weight status of young hildren exploring …...weight status of young children: exploring the...

WEIGHT STATUS OF YOUNG CHILDREN:

EXPLORING THE RELATIONSHIP WITH

SLEEP AND LIGHT EXPOSURE.

Cassandra Lee Pattinson

BAPsycSc (Hons)

Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy

Centre for Children’s Health Research, School of Psychology and Counselling

Institute of Health and Biomedical Innovation

Faculty of Health

Queensland University of Technology

2017

Weight Status of Young Children: Exploring the relationship with sleep and light exposure. i

ii Weight Status of Young Children: Exploring the relationship with sleep and light exposure.

Keywords

Actigraphy; Anthropometry; Body Mass Index; Childcare; Children; Circadian

Rhythms; Cross-Sectional Analysis; Early Childhood Education and Care;

Environmental Light; E4Kids; Homeostatic Sleep; Light; Measurement; Napping;

Preschool; Sleep Duration; Sleep in Childcare; Sleep Parameters

Weight Status of Young Children: Exploring the relationship with sleep and light exposure. iii

Abstract

The problem of paediatric obesity remains significant. It is estimated that 42

million children, under the age of 5 years are classified as overweight or obese. Due

to the significant negative psychosocial and health sequelae associated with obesity

in childhood, both immediate and long-term, early intervention is vital. However,

current interventions directed at diet and physical activity in early childhood have

had limited success in stemming the problem. As such there is a need to identify

modifiable mechanisms which may impact on weight in children, to both increase

knowledge and efficacy of obesity prevention and intervention strategies. This

program of research aimed to investigate the potential influence of two such

environmental mechanisms on children’s weight status, sleep and light exposure. To

do this, a series of three investigations were undertaken, presented as three research

papers. The first addressed methodology by examining the current use of

international growth standards. The second examined the significant sleep

parameters proposed to influence weight status in a large cohort of Australian

children. The third, presented a novel investigation of the effects of sleep and

environmental light exposure on young children’s weight status.

Paper 1 establishes a rationale for selection of growth standards in defining

the prevalence of the obesity problem in child populations. The paper approached

this problem in two ways. Firstly a systematic review of the studies of Australian

preschool children (aged between 3-5 years), conducted between, 2006-2016 (when

all three standards were been available for use) was carried-out. Twenty studies were

identified as part of the review. The majority of studies (16/20) reported weight

status of Australian children, using the IOTF standards. Secondly, an investigation of

iv Weight Status of Young Children: Exploring the relationship with sleep and light exposure.

the prevalence estimates yielded by each of the three international growth standards

was tested on a cohort of Australian preschool aged (3-5 years) children, N = 1,926.

In-line with previous research it was found that the prevalence of overweight and

obesity produced by the three standards varied significantly, Center for Disease

Control (CDC; 33.1%), International Obesity Task Force (IOTF; 21.7%), and the

World Health Organisation (WHO; 9.3%). This paper concludes that judicious

selection of growth standards, taking account of their underpinning methodologies is

needed.

Paper 2, aimed to examine the independent associations of recognised sleep

parameters on young children’s weight status. The sample was derived from the

second year (2011) of the Effective Early Educational Experiences for children

(E4Kids) study (N = 1,111 children aged between 3-6 years old). Associations were

examined and general linear modelling, with adjustment for significant confounding

variables, assessed the impact of the sleep parameters of; night-sleep duration, total

sleep duration, napping frequency, sleep timing (onset, offset and midpoint), and

severity of sleep problems on standardised body mass index (BMI z-score). Separate

models were run for the whole sample and then stratified by gender. In the whole

sample, after adjustment for significant confounding variables, there was a

significant association between short sleep duration, sleeping less than 10 hours per

night and increased BMI z-score. Lending support to current international

recommendations of sleep duration, for children within this age group. Gender

analysis revealed that for girls, there were no associations between any of the sleep

parameters and BMI z-score. However, for boys, after adjustment for significant

control variables, short sleep duration and napping frequency were significant

independent predictors of weight status. These results identify a complex relationship

Weight Status of Young Children: Exploring the relationship with sleep and light exposure. v

between sleep and body mass that implicates gender. Potential mechanisms that

might explain gender differences warrant further investigation.

Paper 3, represents the first investigation of the effect of light exposure on

body mass in young children. Objectively measured environmental light exposure,

sleep, activity and body mass was assessed in 48 preschool-aged children at baseline,

and their body mass was measured again 12 months later. At baseline, after

controlling for sleep duration, sleep midpoint, and activity, moderate intensity light

exposure (~200lux) earlier in the day was associated with increased body mass.

Increased duration of total light exposure (>10 lux) at baseline was predictive of

increased body mass 12-months later, even after controlling for baseline sleep

duration, sleep timing, BMI, and activity. The findings of this paper identify that

light exposure may be a significant contributor to the obesogenic environment during

early childhood.

Collectively this body of research has made significant contributions to

current understanding of child weight status in three ways: through the

documentation of current methodologies used to classify body mass in young

children; adding to the current literature on the potential role of sleep parameters for

child weight; and the first documentation of the significant influence of

environmental light exposure on the weight of preschool aged children. In doing so,

this thesis provides a formative evidence base to inform ongoing research and shines

light on potential pathways for new interventions on child health.

vi Weight Status of Young Children: Exploring the relationship with sleep and light exposure.

Published or Submitted Manuscripts Resulting from the PhD Research

Program

Pattinson C. L., Staton, S. L., Smith, S. S., Trost S. G., Sawyer, E. & Thorpe K. J.

(submitted). Weighing in on international growth standards: Testing the case in

Australian preschool children. Obesity Reviews. (IF = 7.51) (See Chapter 5).

Pattinson C. L., Smith, S. S., Staton, S. L., Trost S. G. & Thorpe K. J. (submitted).

Children’s sleep and weight status: A cohort study of the sleep parameters at

play. Journal of Sleep Research. (IF = 3.09) (See Chapter 6).

Pattinson C. L., Allen, A. C., Staton, S. L., Thorpe K. J. & Smith, S. S. (2016).

Environmental Light Exposure is Associated with Increased Body Mass in

Children. PLOS One. (IF = 3.23, Q1) (See Chapter 7).

Staton, S. L., Thorpe, K. J., Pattinson, C. L., Smith, S. S., & Irvine, S. (2017).

Professional development package and resources for guiding sleep practices in

early childhood education and care services in Queensland, Final Report of

Phase 2. Queensland Government Department of Education and Training.

(Government Report)

Staton, S. L., Thorpe, K. J., Pattinson, C. L., Smith, S. S., Irvine, S., Hassall, S.,

Fuller, T., Wihardjo, K., & Sinclair, D. (2016). Professional development

package and resources for guiding sleep practices in early childhood education

and care services in Queensland, Final Report of Phase 1. Queensland

Government Department of Education and Training. (Government Report)

Staton, S. L., Smith, S. S., Pattinson, C. L., & Thorpe, K. J. (2016). Mandatory

naptimes and group napping trajectories in childcare: An observational study.

Behavioural Sleep Medicine. (IF=1.56).

Weight Status of Young Children: Exploring the relationship with sleep and light exposure. vii

Staton, S. L., Marriott, A., Pattinson, C. L., Smith, S. S., & Thorpe, K.

J. (2016) Supporting sleep in early care and education: An assessment of

observed sleep-times using a sleep practices optimality index. Sleep Health.

Sinclair, D. M., Staton, S. L., Pattinson, C. L., Smith, S. S., Marriott, A., & Thorpe,

K. (2016) What parents want: Parent preference regarding sleep for their

preschool child when attending Early Care and Education. Sleep Health.

Staton, S. L., Irvine, S., Pattinson, C. L., Smith, S. S. & Thorpe, K. J. (2015). The

sleeping elephant in the room: Practices and policies regarding sleep and rest

time in ECEC. Australasian Journal of Early Childhood. (IF=.72).

Staton, S. L., Smith, S. S., Pattinson, C. L., & Thorpe, K. J. (2015). Mandatory

naptimes in childcare and children’s night-time sleep. Journal of Developmental

and Behavioural Pediatrics. (IF=2.12).

Thorpe, K. J., Staton, S. L., Sawyer, E., Pattinson, C. L., Haden, C., & Smith, S. S.

(2015) Napping, development and health from 0-5 years: A systematic review.

Archives of Disease in Childhood. (IF=2.91).

Pattinson, C. L., Staton, S. L., Smith, S. S., Sinclair, D. M., & Thorpe, K. J. (2014).

Emotional climate and behavioural management during sleep time in Early

Childhood Education settings. Early Childhood Research Quarterly. (IF=2.06).

http://eprints.qut.edu.au/92550/




viii Weight Status of Young Children: Exploring the relationship with sleep and light exposure.

Highlighted Presentations and Published Abstracts Resulting from the PhD

Research Program

(Note. Published abstracts are provided in full in Appendix A.)

Pattinson, C., Staton, S., Thorpe, K., & Smith, S. (2016). Naptime practices in

childcare are associated with body mass of preschool children. SLEEP 2016, the

30th

Annual Meeting of the Associated Professional Sleep Societies, Denver, CO.

SLEEP. Vol. 39, Abstract Supplement, pA19.

Smith, S. S., Pattinson, C. L., Thorpe, K. J., Irvine, S. S., Wihardjo, K., & Staton S.

L. (2016). Early childhood educator’s experiences with sleep. SLEEP 2016, the

30th

Annual Meeting of the Associated Professional Sleep Societies, Denver, CO.


Pattinson, C., Allan, A., Staton, S., Thorpe, K., & Smith, S. (2015). Physiological

consequences of light exposure in preschool children. Sleep and Biological

Rhythms. Vol. 13, Supplement S1, p08.

Pattinson, C., Allan, A., Thorpe, K., Staton, S., Smith, S. (2015). Dim light duration

predicts body mass index of young children. SLEEP 2015, the 29th Annual

Meeting of the Associated Professional Sleep Societies, Seattle, WA. SLEEP.

Vol. 38, Abstract Supplement, pA28.

St Pierre, L., Staton, S. L., Pattinson, C. L., Thorpe, K. J., & Smith, S., (2015).

Sleep deprivation and recovery in an expedition adventure race. SLEEP 2015, the

29th Annual Meeting of the Associated Professional Sleep Societies, Seattle, WA.


Staton, S., Smith, S., Hurst, C., Pattinson, C., & Thorpe, K. (2015). Group napping

patterns in relation to duration of mandatory naptimes in childcare settings.

Weight Status of Young Children: Exploring the relationship with sleep and light exposure. ix

SLEEP 2015, the 29th Annual Meeting of the Associated Professional Sleep

Societies, Seattle, WA. SLEEP. Vol. 38, Abstract Supplement, pA28.

Smith, S. S., Neil, E. H., Thorpe, K. J., Pattinson, C. L., & Staton, S. L. (2015).

Characteristics of children who do not nap in childcare. SLEEP 2015, the 29th

Annual Meeting of the Associated Professional Sleep Societies, Seattle, WA.


Pattinson, C., Smith, S., Staton, S., Thorpe, K. (2015). Sleep and weight status of

Australian children: What are the sleep parameters at play? Society for Research

in Child Development (SRCD) 2015 Biennial Meeting. Philadelphia,

Pennsylvania, U.S.A.

Staton, S., Pattinson, C., Smith, S., & Thorpe, K. (2015). Children’s sleep patterns

on days attending and not attending childcare. Sleep and Biological Rhythms. Vol.

13, Supplement S1, p72.


Australian children: The effects of day, night and total sleep. Sleep and Biological

Rhythms, Vol. 12, Supplement 1, p72.

Marriott, A., Staton, S., Thorpe, K., Pattinson, C., Smith, S. (2013) How do current

sleep practices in Early Childhood Education and Care settings reflect current

knowledge about good sleep habits and environments? Sleep Biol Rhythms. Vol.

11, Supplement 2, p15-16. DOI: 10.1111/sbr.12028. (IF=.76)

x Weight Status of Young Children: Exploring the relationship with sleep and light exposure.

Grant Funding and Awards resulting from the PhD Research Program

Staton, S., Thorpe, K., Irvine, S., Smith, S. & Pattinson, C. (2015 - 17). Professional

development package and resources for guiding sleep practices in early

childhood education and care services in Queensland. Queensland Government

Department of Education and Training (Category 3 – Industry Research Grant

$385,000).

Pattinson, C. (2015). Australasian Sleep Association’s Travel Grant 2015 - For

attendance at the 27th

Annual Meeting of the Australasian Sleep Association

Conference, Melbourne, Australia ($500).

Pattinson, C. (2015). Grant-in-Aid; Queensland University of Technology - For

attendance at SLEEP 2015: 29th

Annual Meeting of the Associated Professional

Sleep Societies, Seattle, USA.

Pattinson, C. (2014). Recipient of the School of Psychology and Counselling

Bursary Award - For attendance at HealthCAM 2014 - Australasian

Symposium on Health Communication, Advertising and Marketing. Brisbane,

Australia

Weight Status of Young Children: Exploring the relationship with sleep and light exposure. xi

Masters of Education and Developmental Psychology Co-supervised Thesis

Manuscripts* which align with the PhD Research Program

Annette Marriot Supporting Sleep: Analysis of Observed Practices in Early

Childhood Education and Care Settings (Awarded 2014).

Ruth Blackburn Physiological Response of Children with Behavioural

Difficulties during Rest Activities (Current)

Emma Fitton Does Rest Equal Stress? Examining the impact of rest time

activities on children’s physiological patterns (Current)

Yizhen Teo PEDS and sleep outcomes in young children

Candice Oakes Communicating with Parent’s about sleep and rest in ECEC

*These manuscripts align with the key questions and aims of the PhD program and

were co-supervised by the PhD candidate during the period of their candidature.

xii Weight Status of Young Children: Exploring the relationship with sleep and light exposure.

Table of Contents

Keywords ................................................................................................................................................ ii

Abstract ................................................................................................................................................. iii

Table of Contents .................................................................................................................................. xii

List of Figures ...................................................................................................................................... xiv

List of Tables ........................................................................................................................................ xv

List of Abbreviations ........................................................................................................................... xvi

Preface .............................................................................................................................................. xviii

Statement of Original Authorship ........................................................................................................ xix

Acknowledgements ............................................................................................................................... xx

CHAPTER 1: PAEDIATRIC OBESITY: A PUBLIC HEALTH CRISIS ...................................... 1

1.1 The Problem: paediatric obesity .................................................................................................. 1

1.2 The significance of sleep for child health .................................................................................... 2

1.3 The significance of light for child health ..................................................................................... 4

1.4 The significance of socio-demographic factors for child health .................................................. 5

1.5 The significance of early childhood education and care (ECEC) environments for child health 5

1.6 Context of the research program and thesis outline ..................................................................... 6

CHAPTER 2: PAEDIATRIC OBESITY ........................................................................................... 8

2.1 Prevalence and trends .................................................................................................................. 8

2.2 Defining obesity in paediatric populations .................................................................................. 9 2.2.1 The WHO growth standards ........................................................................................... 11 2.2.2 CDC growth standards.................................................................................................... 12 2.2.3 IOTF growth standards ................................................................................................... 12

2.3 The cost of paediatric obesity. ................................................................................................... 12

2.4 The aetiology of paediatric obesity ............................................................................................ 14

CHAPTER 3: SLEEP, LIGHT EXPOSURE AND THE CIRCADIAN SYSTEM ...................... 17

3.1 Sleep in early childhood ............................................................................................................ 17

3.2 The two process model of sleep ................................................................................................. 18

3.3 The Aetiology of Children’s Sleep Patterning ........................................................................... 21 3.3.1 Family influences ........................................................................................................... 21 3.3.2 Environmental factors..................................................................................................... 22 3.3.3 Child factors ................................................................................................................... 24

3.4 Evidence of a Link between Sleep and Obesity ......................................................................... 28

3.5 Underlying mechanisms hypothesised to link sleep and obesity ............................................... 30

3.6 Circadian rhythms ...................................................................................................................... 34

3.7 Light exposure ........................................................................................................................... 36

3.8 Summary and Implications ........................................................................................................ 38

CHAPTER 4: THESIS METHODOLOGY ..................................................................................... 39

4.1 Methodology .............................................................................................................................. 39

Weight Status of Young Children: Exploring the relationship with sleep and light exposure. xiii

4.2 The E4Kids Study ...................................................................................................................... 42

4.3 The Sleep in Childcare Study ..................................................................................................... 43

4.4 Ethics ......................................................................................................................................... 44

CHAPTER 5: PAPER 1 – WEIGHING IN ON INTERNATIONAL GROWTH STANDARDS:

TESTING THE CASE IN AUSTRALIAN PRESCHOOL CHILDREN ....................................... 45

5.1 Publication Status and Co-Author Contribution ........................................................................ 45 5.1.1 Publication Status and Target Journal............................................................................. 45 5.1.2 Statement of Contribution ............................................................................................... 45

CHAPTER 6: PAPER 2 – BEYOND SLEEP DURATION AND WEIGHT STATUS OF

CHILDREN………….. ....................................................................................................................... 85

6.1 Publication Status and Co-Author Contribution ........................................................................ 85 6.1.1 Publication Status and Target Journal............................................................................. 85 6.1.2 Statement of Contribution ............................................................................................... 85

CHAPTER 7: PAPER 3 - ENVIRONMENTAL LIGHT EXPOSURE IS ASSOCIATED WITH

INCREASED BODY MASS IN CHILDREN. ................................................................................ 115

7.1 Publication Status and Co-Author Contribution ...................................................................... 115 7.1.1 Publication Status and Target Journal........................................................................... 115 7.1.2 Statement of Contribution ............................................................................................. 115

CHAPTER 8: GENERAL DISCUSSION ...................................................................................... 151

8.1 Summary of Key Outcomes ..................................................................................................... 151

8.2 Significance of key outcomes .................................................................................................. 153

8.3 Strengths and limitations of this research program .................................................................. 153

8.4 Implications and future directions for research ........................................................................ 155 8.4.1 Research Agenda .......................................................................................................... 155 8.4.2 Further exploration into the effect of light exposure on young children on both

sleep and weight status ................................................................................................. 156 8.4.3 Should light be added to the WHO list of obesogenic factors? .................................... 157 8.4.4 Theoretical and conceptual advancement ..................................................................... 157

8.5 Concluding Statement .............................................................................................................. 159

BIBLIOGRAPHY ............................................................................................................................. 161

APPENDICES ................................................................................................................................... 195 Appendix A Highlighted Published Abstracts from the PhD Research Program .................... 195 Appendix B Queensland University of Technology Thesis by Published Papers

Guidelines ..................................................................................................................... 204

xiv Weight Status of Young Children: Exploring the relationship with sleep and light exposure.

List of Figures

Figure 2.1. The global trend estimates of overweight and obesity in children aged from birth to

five years between 1990 and 2020. Redrawn from: de Onis, et al., (2010)............................ 9

Figure 2.2. An adaption of the ecological systems theory model, showing the obesogenic

factors associated with childhood weight status (Davison & Birch, 2001). ......................... 16

Figure 3.1. Factors that are associated with sleep patterns and the proposed mechanisms that

underlie the association between sleep and obesity Adapted from Chen et al., (2008),

Staton, (2015), and Taheri (2006). ....................................................................................... 27

Figure 3.2. Interaction between the external environment, the central and peripheral clocks

(adapted from: Archer & Oster, 2015). ................................................................................ 35

Figure 4.1. Methodology and design of the thesis using the E4Kids Study and the Sleep in

Childcare Study. ................................................................................................................... 41

Figure 5.1 An example of the weight status classifications given to one child when applying

the three international growth standards. ............................................................................. 80

Figure 6.1. Mean BMI z-scores observed for boys in each of the napping frequency groups

after adjusting for night sleep duration, parent control, temperament and main

caregiver education. ............................................................................................................. 97

Figure 7.1. Smoothed 7-day light exposure plots from three individual participants ......................... 145

Figure 7.2 Sensitivity Analyses showing Pearson correlations between BMI z score and a

range of MLiT and TAT Light Thresholds (lux) at baseline and follow-up. ..................... 146

Figure 7.3 Representative light exposure profiles (log linear lux) for two individual

participants with “Early” and “Late” light exposure. ......................................................... 147

Figure 8.1. Proposed sleep–light exposure conceptual framework emerging from the thesis. ........... 159

Weight Status of Young Children: Exploring the relationship with sleep and light exposure. xv

List of Tables

Table 2.1. Key biological and psycho-social antecedents proposed to impact on weight status

of children and references for further reading. ..................................................................... 15

Table 5.1. Description of the three international reference values for overweight and obesity5. .......... 71

Table 5.2. Selection of weight standard in Australian pre-school samples 2006-2017 ......................... 73

Table 5.3. Demographic information of children and families participating in the E4Kids study. ....... 78

Table 5.4. Crude prevalence estimates of overweight and obesity in the E4Kids sample

according to the three international standards and by gender. .............................................. 79

Table 6.1. Definition of the sleep parameters assessed in this study. ................................................. 105

Table 6.2. Demographic Information of the 2011 E4Kids Sample included in the final analysis....... 106

Table 6.3. Information about the measured sleep parameters. ............................................................ 107

Table 6.4. General linear model of the significant sleep parameters effect on BMI z-score with

adjustment for significant control variables. ...................................................................... 108

Table 7.1 Participant demographic, sleep, activity, and light characteristics at baseline and

follow-up. ........................................................................................................................... 126

Table 7.2. Linear regression models predicting BMI z score at baseline and follow-up ..................... 129

xvi Weight Status of Young Children: Exploring the relationship with sleep and light exposure.

