Associations between selected dietary behaviours and academic achievement: A study of Australian school aged children

Accepted Manuscript Associations between selected dietary behaviours and academic achievement: A study of Australian school aged children Tracy Burrows, Sharni Goldman, Richard K. Olson, Brian Byrne, William L. Coventry PII:

S0195-6663(17)30374-4

DOI:

10.1016/j.appet.2017.05.008

Reference:

APPET 3459

To appear in:

Appetite

Received Date: 8 March 2017 Revised Date:

2 May 2017

Accepted Date: 3 May 2017

Please cite this article as: Burrows T., Goldman S., Olson R.K., Byrne B. & Coventry W.L., Associations between selected dietary behaviours and academic achievement: A study of Australian school aged children, Appetite (2017), doi: 10.1016/j.appet.2017.05.008. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Title:

Associations between selected dietary behaviours and academic achievement: A study of Australian school aged children. Tracy Burrows, Sharni Goldman, Richard K Olson, Brian Byrne, & William L Coventry.

Burrows T.L. School of Health Sciences, The University of Newcastle, Callaghan, 2308, Australia.

Australia.

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Goldman S.R. School of Health Sciences, The University of Newcastle, Callaghan, 2308,

Olson R.K. Institute of Behavioral Genetics, University of Colorado, Boulder, USA.

Byrne, B & Coventry W.L. School of Behavioural, Cognitive and Social Sciences The University

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of New England, Armidale, 2350, Australia.

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Role of authors:

TB and SG drafted the articles and completed all nutritional analysis, SG undertook this project as part of her Honours program at the University of Newcastle. The grant which funded the Behaviour-Genetic Study of NAPLAN and all recruitment by R.O, B.B and W.C Olson R.K. All authors approved the final manuscript.

Tracy Burrows Phone: 02 49215514 Fax: 02 49213599

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Corresponding author:

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Email: [email protected]

Key words: diet, nutrition, diet behaviour, academic achievement, children, adolescents

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ACCEPTED MANUSCRIPT Abstract

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Background: Research investigating the effects of dietary behaviours on children’s academic

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achievement has predominately focused on breakfast consumption. The aim of this study was

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to conduct secondary analysis to examine associations between a range of dietary behaviours

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and children’s academic achievement. Methodology: Data on five dietary variables (fruit

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intake; vegetable intake; consumption of takeaway; sugar sweetened beverages; and

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breakfast) and scores in the five domains of a standardised academic achievement test known

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as NAPLAN (reading, writing, grammar/punctuation, spelling and numeracy) were obtained

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for Australian children aged 8-15 years in school grades three (n=1185), five (n=1147), seven

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(n=1053) and nine (n=860). Mixed linear models adjusted for socioeconomic status and

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gender were used to examine associations between dietary behaviours and academic scores.

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Results: Greater consumption of vegetables with the evening meal (7 nights/week) was

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associated with higher test scores in the domains of spelling and writing (p=<0.01), with the

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greatest effect observed for spelling with a mean score difference of 86±26.5 NAPLAN points

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between the highest and lowest levels of consumption (95% CI: 34.0 - 138.1; p=<0.01).

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Increased consumption of sugar sweetened beverages was associated with significantly lower

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test scores in reading, writing, grammar/punctuation and numeracy (<0.01). Principle

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conclusions: The findings of this study demonstrate dietary behaviours are associated with

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higher academic achievement. Future research should further explore relationships between a

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wide range of dietary behaviours and children’s academic achievement.

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Keywords: Diet, food, academic achievement

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ACCEPTED MANUSCRIPT Introduction

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The academic achievement of children and adolescents has a significant impact on their future

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outcomes and life opportunities (Florence, Asbridge, & Veugelers, 2008; O'Dea & Mugridge,

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2012; Overby, Ludemann, & Hoigaard, 2013). Academic achievement is associated with

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higher educational attainment, which in turn influences health and social outcomes by

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affecting employment opportunities, socioeconomic status, access to health care, and

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psychosocial wellbeing(Florence et al., 2008; O'Dea & Mugridge, 2012; Overby et al., 2013;

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Ross CE. Wu C, 1995). Given the demonstrated importance of academic achievement,

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identifying factors affecting achievement is a priority for educational authorities and public

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health research (Florence et al., 2008; Littlecott H, Moore G, Moore L, Lyons R, & Murphy

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S, 2015; O'Dea & Mugridge, 2012; Overby et al., 2013).

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A well-recognised factor affecting the academic achievement of children and adolescents is

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socioeconomic status (Coley R, 2002; Considine G & Zappala G, 2002; Ross CE. Wu C,

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1995; Sirin, 2005). Academic achievement is also influenced by sex, genetic endowment, the

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school environment, teacher quality, and authoritative parenting style (Florence et al., 2008;

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Grasby, Coventry, Byrne, Olson, & Medland, 2016; Masud, Thurasamy, & Shakil-Ahmad,

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2015 ; O'Dea & Mugridge, 2012; Overby et al., 2013). Research has examined the effects of

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lifestyle behaviours including physical activity and diet, however, these factors have received

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increased research focus in recent years as a result of global trends in changing food

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environments, rising rates of childhood overweight and obesity, and increased time spent in

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sedentary activities (Cliff et al., 2013; Malik V.S, Willett W.C, & Hu F. B, 2012). A recent

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review of 43 studies examining links between physical activity and academic achievement in

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children, found physical activity was positively associated with higher academic achievement

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in the majority of studies reviewed(Singh et.al, 2012).

