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European Journal of Nutrition (2021) 60:3325–3342
https://doi.org/10.1007/s00394-021-02506-2
ORIGINAL CONTRIBUTION
Ready‑to‑eat cereal andmilk forbreakfast compared
withnobreakfast hasapositive acute effect oncognitive function
andsubjective state in11–13‑year‑olds: aschool‑based, randomised,
controlled, parallel groups trial
KatieAdolphus1 · AlexaHoyland2· JennyWalton3· FritsQuadt4· ClareL.Lawton1 · LouiseDye1
Received: 12 August 2020 / Accepted: 2 February 2021 / Published online: 20 February 2021
© The Author(s) 2021
Abstract
Purpose We tested the acute effect of breakfast (ready-to-eat-cereal [RTEC] and milk) versus (vs.) no breakfast on cognitive
function and subjective state in adolescents.
Methods Healthy adolescents (n = 234) aged 11–13years were recruited to take part in this school-based, acute, randomised,
controlled, parallel groups trial with two interventions; Breakfast or No Breakfast. The breakfast intervention consisted of
adlibitum intake of RTEC (up to 70g) with milk (up to 300ml) administered in a naturalistic school breakfast programme
environment. Cognitive function was assessed at baseline and + 70 and + 215min post-intervention in a group-testing situ-
ation, similar to a school classroom context. The CANTAB test battery included: Simple Reaction Time (SRT), 5-Choice
Reaction Time (5-CRT), Rapid Visual Information Processing (RVIP), and Paired Associates Learning (PAL; primary
outcome). Data collection commenced January 2011 and ended May 2011. This trial was retrospectively registered at www.
clini caltr ials.gov as NCT03979027 on 07/06/2019.
Results A significant effect of the intervention (CMH[1] = 7.29, p < 0.01) was found for the number of levels achieved on the
PAL task. A significant difference between interventions was found when baseline performance reached level 2 (JT, z = 2.58,
p < 0.01), such that 100% of participants in the breakfast intervention reached the maximum level 4 but only 41.7% of those
in the no breakfast intervention reached level 4. A significant baseline*intervention interaction (F[1,202] = 6.95, p < 0.01)
was found for total errors made on the PAL task, indicating that participants who made above-average errors at baseline
reduced the total number of errors made at subsequent test sessions following breakfast consumption whilst those in the no
breakfast intervention did not. There was a positive effect of breakfast on reaction time and visual-sustained attention. The
results also demonstrated interactions of intervention with baseline cognitive performance, such that breakfast conferred a
greater advantage for performance when baseline performance was poorer.
Conclusion Consuming breakfast has a positive acute effect on cognition in adolescents.
Keywords Breakfast· Cognition· Cognitive function· Adolescents· Randomised controlled trial
Introduction
Numerous studies have investigated the effect of breakfast
consumption on cognitive function in children and adoles-
cents [1–5]. Children and adolescents have received par-
ticular attention for a number of reasons. First, breakfast
skipping is common among children and adolescents [6, 7].
Second, breakfast has the potential to improve children’s
cognitive function at school, which may benefit learning and
academic performance [8, 9]. Additionally, children have a
higher brain glucose metabolism compared with adults [10].
* Katie Adolphus
k.adolphus@leeds.ac.uk
1 Human Appetite Research Unit, School ofPsychology,
University ofLeeds, LeedsLS29JT, UK
2 The Kellogg Company, Orange Tower Media City, Salford,
GreaterManchester, UK
3 HarvestPlus, International Food Policy Research Institute,
1201 Eye Street NW, Washington, DC20005, USA
4 Quadt Consultancy BV, Oostvoorne, TheNetherlands
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3326 European Journal of Nutrition (2021) 60:3325–3342
1 3
Furthermore, children and adolescents are subject to a longer
overnight fasting period due to higher sleep demands [11].
Therefore, breakfast consumption may provide energy for
the school morning.
To date, four systematic reviews of the effect of break-
fast on cognitive function in children and adolescents have
been published [12–15]. The findings from acute studies
comparing breakfast vs. fasting demonstrate that breakfast
consumption has a positive, acute, domain-specific effect
on cognition measured within 4h post-ingestion [12, 13].
However, there is inconsistency among results due to meth-
odological issues, which have precluded firm conclusions.
The findings of our systematic reviews [12, 13] informed a
recent methodological critique of this literature [16]. Here,
we reported the key limitations that have hampered a clear
substantiation of the acute effects of breakfast on cognitive
function. These include a lack of research on adolescents,
few ecologically valid breakfast manipulations or testing
environments, small samples, insensitive cognitive tests,
and rare concomitant assessment of subjective state [16].
A key limitation in acute studies examining the effects of
breakfast on cognition is that the cognitive test choice was
not driven by previous evidence showing the task to be sen-
sitive to nutritional manipulations. Moreover, global cogni-
tive function tests, are less likely than domain-specific tests
to be sensitive to small, acute, dietary-induced changes in
healthy children, less directly related to a specific localised
cognitive function and may be more useful if assessed over
a longer time period during which global cognitive function
might vary [17]. Furthermore, most previous acute studies
are laboratory based, with few studies conducted in natural
settings such as the school environment alongside the nor-
mal school day. Previous studies employing breakfast vs. no
breakfast comparisons have used either fixed or adlibitum
breakfast interventions, with the majority using the former.
Whilst a fixed breakfast intervention reduces the variability
in intake within the breakfast intervention, it is less eco-
logically valid and is unlikely to accurately reflect what the
participants might usually consume outside of the study.
This approach also assumes that a prescribed portion size
is suitable for all participants. However, in a heterogeneous
sample of adolescents, there is likely to be a large variation
in body weight, growth trajectories, levels of physical activ-
ity and therefore, energy requirements. It was also deemed
necessary to employ an adlibitum breakfast meal as previ-
ous research has suggested that deviation from habitual meal
size may adversely affect mood and cognitive function [18,
19]. Hence, benefits to subjective mood state and cogni-
tive performance may be most apparent with test meals that
resemble habitual meals.
There is also a lack of research in adolescents. This is
important given that skipping breakfast is more prevalent
in adolescents than any other age group [7] and breakfast
clubs are less prevalent in secondary schools than in primary
schools [20]. Therefore, strategies to increase breakfast con-
sumption in the school environment may be required for cog-
nitive benefits, but previous research is scarce in adolescents.
Adolescence is one of the greatest periods of growth and
change throughout the lifespan. There is a dramatic increase
in energy and nutrient requirements which coincides with
other factors that may affect adolescents’ dietary choices.
These factors include increased independence, a greater need
for acceptance by peers, rebellious or non-conformist behav-
iour, increased time spent out of the home (e.g. for school,
extracurricular, social or work activities), changes in sleep
patterns, reduced parental control and preoccupation with
appearance and body-image. Hence, the cognitive response
to breakfast consumption vs. fasting may be different com-
pared with younger children. This study was, therefore, con-
ducted to address the methodological limitations of previous
research. The aim of this study was to examine the acute
effect of breakfast (ready-to-eat-cereal [RTEC] and milk)
vs. no breakfast on cognitive function and subjective state
in 11–13-year-old adolescents.
Methods
Study design
This study employed an acute, randomised, parallel groups
design with two breakfast interventions: breakfast (ad libi-
tum RTEC and milk) or no breakfast. All procedures were
conducted in the school environment alongside the normal
school day. Data collection commenced January 2011 and
ended May 2011. This trial was retrospectively registered
at www.clini caltr ials.gov as NCT03979027 on 07/06/2019.
Participants
The study sample consisted of males and females aged
11–13years who were recruited to take part in the study
from a UK secondary school. This secondary school had
approximately 1350 pupils, predominantly of low socio-
economic status (68% eligible for Free School Meals).
The inclusion criteria were as follows: aged 11–13years,
willingness to consume RTEC with semi-skimmed cow’s
milk during the study, ability to follow verbal and written
instructions in English, and normal vision with appropri-
ate corrective lenses if required. The exclusion criteria
were as follows: inability to understand the objective
of the cognitive tests or carry out the tests, behavioural
difficulties or attention disorders, administration of any
psychotropic medication in the month prior to testing
or during testing, food allergies or intolerances which
prevent consumption of RTEC and milk (e.g. coeliac,
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3327European Journal of Nutrition (2021) 60:3325–3342
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lactose intolerance), acute illness or feeling unwell within
the week prior to testing or during testing, and hearing
impairment that precluded the normal use of headphones.