List of Abbreviations

AASM American Academy of Sleep Medicine

ABS Australian Bureau of Statistics

ADHD Attention deficit hyperactivity disorder

ALAN Artificial light at night

BMI Body mass index

CDC Centre of Disease Control

CrP C-reactive protein

ECEC Early childhood education and care

EEG Electroencephalogram

EST Ecological systems theory

FFM Fat free mass

FM Fat mass

fMRI functional magnetic resonance imaging

HRQoL Health related quality of life

IOTF International Obesity Task Force

MVPA Moderate-vigorous physical activity

NHMRC National Health and Medical Research Council

NIH National Institutes of Health

NREM Non-rapid eye movement

OECD Organisation for Economic Co-operation and Development

PA Physical activity

REM Rapid eye movement

SCN Superchiasmatic nucleus

Weight Status of Young Children: Exploring the relationship with sleep and light exposure. xvii

SD Standard Deviation

SES Socio-economic status

SPSS Statistical package for social scientists

SWA Slow wave activity

WHO World Health Organization

z-score The deviation of an individual’s value from the median value of a

reference population, divided by the standard deviation of the

reference population (or transformed to normal distribution).

xviii Weight Status of Young Children: Exploring the relationship with sleep and light exposure.

Preface

This thesis is presented in accordance with the guidelines of American

Psychological Association (APA) style detailed by the 6th edition of the manual

(APA, 2010). Because this is a thesis by publication, any articles accepted or

submitted for publication in journals based in America (e.g. PLOS One) are

presented in North American English. Furthermore, alternative house styles based on

the presentation requirements of the corresponding journal have been maintained in

this document. Changes resulting from the publishing process may have been made

to the studies presented in this thesis since they were submitted for publication.

Weight Status of Young Children: Exploring the relationship with sleep and light exposure. xix

Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet

requirements for an award at this or any other higher education institution. To the

best of my knowledge and belief, the thesis contains no material previously

published or written by another person except where due reference is made.

Signature:

Date: February 2017

QUT Verified Signature

xx Weight Status of Young Children: Exploring the relationship with sleep and light exposure.

Acknowledgements

“Ideas are born like babies - messy and a little confused but full of possibilities... It is

only through generous contribution, faith and challenge that they achieve their

potential.”

- Margaret Heffernan

This thesis would not have been possible without the collective efforts and

support of my supervision team. To Professor Karen Thorpe, you are an incredible

inspiration to me with your super-hardworking and dedicated nature; you have been

an amazing support throughout this whole journey. Thank you so much for this

opportunity, your encouragement, mentorship and guiding push to undertake this

PhD. To Associate Professor Simon Smith, you are incredibly wise, calm, and

intelligent. I have thoroughly enjoyed working with you and have learnt so much in

every conversation. I sincerely thank you for your support, knowledge and patience

throughout this time. I would also like to thank Professor Stewart Trost for sharing

your knowledge, time, effort and expertise. Finally, I would also like to thank Dr

Sally Staton for her knowledge, brilliance, and generosity, without which this PhD

would not be the same. It is only through the contribution of this incredible team of

people that I have been able to take these ideas and turn them into a piece of work

that is meaningful.

Any massive undertaking of work of this nature would not be possible without

a team of personal supporters who are there to laugh and cry alongside you. I am

extremely lucky to have some of the best people in the world on this team. To my

loving and supportive Dad, Michael, and Sister, Stephanie, I thank you both for

everything that you have done and continue to do. Your love and encouragement

means the world to me. My Mum and Dad gave me everything that I needed in life to

get me to where I am today. I could not be more grateful for their never ending love

and guidance throughout my life. David and Gemma you have been such an

incredible driving force in getting me across the finish line, my sincerest thanks for

the love you have shared. My friends and colleagues, there are too many

astonishingly beautiful people to name, but you have made this an extraordinary

journey. You are all amazing, have helped ply me with coffee and have been

Weight Status of Young Children: Exploring the relationship with sleep and light exposure. xxi

inspirational in your own ways. A special mention goes to Alicia Allan, for your

statistics skills and contributions. Also special thanks to Mr Christopher Jennings for

your assistance in figure creations, alongside Emma and Spencer for the love, hugs,

home, and laughter.

My sincerest thanks to the wonderful research staff, students and co-authors

involved in both the Sleep in Childcare Research Group and the E4Kids Study. You

have all contributed to this work in a very meaningful way. I am incredibly grateful

to have worked with so many talented and dedicated people. A very special thank

you also goes to the children, their families, services, educators and directors who

participated in this project, without their willingness to participate, this work would

not have been possible. And finally, I would like to thank the Australian Research

Council, the Queensland Government Department of Education and Training, the

Victorian Government Department of Education and Early Childhood Development,

and the Foundation for Children, who provided financial contribution towards the

research included within this thesis and the Australian Commonwealth Government

for providing me with an Australian Postgraduate Award Scholarship.

This thesis is dedicated to my mother, Kerry Patricia Pattinson (16/4/1961 – 21/4/2011)

You are my inspiration, my light, my biggest supporter and the person I am sure has

watched over me throughout this whole PhD. I miss you dearly and I thank you for

everything you have given me.

Chapter 1: Paediatric Obesity: A public health crisis 1

Chapter 1: Paediatric Obesity: A public

health crisis

1.1 THE PROBLEM: PAEDIATRIC OBESITY

Paediatric obesity is a major public health concern, both in Australia and

internationally. Global estimates indicate that 42 million children under the age of 5

were classified as overweight or obese in 2014 (Commission on Ending Childhood

Obesity, 2014). Whilst research has indicated that there has been a recent plateau in

the prevalence of obesity in children (Ogden, Carroll, Kit, & Flegal, 2014a;

Rokholm, Baker, & Sørensen, 2010), current rates remain high. In Australia there has

been a marked increase in the number of children classified as morbidly and severely

obese in the last three decades (Garnett, Baur, Jones, & Hardy, 2016). Further, using

the International Obesity Task Force (IOTF) growth standards, 1 in 5 Australian

children, aged between 2 and 4 years are currently classified as overweight or obese

(Australian Bureau of Statistics, 2015). Paediatric obesity is associated with a range

of negative health sequelae and psychosocial consequences, both throughout

childhood and into adulthood. Moreover, with the direct health care costs of the

Australian overweight and obese population being in excess of $21 billion per annum

(Colagiuri et al., 2010), there are significant fiscal, psychological, and health-related

imperatives for early intervention. However, interventions and preventative strategies

aiming to decrease the incidences of childhood obesity have not, as yet, led to

sustained and effective change in the prevalence of obesity (Hung et al., 2015; Kuhl,

Clifford, & Stark, 2012; Wang et al., 2015; Waters et al., 2011).

Although obesity has a strong genetic component, the rise in the prevalence of

obesity in genetically stable populations suggests that environmental or other

extrinsic factors also contribute significantly to weight gain (Ebbeling, Pawlak, &

Ludwig, 2002; Touchette, Petit, et al., 2008). This program of research aimed to

investigate the potential influence of two such environmental mechanisms on

children’s weight status, sleep and light exposure. An increasing recognition of sleep

and light in affecting biological functioning is emerging in the literature and presents

these as important variables for investigation. The rationale for the focus on these

two factors are detailed below and presented in an extended review in Chapter 3.

2 Chapter 1: Paediatric Obesity: A public health crisis

1.2 THE SIGNIFICANCE OF SLEEP FOR CHILD HEALTH

Sleep is a developmental and restorative process which is necessary to promote

healthy cognitive, emotional, and physiological functioning. Early childhood is a

particularly important time as sleep undergoes substantial transitions in both the

frequency and duration across the 24-hour period. These transitions are significantly

affected by genetic, environmental, and cultural influences (Jenni & O’Connor,

2005; Touchette et al., 2013). For preschool aged children (3-5 years), sleep

problems such as night awakenings, delayed sleep onset and consequent shorter sleep

duration are commonly reported (Hiscock, Canterford, Ukoumunne, & Wake, 2007;

Jenni & Carskadon, 2007; P. Lam, Hiscock, & Wake, 2003). Disruption of sleep may

have physiological consequences, one of which is seen as increased body mass.

Shortened sleep duration has been associated with an increase in body mass

index (BMI) and obesity in children, adolescents and adults. In adults, a U-shaped

relationship between sleep and body mass has been observed, with longer or shorter

sleep duration increasing the risk of obesity (Taheri, Lin, Austin, Young, & Mignot,

2004). However, this relationship in adults has not been consistently supported (see

Marshall, Glozier, & Grunstein, 2008). Research investigating food intake and

metabolic changes in rats revealed that chronic sleep restriction (over 5-days)

followed by sleep allowance (for 2-days) led to significant increases in both food

intake and body weight (Barf, Desprez, Meerlo, & Scheurink, 2012). This increased

food intake has been linked to the physiological regulation of hormones associated

with appetite, satiety, and metabolism (e.g. leptin and ghrelin). Evidence suggests

that homeostatic regulation of appetite is directly affected by sleep restriction or

disruption (Klingenberg, Sjödin, Holmbäck, Astrup, & Chaput, 2012; Spiegel et al.,

2004; Spruijt-Metz, 2011; Taheri et al., 2004; Van Cauter, Spiegel, Tasali, &

Leproult, 2008). In children, the finding of a negative association between sleep

duration and obesity has been relatively robust (see Cappuccio et al., 2008; Chen et

al., 2008; Patel & Hu, 2008; Taheri, 2006). Short sleep duration has been

independently associated with increases in BMI and obesity, even after statistically

controlling for other obesogenic factors (Agras, Hammer, McNicholas, & Kraemer,

2004; J. F. Bell & Zimmerman, 2010; Chaput, Brunet, & Tremblay, 2006; Diethelm,

Bolzenius, Cheng, Remer, & Buyken, 2011; Jiang et al., 2009; Reilly et al., 2005;

Snell, Adam, & Duncan, 2007; Spiegel, Knutson, Leproult, Tasali, & Cauter, 2005;


Taveras, Rifas-Shiman, Oken, Gunderson, & Gillman, 2008; Touchette, Petit, et al.,

2008; von Kries, Toschke, Wurmser, Sauerwald, & Koletzko, 2002).

Although substantial support exists for a link between sleep duration and

obesity in children, experimental trials (e.g. directly manipulating sleep) have not yet

been conducted over the childhood period; as such the level of evidence for

‘causation’ is moderate. Furthermore, not all studies have found an association

between sleep duration and weight status in children. Hiscock, Scalzo, Canterford

and Wake, (Hiscock, Scalzo, Canterford, & Wake, 2011) examined BMI and sleep

duration of Australian infants (N = 3857, age at baseline: 0-1 years; age at follow-up:

2-3 years) and children (N = 3844, age at baseline: 4-5 years; age at follow-up: 6-7

years). Cross-sectional analysis of both cohorts indicated that sleep duration was

comparable across BMI categories at ages 0-1 years, 2-3 years, and 4-5 years. Obese

6-7 year olds slept almost 30 minutes less than did their peers, however, there was no

linear relationship between sleep duration and weight status in this age group.

Moreover, longitudinal analyses revealed that sleep duration at baseline was not a

significant predictor of BMI z-score at follow-up for either cohort (Hiscock et al.,

2011).

The strength of association found between sleep and weight status has also

been brought into question due to the significant methodological variations. These

include inconsistencies in the way that sleep is measured (i.e. parent report vs

observation vs objective accelerometry vs polysomnography vs a combination of

methods), and the sleep parameters assessed (i.e., night-time sleep duration, inclusion

of napping, sleep problems, sleep timing, and regularity). Much focus has been on

sleep duration, yet this has been posited as too simplistic a measure (Golley, Maher,

Matricciani, & Olds, 2013; Jarrin, McGrath, & Drake, 2013; Koulouglioti et al.,

2013), as sleep is not a homogenous state. Sleep is comprised of different stages,

each with specific biological, hormonal and psychological functions (K. F. Davis,

Parker, & Montgomery, 2004; J. C. Lam, Mahone, Mason, & Scharf, 2011; Lavigne

et al., 1999; Weissbluth, 1995). Disruption or deletion of the sleep stage may have

different effects on child health. There is an imperative to elucidate the sleep

parameters that impact on weight status.


1.3 THE SIGNIFICANCE OF LIGHT FOR CHILD HEALTH

The naturally occurring light and dark cycles sustain life on earth, and

represent a marker of the passing of time. Light is known to have direct effects on

health and physiological functioning (Rajaratnam & Arendt, 2001). Exposure to

different intensities, spectra, and timing of light has been shown to impact vigilance

(Phipps-Nelson, Redman, Dijk, & Rajaratnam, 2003), sleep (Cissé, Peng, & Nelson,

2016; Heath et al., 2014; S. Li et al., 2007), mood (Bedrosian & Nelson, 2013), and

weight status (Danilenko, Mustafina, & Pechenkina, 2013; Reid et al., 2014). The

impact of light upon human physiology may be very broad and may involve a

number of mechanisms. For example, direct UVA irradiation of the skin has been

shown to decrease blood pressure in healthy adults (Liu et al., 2014) and exposure to

artificial light at night can negatively impact the efficacy of chemotherapy treatment

for breast cancer patients (Dauchy et al., 2014; Xiang et al., 2015). The adoption of

artificial lighting in modern society, to extend day and work hours has occurred with

little consideration of the impact of light on health and the environment (Wyse,

Biello, & Gill, 2014). Through the adoption of artificial lighting, we have created an

environment of relatively bright nights and dim days, which are largely free from

normal seasonal variations (Wyse et al., 2014). It has been noted that increased use

of artificial lighting has paralleled the global increase in obesity prevalence

(Coomans et al., 2013; Wyse, Selman, Page, Coogan, & Hazlerigg, 2011). In

concordance with this parallel, research in rodents, drosophila, and humans have

shown that environmental light exposure has a profound effect on physiological

functioning. A recent study in human adults showed that exposure to moderate

intensity light (~500lux) earlier in the day, was associated with increased body mass,

independent of sleep duration, sleep timing and activity (Reid et al., 2014). Thus,

habitual environmental light exposure presents a novel, modifiable mechanism to

explore further in young children. If light exposure plays a role in weight status, it

presents a promising place for intervention, as modification of light exposure is

possible through the ‘flick’ of a switch. As such, one of the key aims of this thesis

was to investigate the impact of light exposure on child weight status.


1.4 THE SIGNIFICANCE OF SOCIO-DEMOGRAPHIC FACTORS FOR

CHILD HEALTH

Weight status is not uniform across the population. There are a number of

broad factors which are known to contribute to inter-individual differences - one

such factor consistently proposed to underlie weight status, general health, and even

sleep health is socio-economic status (SES). Although, the mechanisms for these

affects are unclear, there are a range of factors which are associated with low SES

which may increase the risk of higher weight status and poorer sleep health for

children in low SES families. SES has been linked to decreased rates and duration of

breastfeeding (Flacking, Nyqvist, & Ewald, 2007) and higher rates of family chaos

(Kamp Dush, Schmeer, & Taylor, 2013; Vernon-Feagans, Garrett-Peters,

Willoughby, & Mills-Koonce, 2012). Family chaos is associated with decreased

regularity and predictability in everyday life (Kamp Dush et al., 2013). High family

chaos combined with low SES has been associated with shorter sleep duration

(Lumeng et al., 2007), decreased regularity in the timing of the child’s meals and

sleep, and poorer nutrition for children (Fernández-Alvira et al., 2013; Wolfenden et

al., 2011). As such, the SES of the family is important to consider when thinking

about weight and health of young children. This program of research included careful

consideration of SES. Early childhood education and care (ECEC) settings were used

as a sampling frame for the studies within this thesis, so that families from all socio-

economic areas are able to be accessed. By examining the influence of sleep and

light exposure on weight, evidence from this thesis aimed to produce potentially

novel and simple strategies to assist in closing the gap in health outcomes for

children from different backgrounds

1.5 THE SIGNIFICANCE OF EARLY CHILDHOOD EDUCATION AND

CARE (ECEC) ENVIRONMENTS FOR CHILD HEALTH

Early experiences establish lifelong, learning, social functioning, and health

trajectories. During the first five years of life children undergo significant

neurological and physical development. For this reason providing positive early

experiences and early intervention to when problems occur is advocated for

promoting positive development (Campbell et al., 2014). Today, especially within

developed economies, there are ever increasing numbers of children attending early

childhood education and care (ECEC) settings in these formative early years period


(OECD, 2016). In Australia, there is almost universal (~99%) attendance in some

form of care, outside of the family home (e.g. long day care, kindergarten, and family

day care) in the year prior to school (Australian Bureau of Statistics, 2014).

Furthermore, one of the fundamental objectives of ECEC settings is to create positive

lifelong trajectories for children (ACECQA, 2013; Council of Australian

Governments, 2009). Therefore, in line with this objective, ECEC environments

present a unique opportunity for early health intervention. For these reasons, this

thesis comprises of data from two studies based in ECEC settings; the E4kids Study

and the Sleep in Childcare Study. Both studies were designed to capture the effects of

ECEC settings on child outcomes, including health. By observing the current state of

our children’s weight status, sleep, and light exposure in these environments,

evidence from this thesis aims to have direct inputs into development of these

programs and potentially, future child health initiatives.

1.6 CONTEXT OF THE RESEARCH PROGRAM AND THESIS OUTLINE

In commencing the PhD program, the original intention of this thesis was to

examine the impact of day-time sleep on BMI of children with a particular focus on

the effect of ECEC environments on these associations. However, as the PhD

unfolded, there were several discoveries made by the candidate and research team,

and within the field which led to a shift in the direction of the program. Firstly, it was

realised that there was a significant gap in understanding the methodologies used to

define overweight and obesity within the Australian early childhood context.

Secondly, although the candidate contributed to a significant body of work about the

effects of ECEC on both sleep and health outcomes (see appendix A) throughout her

PhD program, there was a realisation that there was still a significant need to identify

the important sleep parameters associated with weight status of children. This was

identified as an important prerequisite step before examination of the contribution of

sleep policy and practice in ECEC settings. Finally, an exciting research paper was

published by Reid and colleagues (2014) showed that light exposure was a

significant independent predictor of adult BMI. After discussion with the research

team, it was decided that this would be a novel, exciting, and exploratory direction

for this thesis program. Thus, this research program was the accumulation of

multiple ideas, led by science, and through discovery of significant gaps within the

current literature. This thesis presents 3 papers which reflect this journey. Paper 1


(Chapter 5), examines the methods used to classify children as overweight or obese,

in a single cohort of Australian preschool children. Paper 2 (Chapter 6), presents an

investigation into the sleep parameters proposed to be involved in weight status.

Finally, paper 3 (Chapter 7) is an investigation of the influences of timing and

intensity of daily light exposure on weight status of young children.

This thesis by published papers is prepared in accordance with the Queensland

University of Technology Thesis by Published Papers Guidelines (See Appendix B).

The thesis is comprised of seven chapters. Chapter 1 provided an overview of the

research background and outlined the purpose of the research and its significance.

Chapter 2 sets the context of this research by providing a detailed overview of the

problem of paediatric obesity, including prevalence, antecedents and costs. Chapter 3

outlines sleep and circadian process and will provide an outline of how these may

impact on weight status of children. Chapter 4 provides an overview of the research

design and methodology. Chapters 5 to 7 include each of the papers currently

published or submitted for publication. Finally, chapter 8 provides an overview of

the research findings and their implications, discusses the strengths and limitations of

the research program, and proposes future directions for research and translation.

8 Chapter 2: Paediatric Obesity

Chapter 2: Paediatric Obesity

Paediatric obesity arises from interactions between genetic predisposition,

epigenetics, individual behaviour and environmental factors. This chapter aims to

review the literature pertaining to this significant health problem. This chapter will

discuss the prevalence and trends of paediatric obesity (section 2.1), how it is defined

(section 2.2) and will also look at the health, social, and economic costs of excessive

weight gain in childhood (section 2.3). The chapter will then introduce the proposed

environmental and biological mechanisms implicated as underlying causes of obesity

in childhood (section 2.4).