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Studies examining associations between dietary behaviours and academic achievement have

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typically focused on food adequacy, micronutrient intakes, and breakfast consumption

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(Burrows, Pursey, Goldman, & Lim, 2016 ; Florence et al., 2008; Fu, Cheng, Tu, & Pan,

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2007; MacLellan, Taylor, & Wood, 2008; Nyaradi A et al., 2015; Overby et al., 2013).

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Multiple studies have shown undernourished children have poorer academic achievement

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compared to well-nourished children(Galal & Hulett, 2003; Kretchmer N, Beard JL, &

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Carlson S, 1996; Meyers A.F, Sampson A. E, & Weitzman M, 1991; Taras. H, 2005), whilst

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regular consumption of three daily meals has been linked with higher academic achievement

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in two studies in Norwegian and Korean adolescents(Kim H.Y & et.al, 2003; Overby et al.,

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2013). Iron intake was associated with higher school grades in two studies in Italian

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adolescent girls and Moroccan children and adolescents(Aboussaleh, El Hioui, Achouri, El

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Marzouki, & Ahami, 2013; Aquilani et al., 2011), and higher intakes of fish rich in omega

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three fatty acids was associated with higher school grades in a study of Swedish

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adolescents(Kim et al., 2010). In a recent review of 36 studies including both observational

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and experimental designs, breakfast quality, frequency and school breakfast programs were

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found to be positively associated with academic achievement in the majority of studies

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reviewed(Adolphus, Lawton, & Dye, 2013).

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Whilst behavioural theories would postulate that observational learning and role modelling

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from parents and peers plays a role in the association between diet and academic achievement

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(Bandura, 1986; Wardle, 1995), current evidence supports an independent effect of dietary

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behaviours on academic achievement. This evidence has limitations including the use of non-

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validated dietary assessment methods, non-standardised subjective measures of academic

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achievement, and inadequate controls for factors known to affect academic achievement

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including socioeconomic status and sex (Adolphus et al., 2013; Burrows et al., 2016 ).

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Limited studies have investigated the effects of a broad range of dietary behaviours more

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reflective of diet quality within a single study (Florence et al., 2008; Nyaradi A et al., 2015;

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Overby et al., 2013), and little is known about the long term effects of dietary behaviours on

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academic achievement as a majority of studies have been cross-sectional(Florence et al.,

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2008).

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National standardised tests have increasingly been used to measure academic achievement in

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developed countries including the United States, Canada and the United Kingdom (Australian

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Curriculum and Reporting Authority, 2013c; O'Dea & Mugridge, 2012). Since 2008, all

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Australian children and adolescents in grades 3, 5, 7 and 9 have sat standardised academic

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achievement tests in the form of the National Assessment Program Literacy and Numeracy,

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referred to as NAPLAN(Australian curriculum and Reporting Authority, 2013a). The purpose

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of this study was to examine associations between multiple dietary behaviours and children’s

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test scores in the five assessment domains of the NAPLAN, using a longitudinal cohort of

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Australian school-aged children and adolescents.

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Methods

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The present study utilised data from the longitudinal Behaviour-Genetic Study of NAPLAN

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(Grasby et al., 2016). Researchers collected data on environmental factors (including living

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arrangements, TV and internet use and home educational facilities) and NAPLAN results in

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pairs of twins. The primary aim of the original study was to investigate the influence of genes

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and the environment on the academic achievement of children and adolescents, with dietary

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intake data collected as a secondary outcome. Given the primary aim, twins were selected as

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the population group as they are the ideal research model. It is common in twins research to

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also explore phenotypic issues, such as dietary intakes, in part because twin studies by their

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very nature demand large numbers, and are therefore an excellent data source for these

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phenotypic studies. Participants for the study were recruited from the National Health and

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Medical Research Council’s Australian Twin Registry (ATR), which is an Australia-wide

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volunteer registry of approximately 33,000 sets of twins willing to participate in health

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research (The Australian Twin Registry, 2015). The recruitment-flow of children (n= 2235) in

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the Behaviour-Genetic Study of NAPLAN is detailed in Figure 1. Recruitment to this study

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was continuous and collected each year, so there was a degree of overlap across data waves

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and grades, although this is not complete. Inclusion criteria for participation included all twins

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registered with the ATR who were in birth cohorts that would enable harvesting of any or all

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NAPLAN results for grades 3, 5, 7 and 9, during the study period 2008 to 2014. All eligible

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children and their families were approached by the ATR for participation in the study.

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Interested families were sent an information pack containing an outline of the study, consent

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forms and a Family-specific Questionnaire. Families willing to participate completed and

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returned the Family-specific Questionnaire to researchers. Following this, families were sent a

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Child-specific Questionnaire and researchers accessed NAPLAN results from the respective

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State Departments of Education. Parents were the initial contact and provided written

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informed consent, and child assent was obtained from participating children. Collected data

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were de-identified and children and their families were given a unique identification number

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to protect privacy. Ethics approval for the Behaviour-Genetic Study of NAPLAN was granted

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by the University of New England and University of Newcastle Human Research Ethics

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Committee.