A power calculation conducted by an independent statisti-
cian estimated that a total of 180 participants (90 partici-
pants in each study intervention) was required to detect
an effect size of 0.42 (based on the outcome measure
“Secondary Memory” reported by Ingwersen etal. [21])
comparing breakfast with no breakfast on the primary
outcome (visual-spatial memory performance) with an
alpha of 0.05 and 80% power. This “Secondary Memory”
outcome measure reflected accuracy scores on immedi-
ate and delayed free word recall, delayed word recog-
nition and a visual memory task. Ingwersen etal. [21]
sample included 32 participants in each group (total of
64 subjects overall) and found a significant effect on the
combined computerised cognitive function tests following
consumption of either high glycaemic index or low gly-
caemic index RTECs. We anticipated that the effect size
demonstrated in a smaller sample and comparing break-
fast types [21] would be smaller than in a fed vs. fasted
comparison and therefore that the present study would be
adequately powered. Participants were randomised into
breakfast and no breakfast interventions. The randomisa-
tion procedure was carried out by the independent statis-
tician prior to screening and revealed to the researchers
via an excel file after the participant screening sessions.
The selected participants for the study were stratified by
class and gender. For each stratum, the interventions were
randomly assigned, such that half of the participants were
assigned to the breakfast intervention and the other half
to no breakfast intervention within each stratum. Hence,
the trial was balanced for intervention comparison and
unbiased with respect to class and gender.
Intervention
There were two interventions in this parallel groups study:
1) Breakfast: Adlibitum RTEC (up to 70g), from a choice
of four commercially available RTECs with 1.8% fat
cow’s milk (up to 300 mls). Adlibitum water intake
was also permitted. The four RTECs were corn flakes,
toasted rice, shredded whole wheat pieces with a sugar
topping, and wheat, corn and oat shapes (Kellogg’s Corn
Flakes, Kellogg’s Rice Krispies, Kellogg’s Mini Max,
and Kellogg’s Start respectively)
2) No breakfast: Adlibitum water intake.
Nutrient composition of the test breakfasts (per maximal
portion) is shown in Table1. On the test day, participants
arrived at school in a fasted state having been asked not
to consume any food or drink after 2100h on the previ-
ous evening (with the exception of adlibitum water intake).
Breakfast was administered in the school dining area within
a typical school breakfast programme environment. Break-
fast preparation and instructions to participants were stand-
ardised. The RTECs were presented in small, individual
plain (unbranded) white boxes in 70g maximal amounts to
each participant. Providing a maximal 70g portion allowed
participants to self-serve and consume a breakfast suitable
for them in terms of portion size, and therefore may better
reflect their habitual intake vs. a standardised portion size.
Milk was served in small, individual glass jugs in 300ml
maximal amounts to each participant. Participants were
permitted to self-serve their chosen RTEC and milk in an
amount habitual for them and were instructed to eat until
they were comfortably full. Participants were required to eat/
drink all of the breakfast/water within 15min. Participants
in both interventions were permitted adlibitum water intake
Table 1 Nutrient composition per maximal portion of the test breakfasts
a Maximal portion size: 70g; nutrition information was provided by the manufacturer (Kellogg’s)
b Maximal portion size: 300ml; nutrition information was provided by the manufacturer (Sainsbury’s)
Corn flakesa (Kel-
logg’s Corn Flakes)
Toasted ricea
(Kellogg’s Rice
Krispies)
Shredded whole wheat sugar topped
piecesa (Kellogg’s Mini Max)
Wheat, corn and oat
shapesa (Kellogg’s Start)
Milkb
Energy (kJ) 1123 1138 1096 1154 618
Energy (kcal) 265 268 259 273 147
Protein (g) 4.9 4.2 7.7 5.6 10.2
Total carbohydrate (g) 58.8 60.9 51.1 55.3 15
Sugars (g) 5.6 7.0 12.6 16.8 15
Total fat (g) 0.6 0.7 1.4 2.5 5.1
Saturated fat (g) 0.1 0.1 0.2 1.4 3
Fibre (g) 2.1 0.7 5.6 3.5 0
Salt (g) 0.9 0.8 0.0 0.7 0.18
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3328 European Journal of Nutrition (2021) 60:3325–3342
1 3
during the 15min breakfast session. This intervention was
chosen to closely resemble a typical school breakfast con-
text and composition. Participants completed a self-report
written questionnaire at the screening. The questionnaire
contained three items relating to the participant’s habitual
breakfast consumption frequency and food type.
Following the breakfast session, the amount of RTEC and
milk leftover was weighed and recorded to determine the
amount consumed. Throughout the remainder of the morn-
ing, participants were permitted adlibitum water intake only
until the school’s scheduled lunch period. The school had a
policy that pupils were not permitted to eat or drink (except
water) during lessons which aided compliance with the fast-
ing regime.
Cognitive function test battery
The Cambridge Neuropsychological Test Automated Battery
(CANTAB; Cambridge Cognition Ltd) was used to assess
cognitive function. The battery was administered on indi-
vidual touchscreen portable computers. Testing was con-
ducted in groups of 15–20 participants, in a quiet classroom
which was consistent across test days. Cognitive testing was
conducted in a group-testing situation to closely resemble a
typical school classroom context. Standardised administra-
tion scripts were used to ensure consistency in administra-
tion. The 25-min cognitive test battery comprised four tests
administered in the following order: Simple Reaction Time
(SRT), 5-Choice Reaction Time (5-CRT), Rapid Visual
Information Processing task (RVIP), and Paired Associates
Learning (PAL). The cognitive tests employed had demon-
strated sensitivity to similar acute nutritional manipulations
in previous studies [21–25]. These tests were grouped into
three cognitive constructs of reaction time, visual-sustained
attention, and visual-spatial memory respectively. Visual-
spatial memory (PAL performance) was the primary out-
come. This was selected as a primary outcome as previous
studies have demonstrated that visual-spatial memory is a
predictor of academic performance, including children’s
reading and mathematics skills [26–28].
Primary outcome: visual‑spatial memory task
The PAL task was employed to measure immediate vis-
ual-spatial memory. The duration of the task is typically
7–9min, depending on response times and level reached.
The task consists of one practice level followed by four
assessed levels. At each level, white boxes are displayed on
the screen and these open in a random order. Depending on
the level, two or more of these boxes contain patterns. After
all boxes have opened, each previously presented pattern
is shown in the centre of the screen and the participant is
required to indicate the previously shown location of the
pattern by touching the relevant white box on the screen.
As the task proceeds, these assessed levels increase in dif-
ficulty by increasing the number of patterns presented. The
number of patterns presented at levels 1, 2, 3 and 4 are 2, 3,
6 and 8, respectively. At each level, the participant is given a
maximum of six attempts (termed “trials”) to recall all of the
correct pattern locations. If a participant is unable to recall
all of the correct pattern locations within six attempts, the
test terminates. Hence, a participant has to succeed at one
level to advance to the next level. Parallel forms were pre-
sented at each test session. Outcome variables for this task
were errors at each level, total errors (adjusted), trials at each
level, total trials (adjusted), correct responses on the first
trial within each level, and levels achieved. The total errors
and total trials outcome variables are adjusted scores. The
total trials (adjusted) variable refers to the number of trials
attempted throughout the entire task. Some participants did
not reach level 4 (8 patterns) because they did not complete
level 3 (6 patterns). Hence, the total trials score is adjusted
for levels that they did not reach (it includes an estimate of
the number of trials they would have attempted on any levels
they did not reach). The total errors (adjusted) variable refers
to the number of errors made throughout the entire task with
an adjustment for any levels that were not reached, as per the
total trials (adjusted) outcome variable.
Secondary outcomes: cognitive function
Reaction time tasks
The SRT and 5-CRT tasks were used to assess reaction
time. The SRT task requires the participant to respond to a
stimulus (yellow dot within a white circle) presented in the
centre of the computer screen by touching the screen within
500ms. The 5-CRT task employs the same paradigm as the
SRT task, except the stimulus appears in one of five loca-
tions on the computer screen requiring the participant to
choose the correct location. Stimulus onset time varied from
750 to 2250ms. Both tasks involve practice (five trials) and
assessed phases (14 trials). Each task lasts approximately
two minutes. Outcome variables on this task were decision
time, movement time, errors of inaccuracy (response was not
within the physical boundaries of the target stimuli), errors
of no response (failure to respond within 500ms), prema-
ture errors (response is made before the target stimulus is
presented), and total errors (sum of all errors).