2.1 PREVALENCE AND TRENDS

Internationally, it has been estimated that 42 million children under 5-years

old, were overweight in 2013 (Commission on Ending Childhood Obesity, 2014). In

2010, the prevalence of obesity and overweight for children was highest in developed

countries (11.7% of all children), however, prevalence is increasing at alarming rates

in developing countries (de Onis, Blössner, & Borghi, 2010). With prevalence rising

rising by more than 30% in low- and middle-income countries in comparison to

developed countries (Commission on Ending Childhood Obesity, 2014). If these

trends continue, it is predicted that there will be 70 million children classified as

overweight or obese by 2025. Furthermore, projected estimates, as illustrated in

Figure 2.1, indicate that if this trend continues, then by 2030, non-communicable

diseases (NCDs) such as overweight/obesity will be responsible for approximately 5

times more deaths than communicable diseases, perinatal, maternal, and nutritional

conditions combined, especially in low- and middle-income countries (de Onis et al.,

2010; de Onis & Blössner, 2000; World Health Organisation, 2011). Indubitably

these trends pose a significant public health concern.

Chapter 2: Paediatric Obesity 9

0

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4

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8

10

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1990 1995 200 2005 2010 2015 2020

Pre

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Developing Countries

Developed Countries

Global Prevalence

In Australia, one in five children aged between 2 and 4 years are considered to

be overweight or obese (Australian Bureau of Statistics, 2015). This prevalence

steadily increases through to a peak in adolescence, where 38.7% of 16 to 17 year

olds are classified as overweight or obese (Australian Bureau of Statistics, 2015).

Although recent research has suggested a plateau in this increase, particularly in

developed countries the problem remains substantial (Ogden, Carroll, Kit, & Flegal,

2014b; Rokholm et al., 2010). Distribution of overweight and obese children within

the population is not equivocal. There is a disproportionate number of children from

low-income and minority populations classified as obese (Sharma et al., 2010)

suggesting social mechanisms are involved. Furthermore, in Australia, research has

shown that there has been an increase in the number of children who are classified as

severely and morbidly obese within the last 30-years (Garnett et al., 2016). The need

for ongoing efforts to establish mechanisms that give rise to obesity and to direct

effective intervention, prevention, and management strategies, both nationally and

internationally is evident. However, controversy regarding the way in which we

define paediatric obesity currently exists.

2.2 DEFINING OBESITY IN PAEDIATRIC POPULATIONS

The World Health Organisation (WHO) defines overweight and obesity as

“abnormal or excessive fat accumulation that may impair health” (World Health

Organisation, 2013, p. 1). As such, measures of body fatness must be utilised to

Figure 2.1. The global trend estimates of overweight and obesity in children aged from

birth to five years between 1990 and 2020. Redrawn from: de Onis, et al., (2010).


diagnose and monitor overweight and obesity status. The composition of the human

body is complex and can be thought of in different ways. Fat-mass (FM; primarily

consists of adipose tissue) and fat-free mass (FFM; all non-adipose tissues, e.g. bones

and vital organs) comprise the two-component model of body composition (Goran,

1998). Adipose tissue consists of two types: brown and white adipose. White adipose

tissue stores energy and is now considered to be a peripheral secretary organ which is

responsive to and sends signals to modulate appetite, energy expenditure, insulin

sensitivity, endocrine functioning and inflammation and immunity responses

(Fantuzzi, 2005). Increased body mass can be due to increases in FM or FFM, or to

both (Eisenmann, 2006). Changes in FM may be due to increases in subcutaneous

and/or visceral adiposity, and have been shown to parallel changes in weight status

during growth (Eisenmann, 2006). Subcutaneous fat can be directly measured

through skin fold-thickness. Visceral adiposity is more strongly associated with an

increased risk of cardiovascular disease and type II diabetes, however direct

measurement of visceral adiposity is difficult (Dietz, 1998; Goran, 1998; Ong &

Loos, 2006; Savva et al., 2000).

Body composition can be measured by a multitude of methods which vary in

cost, feasibility, sophistication, and accuracy. The predominant method used to

measure body fatness is the BMI, a simple ration of mass (kg) to height (cm2).

Although BMI is unable to differentiate between muscle mass and fat mass, it has

been strongly associated with adiposity (both FM and FFM) in both adults and

children (Eisenmann, Heelan, & Welk, 2004; Freedman et al., 2005; Javed et al.,

2015; Must, Jacques, Dallal, Bajema, & Dietz, 1992) and is a relatively inexpensive

method, allowing use in larger populations. In adults, BMI classification cut-points

of overweight and obesity are 25 kg/m2 and 30 kg/m

2 respectively, as these points are

associated with increased risk of adverse health outcomes, mortality, and morbidity

(WHO, 2000). However, due to the marked changes in growth and development, the

meaning associated with BMI differs by age and gender during early childhood. As a

consequence, there is significant variability in the definition of overweight and

obesity in children, which ultimately limits the ability to make comparisons between

studies (Patel & Hu, 2008; Taheri & Thomas, 2008; Taheri, 2006). There are

currently three international growth standards commonly utilised by both researchers

and clinicians, these are published by the World Health Organisation (WHO) –


available for use from birth to 19 years, the Center for Disease Control (CDC) – for

use from 2 to 20 years, and the International Obesity Task Force (IOTF) – for use for

children 2 to 18 years. The 2006 WHO growth standards for infants and toddlers

(birth to 2-years) have been recommended for clinical use in both Australia and the

U.S. (Grummer-Strawn, Reinold, & Krebs, 2010; National Health and Medical

Research Council, 2013). Between the ages of 2 – 18 years, the Australian National

Health and Medical Research Council (NHMRC) guidelines recommend that

clinicians use either the CDC or the WHO growth standards (National Health and

Medical Research Council, 2013). In contrast, there are no such recommendations

for researchers, although some advocate for use of the IOTF standards (Cattaneo et

al., 2010; Monasta, Lobstein, Cole, Vignerová, & Cattaneo, 2011). The reference

populations on which each of the three standards is based are distinct in character

and present different potential inputs of genetic, epigenetic, and social factors, and

the statistical approaches used to calculate the cut-points for overweight and obese

status also differ, which yields different advantages and limitations of each. An in-

depth analysis and comparison of these standards are provided in Paper 1 (chapter 5).

A brief outline is provided here.

2.2.1 The WHO growth standards

The 2006 WHO child growth standards are for children aged between birth and

five years. These standards were intended to represent optimal child development,

that is, for healthy, breastfed infants growing-up in environments free from economic

constraints (WHO Multicentre Growth Reference Study Group, 2006). Based on

pooled samples from 6 countries Brazil, Ghana, India, Norway, Oman and the USA,

participants had to meet strict inclusion criteria, which produced growth charts for

girls and boys with corresponding percentiles and z-scores. Based on the z-scores,

the WHO classifies a child as overweight or obese if they are ≥2SD (97.7th

percentile) and ≥3SD (99.9th

percentile) above the age-specific mean BMI z-score.

As a complement to the 2006 growth standards, the WHO Growth reference

2007 was developed for use in children aged between 5 and 19 years (de Onis et al.,

2007). This release merged the data from the 2006 child growth standards and the

pre-existing 1977 National Center for Health Statistics/WHO growth reference to

ensure there were smooth transitions of growth trajectories between the two samples.

Based on the sex-specific z-scores, children aged 61 months and above are classified


as overweight or obese if they are ≥1SD (85th

percentile) or ≥2SD (95th

percentile)

above the age-specific mean. Statistical smoothing showed that at 19 years old, a z-

score of 1 was equal to a BMI of 25.4 and 25.0 for boys and girls respectively, which

concurs with the existing cut-point of overweight in adults (de Onis et al., 2007).

2.2.2 CDC growth standards

The US 2000 CDC growth charts (Kuczmarski et al., 2000) provide normative

U.S. population data based on the National Health and Nutrition Examination Survey

(NHANES), which was administered five times, between 1971 and 1994. There were

no specific inclusion or exclusion criteria for participants. The CDC also provide age

and gender specific z-scores and percentiles. The statistical cut-point for overweight

and obesity correspond with ≥85th

percentile and ≥95th

percentile, respectively.

2.2.3 IOTF growth standards

Based on multinational survey data from Brazil, Britain, Hong Kong, the

Netherlands, Singapore and the US, the IOTF growth standards developed

international age and gender specific BMI cut-points (Tim J Cole, Bellizzi, Flegal, &

Dietz, 2000). These cut-points were developed to pass through the adult BMI cut-

points of overweight (25) and obesity (30) at 18 years, thus being potentially more

biologically or pathologically meaningful than standards based on distribution alone.

To ensure comparability with other standards, Cole and Lobstein (2012) recently

updated these age- and gender-specific BMI cut-points, so that they can be defined in

terms of centiles.

2.3 THE COST OF PAEDIATRIC OBESITY.

Childhood obesity has been associated with a wide range of harmful health

sequelae including: type II diabetes, hyperinsulinemia, sleep disordered breathing

(sleep apnoea), asthma, hypertension, poor immune functioning/inflammation (e.g.

increased C-reactive protein), polycystic ovary syndrome, musculoskeletal problems

and cardiovascular disease (Castro-Rodriguez, Holberg, Morgan, Wright, &

Martinez, 2001; Tim J Cole et al., 2000; Doak, Visscher, Renders, & Seidell, 2006;

Ebbeling, Pawlak, & Ludwig, 2002; Ford et al., 2001; Lobstein, Baur, & Uauy, 2004;

Reilly et al., 2003). Furthermore, childhood obesity is associated with negative

psychosocial consequences including; depression, low self-esteem, social alienation

and discrimination (Dietz, 1998; Doak et al., 2006; Dockray, Susman, & Dorn, 2009;


Ebbeling et al., 2002; Reilly et al., 2003; Strauss, 2000). A review by Reilly et al.,

(2003) concluded that obese girls were most at risk of psychological or psychiatric

problems associated with weight status. However, ratings of overall quality of life

have been found to be similar for both genders.

Several studies have reported that obese children and their parents have

significantly lower overall ratings on Health Related Quality of Life (HRQoL;

Friedlander, Rosen, Palermo, Redline, & Larkin, 2003; Hughes, Farewell, Harris, &

Reilly, 2006; Schwimmer, Burwinkle, & Varni, 2003; Tsiros et al., 2009).

Alarmingly, Schwimmer et al., (2003) found that severely obese children and

adolescents (M = 12.1 years, SD = 3.0 years) reported HRQoL similar to that of

children with cancer. A population study of 2,863 Australian children (5 to 13 years)

found that obese boys and girls were at risk of poorer health outcomes due to general

health and self-esteem issues in comparison to ‘normal weight’ children (Wake,

Salmon, Waters, Wright, & Hesketh, 2002). Although children scored lower on

general health measures, a high proportion of parents of obese and overweight

children (42% and 81% respectively) did not report concerns about their child’s

weight (Wake et al., 2002). Similarly, international research indicates that parents

accurately identified their child as obese only 11-36% of the time (Baughcum,

Chamberlin, Deeks, Powers, & Whitaker, 2000; Carnell, Edwards, Croker, Boniface,

& Wardle, 2005; Eckstein et al., 2006; Etelson, Brand, Patrick, & Shirali, 2003), with

lower SES families being the most inaccurate in their judgements (Baughcum et al.,

2000) and girls being viewed as most at risk by parents (Maynard, Galuska, Blanck,

& Serdula, 2003). Parent’s perceptions of their children’s weight are influenced by

several factors including the tendency to over-emphasise of the role of genetics, lack

of co-morbid conditions (especially those linked with poorer emotional well-being),

and the normalisation of excess weight gain through the belief that weight will be

completely mitigated by growth or ‘growth-spurts’ (Baughcum et al., 2000; Carnell

et al., 2005; Eckstein et al., 2006; Etelson et al., 2003; Hughes et al., 2006; Jain et al.,

2001; West et al., 2008). These findings may have important implications for the

success of interventions or prevention strategies employed for children in the general

population.

Of particular concern is that obese children are more likely to become obese

adults (Dietz, 1998; Epstein, Myers, Raynor, & Saelens, 1998; Reilly et al., 2003).


Even when controlling for multiple factors including social class and intelligence,

high BMI in adolescence is associated with adverse social and economic outcomes in

adulthood, especially for women (Gortmaker, Must, Perrin, Sobol, & Dietz, 1993;

Sargent & Blanchflower, 1994); however, not all studies have supported this finding

(Viner & Cole, 2005). Overweight and obesity in childhood has been posited as a

mediator of cardiovascular disease (Gunnell, Frankel, Nanchahal, Peters, & Davey

Smith, 1998), and has also been associated with increased mortality and morbidity in

adulthood (Maffeis & Tatò, 2001; Must et al., 1992; Must, 1996; Park, Falconer,

Viner, & Kinra, 2012). Higher weight status in adulthood is also associated with

increased risk of multiple forms of cancer (World Health Organisation, 2011).

The direct health care cost of the Australian overweight and obese population

is in excess of $21 billion per annum (Colagiuri et al., 2010). Furthermore, recent

research indicates that obese children aged between 2 and 5 years, had significantly

higher healthcare costs than children classified as healthy weight (Hayes et al.,

2016). Specifically, over 3 year period, families with an obese child spent between

$825 – 1332 (AUD) of additional healthcare costs in comparison to families with a

healthy weight child (Hayes et al., 2016). This indicates that early intervention is

vital to both short and longer term preservation of health and wellbeing, as well as in

reducing healthcare expenditure. Therefore, there is a significant need to identify

modifiable factors for intervention, especially in early development.

2.4 THE AETIOLOGY OF PAEDIATRIC OBESITY

The aetiology of childhood obesity is multi-faceted, reflecting a complex

interplay of genetic, lifestyle, and behavioural factors (Comuzzie & Allison, 1998;

Faith, Rha, Neale, & Allison, 1999; Lakshman, Elks, & Ong, 2012). Most simply,

homeostasis of bodyweight occurs through physiological regulation which maintains

a balance between the energy consumed and energy expended (Dietz & Gortmaker,

2001; Ebbeling et al., 2002; Eisenmann, 2006; Lakshman et al., 2012). A description

of every identified antecedent of childhood obesity is beyond the scope of this thesis;

however, the reader is directed to a number of review papers, please refer to Table

2.1. There are also several generalist reviews of paediatric obesity which provide

comprehensive breakdowns of each of the proposed antecedents (please refer to:

Dietz & Gortmaker, 2001; Ebbeling et al., 2002; Eisenmann, 2006; Han, Lawlor, &

Kimm, 2010; Lakshman et al., 2012; Lobstein et al., 2004). Medical disorders which


may result in paediatric obesity including congenital and acquired hypothalamic

deficits, endocrine diseases and the use of drugs that alter appetite must also be

considered when assessing child weight status (Han et al., 2010). Furthermore, recent

research has begun to examine the effect of gut microbiomes (DiBaise et al., 2008;

Kumari & Kozyrskyj, 2016; Wu et al., 2011) and circadian factors (e.g. timing of

food intake; Arble, Bass, Laposky, Vitaterna, & Turek, 2009; Garaulet & Gómez-

Abellán, 2014) on weight status of both adults and children.

Table 2.1. Key biological and psycho-social antecedents proposed to impact on

weight status of children and references for further reading.

Antecedent References

Biological

Diet Malik, Pan, Willett, & Hu, 2013;

Moreno & Rodríguez, 2007

Physical activity/sedentary

behaviour

Jiménez-Pavón, Kelly, & Reilly, 2010;

Must & Parisi, 2009;

Pearson & Biddle, 2011;

Pearson, Braithwaite, Biddle, van Sluijs, &

Atkin, 2014;

Wilks, Besson, Lindroos, & Ekelund, 2011

Ethnic background and location

factors (e.g. region - urban or rural)

de Onis & Blössner, 2000;

de Onis et al., 2010;

Wang & Lobstein, 2006

Birth weight Lakshman et al., 2012;

Ong & Loos, 2006

Genetic and Monogenetic variations C. G. Bell et al., 2005;

Farooqi & O’Rahilly, 2004, 2006

Antenatal factors Campión et al., 2009

Timing of adiposity rebound Taylor et al., 2005

Psycho-Social

TV viewing/media use S. J. Marshall et al., 2004


Table 2.1. (Cont.)

Antecedent References

Socioeconomic status Pinot de Moira et al., 2010; Spruijt-Metz, 2011

Developmental barriers (e.g.

temperament, food neophobia,

tantrums and parenting approach)

Kuhl et al., 2012

It is evident that weight status of an individual child is influenced by an

intricate interplay of the child, their family background, and the obesogenic

environment surrounding them. Davison and Birch, (2001) depicted the obesogenic

environment that children are exposed to using an adaption of the ecological systems

theory (EST), shown in Figure 2.2. This conceptualization, whilst comprehensive, is

not completely inclusive. Two factors, entirely missing from this model, that

influence eating behaviour, physical activity, and metabolism, are sleep and light.

Figure 2.2. An adaption of the ecological systems theory model, showing the obesogenic

factors associated with childhood weight status (Davison & Birch, 2001).

Chapter 3: Sleep, Light Exposure and the Circadian System 17

Chapter 3: Sleep, Light Exposure and the

Circadian System

Human sleep and wake cycles typically occur in synchronisation with 24-hour

circadian rhythms. Circadian rhythms continually oscillate but are influenced by the

external environment; light exposure, food intake, and the social environment (Rüger

& Scheer, 2009). In recent years, there has been a convergence of sleep and circadian

science, which has led to better understanding of the bi-directional associations

between sleep and the circadian system. It is known that the circadian system directly

influences the timing, duration, and phasing of sleep (Dijk & Czeisler, 1995). Less is

currently known about the role and potential feedback mechanisms through which

sleep may regulate the circadian system (Archer & Oster, 2015). It has been

hypothesised that sleep timing and duration may indirectly influence the circadian

system by modulating the timing of light exposure (LeBourgeois et al., 2013). Thus,

although sleep and light exposure interact, this interaction is complex and as yet not

well defined in published literature. The literature on sleep and light are currently

distinct. While the purpose of this thesis is to examine both sleep and light exposure

as mechanisms in childhood obesity in review of the literature each will be discussed

separately.

3.1 SLEEP IN EARLY CHILDHOOD

Sleep is vital for neurological, cognitive, and physical development. The first

five years of a child’s life is marked by a rapid evolution in sleep patterns, duration,

and architecture (Carno, Hoffman, Carcillo, & Sanders, 2003; Jenni & LeBourgeois,

2006; Kurth et al., 2016). During infancy, children shift from polyphasic sleep-wake

periods (where a child sleeps multiple times throughout the day and night) to

biphasic patterns in toddlerhood (single day sleep with majority of sleep occurring in

the night; Iglowstein, Jenni, Molinari, & Largo, 2003). Between 2 and 5 years the

majority of children cease day-time napping, and consolidate sleep into the night

period (Acebo et al., 2005; Galland, Taylor, Elder, & Herbison, 2012; Iglowstein et

al., 2003; Thorpe et al., 2015a; Weissbluth, 1995). Duration of sleep significantly

changes across these periods also. Newborn infants (0-2 months of age) sleep

18 Chapter 3: Sleep, Light Exposure and the Circadian System

between 9.3-20 hours, with a mean of 14.6 hours, however, by 4-5 years children are

reported to sleep between 9.1-13.9 hours with a mean of 11.5 hours (Galland et al.,

2012). Changes in day-time sleep duration may best account for this reduction of

total sleep duration across time (Kurth et al., 2016; Sadeh, Mindell, Luedtke, &

Wiegand, 2009).

Night awakenings, delayed sleep onset, and parasomnias such as sleep terrors

and sleepwalking remain a significant developmental issue for children in the

preschool age (3 to 5 years) group (Goodlin-Jones, Tang, Liu, & Anders, 2009; Jenni

& Carskadon, 2007; Jenni, Fuhrer, Iglowstein, Molinari, & Largo, 2005).

Subsequently, children’s sleep habits are a major concern for parents. Between 32%

to 45% of Australian parents report concern about their child’s sleep habits (Hiscock

& Wake, 2001; P. Lam et al., 2003). In a comparison of children with and without

sleep problems (M = 56.9 months, ages ranged from 51 to 67 months), children with

sleep problems had poorer HRQoL, higher rates of parent-reported attention deficit

hyperactivity disorder (ADHD), behavioural problems, and were 37% (95% CI: 8-

75%) more likely to have sustained an injury that required medical attention in the

past 12 months (Hiscock et al., 2007). It is estimated that the primary health care

costs to the Australia Federal Government for children aged between 0 to 7 years

with sleep problems is $27.5 million (95% CI: $9.2-$46.8 million) each year (Quach

et al., 2013). Therefore, there is a significant need to explore the antecedents and

consequences of sleep problems. Changes in both sleep patterning and duration

coincide with development-related brain maturation, and occur simultaneously with

changes in maturation of the biological regulation of sleep and sleep architecture

(Jenni & LeBourgeois, 2006; Weissbluth, 1995). The next section will outline the

development and regulation of sleep and then the child, family, and environmental

factors which affect child sleep will be examined.

