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Journal of School Psychology 104 (2024) 101298
Available online 29 March 2024
0022-4405/© 2024 The Authors. Published by Elsevier Ltd on behalf of Society for the Study of School Psychology. This is an open access article
under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
A healthy breakfast each and every day is important for students'
motivation and achievement
Andrew J. Martin
a
,
*
, Keiko C.P. Bostwick
a
, Emma C. Burns
b
, Vera Munro-Smith
c
,
Tony George
c
, Roger Kennett
a
, Joel Pearson
a
a
School of Education, University of New South Wales, Australia
b
School of Education, Macquarie University, Australia
c
The King's School, Sydney, Australia
ARTICLE INFO
Editor: Craig A. Albers
Action Editor: Laura Pendergast
Keywords:
Breakfast
Diet
Motivation
Achievement
Adolescents
ABSTRACT
Breakfast is often cited as the most important meal of the day and vital for students' academic
functioning at school. Although much research has linked students' breakfast consumption to
better achievement, there has been debate about why and how breakfast has academic benets.
The present study of 648 Australian high school students investigated (a) the role of breakfast
consumption and breakfast quality in students' self-reported motivation and their achievement in
a science test, (b) the role of motivation in mediating the link between breakfast consumption and
quality and students' achievement, and (c) the extent to which breakfast consumption effects are
moderated by the quality of breakfast (e.g., more vegetables, fruit, dairy/protein, wholegrains,
cereals, water; less sugary drinks, processed meat, fast take-away, unhealthy snack foods).
Findings indicated that beyond the effects of personal, home, and classroom factors, breakfast
consumption predicted higher adaptive motivation (p <.05), breakfast quality predicted lower
maladaptive motivation (p <.05), and in turn, students' adaptive (positively, p <.01) and
maladaptive (negatively, p <.01) motivation predicted their achievement. Moreover, adaptive
motivation signicantly mediated the relationship between breakfast consumption and achieve-
ment (p <.05). The effect of breakfast consumption was moderated by the quality of breakfast
such that consuming a high-quality breakfast in the morning was associated with the highest
levels of adaptive motivation (p <.01) and achievement (p <.05) later in the day. Findings have
implications for educational practice and policy seeking to promote a healthy start to the school
day to optimize students' motivation and achievement.
1. Introduction
In 1954, Davis (1954) recommended “Eat breakfast like a king, lunch like a prince, and dinner like a pauper” (p. 19), and since then,
the view that breakfast is the most important meal of the day has attained the status of a near universal truth. Consumption of a healthy
breakfast has frequently been linked to physical, cognitive, and social-emotional health and well-being in many facets of life (Reeves
et al., 2013). The academic domain is no exception, with healthy breakfast consumption associated with increased learning and
achievement (Jackson & Vaughn, 2019). In contrast to many factors that are predominantly beyond a student's control (e.g.,
* Corresponding author at: School of Education, University of New South Wales, NSW 2052, Australia.
E-mail address: andrew.martin@unsw.edu.au (A.J. Martin).
Contents lists available at ScienceDirect
Journal of School Psychology
journal homepage: www.elsevier.com/locate/jschpsyc
https://doi.org/10.1016/j.jsp.2024.101298
Received 2 March 2022; Received in revised form 10 July 2023; Accepted 23 February 2024
Journal of School Psychology 104 (2024) 101298
2
instruction, home background) or factors that can take time to improve (e.g., study skills), eating breakfast is something that students
may have some immediate control over or that can be readily addressed at school (e.g., through curriculum-based health education,
communication to the home, meals/snacks provided by the school). To the extent that breakfast is linked to students' motivation and
achievement, this small and relatively achievable presence in a student's life has the potential to have a signicant positive impact on
their academic development (Hau, 2016). Thus, school psychologists and other educators could view the encouragement of students to
have a healthy breakfast each day as a type of universal intervention.
1.1. Research purpose and foreseen yields
Research into breakfast patterns and links to academic outcomes is important because there are substantial numbers of adolescents
making poor food choices at the start of the day or skipping breakfast altogether (Adolphus et al., 2013; Benton & Jarvis, 2007;
Mhurchu et al., 2010; Rani et al., 2021; Van Horn et al., 2012; Yao et al., 2019). Additionally, relatively few studies have examined the
breakfast habits of adolescents (see Hoyland et al., 2009, for a review). This developmental period is important to study because
adolescents' rapid growth and changes in metabolism may lead to quite distinct breakfast effects (Cooper et al., 2012). Adolescents'
schoolwork also tends to be more complex and more demanding than what they were responsible for as children, and this additional
academic stress could alter nutritional effects on cognitive functioning (Cooper et al., 2012). There also is a lack of research on
breakfast effects among adolescents in ecologically valid contexts, such as in-class tests (Adolphus et al., 2013). In these respects,
adolescence is an important period in which to examine breakfast effects. In the present study, we considered breakfast in relation to
students' motivation and achievement in science. Many students see science as a difcult and stressful school subject (Coe et al., 2008);
furthermore, there have been declines in high school students' participation in science subjects in Australia, which is the context for the
present study (Ofce of the Chief Scientist, 2014). It is also the case that in early to middle high school (Years 7–10), the development
of behaviors, attitudes, and emotions in Science, Technology, Engineering, and Mathematics (STEM) coursework can shape STEM
subject choices in senior high school (Martin et al., 2015). Thus, science represents an important domain in which to explore breakfast
effects.
Our investigation addresses numerous research gaps that pose a barrier to fully understanding the role of breakfast patterns in
adolescents' educational outcomes. First, research typically looks at the association between breakfast consumption and academic
achievement, but overlooks potentially inuential mediators, such as motivation, that may play a role in this link (Corcoran et al.,
2016; Jackson & Vaughn, 2019). To the extent motivation is implicated, educational interventions that do not accommodate moti-
vation will not be sufciently effective or comprehensive. Second, research typically assesses breakfast consumption (i.e., whether a
student eats breakfast) but not the impact of the nutritional quality of that breakfast on academic outcomes (Burrows et al., 2017;
Ptomey et al., 2016). Third, there is limited research on breakfast effects among adolescents and in ecologically valid assessment
contexts such as in-class tests (Adolphus et al., 2013). Fourth, relatively few studies have accounted for a comprehensive range of
personal, home, and classroom factors that must be included in research designs to ascertain the unique effects of breakfast on
motivation and achievement (Adolphus et al., 2013; Whatnall et al., 2019).
With a focus on adolescents in science classrooms, we attended to all four research gaps by investigating (a) the extent to which
breakfast consumption and breakfast quality was associated with motivation and achievement, (b) the role of motivation in mediating
the relationship between breakfast (consumption and quality) and achievement, (c) the extent to which breakfast consumption effects
were moderated by the quality of breakfast, and (d) mediation and moderation effects after accounting for the inuence of a
comprehensive range of personal, home, and classroom attributes (i.e., covariates).
1.2. Key constructs in the present investigation
We draw diverse literatures together to investigate the role of breakfast consumption in students' motivation and achievement. To
appropriately foreground the concepts and research to be reviewed, we rst briey explain key terms as relevant to this investigation.
1.2.1. Breakfast consumption and breakfast quality
Two key dimensions of breakfast that are the focus of most research include breakfast consumption and breakfast quality. Breakfast
consumption refers to whether and/or how regularly breakfast is eaten. Relative to breakfast quality, dening and assessing breakfast
consumption is not overly contentious. In the vast body of research and across multiple research domains (e.g., education, health),
breakfast consumption refers to the consumption of the day's rst meal after waking from a night's sleep (e.g., Yao et al., 2019). In most
studies, breakfast consumption is operationalized in terms of whether breakfast was consumed that morning (i.e., a dichotomous No/
Yes), but it can also be operationalized in terms of how many times in each period breakfast was consumed (i.e., an ordinal or
continuous scale).
Breakfast quality typically refers to how healthy it is, and the denition of “healthy” can be a source of debate as scientic un-
derstanding of diet evolves (Cena & Calder, 2020). The purpose of the present study was not to engage with this debate per se, but
instead posit that the quality of students' breakfast is best assessed in terms of the guidelines set in their national and public health
contexts, and thus may most closely align with the messages (both implicit and explicit) that they receive about “healthy” breakfast.
Our study is set in Australia, so we assess breakfast quality using the Australian Dietary Guidelines published by the National Health
and Medical Research Council (NHMRC, 2013). This frames a healthy diet as comprising a sufcient daily quantity (see the NHMRC
Guidelines for specied amounts) of vegetables, fruit, reduced-fat dairy, wholegrain cereals, small servings of lean meat or sh or eggs,
and water, as well as low intake of take-away and snack foods high in saturated fats (e.g., pizza, deep fried food, burgers, pies), foods
A.J. Martin et al.
