Eur J Nutr
DOI 10.1007/s00394-016-1254-5
ORIGINAL CONTRIBUTION
The effect of the macronutrient composition of breakfast
on satiety and cognitive function in undergraduate students
Christine H. Emilien1 · Robert West2 · James H. Hollis1 Received: 10 August 2015 / Accepted: 21 June 2016
© Springer-Verlag Berlin Heidelberg 2016
Abstract Purpose It is believed that breakfast is an important meal
due to its effect on appetite control and cognitive performance, yet little evidence exists to support this hypothesis.
Methods Using a crossover design, 33 healthy undergraduates (aged 22 ± 2 years with a BMI of 23.5 ± 1.7 kg/m2)
were randomized one of four breakfast treatments: no
breakfast, a low-protein breakfast containing no animal
protein, a high-carbohydrate/low-protein breakfast containing animal protein or a low-carbohydrate/high-protein
breakfast. After an overnight fast, participants reported to
the laboratory and baseline appetite questionnaires and
cognitive tests were completed. A baseline blood sample
was also collected. These measures were repeated at regular intervals throughout the test session. An ad libitum
lunch meal was provided 240 min after breakfast, and the
amount eaten recorded. Diet diaries and hourly appetite
questionnaires were completed for the rest of the day.
Results The no-breakfast treatment had a marked effect
on appetite before lunch (p < .05). Moreover, participants
consumed more energy at lunch following the no-breakfast
treatment (p < .05). There was no difference in appetite
before lunch or food intake at lunch following any treatment when breakfast was eaten. However, food intake over
the entire test day was lowest for the no-breakfast treatment
(p < .05). Plasma glucose and insulin were lower following the high-protein/low-carbohydrate treatment compared
* James H. Hollis
[email protected]
1
Department of Food Science and Human Nutrition, Iowa
State University, 220 MacKay Hall, Ames, IA 50011, USA
2
Department of Psychology, Iowa State University, Ames, IA,
USA
to the low-protein/high-carbohydrate—no animal protein
treatment (p < .05). Participants were less happy when they
missed breakfast (p < .05), but there were no other statistically significant effects of breakfast on mood or cognitive
performance.
Conclusions These results suggest that changing the
macronutrient content of breakfast influences the glycemic
response, but has no effect on the appetitive or cognitive
performance measures used in this present study.
Keywords Protein · Satiety · Cognitive performance ·
Breakfast
Introduction
Throughout the developed world, the number of overweight
and obese adults has risen markedly over the past few decades. This is a public health problem as these conditions
are associated with an increased risk of developing chronic
diseases such as type 2 diabetes [1–3], cardiovascular disease [4] and some forms of cancer [5–7]. Consequently,
reducing the number of overweight or obese individuals is
a leading public health goal. Accumulating evidence suggests that the transition from living at home to university
can be a time of significant and rapid weight gain for undergraduate students, [8–10] with weight gain among university students being nearly six times the population average
[11]. Consequently, university students would appear to be
a group where interventions to prevent weight gain are warranted. These efforts would be supported by research that
identifies dietary strategies that augment satiety and potentially aid weight management.
It is commonly believed that the regular consumption
of breakfast is a key aspect of a healthy diet [12, 13]. In
13
addition to a positive effect on nutrient intake, observational studies indicate that regular breakfast consumers
have a lower body weight [14, 15] and a reduced risk of
weight gain over a 10-year period [16] compared to individuals who do not regularly consume breakfast. However,
data from recent randomized control trials do not support the observational data and do not report a beneficial
effect of consuming breakfast on body weight [17, 18]. It
is possible that the macronutrient composition of the breakfast may influence appetite and food intake at subsequent
meals. For instance, many breakfast foods have a high
glycemic index (GI) and as a consequence may be poorly
satiating [19, 20]. While efforts to encourage consumers to
eat low-GI breakfast foods may improve appetite control,
this strategy may be of limited usefulness as consumers do
not find many low-GI foods to be palatable [21]. Another
strategy is to replace some of the carbohydrates in a highGI breakfast with protein, which has been suggested to be
the most satiating macronutrient [22–24]. To date, only a
limited number of studies have investigated the effect of
increasing intake of protein at breakfast on satiety, and
they report that increasing the protein content of breakfast
increases satiety in adolescents [25] and adults [26–29].