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Demographics

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Demographic data were collected via a Family-specific Questionnaire completed by one

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parent or primary care-giver online or in paper form. The questionnaire contained 25 items

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including the parent completing the questionnaire, education level of the parent completing

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the questionnaire, the birthdate and sex of the twins, and twin type (monozygotic or

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dizygotic). The education level of the parent completing the questionnaire was used as a

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proxy for socio-economic status, consistent with other studies in the socio-economic status

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literature (Sirin, 2005). Parent education level was measured and reported as completion of

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high school; technical/trade including certificate or diploma; undergraduate or postgraduate

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degree which was converted to years of education.

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Dietary Behaviours

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Data on dietary behaviours was collected via a Child-specific Questionnaire. The

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questionnaire was completed separately for each twin by one parent (online or in paper form

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at their discretion.) The diet survey was completed for each year that twins completed the

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NAPLAN tests. Researchers were not insistent that it was the same parent, however, parents

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completed forms separately for each twin. Five questions regarding dietary behaviours were

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included in the questionnaire and were selected from the larger Australian Eating Survey,

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which has been validated for use in children (Burrows, Berthton, Garg, & Collins, 2012;

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Burrows et al., 2013; Burrows, Warren, Colyvas, Garg, & C., 2009; Watson, Collins, Sibbritt,

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Dibley, & Garg, 2009). Selected questions were used in place of the larger survey based on

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space limitations, participant burden and diet being a secondary outcome for the primary

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study. Questions investigated breakfast consumption (times per week); fruit intake (pieces per

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day or week); vegetables with the evening meal (times per week); takeaway meal

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consumption (times per week, day or month) defined as eating out i.e. chips/ fries /pizza,

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hamburger; and sugar sweetened beverage (SSB) consumption (glasses per day), which was

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specified as soft drink or cordial (a concentrated sugar syrup). These dietary behaviours were

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chosen by researchers as they have relevance and are reflective of children’s broader diets and

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provide insight into overall diet quality.

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Academic Achievement

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The NAPLAN is a standardised battery of tests undertaken by Australian children in grades 3,

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5, 7 and 9 in both government and non-government schools(Australian curriculum and

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Reporting Authority, 2013a). Separate tests are undertaken in the five assessment domains of

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reading, writing, spelling, grammar/punctuation and numeracy(Australian curriculum and

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Reporting Authority, 2013a). NAPLAN tests are developed and centrally managed by the

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Australian Curriculum and Assessment Reporting Authority and are delivered in a consistent

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manner guided by the NAPLAN National Protocols for Test Administration(Australian

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curriculum and Reporting Authority, 2013a).

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The results of the NAPLAN tests are calculated as a separate scale score for each of the five

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domains of assessment(Australian curriculum and Reporting Authority, 2013b). Each scale

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spans all school grades from 3 to 9 and ranges from 0 to 1000, with higher scores representing

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higher levels of achievement(Australian curriculum and Reporting Authority, 2013b). The

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NAPLAN score scales correspond to 10 performance bands, which are used to report results

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to individual students and their families(Australian curriculum and Reporting Authority,

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2013b). Band 1 indicates the lowest level of achievement whilst band 10 indicates the

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highest(Australian curriculum and Reporting Authority, 2013b). For each respective school

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grade, a National Minimum Standard of performance represents the minimum level of

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achievement deemed necessary for a student to demonstrate the basic skills of literacy and

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numeracy required for their school grade(Australian curriculum and Reporting Authority,

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2013b). As each performance band contains a range of scale scores and is not a specific cut

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point(Australian curriculum and Reporting Authority, 2013b), the present study used

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NAPLAN scale scores rather than performance bands as a measure of academic achievement

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to increase the sensitivity of statistical analysis, consistent with what has been done in other

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studies using the NAPLAN results(O'Dea & Mugridge, 2012). NAPLAN data were obtained

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for each respective year participating children sat the NAPLAN tests.

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Statistical Analysis

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Descriptive statistics were undertaken and linear mixed models were fitted for test scores in

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each of the five NAPLAN assessment domains. Linear mixed models were appropriate to

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account for the repeated measures of individual and some missing values over time. The

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explanatory variables treated as fixed effects were school grade and the five dietary variables:

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all modelled as categorical variables. The combination of these two variables (diet and school

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grade) represented the primary model, additional variables were added to each model as fixed

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effects including calendar year, parent education level as a proxy for socio-economic status

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(years of education), family and sex.

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significant and either of the main effects of school grade and dietary variables were

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significant. A random intercept for family was used to account for correlation between twins

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within the same family. A residual covariance matrix for school grade was used to model the

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correlation due to repeated NAPLAN measurements for each child as they moved through

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Interactions with sex were examined if sex was

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school grades. The most suitable covariance structure was determined by fitting models and

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choosing the ones with the lowest AIC (Akaike Information Criterion). All results shown are

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from the adjusted model. Using linear mixed models, there was no imputation of missing

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values as this approach runs under the assumption of Missing At Random. Post-hoc model-

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based t-tests were used to determine differences between levels for significant main effects

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and interactions. Effect sizes were calculated using Cohen’s d, statistical significance was set

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at p≤0.05. Statistical analyses were completed using SPSS version 21 (SPSS Inc., Chicago,

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IL, USA).

