The associations between physical activity, sedentary behaviour and academic performance

The associations between physical activity, sedentary behaviour and academic performance

Accepted Manuscript Title: The associations between physical activity, sedentary behaviour and academic performance Author: Carol Maher Lucy Lewis Pet...

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Accepted Manuscript Title: The associations between physical activity, sedentary behaviour and academic performance Author: Carol Maher Lucy Lewis Peter T. Katzmarzyk Dot Dumuid Leah Cassidy Tim Olds PII: DOI: Reference:

S1440-2440(16)00055-4 http://dx.doi.org/doi:10.1016/j.jsams.2016.02.010 JSAMS 1293

To appear in:

Journal of Science and Medicine in Sport

Received date: Revised date: Accepted date:

8-7-2015 10-2-2016 23-2-2016

Please cite this article as: Maher C, Lewis L, Katzmarzyk PT, Dumuid D, Cassidy L, Olds T, The associations between physical activity, sedentary behaviour and academic performance, Journal of Science and Medicine in Sport (2016), http://dx.doi.org/10.1016/j.jsams.2016.02.010 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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The associations between physical activity, sedentary behaviour and academic performance

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Short title: Academic performance & physical & sedentary activity

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Carol Mahera, PhD, Lucy Lewisa, PhD, Peter T. Katzmarzyk,b PhD, Dot Dumuidc, B.Physio (Hons),

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Leah Cassidyd, M.Sci and Tim Oldsa, PhD

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South Australia, Australia; bPennington Biomedical Research Center, Baton Rouge, Louisiana, USA;

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Health and Use of Time Group, School of Health Sciences, University of South Australia, Adelaide,

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School of Health Sciences, University of South Australia, Adelaide, South Australia, Australia; Department for Education and Child Development, Adelaide, South Australia, Australia.

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Address correspondence to: Dr Carol Maher; [email protected]

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15 Acknowledgements

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Leah Cassidy is an employee of the South Australian Department of Education and Child

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Development. Carol Maher is the recipient of a Post-Doctoral Fellowship Award from the Australian

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National Heart Foundation (100188). ISCOLE was funded by The Coca-Cola Company. The funder

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had no role in study design, data collection and analysis, decision to publish, or preparation of this

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manuscript. The other authors have no financial disclosures relevant to this article.

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Abbreviations

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MVPA - moderate to vigorous physical activity

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BMI – body mass index

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ISCOLE - The International Study of Childhood Obesity, Lifestyle and the Environment

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ICSEA - Index of Community Socio-Educational Advantage

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NAPLAN - National Assessment Program – Literacy and Numeracy

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CV - coefficients of variation

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CI - confidence interval

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The relationships between physical activity, sedentary behaviour and academic performance

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Abstract

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Objectives: To examine the relationships between children’s moderate-to-vigorous physical activity

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(MVPA), sedentary behaviours, and academic performance.

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measured physical activity and sedentary behaviour patterns, and academic performance using a

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standardised, nationally-administered academic assessment.

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Methods: A total of 285 Australian children aged 9-11 y from randomly selected schools undertook

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7-day 24 hour accelerometry to objectively determine their MVPA and sedentary behaviour. In the

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same year, they completed nationally-administered standardised academic testing (National

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Assessment Program – Literacy and Numeracy; NAPLAN). BMI was measured, and

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sociodemographic variables were collected in a parent-reported survey. Relationships between

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MVPA, sedentary behaviour and academic performance across five domains were examined using

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Generalized Linear Mixed Models, adjusted for a wide variety of sociodemographic variables.

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Results: Higher academic performance was strongly and consistently related to higher sedentary time,

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with significant relationships seen across all five academic domains (range F = 4.13, p = 0.04 through

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to F = 8.07, p = <0.01). In contrast, higher academic performance was only related to higher MVPA

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in two academic domains (writing F = 5.28, p = 0.02, and numeracy F = 6.28, p = 0.01) and was not

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related to language, reading and spelling performance.

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Conclusions: Findings highlight that sedentary behaviour can have positive relationships with non-

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physical outcomes. Positive relationships between MVPA and literacy and numeracy, as well as the

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well documented benefits for MVPA on physical and social health, suggest that it holds an important

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place in children’s lives, both in and outside of school.