Moreover, another study found that protein consumed at
breakfast is more satiating than protein consumed at lunch
or the evening meal [30]. These data suggest that replacing high-GI carbohydrates with protein at breakfast would
increase or enhance satiety, and may potentially aid weight
management.
In addition to an effect on appetite, it is commonly
believed that eating breakfast is important for cognitive
performance and mood. While it has been shown that skipping breakfast has deleterious effects on cognition [31–34],
less is known about how the macronutrient composition
of breakfast influences cognitive function. While several
studies have shown that the consumption of glucose influences cognitive function [35–37], pure glucose is rarely
consumed as part of a normal diet. A high-GI food may
improve short-term cognitive function due to the rapid
availability of glucose, but the effect may be short-lived
due to the rapid fluctuation in plasma glucose concentration associated with high-GI foods [38]. A limited number
of studies have found that a low-GI breakfast has beneficial
effects on cognitive function in schoolchildren compared to
a high-GI breakfast [39, 40]. The addition of protein to a
high-GI breakfast or the replacement of carbohydrates with
protein may improve cognitive function by reducing the
glycemic response of the meal [41]. Another study found
that a protein-rich or macronutrient-balanced breakfast
meal resulted in better overall cognitive performance [42].
This study determined the effect of breakfasts that differ
in macronutrient content on appetite, mood and cognitive
performance. Part 1 investigates outcomes on appetite, and
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it was hypothesized that increasing the protein content of
a breakfast meal consumed by undergraduates will: reduce
subjective appetite, reduce postprandial plasma insulin concentration, reduce postprandial plasma glucose concentration, and decrease energy intake at the lunch meal. In Part
2, the hypothesis that increasing the protein content of a
breakfast meal will improve cognitive performance and
mood during the morning period is explored.
Part 1: Appetite methods
Recruiting
Thirty-five healthy adults of any gender or ethnic group
were recruited for this study. Participants were recruited
subject to the following inclusion criteria: aged between 18
and 25 years, body mass index 20–25 kg/m2, regularly consumes (>5 days each week) breakfast, a registered undergraduate, rates the palatability of the study foods >6 on a
9-point scale. The exclusion criteria were: an average daily
alcohol intake over 40 g a day, a weight change >3 kg in
previous 3 months, the presence of diagnosed chronic disease (e.g., cancer, heart disease, type 1 or 2 diabetes), uses
tobacco products, recent or planned changes in medication
use, the presence of acute illness, a restrained eater (>13
on the restraint section of the three-factor eating questionnaire) [43], body weight change of >2.5 kg during the
study period. During a screening session, potential participants completed a questionnaire that posed questions about
their general health and dietary habits (including breakfast
consumption) and had their height and weight measured to
determine whether they met the inclusion/exclusion criteria. They also tasted samples of all the test foods and asked
to rate their palatability. The protocol was approved by the
Iowa State University Institutional Review Board, and all
participants signed an informed consent form before being
enrolled in the study.
Study protocol
This study used a randomized, counterbalanced crossover
design. All participants were asked to report to the laboratory on four separate occasions separated by at least one
week. Participants were asked to refrain from drinking
alcohol or strenuous exercise for the 24 h before each test
session. They were also asked to refrain from drinking caffeinated beverages for 12 h prior to each test session. Participants were asked to report to the Nutrition and Wellness Research Center (NWRC) on the day before each test
session to eat all of their meals which were standardized
based on their estimated energy requirements. Estimated
energy requirements were calculated using an age- and
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Table 1 Macronutrient profile of test breakfasts
Breakfast
Carbohydrate Protein
(% total
(% total
calories)
calories)
Fat
(% total
calories)
Fiber (g)
HP/LC
LP/HC-AP
40
59
30
12
30
29
.45
.45
LP/HC-NAP 61
10
29
.45
gender-specific validated equation [44] to calculate basal
metabolic rate. This figure was then multiplied by a physical activity factor of 1.5 to estimate total energy expenditure. Together, the meals provided a macronutrient profile
of 25 % fat, 55 % carbohydrate and 20 % protein of total
energy. For each test session, participants reported to the
NWRC at 7:30 am following an overnight fast (10–12 h).