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Results

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The number of children for which data were provided on diet and academic achievement over

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the study time frame in grades 3, 5, 7 and 9 was n= 1185, n= 1147, n= 1053 and n= 860

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respectively. The sample characteristics of participants are given in Table I. There were no

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differences between the children who were included in this study analysis to those who did

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not have complete data for inclusion. Sex was approximately even across children (females:

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50.8%), and did not differ significantly across school grades (p≥0.05). With respect to twin

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type, 39% of children were monozygotic twins and 61% were dizygotic. Responses for

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Family-specific and Child-specific Questionnaires were reported by mothers for 98.4% of

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children. The education level of the parent completing the Family-specific Questionnaire in

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descending order were: technical or trade certificate or diploma (30.8%); undergraduate

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degree (24.9%); postgraduate degree (24.1%); and completed high school (20.3%). Children’s

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mean scores in all five NAPLAN assessment domains were above the National Minimum

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Standard of performance for all school grades(Australian curriculum and Reporting

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Authority, 2013b). The mean scores in all NAPLAN assessment domains increased

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significantly across school grades (p≤0.01), and the variation in NAPLAN scores between

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twins was observed to decrease over time.

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Children’s dietary behaviours differed significantly across school grades (p≤0.01). For all

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school grades combined, 37.3% of children were reported to consume vegetables every night

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with their evening meal, and 45.3% were reported to meet the National Dietary Guidelines of

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two pieces of fruit per day(National Health and Medical Research Council, 2005). With

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respect to SSB and takeaway meal consumption, 24% of children were reported to consume

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one or more glasses of SSB per day, and 33.6% were reported to consume takeaway meals

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one or more times per week. A majority of children (89.6%) were reported to consume

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breakfast every day.

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Table II. shows mean NAPLAN scores for children by sex and parent education level.

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Females were found to have significantly higher scores than males in four NAPLAN

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assessment domains (spelling, reading, writing and grammar/punctuation) (p≤0.01), whilst

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males were found to have significantly higher scores than females in numeracy (p≤0.01).

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Parent education level was significantly associated with academic achievement in all five

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NAPLAN assessment domains, with children’s NAPLAN scores observed to increase with

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higher levels of parent education (p≤0.01).

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The results of linear mixed models for dietary behaviour variables and NAPLAN scores is

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shown in Table III. Two of five dietary variable’s, vegetable intake and SSB’s were found to

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be consistently associated with domains of academic achievement.

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consumption was the strongest dietary predictor of NAPLAN achievement, having a

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significant association with test scores in four of the five NAPLAN assessment domains

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(reading, writing, numeracy and grammar/punctuation) (p≤0.01). Higher SSB consumption

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was associated with lower NAPLAN scores, with the greatest effect being observed in reading

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score with a mean difference of –46.1±12.9 points between the highest and lowest levels of

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consumption (4-6 glasses/day vs. <1 glass/day), (95% CI: –71.4 - –20.8; p≤0.01). Vegetable

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consumption at the evening meal was significantly associated with test scores in two

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NAPLAN assessment domains, spelling and writing (p≤0.05).

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observed

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grammar/punctuation scores (p= 0.05) and numeracy score (p= 0.06). Higher vegetable

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consumption at the evening meal was associated with higher NAPLAN achievement, with the

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greatest effect being observed in writing with a mean difference of 86.0±26.5 points between

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the highest and lowest levels of consumption (7 evenings per week vs. 0 evenings/week),

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(95% CI: 34.0 - 138.1; p≤0.01). Breakfast and fruit consumption were significantly associated

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with NAPLAN test scores in one academic domain only (writing). Meeting the National

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Dietary Guidelines of two pieces of fruit per day was associated with higher writing scores,

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with a mean difference of 17.0±5.7 points between consuming 2 pieces of fruit per day versus

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3-4 pieces per week (95% CI: 5.8 - 28.1; p= 0.04). Takeaway meal consumption was not

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significantly associated with academic achievement in NAPLAN (p≥0.05). The overall effect

vegetable

consumption

at

the

evening

(p≤0.05).

SSB

A statistical trend was

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sizes for the effect of diet on NAPLAN scores as determined by Cohen’s d were deemed

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small, the largest effect size was found for SSB and reading scores d= 0.1.

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Discussion

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This study aimed to investigate a range of dietary behaviours and effects on children’s

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performance in a standardised academic achievement test. It was found that vegetable intakes

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and SSBs were consistently found to influence children’s academic achievement in addition

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to sex and parent education level. Results of the present study found more than half of all

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children in the present sample reported low consumption of fruit and vegetables and a quarter

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reported high consumption of SSB when compared to National recommendations. Similar

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findings have been observed in population-based research in Australia which found 70% of

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children aged 8-11 years and 95% aged 12-15 years reported inadequate consumption of both

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fruit and vegetables, whilst 50% aged 9-13 years and 62% aged 14-18 years reported

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consuming one or more SSB per day (Australian Bureau of Statistics, 2015). Dietary

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behaviours examined in this study were found to differ significantly across schools’ grades.

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SSB and takeaway meal consumption increased from grades 3 to 9 whilst fruit and breakfast

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consumption decreased. This is in line with previous research, which has demonstrated

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increased consumption of energy dense, nutrient poor foods such as SSB and takeaway meals

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and concurrent displacement of nutrient dense foods as children age(Frary CD, Johnson RK,

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& Wang MQ, 2004; Kant A.K, 2003; Rangan, Randall, Hector, Gill, & Webb, 2008). This is

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likely a result of children exerting increasing levels of independence over their food choices

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with increasing age(Rangan et al., 2008).