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Key Words: physical activity, school, academic, sedentary

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Introduction

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Physical activity is recognized to have wide-ranging benefits for children’s physical and mental

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health.1 In contrast, sedentary behaviour is increasingly recognized for having important negative

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health impacts.2 Physical activity is frequently touted as being important to maximise academic

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performance, with proponents recommending regular bouts of exercise throughout the school day to

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optimize attention and learning.3

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A number of cross-sectional studies have examined the relationship between physical activity and

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academic outcomes. The findings are mixed, with one Australian study finding positive relationships

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between numeracy and literacy and physical education 4; and another finding a strong positive,

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relationship between academic scores (reading, numeracy and writing), and physical fitness/physical

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activity at the school level 5. Other studies indicate weak, positive associations 6, 7, though many

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studies have reported no association 8, 9. However, such studies have typically been limited by

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methodological considerations. Several studies have failed to adjust for key socio-demographic

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confounders (e.g. socio-economic status).10, 11 Furthermore, many studies have employed

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measurement tools that have limited ability to accurately characterize typical physical activity. For

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example, some studies have examined the association between academic performance and sports or

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school-based physical education participation, without considering other forms of physical activity,

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like extracurricular sports and informal physical activities. 12, 13 Other studies have relied on subjective

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reports of physical activity from children or their teachers14; tools which are limited in their reliability

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and validity.

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To date, few studies have employed objective physical activity measurement techniques, such as

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accelerometers, to examine the links between physical activity and academic outcomes. In Sweden,

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Kwak et al.15 related school report grades to accelerometry data in 239 students (mean age of 16 y)

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and found no significant associations, with the exception of a significant, positive correlation between

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vigorous physical activity and academic performance amongst girls. More recently, LeBlanc et al.16 4 Page 4 of 20

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found no correlation between accelerometer-measured physical activity and academic achievement

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among students in Louisiana, USA (n=261, mean age 10 y).

88 Several studies have examined the relationship between screen time and academic achievement, with

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most reporting a negative association,12 with some exceptions.17, 18 Otabe et al measured self-reported

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total sedentary time in 140 13 year old Japanese students, and found that it was not associated with

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academic performance on weekend days, but that weekday self-reported sedentary time was positively

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associated with academic achievement.19

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To our knowledge, only one study has examined the association between objectively measured

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sedentary behaviour and academic achievement. Syvaoja et al9 used accelerometers to characterize 7-

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day physical activity and sedentary behaviour in 277 Finnish children (mean age of 12.2 y). No

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significant correlations were found between either physical activity levels or sedentary behaviour and

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academic performance. However, a limitation of this study was that academic achievement was

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reported by classroom teachers, and the reliability of such reports, is unclear.

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This study aimed to address the gaps in the current literature, by examining the associations between

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children’s accelerometer-measured physical activity and sedentary behaviour patterns, and academic

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performance, using a standardised, nationally-administered academic assessment.

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Methods

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Participants for this study were drawn from the Australian arm of the International Study of

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Childhood Obesity, Lifestyle and the Environment (ISCOLE). ISCOLE is a multi-national cross-

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sectional study involving around 7000 children from 12 countries. See Katzmarzyk et al. for full

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methodological details.20

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Participants included in this analysis were Australian children in the 5th grade (aged 9-11 y) from 26

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randomly-selected schools from the city of Adelaide and surrounding areas. A list of all government, 5 Page 5 of 20

Catholic and independent school in Adelaide and surrounding areas was compiled. Schools were

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linked to socioeconomic status data, based upon ‘Index of Community Socio-Educational Advantage’

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(ICSEA) scores - basket socioeconomic indicators published by the Australian government on the

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‘Myschool” website.21 The list was then sorted into socioeconomic status tertiles. Schools were

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randomly selected from tertiles using a random-number sequence, to ensure tertiles were equally

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represented. The school participation rate was 43%, and the child participation rate was 57%. Data

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collection occurred between October 2011 and December 2012. In all, the Australian ISCOLE sample

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comprised 528 children. To be included in this particular analysis, participants were required to have

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valid accelerometry (n=491), academic performance (n=333), BMI (n=333), and complete

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sociodemographic covariate data (n=285). Thus the total sample included was 285.