Journal of School Psychology 104 (2024) 101298
3
and drinks high in sugar content (e.g., sweet bakery goods and energy and soft drinks) or salt content (e.g., highly processed foods).
Thus, a breakfast comprising ingredients such as wholegrain toast with egg or lean meat, or reduced fat low-sugar yogurt, or natural
oats muesli with reduced fat milk, or fruit would provide examples of a healthy breakfast in the Australian context.
1.2.2. Motivation
Motivation refers to the energy, drive, and inclination to learn (Martin, Ginns, Anderson, Gibson, & Bishop, 2021). Researchers
have identied the yields of studying motivation from a multidimensional perspective—including positive and negative dimensions of
motivation (Martin, 2023; Wigeld & Koenka, 2020). Researchers have consistently identied that positive and negative dimensions
of motivation are not simply opposite ends of the same motivational spectrum (Martin, 2009); rather, positive and negative dimensions
can occur, leading to differential outcomes for students (e.g., engagement, achievement). Said another way, optimizing students'
motivation is not only about promoting adaptive dimensions (akin to the idea of “switching on” in academic engagement literature;
Collie et al., 2019), but also about redressing any maladaptive motivation (“switching off”). Following prior motivation research in
school science (Martin, Ginns, Burns, Kennett, Munro-Smith, et al., 2021; Martin, Ginns, Burns, Kennett, & Pearson, 2021; Martin,
Kennett, et al., 2021), which is also the context for the present study, we adopted the multi-dimensional Motivation and Engagement
Wheel (Martin, 2007, 2009) as the operational framework for the present research. The Wheel comprises six rst-order factors sub-
sumed under positive motivation (i.e., self-efcacy, valuing school, and mastery orientation) and negative motivation (i.e., anxiety,
failure avoidance, and uncertain control). The Wheel is aligned with other conceptualizations from Pintrich (2003), who outlined
major areas for an integrative motivational science that drew on theories of self-efcacy, attributions, valuing, control, self-
determination, goal orientation, need achievement, and self-worth. Therefore (and as fully detailed elsewhere; e.g., Liem & Martin,
2012; Martin, 2009, 2013), (a) social-cognitive (self-efcacy) theory (Bandura, 2001) is reected in the self-efcacy dimension of the
Wheel, (b) attribution and control theories are reected in the uncertain control dimension (Weiner, 2010), (c) situated expectancy
value theory (Eccles & Wigeld, 2020) is seen in a valuing dimension, (d) self-determination theory (especially by way of intrinsic
motivation; Ryan & Deci, 2000) and goal theory (Elliot, 2005) are reected in a mastery orientation dimension, and (e) need
achievement and self-worth theories (Covington, 2000) are reected in failure avoidance and anxiety.
1.3. Theories and mechanisms: the how and why of breakfast effects
Theories and mechanisms explaining how and why breakfast (or diet more generally) impacts physical, cognitive, and social-
emotional functioning traverse physiological and psychological domains (Bellisle, 2004). Indeed, the integration of these domains
in understanding human behavior is not new, with major theories emphasizing the close links between “body and mind” (Blascovich,
2008; Martin, Kennett, et al., 2021; Schultheiss & Wirth, 2018). For example, biopsychological perspectives emphasize the importance
of understanding the joint operation of physiological and psycho-social processes involved in how individuals navigate their world (e.
g., Blascovich, 2008), including their academic life (Martin et al., 2023; Martin & Burns, 2023; Martin, Kennett, et al., 2021). Adopting
an integrative approach that takes these processes into account augments practitioners' understanding of the most salient factors
implicated in students' academic outcomes.
1.3.1. Physiological processes
The relationship between food intake and energy expense is an important one. When food is metabolized, the body produces
glucose, a primary energy source relied on throughout the day. Therefore, eating breakfast is critical for the sustained release of
glucose into the blood stream and to the brain through the morning (Mahoney et al., 2005; see Adolphus et al., 2013, Hasz & Lamport,
2012, and Rani et al., 2021, for reviews). Thus, for example, Murphy (2007; see also Adolphus et al., 2013) theorized that breakfast
consumption provides the energy available for the cognitive processes involved in navigating the academic demands of a school day. A
good quality diet can also correct nutritional deciencies, such as iron and iodine deciencies, that are involved in cognitive func-
tioning that in turn impact academic performance (Adolphus et al., 2013). Whatnall et al. (2019) indicated that there are many nu-
trients known to have vital roles in brain function and development (e.g., folate, iron, omega 3) and the link between achievement and
diet quality may be explained by better nutrient intakes (see also Hasz & Lamport, 2012; Hoyland et al., 2009; Masoomi et al., 2020;
Pe˜
na-Jorquera et al., 2021).
Particularly relevant to the present study is the role of physiological aspects of diet in students' motivation. There is not a lot of
research into this, but we infer from related constructs to draw relevant connections. For example, Mahoney et al. (2005) found that
breakfast containing more ber and protein was associated with differences in reported motivation. According to Widenhorn-Müller
et al. (2008), breakfast impacts performance via the energy (that may be construed as motivation from a drive perspective) caused by
the macronutrient composition of a meal. The concept of homeostatic energy regulation (to maintain energy levels) has been cited in
relation to goal pursuit with, for example, Jackson and Vaughn (2019) nding that many people are driven to eat to acquire the
necessary energy to achieve goals. Indeed, Jackson and Vaughn (2019) found that diet was associated with goals, with increased
consumption of high-caloric foods being associated with aspects of motivation such as performance goals. Some foods that can be part
of a good quality breakfast are also rich in elements that target serotonin receptors (Mahoney et al., 2005) that play a role in regulation
of mood (Bamalan et al., 2022; Berger et al., 2009), thereby providing a potential explanation for how a quality breakfast (one part of a
quality diet) may impact aspects of students' motivation such as their academic anxiety (Stuntz et al., 2017). Indeed, this latter point is
part of our rationale for adopting a multidimensional motivation framework where maladaptive dimensions of motivation (such as
anxiety) are included. Investigating the association between breakfast consumption/quality and both adaptive and maladaptive di-
mensions of motivation afforded a more comprehensive insight into breakfast effects and guarded against the risk of neglecting
A.J. Martin et al.
Journal of School Psychology 104 (2024) 101298
4
important motivational constructs (e.g., academic anxiety) that play a notable role in students' academic experience.
1.3.2. Psychological processes
Many psychological theories, including psycho-educational theories, cite diet and/or food as inuencers of motivational states and
behavior. In early work, theorists and researchers identied satisfying physiological needs as the primary driver or goal of motivated
behavior (e.g., Freud, 1933/1965; Hull, 1952; Maslow, 1943). For example, under drive theory, individuals are motivated to satisfy
fundamental drives such as hunger and thirst (Hull, 1952). However, this and subsequent adaptations (e.g., Berlyne, 1950; Harlow,
1950) did not adequately explain the relationship between drives and intrinsic motivation. Consequently, a cognitively oriented shift
in the eld was suggested that unied the satisfaction of physiological needs with psychological needs. This substantially inuenced
theory and research in education and psychology. As a case in point, under self-determination theory, Deci and Ryan (1985) identied
the importance of satisfying “primary tissue needs” (p. 21; also see Ryan & Deci, 2020) for intrinsic motivation and highlighted the
need to eat for the regulation of energy expenditure as a “homeostatic urgency” (p. 234) that can disrupt intrinsic motivation if not met.
Stuntz et al. (2017) accordingly found that healthier lifestyle patterns (including a healthy breakfast) impacted satisfaction of psy-
chological needs and self-determined motivation that in turn led to enhanced behavior (that in our study is posited in terms of aca-
demic achievement).
1.4. Breakfast and academic achievement
Much of the school-based breakfast research investigates effects of breakfast consumption and quality on academic achievement
and cognitive functioning. Breakfast consumption is positively associated with higher academic achievement and cognitive func-
tioning among children and adolescents (see Adolphus et al., 2013, Hasz & Lamport, 2012, Illøkken et al., 2022, Liu et al., 2021,
Mahoney et al., 2005, Murphy, 2007, Ptomey et al., 2016, and Yao et al., 2019, for research and reviews). Conversely, skipping
breakfast has been associated with poorer memory and recall and interferes with adolescents' learning (Basch, 2011).