Visual‑sustained attention task
The RVIP task was used to measure visual-sustained atten-
tion. Participants are required to detect a 3-digit target
sequence within a continuous, rapidly presented digit series
on the computer screen within 1700ms. Participants respond
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3329European Journal of Nutrition (2021) 60:3325–3342
1 3
by pressing a press pad upon detection of the consecutive
target sequence “3–5–7”. The task consists of a 2min prac-
tice phase followed by a 7min assessed phase. The first
minute of the assessed stage is a ‘run-in’ period; therefore
responses from the last 6 min are included as outcome vari-
ables. These 6 min (termed blocks 1–6) contain nine target
sequences each (54 in total). Outcome variables for this
task were correct targets by block (blocks 1–6), total cor-
rect targets, false alarms, correct rejections, reaction time,
and guessing tendency (A Prime [A′]; B Double Prime [B″])
[29].
Secondary outcomes: subjective state
andsubjective cognitive test performance
Subjective state was a secondary outcome measure. Con-
comitant ratings of subjective hunger, cheerfulness, energy,
distractibility, ease of focus, bad temper, keenness to try
hard, and feeling awake were taken throughout the test morn-
ing using 8 unipolar Visual Analogue Scales (VAS). The
VAS descriptors were chosen and adapted from those used
in previous studies [13] to reflect dimensions of motivation,
alertness and mood. The mood descriptors were piloted in
a small sample of 11-year-olds to ensure suitability for the
study population. VAS were presented electronically using
the CANTAB equipment and processed by Cambridge Cog-
nition Ltd. Participants responded to each VAS using the
touchscreen by moving the cursor along a 100mm line with
extreme anchors at each end. The initial location of the cur-
sor was at the 50mm mark. There were 100 points on the
scale, yielding possible scores of 0–100. Participants were
asked to rate their subjective state immediately before and
after breakfast and each cognitive test battery. At each meas-
urement point, participants completed a total of 8 or 12 VAS
items. The 8-item VAS (pre-cognitive testing and following
breakfast) assessed hunger, cheerfulness, energy, distract-
ibility, ease of focus, bad temper, keenness to try hard, and
feeling awake and the 12-item VAS (post cognitive testing
only) contained an additional four items relating to perceived
test battery difficulty and perceived performance, concentra-
tion and frustration during the test battery. The 8-item VAS
took approximately 3min to complete and the 12-item VAS
took approximately 4min to complete.
Procedure
Participants attended two screening sessions in the week
prior to the scheduled test day. At the first screening session,
participants completed a self-report written questionnaire
to obtain information on habitual breakfast intake, medical
conditions, food allergies and intolerances. The height and
weight of each participant were measured and recorded by
trained researchers to determine Body Mass Index standard
deviation scores (BMI SDS) based on the British 1990
growth reference data [30]. Participants were also tested for
colour vision. Lastly, participants were given the opportunity
to try a small amount of each RTEC (with milk) and choose
the RTEC they wished to consume as a test breakfast. Addi-
tionally, the following demographic measures were taken
from school records: age, gender, ethnicity, and Cognitive
Abilities Test (CAT) score. The CAT is carried out routinely
by UK schools at the beginning of year 7 and 8. The CAT
has three timed, multiple-choice test batteries which yields
scores for verbal, nonverbal, and quantitative reasoning abil-
ity [31]. A mean CAT score was calculated as the average
of the three subtest scores. Mean CAT score was used as an
indication of the cognitive abilities of the sample and is a
proxy for Intelligence Quotient (IQ).
The full test day schedule and concomitant school activity
are given in Table2. Three cognitive and subjective state
testing batteries were administered on the test day. The
baseline battery was administered at 0840h (− 25min pre-
intervention). At 0905h, participants were served breakfast
or no breakfast in the school dining area with 15min allowed
for consumption. The second battery was administered at
1015h (+ 70 min post-intervention). The third battery
was conducted in the late-morning at 1240h (+ 215min
post-intervention).
Ethical considerations
Prior to commencement of the study, ethical approval was
obtained from the School of Psychology Research Ethics
Committee at the University of Leeds, UK (Reference:
10-0105, Date: 27/12/2010). All researchers involved in the
study obtained Disclosure and Barring Service clearance.
To recruit participants, letters were sent home to each par-
ent/guardian of school pupils aged 11–13years, containing
a cover letter and information sheet for the parent/guardian
and an information sheet for the school pupils. The pupil
version was specifically designed, in terms of readability
and content, to aid understanding. For the pupils, this infor-
mation was reiterated at screening and they were given the
opportunity to ask questions. Participants and their parents/
guardians were told that participants could withdraw at any
point before or during the study without giving a reason.
The tasks were not expected to induce any pupil distress and
there were no adverse events related to pupil participation.
Informed consent was obtained from parents using passive
consent (opt-out), and each child gave his/her own verbal
assent to participate in the study at screening (opt-in) after
reading the pupil information sheet and a presentation on the
study from the researchers. Parents/guardians were informed
that if they were happy for their child to take part in the
study they did not need to respond to the letter or notify
the researchers, and consent would be assumed but children
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3330 European Journal of Nutrition (2021) 60:3325–3342
1 3
opted in on the day and had the opportunity to withdraw
at any time. Participants and their parents did not receive
payment or another reward for taking part in the research.
However, the participants received a certificate at the end of
the research to thank them for taking part.
Statistical analysis
All analyses were performed using SAS version 9.2 (SAS
Institute). Data for which residuals illustrated a skewed
distribution were normalized by transformation of the data
(logarithm of the data) and/or the removal of outliers (where
the studentised residual > 3). Baseline participant character-
istics were compared using independent groups t tests for
continuous variables and Pearson’s chi-squared (χ2) tests for
categorical variables.
Cognitive function data that complied with paramet-
ric assumptions were analysed using mixed ANCOVA
models with the intervention (2 levels; breakfast and no
breakfast) as the between-subject factor and session (2 lev-
els; test session one and test session two) as the repeated
measures factor with baseline cognitive test performance
included as a varying covariate. All main effects and their
interactions (baseline*intervention; baseline*session;
intervention*session; baseline*intervention*session) were
requested in the first model, and all covariates including age,
gender, school year, school class, habitual breakfast intake,
and CAT score. The model fit, F values and significance of
main effects and interactions were examined in each model.
Non-significant interactions and covariates were removed,
starting with the highest order interactions, and the result-
ing model was compared to the previous model using the
McQuarrie Tsai corrected Akaike Information Criterion
(AICc) [32]. The AICc gives an indication of the amount of
remaining unexplained variance after the model has been fit-
ted, in which a smaller AICc value indicates a better model.
Models were chosen on the basis of ‘best fit’, and interac-
tion terms that improved the fit were retained. The reported
ANCOVAs are the best fit (i.e., lowest AICc) models. In
ANCOVA models, the main effects are a test of the differ-
ence at the intercept, where baseline is equal to zero. There-
fore, these main effects are informative only when there are
no interactions with baseline. As the ANCOVA included
baseline as a continuous covariate, the test for significant dif-
ferences by the intervention was based on the Least Square
Means (LSMeans). Where a baseline*intervention interac-
tion was present, the LSMeans test indicated the magnitude
of the difference between the two interventions at different
levels of baseline. Significant baseline*intervention inter-
actions were also explored by a scatterplot of baseline on
post-intervention cognitive performance according to inter-
vention for each outcome as required.
Cognitive function data that did not satisfy parametric
assumptions were subjected to the Poisson dispersion test.
A non-significant test indicates that a Poisson distribution
is adequate and the mean events occur at a constant rate in a
typical Poisson distribution. Where the Poisson dispersion
test returned a significant result, indicating the absence of a
Poisson distribution, the Cochran-Mantel–Haenszel (CMH)
test was used as a non-parametric equivalent of ANCOVA
with baseline as a covariate. Where a significant CMH test
was coupled with a baseline*intervention interaction, the
baseline response at which the difference between interven-
tions was statistically significant was determined using the
Jonckheere-Terpstra (JT) test [33, 34].