Each participant’s weight was measured using clinical
weighing scales (Detecto 758C, Cardinal Scale Manufacturing Company, Webb City, MO) at the start of each test
session. An indwelling catheter was placed into the participant’s non-dominant arm by the study nurse, and the
participant allowed to rest for 30 min to acclimatize to the
catheter. Then, a baseline blood sample was collected, and
the participant asked to complete an appetite questionnaire
and to complete a battery of cognitive performance and
well-being tests. The participant was then provided with
one of four meals: no breakfast (NB), low protein/high carbohydrate—no animal protein (LP/HC-NAP), low protein/
high carbohydrate—with animal protein (LP/HC-AP) or
high protein/low carbohydrate—with animal protein (HP/
LC). Participants were randomized to a treatment order.
Macronutrient profiles for each of the three test meals are
reported in Table 1. Participants were asked to eat this meal
in its entirety within 15 min. On completion of the meal,
another blood sample was collected and an appetite questionnaire completed (t0). Further blood samples were collected and appetite questionnaires completed at t0 + 15,
30, 60, 120, 180 and 240 min. After the final blood sample
was collected, the indwelling catheter was removed from
the participants arm and they were allowed to rest for 5 min
before being presented an ad libitum lunch meal for which
they were instructed to eat until comfortably full. Participants ate this meal seated at an individual table and were
not allowed to interact with other participants.
Test meals
The test meals used in this study comprised of commonly
eaten breakfast foods: English muffin (Thomas’ Brand, Fort
Worth, TX), low-fat spread (Unilever, London, England)
and Tropicana orange juice (PepsiCo, Purchase, NY). For
the two animal protein-containing treatments (LP/HC-AP
and HP/LC), a low-fat, pork breakfast sausage was made
using ground lean pork loin (Hormel, Austin, MN). The
breakfast meal provided 20 % of the participant’s estimated daily total energy expenditure. For the ad libitum
lunch meal, participants were served a meal of pasta and
tomato sauce (Barilla Group, Parma, Italy). For this meal,
dry pasta was cooked, according to the manufacturer’s
instructions. Drained pasta and tomato sauce were weighed
out into individual portions, thoroughly mixed and frozen.
These individual portions were reheated using a microwave
oven. The time each meal was reheated for was standardized. After reheating and just before serving, 35 g of shredded parmesan cheese (Kraft Food Groups Inc, Northfield
Il) was mixed into create a 900-kcal portion for service.
Subjective appetite measurement
Subjective appetite was determined using a questionnaire
that posed standard appetite questions. These were: How
hungry do you feel right now? How full do you feel right
now? What is your desire to eat right now? What is your
prospective consumption right now? How thirsty are you
right now? Answers were captured on a visual analog scale.
The appetite questionnaire was presented on a Palm Pilot
handheld computer. Answers were captured and stored with
a time and date stamp so that compliance to the study protocol could be determined. Participants were required to
maintain this appetite log at set times while in the laboratory and once every hour for the remainder of their waking
hours outside the laboratory. For data collected outside the
laboratory, only time points where 75 % of the participants
responded within 10 min of the correct time were used in
the analysis.
Measurement of food intake
After leaving the laboratory, the participant was required to
maintain a diet diary for the rest of the day to determine
their food intake. Participants were trained in the use of
a food log before leaving the laboratory during the first
test session. Data from the food logs were analyzed using
Nutritionist Pro™ Diet Analysis Software (version 2.1.13;
First DataBank, San Bruno, CA).
Glycemic response
Blood was drawn into EDTA-coated vacutainers and centrifuged at 3000 g for 15 min at 4 C. The plasma was collected and stored at −80 C until assayed. All assays were
completed within one month of the test session. Plasma
was assayed for insulin using radioimmunoassay as previously described [45] and glucose assayed using a metabolic analyzer (YSI Life Sciences, Model 2700 select). All
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Fig. 1 VAS scores (means and standard error) for hunger (a), fullness (b), desire to eat (c) and prospective consumption (d) rated from
baseline through 780 min post-consumption of no breakfast (diamonds), low-protein/high-carbohydrate—no animal protein (squares),
low-protein/high-carbohydrate—animal protein (triangles) and highprotein/low-carbohydrate (circles) breakfast meals. Different letters
indicates statistically significant difference in AUC (p < .05)
samples were run in duplicate, and all samples from each
participant were analyzed within the same batch.
using SPSS for Windows or Mac (version 16.0; SPSS, Chicago, IL, USA).