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Ethical approval for the Australian protocol was provided by the University of South Australia

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Human Research Ethics Committee, the South Australian Department of Child Development, and the

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Catholic Education Department of South Australia. ISCOLE is registered on ClinicalTrials.gov,

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Identifier: NCT01722500.

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Objective physical activity and sedentary behaviour were measured using 7 day, 24 h accelerometry.

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Participants wore the Actigraph GT3X+ accelerometer (ActiGraph, Ft. Walton Beach, FL) on their

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right hip. The minimal amount of accelerometer data that was considered acceptable was 4 days with

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at least 10 hours of waking-hours wear time per day, including at least one weekend day. Full details

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of the ISCOLE accelerometry protocol have been described elsewhere.22

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Algorithms developed at the ISCOLE coordinating center were used to carry out quality control

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procedures, derive waking-hours wear time, and summarise minute-to-minute data.22 Data were

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processed in one-minute epochs using Treuth et al.’s23 cut-offs (i.e. sedentary <100 cpm; light 100 to

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≤ 3000 cpm; moderate >3000 to ≤5200; vigorous >5200). Participants’ mean MVPA and mean

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sedentary time were calculated for: weekdays, weekend days, all days (mean of weekday and

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weekend day values, weighted 5:2), weekday in-school time (between the hours of 0900 and 1500)

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and weekday “critical window”24 time (between 1530 and 1830).

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and Numeracy (NAPLAN) grade 5 results. NAPLAN is a standardised, compulsory, annual

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assessment undertaken in all Australian schools. Standardised scores are produced across five

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academic domains: language (grammar and punctuation), reading, writing, spelling and numeracy,

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where the mean is 500, and the standard deviation is 100, and a higher score indicating greater

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academic competence. Overall academic performance was determined by calculating the mean of the

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five domain scores. Academic performance data was collected separately to the ISCOLE protocol,

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and was accessed via the Department of Children’s Education and Development with parentss

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permission.

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Body mass index was calculated, and converted to BMI z-scores using the World Health Organisation

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system.25 Family SES was determined from parent-reported household income and highest education

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level. Annual gross household income (excluding lump sums, employer contributions, tax returns and

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insurance payments) was collected in the following Australian dollar categories (note $1AUD equals

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approximately $0.75USD): 1 = <$10,000; 2 = $10,000 to $29,999; 3 = $30,000 to $49,999; 4 =

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$50,000 to $69,999, 5 = $70,000 to $89,999, 6 = $90,000 to $109,999; 7 = $110,000 to $139,999; 8 =

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≥$140,000. The highest educational level attained by either parent was collapsed into the following

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categories: 1 = < high school; 2 = some high school; 3 = completed high school; 4 = some post-

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secondary (e.g. vocational diploma or certificate); 5 = bachelor degree; 6 = post-graduate. Number

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siblings and parents in the household was determined from parent-report (note that parental figures

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could include biological mothers, biological fathers, step mothers, step fathers and legal guardians).

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Sedentary behaviour, academic performance (NAPLAN scores) and BMI z-scores were normally

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distributed. MVPA data were skewed, so were log-transformed prior to analysis. To account for the

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nested survey design (participants nested within schools), multilevel analyses were conducted using 7 Page 7 of 20

Generalized Linear Mixed Models in SPSS Version 21. Models treated academic performance as the

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dependent variable, participant and school as random effects and MVPA and sedentary behaviour as

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fixed effects. Additionally, socio-demographic covariates (child BMI z score, ethnicity, gender,

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highest household education, household income, marital status, mothers employment hours, number

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of siblings) were included as fixed effects. Models were repeated focussing on MVPA and sedentary

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behaviour across different time periods - whole week, weekend days, within school hours on

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weekdays, and during the critical window on weekdays.

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The combined relationships between MVPA and sedentary behaviour and academic performance

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were further examined using mixed models. To facilitate easy interpretation of these findings,

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participants were dichotomised into high and low MVPA and sedentary groups based on sex-specific

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median splits, and four combined groups formed (low MVPA/low sedentary; high MVPA/low

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sedentary; low MVPA/high sedentary and high MVPA/high sedentary). Generalized Linear Mixed

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Models were then used to determine whether overall academic performance varied according to

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group. Significance for all analyses was set at p<0.05 without adjustment for multiple comparisons,

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however exact p values have been reported.