Table 2 Test day schedule
T time point, VAS visual analogue scale
Time Time relative to
the intervention
Activity Concomitant school activity
0835 − 30 Registration and arrival at testing classroom Lesson 1
0840 − 25 Baseline measures
VAS 8-item (T1)
Baseline cognitive test batteryVAS 12-item (T2)
Lesson 1
0905 0 Study intervention Lesson 1
0920 + 15 Post-intervention measures
VAS 8-item (T3)
Lesson 1
1015 + 70 Test session 1 measures
VAS 8-item (T4)
Test session 1 cognitive test battery
VAS 12-item (T5)
Lesson 2 (ends at 10:30)
Break-time (10:30 -10:45)
1240 + 215 Test session 2 measures
VAS 8-item (T6)
Test session 2 cognitive test battery
VAS 12-item (T7)
Lesson 4 (ends at 12:45)
School lunch period
1310 + 245 End of test day School lunch period
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3331European Journal of Nutrition (2021) 60:3325–3342
1 3
All VAS data were analysed using mixed ANCOVA
models with intervention (2 levels; breakfast and no break-
fast) as the between-subject factor and the time point (T) of
measurement (see Table2) as the repeated measures factor
(6 levels; T2–T7 for the 8-item VAS ratings or 2 levels; T5
and T7 for VAS ratings from the additional four items in the
12-item VAS) and baseline VAS ratings as the covariate.
Age, gender, school year and school class were included as
covariates. The LSMeans test was employed between the
interventions at each of the time points to indicate the mag-
nitude of the difference between the two interventions at
each post-intervention time point.
Results
Participant characteristics
Flow of participants through the phases of the study is
shown in Fig.1. A total of 369 school pupils were invited to
take part, of which a total of 111 pupils were excluded (see
Fig.1). Of the 258 participants enrolled, 24 were excluded
from the analysis due to lack of compliance on the test day.
This gave a final sample size of 234 participants of which
113 were randomly allocated to the breakfast intervention
and 121 to the no breakfast intervention. Hence, this sample
provided adequate power (80% power with an alpha of 0.05)
based on the power calculation reported in “Participants”,
which suggested 180 participants were required. Partici-
pant characteristics according to intervention are shown in
Table3. There were no significant differences between the
characteristics of participants assigned to each study inter-
vention. The sample consisted of habitual breakfast consum-
ers and non-breakfast consumers, such that 42.7% of partici-
pants reported that they consumed breakfast every day (7/
days a week) or nearly every day (5–6days/week), 23.5% of
participants reported that they consumed breakfast occasion-
ally (3–4days/week) and 33.8% of participants reported that
they rarely consumed breakfast (0–2days/week). RTECs
were the most frequently consumed food for breakfast on
school days (42.9%). Hence, it is likely that the breakfast
food provided in the breakfast intervention broadly reflected
habitual breakfast intake and was, therefore, ecologically
valid.
RTEC choice andself‑serve RTEC intake
Within the breakfast intervention, eight (7.1%) participants
chose to consume Kellogg’s Corn Flakes, 12 (10.6%) par-
ticipants chose to consume Kellogg’s Rice Krispies, 30
(26.5%) participants chose to consume Kellogg’s Mini
Max, and 63 (55.8%) participants chose to consume Kel-
logg’s Start. Across all four RTEC types, total mean RTEC
intake was 49.5g ± 17.6 g. Total mean intake of milk
was 133.5g ± 79.4g. The overall mean intake of energy
was 1059 ± 359 kJ. Overall macronutrient intake was:
44.5g ± 15.0g total carbohydrate, 16.0g ± 6.0g of which
sugars, 9.0g ± 3.8g protein, 3.7g ± 1.6g total fat, and
2.7g ± 1.6g fibre.
Cognitive function: primary outcome
Visual‑spatial memory
A significant effect of the intervention (CMH[1] = 7.29,
p < 0.01) was found for the number of levels achieved on
the PAL task (i.e. the number of levels successfully passed
by a participant). Further analysis using the JT test showed
a significant difference between interventions when baseline
performance reached level 2 (JT, z = 2.58, p < 0.01) with no
significant difference when baseline performance reached
level 3 or 4. Figure2a demonstrates that for participants
with baseline performance at level 2, 100% of participants in
the breakfast intervention reached level 4 but only 41.7% of
those in the no breakfast intervention reached the maximum
level 4. Hence, more of those participants who performed
poorly at baseline (i.e. those who reached a low level on
the task at baseline) improved their performance across the
morning following breakfast consumption relative to fasting.
For the total errors made on the PAL test, the distribution
of residuals showed a positive skew and was normalised by
the removal of eight outliers. The analysis showed a sig-
nificant baseline*intervention interaction (F[1,202] = 6.95,
p < 0.01) for total errors made on the PAL test. The LSMeans
comparison showed no difference between interventions
when baseline = 10 (t[202] = − 0.25 ns; Table4) and when
baseline = 0 (t[202] = − 1.85 ns). However, the LSMeans
comparison between interventions when baseline = 50 was
significant (t[202] = − 2.43, p < 0.05). Figure2b shows a
scatterplot of baseline total errors against total errors pooled
across test session one and two according to breakfast inter-
vention. Participants who made above-average errors at
baseline reduced the total number of errors made at subse-
quent test sessions following breakfast consumption whilst
those in the no breakfast intervention did not (Fig.2b). There
were no significant effects of the intervention on all other
PAL outcome variables.
Cognitive function: secondary outcomes
Reaction time
A significant effect of the intervention was shown for SRT
accuracy (CMH[1] = 8.67, p < 0.01). A larger proportion of
participants increased the number of errors of no response
they made across the morning relative to baseline in the no
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3332 European Journal of Nutrition (2021) 60:3325–3342
1 3
breakfast intervention (14.8%) compared with the breakfast
intervention (5.9%; Fig.3a).
For 5-CRT movement time (ms), the distribution of
residuals showed a positive skew and was normalised by
the removal of eleven outliers. The final ANCOVA model
for 5-CRT movement time (ms) demonstrated a signifi-
cant main effect of the intervention (F[1,204] = 9.90,
p < 0.01) and a significant baseline*session interaction
(F[1,203] = 12.75, p < 0.001). LSMeans comparisons
indicated that at mean baseline performance the differ-
ence between interventions was significant (t[204] = 3.15,
p < 0.01; Table4). As shown in Fig.3b, movement time
was faster following breakfast vs. no breakfast at test ses-
sions one and two. There were no significant effects of
the intervention on all other SRT and 5-CRT outcome
variables.
Randomised (n= 369)
Randomised to breakfast intervention (n=185) Randomised to no breakfast control (n=184)
Assessed for eligibility (n=272)
Excluded (n=14)
Not meeting inclusion criteria (n=7)
Participant left the school (n=7)
Received allocated breakfast intervention
(n=133)
Received allocated no breakfast control
(n=125)
Analysed (n=113)
Excluded from analysis (n= 20)
< 15g of RTEC consumed (n=17)
Did not comply with overnight fasting
requirement (n=3)
Analysed (n=121)
Excluded from analysis (n=4)
Did not comply with fasting
requirements (n=4)
Enrolled (n=258)
Identification of eligible participants (n= 369)
Excluded (n=97)
•Child or parent declined to participate (n=33)
•Absent from school during screening sessions (n=64)
Fig. 1 Participant flow chart. RTEC, ready to eat cereal
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3333European Journal of Nutrition (2021) 60:3325–3342
1 3
Visual‑sustained attention
The distribution of residuals for the number of correct
targets for blocks 3 and 4 showed a negative skew and was
normalised by the removal of three outliers. The analysis
demonstrated a significant main effect of the interven-
tion for Block 3 (F[1,202] = 6.00, p < 0.05), a significant
baseline*intervention interaction for Block 3 and 4 (Block
3: F[1,202] = 6.29, p < 0.05; Block 4: F[1,202] = 4.01,
p < 0.05), and a significant baseline*session interaction
for Block 4 (F[1,202] = 4.54, p < 0.05). The LSMeans
comparison indicated no difference between interventions
at mean baseline for Block 3 and 4 across test sessions
one and two (Block 3: mean baseline 7.44; t(202) = 0.02
ns; Block 4: mean baseline 7.42; t(202) = 1.25 ns;
Table4). However, for block 3, the LSMeans compari-
son between interventions was only significant when
baseline = 0 (t[202] = − 2.45, p < 0.05), when base-
line = 2 (t[202] = − 2.41, p < 0.05) and when baseline = 10
(t[202]2.16, p < 0.05). For block 4, the LSMeans compari-
son between interventions was only significant when base-
line = 9 (t[202] = 2.30, p < 0.05). Performance across test
sessions one and two was better following breakfast vs. no
breakfast in participants with low performance at baseline
only in block three. Conversely, performance across test
sessions one and two was better following no breakfast vs.
breakfast in participants with high performance at baseline
only in block three and four.