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The effects of diet in this study remained after controlling for SES as approximated by parent

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education level, and child sex within the statistical models. A potential mechanism underlying

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the observed association between diet and academic achievement is that fruits, vegetables and

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fortified breakfast cereals are rich dietary sources of antioxidants shown to maintain cellular

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mitochondria DNA. Moreover, other dietary factors and components have been shown to

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influence neuronal activity and synaptic plasticity, which may promote better cognitive

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function(Gomez - Pinilla F, 2008).

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Dietary behaviours characteristic of lower diet quality, specifically higher SSB consumption

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in this study, were significantly associated with lower NAPLAN test scores in reading,

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writing, grammar/punctuation and numeracy. Limited studies have investigated the effects of

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a broad range of dietary behaviours and overall diet quality on children’s academic

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achievement (Florence et al., 2008; Overby et al., 2013). One recent Canadian study of 5200

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fifth grade school children examined links between diet quality, measured by the validated

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Diet Quality Index International (DQI-I), and children’s achievement in a standardised

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literacy assessment(Florence et al., 2008). Children with higher diet quality scores,

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characterised by greater intakes of fruits and vegetables, were found to be significantly less

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likely to fail the standardised literacy assessment compared to children with lower diet quality

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scores, characterised by greater intakes of SSB and fast food / takeaway meals (OR: 0.70

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(0.56 - 0.88), p≤0.05)(Florence et al., 2008). Similarly, a Norwegian study of 475 ninth grade

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school children examined associations between healthy versus non-healthy dietary patterns

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and children’s self-reported learning difficulties in literacy and mathematics(Overby et al.,

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2013).

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Dietary behaviours characteristic of a healthy dietary pattern including consuming breakfast

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often (six or more times per week) and fruit (once a day or more) were associated with

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significantly decreased odds of learning difficulties in literacy (Breakfast often OR: 0.44

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(0.23-0.83); p= 0.01) and mathematics (Breakfast often: 0.33 (0.19-0.55); p≤0.01; Fruit often

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OR: 0.57 (0.34 -0.93); p=0.02)(Overby et al., 2013). Conversely, dietary behaviours

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characteristic of a non-healthy dietary pattern including higher SSB consumption (1-3 times

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per week or more) and fast food / takeaway meal consumption (4-6 times per week or more)

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were associated with significantly increased odds of learning difficulties in mathematics (SSB

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OR: 2.48 (1.33-4.62); p=0.04; Fast food OR: 5.96 (2.0-17.43); p=<0.01)(Overby et al., 2013).

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Previous research has linked high consumption of foods high in refined carbohydrates such as

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SSBs, and saturated fats such as takeaway meals, with neurological and cognitive dysfunction

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through hippocampal and frontal lobe volume loss and dysfunction(Gomez - Pinilla F, 2008;

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Kanoski S.E, 2011). The results of the present study did not find a significant association

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between takeaway meal consumption and children’s academic achievement, in contrast to

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these previous reports. Possible explanations for this include differences in the assessment of

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both diet and academic achievement, and the relatively small number of children who had

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takeaway meal consumption reported at higher levels (more than 3-4 times per week) limiting

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the variation. The selection of academic outcomes investigated in this study was opportunistic

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and were all those available through NAPLAN. The authors deemed this an appropriate

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approach as we had no hypothesis as to whether one domain would be more affected by

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various dietary influencers than another. It is not clear why specific dietary factors were

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associated with specific academic outcomes, however, future research should investigate this

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and consider the effects of healthy versus non-healthy meal patterns and overall diet quality

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on children’s academic achievement.

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The findings of this study have several implications. A majority of research investigating

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links between dietary behaviours and children’s academic achievement has focused on

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breakfast consumption (Florence et al., 2008; Nyaradi A et al., 2015; Overby et al., 2013).

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This study extends current understandings through demonstrating additional dietary

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behaviours are significantly associated with children’s academic achievement. The present

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study found NAPLAN test score differences of 46.1 and 86.0 points related to SSB and

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vegetable consumption respectively. These score differences equate to a change of one to

338

several performance bands, or between meeting or not meeting the NAPLAN National

339

Minimum Standard of Performance(Australian curriculum and Reporting Authority, 2013b).

340

Schools face increasing pressure to continually improve academic outcomes(Wilkins et.al,

341

2003). As a result, school time dedicated to health education including nutrition and physical

342

activity may be reduced to increase instructional time for mathematics, English, and

343

science(Wilkins et.al, 2003). The findings of this study add to a growing body of research

344

demonstrating the importance of nutrition and health education in schools to not only promote

345

children’s general health, but to also promote academic achievement(2). The results may have

346

the potential to improve the commitment of parents and schools with health promotion. This

347

has implications for educational policy makers and authorities(Bostic J.Q, 2012; O'Dea &

348

Mugridge, 2012).