The quality of breakfast (as separate from its consumption) also impacts children's and adolescents' achievement and cognitive
performance. Ptomey et al. (2016; see also Adolphus et al., 2013; Hasz & Lamport, 2012; Pe˜
na-Jorquera et al., 2021) found a positive
link between the nutritional quality of food consumed (especially whole-grains) at breakfast and achievement, but their study was of
young children; they recommended similar research be conducted among adolescents. Whatnall et al. (2019) found that a diet better
aligned with nutritional guidelines (e.g., higher diet quality, such as those that include more vegetables and fruit) was associated with
higher grade point averages (GPA) among university students, as did O'Dea and Mugridge (2012) with regards to literacy achievement
among a sample of children in Grades 3–7. In contrast, high-calorie, nutrient-poor foods can impede performance (Cornil et al., 2020)
and consumption of snack food and low nutrient-dense food (or a diet low in dairy content) was associated with poor school per-
formance among adolescents (Faught et al., 2017).
The weight of evidence thus suggests a positive association between breakfast consumption and quality and academic achievement.
Where breakfast is skipped or where the quality is questionable, there appears to be adverse implications for students' academic
performance. We therefore hypothesized that breakfast consumption and quality would be positively associated with academic
achievement in the present study.
1.5. Breakfast and academic motivation
We also posit that breakfast has implications for students' academic motivation. In most breakfast research, motivation is typically
assessed by way of an inferred or implied motivation construct (e.g., school happiness, connectedness, mood at school) or in classic
“drive” and physiological terms (e.g., energy, alertness). Using these inferred indicators of motivation, breakfast consumption has been
associated with better psychosocial functioning among children at school (Mhurchu et al., 2010). Adolescent students have also re-
ported more positive feelings, alertness, and interest in their schoolwork following breakfast consumption (Widenhorn-Müller et al.,
2008), whereas regular breakfast intake has been associated with greater connectedness among adolescents at school (Sampasa-
Kanyinga & Hamilton, 2017). However, it should be noted that there is less research examining “classic” motivational constructs, such
as self-efcacy, that reside under educational and school psychology theories (see Pintrich, 2003, for review) and thus there is a need to
further explore these associations.
Relatively less research has investigated the role of breakfast quality in students' motivation. Stuntz et al. (2017) indicated that
eating fruit and vegetables was associated with psychological need satisfaction and intrinsic motivation among university students.
Similarly, among adolescents, improving nutrition has been linked to motivation to learn (Basch, 2011), whereas a high-ber breakfast
has been associated with improved psychosocial functioning at high school (Godin et al., 2018). Mahoney et al. (2005) found that
breakfast containing more ber and protein led to greater alertness and motivation among elementary students. In contrast, a poor-
quality breakfast has been associated with problematic motivation. For example, malnourished elementary students were more likely
to be anxious (Murphy et al., 1998). Additionally, a poor nutrition breakfast has been associated with school disengagement and low
academic expectations in childhood and adolescence (Jackson & Vaughn, 2019; Michael et al., 2015).
We expand on this research base by investigating breakfast and motivation in more well-established psycho-educational terms via
adaptive motivation (comprising self-efcacy, valuing, and mastery orientation) and maladaptive motivation (comprising anxiety,
failure avoidance, and uncertain control) consistent with psycho-educational research over the past 2 decades (e.g., Elphinstone &
Tinker, 2017; Martin, 2009; Martin, Ginns, Anderson, Gibson, & Bishop, 2021; Plenty & Heubeck, 2013). Because there has been no
breakfast research investigating these psycho-educational motivation constructs, we infer from the cognate research summarized
A.J. Martin et al.
Journal of School Psychology 104 (2024) 101298
5
above to hypothesize a positive association between breakfast consumption and quality and students' adaptive motivation and an
inverse association between breakfast consumption and quality and students' maladaptive motivation.
1.6. Does motivation mediate the link between breakfast and achievement?
Researchers have also suggested it is important to examine the extent to which breakfast may be linked to school students'
achievement via key mediating factors (Corcoran et al., 2016). Motivation has been theorized as one mediator, often in terms of the
improved energy and alertness afforded by the consumption of breakfast by children and adolescents (e.g., Adolphus et al., 2013;
Murphy, 2007; Widenhorn-Müller et al., 2008). Prior research has also demonstrated that motivation is a key mediator between other
personal (e.g., home life) and contextual factors (e.g., teacher-student relationships) and students' academic performance. It may be
that motivation plays a similar mediating role for the effects of breakfast consumption and quality. The present study sought to expand
on this by investigating the mediating role of motivation factors typically assessed under psycho-educational paradigms, including (a)
adaptive motivation (e.g., self-efcacy, valuing, mastery orientation) and (b) maladaptive motivation (e.g., anxiety, failure avoidance,
uncertain control; Martin, Ginns, Anderson, Gibson, & Bishop, 2021). Considering the research summarized above suggesting that
breakfast is associated with motivation and prior research demonstrating that the adaptive and maladaptive motivation factors are
positively and negatively (respectively) associated with academic achievement (see Liem & Martin, 2012, for a review), we hy-
pothesized a signicant indirect effect between breakfast and achievement via motivation in addition to the bivariate path between
breakfast and achievement.
Diverse research has identied motivation as an important precursor to academic outcomes (e.g., engagement, achievement). For
example, Reeve (2012) described how students' inner motivational resources allowed them to engage and perform in the classroom.
Cleary and Zimmerman (2012) distinguished between ‘will’ and ‘skill’, with the former indicating motivation and the latter indicating
performance-related dimensions. Schunk and Mullen (2012) detailed the energizing role of motivation and the impact of this on
achievement. This motivation to achievement link may provide some insight into better explaining and understanding how breakfast is
associated with achievement. Thus, in addition to the well-established physiological mediators involved in the link between breakfast
and achievement (e.g., via cognitive functioning, mood), our study explored the potential role of motivational mediators to gain a more
complete picture of how breakfast might inuence achievement. The extent to which breakfast consumption and quality are associated
with motivation and, in turn, achievement, provides information for educators on what factors may also be addressed to augment
motivation interventions. Given that breakfast is amenable to effective educational policy and school practice (Hau, 2016), it con-
stitutes a sustainable and scalable means to advance educators' motivation efforts.
1.7. Do the effects of breakfast consumption vary as a function of breakfast quality?
It is also possible that the effects of breakfast consumption for school students may vary as a function of its quality (Anzman-Frasca
et al., 2015), suggesting the need to test for interactions between breakfast consumption and breakfast quality. For example, regular
consumption of a high quality (vs. irregular consumption or a low quality) breakfast may have benets for both motivation and
achievement. A review of school performance by Taras (2005) found that a regular and high-quality breakfast enhanced cognitive
functioning. Similarly, Adolphus et al. (2013) reported that frequency and quality of diet was positively linked to academic perfor-
mance, particularly for undernourished children and adolescents (see also Antonopoulou et al., 2020, in relation to university students;
Hoyland et al., 2009, in relation to elementary and high school students). We therefore tested the interaction between breakfast
consumption that morning (No/Yes) and the quality of breakfast typically consumed (a continuous diet quality score). We hypoth-
esized that consumption of a high-quality breakfast that morning (relative to a low-quality breakfast that morning or skipping
breakfast) would be associated with more adaptive motivation and achievement.
1.8. Personal, home, and classroom factors important to accommodate
The links between breakfast and motivation, breakfast and achievement, and motivation and achievement (i.e., the key re-
lationships in this study, see Fig. 1 and Fig. 2) must be disentangled from personal, home, and classroom factors that are known to be
signicantly implicated in students' academic development. Many studies of breakfast do not adequately account for enough cova-
riates and confounds, which is a limitation that needs to be addressed to ascertain the unique effects of breakfast on students' academic
outcomes (Adolphus et al., 2013; Whatnall et al., 2019). These variables may precede or co-occur with the consumption of breakfast
and thus may explain variation in student motivation and achievement that must be accounted for when seeking to determine the
unique effects of breakfast.
Fig. 1. The study's conceptual model.
A.J. Martin et al.
Journal of School Psychology 104 (2024) 101298
6
1.8.1. Personal factors
There are several background socio-demographic (e.g., age, gender, language background) and personal attribute (e.g., consci-
entiousness, prior achievement, physical activity) factors that are relevant to these processes that ought to be accounted for in
modeling. Including age as a factor in our study is important because research reveals age-related declines in motivation (e.g., Burns
et al., 2019; Fredricks & Eccles, 2002). There are also gender differences to recognize, with females in high school generally more
motivated than males (Martin, 2009) and more likely to skip breakfast (Cohen et al., 2003). Given the Australian context for this
research, non-English-speaking background (NESB) was also included as research has revealed mixed motivation (Martin et al., 2011)
and achievement (Glick & Hohmann-Marriott, 2007) ndings relating to language background. Students' psychological attributes also
seem relevant. For example, it is unclear if breakfast is uniquely linked to motivation and achievement and/or whether students with
conscientious and hardworking traits (who are also likely motivated and achieving; Ginns et al., 2014) tend to have more regular and
healthier breakfast patterns (Hau, 2016); thus, conscientiousness was included in the present study to separate this personal trait from
more situation-specic motivation and achievement. Additionally, prior achievement is identied as being related to subsequent
motivation and achievement (Hattie, 2009), suggesting the need to account for students' prior academic performance when examining
breakfast effects. Finally, given that metabolism and energy are implicated in breakfast effects, students' level of physical activity,
which has metabolic consequences, is important to control for, especially given the positive links between students' physical activity
and academic outcomes (Donnelly & Lambourne, 2011).