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Results

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Participants’ characteristics and breakdown of NAPLAN scores are shown in Table 1. Additionally,

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demographic characteristics of participants excluded from the analysis (due to missing data for

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MVPA, sedentary behaviour, academic performance or covariates) are presented. There were limited

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differences between children included and excluded in the analyses; they did not differ based upon

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sex, BMI z-score, MVPA, sedentary behaviour, parental education level or number of siblings,

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however, excluded children were slightly older, came from households with fewer parents, and had

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lower academic performance. NAPLAN scores across the five academic domains were similar to

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national averages (500), though the standard deviations were smaller than national averages.

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****Insert Table 1 about here**** 8 Page 8 of 20

197 Table 2 shows the relationships between MVPA, sedentary behaviour and NAPLAN scores. Across

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the week as a whole, sedentary behaviour was consistently, positively related to academic

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performance in all academic domains (see Table 2). Children with high levels of sedentary behaviour

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scored, on average, 24 points more than those with low sedentary time (mean overall proficiency

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score 505.3 versus mean 481.3, where the “high” sedentary group got 433 minutes per day of

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sedentary time, versus 331 minutes in the “low” sedentary group).

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Academic performance was inconsistently related to MVPA across the whole week. Writing

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achievement (F = 5.28, p = 0.02) and numeracy achievement (F = 6.28, p = 0.01) were positively,

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significantly related to MVPA, as was overall proficiency score (F = 4.43, p = 0.04). The F-values

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revealed that the relationships between academic performance and MVPA (F = 4.43, p = 0.04) were

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smaller in magnitude than those between academic performance and sedentary behaviour (F = 14.04,

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p = <0.01). Children categorised as having high MVPA scored, on average, 10 points more than those

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with low MVPA (overall proficiency score 497.9 versus mean 488.2, where the “high” MVPA group

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achieved a mean of 45 minutes per day versus a mean of 19 minutes for the “low” MVPA group).

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Table 2 also presents the relationships between physical activity, sedentary behaviour, and NAPLAN

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scores across specific day types and segments. For MVPA and sedentary behaviours performed within

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school hours, there were few significant relationships with academic performance. During the

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weekday “critical window” (the 3 hours after school finishes), sedentary time was positively related to

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overall academic performance (F = 6.87, p < 0.01), and more specifically, language (F = 3.84, p =

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0.05), reading (F = .65, p = 0.01), and spelling performance (F = 4.88, p = 0.03). Children with high

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levels of sedentary behaviour during the critical window scored, on average, 5 points more than those

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with low sedentary time (mean overall proficiency score 495.0 versus mean 490.5). During the 9 Page 9 of 20

weekend time period, sedentary time was positively related to overall academic performance (F=6.87,

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p<0.01), and specifically, language (F = 7.53, p = <0.01), reading (F = 7.19, p = <0.01), and spelling

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performance (F = 4.86, p = 0.03). Children with high levels of sedentary behaviour on weekends

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scored, on average, 16 points more than those with low sedentary time (mean overall proficiency

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score 501.1 versus mean 485.4). There were no relationships between academic performance and

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MVPA during the critical window and the weekend, except for a significant, positive relationship

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between writing and MVPA during the critical window (F = 6.80, p = 0.01).

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The combined impact of sedentary behaviour and MVPA on academic performance was examined

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using mixed model analyses, adjusting for school. Results confirmed that there were significant

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differences across combined MVPA/sedentary categories (Figure 1). Post hoc analyses revealed that

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children in the low MVPA/low sedentary group achieved significantly lower NAPLAN results than

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children in any other MVPA/sedentary behaviour combination. Amongst children with low sedentary

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time, higher MVPA was related to significantly higher academic achievement. However, amongst

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children with high sedentary time, higher MVPA was unrelated to higher academic performance

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(p=0.38).

