productive study and translation of nutrition science. Too often, honest differences of opinion are cast as conflicts instead of areas of needed collaboration. Recognition of the value (and limitations) of contributions from well-executed nutrition science derived from the various approaches used in the discipline, as well as appreciation of how their layering will yield the strongest evidence base, will provide a basis for greater productivity and impact. Greater collaborative efforts within the field of nutrition science will require an understanding that each method or approach has a place and function that should be valued and used together to create the nutrition evidence base. Precision nutrition was identified as an important emerging nutrition topic by the preponderance of task force members, and this theme was adopted for the report because it lent itself to integration of many approaches in nutrition science. Although the primary audience for this report is nutrition researchers and other nutrition professionals, a secondary aim is to develop a document useful for the various audiences that translate nutrition research, including journalists, clinicians, and policymakers. The intent is to promote accurate, transparent, verifiable evidence-based communication about nutrition science. This will facilitate reasoned interpretation and application of emerging findings and, thereby, improve understanding and trust in nutrition science and appropriate characterization, development, and adoption of recommendations.
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Systematic reviews and meta-analyses are essential to summarize evidence relating to efficacy and safety of health care interventions accurately and reliably. The clarity and transparency of these reports, however, is not optimal. Poor reporting of systematic reviews diminishes their value to clinicians, policy makers, and other users.Since the development of the QUOROM (QUality Of Reporting Of Meta-analysis) Statement--a reporting guideline published in 1999--there have been several conceptual, methodological, and practical advances regarding the conduct and reporting of systematic reviews and meta-analyses. Also, reviews of published systematic reviews have found that key information about these studies is often poorly reported. Realizing these issues, an international group that included experienced authors and methodologists developed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) as an evolution of the original QUOROM guideline for systematic reviews and meta-analyses of evaluations of health care interventions.The PRISMA Statement consists of a 27-item checklist and a four-phase flow diagram. The checklist includes items deemed essential for transparent reporting of a systematic review. In this Explanation and Elaboration document, we explain the meaning and rationale for each checklist item. For each item, we include an example of good reporting and, where possible, references to relevant empirical studies and methodological literature. The PRISMA Statement, this document, and the associated Web site (http://www.prisma-statement.org/) should be helpful resources to improve reporting of systematic reviews and meta-analyses.
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Previous research has shown that oral processing characteristics like bite size and oral residence duration are related to the satiating efficiency of foods. Oral processing characteristics are influenced by food texture. Very little research has been done on the effect of food texture within solid foods on energy intake. The first objective was to investigate the effect of hardness of food on energy intake at lunch, and to link this effect to differences in food oral processing characteristics. The second objective was to investigate whether the reduction in energy intake at lunch will be compensated for in the subsequent dinner. Fifty subjects (11 male, BMI: 21±2 kg/m2, age: 24±2 y) participated in a cross-over study in which they consumed ad libitum from a lunch with soft foods or hard foods on two separate days. Oral processing characteristics at lunch were assessed by coding video records. Later on the same days, subjects consumed dinner ad libitum. Hard foods led to a ∼13% lower energy intake at lunch compared to soft foods (P<0.001). Hard foods were consumed with smaller bites, longer oral duration per gram food, and more chewing per gram food compared to the soft foods (P<0.05). Energy intake at dinner did not differ after both lunches (P = 0.16). Hard foods led to reduced energy intake compared to soft foods, and this reduction in energy intake was sustained over the next meal. We argue that the differences in oral processing characteristics produced by the hardness of the foods explain the effect on intake. The sustained reduction in energy intake suggests that changes in food texture can be a helpful tool in reducing the overall daily energy intake.
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The current obesogenic food environment is characterized by energy dense, softly textured foods that can be eaten quickly. Previous studies suggest that oral processing characteristics such as large bite size, low number of chews and low orosensory exposure contribute to the low satiating efficiency of these foods. The current study investigated the oral processing characteristics of 35-solid meal components and examined associations between oral processing characteristics, food composition, sensory properties and expected satiation. A panel of 15 subjects consumed 50 g of 35-foods representing various staples (potatoes, rice, pasta), vegetables (broccoli, carrot), and protein rich foods such as meat and fish. Subjects were video-recorded during consumption of each food and their eating behaviours were coded for the number of bites, chews and swallows and derived measures such as chewing-rate, eating-rate, bite-size, and orosensory residence time. Subjects rated expected fullness for the 35-foods and the sensory differences were quantified using a separate trained sensory panel. Oro-sensory residence time was highly correlated with the number of chews (R2 = 0.98) and chew-rate was relatively constant at approximately 1 chew/s. Expected fullness was positively correlated with energy density (R2 = 0.40), protein (R2 = 0.33) and the sensory attributes chewiness (R2 = 0.15) and saltiness (R2 = 0.12). Foods that were consumed in smaller bites, were chewed for longer and were expected to impart a higher satiation. This information may be used to design foods/meals lower in energy and higher in satiating efficiency per kcal consumed.