1.8.2. Home factors
Various home factors are also relevant to our focal variables. Researchers have identied socio-economic status (SES) as a factor
positively associated with students' motivation (Martin, Ginns, Anderson, Gibson, & Bishop, 2021), achievement (Sirin, 2005), and
food insecurity and students' access to quality breakfast (Dykstra et al., 2016). It has also been suggested that intrinsic motivation may
be predicted more by time spent with and support from family than by breakfast intake (Tanaka & Watanabea, 2012), pointing to the
Fig. 2. The study's hypothesized model.
Note. Observed/single-item breakfast variables are represented in bolded rectangles. Latent motivation and achievement variables are represented in
ellipses (as described in Materials, achievement was an error-adjusted score and thus depicted here in an ellipse). Covariates are represented in
dashed parentheses (all were observed/single-item indicators except instruction, which was estimated as a 5-item latent factor). The breakfast
variables were correlated and each predicted motivation and achievement. The covariates were correlated with each other and with breakfast
consumption, breakfast quality, and the interaction to control for shared variance among them. Each covariate predicted motivation and
achievement.
A.J. Martin et al.
Journal of School Psychology 104 (2024) 101298
7
salient role of home assistance. In any case, home/parental support may also be correlated with provision and consumption of chil-
dren's regular and healthy eating habits (Faber et al., 2018) and with motivation (George et al., 2012).
1.8.3. Classroom factors
Classroom factors are relevant to students' motivation and achievement and thus important to control for when seeking to ascertain
the unique effects of breakfast on motivation and achievement. For example, improvements in motivation and achievement are known
to be associated with instruction (Hattie, 2009). Interestingly, the nature of instruction may also be implicated in metabolic processes.
More cognitively demanding tasks require an increase in metabolic resources to successfully perform them (Defeyter & Russo, 2013;
Spence, 2017). Thus, teachers who do not adequately adjust instruction to the cognitive demands of academic tasks may yield dif-
ferences in students' achievement that are in part a function of differences in their metabolic processes. This being the case,
instructional practices that help students manage cognitive burden are relevant (Sweller, 2012). Load reduction instruction is one such
pedagogical practice (Martin & Evans, 2018, 2021; Martin, Ginns, Burns, Kennett, Munro-Smith, et al., 2021; Martin, Ginns, Burns,
Kennett, & Pearson, 2021) and was included in the present study as a covariate. In addition, because the metabolic effects of breakfast
consumption vary throughout the course of the day, it is important to account for the timing of the lesson in which students' motivation
and achievement are assessed (Stuntz et al., 2017). The present study did so by including lesson timing as a covariate as both a linear
effect and a non-linear (quadratic) effect to account for a possible rise and fall (or fall and rise) through the school day.
1.9. Aims of the present study
Our aims for this study were to identify (a) the role of breakfast consumption and breakfast quality in high school students'
motivation and achievement, (b) the role of motivation in mediating the link between breakfast and achievement, (c) the extent to
which breakfast consumption effects are moderated by the quality of breakfast, and (d) these mediation and moderation effects after
accounting for the inuence of a comprehensive suite of personal, home, and classroom attributes (covariates). Fig. 1 shows the basic
conceptual model guiding the study and Fig. 2 shows the detailed hypothesized model. We hypothesized that breakfast consumption
and breakfast quality would positively predict adaptive motivation and inversely predict maladaptive motivation. We hypothesized
that breakfast consumption and breakfast quality would positively predict achievement. We hypothesized a signicant indirect
relationship between breakfast (i.e., consumption and quality) and achievement via motivation. Finally, we hypothesized that con-
sumption of a high-quality breakfast that morning (relative to a low-quality breakfast that morning or skipping breakfast) would be
associated with more positive patterns of motivation and achievement.
2. Method
2.1. Participants and procedure
The lead researcher's university provided human ethics approval. Approval was then provided by school principals. Parents/
caregivers and participating students then provided their consent. Participants in this study were 648 Australian high school students
from ve schools. The schools were in a major city in New South Wales (NSW) and from the independent (non-government) school
sector. Two of these schools were single-sex boys' schools, two were single-sex girls' schools, and one school was co-educational. Sixty-
one percent of students were boys. Students were in Year 7 (32%), Year 8 (35%), and Year 9 (33%), which are the rst 3 years of high
school in Australia. The mean age of students was 13.52 years (SD =0.98). Eight percent of students spoke a language other than
English at home (the largest group was Mandarin/Cantonese at 2.8%, followed by Spanish, German, Arabic, and Filipino/Tagalog each
Table 1
Correlations with substantive factors (from CFA).
Breakfast Consumption Breakfast Quality Adaptive Motivation Maladaptive Motivation Achievement
Age 0.015 0.068 -0.092 0.108 0.031
Gender (Male/Female) -0.159*** 0.051 -0.071 0.261*** -0.048
NESB (No/Yes) 0.061* -0.063 0.141*** -0.117* 0.132***
Prior achievement 0.114** 0.088 0.390*** -0.377*** 0.374***
Conscientiousness 0.059 0.171*** 0.353*** -0.231*** 0.150***
Physical activity 0.093** 0.059 0.056 -0.078 0.039
SES -0.048 0.113* 0.050 -0.017 0.044
Home assistance 0.056 -0.061 0.257*** -0.075 0.031
Instruction (LRI) 0.104** -0.020 0.617*** -0.332*** 0.147**
Lesson timing 0.005 0.074 0.088 -0.017 0.032
Breakfast consumption – 0.111** 0.205*** -0.203*** 0.084*
Breakfast quality – 0.093* -0.114** 0.096*
Adaptive motivation – -0.449*** 0.329***
Maladaptive motivation – -0.288***
Achievement –
Note. NESB =non-English speaking background; SES =socio-economic status; LRI =load reduction instruction.
*p <.05. **p <.01. ***p <.001.
A.J. Martin et al.
Journal of School Psychology 104 (2024) 101298
8
with <1%, and ‘Other non-specied’ at 3.7%; however, we did not explicitly ask students about their ethnic/cultural background in
the survey). Students varied in SES (range =866–1181, M =1121, SD =46) based on the Australian Bureau of Statistics Index of
Relative Socio-Economic Advantage and Disadvantage classication with higher scores indicating higher SES; in aggregate, students
were higher than the Australian average of 1000 (SD =100). Sixteen percent reported they had not eaten breakfast the morning of data
collection (Table 1 provides data on what student characteristics were associated with breakfast consumption).
The survey (including items about breakfast quality and consumption) and science test were all conducted via an online survey in a
single sitting of a normally scheduled science class in Term 2 (of four school terms) of the school year. The study was part of a larger
research program exploring psychological and physiological responses in students' motivation and learning in STEM courses. Several
datasets were collected through this research program, including the present dataset relevant to students' dietary habits and potential
impacts in each classroom lesson at school (in this case, a science lesson). Participating schools were those opting in from a Heads of
Science schools network invited to be part of the larger research program. For students from these schools to be eligible to participate,
they needed to be in the early to middle years of high school (comparable to Grades 7–9 in the US) and have parent/guardian consent;
no other inclusion or exclusion criteria were applied.
Although the study was conducted in Australia, we believe it has generalizability to diverse national contexts. For example, the
measures we used for motivation have been validated in other countries (e.g., in the UK, US, Canada, and China; Martin et al., 2016,
2017) and thus our motivation ndings may have broader applicability. The science achievement test we administered comprises
items that are common across other national science syllabi (e.g., UK, US; Mullis et al., 2016) and thus may have broad applicability.
Our breakfast quality assessment was based on a healthy eating diet quiz that was developed in consultation with international health
guidelines and literatures (NHMRC, 2013). Finally, our hypotheses were based on the international literature reviewed in the
Introduction; the extent to which these hypotheses are supported by the results of this study will further underscore the generalizability
of our research.
2.2. Materials
Materials were comprised of items asking students about breakfast consumption, breakfast contents (to indicate quality), moti-
vation, and achievement, as well as various personal, home, and lesson attributes that were modeled as covariates. Descriptive sta-
tistics and reliability are shown in Table 2.