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Discussion

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This study examined the relationships between physical activity, sedentary behaviour and academic

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performance. It was found that academic performance was largely unrelated to physical activity, with

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a few exceptions in which higher MVPA was weakly related to higher academic achievement,

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particularly in the domains of writing and numeracy. In contrast, higher sedentary time was quite

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consistently, and more strongly, related to higher academic performance. These relationships were

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seen across academic domains, and for sedentary behaviours undertaken on weekends, and during the

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“critical” window after school on weekdays, though less so for sedentary behaviours undertaken

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during school hours. 10 Page 10 of 20

253 To our knowledge, this is the first study to consistently identify relationships between total sedentary

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time and academic performance. There has recently been a great deal of interest in the health

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associations with sedentary behaviour, with many studies reporting deleterious associations. In

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contrast, findings of the current study highlight that sedentary behaviour can have favourable

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relationships with academic performance. A finding which is consistent with that reported by Otabe et

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al19, however the current study offers additional support given that it was conducted in a larger sample

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and using an objective, rather than subjective, measure of sedentary time. Taken together with

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findings from previous studies focused on screen-based sedentary time which have shown, in general,

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a negative relationships with academic performance, this may suggest that non-screen-based

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sedentary behaviours may be underpinning the positive relationships. Evidence links academic

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performance with extra-curricular study time 26 in older children, however it is unclear whether this

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would be a strong mediating factor in the age group in the current study (9-11 year olds). Other non-

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screen sedentary behaviours, such as recreational reading, may be contributing to the relationships

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observed in this study.

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After adjusting for socio-demographic confounding factors, the relationships between academic

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performance and MVPA were weak, which is broadly consistent with previous research.7, 10, 11

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Statistically-significant relationships between academic performance and MVPA were observed in the

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specific academic domains of writing and numeracy. Impacts of MVPA and in particular, aerobic

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exercise, on cerebral blood flow, and neuroplasticity and executive function, 27 may underpin these

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associations. The patterns in the relationships between academic performance, sedentary behaviour

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and MVPA were remarkably consistent across non-school days/day-segments (e.g. weekend days and

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weekday “critical window”) but less so for in-school activities, when academic performance was

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unrelated to MVPA and sedentary behaviour.28 It is possible that the structure and routine of the

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school day limit the variability in MVPA and sedentary behaviours between children during school

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hours. Certainly, in the current dataset, the coefficients of variation (CV) for MVPA and sedentary

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behaviour on weekdays (MVPA CV = 18.8, 95% CI 17.2 - 20.5; sedentary behaviour CV = 17.5, 95%

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CI 16.0-19.0) were lower than on weekend days (MVPA CV = 40.5, 95% CI 36.7 – 40.5; sedentary

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behaviour CV = 26.4, 95% CI 24.1 – 28.7) offering support to this notion. Other studies have noted

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the ‘equalising’ influence of school, that is, there is less variation in the activities undertaken by

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students of high vs low SES when they are at school compared with outside of school.31 The fact that

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there were consistent relationships between activity patterns outside of school and academic

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performance highlight the importance of the family environment for achieving optimal academic

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outcomes, an issue widely recognised in the literature.29

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Key strengths of the current study were that it used high quality, objective measures of physical

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activity, sedentary behaviour and academic performance, which minimises the risk of bias impacting

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results. Relationships were examined across different day segments and types. The study included a

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relatively large sample, and the multilevel analysis method accounted for potential clustering of

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results within schools. Furthermore, a wide range of potential child- and family-level confounders

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were included in the multivariate models.

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Limitations must also be acknowledged. The participants, whilst randomly selected, were drawn from

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a single city in Australia. The school (43%) and child (57%) participation rates were modest, and are

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fairly typical of Australian school-based physical activity research. Schools that declined participation

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typically cited that they were too busy or already involved in other research projects, however it is

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possible that the moderate participation rates may have introduced bias. The included participants had

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higher NAPLAN scores, were older, and had fewer parents in household than participants with

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missing data. Therefore it is unclear whether the findings are generalizable to other geographic

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locations and wider demographic groups. Finally, although we used a wide range of socio-

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demographic characteristics as covariates, the possibility of residual confounding (for example,

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through differing parenting styles) cannot be excluded.