3.2 THE TWO PROCESS MODEL OF SLEEP

The regulation of sleep and wake has been posited as a two-process model,

consisting of two interacting biological processes; homeostatic and circadian

(Achermann, 2004; Borbély & Achermann, 1999; Borbély, 1998; Jenni &

LeBourgeois, 2006; Markov & Goldman, 2006). One of the most basic human needs

is sleep and this need/pressure to sleep is known as the homeostatic process (S). If

the amount of time spent awake is prolonged, then sleep pressure and sleepiness


increases until sleep occurs. Consequently, Process S dissipates with sleep onset and

gradually declines as sleep progresses with the lowest levels occurring upon

awakening. Homeostatic regulation of sleep is evidenced from infancy. Newborn

babies are highly sensitive to sleep loss, being unable to sustain long periods of

wakefulness, any short periods of sleep deprivation are typically followed by

increased sleep duration and intensity (Jenni & Carskadon, 2007). The circadian

process (C; see section 3.6 for review) is an endogenous biological clocklike

mechanism that is synchronised with environmental cues of the light cycle

(Achermann, 2004; Markov & Goldman, 2006). Circadian processes have been

shown to develop in utero, with distinct sleep-wake patterns consolidating into the

night period and circadian-driven hormonal regulation occurring between 2 to 3

months of age (Heraghty, Hilliard, Henderson, & Fleming, 2008; Jenni & Carskadon,

2007; McMillen, Kok, Adamson, Deayton, & Nowak, 1991; Mirmiran & Kok, 1991;

Rivkees, 2003). Therefore, consolidated sleep-wake patterns are the result of Process

S being at a high level (high sleep need) interacting with Process C nearing the end

of the 24hour cycle, typically in conditions of darkness (night). As homeostatic sleep

need declines, and the circadian-mediated processes promoting sleep shift, the two

processes again interact in the morning to promote wakefulness and increase

alertness.

Process S can be measured by the propensity for slow wave activity (SWA) in

the electroencephalogram (EEG) during sleep (Achermann, 2004; McDevitt,

Alaynick, & Mednick, 2012). SWA is determined by the alternating rapid-eye-

movement (REM) and non-REM (NREM) sleep cycles (Achermann, 2004). SWA

significantly increases as sleep deprivation is prolonged, and declines during sleep

(Dijk, Brunner, Beersma, & Borbély, 1990). Process C can be measured in different

ways including; core body temperature, alertness, and hormone expression (e.g.

melatonin). Process C modulates the NREM/REM sleep cycle and the release of

hormones such as cortisol and melatonin (Borbély, 1998; Markov & Goldman,

2006). This ultradian process which controls the fluctuations between REM and non-

REM, is commonly referred to as sleep architecture (Borbély & Achermann, 1999;

Markov & Goldman, 2006).

Recently, the American Academy of Sleep Medicine (AASM) introduced a

new classification system for sleep architecture for paediatric populations (>2


months post term until ~13 years) (Berry et al., 2013). Instead of the classic

representation of a 5-staged pattern of sleep, the AASM have proposed that the sleep

architecture of children should be described by 6 distinct phases: Stage W

(Wakefulness), Stage N1 (NREM 1), Stage N2 (NREM 2), Stage N3 (NREM 3),

Stage N (NREM) and Stage R (REM; Berry et al., 2013; Grigg-Damberger et al.,

2007; Moser et al., 2009). Stage W is the waking state which ranges from alert to

drowsy and is measured in children by the slowness in eye movements and the

frequency of a child’s eye blink (Berry et al., 2013). Stage N1, is the first phase of

NREM sleep and is known as ‘shallow’ sleep, as an individual in this stage is

aroused easily and may or may not know that they have been in a sleep state. During

N2, sleep spindles and k-complexes arise in the EEG. These conspicuous features

have been shown to play a role in cognition, declarative memory, and preserving

sleep continuity (De Gennaro & Ferrara, 2003; Kurdziel, Duclos, & Spencer, 2013).

Stage N3 is known as ‘deep’ sleep as the arousal threshold is high and slow (delta)

EEG waves are predominant. Stage N is only evident in the paediatric population due

to the large variability of sleep in infants. Typically Stage N can be classified as

either N1, N2 or N3 by 5 to 6 months of age (Berry et al., 2013; Moser et al., 2009).

Stage R (REM sleep) is the state in which people typically report dreaming (activated

state), have rapid eye movements, and experience muscle atonia. During Stage R

heart rate and breathing also become irregular in comparison to NREM sleep.

Rhythmical cycles of rest-activity patterns, lasting between 40 and 60 minutes, have

been recorded in utero, beginning around 20 and 28 weeks (Kahn, Dan, Groswasser,

Franco, & Sottiaux, 1996). However, REM/NREM sleep states only begin to emerge

and consolidate into adult-like patterns between 3 to 6 months of age (Jenni &

Carskadon, 2007; Kahn et al., 1996). These sleep cycles typically oscillate in 30-70

minute blocks in infancy, with adult ~90 minute cycles emerging around 5 years of

age (Jenni & Carskadon, 2007; Kahn et al., 1996; Markov & Goldman, 2006).

The sleep-wake system is complex. Due to this complexity, the system is

incredibly vulnerable to disruption. Societal influenced routines including child

care/work, social schedules, and timing of exposure to light or food intake, can have

a profound effect on this system. Thus for young children, consideration of the

genetic and environmental factors that influence sleep are important. These are

discussed in the following sections.


3.3 THE AETIOLOGY OF CHILDREN’S SLEEP PATTERNING

Research indicates that there is significant inter-individual variability in sleep

duration, especially in childhood (Acebo et al., 2005; Friedman, Corley, Hewitt, &

Wright, 2009; Iglowstein et al., 2003; LeBourgeois et al., 2013; Maire, Reichert, &

Schmidt, 2013). Although sleep is a biologically-driven process, some of the

variability observed may be due to cultural and environmental effects (Fisher, van

Jaarsveld, Llewellyn, & Wardle, 2012; Jenni & O’Connor, 2005). Thus, there is a

need to examine the factors that influence children’s sleep patterns. These factors can

be grouped into three broad categories; Family influences (relating to parent and the

family environment), Environmental influences (physical and social environment of

sleep) and Child influences (including genetic factors and individual characteristics).

Each of these categories will be briefly outlined in relation to children’s sleep

patterns.

3.3.1 Family influences

A number of family factors have been shown to influence the development and

maintenance of children’s sleep patterns. Family SES, family structure (i.e. the

number and age of siblings), parent education and parent age are all associated with

children’s sleep-wake patterns (Hale, Berger, LeBourgeois, & Brooks-Gunn, 2011;

Sadeh, Raviv, & Gruber, 2000) reflecting underlying social mechanisms that vary

systematically for different family types and lifestyles. Family lifestyles, such as

parent’s work schedules (including shift-work), and school/ECEC start times, may

have a direct impact on the duration and timing of a child’s sleep (Iwata, Iwata,

Iemura, Iwasaki, & Matsuishi, 2011; S. Li et al., 2010). Family background factors

such as SES and culture may also influence parent’s approaches to and beliefs about

their child’s sleep. Parenting strategies and parent imposed sleep routines have been

shown to have a direct influence over children’s sleep onset times, sleep duration,

and emerging sleep difficulties (Hale, Berger, LeBourgeois, & Brooks-Gunn, 2009;

Hale et al., 2011; Mindell et al., 2011; Mindell, Meltzer, Carskadon, & Chervin,

2009; Mindell, Sadeh, Kohyama, & How, 2010; Morrell & Cortina-Borja, 2002;

Sadeh et al., 2009; Touchette et al., 2005). In addition, there are culturally dependent

differences in sleep patterns, such as co-sleeping practices and siesta cultures

(Galland, Taylor, Elder, & Herbison, 2012; Hense, Barba, et al., 2011; Owens, 2004,

2005). Daytime activities including, amount of physical activity, attending ECEC


settings, and hours of sedentary activities, have also been shown to impact on child

sleep, both quality and duration (Iwata et al., 2011; S. Li et al., 2010; Nevarez, Rifas-

Shiman, Kleinman, Gillman, & Taveras, 2010). Perinatal adversity due to maternal

smoking, alcohol and/or consumption of non-prescription medication during

pregnancy has also been associated with an increased risk of several types of sleep

problems (Armstrong, O’Donnell, McCallum, & Dadds, 1998; El-Sheikh, Buckhalt,

Granger, Erath, & Acebo, 2007; Shang, Gau, & Soong, 2006) and increased weight

status (Hart & Jelalian, 2008; Reilly et al., 2005; Touchette, Mongrain, Petit,

Tremblay, & Montplaisir, 2008; Yolton et al., 2010). As such, family background

factors (psychological, social and cultural) are an important consideration when

examining children’s sleep patterns, and variations in their sleep patterns. Many of

these family factors were captured as part of both the E4Kids and Sleep in Childcare

study, thus where appropriate, statistical analyses controlled for these variables.

3.3.2 Environmental factors

Children’s sleep patterns have been shown to be influenced by three types of

environments: physical, emotional, and behavioural environment (i.e. parenting

strategies, parent stress and marital instability)

The physical sleeping environment is a fundamental and malleable aspect of

sleep which may influence sleep duration and quality. Premature or frequent night

awakenings, and delayed sleep onset, can be a direct consequence of the ambient

room temperature (Mao, Pan, Deng, & Chan, 2013; Zhou, Lian, & Lan, 2013), noise

(Eberhardt, Stråle, & Berlin, 1987; Griefahn & Gros, 1986; Griefahn, 2002; Perron et

al., 2016), and the physical comfort of bedding (Verhaert et al., 2012). Use of light-

emitting media devices (e.g. TV, mobile phones or computers) either in the bedroom

or within the hour before bed-time, have been shown to directly influence the sleep

patterns of children (Cain & Gradisar, 2010; Garrison, Liekweg, & Christakis, 2011;

S. Li et al., 2007; Mirmiran & Kok, 1991; Nevarez et al., 2010; Rivkees, 2003;

Vandewalle et al., 2011; Wells et al., 2008; Wyse et al., 2011). Light exposure has

been shown to suppress the secretion of the circadian-driven hormone melatonin

(Cajochen et al., 2005; Gooley et al., 2010; Lockley, Brainard, & Czeisler, 2003)

which may result in delayed sleep onset and shift circadian phase (see sections 3.6

and 3.7 for more detail). Similarly, circadian timing and sleep are influenced by the

geographical location of the family, with ambient temperature and light exposure


(hours and intensity) varying significantly by topography (Hense, Pohlabeln, et al.,

2011; Mindell et al., 2010; Olds, Maher, & Matricciani, 2011; Wyse et al., 2011).

Sleep routines, which can occur both within and outside of the home (e.g.

ECEC), affect the emotional (i.e. calmness of the environment) and behavioural

(physical routines in place to signal sleep) environment in which sleep occurs and

can work to either promote or impair a child’s ability to sleep. Behavioural factors

focus on sleep hygiene and pre-sleep routines (Cain & Gradisar, 2010; Mindell et al.,

2009; Spruyt, O’Brien, Cluydts, Verleye, & Ferri, 2005), “Sleep hygiene” refers to

the malleable parent and child practices which are thought to promote sleep quality

and duration as well as increase daytime alertness (LeBourgeois, Giannotti, Cortesi,

Wolfson, & Harsh, 2005; Mindell et al., 2009). Recommendations for good sleep

hygiene include:

- consistent sleep-wake times;

- regular and predictable bedtime routines;

- comfortable bedding and bedroom environment;

- removing TV and other media from the bedroom; and

- restricting caffeine intake, especially late in the day (Galland & Mitchell,

2010; LeBourgeois et al., 2005; Mindell et al., 2009; Taheri, 2006).

Poor sleep hygiene including inappropriate bedtime routines, which promote

negative sleep associations, parental presence (e.g. rocking or patting child to sleep)

or inconsistent sleeping locations, have been shown to increase both sleep onset

latency and sleep disruptions (Gaylor, Burnham, Goodlin-Jones, & Anders, 2005;

Goodlin-Jones et al., 2009). Furthermore, inconsistent sleep-wake times have been

associated with shortened total sleep duration (Mindell et al., 2009), and have also

been posited as having a direct effect on childhood obesity (Golley et al., 2013;

Jarrin et al., 2013; Kjeldsen et al., 2014). Sleep hygiene is important both within and

outside of the home care environment. Recent research has shown that within

Australian ECEC environments, although providing opportunities for sleep and rest,

the majority (64%) do not engage in practices which support sleep, such as consistent

scheduling, and providing pre-sleep routines (Staton, Marriott, et al., 2016).


Emotionally supportive environments decrease stress and provide for safety

and comfort, are helpful to promote sleep across the lifespan. As such the emotional

environment in which sleep occurs has been shown to be influenced by family stress

(Sadeh et al., 2000) and marital instability/hostility (R. J. Kelly & El-Sheikh, 2011,

2013; Mannering et al., 2011). Furthermore, research indicates that in ECEC

environments catering for preschool aged children, there is an increase in negative

emotional support such as yelling and threats which has been hypothesised to

increase stress (Pattinson, Staton, Smith, & Thorpe, 2014; Staton, Marriott, et al.,

2016). Therefore, there are a multitude of environmental factors which may either

promote or impede sleep. Some research suggests that child factors (e.g.

temperament) may mediate the effects of the environment. Therefore, both child and

environmental factors will be investigated in this thesis. Furthermore, where possible

the thesis aimed to statistically control for significant environmental influences on

sleep.

3.3.3 Child factors

Child age is one of the strongest predictors of sleep variations and patterns

(Acebo et al., 2005; Blair et al., 2012; Iglowstein et al., 2003). Nevertheless, there

are a number of genetic and individual child factors that impact on sleep including;

child temperament (Goodnight, Bates, Staples, Pettit, & Dodge, 2007; Keener,

Zeanah, & Anders, 1988), neurobiological and developmental disorders (Alfano &

Gamble, 2009), perinatal adversity (Armstrong et al., 1998; El-Sheikh et al., 2007)

and gender (Acebo et al., 2005). Difficult and impulsive temperament types have

been implicated in shortened sleep duration, more frequent night waking, delayed

sleep onset and increased weight status in both infants in children (Keener et al.,

1988; Kuhl et al., 2012; Palmstierna, Sepa, & Ludvigsson, 2008; Ward, Gay, Alkon,

Anders, & Lee, 2008; Watamura, Sebanc, & Gunnar, 2002), although there is debate

around interpretation, as the direction of effect in these studies is not clear (Scher,

Epstein, Sadeh, Tirosh, & Lavie, 1992; Touchette, Petit, Tremblay, & Montplaisir,

2009). Developmental disorders further complicate potential to understand the

relationship. Children with neurobiological and developmental disorders (e.g.,

autism, intellectual disabilities, developmental delay and ADHD) have been shown

to experience greater sleep disruption, greater decline in both sleep duration and

efficiency, as well as more daytime sleepiness than do age-matched typically


developing children (Anders, Iosif, Schwichtenberg, Tang, & Goodlin-Jones, 2012;

Goodlin-Jones et al., 2009; Richdale, 1999; Schwichtenberg, Iosif, Goodlin-Jones,

Tang, & Anders, 2011; Souders et al., 2009). An effect of child gender has also been

proposed. One recent study showed that children’s diets differed according to gender

and sleep duration (Tatone-Tokuda et al., 2012). Boys with shorter sleep patterns

were reported to be more likely to eat at irregular hours or eat too much/fast,

however girls with shorter sleep patterns consumed less fruits, vegetables and milk,

and had more frequent intake of soft-drinks. In-line with these findings, a recent

study showed differential risk patterns for boys (higher TV viewing duration, BMI

and parental presence when falling asleep) and girls (lower fruit and vegetable

intake) associated with risk of shorter sleep duration (Plancoulaine et al., 2015).

However, whether gender differences reflect gender-specific sleep physiology or

differential parenting behaviours between genders is unknown (Atkinson &

Davenne, 2007; Blair et al., 2012; Iwata et al., 2011). Acebo and colleagues (2005)

have proposed that discrepant results regarding gender differences in the

development of sleep patterns are a direct consequence of the measures used and the

possibility of an interactional effect between age and gender.

Sleep is influenced by a dynamic interplay of genetics and environment. In a

comparative twin study, Touchette et al., (2013) examined the relative influence of

genetic and environmental factors on daytime and night-time sleep duration in

infants (N = 995 twins, 40.7% were monozygotic, 58.9% were dizygotic). Maternal

reports of daytime and night-time sleep duration at 6-, 18-, 30- and 48-months of age

were used to assess variance accounted for by genetic factors, the shared

environment, and the unique environment. Night-time sleep duration and

consolidation was most influenced by genetic factors. However, at 18 months there

was a strong effect of the shared environment. On the other hand, daytime sleep

duration, for all ages, was accounted for by both the shared and unique environments

of the children (Touchette et al., 2013).

3.3.4 Summary of the factors that influence child sleep

Figure 3.1 illustrates the family, child, and environment factors which may

influence the duration and/or quality of a child’s sleep. Poor sleep is associated with

an array of significant outcomes including: daytime sleepiness and fatigue, decreased

physical activity, cognitive impairments, metabolic hormone disruption, increased


emotionality, reactivity, behavioural difficulties, and weight status (Alfano &

Gamble, 2009; Y. Kelly, Kelly, & Sacker, 2013; J. C. Lam et al., 2011; Lavigne et

al., 1999; Y. Li, Jin, Owens, & Hu, 2008; Sadeh, 2011). Figure 3.1, identifies

specific mechanisms that are hypothesised to underlie the association between sleep

and increased weight (Cappuccio et al., 2008; Garaulet et al., 2011; Knutson & Van

Cauter, 2008; Olds et al., 2011). The next section will examine the evidence of the

link between sleep and obesity (section 3.4), followed by an evaluation of the

proposed mechanisms of association (section 3.5).


Family Background

Factors

- Parent age - Parent education

- SES

- Racial/ethnic identity

- Family structure

- Family lifestyle

Child Factors

- Age - Gender

- Temperament

- Neurological disorders

- Developmental

disorders

- Perinatal adversity

Increases in children’s weight status

Decline in

Physical Activity

Decreased

inhibition/self -

regulation

Poor food

choices

More

opportunity to

eat

Environmental Factors

- Sleep hygiene (i.e. Noise, temperature)

- Media use - ECEC sleep practices - Calm environment - Sleep routines

Sleep disruption/loss

Prefrontal Cortical

Dysfunction

Disruption of

Hormone

Regulation

Increased fatigue Increased time

awake

Increased

Hunger

Decreased

appetite

suppression

Figure 3.1. Factors that are associated with sleep patterns and the proposed mechanisms that underlie

the association between sleep and obesity Adapted from Chen et al., (2008), Staton, (2015), and Taheri

(2006).


3.4 EVIDENCE OF A LINK BETWEEN SLEEP AND OBESITY

Research has shown that short sleep duration is associated with higher weight

status in children. A recent meta-analysis of 25 prospective cohort studies of children

and adolescents, found that children and adolescents sleeping less than ~10 hours per

night were 76% more likely to be overweight or obese (Ruan, Xun, Cai, He, & Tang,

2015). In a 32-year prospective birth cohort study shorter childhood sleep duration

(at age 5, 7, 9, and 11 years) were associated with higher BMI in adulthood, even

after adjustment for confounding variables such as childhood SES, child and adult

TV viewing, adult physical activity and smoking status (Landhuis, Poulton, Welch,

& Hancox, 2008). Additionally, Taveras et al., (2008) tracked 915 children from 6

months through to 3 years of age. They found that total sleep duration of <12 hours

in infancy was associated with a greater risk of being overweight at age 3. J. F. Bell

and Zimmerman (2010) also found that night-time sleep duration at baseline was

associated with BMI at the five year follow-up for the younger cohort (birth to 59

months old) but not for the older cohort (60 to 154 months). These findings lead

them to surmise that, prior to age five, there is a ‘critical window’ during which

night-time sleep duration is pivotal for subsequent vulnerability to obesity. Echoing

these findings, the first comprehensive longitudinal study of sleep and obesity was

conducted in New Zealand – the FLAME study. Using accelerometry to measure

sleep and physical activity objectively, alongside bioelectrical impedance and dual x-

ray absorptiometry methods to measure BMI, FM and FFM, the study examined the

association between sleep duration and obesity in 244 children from age 3 through to

age 7 years (Carter, Taylor, Williams, & Taylor, 2011). They found that by age 7

years there was a significant reduction in BMI, and to the risk of being overweight,

for every additional hour of sleep at 3-5years, even after adjusting for confounding

variables including baseline BMI. Furthermore, the differences in BMI, for both

males and females, were explained by changes in FM, rather than changes in FFM.