2.2.1. Breakfast consumption and breakfast quality
Following prior recommendations (i.e., Burrows et al., 2017; Ptomey et al., 2016), students reported on their breakfast on the same
day as they completed the achievement test and completed the motivation measures. Self-reports of breakfast have been shown to be
valid for children over ages 8 years (Burrows et al., 2013). For breakfast consumption, participants were asked “Did you eat breakfast
this morning?”, to which they responded either No (scored 0) or Yes (scored 1). Breakfast quality was a summed index based on eight
survey indicators that were scored to align with the Australian Dietary Guidelines Quiz (NHMRC, 2013, p. 6). Using a question stem
(“Each week, how often do you usually eat the following for breakfast before school?”), participants were asked to rate on a 4-point
scale (1 =Never to 4 =Five days) how often they ate/drank (a) vegetables and fruit, (b) dairy and protein, (c) wholegrains and cereals,
(d) water, (e) sugary soft drink (reverse scored), (f) processed meat (reverse scored), (g) fast take-away food (reverse scored), (h)
unhealthy bakery goods (reverse scored), and (i) unhealthy snack foods (reverse scored). Thus, following Whatnall et al.'s (2019)
recommendations, a quality indicator of breakfast quality comprised both healthy and unhealthy food. Indeed, the range of breakfast
quality indicators was part of our rationale for asking about breakfast quality across the course of a school week (rather than just that
morning) to capture a valid measure of breakfast quality as best we could. Also, if we restricted our breakfast quality measure to just
Table 2
Descriptive and reliability statistics.
Range M SD Skew Kurtosis Reliability
Age (years) 12–16 13.52 0.98 0.00 −0.96 –
Gender (Male/Female) 0–1 0.61 0.49 −0.45 −1.81
NESB (No/Yes) 0–1 0.08 0.27 3.22 8.38
Prior achievement 1–3 2.34 0.65 −0.49 −0.71 –
Conscientiousness 1–7 5.46 1.24 −0.93 1.05 –
Physical activity 1–4 3.40 0.72 −1.14 1.22 –
SES 866–1181 1121.26 45.95 −1.85 5.19 –
Home assistance 1–5 1.89 1.13 1.10 0.19 –
Instruction (LRI) 1–7 5.26 1.18 −0.73 0.03 0.872
Lesson timing 1–7 3.16 1.70 0.39 −0.65 –
Breakfast consumption 1–2 1.84 0.37 −1.84 1.39 –
Breakfast quality 0–8 4.55 1.39 −0.78 1.20 –
Adaptive motivation 1–7 5.68 0.89 −0.79 0.19 0.831
Maladaptive motivation 1–7 3.81 1.13 0.00 −0.44 0.700
Achievement 0–12 6.41 2.38 −0. 05 −0.32 0.600
Note. NESB =non-English speaking background; SES =socio-economic status; LRI =load reduction instruction. Reliability (coefcient omega) is not
available for single-item factors, as indicated by a dash.
A.J. Martin et al.
Journal of School Psychology 104 (2024) 101298
9
the morning of the survey, we would not know the effects of skipping breakfast for students who otherwise typically have (un)healthy
breakfast habits; hence, our decision to ask about breakfast quality across the week and consumption that morning. Consistent with the
Australian Dietary Guidelines Quiz, participants answering a 4 (i.e., 5 days a week for each school day) received one point and all other
responses received zero points. Based on the relevant survey items, participants could receive a maximum score of 8, indicating regular
high-quality breakfast (as shown in Table 2; M =4.55, SD =1.39, approximately normally distributed). Thus, this measure is a
formative sum (not a latent factor; Diamantopoulos & Winklhofer, 2001) consistent with the NHMRC (2013) and validity data by
Marshall et al. (2012). Diet scores that are the sum of healthy/unhealthy foods eaten have been used in prior research linking diet to
achievement (Gibney et al., 2018; Whatnall et al., 2019); Florence et al. (2008) suggested a diet score is preferable over modeling of
individual nutrients because individuals do not consume single nutrients, but rather consume combinations of foods that are reected
in diet scores. Taken together, breakfast consumption indicated whether participants had eaten breakfast that morning and breakfast
quality was an index of the typical quality of breakfast eaten.
2.2.2. Motivation
Motivation was assessed using the domain-specic (science) form of the Motivation and Engagement Scale–High School (MES-HS;
Martin, 1999–2021), validated by Green et al. (2007). Following Martin et al. (2013), an adaptive motivation latent factor and a
maladaptive motivation latent factor were modeled. Adaptive motivation comprised items (a) asking about self-efcacy which refers
to students' belief in their ability to understand or to do well in their schoolwork (four items; e.g., “If I try hard, I believe I can do well in
this science class”); (b) valuing that connotes students' beliefs that what they learn is useful, important, and relevant (four items;
“Learning in this science class is important”); and (c) asking about mastery orientation, which is being focused on learning, effort-
oriented mastery, and developing skills (four items; e.g., “I feel very pleased with myself when I do well in this science class by
working hard”). Maladaptive motivation comprised items asking students about their anxiety (i.e., feeling nervous and worrying about
schoolwork and assessment; four items; e.g., “When exams and assignments are coming up for this science class, I worry a lot”), failure
avoidance (i.e., students' motivation to do their schoolwork to avoid doing poorly or being seen to do poorly; four items; e.g., “Often
the main reason I work in this science class is because I don't want people to think that I'm dumb”), and uncertain control (students'
uncertainty about how to attain success or avoid poor performance; four items; e.g., “I'm often unsure how I can avoid doing poorly in
this science class”). Each item was rated on a scale of 1 (strongly disagree) to 7 (strongly agree). Each factor was approximately normally
distributed and had adequate reliability (see Table 2).
2.2.3. Achievement
Achievement was assessed via 12 questions in an online science test that was part of the online survey. Martin, Ginns, Burns,
Kennett, and Pearson (2021) described instrument piloting, development, and validation (see also Burns et al., 2023, for further
validation evidence). Test items were aligned with the participants' state science syllabus; thus, two parallel forms were devised, with
one form to match the Stage 4 (Years 7 and 8) state science syllabus and the other form to match the Stage 5 (Years 9 and 10) state
science syllabus. This also ensured the tests were appropriately challenging for a given stage, which is important given that breakfast
may benet emotional responses when performing demanding tasks (Benton et al., 2001). Test items were based on syllabus units
relevant to Physical World, Earth and Space, Living World, and Chemical World and tapped into the following skills: questioning and
predicting, planning and conducting investigations, processing and analyzing data, and problem solving. The test was aimed to have an
approximately 30/70 ratio of content-focused to skill-focused questions. The relatively easier questions focused on content and the
more difcult questions focused on skill application. All answers were in multiple-choice format and recoded as dichotomous (0 =
incorrect; 1 =correct). Correct answers were summed to generate a total score. We then standardized these total scores by year level
(M =0; SD =1 for each year level); consistent with prior research using this achievement measure (Martin, Ginns, Burns, Kennett, &
Pearson, 2021), error-adjusted scoring was used to avoid inated or unreliable standard errors (Kline, 2016). We did so by using the
following equation:
σ
h
2
* (1 -
ω
h
), where
σ
h
2
was the estimated variance of achievement and
ω
h
was the reliability estimate of
achievement (h). The intraclass correlation for achievement was 0.07 (see Data Analysis for our approach to adjusting for clustering of
students within classrooms). The test had adequate reliability and scores were approximately normally distributed (see Table 2).
2.2.4. Background attributes
As described in the Introduction, to establish the unique effects of breakfast consumption, including a comprehensive range of
covariates was important in this study. Three groups of student-reported covariates were thus included: (a) personal (i.e., age, gender,
language background, prior science achievement, physical activity, and conscientiousness from Gosling et al., 2003), (b) home (i.e.,
socio-economic status and home/parent assistance via provision of science learning resources), and (c) classroom (i.e., timing of
science lesson in the day, including linear and quadratic effects, as well as load reduction instruction from Martin & Evans, 2018). The
scoring and range of these variables are shown in Table 2.
2.3. Data analysis
Conrmatory factor analysis (CFA) was used to rst ascertain the factor structure and latent correlations for the full set of measures.
Following this, structural equation modeling (SEM) was used to test the hypothesized model. Both were conducted with Mplus version
8.70 (Muth´
en & Muth´
en, 1998–2021). We used the MLR (maximum likelihood robust to non-normality) estimator that provides
parameter estimates with standard errors and a chi-square test statistic that are robust to non-normality (Muth´
en & Muth´
en,
1998–2021). Adequate model t was indicated where the Comparative Fit Index (CFI) is >0.90 and Root Mean Square Error of
A.J. Martin et al.