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The implications of this study’s findings should be considered. Firstly, the study highlighted that

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sedentary behaviours (particularly non-screen based behaviours) are favourably related to academic 12 Page 12 of 20

performance. In recent years there have been strong public health messages encouraging people to

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reduce their sedentary time. Since 2005, Australia’s physical activity guidelines for children have

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encouraged limiting of children’s screen-based sedentary time, and in the most recent iteration, non-

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screen sedentary time has also been targeted. Whilst inclusion of sedentary behaviour guidelines was

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done based upon expert advice, there is not scientific consensus on this issue, and some experts have

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questioned whether there is sufficient empirical evidence regarding the independent effects of

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sedentary behaviour to warrant sedentary behaviour public health guidelines.30 It appears that future

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research examining the relationships between sedentary time (particularly non-screen sedentary time)

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and a wide range of social and academic outcomes, rather than purely physical health outcomes, is

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warranted.

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The lack of strong and consistent relationships between physical activity and academic performance

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should be considered in the context of other research. This study suggests that physical activity may

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be beneficial for numeracy and literacy development, and furthermore, provides no evidence that it

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jeopardises academic performance. Given the wealth of quality scientific data showing that physical

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activity has many important and varied benefits for children’s physical and emotional health,1 we

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advocate that physical activity represents an important component of school curriculum and children’s

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out of school activities.

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Conclusions

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In conclusion, this study found that higher sedentary time was related to better academic performance

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across a variety of academic domains whilst higher MVPA was inconsistently related to higher

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writing and numeracy scores, but not other aspects of academic performance. This finding, whilst

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cross-sectional, suggests that sedentary behaviour, often considered detrimental for physical health,

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may have positive relationships for non-physical outcomes. The positive relationships between

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MVPA and writing and numeracy suggested by this study, as well as MVPA’s well documented

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benefits for physical and social health, suggest that it holds an important place in children’s lives, both

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within and outside of school. 13 Page 13 of 20

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writing performance. -

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Higher moderate to vigorous physical activity appears to be related to higher numeracy and

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Practical Implications

Higher sedentary time appears to be consistently related to higher performance across all academic domains, including language, reading, spelling, writing, and numeracy.

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Whilst sedentary behaviour has commonly been related to detrimental outcomes (particularly health related outcomes), our findings highlight that detrimental relationships are not

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universal, as sedentary behaviour appears to have beneficial relationships with academic

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performance.

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Acknowledgements

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L.C. is an employee of the South Australian Department of Education and Child Development. C.M.

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is the recipient of a Post-Doctoral Fellowship Award from the Australian National Heart Foundation

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(100188). ISCOLE was funded by The Coca-Cola Company. The funder had no role in study design,

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data collection and analysis, decision to publish, or preparation of this manuscript. The other authors

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have no financial disclosures relevant to this article.