The distribution of residuals for RVIP false alarms
showed a positive skew and was normalised by the removal
of eight outliers. Analysis of RVIP false alarms showed a
significant main effect of the intervention (F[1,202] = 3.92,
p < 0.05) and a significant baseline*intervention interac-
tion (F[1,202] = 8.19, p < 0.01). The LSMeans comparison
indicated no difference between interventions when base-
line = 5.71 across test sessions one and two (t(202) = − 0.00
ns; Table 4). However, the LSMeans comparison
between interventions was significant when baseline = 20
(t[202] = 2.58, p < 0.05), when baseline = 50 (t[202] = 2.82,
p < 0.01) and when baseline = 0 (t[202] − 1.98, p < 0.05).
The advantage for breakfast was evident only for participants
with high baseline values (i.e. poorer baseline performance)
across test sessions one and two.
The residuals for guessing tendency (B″) showed a
negative skew and were normalised by the removal of 14
outliers. For guessing tendency (B″), the analysis dem-
onstrated a significant main effect of the intervention
(F[1,218] = 10.24, p < 0.01) and a baseline*intervention
interaction (F[1,218] = 9.74, p < 0.01). LSMeans com-
parison when baseline = 0.84 (mean baseline) and when
baseline = 1.00 did not confirm a significant difference
between the interventions overall across test sessions
Table 3 Participant
characteristics according to
interventiona
CAT Cognitive Abilities Test, SAS Standard Age Score, SDS Standard Deviation Score
a Values are means ± SEs unless otherwise indicated
All participants
(n = 234)
Breakfast intervention
(n = 113)
No breakfast
intervention
(n = 121)
Gender, n (%)
M 125 (53.4) 64 (52.9) 61 (54.0)
F 109 (46.6 57 (47.1) 52 (46.0)
Ethnicity, n (%)
White British 159 (67.9) 79 (65.3) 80 (70.8)
Asian/ British Asian 47 (20.1) 29 (24.0) 18 (15.9)
Black British/African/Caribbean 15 (6.4) 8 (6.6) 7 (6.2)
Mixed 8 (3.4) 3 (2.5) 5 (4.4)
Other 3 (1.3) 1 (0.8) 2 (1.8)
Missing data 2 (0.9) 1 (0.8) 1 (0.9)
Habitual breakfast consumption—fre-
quency/week, n (%)
0 18 (7.7) 10 (8.3) 8 (7.1)
1–2 61 (26.1) 23 (19.0) 38 (33.6)
3–4 55 (23.5) 32 (26.4) 23 (20.4)
5–6 38 (16.2) 22 (18.2) 16 (14.2)
7 62 (26.5) 34 (28.1) 28 (24.8)
Age, years 12.43 ± 0.04 12.45 ± 0.05 12.42 ± 0.05
BMI SDS 0.69 ± 0.08 0.60 ± 0.11 0.78 ± 0.12
CAT SAS score 90.51 ± 0.72 90.53 ± 0.98 90.48 ± 1.07
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3334 European Journal of Nutrition (2021) 60:3325–3342
1 3
one and two (t[218] = − 0.76 ns and t[218] = 1.95 ns
respectively; Table4). However, LSMeans comparison
between interventions when baseline = 0.20 was significant
(t[202] = − 3.21, p < 0.01). The interaction was driven by
lower levels of baseline, such that the beneficial effect of
breakfast across test sessions one and two was evident for
those with a poorer performance at baseline only. There
were no significant effects of the intervention on all other
RVIP outcome variables.
Secondary outcomes: subjective state
andsubjective cognitive test performance
The analysis of ratings of perceived hunger and energy lev-
els showed a similar pattern of results to each other. For
subjective ratings of perceived hunger and energy levels,
the ANCOVAs demonstrated a main effect of the interven-
tion (smallest F[1,212] = 54.13, p < 0.0001) and significant
intervention*time (smallest F[5,1130] = 2.54, p < 0.05)
8.3
50.0
41.7
100
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
234
Percentage of participants
Level achieved
PAL-Levels achieved when baseline performance reached level 2
No breakfast Breakfast
A
Baseline total errors
Total errors pooled across test sessions 1 and 2
PAL total errors (adjusted)
B
Breakfast
No breakfast
Fig. 2 a Number of levels achieved on the PAL task according to
intervention pooled across test sessions one and two. Figure shows
percentage of participants reaching each level when baseline perfor-
mance reached level 2 only. b Scatterplot of baseline performance
against post-intervention PAL task total number of errors (adjusted)
pooled across test sessions one and two by intervention
Table 4 Cognitive function data that complied with parametric assumptions by intervention and test session
Values are LSmeans ± SEs unless otherwise indicated
B″ B double prime, TS test session
a Values are means ± SEs. Baseline cognitive test performance was included as a covariate in the analysis
Cognitive function outcome variable BaselineaTS1 TS2
Breakfast No Breakfast Breakfast No Breakfast Breakfast No Breakfast
PAL total errors adjusted 9.32 ± 1.15 9.78 ± 1.14 8.27 ± 0.72 8.63 ± 0.68 8.51 ± 0.73 7.88 ± 0.68
5-CRT movement time (ms) 279.15 ± 5.52 292.69 ± 7.29 277.35 ± 4.58 290.68 ± 4.20 274.04 ± 4.62 287.85 ± 4.32
RVP correct targets block 3 7.43 ± 0.16 7.47 ± 0.14 7.17 ± 0.15 7.11 ± 0.14 6.97 ± 0.15 7.03 ± 0.14
RVP correct targets block 4 7.37 ± 0.15 7.50 ± 0.15 7.04 ± 0.14 7.26 ± 0.13 6.95 ± 0.14 7.07 ± 0.13
RVP false alarms 5.99 ± 0.65 5.46 ± 0.61 7.22 ± 0.52 7.21 ± 0.50 8.10 ± 0.54 8.11 ± 0.50
RVP B″0.83 ± 0.13 0.84 ± 0.13 0.83 ± 0.01 0.81 ± 0.01 0.80 ± 0.01 0.79 ± 0.01
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3335European Journal of Nutrition (2021) 60:3325–3342
1 3
interactions. Significant baseline*intervention*time
(F[5,1130] = 4.66, p < 0.001) and baseline*time
(F[5,1130] = 4.39, p < 0.001) interactions were also dem-
onstrated for hunger ratings and a baseline*intervention
(F[1,212] = 19.85, p < 0.0001) interaction for energy ratings.
LSMeans comparisons between the interventions at each of
the time points indicated that hunger ratings were signifi-
cantly higher and energy ratings lower in the no breakfast
intervention at T3, T4, T5, T6 and T7 (largest p = 0.0082;
Table5).
The analysis of perceived cheerfulness, keenness to
try hard, ease of distractibility, ease of focussing, and feel-
ing awake showed a similar pattern of results to each other.
For ratings of perceived cheerfulness, keenness to try hard,
perceived ease of distractibility, perceived ease of focus-
sing, and ratings of feeling awake, the ANCOVA models
demonstrated significant main effects of the intervention
[smallest (F[1,212] = 3.92, p < 0.05)]. Furt hermore, signifi-
cant intervention*time interactions were demonstrated for
perceived cheerfulness, keenness to try hard and ratings of
feeling awake [smallest F[5,1129] = 2.59, p < 0.05)] and
significant baseline*intervention interactions for ratings of
perceived ease of distractibility and perceived ease of focus-
sing (smallest F[1,212] = 15.05, p < 0.0001). A significant
baseline*time (F[5,1128] = 4.13, p < 0.001) interaction was
demonstrated for ratings of feeling awake and a significant
baseline*intervention*time (F[5,1129] = 2.56, p < 0.05) inter-
action for perceived ease of distractibility. LSMeans com-
parisons between the interventions at each of the time points
indicated that participants who consumed breakfast felt more
keen to try hard, able to focus, awake, and less distractible than
those in the no breakfast intervention at T3, T4 and T5 (largest
p = 0.02; Table5).