Despite the number of epidemiological studies which report an association between

shortened sleep duration and weight status, debate has arisen.

Researchers have argued that sleep duration is too simplistic a measure, with

other dimensions of sleep quality (e.g. sleep midpoint, sleep-wake patterns)

potentially more important than total duration (Adamo, Wilson, Belanger, & Chaput,

2013; Anderson, Andridge, & Whitaker, 2016; Golley et al., 2013; Jarrin et al., 2013;


Koulouglioti et al., 2013; Scharf & DeBoer, 2015). Two studies of Australian

adolescents aged between 9 and 16 years found that sleep-wake patterns were

important in explaining the relationship between sleep and obesity (Golley et al.,

2013; Olds et al., 2011). Specifically, young adolescents classified as “late bed - late

rise” showed decreased physical activity and increased weight status when compared

to children classified as “early bed - early rise”, despite adolescents in each of these

groups having similar sleep durations (Olds et al., 2011). Similarly, later sleep

midpoint, a marker of circadian timing (S. K. Martin & Eastman, 2002), has been

associated with higher weight (kg) even though sleep duration was the same between

“late” vs “normal” sleepers (Thivel et al., 2015). A longitudinal examination of 1,441

children aged between 3 and 12 years at baseline found, even after controlling for

baseline BMI, children with shorter sleep durations, later bed-times and earlier wake

times, had higher BMI and were more likely to be overweight, 5 years later (Snell et

al., 2007). Recent findings from a longitudinal study of young children indicated that

shorter night sleep duration and later bedtime at age 4 was associated with increased

BMI z-score at 5 years (Scharf & DeBoer, 2015). However, Scharf and DeBoer

(2015), did not control for day-time napping in this study, which is important as

between the ages of 2 and 5 years, children are typically within a transition phase,

when napping begins to cease (Iglowstein et al., 2003; Jenni & Carskadon, 2007).

Hiscock and colleagues (2011) examined children’s 24-hour total sleep duration

(combining both daytime and night-time sleep) and found no association with weight

status. However, Agras and colleagues (2004), who found that short total sleep

duration in childhood was associated with higher BMI at 9.5 years, noted that the

differences in children’s total sleep durations across time were almost exclusively

due to changes in day-time sleep duration. In contrast, J. F. Bell and Zimmerman,

(2010) found that napping was not associated with obesity and thus surmised that

day-time napping does not substitute for night-time sleep as a strategy for obesity

prevention, a finding echoed by others (Jiang et al., 2009; Touchette, Petit, et al.,

2008). However, research indicates that napping may impact both night-time sleep

duration (Staton, Smith, Hurst, Pattinson, & Thorpe, 2016; Thorpe et al., 2015b), and

night-time neurophysiology of sleep as seen on EEG (Kurth et al., 2016; Lassonde et

al., 2016).


3.5 UNDERLYING MECHANISMS HYPOTHESISED TO LINK SLEEP

AND OBESITY

The underlying mechanisms through which sleep affects BMI and obesity are

largely unknown, however, four main hypotheses (see Figure 3.1) have emerged

from the literature (Chaput, 2016; Knutson, Spiegel, Penev, & Van Cauter, 2007;

Taheri, 2006). The first hypothesis stipulates that shortened sleep duration increases

tiredness which reduces physical activity, disrupting the balance between energy

consumed and energy expended, leading to obesity. Recent research indicates that

healthy adults who undergo acute sleep restriction have an associated decrease in

both the amount and intensity of physical activity the subsequent day (Brondel,

Romer, Nougues, Touyarou, & Davenne, 2010; Schmid et al., 2009). Similar

findings were evident in adolescents where physical activity declined by 3% for

every additional hour of sleep disruption (N. K. Gupta, Mueller, Chan, & Meininger,

2002). The impact of sleep loss on activity in children has been more controversial.

Some studies have argued that reductions in physical activity for children may

actually have a causal role in decreased sleep duration, through declines in

homeostatic sleep drive and increasing sleep onset latency, subsequently leading to

increases in BMI through the reduction in caloric output (Agras et al., 2004; Nixon et

al., 2009). In contrast, Ekstedt, Nyberg, Ingre, Ekblom and Marcus, (2013) found

that moderate-to-vigorous physical activity (MVPA) promoted sleep efficiency,

however neither sleep duration or efficiency influenced the level of physical activity

on the following day. When looking at sleep-wake patterns of 9 to 16 year olds,

children and adolescents that went to bed late and woke up late were 2.16 times more

likely to be obese, 1.77 times more likely to have low MVPA and have increased

screen time than children that were going to bed early and rising early (Olds et al.,

2011). It is evident that the relationship between sleep and physical activity needs to

be elucidated more fully.

The prefrontal cortex (PFC) plays a pivotal role in sleep physiology, dreaming,

alertness and has been shown to be significantly affected by sleep-deprivation and

restriction (Alhola & Polo-Kantola, 2007; Horne, 1993; Muzur, Pace-Schott, &

Hobson, 2002; Orzeł-Gryglewska, 2010). Horne, (1993) proposed the PFC

vulnerability hypothesis, which posits that the PFC shows marked benefits from

sleep and as a consequence is also highly sensitive to sleep loss. Specifically, sleep


loss compromises cognitive functioning dependent on the PFC, including, executive

functioning (i.e., memory and attention), inhibitory control, emotional reactivity and

affect (Orzeł-Gryglewska, 2010). Furthermore, the PFC has also been associated

with reward behaviours in response to food stimuli. Functional magnetic resonance

imaging (fMRI) of sleep deprived obese adults revealed that obese participants had

greater frontal cortical activity in response to food stimuli than healthy weight

subjects (L. E. Martin et al., 2010). Thus the second hypothesis stipulates that short

sleep duration results in decreases in a child’s ability to inhibit food intake and

increases the likelihood of making poor food choices due to PFC dysfunction. It

would be expected that these effects would be particularly detrimental to children, as

between 3 and 6 years old, the PFC undergoes a period of rapid development and

maturation, a change which has been noted to coincide with napping cessation (J. C.

Lam et al., 2011). In support of this hypothesis, research has indicated that when

suffering from sleep loss, young children can exhibit more impulsive behaviour and

increased emotional reactivity symptomatic of ADHD (Beebe & Gozal, 2002;

Medeiros, Carvalho, Silva, Prado, & Prado, 2005; Touchette et al., 2009).

Furthermore, a study of adolescence revealed that even mild sleep deprivation was

associated with significant changes in affect and impulsive behaviours (Rossa,

Smith, Allan, & Sullivan, 2013). Other data support this broad association, for

example Friedman, Corley, Hewitt and Wright (2009) found that sleep problems at

age 4 predicted later cognitive executive control. A recent meta-analysis revealed

that impulsivity was greater in overweight/obese children than in healthy weight

children (Thamotharan, Lange, Zale, Huffhines, & Fields, 2013). Thus, PFC

dysfunction as a result of short sleep duration may be associated with child weight

status through increased hedonic response to food stimuli as well as lowered

inhibitory control.

The third hypothesis is that sleep restriction results in changes to the hormones

responsible for regulating hunger and satiety. These hormones include; leptin

(controls appetite and high levels promote satiety), ghrelin (stimulates appetite and

signals hunger), interleukin 6, C-reactive protein (CrP), insulin, cortisol and growth

hormone (Carlson, 2005; Spiegel et al., 2004; Taheri et al., 2004; Taheri, 2006).

Human appetite is regulated by the gut-brain axis, which is sensitive to levels of

adipose (fat) tissue (Buchwald, Cowan, & Pories, 2007; Carlson, 2005; Spruijt-Metz,


2011; Ueda et al., 2013). Adipose tissue releases adipokines, such as leptin.

Suppression of leptin indicates starvation and subsequently the brain works to

increase hunger and suppress energy expenditure (Flier, 2004; Ueda et al., 2013).

Leptin and other hormones such as insulin are released in proportion to the amount

of body fat and exert sustained inhibitory effects on food intake and increase energy

expenditure whilst working antagonistically with gastrointestinal peptides such as

ghrelin (Carlson, 2005; Van Cauter et al., 2008). Ghrelin is released rapidly prior to

eating, declines in response to food intake, and can slow the rate of metabolism of

fats when necessary (Van Cauter et al., 2008). Both leptin and ghrelin are part of the

orexin system which is integral to energy expenditure, eating, and wakefulness. The

orexin system influences the central nervous system through the ventromedial and

arcuate nuclei of the hypothalamus or “appetite centre” of the brain (Carlson, 2005).

Sleep duration has been shown to influence the regulation of both leptin and ghrelin.

In an adult sample, Taheri and colleagues (2004) found that participants who had

habitually shorter sleep patterns (5 hours) had significantly lowered levels of leptin

and elevated ghrelin in comparison to people who slept 8 hours per night. A number

of adult studies have also shown a strong effect of shortened sleep duration on both

leptin and ghrelin (Brondel et al., 2010; Mullington et al., 2003; Spiegel et al., 2004;

Spiegel, Leproult, & Cauter, 1999). However, others have not found the same

variations in leptin/ghrelin in response to sleep curtailment (Nedeltcheva, Kessler,

Imperial, & Penev, 2009; Schmid et al., 2009). Furthermore, studies of the effects of

sleep duration in adults have difficulties in distinguishing whether obesity leads to

lowered levels of leptin and ghrelin (e.g. obesity leads to decreases in night sleep due

to development of sleep apnoea) or, whether this hormone disruption (resulting from

shortened sleep) leads to increased appetite and deregulation of satiety, heightening

the risk of obesity. It is possible that these hormonal changes may begin in childhood

and influence the trajectory of weight gain.

Thus far, there have been few studies to examine the association between sleep

duration, obesity and metabolism in childhood, although evidence is rapidly

beginning to emerge. Studies of the effects of short sleep duration on BMI in

adolescents have shown that leptin is negatively associated with sleep duration, only

in girls (Hitze et al., 2008) and was also associated with binge eating (Miller et al.,

2013). One experimental study of 37 children (Age Range: 8 – 11 years) manipulated


the amount of sleep each child received over a three week period. When sleep was

extended (by ~ 1.5 hours) children consumed less calories, had lower fasting leptin

levels and lower weight than when in the decreased sleep condition. However,

Kjeldsen and colleagues (2014) found that when they controlled for covariates such

as age, sex, pubertal status, height and weight, neither leptin or ghrelin was

associated with sleep duration. CrP is a marker of inflammation has been shown to

be elevated in both obese and sleep-deprived people and has been shown to augment

the sleep related effects of leptin (Bayer, Rosario, Wabitsch, & von Kries, 2009;

Knutson et al., 2007). Thus CrP has been used in some studies as a proxy measure of

leptin. In support of the use of CrP one study did find that shorter sleep durations was

associated with higher levels of CrP independent of age, sex, pubertal status and

MVPA. However, Börnhorst and colleagues (2012) found that for children (2 to 9

years) the significant relationship between sleep and obesity was not attenuated by

CrP, but was significantly influenced by insulin. It is evident that there is much

variability in the literature regarding the role of metabolic hormones in the

association of sleep and childhood obesity.

The final hypothesis proposes that people who sleep less are awake longer,

having more opportunity to eat, resulting in increased total caloric intake and

consequently obesity (Sivak, 2006). Findings are mixed, some studies do not report

any differences between energy consumption and short sleep duration (Reilly et al.,

2005; Schmid et al., 2009; von Kries et al., 2002), while evidence from other adult

and animal studies indicate that sleep restriction is associated with increases in both

caloric intake and the portion sizes of meals (Barf et al., 2012; Brondel et al., 2010;

Hogenkamp et al., 2013; Knutson et al., 2007; Morselli, Leproult, Balbo, & Spiegel,

2010; Orzeł-Gryglewska, 2010; Taheri et al., 2004). However, disentangling the

direction of this effect is difficult, whether the adults and rats are eating more

because they had more time in the day or because they were tired and having to

remain awake is currently unknown. This effect is further complicated in children

where parental influences in relation to access to food, feeding practices and

environments outside of the home all impact on children’s ability to obtain and eat

certain foods (Agras et al., 2004).

In summary, there have been a number of theories proposed to underlie the

association between sleep and weight status. These mechanisms lay out testable


hypotheses with different implications for intervention depending on the mechanism

supported. Thus, it is evident that there is an imperative to investigate these

mechanisms; however, many challenges to testing these theories remain.

3.6 CIRCADIAN RHYTHMS

The circadian system is the “internal body clock”, which oscillates on an

approximate 24-hour period to drive our biological and behavioural processes,

including the regulation of sleep-wake cycles, metabolism, emotions, and weight

(Mirmiran & Kok, 1991; Rüger & Scheer, 2009). The circadian system receives time

cues from light, food, and activity to allow an organism to adapt to changes in the

environment due to the rotation of the earth. In all mammals, circadian timing is

driven by a central pacemaker located in the suprachiasmatic nuclei (SCN), within

the basal hypothalamus (Archer & Oster, 2015; Rüger & Scheer, 2009). Information

about the time of the day is received by the SCN through projections directly from

photosensitive ganglion cells in the retina (Foster & Helfrich-Förster, 2001; Lucas et

al., 2014; Zele, Feigl, Smith, & Markwell, 2011). From these time cues, the SCN

modulates the timing of secondary peripheral clocks, such as those in the organs (e.g.

the liver, and stomach), which subsequently synchronise with each other and with

external time (see Figure 3.2; Archer & Oster, 2015). The circadian system works in

conjunction with the homeostatic sleep drive to determine the timing of sleep/wake,

food intake, activity/rest and body temperature, however this is modulated by daily

routines and obligations. As such, synchrony between these rhythms promotes health

and well-being, whilst disruptions that lead to desynchronization are associated with

negative impacts for health, as evidenced for jet-lag and shift work (Markov &

Goldman, 2006; Rüger & Scheer, 2009).


Timing of food intake has been shown to have an influence on the timing of

peripheral clocks, especially in the stomach and liver (Garaulet & Gómez-Abellán,

2014). Furthermore, the timing of food intake when misaligned with circadian

timing, has been shown to directly affect weight status and work performance, over

and above that of caloric value of the food eaten (Arble et al., 2009; Garaulet &

Gómez-Abellán, 2014; C. C. Gupta et al., 2016; Salgado-Delgado, Angeles-

Castellanos, Saderi, Buijs, & Escobar, 2010). However, it is recognised that social

factors such as work, education, and travel, modulate the circadian system, affecting

both light exposure and timing of food intake i.e. social jet-lag (Biggs, 2013; Juda,

Vetter, & Roenneberg, 2013; Wittmann, Dinich, Merrow, & Roenneberg, 2006), and

the significant health-related effects of shift work (S. Davis, Mirick, & Stevens,

2001; Erren, 2013; Fonken et al., 2010; Rosa, 1993). However, research indicates

that light exposure is vital for circadian timing in all species. Converging research

has shown that light exposure may also play a direct role in weight status and as

such, daily environmental light exposure was a variable of interest for this thesis.

Figure 3.2. Interaction between the external environment, the central and

peripheral clocks (adapted from: Archer & Oster, 2015).


3.7 LIGHT EXPOSURE

Light is the principal cue for circadian entrainment in all species (Cao et al.,

2015). Through the adoption and use of artificial lighting, humans have, created an

environment of relatively dim days and bright nights (Gaston, Visser, & Hölker,

2015; Wyse et al., 2014, 2011). Manipulation of the timing, intensity, and duration of

light exposure to suit contemporary lifestyles has occurred with limited consideration

of its effects on health, behavioral, and environmental outcomes. An understanding

of these effects is only now beginning to emerge (Bedrosian & Nelson, 2013; Brooks

& Canal, 2013; Gaston et al., 2015; Wyse et al., 2014).

Studies of the natural environment indicate that increased artificial light at

night (ALAN), both through direct illumination (e.g. structural, security, street, and

advertising lighting) and skyglow, affect the reproductive, migration, and daily

movement behaviors of multiple plant and animal populations (Evans, Akashi,

Altman, & Manville, 2007; Gaston, Duffy, Gaston, Bennie, & Davies, 2014;

Kempenaers, Borgström, Loës, Schlicht, & Valcu, 2010; Stone, Jones, & Harris,

2009). ALAN has even been shown to affect aquatic animals, with coastal lighting

disorientating turtle hatchlings, and affecting migration patterns of fish and other

marine life (Davies, Duffy, Bennie, & Gaston, 2014). In tropical areas, research has

shown that ALAN has affected the habitat and flight patterns of nocturnal seed

dispensing bats, which due to avoidance of lit areas, has been linked to forest

succession (Gaston et al., 2015). The costs of the changes to the environment

introduced by ALAN are not yet fully understood.

Animal studies indicate that the timing and intensity of light exposure is critical

for metabolic functioning and weight status. Rodents exposed to continuous white

light, even at low levels, exhibited symptoms of metabolic syndrome, increased

adiposity, glucose intolerance (Fonken, Lieberman, Rebecca, Weil, & Nelson, 2013;

Fonken et al., 2010), and reduced sympathetic activity in brown adipose tissue

(Kooijman et al., 2015), independent of their caloric intake and locomotor activity.

Many of these symptoms were abolished when regular light-dark cycles were

reinstated (Fonken, Weil, & Nelson, 2013).

In adult humans, morning bright light treatment has been shown to reduce body

fat and appetite (Danilenko et al., 2013; Dunai et al., 2007), improve mood (Dunai et

al., 2007), and modulate concentrations of the appetite regulating hormones; leptin


and ghrelin (Figueiro, Plitnick, & Rea, 2012). Research has also shown that when

sleeping under bright light conditions, participants experienced heightened heart rate

variability and variations in breathing, similar to a stress response (Yamauchi et al.,

2014). Commensurately, recent evidence shows that exposure to light of moderate

intensity (~500 lux) earlier in the day is associated with lower body mass,

independent of sleep timing, total sleep duration, and activity in adults (Reid et al.,

2014). Adolescents have also been shown to have heightened sensitivity to light

exposure when compared to older adults (Crowley, Cain, Burns, Acebo, &

Carskadon, 2015; Figueiro & Overington, 2015). This sensitivity has been shown to

directly affect melatonin suppression which may indicate that ALAN is particularly

disruptive for regulation of sleep in this already vulnerable age group (Crowley et al.,

2015). The increased use of electronic equipment such as night lights, tablets, mobile

phones, and televisions has been well-documented for children 3 – 5 years (Cox et

al., 2012; Dennison, Erb, & Jenkins, 2002). Taken together, these data indicate that

the timing, duration, and intensity of light exposure has a potent role in metabolic

and physiological functioning. Early childhood is a pivotal time in the establishment

of lifelong growth and adiposity trajectories (Campbell et al., 2014). However, to

date, no studies have examined the effect of habitual light exposure on body mass in

children.

Unlike activity, dietary intake, and sleep duration, light exposure is easily and

directly manipulated; literally through the flick of the switch. The current ubiquitous

social, industrial, and culturally driven manipulation of our environmental light may

impact on body mass through three very broad mechanisms that warrant exploration.

Firstly, increased light duration may provide insufficient dark, and insufficient

metabolic ‘down time’, for normal recuperative processes to occur. Indeed,

depending on geographical location, skyglow and other artificial light at night

sources are increasing at rates of up to 20% per year (Hölker et al., 2010). Children

are increasingly exposed to broader spectral signatures and more diverse intensity

profiles of light (Gaston et al., 2014). Secondly, chronically increased daily light

duration may provide a biological signal analogous to endless summer days, with the

potential to amplify any seasonally-driven metabolic processes, such as body mass

acquisition (Ebling, 2014; Simmen, Darlu, Hladik, & Pasquet, 2015). Alternatively, a

child’s initial light state may promote some mediating phenomena such as


problematic behavior, physiological or metabolic changes, which in turn, promote

changes in BMI. One example of light states interacting with physiological behavior

is in the case of sleep. Multiple studies document an association between short sleep

duration and variability in sleep timing with increased body mass in pediatric

populations (J. F. Bell & Zimmerman, 2010; Golley et al., 2013; Scharf & DeBoer,

2015). Thus, a confounding relationship between sleep and light exposure is

expected as sleep timing and duration likely influence the timing and duration of

light exposure. However, no published studies have addressed this association in

young children. As such, this thesis aimed to address this gap by examining the

effects of sleep and light on weight status in preschool aged children.

3.8 SUMMARY AND IMPLICATIONS

In summary, current evidence indicates that sleep and exposure to light have

significant implications for health. Cross-sectional, prospective, and longitudinal

studies have identified that short sleep duration in early childhood is associated with

increased body mass and increased risk of being classified as overweight. However,

controversy regarding methodology of measurement and the role of sleep parameters

endures. Coinciding with current sleep science, circadian researchers have noted the

profound effects that light exposure has for metabolic and physiological functioning

in animals and humans. A study of sleep and light on weight status is emergent.