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Penedo, FJ, Dahn, JR. Exercise and well-being: a review of mental and physical health benefits associated with physical activity. Curr Opin Psychiatry 2005. 18(2): 189-193. Tremblay, MS, LeBlanc, AG, Kho, ME et al. Systematic review of sedentary behaviour and health indicators in school-aged children and youth. Int J Behav Nutr Phys Act 2011. 8(1): 98. Budde, H, Voelcker-Rehage, C, Pietraßyk-Kendziorra, S et al. Acute coordinative exercise improves attentional performance in adolescents. Neurosci Lett 2008. 441(2): 219-223. Telford, RD, Cunningham, RB, Fitzgerald, R et al. Physical education, obesity, and academic achievement: a 2-year longitudinal investigation of Australian elementary school children. Am J Public Health 2012. 102(2): 368-374. Telford, RD, Cunningham, RB, Telford, RM et al. Schools with fitter children achieve better literacy and numeracy results: Evidence of a school cultural effect. Pediatr Exerc Sci 2012. 24(1): 45. Keeley, TJ, Fox, KR. The impact of physical activity and fitness on academic achievement and cognitive performance in children. Int Rev Sport Exerc Psychol 2009. 2(2): 198-214. Trudeau, F, Shephard, RJ. Physical education, school physical activity, school sports and academic performance. Int J Behav Nutr Phys Act 2008. 5(1): 10. Esmaeilzadeh, S, Kalantari, H-A. Physical fitness, physical activity, sedentary behavior and academic performance among adolescent boys in different weight statuses. Mediterranean Journal of Nutrition & Metabolism 2013. 6(3): 207-216. Syväoja, H, Kantomaa, MT, Ahonen, T et al. Physical activity, sedentary behavior, and academic performance in Finnish children. Med Sci Sports Exerc 2013. 45(11): 2098 - 2104. Coe, DP, Pivarnik, JM, Womack, CJ et al. Effect of physical education and activity levels on academic achievement in children. Med Sci Sports Exerc 2006. 38(8): 1515. Ahamed, Y, Macdonald, H, Reed, K et al. School-based physical activity does not compromise children's academic performance. Med Sci Sports Exerc 2007. 39(2): 371. Carlson, SA, Fulton, JE, Lee, SM et al. Physical education and academic achievement in elementary school: data from the early childhood longitudinal study. Am J Public Health 2008. 98(4): 721-727. Schumaker, JF, Small, L, Wood, J. Self-concept, academic achievement, and athletic participation. Percept Mot Skills 1986. 62(2): 387-390. Kristjnsson, lL, Sigfsdttir, ID, Allegrante, JP et al. Adolescent health behavior, contentment in school, and academic achievement. Am J Health Behav 2009. 33(1): 69-79. Kwak, L, Kremers, SP, Bergman, P et al. Associations between physical activity, fitness, and academic achievement. J Pediatr 2009. 155(6): 914-918. e1. LeBlanc, MM, Martin, CK, Han, H et al. Adiposity and physical activity are not related to academic achievement in school-aged children. J Dev Behav Pediatr 2012. 33(6): 486-94. Drummond, A, Sauer, JD. Video-games do not negatively impact adolescent academic performance in science, mathematics or reading. PLoS ONE 2014. 9(4). Haapala, EA, Poikkeus, A-M, Kukkonen-Harjula, K et al. Associations of physical activity and sedentary behavior with academic skills–a follow-up study among primary school children. PLoS ONE 2014. 9(9). Otabe, Y, Hattori, S, Yamatsu, K. Sedentary behavior and academic performance in Japanese junior high school students. Sci Sport 2014. 29(0): S19. Katzmarzyk, PT, Barreira, TV, Broyles, ST et al. The International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE): design and methods. BMC Public Health 2013. 13(1): 900. 15 Page 15 of 20

25. 26. 27. 28. 29. 30.

ip t

24.

cr

23.

us

22.

Australian Curriculum, Assessment and Reporting Authority, My School: Guide to understanding 2012 Index of Community Socio-educational Advantage (ICSEA) Values. 2013. Tudor-Locke, C, Barreira, TV, Schuna, JM et al. Improving wear time compliance with a 24hour waist-worn accelerometer protocol in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE). Int J Behav Nutr Phys Act 2015. 12(1). Treuth, MS, Schmitz, K, Catellier, DJ et al. Defining accelerometer thresholds for activity intensities in adolescent girls. Med Sci Sports Exerc 2004. 36(7): 1259 – 1266. Atkin, AJ, Gorely, T, Biddle, S et al. Critical hours: physical activity and sedentary behavior of adolescents after school. Pediatr Exerc Sci 2008. 20(4): 446-56. De Onis, M, WHO child growth standards: length/height-for-age, weight-for-age, weight-forlength, weight-for-height and body mass index-for-age: methods and development, W.H. Organisation, Editor. 2006: Geneva. Duckworth, AL, Seligman, ME. Self-discipline outdoes IQ in predicting academic performance of adolescents. Psychol Sci 2005. 16(12): 939-944. Van Praag, H, Christie, BR, Sejnowski, TJ et al. Running enhances neurogenesis, learning, and long-term potentiation in mice. Proc Natl Acad Sci 1999. 96(23): 13427-13431. Downey, DB, Von Hippel, PT, Broh, BA. Are schools the great equalizer? Cognitive inequality during the summer months and the school year. Am Sociological Rev 2004. 69(5): 613-635. Davis-Kean, PE. The influence of parent education and family income on child achievement: the indirect role of parental expectations and the home environment. J Fam Psychol 2005. 19(2): 294. Katzmarzyk, P. Increasing physical activity or decreasing sedentary behavior to improve children's health? in Global Summit on the Physical Activity of Children, May 19-22. 2014. Toronto, Canada.