For ratings of perceived bad temper, the ANCOVA dem-
onstrated a significant baseline*intervention*time interaction
(F[5,1127] = 3.69, p < 0.01) and a significant main effect of the
intervention (F[1,212] = 7.26, p < 0.01). LSMeans compari-
sons between the interventions at each of the time points indi-
cated that those who skipped breakfast felt significantly more
bad tempered immediately post breakfast (T3, p < 0.0001) and
immediately following Test Session 1 (T5, p < 0.001) than
those who had eaten breakfast (Table5).
For ratings of perceived concentration during the cognitive
test battery, the ANCOVA revealed a significant main effect
of the intervention (F[1,211] = 7.83, p < 0.01). LSMeans com-
parisons between the interventions at the two post-intervention
test sessions indicated that participants in the breakfast inter-
vention reported concentrating significantly more than those
in the no breakfast intervention at Test Session 1 (p < 0.01)
(Table6). The ANCOVA models revealed no significant
effects of the intervention on perceived performance, ratings
of frustration during the testing battery, and perceived test bat-
tery difficultly and, therefore, the LSMeans comparison were
not consulted.
9.7
75.5
14.8
14.9
79.2
5.9
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
Decrease in Errors No change Increase in errors
Percentage of participants
Change in number of errors
SRT - errors of no response
No Breakfast Breakfast
A
290.68 287.85
277.35 274.04
100
150
200
250
300
350
Test Session 1Test Session 2
Movement time (ms)
Session
5-CRT -movement time
No BreakfastBreakfast
B
Fig. 3 a Percentage of participants who made fewer, more, or no
change in the number of errors of no response on SRT relative to
baseline pooled across test sessions one and two. SRT Simple Reac-
tion time. b Least Squares Mean ± SE movement time (ms) for 5-CRT
according to intervention and test session. 5-CRT 5 choice reaction
time
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3336 European Journal of Nutrition (2021) 60:3325–3342
1 3
Discussion
Principal findings
The findings of this study demonstrated that breakfast con-
sumption vs. no breakfast has a positive acute effect on cog-
nitive function and subjective state in 11–13-year-olds. This
study employed a randomised controlled trial design and
recruited one of the largest samples of adolescents reported
in the literature to date. Furthermore, the study used a battery
of cognitive tests with previously demonstrated sensitivity to
similar acute nutritional manipulations to ensure null find-
ings are due to true lack of effect rather than test insensitivity
[17]. The study extends previous research by providing new
evidence under highly ecologically valid research conditions
by including a school-based testing environment alongside
the normal school day and an adlibitum breakfast served
in a naturalistic school breakfast programme environment.
There was a positive effect of breakfast on each of the
cognitive tasks included in the battery, which measured
Table 5 VAS ratings of subjective state by intervention and test session
Values are LSmeans ± SEs unless otherwise indicated. T1–T7 corresponds to test day schedule (see Table2)
T time point, TS test session VAS, VAS Visual Analogue Scale
a p values are LSMeans comparisons between interventions at T3-T7
b Values are means ± SEs. Baseline VAS ratings were as a covariate in the analysis
VAS descriptor Baseline (T1)bBaseline (T2)bPost-intervention
(T3)
Pre TS one (T4) Post TS one (T5) Pre TS two (T6) Post TS two (T7)
Hunger
Breakfast 66.70 ± 2.90 63.42 ± 2.89 19.86 ± 2.33 40.96 ± 2.99 38.30 ± 3.06 75.91 ± 2.45 77.53 ± 2.48
No breakfast 66.02 ± 2.88 64.54 ± 2.60 72.46 ± 2.84 72.81 ± 2.85 74.62 ± 2.80 82.53 ± 2.38 85.49 ± 2.08
p valuea– – < 0.0001 < 0.0001 < 0.0001 0.0042 0.0016
Cheerfulness
Breakfast 51.34 ± 2.96 51.78 ± 2.76 76.35 ± 2.41 65.84 ± 2.83 62.52 ± 2.96 62.40 ± 3.09 55.31 ± 3.25
No breakfast 55.01 ± 2.66 53.30 ± 2.68 50.08 ± 3.08 49.41 ± 3.15 49.76 ± 3.15 55.57 ± 3.21 56.96 ± 3.22
p valuea– – < 0.0001 < 0.0001 0.0017 0.06 0.73
Bad temper
Breakfast 26.99 ± 2.41 28.88 ± 2.55 17.21 ± 2.23 23.60 ± 2.70 23.06 ± 2.85 26.24 ± 2.85 28.59 ± 3.10
No breakfast 23.24 ± 2.50 23.58 ± 2.51 30.98 ± 3.14 26.61 ± 2.79 32.70 ± 3.29 24.68 ± 2.83 25.61 ± 3.12
p valuea– – < 0.0001 0.20 0.0008 0.89 0.96
Energy
Breakfast 44.59 ± 2.66 44.34 ± 2.77 76.49 ± 2.21 70.12 ± 2.48 69.14 ± 2.61 58.92 ± 3.04 52.28 ± 3.06
No breakfast 45.77 ± 2.31 45.59 ± 2.45 40.29 ± 2.91 41.57 ± 2.85 41.79 ± 2.89 40.84 ± 2.89 40.66 ± 3.05
p valuea– – < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.0082
Keenness to try
hard
Breakfast 63.34 ± 2.50 62.42 ± 2.75 74.72 ± 2.42 67.85 ± 2.66 69.68 ± 2.69 67.03 ± 2.89 62.80 ± 3.07
No breakfast 63.77 ± 2.61 61.00 ± 2.61 54.06 ± 3.03 58.26 ± 2.97 55.17 ± 3.11 62.54 ± 3.05 61.86 ± 3.09
p valuea– – < 0.0001 0.0067 < 0.0001 0.23 0.65
Distractibility
Breakfast 45.27 ± 2.98 41.25 ± 3.00 36.13 ± 3.13 41.22 ± 3.04 38.13 ± 3.17 43.64 ± 3.17 48.49 ± 3.43
No breakfast 45.21 ± 2.76 40.20 ± 2.96 50.72 ± 3.17 49.01 ± 3.29 46.30 ± 3.31 46.58 ± 3.36 46.31 ± 3.41
p valuea– – 0.0002 0.02 0.01 0.20 0.65
Ease of focus
Breakfast 63.03 ± 2.65 59.29 ± 2.75 74.10 ± 2.59 73.21 ± 2.46 71.00 ± 2.73 65.53 ± 2.92 65.02 ± 3.07
No breakfast 65.09 ± 2.69 60.78 ± 2.77 57.71 ± 2.90 62.35 ± 2.95 61.98 ± 2.93 64.66 ± 2.88 62.51 ± 3.01
p valuea– – < 0.0001 0.0006 0.0012 0.69 0.20
Awake
Breakfast 48.37 ± 3.00 49.84 ± 2.96 78.15 ± 2.30 74.43 ± 2.59 71.79 ± 2.78 69.94 ± 2.82 68.85 ± 3.07
No breakfast 49.05 ± 2.78 47.10 ± 2.80 51.87 ± 3.21 55.88 ± 3.31 50.70 ± 3.32 62.57 ± 3.25 61.26 ± 3.26
p valuea– – < 0.0001 < 0.0001 < 0.0001 0.23 0.11
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3337European Journal of Nutrition (2021) 60:3325–3342
1 3
reaction time, visual-sustained attention and visual-spatial
memory. The functions assessed have some wider impact
on learning in the classroom. Measures of cognitive per-
formance provide a proxy for cognitive abilities such as
the ability to concentrate, react and remember, all of which
are key processes for effective learning in school [17, 35,
36]. The positive effects of breakfast consumption on the
study’s primary outcome (visual-spatial memory) suggest
that breakfast may help children learn at school and could
improve academic attainment. Previous studies have demon-
strated that visual-spatial memory is a predictor of children’s
academic performance [26–28]. The findings suggest that
breakfast omission may be associated with poorer cognitive
performance on domains that impact negatively on every-
day functioning at school. However, the clinical significance
of the results is unclear. Pham and Hasson [26] examined
the association between visuospatial working memory and
reading ability in a sample of schoolchildren. The inclu-
sion of visuospatial working memory into a hierarchical
regression model provided significant results, contributing
an additional 4% of unique variance to reading comprehen-
sion. Whilst Pham and Hasson’s findings [26] suggest that
the positive effects of breakfast consumption on the current
study’s primary outcome (visual-spatial memory) may have
a clinical significant effect on reading ability, our study used
different tests of visual-spatial memory and statistical analy-
ses. Therefore, it would be tenuous to directly translate our
findings into changes to academic performance.