Statistical analysis
Appetite results
Means and standard errors were calculated for all study variables, and data were checked for normality of distribution.
Area under the curve (AUC) for biomarkers and subjective
appetite was calculated using the trapezoid method [46].
The main effect of treatment on food intake and appetite
was assessed using a one-way, repeated-measures ANOVA.
For all measures, post hoc analysis was performed using
a Bonferroni-adjusted pairwise comparison of treatment
effects.
A power calculation was conducted, and a sample size
of 28 was estimated to be sufficient to detect a difference
of 10 % in food intake, subjective appetite and physiological markers of appetite using an alpha level of .05 and a
desired power of .80. Data from previous studies conducted in our laboratory were used to estimate the standard
deviation for this calculation. Thirty-five participants were
recruited to allow for attrition. Statistical significance was
set at p < .05, two-tailed. Statistical analysis was conducted
Subject demographics
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Thirty-five participants were randomized to the study. Two
participants dropped out after before completing all sessions due to scheduling conflicts. No participants were
excluded from the results due to weight gain of >2.5 kg
during the study period. In total, 33 participants, 19 males
and 14 females, completed the study. The average age of
the participants was 22 ± 2 years, and average BMI was
23.5 ± 1.7 kg/m2.
Subjective appetite
As shown in Fig. 1, there was a significant main effect of
treatment on hunger, fullness, desire to eat and prospective
consumption (p < .001). Post hoc analysis revealed significant treatment differences between the NB condition and
each condition where breakfast was provided (p < .05).
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There was no statistically significant difference between
the HP/LC, LP/HC-AP and LP/HC-NAP breakfasts for any
subjective appetite measure (p < .05).
Food intake
There was a significant main effect of treatment on energy
intake at lunch[F(3, 96) = 4.671, p = .004] with post hoc
analysis revealing lower energy intake after the LP/HCNAP and HP/LC breakfasts compared to the no-breakfast
treatment (p = .04 in both cases, see Table 2). There was no
significant treatment effect on energy intake after the participants left the laboratory [F (3, 96) = 1.070, p = .366].
When the calories provided by the breakfast meals were
included in the analysis (i.e., total daily food intake),
there was a main effect of treatment [F (3, 96) = 8.924,
Table 2 Average caloric intake by treatment and meal (mean
kcal ± standard error)
Treatment
NB
Breakfast
0 ± 0a
b
LP/HC-NAP 508 ± 13
LP/HC-AP 508 ± 13b
HP/LC
Ad libitum
lunch
Diet diary
638 ± 62a
527 ± 38b
556 ± 48a, b
1456 ± 102a 2095 ± 130a
1520 ± 112a 2626 ± 123b
1415 ± 109a 2548 ± 127b
508 ± 13b 510 ± 46b
Total
1621 ± 107a 2709 ± 111b
Difference letters for a given column indicate statistically significant
difference for that meal (p < .05)
Fig. 2 Plasma responses (means and standard error) for (a) glucose
and (b) insulin measured from baseline through 240 min post-consumption of no breakfast (closed diamonds), low-protein/high-carbohydrate—no animal protein (closed squares), low-protein/high-car-
p < .0001]. Post hoc analysis revealed that participants consumed fewer calories following the NB treatment on the nobreakfast treatment day compared to each of the treatment
breakfasts provided (p = .001, .003 and .001 for the LP/
HC-NAP, LP/HC-AP and HP/LC treatments, respectively).
Glycemic response
The postprandial plasma insulin and glucose response is
illustrated in Fig. 2. There was a significant main effect of
treatment on plasma glucose [F(3, 96) = 5.77, p = .001].