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Background: A number of studies have shown that bite and sip sizes influence the amount of food intake. Consuming with small sips instead of large sips means relatively more sips for the same amount of food to be consumed; people may believe that intake is higher which leads to faster satiation. This effect may be disturbed when people are distracted. Objective: The objective of the study is to assess the effects of sip size in a focused state and a distracted state on ad libitum intake and on the estimated amount consumed. Design: In this 3×2 cross-over design, 53 healthy subjects consumed ad libitum soup with small sips (5 g, 60 g/min), large sips (15 g, 60 g/min), and free sips (where sip size was determined by subjects themselves), in both a distracted and focused state. Sips were administered via a pump. There were no visual cues toward consumption. Subjects then estimated how much they had consumed by filling soup in soup bowls. Results: Intake in the small-sip condition was ∼30% lower than in both the large-sip and free-sip conditions (P<0.001). In addition, subjects underestimated how much they had consumed in the large-sip and free-sip conditions (P<0.03). Distraction led to a general increase in food intake (P = 0.003), independent of sip size. Distraction did not influence sip size or estimations. Conclusions: Consumption with large sips led to higher food intake, as expected. Large sips, that were either fixed or chosen by subjects themselves led to underestimations of the amount consumed. This may be a risk factor for over-consumption. Reducing sip or bite sizes may successfully lower food intake, even in a distracted state.
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Objective: Eating slowly increases the postprandial responses of some anorexigenic gut hormones in healthy lean subjects. As the rate of food intake is positively associated with obesity, the aim of the study was to determine whether eating the same meal at different rates evokes different postprandial anorexigenic responses in obese adolescent and adult subjects. Design and methods: Eighteen obese adolescents and adults were enrolled. A test meal was consumed on two different sessions by each subject, meal duration taking either 5 min (fast feeding) or 30 min (slow feeding). Circulating levels of glucagon-like peptide 1 (GLP1), peptide YY (PYY), glucose, insulin, and triglycerides were measured over 210 min. Visual analog scales were used to evaluate the subjective feelings of hunger and satiety. Results: fast feeding did not stimulate GLP1 release in obese adolescent and adults, whereas slow feeding increased circulating levels of GLP1 only in obese adolescents. Plasma PYY concentrations increased both in obese adolescents and in adults, irrespective of the eating rate, but slow feeding was more effective in stimulating PYY release in obese adolescents than in adults. simultaneously, slow feeding evoked a higher satiety only in obese adolescents compared with fast feeding but not in obese adults. in obese adolescents, slow feeding decreased hunger (only at 210 min). irrespective of the eating rate, postprandial responses of insulin and triglycerides were higher in obese adults than in obese adolescents. Conclusion: Slow feeding leads to higher concentrations of anorexigenic gut peptides and favors satiety in obese adolescents, but this physiological control of food intake is lost in obese adults.
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To determine the influence of masticatory efficiency on postprandial satiety and glycaemic response, twenty-one healthy males were recruited for this randomised cross-over trial. The participants consumed a fixed amount of pizza provided in equal-sized portions by chewing each portion either fifteen or forty times before swallowing. Subjective appetite was measured by appetite questionnaires at regular intervals for 3 h after the meal and plasma samples were collected for the measurement of selected satiety-related hormones, glucose, insulin and glucose-dependent insulinotropic peptide (GIP) concentrations. An ad libitum meal was provided shortly after the last blood sample was drawn and the amount eaten recorded. Compared with fifteen chews, chewing forty times per portion resulted in lower hunger (P = 0·009), preoccupation with food (P = 0·005) and desire to eat (P = 0·002). Meanwhile, plasma concentrations of glucose (P = 0·024), insulin (P < 0·001) and GIP (P < 0·001) were higher following the forty-chews meal. Chewing forty times before swallowing also resulted in a higher plasma cholecystokinin concentration (P = 0·045) and a trend towards a lower ghrelin concentration (P = 0·051). However, food intake at the subsequent test meal did not differ (P = 0·851). The results suggest that a higher number of masticatory cycles before swallowing may provide beneficial effects on satiety and facilitate glucose absorption.