Journal of School Psychology 104 (2024) 101298
10
Approximation (RMSEA) and Standardized Root Mean Square Residual (SRMR) are <0.08 (Kline, 2016). Missing data (0.21% total
missingness, with most missing on SES [1.5%, n =9] and conscientiousness [2.5%, n =16]) were handled via the Mplus default, Full
Information Maximum Likelihood (FIML; Arbuckle, 1996). We implemented the “cluster” and “type =complex” commands in Mplus to
adjust standard errors for students nested within the 88 classrooms (with an average class size of approximately 11 students, which is
not unduly disproportionate to the staff-to-student ratio for high schools in the independent school sector, taking into account non-
teaching staff numbers, non-participation, and student absences; Australian Bureau of Statistics, 2019).
For the CFA, the following factors were included: breakfast consumption (single-item indicator, binary), breakfast quality (single-
item indicator, summed index), adaptive motivation (latent factor), maladaptive motivation (latent factor), achievement (single-item
indicator, summed error-adjusted index), and personal, home, and lesson covariates (each single-item indicators, with the exception of
instruction which was estimated as a latent factor), thus, this was a 15-factor CFA. All single-item indicators had their loading set at
1.00 and residual at 0.
In the SEM, (a) breakfast consumption, breakfast quality, their interaction, and all personal, home, and lesson covariates predicted
adaptive and maladaptive motivation and in turn, (b) these factors, including adaptive and maladaptive motivation, predicted
achievement (thus, a “fully-forward” model). The covariates were correlated with breakfast consumption, breakfast quality, and their
interaction to control for shared variance among them. The interaction term (breakfast consumption x breakfast quality) was created
by nding the product of zero-centered breakfast consumption and breakfast quality (Aiken et al., 1991) and then included as a
correlated predictor of motivation and achievement alongside its lower-order terms (as per Hayes, 2022; moderated mediation with a
moderated direct effect). When modeled as such, the lower-order effects of breakfast consumption, for example, can be interpreted as
the difference in motivation scores between participants who did and did not consume breakfast on data collection day when breakfast
quality for the past week was average. In the event of any signicant interaction effects (i.e., breakfast consumption x breakfast quality
effects), follow-up simple slope effects were graphed to understand the nature of this interaction (with low/high groups determined as
1 SD below or above the mean, as per Dawson & Richter, 2006). Our data also enabled tests of indirect (mediation) effects which were
conducted in subsidiary analyses. This involved parametric bootstrapping and explored the extent to which motivation mediated the
relationship between breakfast factors (i.e., consumption, quality, and consumption x quality; Hayes, 2022) and students' achieve-
ment. These analyses were based on bootstrapped standard errors of 1000 draws (MacKinnon et al., 2002; Shrout & Bolger, 2002).
3. Results
3.1. Correlations among key factors
A CFA was conducted in which all covariates and substantive factors were included. This yielded an acceptable t to the data,
χ
2
=
415.152, df =137, p <.001, CFI =0.912, RMSEA =0.057, SRMR =0.040. This CFA also generated a correlation matrix for all factors.
Table 1 shows bivariate correlations with the substantive factors. Breakfast consumption was associated with higher adaptive moti-
vation (r =0.21, p <.001) and achievement (r =0.08, p <.05) and associated with lower maladaptive motivation (r = − 0.20, p <
.001). Thus, having breakfast in the morning was linked with more adaptive motivation and achievement later that school day.
Breakfast quality was associated with higher adaptive motivation (r =0.09, p <.05) and achievement (r =0.10, p <.05) and asso-
ciated with lower maladaptive motivation (r = − 0.11, p <.01). Thus, a better-quality breakfast was associated with more positive
motivation and achievement. Breakfast consumption was also associated with higher breakfast quality (r =0.11, p <.01), adaptive
Table 3
Standardized beta coefcients from hypothesized model.
Adaptive Motivation Maladaptive Motivation Achievement
Covariates
- Age 0.000 0.109* 0.044
- Gender (Male/Female) -0.030 0.247*** 0.033
- NESB (No/Yes) 0.031 −0.005 0.083*
- Prior achievement 0.255*** −0.307*** 0.227***
- Conscientiousness 0.190*** −0.131** 0.018
- Physical activity -0.060 0.027 0.007
- SES 0.032 −0.037 0.020
- Home assistance 0.167*** −0.005 −0.051
- Instruction (LRI) 0.526*** −0.236*** −0.076
- Lesson timing (linear) 0.009 0.089 −0.018
- Lesson timing (quadratic) -0.079 0.003 0.111*
Breakfast
- Breakfast consumption 0.109** −0.080 0.017
- Breakfast quality 0.047 −0.089* 0.021
- Consumption x Quality 0.069** 0.054 0.089*
Mediators
- Adaptive motivation – – 0.219**
- Maladaptive motivation – – −0.123**
Note. NESB =non-English speaking background; SES =socio-economic status; LRI =load reduction instruction.
*p <.05. **p <.01. ***p <.001.
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Journal of School Psychology 104 (2024) 101298
11
motivation was associated with higher achievement (r =0.33, p <.001), and maladaptive motivation was associated with lower
achievement (r = − 0.29, p <.001). Taken together, these bivariate associations provide preliminary support for the hypothesized
connections between breakfast, motivation, and achievement. Analyses thus proceeded to test the hypothesized model in Fig. 2.
3.2. Effects in the hypothesized model
Structural equation modeling yielded an acceptable t to the data,
χ
2
=433.417, df =153, p <.001, CFI =0.912, RMSEA =0.054,
SRMR =0.038. Table 3 reports paths in the nal model; Fig. 3 shows only the substantive paths that were signicant in this nal
model. All signicant and non-signicant paths (including effects of covariates) are presented in Table 3. Breakfast consumption
predicted higher adaptive motivation (β =0.11, p <.01) and this effect was accompanied by a signicant interaction with breakfast
quality (described below). Breakfast quality predicted lower maladaptive motivation (β = − 0.09, p <.05). In turn, adaptive motivation
predicted higher achievement (β =0.22, p <.01) and maladaptive motivation predicted lower achievement (β = − 0.12, p <.01).
Although breakfast consumption and quality were both associated with academic achievement in the bivariate correlations in the CFA,
they demonstrated no predictive association with achievement in the SEM after accounting for all other paths.
3.3. Interaction effects in the hypothesized model
There were two signicant interaction effects such that breakfast quality moderated the effect of breakfast consumption on
adaptive motivation (β =0.07, p <.01) and achievement (β =0.09, p <.05). Results of follow-up tests of the interactions are shown in
Fig. 4 and Fig. 5 (which present effects adjusted for all other predictors in the model). In Fig. 4, students who typically eat high quality
breakfast and ate breakfast that morning showed the highest levels of adaptive motivation. In contrast, relatively lower levels of
adaptive motivation were observed for students who ate that morning but typically ate a poor-quality breakfast or consumed no
breakfast that morning (regardless of whether they typically eat a poor- or high-quality breakfast). In Fig. 5, consuming a high-quality
breakfast that morning was associated with the highest levels of test achievement later that day. Students scoring lowest on the
achievement test were those who consumed a poor-quality breakfast that morning or consumed no breakfast that morning when they
would typically eat a high-quality breakfast.
3.4. Indirect effects in the hypothesized model
Indirect effects were examined to determine the extent to which motivation may have mediated the association between students'
breakfast consumption and breakfast quality and students' achievement. Of the six possible indirect effects, three were tested because
they represented signicant predictive paths between the predictor → mediator and between the mediator → outcome (i.e., in cases
where both components of an indirect effect were signicant): breakfast consumption → adaptive motivation → achievement;
breakfast quality → maladaptive motivation → achievement; breakfast consumption x quality → adaptive motivation → achievement.
Of the three indirect effects tested, one was statistically signicant at p <.05, as follows: breakfast consumption → adaptive motivation
→ achievement (β =0.024, p <.05). This result suggests that students' adaptive motivation mediated the connections between
breakfast consumption and students' achievement. Indeed, combined with the lack of bivariate paths between breakfast consumption
and students' achievement, motivation emerged as a noteworthy factor in explaining how breakfast may play a role in students' ac-
ademic performance.
Breakfast Consumption
Consumption x Quality
Breakfast Quality
.07**
.11**
-.09*
.09*
Maladaptive Motivation
.22**
-.12**
Adaptive Motivation
Achievement
Fig. 3. Signicant predictive (standardized beta) paths.
Note. The gure displays only the substantive paths that were signicant in the nal model. Table 3 reports on all paths (signicant and non-
signicant) in the nal model.
*p <.05. **p <.01.
A.J. Martin et al.
Journal of School Psychology 104 (2024) 101298
12
4. Discussion
Although a good deal of research has linked students' breakfast consumption to better achievement at school, there has been debate
about why and how breakfast has its academic benets. It is also unclear whether breakfast consumption has positive effects for
academic outcomes after controlling for a range of previously unexamined and potentially confounding background factors. The
present study addressed these concerns. Results demonstrated signicant associations between students' breakfast habits and their
motivation and achievement, beyond the effects of numerous background factors. Moreover, because preliminary bivariate correla-
tions revealed that some of these previously unexamined background factors were signicantly associated with the central substantive
factors, the multivariate analyses meaningfully add to current understanding of the unique effects of breakfast and students' motivation
and achievement.