Sleep and light exposure present two environmental and modifiable factors which

may influence health, yet no published literature has looked at these associations in

young children aged between 3 and 5 years.

Chapter 1, provided an outline of the problem of paediatric obesity and the

importance of early intervention, especially accounting for significant socio-

demographic factors. The costs and significance of the paediatric obesity problem

was provided in Chapter 2. This study presents the first to investigate sleep and light

exposure in children. The findings present the potential to have wide reaching

implications for child health and development.

Chapter 4: Thesis Methodology 39

Chapter 4: Thesis Methodology

This chapter provides an overview of the methodology and design of the

studies used within the thesis. Section 4.1 outlines the research program that was

followed by the PhD candidate; section 4.2 details the E4Kids Study, which provided

the data for papers 1 and 2; finally, section 4.3 outlines the design of the Sleep in

Childcare Study, which provided the data for paper 3. Additional details of the

individual measures used for each paper are provided within the methods section of

each of the papers.

4.1 METHODOLOGY

The research program undertaken by the PhD candidate includes three studies

using data from two key research projects: the E4Kids Study and the Sleep in

Childcare Study. An overview of how each of the studies were utilised in this thesis

is presented in the figure below (Figure 4.1). Papers 1 and 2 utilised data from the

first (2010) and second (2011) year of the E4Kids Study. Paper 3 used baseline

(2012) and follow-up (2013) data from the Sleep in Childcare Study.


Figure 4.1. Methodology and design of the thesis using the E4Kids Study and the Sleep in Childcare Study.


4.2 THE E4KIDS STUDY

This thesis utilised secondary data analysis from the ‘Assessing the

effectiveness of early childhood education and care programs in Australian

communities’ (E4Kids Study). The E4Kids study is an Australian Research Council

Linkage project (ARC LP0990200), which was conducted in collaboration between

QUT and The University of Melbourne. The E4Kids study is a 5-year longitudinal

study, which commenced in 2010, tracking the effects of early childhood education

and care settings on child health, education and equity outcomes in a large cohort of

over 2,000 children aged from 3 to 5 years from ECEC services in four research

sites; Brisbane (metropolitan) and Mt Isa (remote) in Queensland, Melbourne

(metropolitan) and Shepparton (rural) in Victoria. Using stratified random sampling,

the E4Kids study aimed to recruit a representative sample of Australian children

attending licenced care services, which were stratified by socio-economic index for

area (SEIFA); service type (e.g. long day care, kindergarten, family day care); and

location (remote, rural and metropolitan) (Tayler et al., 2016). Within each service,

rooms catering for 3 to 5 year old children were targeted for recruitment, with all

children attending these rooms invited to participate. Please refer to

http://www.e4kids.org.au for more information about the E4Kids project.

The data collected as part of the E4Kids project includes a range of information

on children’s home environment, including child and family demographic

information, child temperament measures, information on child sleep behaviours

(added to the E4Kids study in 2011), as well as information on transition to school.

Direct observation of ECEC environments and child testing (including

anthropometric measures of height and weight) was also conducted. The direct

testing and survey data from parents, teachers and directors/principals has occurred

yearly from 2010 to 2015. The candidate worked as a team leader, and subsequently

as a fieldwork manager on the E4Kids project from 2010 to 2014. Her

responsibilities have included; recruitment of participants and centres, data

collection, entry and management, co-ordination, training, as well as, liaising with

key stake holders; i.e., parents, children, childcare staff and directors.

http://www.e4kids.org.au/


4.3 THE SLEEP IN CHILDCARE STUDY

The Sleep in Childcare Study was conducted as part of a research grant from

the Financial Markets Foundation for Children (Australia) Grant (2012-213). The

study sample was six long-day care services from the Brisbane metropolitan area that

were selected from a pool of 130 ECEC services which had taken part in the

Queensland - E4Kids study in 2011. Sampling was based on:

1) service type: long-day care

2) located in high SES area (SIEFA ranking >8), and

3) sleep practices:

a. mandated sleep-rest time; a period of at least 60 minutes were

children are required to lay on their beds without alternative

activities, regardless of whether they sleep or not, versus

b. flexible sleep-rest time; services had to specify that they had a

sleep/rest period for at least 60 minutes however, children were

given alternative activities or options if they were not sleeping.

The final sample consisted of four centres with mandatory sleep practices and

two centres with flexible sleep practices. From each service, one pre-school room,

catering for children aged between 3 and 5 years, was selected as the target room. All

children within each of the target rooms were invited to participate in the study.

Sixty-two children were recruited into the study, age range: 3 - 6 years M = 4.7

years.

Sleep, activity and light exposure, both within and outside of the childcare

setting, was measured using actigraphy. Participating children wore an Actiwatch 2

(MiniMitter Phillips) for a two week period. During this period parents were asked to

complete a sleep diary and parent survey which asked about sleep scheduling, sleep

behaviours, health and family demographics (including parental BMI and education).

Detailed observation of the sleep period and daily schedule were taken on two

occasions (same day each week) during the fortnight. Salivary cortisol was also taken

4 times during these two study days. Direct measurement of children’s height, weight

and waist circumference, was conducted by trained fieldworkers at the ECEC

services. A 12 month follow-up through parent questionnaire regarding sleep

44 Chapter 4: Thesis Methodology

behaviours, sleep scheduling and family demographics, (including parental and child

BMI) was completed in November 2013. The candidate was the Senior Research

Assistant on this project. As such, was involved in the development of the

observation protocol and training of the research staff. The candidate also conducted

all recruitment of the centres and families involved in the project, assisted with ethics

submissions and variations, as well as data collection, entry and management.

4.4 ETHICS

This study involved the active participation of children, educators, parents and

centre directors in the completion of survey, direct testing and observational

measures and in all cases complied with the requirements of the National Statement

on Research involving Human Participation. Ethics variations to use the data

collected from both the E4Kids (approval number 1000000172) and Sleep in

Childcare Research (approval number 1200000046) projects has been approved by

QUT University Human Research Ethics Committee (UHREC). Workplace health

and safety risk assessments were conducted and approved by the Faculty of Health

H&S Officer.

Chapter 5: Paper 1 – Weighing in on international growth standards: Testing the case in Australian preschool

children 45

Chapter 5: Paper 1 – Weighing in on

international growth standards:

Testing the case in Australian

preschool children

5.1 PUBLICATION STATUS AND CO-AUTHOR CONTRIBUTION

5.1.1 Publication Status and Target Journal

This paper is currently in press in the journal of Obesity Reviews (IF=7.51). This

is the official journal of reviews, produced monthly by the World Obesity

Federation. When ranked by impact factor, this is the highest rank obesity journal.

The aim of the journal is to print reviews about obesity and related comorbidities. It

aims to appeal to a wide readership of medical practitioners, researchers and policy

makers through its diverse scope of publishing basic and behavioural science,

clinical treatment and outcomes, epidemiology, prevention, and public health

research. The following paper has been formatted in accordance with the

requirements of Obesity Reviews.

5.1.2 Statement of Contribution

Ms Pattinson conceptualized and designed the study, developed the review

protocol, undertook database searches and screening, contributed to interpretation

of the data, and drafted the manuscript; Dr Staton assisted with the development of

the review protocol, assisted with screening of articles, contributed to

interpretation of data, and critically reviewed the manuscript; Dr Smith

conceptualized and designed the study, supervised data collection and screening,

contributed to interpretation of data and critically reviewed the manuscript.

Professor Trost also assisted with conceptualising and designed the study,

contributed to interpretation of data and critically reviewed the manuscript. Ms

Sawyer undertook database searches and screening, and assisted in the analysis and

interpretation of the data. Professor Thorpe conceptualized and designed the study,

supervised database searches and screening, analysed and interpreted the data,

and drafted the final manuscript; all authors approved the final manuscript.

Principal Supervisor Confirmation I have sighted email or other correspondence from all Co-authors verifying their authorship. Professor Karen Thorpe 18/11/2016 _______________________ ____________________ ______________________ Name Signature Date

46Chapter 5: Paper 1 – Weighing in on international growth standards: Testing the case in Australian preschool children

Title: Weighing in on international growth standards: Testing the case in Australian

preschool children.

Authors: Cassandra L. Pattinson1,2

, Sally L. Staton1,2

, Simon S. Smith3, Stewart G.

Trost1, 4

, Emily F. Sawyer5, Karen J. Thorpe

1,2,6

1Institute for Health and Biomedical Innovation, Centre for Children’s Health

Research, Queensland University of Technology, Queensland, Australia.

2School of Psychology and Counselling, Queensland University of Technology,

Queensland, Australia.

3Recover Injury Research Centre, Faculty of Health and Behavioural Sciences, The

University of Queensland, Queensland, Australia.

4School of Exercise and Nutrition Sciences,

Centre for Children’s Health Research,

Queensland University of Technology, Queensland, Australia.

5School of Medicine and Dentistry, James Cook University, Queensland, Australia.

6Institute of Social Science Research, The University of Queensland, Queensland,

Australia

Key Words: Preschool, Body Mass Index, Overweight, Obesity, Growth Standards,

Children

Running Title: Weighing in on international growth standards

Acknowledgments: The sampling derives from an Australian longitudinal cohort

study of ECEC effectiveness, Effective Early Educational Experiences for Children

(E4Kids). E4Kids is a project of the Melbourne Graduate School of Education at The

University of Melbourne and is conducted in partnership with the Queensland

University of Technology. E4Kids is funded by the Australian Research Council

Linkage Projects Scheme (LP0990200), the Victorian Government Department of

Education and Early Childhood Development, and the Queensland Government


children 47

Department of Education and Training. E4Kids is conducted in academic

collaboration with the University of Toronto Scarborough, the Institute of Education

at the University of London and the Royal Children’s Hospital in Melbourne. The

E4Kids team would like to sincerely thank the ECEC services, directors,

teachers/staff, children and their families for their participation in this study. We also

thank Christopher Jennings for his assistance in the development of Figure 1.

Address of Corresponding Author: Cassandra L. Pattinson, Centre for Children’s

Health Research (CCHR), Level 5, 62 Graham Street, South Brisbane, Queensland

University of Technology, QLD, 4101, Australia.

Ph: +61 (07) 3069 7288; Fax: +61 (07) 3138 0486; Email:

[email protected]

Potential Conflicts: The authors have no conflicts of interest to declare.

mailto:[email protected]


Abstract

Overweight and obesity in preschool-aged children is a major health concern.

Accurate and reliable estimates of prevalence are necessary to direct public health

and clinical interventions. There are currently three international growth chart

standards used to determine prevalence of overweight and obesity, each using

different methodologies: Center for Disease Control (CDC), World Health

Organisation (WHO), and International Obesity Task Force (IOTF). Adoption and

use of each method was examined through a systematic review of Australian

population studies (2006-2017). For this period, systematically identified population

studies (N = 20) reported prevalence of overweight and obesity ranging between 15

and 38% with most (n = 16) applying the IOTF standards. To demonstrate the

differences in prevalence estimates yielded by the IOTF in comparison to the WHO

and CDC standards, methods were applied to a sample of N = 1,926 Australian

children, aged 3-5 years. As expected, the three standards yielded significantly

different estimates when applied to this single population. Prevalence of

overweight/obesity was WHO - 9.3%, IOTF - 21.7% and CDC - 33.1%. Judicious

selection of growth standards, taking account of their underpinning methodologies

and provisions of access to study datasets to allow prevalence comparisons are

recommended.

Introduction

Paediatric obesity is a global public health concern. While there are suggestions of a

plateau in the prevalence of childhood overweight and obesity in Western developed

societies 1,2

, approximately one in five Australian children aged between 2 and 4

years are currently classified as overweight/obese 3. However, the way in which we

determine the prevalence of overweight and obesity is inconsistent 4–6

. Since 2006


children 49

there have been three international standard methods available to identify overweight

and obesity, estimate population prevalence, and guide clinical decision making. In

this paper we assess patterns of selection and use of these standards, and the

population estimates they yield, within studies focused on Australian pre-school

children. Further, we demonstrate the differences in prevalence estimates produced

by these standards in a population-based cohort of Australian preschool children.

The three commonly used international growth standards for BMI-for-age were

determined by the World Health Organisation (WHO)7; the US Center for Disease

Control and Prevention (CDC)8; and the International Obesity Task Force (IOTF)

9.

Each of these standards are based on historical survey data, with distinct reference

populations and methodologies for determining the cut-points that define overweight

and obese status of children aged between 2-18years (see Table 1). The WHO

Multicentre Growth Reference Study (MGRS) is premised on optimal child

development, and produced growth curves for children raised in healthy and socially

advantaged environments from birth to 5 years. The referent population for the WHO

Child Growth Standards is a pooled sample from six countries, collected between

1997 and 2003, and comprised of children that met specific inclusion criteria to

represent optimal healthy growth10

. Inclusion criteria for the study involved meeting

MGRS’s recommendations for breast feeding duration, a non-smoking mother

(before and after birth), and a healthy singleton birth. The WHO Child Growth

Standards determines overweight and obese status using sex- and age-specific z-

scores and percentiles. However, the WHO standards have been criticised; 1) due to

the stringent inclusion criteria of the reference sample which potentially classifies

healthy children at the extreme ends of the scale as unhealthy 4 and 2) failure to

account for other significant environmental and genetic factors influencing weight


and growth in the sample 11

. The CDC 2000 Growth Charts are also based on sex-

and age-specific percentiles; however, the reference population in this case are serial

representative samples of children from the USA. This population was drawn from 5

cross-sectional administrations of the National Health and Nutrition Examination

Survey (NHANES), collected between 1971 and 1994 with no specific inclusion or

exclusion criteria. It is important to note there have been several changes since the

development of the CDC references to infant feeding, including increased

breastfeeding rates 12

and lower protein content in today’s infant formula 13,14

, both

of which may affect growth trajectories. However, the major criticism of both the

CDC and the WHO references is that they both determine prevalence of overweight

and obesity using arbitrary statistical cut-points which are not explicitly associated

with health outcomes 10,15

. While the IOTF 9 also use smoothed centile curves to map

growth, one of the key differences is that children’s weight status is determined by

backward mapping from adult BMI cut-points for overweight and obesity (25 and 30

kg/m2 respectively), onto age- and sex-specific BMI z-scores. The IOTF used six

large nationally representative cross-sectional survey studies on growth, collected

between 1963 and 1993, from Brazil, Great Britain, Hong Kong, the Netherlands,

Singapore and the USA (including the NHANES years I & II data as used in the

CDC standards) to create the BMI cut-points for overweight and obesity between

birth and 20 years. One of the aims of development of the IOTF was to assist with

international comparison. However, one critique is that due to the backward mapping

from adult cut-points, the sensitivity of the IOTF definition of obesity is much lower

than that of other reference data, with research indicating that this approach does not

classify 40 – 50% of obese children correctly, with marked differences observed in

this sensitivity between the sexes 4,16,17

. This effect is partly due to the decision of the


children 51

IOTF to backward map growth from 18. Although selecting 18 allowed Cole and

colleagues to average six datasets, research indicates that BMI plateaus earlier in

females but continues to increase in males into the early twenties, potentially leading

to gender bias 4,16

. Furthermore, the IOTF approach of backwards mapping has yet

to be validated prospectively via trajectories from early childhood (< 5 years) weight

status to adult health outcomes, with more consistent patterns of growth and health

trajectories being observed in older children and adolescents 18,19

. In sum, the

reference populations on which each of the three standards is based are distinct in

character, with different selection factors, sampling time frames, and limitations.

Given the different sampling and statistical methodologies it is not surprising that

studies using these standards report discrepant estimates of overweight and obesity in

child populations 4,20-22

. Further, studies that specifically compare the prevalence of

overweight and/or obesity yielded by the different international standards identify

inconsistent patterns of prevalence. Flegal and colleagues 20

compared the IOTF and

CDC standards in a population of U.S. children aged between 2 and 19 years. They

found that the IOTF produced lower prevalence estimates for overweight and obesity

than did the CDC in younger children, but higher estimates for older children. These

results may be indicative of systematic differences in the distribution of BMI with

age between the US and other countries 20

. Within the Australian context, this may

be an important consideration given that Australia is not included within any of the

three international growth standard’s sample populations and this might be an issue

for other countries not included in the sampling. Monasta and colleagues 4 compared

the IOTF and WHO standards in a Czech population of children aged 2 to 5 years

and reported that the WHO standards produced much lower estimates of overweight

than did the IOTF standards, these differences were particularly marked in girls.


Conversely, a study of Canadian pre-schoolers 22

(mean age of 4.5 years) found that

WHO classified a higher prevalence of children as overweight than either CDC or

IOTF. Yet the CDC standards yielded higher obesity prevalence than both the WHO

and IOTF standards, with similar results observed for both boys and girls.

Discrepant prevalence estimates present a problem; the choice of standard can result

in substantially different individual, group, and population estimates of overweight

and obesity, with attendant effects on research findings, public health interventions,

and clinical practice. In this paper we examine these potential biases through a

systematic review of research studies relating to paediatric obesity (3 – 5 years) in

Australia conducted between 2006 and 2017, the period when all three standards

were available, to assess the use of each of these standards in reporting overweight

and obesity prevalence in pre-school populations. To demonstrate the magnitude of

potential differences in prevalence estimates of overweight and obesity through the

application of each standard, we apply all three international reference standards of

BMI-for-age to a single population. Our focus sample comprised 1,926 Australian

preschool children, drawn from a Western developed economy but one that is not

part of any of the current international reference populations. Our aim was to inform

the rationale for selection of growth standards in defining the extent and nature of the

obesity problem in child populations.

Systematic Review

Method

Search strategy

We conducted a systematic search of research literature published between 2006 and

2017 relating to paediatric obesity (children 3-5 years) in Australia. This search is

current as of 30th of January 2017 and used the following international databases:


children 53

PubMed, Embase, PsycINFO [via EBSCOhost], CINAHL, Web of Science,

SCOPUS, Science Direct, PROQUEST, The Cochrane library database, and JSTOR.

The reference lists from identified review articles and papers were also examined to

identify potential papers for inclusion. Key words included ‘Australia AND BMI

AND Obesity AND Overweight AND Pre-school’ and where possible date limit

from 2006 – 2017 OR last 11 years. Search strategy in PsycINFO (AB: Australia*)

AND (AB: BMI OR “Body Mass Index” OR BMIz-score OR Obesity OR

Overweight OR Anthropometry OR prevalence) Limited by Age: Pre-school Age (2-

5 yrs) AND Publication Year 2006 – 2017. Searches in other databases were based

on these terms (see Table S1).

Inclusion and exclusion criteria

Studies were included if they were intervention studies, case control, observational

studies, cross-sectional, longitudinal or national data analyses that report prevalence

of overweight and obesity in a 3-5 year old cohort. The inclusion criteria specified

that study participants needed to be aged between 3 and 5 years (<72 months) and

recruited in Australia. Studies that included children outside of the specified age

range where allowable, only if the prevalence of overweight and obesity for children

within the 3-5-year old age range was identifiable. Studies were excluded if they

were case studies, letters, commentaries, review articles, or conference abstracts with

full text unavailable. Studies that examined sub-populations of Australians (e.g.

ethnic minorities, children with specific health problems which are related to

increased BMI, and studies specifically targeting groups with high BMI) were

excluded. Intervention studies were included in the final pool only if baseline

measurements of weight status were provided or able to be sourced. Further, if


intervention and control groups differed in baseline weight status, only the control

group was reported to limit bias in weight status categories.

Adopting the PRISMA approach, after the initial search was conducted, two authors

(E.F.S., C.L.P.) examined the title and abstracts of all records to determine if they

met the inclusion criteria. Then, full-text versions of identified studies were reviewed

for consistency with inclusion criteria (C.L.P.). Any concerns about inclusion or

exclusion of articles were discussed with three members of the research team (K.J.T.,

S.L.S., S.S.S.). Full details of the number of articles identified and included at each

stage are provided in Fig. S1. Where multiple studies reporting on the same study

sample were found, the publication that identified overweight/obesity prevalence in

the closest age (or age range) according to our criteria or the publication with the

largest sample size was retained. As a result, out of the total of 39 studies identified

as meeting the inclusion criteria, 19 studies were then excluded from the final

analysis due to duplication of the study sample. The representative studies included

in the final analysis are identified in Table 2.

Results

The systematic review of Australian pre-school cohort studies identified 20 articles.