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331

This study is limited by the use of self-reported dietary measures, a parent-proxy for dietary

351

reporting, which may have also be influenced by socioeconomic status, and the extent to

352

which a standardised test can reflect children’s academic achievement. The five dietary

353

variables have not been validated on their own, but were selected from a larger food

354

frequency questionnaire. Two major factors known to predict academic achievement were

355

accountedfor in this study, namely socioeconomic status and sex. Parents who promote higher

356

academic achievement are also likely to be from higher SES levels and may be more likely to

357

provide healthy foods(Darmon & Drewnowski, 2008), so this should be considered when

358

interpreting results in addition to the small effect sizes detected. Other significant drivers of

359

academic achievement may have been present, which were not accounted for in analyses such

360

as school and teacher effects(Rivkin, Hanushek, & Kain, 2005). Parenting styles were also not

361

assessed as part of the current study; these have been shown to affect both academic

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ACCEPTED MANUSCRIPT

achievement and dietary intake(Burrows, Warren, & Collins, 2009; Collins, Duncanson, &

363

Burrows, 2014; Masud et al., 2015 ). Behavioural theories suggest that parental attitudes,

364

which were not assessed can affect children indirectly through food purchased and served in

365

the household as this influences exposure, habits and preferences(Wardle, 1995). In addition,

366

behaviours such as those related to diet and those related to academic achievement are

367

influenced by peers as well as by parents (Bandura, 1986). Results could not be separated for

368

public vs private schools. Furthermore, accuracy in measuring SES increases when it is

369

measured in a tripartite manner including parent education, income, and occupation, therefore

370

the use of parent education alone in this study has limitations as an indicator of SES(Sirin,

371

2005). This study has several strengths. Limited comparable studies have examined

372

associations between multiple dietary behaviours and children’s academic achievement in a

373

large longitudinal cohort; whilst accounting for known confounders including sex and socio-

374

economic status (Florence et al., 2008; O'Dea & Mugridge, 2012) (Nyaradi et al., 2016). This

375

study measured academic achievement using a national standardised assessment, minimising

376

bias in the assessment of children’s academic achievement(Australian Curriculum and

377

Reporting Authority, 2013c; Nyaradi A et al., 2015; O'Dea & Mugridge, 2012). For a

378

majority of children in the study sample (98.4%) the reported parent education level was

379

reflective of the mother’s education, which has been shown to be a more sensitive indicator of

380

socio-economic status compared to father’s education level(Ball K & Mishra G, 2006;

381

Duncan, Daly, McDonough, & Williams, 2002).

382

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362

Conclusion

384

This study investigated the effects of a range of dietary behaviours on children’s achievement

385

in five domains of a standardised academic achievement test. Greater consumption of

386

vegetables at the evening meal, adequate fruit intake, regular breakfast consumption and

387

lower consumption of SSBs was associated with significantly higher test scores – vegetables

388

(writing & spelling); breakfast (writing); fruit (writing); SSBs (reading, writing,

389

grammar/punctuation & numeracy). This study furthers understandings of the link between

390

dietary behaviours and academic achievement, and suggests behaviours characteristic of

391

higher diet quality may have a positive effect on children’s academic achievement. The

392

findings of this study have implications for future interventions by identifying the specific

393

dietary behaviours that have the potential to improve children’s academic achievement.

394

Further research into the relationship between children’s dietary behaviours, overall diet

395

quality and academic achievement is warranted.

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ACCEPTED MANUSCRIPT 396

Funding

398

The Longitudinal Behaviour Genetic Study of NAPLAN Results received funding from an

399

Australian Research Council (ARC) grant (2012-2014) to WLC, BB and RKO. An ARC

400

Discovery Project grant to the same authors was awarded in 2014 to fund continued study

401

(2015-2017).

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402

Acknowledgements

404

This study was undertaken as partial requirement for the degree Bachelor of Nutrition and

405

Dietetics Honours Program. All authors contributed to reviewing, editing and approving the

406

final version of the paper. We would like to thank our PhD student, Katrina Grasby, for

407

helping to develop the questions, our research assistants, Sally Larsen and Emma Slade, and

408

the twins and their families for participating.

M AN U

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Transparency Declaration

411

The lead author affirms that this manuscript is an honest, accurate, and transparent account of

412

the study being reported, that no important aspects of the study have been omitted and that

413

any discrepancies from the study as planned (and registered with) have been explained. The

414

reporting of this work is compliant with STROBE guidelines.

415 416 417

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References

419

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ACCEPTED MANUSCRIPT Table I. Sample characteristics of children by school grade, reported as mean ± S.E or % frequency. Characteristic

Grade 3

Grade 5

Grade 7

Grade 9

N

1185

1147

1053

860

Age in years

8.6±0.41

10.6±0.44

12.6±0.46

14.5±0.48

Male

51.1

52.0

47.3

Female

48.9

48.0

52.7

P-value

RI PT

Gender (%) 46.4 53.6

Score range: 0 – 1000

Reading

448.2±91.3

523.7±81.2

Writing

431.7±63.1

498.9±70.1

Grammar/Punctuation

451.7±94.7

526±87.7

Spelling

424.6±75.3

506.4±70.6

Numeracy

423.6±77

<0.01*

572.4±76.4

607.9±74.3

<0.01*

560±63.8

601.6±66.7

<0.01*

510.5±72.8

575.6±73.5

621.3±73.3

<0.01*

0.2

0.5

0.6

1.1

0.7

0.9

Never

0.4

<1/week

1.3

1-2/week

3.4

2.6

2.9

15.1

14.7

11.4

10.6

47.1

45.9

45.1

42.5

32.2

34.7

39.7

42.5

0.2

0.8

1.1

1.3

1.3

1.0

1.6

2.1

2.0

2.8

4.5

4.4

4.7

6.7

8.7

9.8

5.7

7.4

7.1

7.5

1/day

30.8

31.1

30.0

29.6

2-3/day

50.6

46.7

43.6

40.4

4/day

4.8

3.5

3.2

4.9

0.0

0.2

0.6

1.8

7/week

None <1/week 1-2/week 3-4/week 5-6/week

AC C

Pieces of fruit

EP

3.8

5-6/week

Breakfast Never

<0.01*

595.1±83.5

TE D

Vegetables with evening meal

616.6±67.6

552.6±74.4

M AN U

Dietary Behaviours (%)