The results from the reaction time tasks indicate that
reaction time was significantly faster following breakfast
compared with no breakfast. Both reaction time (faster psy-
chomotor speed) and accuracy were improved by breakfast
consumption. Breakfast-induced improvements in reaction
time have been previously reported in adolescents, suggest-
ing that this finding is reliable [3, 23]. Notably, in a similar
study to the current study, Cooper etal. [3] conducted a
school-based, randomised controlled, crossover study com-
paring the effects of consuming an ad-libitum breakfast
relative to fasting in 40 healthy British adolescents aged
12–15years. The results demonstrated that accuracy on
SRT was superior + 20min post-breakfast consumption vs.
fasting [3].
The results of this study also show an advantage for
breakfast on visual-sustained attention, evidenced by a sig-
nificantly greater number of correct responses in Blocks 3
and 4, fewer false alarms, and less guessing. In our previ-
ous systematic reviews, we reported that tasks that required
attention were facilitated most consistently by breakfast
consumption relative to fasting [12, 13]. In a similar study
to the current study, Wesnes etal. [23] demonstrated that
the consumption of a 45g portion of RTEC with milk for
breakfast relative to fasting reduced the decline in ‘Power of
Attention’ factor scores, which included response times on
digit vigilance, across the morning in 9–16-year-olds [23].
Taken together, the findings indicate that breakfast con-
sumption facilitates adolescents’ ability to sustain attention
Table 6 VAS ratings of
subjective cognitive test
performance by intervention
and test session
Values are LSmeans ± SEs unless otherwise indicated. T2, T5, and T7 corresponds to test day schedule (see
Table2)
T time point, TS test session VAS Visual Analogue Scale
a p values are LSMeans comparisons between interventions at T5 and T7
b Values are means ± SEs. Baseline VAS ratings were included as a covariate in the analysis
VAS descriptor Baseline (T2)bPost TS one (T5) Post TS two (T7)
Perceived difficulty
Breakfast 33.84 ± 2.47 31.38 ± 2.75 38.61 ± 3.00
No breakfast 38.40 ± 2.43 35.93 ± 2.76 36.37 ± 3.07
p valuea– 0.54 0.32
Perceived level of concentration
Breakfast 71.86 ± 2.22 75.80 ± 2.26 72.45 ± 2.46
No breakfast 74.57 ± 2.10 69.35 ± 2.65 70.68 ± 2.71
p valuea– 0.0012 0.33
Perceived performance
Breakfast 65.56 ± 2.26 71.40 ± 2.29 68.19 ± 2.73
No breakfast 62.52 ± 2.31 66.29 ± 2.63 66.13 ± 2.77
p valuea– 0.46 0.98
Perceived frustration
Breakfast 37.96 ± 2.50 39.57 ± 3.08 49.09 ± 3.44
No breakfast 37.34 ± 2.58 43.53 ± 3.00 40.32 ± 3.35
p valuea– 0.30 0.01
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3338 European Journal of Nutrition (2021) 60:3325–3342
1 3
across time and the ability to pick out salient information
and ignore irrelevant information.
Visual-spatial memory was better following breakfast
compared with no breakfast in the current study. Partici-
pants in the breakfast intervention were able to recall the
locations of a greater number of stimuli. More children who
ate breakfast progressed to the highest, most difficult level
of the task and made fewer errors compared to those who
skipped breakfast. An advantage for breakfast on visual-
spatial memory has been demonstrated in previous studies
in adolescents [23, 37].
There were several indications that the effects of breakfast
on cognitive performance differed according to cognitive
performance at baseline, rarely examined in previous studies
of healthy well-nourished adolescents. The interaction of
intervention with baseline cognitive performance indicated
a greater advantage for breakfast when baseline performance
was poorer. Similarly, when IQ scores were included as a
covariate, consumption of breakfast benefitted those with a
lower IQ to a greater extent [38, 39]. Furthermore, previous
studies have shown that the positive effects of breakfast con-
sumption relative to fasting tended to be more consistent in
undernourished children (typically defined as below-normal
height or weight for age). These children also performed
more poorly on the cognitive tasks [40–42] and therefore
had greater scope for improvement. This demonstrates the
importance of the choice of the cognitive task such that floor
effects in undernourished participants and ceiling effects
particularly in well-nourished adolescents are avoided. This
also highlights the importance of sampling so that adoles-
cents with a broad range of cognitive ability are included
rather than those at the upper end of the distribution whose
cognitive reserve is likely to protect them from the detrimen-
tal effects of breakfast omission [38, 39].
Clear positive effects of breakfast consumption were dem-
onstrated on subjective VAS ratings of hunger, mood, moti-
vation, and alertness. Furthermore, these effects were appar-
ent immediately after consuming breakfast and continued
until the mid- or late-morning. These findings concur with
previous findings demonstrating consistent advantageous
effects on subjective feelings of mood, motivation and alert-
ness following breakfast consumption relative to no break-
fast in adolescents [3, 4, 37]. Subjective state, such as mood,
is an important outcome in its own right, but mood can influ-
ence cognitive function [43–45]. Breakfast may affect cogni-
tion indirectly through changes in feelings or subjective state
(e.g., mood or alertness). The positive changes in mood,
alertness, and motivation after breakfast may facilitate cog-
nitive function by increasing the ability to concentrate and/or
motivation to try hard on cognitive tasks. There is evidence
that mood state modulates cognitive function, but the nature
of the relationship is not straightforward. Studies in adoles-
cents have shown that mood and cognitive performance are
related, but the nature of the relationship differs before and
after cognitive testing. Before cognitive testing, ratings of
‘happy’, ‘friendly’, ‘relaxed’, ‘calm’, ‘angry’, ‘sad’ and ‘dis-
satisfied’ are negatively associated with thhheee cognitive
performance [46, 47]. Feeling more nervous before the cog-
nitive testing is positively associated with thhhe cognitive
performance [46]. After cognitive testing, feelings such as
‘friendly’,’ calm’, ‘happy’, ‘contented’ are negatively associ-
ated with cognitive performance. Feelings such as ‘drowsy’,
‘sluggish’, ‘tired’ are positively associated with performance
[46, 47]. The unexpected finding that feeling more friendly
and happy is associated with poorer performance may be
because these adolescents feel more relaxed and friendly
towards the researchers and are, therefore, not motivated
or aroused by the testing situation. Similarly, adolescents
who felt more nervous before the cognitive testing may have
performed better because they were more aroused by the
testing situation which in turn enhanced their attention and
response. Negative feelings such as ‘sluggish’, ‘drowsy’ after
the cognitive testing may have been associated with superior
performance because these participants tried harder or were
more engaged with the cognitive tasks and so were feeling
more fatigued after trying to perform well. However, studies
in children and adolescents have shown that acute improve-
ments in subjective feelings of mood, motivation and alert-
ness are not always accompanied by improvements in cogni-
tive performance [4, 48] which suggests other mechanisms
may facilitate cognitive performance.
This sample of adolescents consisted of a mixed sample
of habitual breakfast consumers and non-breakfast consum-
ers. It was deemed important to establish if any differences
in habitual breakfast behaviour existed across study break-
fast interventions and include this variable as a covariate
as this would be likely to influence the effects of breakfast
consumption, and breakfast omission, on cognitive perfor-
mance and subjective state. For example, habitual breakfast
consumers are likely to be accustomed to regular breakfast
and, therefore, fasting may have more adversely affected
cognitive performance and subjective state relative to non-
breakfast consumers.