These are summarised in Table 2. The studies had a wide range of sample sizes

(from 84 to 114,925 children). Nineteen out of the 20 studies directly measured child

weight and height, thus reducing the error associated with self-report data. Most

studies (80%) used the IOTF standards to describe the prevalence of overweight and

obesity. Three studies used the CDC standards and one study used the WHO

standards to determine prevalence. It is important to note that one study 23

reported

using the WHO software to obtain BMI z-scores and percentiles, however, when it

came to classifying children as overweight and obese they used cut-points of 85th

and


children 55

95th

percentiles respectively, which indicates that the authors elected to use the lower

CDC recommended percentiles for overweight and obesity, instead of the WHO

recommended 2SD and 3SD z-scores. This would have contributed to the high

prevalence of overweight/obesity (33%) reported in their 4- to 5-year old children. In

general, the proportion of children in each weight category varied. For the studies

that used the IOTF standards, the weighted average of overweight/obese prevalence

was 18.3% (Range: 13.0 to 29.6%). From the three studies that used the CDC

standards, the weighted average of children classified as overweight/obese was

25.5% (Range: 14.8 to 33.0%). As only one study utilised the WHO standards

weighted averages were unable to be computed. The authors report that 38% of

children participating were classified as overweight/obese.

Prevalence Estimates in an Australian Population

Method

Participants

A total of 2,489 children were recruited into the Effective Early Educational

Experiences (E4Kids) Study, an Australian longitudinal cohort. Recruitment and

sampling was designed to capture a representative sample of Australian children

attending licensed Early Childhood Education and Care (ECEC) environments.

These methods have been detailed elsewhere 24

. Briefly, the E4Kids Study is a 5-year

longitudinal study that commenced in 2010. To represent the diversity of licensed

ECEC provision in Australia, a random sampling frame, stratified by service type

(Long Day Care, Kindergarten and Family Day Care) and socioeconomic status

(SES) was used to recruit 140 licensed ECEC services in the states of Queensland

and Victoria. Within each service, any room with children in the year prior to school

(aged 3-5 years) were identified for recruitment. All children and their families in


these rooms were invited to participate. Written informed consent to participate was

provided by each child’s parent or legal guardian. Ethics approval for the study was

granted by the Human Research Ethics Committees of both The University of

Melbourne and Queensland University of Technology.

Measures

Trained research staff measured each child’s height and weight at their ECEC service

using calibrated stadiometers (SECA Leicester Portable Height Measure) and floor

scales (HD-316, Wedderburn Scales; Tanita Corporation, Tokyo, Japan). Children

were dressed in light clothing and without shoes, in accordance with the standardised

procedures outlined by the World Health Organisation7. Children were measured

twice; if measurements differed (weight >0.1kg; height >0.5cm), a third

measurement was taken by the researcher with the mean of these measurements used

to calculate BMI. The main caregiver of the participating child also completed a

questionnaire which provided details about the child and family demographics.

Defining overweight and obesity

BMI for each child was calculated (weight (kg) / height (m) 2

) from anthropometric

data. Children were then classified according to the published WHO, CDC and IOTF

cut-points.

The World Health Organisation: The WHO’s Anthro program (version

3.2.2) was used to transform raw anthropometric data into sex- and age-specific z

scores. In accordance with WHO standards, overweight for children under 5 years of

age is classified as a BMI z-score ≥2 standard deviations above the mean, and a

classification of obese given to children with a BMI z-score ≥3 standard deviations

above the mean.


children 57

The Center for Disease Control: Using the SAS (version 9.5) macro for the

2000 CDC Growth Charts, children’s raw anthropometric data were also transformed

into sex- and age- specific z-scores. With children classified as overweight if their

BMI z-score was ≥ 85th

percentile and obese, if their BMI z-score was ≥ 95th

percentile.

The International Obesity Task Force: Using the guidelines published by

Cole and Lobstein 25

children were classified as overweight or obese by age (rounded

to the nearest whole month i.e., 38.6 = 39 months old) and gender specific BMI score

which corresponds to a BMI of ≥25 and ≥30 at age 18, respectively.

Analysis

Initial data cleaning and checks were conducted to remove all children labelled as

having biologically implausible values on height, weight, and BMI-for-age

measurements. Height and weight measurements were available for 2,038 children

(81.9%). Children who were considered age outliers were removed from the sample;

50 aged < 3 years, and 36 children aged > 5.1 years were removed from analyses.

The WHO and CDC exclusion ranges were utilised and any children identified as

having implausible biological values (i.e. likely the result of a recording or

calculation error) were examined for possible exclusions; 26 children were

subsequently excluded. Applying the WHO criteria there were 12 exclusions. More

stringent criteria from the CDC resulted in an additional 14 children being flagged

and excluded as having biologically implausible values. Eleven of the 12 children

identified in the WHO flags were also identified in the CDC analysis. Our final

sample consisted of 1,926 children (985 [51.1%] boys; Mean age = 48.65 months ±

6.21 [SD]; Age range: 36.00 – 60.95 months).


Differences in prevalence estimates for each weight status category were tested for

statistical significance using a z-test for difference in proportions. We also examined

if there were any gender differences using the same methods.

Results

Demographic information about the final sample is provided in Table 3. Over 90% of

participating children had been breastfed at some point in infancy and 1.9% of

children were identified as being of Aboriginal and/or Torres Strait Islander origin.

The majority of respondents were born in Australia. Of the responding main

caregivers 53.3% held university degrees and 28% held a technical qualification.

These figures (81.3%) are higher than national statistics that show 59% of

Australians (aged between 15 and 64 years) have a post-school qualification 26

but

consistent with recruitment through childcare, where most caregivers are employed

or studying 27

.

Table 4 shows the crude prevalence estimates of normal weight, overweight, and

obese children resulting from application of each of the three international reference

standards. The proportion of children classified as overweight or obese significantly

differed across each of the three reference values. Using the WHO reference values,

significantly fewer children were classified as overweight compared to the CDC or

IOTF cut-points. There were significantly more children classified as obese when

using the CDC reference, than either the IOTF or WHO.

Prevalence rates were also examined by gender (Table 4). For males, there were

significantly fewer children classified as overweight using the WHO standards than

when using the CDC standards. For females there were significantly fewer children

classified as overweight when using the WHO reference versus either IOTF or CDC

standards.


children 59

Discussion

We assessed the use of each of the standards in all published literature relating to

preschool aged children in Australia in the period when all three international

standards were available for selection (2006-2017). We found a distinct preference

among the research outputs, with 80% (16/20) of the identified papers employing the

IOTF standards. Furthermore, the weighted averages from papers using IOTF gave

much lower prevalence estimates in comparison to the paper’s employing the CDC

standards. The single study28

utilising the WHO standards, reported overweight and

obesity as 38% however, this study was of a low income sample which may account

for the high prevalence observed. The preference for using the IOTF identified in the

Australian studies may be explained in two ways. First, the rationale may be

substantive and based on a concern to use a reference that is based on adult BMI

standards and is internationally comparable. Alternatively, it may be that consensus

and publication patterns drive selection. That is, researchers show a comparison and

consistency bias in which reference standard is selected on the basis that other

studies and research teams have also made this choice.

Alongside the strong preference for IOTF, as demonstrated in our Australian cohort,

the estimates of overweight and obesity in the same preschool children vary

significantly based on choice of BMI-for-age standards. Specifically, in our sample,

the WHO and IOTF identified 1.6% and 4.0% of children as obese, respectively. In

contrast, the CDC standards identified 13.1% of children as obese. Our findings, like

those of several other international comparisons, show that international standards

produce substantially disparate estimates 4,13,20,21

. Figure 1 illustrates how an

individual child from the E4Kids data set, aged 3.5years with a height of 103.5cm

and weight of 19.3kg was classified using the three standards and show that this


same child could be simultaneously classified as normal weight, overweight and

obese. Such variation derives from differences in reference populations and

methodologies to determine categorical cut-points. These variations are also

dependent on the sample that is being examined. For example, the IOTF cut-point for

overweight corresponds with the 85th

percentile in US children, but around the 90th

percentile in children from the UK 5,15,17

. It is evident that in the current sample, the

IOTF cut-points for overweight align closely with the CDC’s 85th

percentile, yet the

IOTF obesity cut-points align more closely with WHO’s 3SD (see Table 1). This is

an important consideration for both researchers and clinicians working with

Australian, and indeed other ethnically diverse countries with children who are not

included in the current international standards.

The Australian National Health and Medical Research Council guidelines

recommend the use of either the CDC or WHO growth charts for children aged 2-18

years in clinical practice 29

. While recognising that clinical application may involve

additional considerations relating to sensitivity and specificity 30

(e.g. if adopted for

screening or for diagnosis), differential use of these standards means that

interpretation will vary widely, with more or fewer individual children identified for

further investigation or intervention, depending on the standard used. The results of

this study indicate that there is a need for more definitive guidelines for Australian

clinicians with the selection of a single standard, for example recommending use of

the WHO standards would be advisable. Whilst a statistical issue remains around

changing the definition of overweight/obesity between 5.0 and 5.1 years, causing a

potential ‘step’ effect in categorization of individual children, the WHO standards

have the benefit of presenting a standard based on optimal child development.


children 61

In the context of research, there are currently no specific guidelines regarding

appropriate choice of standard, as such standard selection is relative to the research

aim. Some researchers have advocated for the use of IOTF in epidemiological

research as this standard is modelled from adult based BMI cut-offs, and provides

possibilities for international comparison 4,31,32

. The WHO, however, presents

alternative benefits based on health characteristics that affect growth trajectories and

long-term health outcomes (e.g. breastfeeding) 5,8

. A recent study comparing the use

of the CDC and WHO standards on the height of Australian children (2–16 years),

found that neither standard accurately reflected the contemporary Australian child

population, prompting authors to call for the development of local growth charts

specifically for Australian children 33

. However, this approach may be problematic as

1) if one were to construct standards using contemporary Australian data, it would

reflect a less than healthy population; and 2) continually updating the BMI standards

would mean that the prevalence of overweight and obesity would not appear to

change at all. Which begs the question; where to from here?

Implications for future research

The findings presented in this study, and others, leads us to question the arbitrary

statistical cut-points used by these international growth standards. Given the

discordance between standards there is a need to revise the language commonly used

to pathologise children; especially in applying the categories of “overweight” and

“obese”. An alternative approach is provided by Ogden and colleagues 2 who focus

on percentile to more accurately characterise children’s weight status, for example

using the term “high percentile for age”. Such an approach is more likely to limit

harm from classification, or misclassification, associated with arbitrary variation in

category derivation, while still conveying weight status in interpretable terms. The


current categorical approach to classification has implications for statistical analyses

also, and may have obscured some relationships between weight status, predictors of

weight status, and outcomes associated with weight status. Conceptualising BMI or

BMI z-score as a continuous variable will culminate in analyses and associated

findings being more telling. If studies do choose to classify children in accordance

with a fixed cut-point, sensitivity analyses using all three standards may be necessary

to determine that the effects found exist regardless of the standard used.

Our review highlights the need for more sophisticated measures to identify children

and adolescents with unhealthy body mass and allow for increased ability to predict

risk of long term pathology. Indeed, efforts have already begun to assess risk using

behavioural and genetic/proteomic biomarkers. A recent review advocated for the

use of in-depth phenotype analysis using fat mass and related biomarkers (e.g.

insulin resistance and glucose tolerance) to identify cut-offs more sensitive for

disease risk than BMI alone 34

while lifestyle guidelines present measures of activity,

sedentary behaviour, sleep, and nutrition that together with BMI present potentially

more accurate assessments of risk. Cross-sectional population data provide point

prevalence for weight status, but longitudinal tracking of a child’s weight status to

identify anomalous growth is more salient in the clinical context and more congruent

with contemporary patient-centred care. Indeed, the BMI standards were specifically

developed to track weight status over time. In the research context measurement of

growth trajectories, using latent growth modelling, and categorisation of growth

patterning present new opportunities to identify higher risk growth trajectories. In

today’s ‘big data’ age, encouraging researchers to publish raw height, weight, and

age data, and longitudinally tracking the development of children, provides

significant opportunity to advance knowledge of growth and weight status.


children 63

Accumulation of data in this way will ensure comparability across studies and allows

iteration of determinants of increased body mass and the point at which body mass

becomes a risk for negative health outcomes.

Finally, an extension of this systematic review to expand the data on BMI standard

usage beyond Australian populations to other international populations and

corresponding prevalence estimates reported would further inform understanding.

Such data will provide insight as to the functioning of the three international growth

standards across countries and between ethnically diverse populations in prevalence

estimates for overweight and obesity.

Conclusion

In conclusion, paediatric obesity remains a significant public health concern both in

Australia and internationally. Accurate assessment of the clinical burden is vital to

inform action. In the Australian context our review suggests that there is a heavy

reliance on the IOTF standards for 3 – 5 year old populations yet when the three

international standards commonly used to classify weight status are compared they

produce significantly different prevalence estimates. These findings raise broader

issues regarding current approaches to the classification of weight status and

prediction of health risk. In the short-term, our findings suggest that researchers

should give careful consideration to their research aims and sample population when

selecting a growth standard and make raw anthropometric data available to the

research community. In the longer-term, given the varied strengths and limitations

that produce discordance between standards, ongoing longitudinal tracking of

growth, with the emphasis on percentiles, rather than the pathologising categories of

overweight and obesity in young children presents a productive way forward.


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40. Franzon J, Hugo G, Wittert G, Wilson D. Overweight and obesity in 4-year-old South

Australian children and the stability of IOTF cut points in this age group. Obes Res

Clin Pract 2008; 2: 247–250.

41. Gopinath B, Baur LA, Garnett S, Pfund N, Burlutsky G, Mitchell P. Body Mass

Index and Waist Circumference Are Associated With Blood Pressure in Preschool-

Aged Children. Ann Epidemiol 2011; 21: 351–357.

42. Jones RA, Okely AD, Gregory P, Cliff DP. Relationships between weight status and

child, parent and community characteristics in preschool children. Int J Pediatr Obes

2008; 4: 54–60.


children 69

43. Kremer PJ, Bell AC, Sanigorski AM, Swinburn BA. Overweight and obesity

prevalence in children based on 6-or 12-month IOTF cut-points: does interval size

matter? Int J Obes 2006; 30: 603–605.

44. Nichols MS, de Silva-Sanigorski AM, Cleary JE, Goldfeld SR, Colahan A, Swinburn

BA. Decreasing trends in overweight and obesity among an Australian population of

preschool children. Int J Obes 2011; 35: 916–924.

45. Pettman T, Magarey A, Mastersson N, Wilson A, Dollman J. Improving weight status

in childhood: Results from the eat well be active community programs. Int J Public

Health 2014; 59: 43–50.

46. Spurrier NJ, Magarey AA, Golley R, Curnow F, Sawyer MG. Relationships between

the home environment and physical activity and dietary patterns of preschool

children: a cross-sectional study. Int J Behav Nutr Phys Act 2008; 5: 31.

47. Spurrier NJ, Volkmer RE, Abdallah CA, Chong A. South Australian four-year-old

Aboriginal children: residence and socioeconomic status influence weight. Aust NZ J

Public Health 2012; 36: 285–290.

48. Tai A, Volkmer R, Burton A. Association between asthma symptoms and obesity in

preschool (4-5 year old) children. J Asthma 2009; 46: 362–365.

49. Tey C, Wake M, Campbell M, Hampton A, Williams J. The light time-use diary and

preschool activity patterns: Exploratory study. Int J Pediatr Obes 2007; 2: 167–173.

50. Wake M, Nicholson JM, Hardy P, Smith K. Preschooler obesity and parenting styles

of mothers and fathers: Australian National Population Study. Pediatrics 2007; 120:

E1520–E1527.


51. Wolfenden L, Hardy LL, Wiggers J, Milat AJ, Bell C, Sutherland R. Prevalence and

socio-demographic associations of overweight and obesity among children attending

child-care services in rural and regional Australia. Nutr Diet 2011; 68: 15–20.

52. Zuo Y, Norberg M, Wen LM, Rissel C. Estimates of overweight and obesity among

samples of preschool-aged children in Melbourne and Sydney. Nutr Diet 2006; 63:

179–182.


children 71

Table 5.1. Description of the three international reference values for overweight and

obesity5.

Standards/References Overweight Obesity Reference

population

Sample

Strategy

WHO Growth

Standards for pre-

school children 2006 7

Children aged between

Birth and 5 years (or

60.99 months)

BMI-for-age

z-score >2

standard

deviations

above the

mean

BMI-for-

age z-

score >3

standard

deviations

above the

mean

Multicentre

Growth

Reference

Study from

Brazil,

Ghana, India,

Norway,

Oman and the

USA

(1997 – 2003)

Children

meeting

specific

inclusion

criteria (i.e.

breastfeeding

duration and

healthy

singleton

birth) which

represents

optimal

growth –

healthy

children

CDC Growth Charts –

2000 8

Children aged between

2 and 19 years

≥85th

percentile

≥95th

percentile

US NHANES

data (1971-

1994)

Normative

USA

population

data – no

specific


inclusion or

exclusion

criteria

IOTF Reference

Values – 2000 9

International BMI cut-

points for children

aged between 2 to 18

years

≥BMI-for-

age cut-offs

(pass

through BMI

of 25 at age

18)

≥ BMI-

for-age

cut-offs

(pass

through

BMI of 30

at age 18)

Multinational

Surveys from

Brazil (1989),

Great Britain

(1978-93),

Hong Kong

(1993), the

Netherlands

(1980),

Singapore

(1993) and

the US

(1963–80)

(1963 – 1993)

Normative

population

data from

national

growth and

school health

surveys – with

quality

control

measures.


children 73

Table 5.2. Selection of weight standard in Australian pre-school samples 2006-2017 Author Sample,

Age; Female (%)

BMI

Reference

Age BMI

reported

Not

Overweight

%

Overweight

%

Obese

%

Campbell et

al.,

2006 35

Cohort follow-up of

subset of the Parent

Education And Support

[PEAS] (N = 324)

4 yrs; 51.2

IOTF M = 4.2

(SD = 0.2)

81.0 17.0 3.0

Cox et al.,

2012 36

Cross-sectional survey of

children and their

mothers in Melbourne,

Australia (N = 135)

2 – 6 yrs; 60.0

CDC† M = 4.5

(SD =

0.84)

85.2 11.1 3.7

Cretikos et

al.,a

2008 37

Sub-sample of the

Bettering the Evaluation

and Care of Health

[BEACH] program (N =

12,925) a

2-17 yrs; 71.3

IOTF†

(CDC and

ABS data

used to

identify BIV)

2 – 4

(n =

3,015)

71.2 14.7 14.1

Crouch et

al.,

2007 38

Mothers and their

children, attending swim

lessons

at a Central Coast swim

school in NSW

(N = 111)

2 – 6 yrs; 48.6

IOTF M = 4.42

(SD =

1.35)

78.4 15.3 6.3

de Silva-

Sanigorski

et al.,

2010 39

Romp and Chomp

community-based obesity

prevention RCT,

conducted in Victoria (n

= 15,838)

3.5 yr cohort; 48.8

IOTF

(BMIz and

BMI used the

CDC STATA

program)

^C group:

M = 3.65

(SE =

.001)

n = 14,647

83.6 13.2 3.2


Table 5.2. (Continued)

Author Sample,

Age; Female (%)

BMI Reference Age BMI

reported

Not

Overweight

%

Overweight

%

Obese

%

Franzon et al.,

2008 40

Sate wide administrative

data set of preschool

children in South Australia

[SA] (N = 114,925)

4 – 5 yrs; 49.1

IOTF 4.5* 82.9 12.6 4.5

Gopinath et al.,

2011 41

Sydney Paediatric Eye

Disease Study [SPEDS] (N

= 1,249)

3 – 6 yrs; 47.4

CDC 3

(n = 333)

77.2 15.3 7.5

4

(n = 333)

72.4 15.0 12.6

5

(n = 321)

71.7 16.8 11.5

Hayes et al.,

201628

The Healthy Beginnings

Trial [HBT] (N = 350)

WHO 2-<5 yrs 61 29 9

Jones et al.,

2008 42

Pre-school Activity ‘N’

Dietary Adiposity

[PANDA] (N = 138)

2 – 6 yrs; 48.6

IOTF M = 4.3

(0.7)

80.4 19.6

Kremer et al.,b

2006 43

Representative sample of

children in Barwon-South

Western Victoria (N =

2,178)

4 – 12 yrs; 52.1

IOTF 4*

(n = 176)

70.5 23.3 6.3

5*

(n = 247)

75.3 15.8 8.9

Nichols et al.,

2011 44

Maternal and Child Health

Services data on Victorian

children (N = 96,164)

3.5 yr cohort; 49.1

IOTF

(WHO growth

standards used to

attain BIV)

M = 3.64

(SD = .16)

80.7 16.1 3.2


children 75


Author

Sample,

Age; Female (%)

BMI Reference

Age BMI

reported

Not

Overweight

%

Overweight

%

Obese

%

Pettman

et al.,

2014 45

Evaluation of the Eat Well Be

Active [EWBA] community

program and intervention

targeted 0-18yr olds living in

SA. (n = 1,005)

4-5 yr cohort; NR

IOTF ^^C group:

M = 4.8 (SD =

0.23)

(n = 541)

77.1 17.6 5.4

Spurrier

et al.,

2008 46

Families recruited through

preschools, in the southern

region Adelaide, SA

(280)

4.1 – 5.4 yrs; 50.0

IOTF M = 4.8 (SD =

.21)

79.0 15.0 6.0

Spurrier

et al. c,

2012 47

The Children, Youth,

Women’s Health Service

[CYWHS] school entry health

assessment in SA in 2009 (N

= 11,025)

3.5 - 5.9 yrs; 48.7

IOTF

(Also used BMIz

– 1990 British

Growth

Reference Data)

M = 4.76 (SD =

.24)

75.4 14.2 4.4

Tai et

al.,

2009 48

The Children, Youth,

Women’s Health Service

[CYWHS] school entry health

assessment in SA in 2006 (N

= 1,509)

4 – 5 yrs; 49.0

IOTF M = 4.6 (SD =

.04)

(n = 1,457 with

anthropometric

data)

80.6 13.7 5.7

Tey et

al.,

2007 49

Subset of ‘PEAS Study’ re-

enrolled into the PEAS Kids

Growth Study (N = 84)

M = 5.1 yrs; 57.0

IOTF M = 5.1 (SD =

0.1)

87 7 6



Author Sample,

Age; Female (%)

BMI

Reference

Age BMI

reported

Not

Overweight

%

Overweight

%

Obese

%

Wake et

al., d

2007 50

Longitudinal Study of

Australian Children

[LSAC]

(n = 4,934; wave 1)

4 – 5yr; 49.1

IOTF 4-5 79.3 15.2 5.5

Wolfenden

et al.,

2011 51

Data collected as part of

Good for Kids, Good for

Life, NSW (N = 764)

2 – 5 yrs; 50.0

IOTF M = 3.89

(SD = 0.79)

83.3 12.7 4.0

Zhou et al.,

2012 23

Cross-sectional survey of

children aged living in

Adelaide, SA (N = 300)

1 – 5 years; (1) 40.0 (2)

53.0

CDC (1) 3 – 4

(n = 70)

71 16 13

(2) 4 – 5

(n = 68)

67 15 18

Zuo et al.,

2006 52

Two cross-sectional

surveys of children

attending pre-school in

Melbourne (M)

and Sydney (S). The data

sets were collected

independently both focus

on preschool children (M,

N = 196) (S, N = 325)

2.0 - 5.4 yrs; M 50.0, S 53.8

IOTF Melbourne

4.0 – 4.4

(n = 51)

78.4

19.6

2.0

4.5 – 4.9

(n = 83)

72.3 16.9 10.8

5.0 – 5.4

(n = 62)

79.0 12.9 8.1

Sydney

3.0 – 3.4

(n = 58)

74.1

19.0

6.9

3.5 – 3.9

(n = 37)

70.8 21.1 8.1

4.0 – 4.4

(n = 18)

72.2 22.2 5.6

†Parent reported weight and height in this study.


children 77

aThis study is representative, as one other paper was identified using the same

BEACH data-set.

bThis study is representative, as one other paper was identified using the same

Victorian data-set.

cThis study is representative, as one other paper was identified using data from the

same year as this study.

dThis study is representative, as 16 other papers were identified using the same

LSAC data-set.