577.5±68.7

SC

NAPLAN Scores

3-4/week

NS

<0.01*

<0.01*

ACCEPTED MANUSCRIPT 1-2/week

0.2

1.1

1.6

2.3

3-4/week

1.1

1.3

3.2

4.3

5-6/week

3.8

5.3

6.6

7.9

7/week

94.8

92.1

87.9

83.7

Never

3.7

4.7

3.6

5.0

<1/week

66.3

66.3

67.6

1-2/week

29.1

28

27.5

3-4/week

0.6

0.8

1.4

5-6/week

0.2

0.3

0.0

1/day

0.1

0.0

>1/day

0.0

0.0

<0.01*

79.9

1/day

13.5

2-3/day

5.4

4-6/day

1.1

>7/day

0.1

EP

1.0

<0.01*

SC

0.0

0.1

0.0

0.0

74.1

72.7

15.5

16.9

19.0

6.1

8.0

7.5

1.1

1.0

0.7

0.0

0.0

0.0

*Statistically significant, NS not significant, SSB (sugar sweetened beverage).

AC C

28.5

77.3

TE D

<1/day

65.3

0.0

M AN U

Glasses of SSB

RI PT

Takeaway meals

<0.01*

ACCEPTED MANUSCRIPT

Table II. Child NAPLAN scores by gender and parent education level. Results are reported as mean ± S.D and 95% confidence level.

Spelling

Grammar/Punct

Reading

Writing

Numeracy

Male

507.3 ± 97.9

523.9 ± 105.1

528.8 ± 103.8

498.4 ± 95.1

531.4 ± 109.8

Female

524.6 ± 92.8

542.5 ± 99.2

540.2 ± 97.3

526.8 ± 91.2

517.3 ± 98.9

Mean difference with 95% C.I

17.4 (11.6–23.2)

18.6 (12.3–24.8)

SC

RI PT

NAPLAN scores

11.4 (5.2–17.6)

28.4 (22.7–34.1)

14.1 (7.7–20.5)

P-Value

<0.01*

<0.01*

<0.01*

<0.01*

<0.01*

High school

506.8 ± 95.9

511.1 ± 105.1

514.7 ± 102.7

499.4 ± 90.9

507.6 ± 99.2

Technical/Trade including certificate or diploma Undergraduate degree

502.8 ± 98.0

515.4 ± 104.9

513.8 ± 102.3

496.7 ± 90.9

511.9 ± 105.5

531.7 ± 90.9

550.6 ± 96.6

550.3 ± 91.4

528.5 ± 95.5

544.7 ± 102.0

Postgraduate degree

533.6 ± 90.1

559.5 ± 93.5

562.1 ± 92.5

534.7 ± 91.4

546.3 ± 102.5

P-Value

<0.01*

<0.01*

<0.01*

<0.01*

<0.01*

EP

TE D

Parent Education Level

M AN U

Gender

AC C

*Statistically significant P- value indicates significance of difference in NAPLAN scores between categories of gender and parent education level.

ACCEPTED MANUSCRIPT

Table III. Results of linear mixed models analysis of child NAPLAN scores with dietary behaviours for grades 3, 5, 7 and 9 combined.

RI PT

Results are reported as mean ± S.E.