Post hoc analysis revealed that plasma glucose was lower
following the NB treatment compared to all other treatments (p < .05). Moreover, plasma glucose was lower following the HP/LC treatment compared to the LP/HC-NAP
treatment (p = .005). There was a significant main effect of
treatment on plasma insulin [F(3, 96) = 22.5, p < .0001].
Post hoc analysis revealed that plasma insulin was higher
following the LP/HC-NAP, LP/HC-AP and HP/LC treatments compared to no breakfast (p < .001).
Appetite discussion
This present study found that increasing the amount of protein consumed at breakfast at the expense of carbohydrate
had no effect on subjective appetite or food intake. When
the breakfast provided <15 % of the energy from protein,
there was no benefit of consuming protein from animal
sources over plant sources. However, total food intake over
bohydrate—with animal protein (open triangles) and high-protein/
low-carbohydrate (open circles) breakfast meals. Different letters
indicates statistically significant difference in AUC (p < .05)
13
the entire test day was lower when participants did not eat
breakfast. The HP/LC breakfast resulted in a lower postprandial plasma glucose response compared to the LP/HCNAP breakfast. As expected, plasma insulin was lower following the NB treatment compared to all other treatments.
While the NB treatment resulted in higher hunger, desire
to eat and prospective consumption ratings and lower fullness during the morning these differences disappeared after
lunch was eaten. Moreover, the macronutrient content of
the breakfast had no effect on subjective appetite at any
point during the test day. This finding contradicts previous
studies that suggest that the consumption of a high-protein
breakfast meal reduces appetite [47–49]. However, a key
difference between the present study and previous research
concerns the amount of protein provided in the breakfast meal. In this present study, the HP/LC breakfast was
30 % protein (as a % of energy) compared to other studies
that used breakfasts that provided 40–70 % of the energy
from protein [47–50]. It is possible that there is a threshold amount of protein (either by percentage of energy or
an absolute amount by weight) that has to be provided to
elicit an effect on appetite and food intake. Further research
is required to fully understand the influence of the macronutrient composition of breakfast on appetite and weight
management.
While energy intake at lunch was higher following the
NB treatment, the macronutrient composition of the breakfast had no statistically significant effect on food intake at
lunch. However, it is important to note that while participants consumed a larger lunch following the NB treatment,
the increase was not sufficiently large to compensate for
the energy reduction due to skipping breakfast and energy
intake over the test day was lowest when participants did
not eat breakfast. This combination of higher intake at an
ad libitum test meal with lower overall energy intake for
the test day following breakfast omission has been shown
in numerous previous intervention trials [51–54]. This finding shows that the calorie deficit resulting from skipping
breakfast is not fully compensated for at the lunch meal.
Given that after the lunch meal, there are no treatment differences observed in any measure of subjective appetite, no
further compensation in intake would be expected. Therefore, skipping breakfast combined with incomplete caloric
compensation at subsequent meals results in a lower daily
energy intake. Furthermore, even in studies where no treatment difference at the lunch meal are observed, breakfast
skipping is still consistently associated with lower caloric
intake over the testing period [54–57].
While these results suggest that skipping breakfast may
be a useful strategy to lose weight, these data must be interpreted cautiously. A limitation of this present study is that
it only covers a single day and it is likely that this is too
short a period for physiological mechanisms that regulate
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appetite to have a meaningful impact on body weight. Consequently, the long-term effects on food intake and appetite
are not known. At this time, few long-term studies examining the role of breakfast in body weight management have
been conducted. In a study by Schlundt et al. [58], there
was no advantage to eating breakfast on body weight or
body composition. However, eating breakfast was associated with an improvement in nutrient intake and a reduction in impulsive snacking. These results led the authors to
conclude that encouraging people to eat breakfast should
be recommended. Recent studies found that recommending the consumption of breakfast had no discernable effect
on body weight or adiposity [18, 59]. Still, the studies by
Betts, Schlundt and Dhurandhar were relatively short term
(6, 12 and 16 weeks, respectively), and longer-term studies
to determine the effect of breakfast consumption on appetite and body weight are required.