There are several possible mechanisms of action for the
observed acute cognitive effects of breakfast consumption.
These include increased brain glucose availability, glucose-
mediated insulin delivery to the brain, increased acetylcho-
line synthesis, and amplification of the cortisol response.
These potential mechanisms have been discussed in detail
in our previous systematic reviews [12, 13].
Limitations
The limitations of this study should be considered when
interpreting the findings. The school testing environment
is a key strength in terms of ecological validity, but also
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
3339European Journal of Nutrition (2021) 60:3325–3342
1 3
a limitation. This trade-off between experimental control
and ecological validity caused a significant loss of control
over the study procedures and extraneous variables. Simi-
larly, there was a trade-off between the ecological validity
provided by the ad-libitum breakfast manipulation and the
variability in intake between participants which was not
controlled. In this situation, the four RTECs did not provide
matched macro- or micronutrients, but the RTECs were all
high carbohydrate and reasonably matched. Moreover, this
study only compared one type of breakfast (RTEC and milk)
vs. no breakfast. Hence, as a breakfast vs. no breakfast com-
parison, the results of this study are not able indicate the
optimal breakfast composition for cognitive function. How-
ever, other breakfast types have also demonstrated positive
effects on cognition in adolescents [12, 13]. This may have
influenced the results of the study. For example, within the
limited number of studies comparing breakfast type, there is
some evidence that suggests that lower glycaemic index (GI)
or glycaemic load (GL) breakfasts may facilitate cognitive
function relative to higher-GI or GL breakfasts [13]. This
suggests that the lower GL/GI RTEC breakfasts included in
the current study’s breakfast intervention, that elicit a gly-
caemic response characterised by less oscillating glucose
concentrations and a sustained blood glucose concentration
above fasting concentrations, may have facilitated cogni-
tive function to a greater extent. However, the previous evi-
dence is not consistent [15, 49]. Furthermore, simultaneous
blood glucose measures are not always taken in studies that
reported anadvantage of lower-GI or -GL breakfasts on cog-
nition [21, 23, 50]. Moreover, in studies that used continuous
blood glucose monitoring, the evidence indicated that large
differences in postprandial glycaemic responses elicited by
high- and low- GL breakfast interventions were apparent
in the absence of any cognitive performance effects [49].
Additionally, there is evidence that positive cognitive effects
are apparent when postprandial blood glucose concentra-
tions had returned to baseline [13]. These temporal relations
suggest that other factors associated with ingestion of these
low-GI breakfast meals, rather than glucose response per
se, may mediate the effects on cognitive performance [15].
A major limitation of employing a breakfast vs. no
breakfast comparison is the inherent inability to blind
participants to the study interventions. The potential bias
caused by the inability to blind participants to treatment
interventions is exacerbated in a repeated measures design
because it increases expectancy effects due to increased
familiarity with the study procedures and intervention.
Hence, the use of a parallel groups design was preferred
for the current study. However, it is likely that this design
introduced additional variation between interventions.
Furthermore, the effects of breakfast on actual academic
performance and the chronic effects of consuming break-
fast were not examined. Therefore, it is not possible to
confidently conclude that acute changes in cognitive per-
formance will translate to changes in academic perfor-
mance over time. Nonetheless, the present study adds to
an increasing body of literature suggesting the benefits
of regular breakfast intake for academic performance [8].
Breakfast cereals are a commonly consumed breakfast
food in British adolescents and, therefore, offers good
ecologically validity for the sample under study [7, 20].
However, we acknowledge that this type of breakfast may
not be generalizable to European adolescents breakfast
consumption habits [51]. Finally, statistical correction for
multiple testing of the secondary outcomes was not con-
ducted and hence the probability of obtaining significant
results will have increased merely because of the number
of comparisons. Therefore, the results of the secondary
outcomes analyses should be considered exploratory.
Implications
The findings from this ecologically valid school-based
study could have implications for school food provision,
such as school breakfast clubs and programmes. Breakfast
clubs may offer an avenue by which to increase breakfast
consumption by providing an opportunity to eat breakfast
immediately before school with peers. Schools also have
an important role to play as they present a setting to pro-
vide healthy food at breakfast and apply healthy eating
messages as part of the curriculum. Moreover, a review
of the benefits of school breakfast clubs reported that
breakfast clubs offer benefits to cognitive and academic
performance and social development, which may be more
pronounced in breakfast clubs operating in deprived areas
[52]. Encouragingly, many schools have school breakfast
programmes, but the availability is greater in primary than
secondary schools [20]. Hence, the findings of this study
suggest that adolescents also represent an important target
population for promoting breakfast consumption, possibly
via the provision of breakfast clubs, which may benefit
cognitive function and learning at school. Furthermore,
the findings highlight the need for national school food
policy to consider the universal provision of school break-
fast, particularly in adolescents.
Another area of work which requires attention is the acute
effect of breakfast composition on cognitive performance.
Previously systematic reviews have demonstrated a shortage
of studies and problematic designs [12, 13]. Further studies
are needed with well-matched study interventions to estab-
lish the role of breakfast composition in schoolchildren’s
cognitive performance. This may help make feasible rec-
ommendations on the type of breakfast that is beneficial for
cognitive performance in schoolchildren to serve in school
breakfast programme environments.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
3340 European Journal of Nutrition (2021) 60:3325–3342
1 3
Conclusion
To conclude, breakfast consumption has a positive acute
effect on subjective state, attention, reaction time, and mem-
ory in adolescents. These findings have important implica-
tions because adolescents often skip breakfast on school
days. Moreover, these findings have important implications
because breakfast consumption represents a modifiable
lifestyle factor which could be manipulated to enhance the
learning of children and adolescents. Efforts that encourage
the regular consumption of breakfast on school days (e.g.
provision of free school breakfasts) are, therefore warranted.
Acknowledgements This study was conducted as part of a Knowledge
Transfer Partnership between the University of Leeds and The Kellogg
Company funded by ESRC, BBSRC, TSB and The Kellogg Company.
The study was funded by additional funding from The Kellogg Com-
pany to supplement the ongoing Knowledge Transfer Partnership. The
authors would like to thank Rebecca Pyatt for her help with the data
collection.
Funding This study was conducted as part of a Knowledge Transfer
Partnership between the University of Leeds and The Kellogg Com-
pany funded by ESRC, BBSRC, TSB and The Kellogg Company. The
study was funded by additional funding from The Kellogg Company to
supplement the ongoing Knowledge Transfer Partnership.
Compliance with ethical standards
Conflict of interest This study was conducted as part of a Knowledge
Transfer Partnership between the University of Leeds and The Kellogg
Company funded by ESRC, BBSRC, TSB and The Kellogg Company.
The study was funded by additional funding from The Kellogg Com-
pany to supplement the ongoing Knowledge Transfer Partnership. Al-
exa Hoyland is currently an employee of The Kellogg Company. Katie
Adolphus and Jenny Walton were previous employees of The Kellogg
Company. At the time that the study was conceptualised, designed, and
conducted, Alexa Hoyland was an employee of the University of Leeds
and was the Knowledge Transfer Associate on the Knowledge Transfer
Partnership between the University of Leeds and The Kellogg Compa-
ny funded by ESRC, BBSRC, TSB and The Kellogg Company. At the
time that the study was conceptualised, designed, and conducted, Katie
Adolphus was a PhD student funded by the ESRC and the Schools
Partnership Trust Academies.
Ethical approval Prior to the commencement of the study, ethical
approval was obtained from the School of Psychology Ethics Research
Committee at the University of Leeds, UK (Reference: 10-0105, Date:
27/12/2010) and was conducted in accordance with the Helsinki Dec-
laration of 1964 and its later amendments.
Consent to participate Informed consent was obtained from parents
using passive consent (opt-out), and each child gave his/her own verbal
assent to participate in the study at screening (opt in).
Author contributions to the manuscript The authors’ responsibili-
ties were as follows: AH, JW, CLL, KA, and LD conceptualised and
designed the research. AH and KA prepared the study materials and
collected the data. FQ performed the statistical analysis. KA wrote the
first draft of the manuscript. AH, JW, CLL, KA, and LD commented
on and edited the manuscript. All authors read and approved the final
manuscript.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/.
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