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Background Slow eating has been associated with enhanced satiation, but also with increased water intake. Therefore, the role of water ingestion in regard to eating rate needs to be discerned. This study examined the influence of eating rate on appetite regulation and energy intake when water intake is controlled. Methods In a randomized design, slow and fast eating rates were compared on two occasions, in 30 women (22.7±1.2y; BMI=22.4±0.4kg/m2) who consumed an ad libitum mixed-macronutrient lunch with water (300 mL). Satiation was examined as the main outcome by measuring energy intake during meals. At designated times, subjects rated hunger, satiety, desire-to-eat, thirst, and meal palatability on visual analogue scales. Paired t-tests were used to compare hypothesis-driven outcomes. Appetite ratings were compared across time points and conditions by repeated measures analysis of variance (ANOVA) using a within-subject model. Results Energy intake and appetite ratings did not differ between conditions at meal completion. However, subjects rated less hunger and tended to rate lower desire-to-eat and greater satiety at 1 hour following the slow condition. Conclusions Results tend to support a role of slow eating on decreased hunger and higher inter-meal satiety when water intake is controlled. However, the lack of significant differences in energy intake under these conditions indicates that water intake may account for the effects of eating rate on appetite regulation.
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Background A higher eating rate leads to a higher food intake, possibly through shorter orosensory exposure to food. The transit time in the oral cavity and the number of bites or sips per gram (inversely related to bite or sip size) are main contributors that affect eating rate. The separate role of these two aspects on satiation and on orosensory exposure needs further clarification. Objective The objective of the first study was to investigate contributions of the number of sips per gram (sips/g) and oral transit time per gram (s/g) on ad libitum intake. The objective of the second study was to investigate both aspects on the total magnitude of orosensory exposure per gram food. Methods In study 1, 56 healthy male subjects consumed soup where the number of sips and oral transit time differed by a factor three respectively: 6.7 vs. 20 sips/100 g, and 20 vs. 60 s/100 g (2 × 2 cross-over design). Eating rate of 60 g/min was kept constant. In study 2, the effects of number of sips and oral transit time (equal as in study 1) on the total magnitude of orosensory exposure per gram soup were measured by time intensity functions by 22 different healthy subjects. Results Higher number of sips and longer oral transit time reduced ad libitum intake by respectively ∼22% (F(1, 157) = 55.9, P < 0.001) and ∼8% (F(1, 157) = 7.4, P = 0.007). Higher number of sips led to faster increase in fullness per gram food (F(1, 157) = 24.1, P < 0.001) (study 1). Higher number of sips and longer oral transit time both increased the orosensory exposure per gram food (F(1, 63) = 23.8, P < 0.001) and (F(1, 63) = 19.0, P < 0.001), respectively (study 2). Conclusion Higher number of sips and longer oral transit time reduced food intake, possibly through the increased the orosensory exposure per gram food. Designing foods that will be consumed with small sips or bites and long oral transit time may be effective in reducing energy intake.
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Objective: Energy density (ED) and eating rate (ER) influence energy intake; their combined effects on intake and on postprandial pancreatic and gut hormone responses are undetermined. To determine the combined effects of ED and ER manipulation on voluntary food intake, subjective appetite, and postprandial pancreatic and gut hormone responses. Design and methods: Twenty nonobese volunteers each consumed high (1.6 kcal g(-1) ; HED) and low (1.2 kcal g(-1) ; LED) ED breakfasts slowly (20 g min(-1) ; SR) and quickly (80 g min(-1) ; FR) ad libitum to satiation. Appetite, and pancreatic and gut hormone concentrations were measured periodically over 3 h. Ad libitum energy intake during the subsequent lunch was then measured. Results: Main effects of ED and ER on energy intake and a main effect of ER, but not ED, on mass of food consumed were observed, FR and HED being associated with increased intake (P < 0.05). Across all conditions, energy intake was highest during FR-HED (P ≤ 0.01). Area under the curve (AUC) of appetite ratings was not different between meals. Main effects of ED and ER on insulin, peptide-YY, and glucagon-like peptide-1 AUC (P < 0.05) were observed, FR and HED being associated with larger AUC. No effects on active or total ghrelin AUC were documented. Total energy intake over both meals was highest during the FR-HED trial with the greatest difference between FR-HED and SR-LED trials (P ≤ 0.01). Conclusion: Consuming an energy dense meal quickly compounds independent effects of ER and ED on energy intake. Energy compensation at the following meal may not occur despite altered gut hormone responses.