The glycemic response observed in this study supported the hypothesis that exchanging protein for carbohydrate would reduce the postprandial insulin and glucose
response. Although insulin AUC for the NB treatment was
significantly lower than the three breakfast treatments, glucose AUC for the NB treatment was only lower than the
LP/HC-NAP treatment. Although both the LP/HC-AP
and HP/LC treatments showed a markedly higher glucose
concentration than the no-breakfast treatment during the
first 30 min post-consumption, glucose concentrations for
these groups were lower than the NB treatment from 60
to 180 min post-consumption. This response, though not
resulting in statistically significant AUC differences, is
interesting, as the dip in glucose concentration below baseline measures was not observed for the LP/HC-NAP group.
Finally, the presence of animal protein in the absence of a
significant decrease in the meal’s carbohydrate content was
not sufficient to elicit a change in glycemic response as
indicated by there being no statistically significant difference between the LP/HC-AP and LP/HC-NAP groups. The
observed glycemic effect for the HP/LC group is therefore
equally explained both by the higher protein and lower carbohydrate content of the meal.
Part 2: Cognitive methods
General methods
Cognitive outcomes were assessed on the same test days as
appetite response using the same recruiting, study protocol
and treatments described in Part 1. A battery of cognitive
tests was administered at baseline before any breakfast
treatments were consumed. Additional cognitive tests were
administered, at t = 30, 60, 120, 180 and 240 min postbreakfast consumption.
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Cognition and affect assessment
The psychological battery used was designed to measure
five constructs that are known to vary systematically over
time and to be sensitive to intrinsic within-person variables
(e.g., stress, circadian variation, mood). Additionally, previous research demonstrates that each of the constructs can
be reliably measured over time within participants [60].
This is an essential element of the measures given that each
test was administered 24 times to participants.
Short positive affect and negative affect scale (PANAS)
Variation in mood was measured by having individuals
rate their current mood on eight adjectives (bored, sad,
energetic, amused, calm, angry, happy and anxious) at
six points across the session. Ratings were made by having individuals place a mark on a 10-cm line bound by the
statements “not at all” or “extremely” with a pencil. The
use of the bounded line rather than discrete values (e.g.,
1–10) was designed to reduce subjects’ reliance on memory
of previous responses in making their rating and instead
to respond based upon their current mood. Placement of
the mark on the line for the eight adjectives served as the
dependent measure and was measured in millimeters from
the left edge of the line (“not at all”).
Immediate and delayed memory
A verbal learning task was used to assess immediate and
delayed memory. In this task, individuals encoded and
recall a 15-item list of words. The task included two learning trials and a delayed recall trial for each points of measurement. For the learning trials (immediate memory),
subjects heard the words presented one at a time and then
recalled as many of the words as possible. For the retention trial (delayed memory), subjects were asked to recall
as many of the words as they could. The fluency, Stroop
and speed of processing tasks were completed between the
learning trials and the retention trial. Twenty-four study–
test lists were generated for the study using the Paivio,
Yuille and Madigan (1968) norms [61]. The dependent
measures included: (1) short-term retention representing
recall for learning trials 1 and 2; and (2) retention from
delayed recall. These measures are sensitive to within-person variability related to circadian variation over days of
testing [62].
Letter fluency [63]
In the task, individuals were asked to generate as many
words as possible beginning with a given letter in 1 min (F,
A, S, C, M, L). The two rules were that individuals should
not repeat words or use minor variations of words (e.g.,
jump, jumping, jumper). The outcome represents the number of unique words generated in 1 min.
Stroop task [64, 65]
In the task, individuals named the color of neutral stimuli
(e.g., XXXX present in red) or incongruent stimuli (e.g.,
RED presented in blue). In the task, individuals named
the color of 40 stimuli presented on a card. The dependent
measure represented the change in the Stroop effect (i.e.,
color–word response time minus neutral response time)
from baseline.
Speed of processing
The pattern comparison task [66] was used to measure
speed of processing. In the task, individuals viewed strings
of letters and digits (e.g., XO3A___XO3A or XO3A___
X23A) and decide whether or not the two strings were
identical. For each measurement, individuals completed 20
comparisons including ten match trials and ten non-match
trials. The dependent variable represented the time required
to complete the 20 stimuli.