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21.

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401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424

pt Ac ce

426

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425

16 Page 16 of 20

426 427

Figure Legend

428

FIGURE 1 The associations between combined MVPA and sedentary behaviour categories, and

429

academic performance.

ip t

430

Ac ce

pt

ed

M

an

us

cr

431

17 Page 17 of 20

FIGURE 1 The associations between combined MVPA and sedentary behaviour categories,

432

and academic performance

an

us

cr

ip t

431

M

433 434

a denotes significant difference from low MVPA/low sedentary category

436

b denotes significant difference from high MVPA/low sedentary category

437

c denotes significant difference from low MVPA/high sedentary category

438

d denotes significant difference from high MVPA/high sedentary category

440

pt

Ac ce

439

ed

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18 Page 18 of 20

TABLE 1 Demographic characteristics of participants included and excluded in analyses Excluded from

p (t-test/

analysis

analysis*

chi-square)

n (% males)

285 (44.9% males)

243 (43.4% males)

0.64

Age, years (SD)

10.2 (0.6)

10.4 (0.5), n=243

<0.001

BMI z-score (SD)

0.60 (1.16)

0.62 (1.07), n=243

0.84

Daily MVPA; mean minutes (SD)

31.5 (18.4)

34.1 (17.2), n=206

0.10

Daily sedentary; mean minutes (SD)

382.0 (65.9)

375.7 (63.6), n=206

0.27

0.005

ip t

Included in

0

0 (0.0)

5 (2.1)

(%)

1

42 (14.7)

50 (20.6)

2

243 (85.3)

178 (73.3)

0

14 (4.9)

1

125 (43.9)

2

85 (29.8)

3+

59 (20.7)

us 22 (9.1)

105 (43.2) 63 (25.9) 40 (16.5)

Highest parental

≤ High school

54 (18.9)

44 (18.1)

education; n (%)

Diploma

104 (36.5)

85 (35.0)

≥ Bachelor

127 (44.6)

82 (33.7)

496.7 (56.1)

467.6 (60.1), n=75

Mean standardised NAPLAN scores

0.44

<0.001

496.4 (SD 83.9)

pt

Language

0.17

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M

Number of siblings; n (%)

cr

Parents in household; n

ed

440

442

503.4 (SD 78.6)

Spelling

496.9 (SD 69.7)

Ac ce

441

Reading Writing

474.2 (SD 55.2)

Numeracy

483.1 (SD 63.9)

* n doesn’t always add up to 243 due to missing data

443

19 Page 19 of 20

ip t cr

school hours and critical window), and on weekends. Language

Reading

Spelling

P Value

F

P Value

F

LOG MVPA

1.97

0.16

0.91

0.34

SB

8.07

<0.01

18.65

LOG MVPA

0.19

0.66

0.01

SB

0.51

0.47

F

P Value

F

P Value

2.10

0.15

5.28

0.02

6.28

0.01

4.43

0.04

7.22

<0.01

4.13

0.04

5.46

0.02

14.04

<0.01

0.92

0.69

0.41

4.59

0.03

2.38

0.12

0.72

0.40

4.08

0.04

0.40

0.53

0.27

0.61

3.16

0.08

1.96

0.16

0.86

0.02

0.88

1.62

0.20

6.80

0.01

1.72

0.19

1.17

0.28

3.84

0.05

0.65

0.01

4.88

0.03

3.71

0.06

1.55

0.21

6.87

<0.01

LOG MVPA SB

Ac c

Weekend

M

d

<0.01

ep te

SB

Scores Average

P Value

Within school hours

0.03

Numeracy

F

Whole week

LOG MVPA

Writing

P Value

an

F

Critical window

us

TABLE 2 The associations between academic performance and MVPA and sedentary behaviour, across the whole week, on weekdays (within-

0.58

0.45

0.00

>0.99

0.00

0.97

0.00

1.00

2.38

0.12

1.17

0.28

7.53

<0.01

7.19

<0.01

4.86

0.03

1.28

0.26

2.76

0.10

6.87

<0.01

Models adjusted for school clustering, MVPA, sedentary behaviour, child BMI z score, ethnicity, gender, highest household education, household income, marital status, mothers employment hours and number of siblings.

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