Spelling

Grammar/Punct

Reading

Writing

Numeracy

Never

518.9 ± 21.0

485.6 ± 29.3a,b

539.4 ± 26.3

447.7 ± 26.5a.b,c,d,e

498.2 ± 24.0

<1

539.9 ± 13.0

540.8 ± 17.4

SC

NAPLAN scores Dietary Behaviours

543.1 ± 15.8

512.6 ± 15.6a

536.1 ± 14.5

1-2

515.6 ± 6.4b

530.4 ± 9.1c

538.2 ± 7.9a

518.8 ± 8.7b

527.1 ± 7.3

3-4

520.2 ± 4.1a,c

537.7 ± 5.4d

542.1 ± 4.8b

515.0 ± 4.9c,f

532.8 ± 4.5

5-6

527.1 ± 3.2a,d

544.3 ± 3.9a

544.1 ± 3.7c

521.7 ± 3.3d,g

537.2 ± 3.4

7

533.1 ± 3.4b,c,d

550.1 ± 4.3b,c,d

554.2 ± 4.0a.b,c

533.7 ± 3.6e,f,g

543.5 ± 3.7

P value

<0.01*

0.05+

0.05+

<0.01*

0.06+

547.0 ± 3.6a,c

550.1 ± 3.4a,c

527.4 ± 3.0a

542.1 ± 3.2a.b,c

543.8 ± 4.9b

544.2 ± 4.4b,d

521.5 ± 4.3b

531.2 ± 4.1a.d

525.5 ± 7.0a,b

531.7 ± 6.2a,b,e

502.5 ± 6.3a.b

522.5 ± 5.7b

TE D

Glasses of SSB

M AN U

Vegetables with evening meal

528.8 ± 3.0

1

527.7 ± 3.8

2-3

522.4 ± 5.1

4-6

506.7 ± 10.3

514.3 ± 15.6c

504.0 ± 13.1c,d,e

499.8 ± 15.7

506.4 ± 12.0c,d

≥7

N.R

N.R

N.R

N.R

N.R

P value

0.11

<0.01*

<0.01*

<0.01*

<0.01*

570.8 ± 14.9

587.0 ± 25.2

573.1 ± 20.6

539.7 ± 23.8a

556.4 ± 17.8

AC C

EP

<1

Breakfast consumption 0

ACCEPTED MANUSCRIPT

1-2

515.7 ± 13.1

523.5 ± 17.3

532.2 ± 16.4

485.1 ± 13.7a,b,c

520.6 ± 13.7

3-4

530.5 ± 7.9

551.4 ± 11.7

542.1 ± 10.7

516.0 ± 10.1

539.0 ± 9.3

b

536.1 ± 5.2

c

540.4 ± 6.6

546.3 ± 5.7

515.8 ± 6.0

RI PT

528.8 ± 4.7

7

527.9 ± 2.9

544.7 ± 3.5

546.9 ± 3.3

525.6 ± 2.9

539.5 ± 3.1

P value

0.79

0.60

0.91

0.02*

0.56

0

528.3 ±16.9

546.0 ± 23.0

540.8 ± 21.0

503.4 ± 19.0

538.6 ± 18.5

<1 a week

539.7 ± 9.7

528.3 ± 13.6

SC

5-6

552.4 ± 12.1

512.2 ± 12.1

543.8 ± 10.9

1-2 a week 3-4 a week

M AN U

Pieces of fruit

520.0 ± 5.8

535.8 ± 8.5

522.3 ± 4.5

537.2 ± 6.3

531.2 ± 7.4 544.2 ± 5.6

a

529.0 ± 6.6

b

535.7 ± 5.0

c

515.5 ± 7.6 513.7 ± 5.6

5-6 a week

523.0 ± 4.5

540.6 ± 6.2

544.1 ± 5.4

517.0 ± 5.7

535.6 ± 5.0

1 a day

527.2 ± 3.3

542.6 ± 4.2

546.4 ± 3.8

524.2 ±. 6

535.6 ± 3.5

≥4 a day

532.5 ± 6.0

P value

0.34

Takeaway meals

549.6 ± 4.0

549.1 ± 3.7

530.7 ± 3.4

541.5 ± 3.4

543.7 ± 8.6

547.2 ± 7.5

530.2 ± 7.9

544.9 ± 6.8

0.30

0.42

0.04*

0.31

534.8 ± 8.8

542.0 ± 7.6

523.2 ± 8.1

537.8 ± 7.0

TE D

529.3 ± 3.2

EP

2-3 a day

a,b,c

532.7 ± 6.1

<1 a week

528.8 ± 3.0

547.5 ± 3.7

548.1 ± 3.5

526.9 ± 3.1

540.1 ± 3.2

1-2 a week

525.1 ± 3.5

539.4 ± 4.4

544.1 ± 4.0

519.7 ± 3.7

534.0 ± 3.7

3-4 a week

531.3 ± 13.3

523.4 ± 20.8

561.5 ± 17.5

478.4 ± 19.3

525.5 ± 16.2

5-6 a week

454.2 ± 23.7

474.9 ± 39.8

477.4 ± 33.7

464.4 ± 35.6

494.4 ± 28.3

1 a day

554.8 ± 50.2

548.7 ± 59.8

553.7 ± 56.1

549.5 ± 52.8

551.1 ± 52.2

AC C

Never

ACCEPTED MANUSCRIPT

>1 a day P Value

N.R

N.R

N.R

N.R

N.R

0.61

0.19

0.67

0.07

0.31

+

RI PT

*Statistically significant; Statistical trend; SSB (sugar sweetened beverage); NR (not reported). Analysis carried by Linear mixed models with calendar

year, parent education level as a proxy for socio-economic status (years of education), family and sex added as fixed effects.

AC C

EP

TE D

M AN U

SC

P-value indicates significance of difference in NAPLAN scores between categories for dietary behaviours based on pairwise comparison of dietary behaviour categories, indicated by a, b, c, d, e, f

1

ACCEPTED MANUSCRIPT ATR subjects eligible to participate n=6854

Did not provide NAPLAN and dietary data n=38 - Did not return Child Specific Questionnaire - NAPLAN results not accessed

M AN U

Child Specific Questionnaires sent - Data on dietary behaviours - Questionnaire completed each year child sat NAPLAN tests

RI PT

Consented to participate n=2273 - Completed consent forms - Completed Family Specific Questionnaire

Did not consent to participate n = 4581 - Declined participation n = 1116 - Did not respond n = 3465

SC

Information pack sent to families - Consent forms - Family Specific Questionnaire - Data on demographics

TE D

Provided NAPLAN and dietary data for some or all school grades 3, 5, 7 and 9 n=2235 - Returned Child Specific Questionnaire - NAPLAN test results accessed - Repeat measures of some individual subjects

AC C

EP

Reported NAPLAN and dietary data for Grade 3 n=1185

Reported NAPLAN and dietary data for Grade 5 n=1147

Reported NAPLAN and dietary data for Grade 7 n=1053 Reported NAPLAN and dietary data for Grade 9 n=860

Figure 1. Flow of subjects through the Behaviour-Genetic Study of NAPLAN