Statistical analysis
Means and standard errors were calculated for all study
variables, and data were checked for normality of distribution. For the cognitive and affective measures, missing values were interpolated at the participant level by using the
mean performance for the available sessions within a given
day (breakfast treatment). The cognitive measures were
analyzed in a set of 4 (breakfast) × 5 (time) MANOVAs
where the dependent measure was the difference in performance from the pre-breakfast baseline to the post-breakfast
point of measurement. This served to focus the analysis on
the change in performance related to the different breakfast
and eliminate random variation in initial test performance.
The affect measures were analyzed in a set of 4 (breakfast) × 6 (time) MANOVAs to represent variation in initial
affect and change in affect over time.
Cognitive results
Affect
There was a main effect of time for boredom, sadness and
energetic (see Fig. 3). The level of boredom increased from
baseline through t = 180 post-breakfast consumption and
then decreased from 180 to 240 min [F(5, 28) = 6.14,
p = .001], while the level of sadness declined over the
entire testing session [F(5, 28) = 3.18, p = .021]. For
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Fig. 3 Affect responses (means and standard error) for a boredom, b
energetic, c happy and d sad measured from baseline through 240 min
post-consumption of no breakfast (closed diamonds), low-protein/
high-carbohydrate—no animal protein (closed squares), low-protein/
high-carbohydrate—with animal protein (open triangles) and highprotein/low-carbohydrate (open circles) breakfast meals. Asterisk indicates significant effect of treatment as compared to all other treatments
energetic, the main effect of time revealed a consistent
sharp rise across treatments at 240 min [F(5, 28) = 5.88,
p < .001]. A smaller, yet significant, effect of time was
observed for amusement [F(5, 28) = 3.06, p = .026] and
anxiety [F(5, 28) = 3.14, p = .023]—data not shown.
For happy (Fig. 3), the main effect of breakfast was significant [F(3, 30) = 4.39, p = .011] with individuals being
less happy in the no-breakfast group than when breakfast
was consumed. The main effect of time was also significant [F(5, 28) = 4.76, p = .003] with the level of happiness
increasing at the end of the test session (240 min). For calm
and angry, the neither of the main effects nor the interaction
was significant—data not shown.
For delayed recall (Fig. 4a), there was a decrease in memory
over time (F(4, 29) = 28.69, p < .001). The main effect of
breakfast (F(3, 30) = 2.83, p = .056) and the breakfast ×
time interaction (F(12, 21) = 2.03, p = .077) had a trend
toward significant. The effect of breakfast reflected poorer
memory for the NB and LP/HC—NAP as compared to the
LP/HC—AP and HP/LC treatments, and the interaction
reflected a decrease in the effect of breakfast over time.
Cognitive performance
Immediate and delayed memory For immediate recall, the
main effect of time was significant in the change score analysis for both list 1 (F(4, 29) = 5.42, p = .002) and list 2 (F(4,
29) = 8.92, p < .001), and represented an overall decrease
in memory performance over time—data not shown. This
likely reflects the buildup of proactive interference from
the previous lists. There was no treatment effect or treatment × time interaction observed for either list 1 or list 2.
13
Letter fluency An analysis of change in the total number
of words generated revealed a significant main effect of time
[F(4, 29) = 21.55, p < .001] that represented an increase
in the number of words generated over time (Fig. 4b). The
main effect of breakfast [F(3, 30) = .36, p = .78] and the
breakfast × time interaction [F(12, 21) = .93, p = .53] were
not significant.
Stroop task As a manipulation check, response time for
color–word cards (M = 18.36, SE = .66) was compared
to response time for color cards (M = 15.17, SE = .66).