4.1. Findings of note
The ndings revealed that different aspects of breakfast were associated with different dimensions of motivation: breakfast con-
sumption was positively associated with adaptive motivation (and we also note the effect of breakfast consumption here was
accompanied by an interaction with breakfast quality), whereas breakfast quality was negatively associated with maladaptive
motivation. Breakfast consumption may be considered an energizing factor that was positively implicated in students' adaptive
motivation. This is consistent with the literature suggesting that breakfast consumption fuels the individual (Jackson & Vaughn, 2019)
and provides energy and drive (Murphy, 2007; Widenhorn-Müller et al., 2008) that connotes positive motivation. Conversely, the
benet from better breakfast quality seemed to be a protective factor in that it was associated with lower levels of maladaptive
motivation. Why this is the case requires further investigation, but it appears that a healthy breakfast may be helpful for students
seeking to reduce academic anxiety, fear, and other variables in line with other accounts suggesting the benets of a healthy breakfast
and diet for reducing ill health (Jackson & Vaughn, 2019; Michael et al., 2015; NHMRC, 2013). Taken together, both the consumption
of breakfast and the quality of what is consumed were associated with students' academic motivation. To the extent this is the case, it
appears that simply having breakfast is not sufcient; to gain the full benets of eating breakfast, its quality is also important for
optimal motivation. Interestingly, neither consumption nor quality had effects on achievement, but they did have moderated and
mediated roles.
5.613
5.569
5.669 5.869
5.2
5.45
5.7
5.95
6.2
Typically Unhealthy Typically Healthy
Adapve Movaon
Breakfast No
Breakfast Yes
Fig. 4. Plot for effects of breakfast interaction (breakfast consumption x breakfast quality) on adaptive motivation.
Note. Adaptive motivation is on original scale of 7-point rating.
0.058
-0.092
-0.100
0.134
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
Typically Unhealthy Typically Healthy
Achievement
Breakfast No
Breakfast Yes
Fig. 5. Plot for effects of breakfast interaction (breakfast consumption x breakfast quality) on achievement.
Note. Achievement is on standardized scale where M =0.
A.J. Martin et al.
Journal of School Psychology 104 (2024) 101298
13
Findings supported the well-established connection between motivation and achievement (Liem & Martin, 2012). In educational
research, this is not headline news. But in health research, it is noteworthy. Specically, although there is a good deal of health
research demonstrating a direct link between breakfast and achievement (see Adolphus et al., 2013, Burrows et al., 2017, Faught et al.,
2017, Hasz & Lamport, 2012, Mahoney et al., 2005, Murphy, 2007, Ptomey et al., 2016, and Yao et al., 2019, for research and reviews),
there remain unanswered questions about how exactly breakfast translates into better achievement. There has been speculation about
possible mediating factors that explain the link (Corcoran et al., 2016). One thesis is that breakfast fuels and energizes the individual,
which provides the impetus for enhanced performance (Murphy, 2007; Widenhorn-Müller et al., 2008). This brings into focus the role
of motivation (e.g., Murphy, 2007) and our study indicated that (a) breakfast predicted motivation, (b) motivation predicted
achievement, and (c) breakfast did not predict achievement (although it did as an interaction effect). Moreover, adaptive motivation
signicantly mediated the relationship between breakfast consumption and achievement. This indirect effect suggests that a
comprehensive response to enhancing students' achievement must not only address their adaptive motivation, but also attend to other
antecedents in their lives, such as their breakfast consumption.
Notably, breakfast consumption and breakfast quality did not just operate as independent effects as they also interacted such that
some effects of breakfast consumption were moderated by its quality. Specically, the ndings demonstrated that for adaptive
motivation, it was not only important to have breakfast on a given morning, but to also ensure it was of high quality (see also Adolphus
et al., 2013; Taras, 2005). Interestingly, the tests for moderation also revealed that students low in adaptive motivation and
achievement were those who typically ate a healthy breakfast, but who had skipped breakfast that morning. The precise reason for this
requires further research; for example, is it a result of nutritional disruption or because a breakfast routine in the home was disrupted
by an event such as an argument with parents that led to reduced motivation and achievement later that day (e.g., George et al., 2012)?
Alternatively, it may reect a broader trend of inconsistent consumption of breakfast. In any case, it is a novel contribution because the
effect of a disrupted breakfast routine on academic motivation (for example) has not received research attention, although Mahoney
et al. (2005) and Benton et al. (2001) suggested that this disruption could adversely affect children's cognition, mood, and behavior.
This nding reinforces the importance of maintaining a healthy breakfast each morning.
A strength of the study was the inclusion of multiple covariates and potential confounds that the literature has identied as critical
to account for when seeking to ascertain the unique effects of breakfast. The ndings demonstrated that beyond salient personal, home,
and classroom factors, breakfast consumption and breakfast quality were uniquely associated with motivation and achievement.
Researchers have emphasized some key factors to disentangle from breakfast effects; for example, research has suggested that older
school students, girls, and students from low SES backgrounds are more likely to skip breakfast (Mhurchu et al., 2010; Widenhorn-
Müller et al., 2008; Yao et al., 2019), whereas higher achieving students may make more informed choices about breakfast (Whatnall
et al., 2019). By including these background factors as predictors (covariates), we could ascertain breakfast effects controlling for
variance attributable to these factors. Indeed, these background attributes are also implicated in students' motivation and achievement
and so their presence in the model held even greater signicance for interpreting breakfast effects.
4.2. Implications for theory
The ndings are a further contribution to theories that emphasize the critical nexus between “body and mind” in human func-
tioning (e.g., Blascovich, 2008; Martin, Kennett, et al., 2021; Schultheiss & Wirth, 2018), including young people's academic func-
tioning (Bellisle, 2004; Martin et al., 2023; Martin & Burns, 2023; Martin, Kennett, et al., 2021). For psycho-educational theorizing, the
study reinforces the importance of holistic perspectives that account for the health and physiological factors involved in students'
motivation. Of course, seminal psycho-educational theories often do recognize these factors (e.g., self-determination theory; Deci &
Ryan, 1985), but we posit that the present study adds more concretely to these theories in that it explicitly positions breakfast con-
sumption and quality in this educational space. Thus, for example, theories around learning might not only attend to the relevant
instructional inputs, but also to how well the students started the day by way of adequate nutritional input via breakfast consumption
and breakfast quality.
The ndings also suggest the importance of incorporating psycho-educational perspectives in models and processes that are central
to health-related disciplines. In the present study, academic motivation appeared to be a potentially energizing mechanism mediating
the link between breakfast and achievement. Whereas educational and school psychology have long promoted motivation as a key
factor in learning and achievement, the present study formally links this psycho-educational concept to health and lifestyle ante-
cedents. This has implications for health and related literatures seeking to conduct more encompassing and integrative research to
better understand young people's development.
Our study also reinforces the importance of integrative multi-dimensional perspectives on student motivation in this space.
Motivation research is populated by diverse theories and multitudes of constructs that can present problems for researchers and
practitioners whose objective is to implement parsimonious and cohesive motivational approaches to support students' educational
development (see Hattie et al., 2020, Martin, 2023, Wigeld & Koenka, 2020, and Wong & Liem, 2021, for reviews). To respond,
researchers have recommended studying motivation from multidimensional perspectives, including in terms of its positive and
negative dimensions (Martin, 2023). In our Introduction, we identied the rationale for adopting a motivation framework where
maladaptive dimensions of motivation (such as anxiety) were included. We argued it was important to understand the association
between breakfast and both adaptive and maladaptive dimensions of motivation as this would enable a more comprehensive insight
into breakfast effects and reduce the risk of missing important motivational constructs (such as maladaptive dimensions) that play a
notable role in students' academic experience. Our ndings quite clearly indicate that this cohesive approach representing both
adaptive and maladaptive dimensions was warranted as not only was breakfast associated with these two motivation dimensions in
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distinct ways, but the two dimensions were each uniquely associated with achievement.
4.3. Implications for practice
The results reveal three main points for educational intervention: (a) incorporating a healthy breakfast or morning snack into the
school day, (b) ensuring information about breakfast is included in school curricular/syllabus units (and communicated to home), and
(c) addressing aspects of motivation that mediate the link between breakfast and achievement. These are discussed more below.