The comparison revealed a significant Stroop effect (F(1,
32) = 82.69, p < .001). The main effect of time was significant in the change score analysis (F(4, 29) = 6.14, p = .001)
reflecting a decrease and then an increase in interference
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Fig. 4 Change from baseline in a number of words recalled for
delayed memory test, b number of unique words from fluency test, c
seconds to complete speed of processing test and d seconds to complete Stroop test for no breakfast (closed diamonds), low-protein/
high-carbohydrate—no animal protein (closed squares), low-protein/
high-carbohydrate—with animal protein (open triangles) and high-
protein/low-carbohydrate (open circles) breakfast meals. No statistically significant differences were observed (p < .05); however, for
delayed memory task, the NB and LP/HC—NAP groups had a trend
toward poorer memory as compared to the LP/HC—AP and HP/LC
treatments
from baseline (Fig. 4c). The main effect of breakfast (F(3,
30) = .78, p = .51) and the breakfast × time interaction
(F(12, 21) = 1.26, p = .31) were not significant.
groups to be more susceptible to forgetting early in the session. Although this finding was not statistically significant,
it may be consistent with the finding that glucose administration may have a greater effect on delayed memory than
working memory that would contribute to performance
for the two immediate memory trials [67]. Verbal fluency
and Stroop interference were not sensitive to the breakfast
manipulation. This finding stands in contrast to those of
Fischer et al. [42] who did find an effect of breakfast content on measures of short-term memory and attention. The
tasks used by Fischer et al. are rather different from those
used in the current study, which might account for the differences observed between studies. There was variation
over time in six of the eight measures of affect that follow
a predictable pattern (e.g., level of boredom increased as
the long repetitive session continued or level of happiness
or energy increased toward the end of the session). Breakfast did have a significant effect on level of happiness,
with happiness being lower in the NB group relative to the
breakfast groups. This effect did not interact with time and
may therefore reflect lowering of positive emotion related
to the realization that a meal would be delayed until the end
of the morning session.
Speed of processing The main effect of time was significant in the change score analysis (F(4, 29) = 3.19, p = .028)
in processing speed (Fig. 4d). This effect represented a significant cubic relationship (F(1, 32) = 10.70, p = .003) that
is not easy to interpret. The main effect of breakfast (F(3,
30) = .78, p = .51) and the breakfast × time interaction
(F(12, 21) = 1.26, p = .31, η2p = .42) were not significant.
Cognitive discussion
In this present study, the psychological data reveal minimal
effects of eating breakfast on cognition or affect. Five of
the six measures of different aspects of cognition revealed
significant effects of time across the session indicating that
the tasks were sensitive to within-person variation over this
interval. The only suggestion of an effect of breakfast on
cognition was related to delayed memory, wherein there
was a trend for individuals in the NB and LP/HC-NAP
13
Conclusion
This present study investigated the effect of changing the
macronutrient content of breakfast on appetite, food intake
and cognitive performance. While this present study suggests
that skipping breakfast may result in reduced food intake
over a 24-h period, this study has several limitations that
should be considered. First, the study duration was short and
it is possible that, in time, participants would have adapted
to consuming breakfast by eating less at subsequent meals
thus reducing the differences in intake observed between NB
and other test conditions. Second, food intake outside the
laboratory was measured using self-report which is a method
that has substantial associated errors. Third, the study group
was narrowly defined, using only registered undergraduates,
and the results are not generalizable to the wider population.
However, undergraduates are a group at potentially increased
risk of poor dietary habits, and strategies to improve diet in
this group are likely of public health importance. Fourth,
similar to many diet studies, it was not possible to adequately
blind the participants or the researchers to the study intervention. The participants were not informed about the purpose of
the study until after it was completed, but it was likely they
deduced the study objectives. Furthermore, the researchers
who conducted the study also would have been aware of the
study objectives although the statistical analysis was conducted by an individual not involved in data collection. The
lack of adequate blinding increases the risk of biased results.
Fifth, participants were not familiarized with the test meal. It
is likely with repeated exposures that their response would
change and the results provided by this present study should
not be extrapolated to the long term. Sixth, previous research
indicates that the menstrual cycle may influence appetite
and food intake. In this present study, we did not control for
the menstrual cycle which may have influenced the female
appetitive response and food intake. Seventh, the participants
were provided with large amounts of food at no cost to them.
This may have encouraged overconsumption and masked an
effect of the intervention on food intake.
Due to the common perception that breakfast is an
important meal for weight management and cognitive performance, further research is warranted to determine the
importance of breakfast as part of a healthy diet.
Acknowledgments We thank Visha Arumugam for her technical
assistance and dedication. This study was funded by the National
Pork Board.
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