First, regarding the provision of a healthy breakfast or morning snack at school, Faught et al. (2017; see also Oostindjer et al., 2017)
suggested that schools are a critical site where students' breakfast habits are addressed with a view to optimizing their academic
development. Free school-based breakfast programs are one avenue for this (O'Dea & Mugridge, 2012). Alternatively, schools might
consider providing students with a snack later in the morning (Benton et al., 2001). Because breakfast (or snack) intervention is
logistically straightforward (Hasz & Lamport, 2012; Hau, 2016) and cost-effective (e.g., a piece of fruit and a glass of milk are cost
effective relative to tutoring intervention conducted at scale), this has been recommended as a front line for intervention (Yao et al.,
2019), especially for students from disadvantaged homes, food-insecure homes, or food-scarce neighborhoods (Jackson & Vaughn,
2019). Indeed, the bivariate correlations in the present investigation suggested that socio-economic status was signicantly associated
with breakfast quality. This relationship reinforces the need to consider such interventions at a policy level rather than by individual
school. More specically, this nding suggests the importance of considering funding for such programs across schools to ensure the
schools that cater to students who have least access to (quality) breakfast can access it. In addition, if a school breakfast (or snack)
program is implemented, there is also a need to ensure that barriers to participation are addressed (Dykstra et al., 2016), potentially via
encompassing health educational programs. For example, a desire to lose weight and body image concerns (Godin et al., 2018; O'Neil
et al., 2014) may impede students' adoption of breakfast programs if not effectively countered through other educational and health
avenues.
Second, Jackson and Vaughn (2019) emphasized the importance of educating students about the inter-connectedness of breakfast
consumption and academic well-being. This can be via curricular/syllabus units at school (e.g., health and physical activity subjects).
O'Neil et al. (2014) recommended that achieving the goals of a quality breakfast is best done when it is seen as a partnership between
school, home, and student. Thus, O'Neil et al. (2014) advised communication to the home and via in-school parent information sessions
aimed at empowering and building condence, such as strategies for preparing food, getting portable and non-perishable items ready
for the school week, and buying in bulk so there is enough food for the week.
Finally, motivation was a signicant mediator in the study and so interventions that promote motivation are also relevant.
Consistent with Martin, Ginns, Anderson, Gibson, and Bishop (2021), the adaptive motivation construct comprised self-efcacy,
valuing, and mastery orientation, and each of these has well-established practice guidelines. For example, boosting self-efcacy en-
tails individualizing tasks so students have opportunity to succeed as a basis for condence (McInerney, 2000); it also entails
encouraging students to develop more positive beliefs about themselves and their capacities (Wigeld & Tonks, 2002). To enhance
valuing, educators can encourage students to see the relevance and importance of school subjects or topics (Eccles & Wigeld, 2020)
and model their own valuing of these subjects/topics to students (Bandura, 2017). For mastery orientation, it can be helpful to direct
students' focus on effort, skill development, and mastery, with less focus on comparisons and competition (Elliot, 2005). Maladaptive
motivation in this study comprised anxiety, failure avoidance, and uncertain control (following Martin, Ginns, Anderson, Gibson, &
Bishop, 2021). For anxiety and failure avoidance, fear of failure is a contributing factor (Covington, 2000) and can be addressed by
encouraging students to see that mistakes provide diagnostic information about how to improve and do not imply that the student is
lacking in worth (Covington, 2000). For uncertain control, educators might like to direct students' attention to aspects of schoolwork
they do control, such as their effort, strategy, and attitude (Martin, 2010).
4.4. Limitations and future directions
There are several study limitations to note when interpreting ndings and that also provide direction for future research. First, the
diet score was based on students' self-reports and there are limitations with these types of data, such as retrospective bias and inac-
curate recall, although we note that self-report dietary intake has been found to be valid (Burrows et al., 2013). Future research might
look at contemporaneous food diaries or parent reports of breakfast habits. The central motivation measures were also self-reported,
but as these are intra-psychic constructs, the use of self-report is deemed feasible. Nonetheless, future research might look to also
garner teacher reports of students' motivation. Regarding achievement, however, we point out that this was an actual test and so was
free of the potential biases implicated in self-report. It is also the case that diet surveys vary (Gibney et al., 2018; O'Neil et al., 2014).
The present ndings must be interpreted in the context of a survey developed around national guidelines relevant to our Australian
sample (viz. NHMRC, 2013). We also point out that our measure of breakfast quality reected students' typical diet (across the course
of the week) and not just for the morning of the survey. In the Method section, we explained that this was to ensure valid (and avoid
narrow) measurement of breakfast quality and to capture typical breakfast quality for students who do eat breakfast but did not do so
that morning. Indeed, this approach revealed ndings suggesting the importance of maintaining a quality breakfast each day. That
said, we urge some caution when interpreting ndings because we do not know if students ate a quality breakfast on the morning of the
survey (we only know the typical quality across the course of the week) or the role of that morning's breakfast quality in students'
motivation and achievement in science later that day. We also do not know the students who never eat breakfast (we only know if they
did not eat breakfast that morning). Our breakfast quality measure was 1 =Never to 4 =Five days, but a rating of Never here signals the
frequency of consumption of particular food types; it does not mean they never eat breakfast. In addition, regardless of whether a
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student ate breakfast that morning, we do not know if they were feeling hungry in the science lesson, which is important as feeling
hungry has been negatively associated with students' learning (International Association for the Evaluation of Educational Achieve-
ment, 2020). Future research is needed to disentangle these issues, with a particular focus on including a measure of that morning's
breakfast quality, an item that asks students how often they eat breakfast (regardless of its quality), and how hungry they are feeling at
the time of the survey.
Second, our data were collected in a single online survey sitting and in this sense were cross-sectional. That said, students were
reporting on food eaten that morning (which necessarily preceded the science lesson). In addition, the role of motivation in leading to
achievement is a well-established one, both theoretically and empirically (e.g., Lazowski & Hulleman, 2016; Marsh & Martin, 2011).
Thus, although our data were collected at the one time, there was a logic to modeling in the way we did (breakfast → motivation →
achievement). Nonetheless, future research design needs to be implemented over the medium to longer term to verify the ordering of
factors we hypothesized. Third, although we included many covariates, there is a need to investigate other factors. For example,
Benton et al. (2001) observed that poor glucose tolerance was associated with poor cognitive performance and thus more detailed
information on glucose intake and tolerance would be helpful. Similar information on vitamin and mineral intake would be infor-
mative as well. Another factor is students' knowledge of nutrition as it is possible that students are not making unhealthy choices, but
rather they simply do not know what a healthy diet is (Cornil et al., 2020). Knowing the size of the student's breakfast may also be
important (Nyaradi et al., 2016). There are also cultural factors we could not account for, such as Ramadan which fell in part of the
term two data collection period. Although we do not have data on which students were observing Ramadan, we do not anticipate it
unduly affected results (e.g., <1% of students reported Arabic as their home language, and we included non-English speaking
background as a covariate). In addition, although we included an indicator of home assistance, we did not have more detailed in-
formation about students' attachments to parents which is a factor associated with their eating habits (e.g., Faber et al., 2018).
Fourth, we only assessed one aspect of students' diet: breakfast. It is important to ascertain breakfast effects in the context of
students' broader dietary intake to better understand the unique effects of breakfast (Burrows et al., 2017). Related to this was the fact
we did not have information about whether students had eaten sometime between breakfast and the science lesson. In part we dealt
with this using lesson timing (time in the day of the science lesson) as a covariate and something of a proxy control for aspects of the
daily routine (including snacking) that are important to disentangle from breakfast consumption. But including lesson timing does not
directly or sufciently deal with this potential confound and so future research should also include post-breakfast food intake mea-
sures. Finally, our schools were higher in SES than the national average. This being the case, our students may be considered relatively
well-nourished or have greater access to nutritious foods. It is unclear how our ndings generalize to students who are lower in SES. In
a follow-up analysis, we tested the extent to which SES moderated the association between breakfast consumption (via a consumption
* SES interaction term), breakfast quality (quality * SES), and consumption by quality (consumption * quality * SES), and students'
motivation and achievement. No interaction terms attained statistical signicance at p <.05; nevertheless, we recommend further
research among schools serving lower SES communities.
5. Conclusions
A healthy breakfast has traditionally been associated with improved cognitive and academic performance, but research has been
piecemeal and the motivational factors that are implicated in the process have not been well understood. The present study brought
together a wide variety of dietary, academic, personal, home, and classroom factors and demonstrated a positive role for a regular
healthy breakfast for students' academic motivation and achievement. We conclude that this small and relatively achievable change in
a student's life has the potential to have positive implications for their academic outcomes.
Acknowledgements
The authors thank the participating schools for assisting with data collection and Rebecca Reynolds, Carolyn Imre, and Brad
Papworth for advice on instrumentation, study design, and analysis. This study was funded by the Australian Research Council (Grant
#LP170100253) and The Future Project at The King's School.
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