^Reported here is the Baseline data from comparison (C) group only in the 3.5 year -

old sample.

^^Reported here is the Baseline data from the comparison (C) group of the pre-school

aged children.

*Mean of the 6-month cut-points reported here

NR – Not Reported/not able to be determined


Table 5.3. Demographic information of children and families participating in the

E4Kids study.

Characteristic Descriptive Sample Size

(n)

Child age in years, Mean (SD) 4.05 (0.52) 1926

Child gender (% Female) 48.9 1926

Child ever breastfed? (% Yes) 91.2 1332

Child is Aboriginal and/or Torres Strait Islander

origin (%)

1.9 1129

Family Characteristics

Main Caregiver born in Australia (%) 80.6 1120

Highest Level of Education of Main Caregiver

(%)

High school or did not complete high school

Technical certificate or diploma

University bachelor degree

Postgraduate university degree

17.8

28.0

33.1

21.2

1059

188

297

350

224


children 79

Table 5.4. Crude prevalence estimates of overweight and obesity in the E4Kids

sample according to the three international standards and by gender.

a,b,c Denotes the z-score test of proportional difference.

Note: The first set of letters denotes the difference between IOTF (a) and the prevalence estimates of each of the other

standards. The second set of letters is a direct comparison between the CDC (a) and the WHO standards. If the letter subscripts

differ from ‘a’ this indicates a statistically significant difference at the level of p < .05.

Category IOTF CDC WHO

n % n % n %

Overall (n = 1,926)

Non-

overweight/obese

1508 78.3 a 1289 66.9

b 1744 90.6

c,b

Overweight 341 17.7a 385 20.0

a 151 7.8

b,b

Obese 77 4.0a 252 13.1

b 31 1.6

a,a

Male (n = 985)

Not

overweight/obese

786 79.8 a 635 64.5

b 876 88.9

c,b

Overweight 161 16.3 a 210 21.3

a 88 8.9

a,b

Obese 38 3.9a 140 14.2

a 21 2.1

a,a

Female (n = 941)

Not

overweight/obese

722 76.7 a 654 69.5

b 868 92.2

c,b

Overweight 180 19.1 a 175 18.6

a 63 6.7

b,b

Obese 39 4.1 a 112 11.9

a 10 1.1

a,a


Figure 5.1 An example of the weight status classifications given to

one child when applying the three international growth standards.

Figure 5.1.

An example of the weight status classifications given to one child when applying the

three international growth standards.


children 81

Supporting Information

Weighing in on international growth standards: Testing the case in Australian

preschool children.

Cassandra L. Pattinson1,2

, Sally L. Staton1,2

, Simon S. Smith3, Stewart G. Trost

1, 4,

Emily F. Sawyer5, Karen J. Thorpe

1,2,6

1Institute for Health and Biomedical Innovation, Queensland University of

Technology, Victoria Park Rd, Kelvin Grove, Queensland, Australia.

2School of Psychology and Counselling, Centre for Children’s Health Research,

Queensland University of Technology, South Brisbane, Queensland, Australia.

3Recover Injury Research Centre, Faculty of Health and Behavioural Sciences, The

University of Queensland, Queensland, Australia.



Queensland University of Technology, South Brisbane, Queensland, Australia.

5School of Medicine and Dentistry, James Cook University, Queensland, Australia.

6Institute of Social Science Research, The University of Queensland, Queensland,

Australia

Corresponding author: CL Pattinson Centre for Children’s Health Research (CCHR),

62 Graham Street, South Brisbane, Queensland University of Technology, QLD,

4101, Australia. Email: [email protected]



Table S1. Search terms used for each database.

Database Search strategy Results

PubMed (((Australia[Title/Abstract]) AND (BMI[Title/Abstract] OR “Body

Mass Index”[Title/Abstract] OR “BMI z-score”[Title/Abstract]

OR Obesity[Title/Abstract] OR Overweight[Title/Abstract] OR

Anthropometry[Title/Abstract])) AND (preschool[Title/Abstract]

OR “preschool students”[Title/Abstract] OR “Preschool

Child”[Title/Abstract])) AND ("2006"[Date - Publication] :

"3000"[Date - Publication])

24

Embase

ONLY

Embase.com

australia* AND ('body mass index'/exp/mj OR 'body mass

index'/mj OR 'bmi z score' OR 'obesity'/exp/mj OR

'overweight'/exp/mj OR anthropometry) AND ('preschool students'

OR 'preschool child'/mj) AND [2006-2017]/py AND [embase]/lim

16

PsycINFO

(via

Ebscohost),

(AB: Australia*) AND (AB: BMI OR “Body Mass Index” OR

BMIz-score OR Obesity OR Overweight OR Anthropometry)

Limited by Age: Preschool Age (2-5 yrs) AND Publication Year

2006 – 2017

108

CINAHL

(via

Ebscohost),

(Australia*) AND (BMI OR “Body Mass Index” OR BMIz-score

OR Obesity OR Overweight OR anthropometry) Limiters:

SubjectAge: preschool: 2-5 yearsPublished Date: 2006 – 2017 In

AB

93

Cochrane '(Australia*) AND (BMI OR "Body Mass Index" OR bmi z score

OR obesity OR overweight OR anthropometry) AND (preschool

OR "preschool students" OR ”preschool child”) in Abstract ,

Publication Year from 2006 to 2017 in Trials”

3 trials

Scopus ABS ( ( australia* ) AND (bmi OR "Body Mass

Index" OR “bmi z-score “ OR obesity OR overweight OR

anthropometry) AND (preschool OR "preschool

students" OR "Preschool Child") ) AND PUBYEAR > 2005

142

Web of

Science

(Australia*) AND (BMI OR “Body Mass Index” OR BMIz-score

OR Obesity OR Overweight OR anthropometry) AND (preschool

OR “preschool students” OR “Preschool Child”))

Timespan: 2006-2017. Search language = Auto In all fields

166

ProQuest ab(Australia*) AND ab(BMI OR "Body Mass Index" OR BMIz-

score OR Obesity OR Overweight OR Anthropometry) AND

ab(preschool OR "preschool students" OR “Preschool Child”)

Date Limiter: from 2006 to 2017

45

Science

Direct

pub-date > 2005 and TITLE-ABSTR-KEY (Australia*) and

TITLE-ABSTR-KEY (BMI OR “Body Mass Index” OR BMIz-

score OR Obesity OR Overweight OR anthropometry AND

preschool OR “preschool students” OR "Preschool Child").

8

JSTOR (((ab:(Australia*)) AND ab:(BMI OR "Body Mass Index" OR bmi

z score OR obesity OR overweight OR Anthropometry)) AND

ab:(preschool OR "preschool students" OR preschool child))

5


children 83

Fig. S1.

Systematic identification and exclusion of relevant papers for final analysis.

Chapter 6: Paper 2 – Beyond Duration: Investigating the association between sleep parameters and the weight

status of children 85

Chapter 6: Paper 2 – Beyond Duration:

Investigating the association

between sleep parameters and

the weight status of children



This paper is currently under review in the Sleep Health Journal. This is an

international journal of the National Sleep Foundation. It is a multidisciplinary

journal that examines sleep and health from both a population health and social

science perspective. Please note that the following paper has been formatted in

accordance with the requirements of the journal.


Ms Pattinson conceptualised and design of the study, supervised and

performed data collection, analysed and interpreted the data, and drafted the

manuscript; Dr Smith conceptualized and designed the study (as associate

supervisor), contributed to interpretation of data, contributed to the drafting of the

manuscript and critically reviewed the manuscript; Dr Staton conceptualized and

designed the study, contributed to interpretation of the data, and critically

reviewed the manuscript; Professor Trost (as associate supervisor), contributed to

interpretation of data and critically reviewed the manuscript; Professor Thorpe

conceptualized and designed the study (as principle supervisor), supervised data

collection, contributed to interpretation of data and contributed to the drafting of

the manuscript. All authors approved the final manuscript as submitted.


86Chapter 6: Paper 2 – Beyond Duration: Investigating the association between sleep parameters and the weight status of children

Beyond duration: Investigating the association between sleep parameters and the

weight status of children

Running head: Beyond duration: Sleep parameters and weight status

Cassandra L. Pattinson1,2*

, BPsySci, Simon S. Smith1,3

, PhD, Sally L. Staton1,2

, PhD,

Stewart G. Trost1, 4

, PhD, Karen J. Thorpe1,2,5

, PhD

1Institute for Health and Biomedical Innovation, Centre for Children’s Health

Research, Queensland University of Technology, 62 Graham St, South Brisbane,

Australia, 4101.

2School of Psychology and Counselling, Queensland University of Technology, 62

Graham St, South Brisbane, Australia, 4101.

3Recover Injury Research Centre Faculty of Health and Behavioural Sciences, The

University of Queensland, Herston, Australia, 4006.



Queensland University of Technology, 62 Graham St, South Brisbane, Australia,

4101.

5Institute of Social Science Research, The University of Queensland, 80 Meiers Rd,

Indooroopilly, Australia, 4068

*Corresponding Author: Cassandra L. Pattinson, Centre for Children’s Health

Research (CCHR), Level 5, 62 Graham Street, South Brisbane, Queensland

University of Technology, QLD, 4101, Australia, Ph: +61 (07) 3069 7288, Fax: +61

(07) 3138 0486 Email: [email protected]

Manuscript word count: 3,852 words

Conflict of interests: The authors have no conflicts of interests to disclose

Author contributorship: Conceived and designed the study: SLS KJT SSS CLP SGT.

Performed data collection: CLP SLS. Supervised data collection: KJT. Analysed the

data: CLP SSS KJT SLS. Wrote the paper: CLP SSS KJT. Contributed to

revision/editing of manuscript: SLS SGT




Abstract

Objectives: To examine the associations between sleep parameters and weight status

in a large sample of preschool children.

Design: Cross-sectional survey data from the Effective Early Educational

Experiences for children (E4Kids) study were analysed.

Participants: 1,111 children aged 3 to 6 years in Queensland and Victoria, Australia.

Measurements: General linear modelling (GLM), with adjustment for significant

control variables, assessed the impact of night-sleep duration, total sleep duration,

napping frequency, sleep timing (onset, offset and midpoint), and severity of sleep

problems on standardised body mass index (BMI z-score). GLM was conducted for

the total sample and then, separately by gender.

Results: For the total sample, there was a significant association between short sleep

duration (≤10 hours) and increased BMI z-score. No other sleep parameters were

associated with BMI z-score in this sample. Analyses by gender revealed that among

girls, there were no associations between any sleep parameter and BMI z-score.

However, among boys short sleep duration and napping frequency were both

significantly associated with weight status, even after adjustment for controls.

Conclusion: Sleep duration is a consistent independent predictor of body mass in

young children. These results identify a complex relationship between sleep and

body mass that implicates gender. Potential mechanisms that might explain gender

differences warrant further investigation.


Key Words: Sleep, Body Mass Index, Preschool Children, Gender, Australia



Beyond duration: Investigating the association between sleep parameters and the

weight status of children

Paediatric obesity is a significant public health concern with negative psychosocial and

health sequelae in childhood and across the life course 1,2

. Globally, it is estimated that 42

million children under the age of 5 are classified as overweight or obese 3. In Australia, 23%

of children aged between 2 and 4 years are classified as overweight or obese, with slight

increases in prevalence observed through to adolescence 4. To date, intervention strategies

have been directed to the immediate problem of caloric intake and energy expenditure (e.g.

Get Up & Grow (AUS), Hip-Hop to Health (USA), MAGIC (UK) campaigns); however, the

problem remains significant. For this reason attention has turned to identifying other

modifiable mechanisms implicated in weight status. Sleep has emerged as a significant

candidate for investigation.

Meta-analyses have identified an association between shortened sleep duration and increased

risk of obesity in children 5,6

. However, not all studies have found an association between

sleep duration and weight status in young children (e.g. Hiscock et al., 2011). Furthermore,

recent research suggests that alternative sleep parameters, including sleep midpoint and

timing of sleep onset or offset, potentially exert a greater influence on weight status than

duration alone 8–12

. For example, Olds et al. (2011) report that children and adolescence

classified as “late bed - late rise” had decreased physical activity and increased weight status

compared to those classified as “early bed - early rise” despite these groups having similar

sleep durations. Similarly, in a study of “late” vs “normal” sleepers, later sleep midpoint was

associated with higher weight status, even though sleep duration did not differ 11

. In

longitudinal analysis of younger children (aged 4-5 years), both shorter night sleep duration

and later sleep onset at age 4 was associated with increased body mass between 4 and 5 years

of age 9. This may be indicative of early dysregulation of circadian timing, resulting in

metabolic hormone disruption (e.g. leptin and ghrelin) leading to increased body mass 13

.


Considering the independent effect of a multiplicity of sleep parameters on the weight status

of young children may provide greater understanding of the mechanisms at play. Therefore,

the aim of this study was to investigate associations between multiple sleep parameters

(daytime napping, night time sleep duration, sleep timing and sleep problems) and weight

status in a large sample of Australian children aged 3 to 6 years (N=1,111). We

hypothesized that sleep parameters indicative of poorer sleep would be associated with

increased body mass. Further, due to recent research indicating significant gender differences

associated with shortened sleep duration 14

and subsequent increased BMI z-score 15

, we

conducted exploratory analyses stratified by gender.

Method

Participants

The children were participants in the Effective Early Educational Experiences for

children (E4Kids), a 5-year Australian longitudinal study of the developmental

impact of early childhood education and care (ECEC) services. The sample and

design of the study have been detailed elsewhere 16

. Briefly, children and families

were recruited in 2010 from childcare services across four locations in two states:

Queensland – Brisbane (metropolitan) and Mt Isa (remote), Victoria – Melbourne

(metropolitan) and Shepparton (rural). Stratified random sampling was used to

capture the range of licensed service types (Long Day Care, Kindergarten and Family

Day Care) and ensure representation of both high and low socioeconomic states

(SES). Recruitment was focussed on children aged between 3 and 5 years of age,

with any child within identified ECEC rooms invited to participate. Written informed

consent was provided by main caregivers and children gave verbal consent. Ethical

approval was provided by both The University of Melbourne and Queensland

University of Technology Human Research Ethics Committees. A total of 2,488



children were recruited in 2010 from 140 services. These data are from the second

year (2011) of the study, when height and weight were collected for 1,945 children

(78.2%). Parents of 1,288 children completed the sleep items. Complete data were

available for a total of 1,112 children.

Analyses were conducted to investigate if there were any significant differences

between parents who did and did not complete the main caregiver sleep items in

2011. Parents who did not complete the items were significantly younger (M = 30.56

(.23)) than parents who completed (M = 32.20 (.14)) the survey (t (1,129.56) = -6.11,

p <.001). Furthermore, parents who completed the survey had significantly higher

relative advantage on SES (M = 1031.54 (2.1)) than parents who did not complete (M

= 1000.96 (2.16)) the survey (t (1281) = -10.13, p <.001). No other significant

differences were found between parents who completed or did not complete the

study in 2011.

Weight Status

Height and weight were measured by trained fieldworkers to WHO standards 17

within the child’s ECEC service. Children were dressed in light clothing and without

shoes and were measured using calibrated stadiometers (SECA Leicester Portable

Height Measure) and floor scales (HD-316, Wedderburn Scales; Tanita Corporation,

Tokyo, Japan). Children were measured twice; if measurements differed (weight

>0.1kg; height >0.5cm) a third measurement was taken by the researcher with the

mean of these measurements used to calculate BMI. The WHO’s Anthro (version

3.2.2) and AnthroPlus (for children over 5.1 years) programs were then used to

transform raw anthropometric data into sex- and age-specific z scores. To ensure

comparability, weight status was reported using international cut-points 18,19

.


Biologically implausible values (BIV) were identified using the WHO guidelines,

with one child identified and subsequently removed.

Sleep Parameters

The main caregiver reported their child’s typical night sleep; (1) “On a typical night,

when does the study child usually go to sleep?” – responses were indicated by a time

between “before 5 pm” through to “midnight or after midnight” presented in half

hour increments, (2) “On a typical morning, when does the study child usually wake

up?” - respondents indicated a time between “before 4am” through to “after 10am”

presented in half hour increments. The main caregiver also reported on their child’s

napping frequency and duration; (1) “Does the study child ever nap (sleep during the

day)?” – response category of yes/no, (2) “In a typical week, on how many days does

the study child usually nap?” – using an eight-item Likert scale response item from

“only some weeks/less than 1 day per week – 7 days per week”, (3) “On days when

the study child naps, how long do they usually nap for?” – responses was on a seven-

point Likert scale from “0-15mins – more than 2hours”. Finally, the main caregivers

were also asked if their child “ever have problems with their sleep” and how severe

they thought these problems were: Mild / Moderate / Severe. Using the information

collected the sleep parameters were created as described in Table 1.

Control Variables

Child Factors

Child gender and date of birth were reported by the main caregiver. The main

caregiver also completed information about child temperament, using the Short

Temperament Scale for Children 20

. Inflexible (aka reactive) temperament was

measured as temperament has been shown to be associated with both increased

weight status 21

and decreased sleep duration 22

in young children. The question, “In



the last 24 hours, how often has your child had biscuits, doughnuts, cake, pie or

chocolate?” was used to measure frequency of discretionary food intake with

dichotomous responses categories of “not at all” and “at least once”. The main

caregiver also provided information on the frequency of their child’s physical

activity and sedentary behaviour. A question from the home learning environment

scale 23

was used as a proxy of physical activity (PA) frequency. This question asks

parents to report the days per week (0 – 7 days) that they participated in outdoor

activities with their child like walking, swimming or cycling. Furthermore, two

questions from the social learning environment scale 24

in which parents reported the

number of days per week (0 – 7 days) in which a child watches TV or movies either

with an adult, or alone, were averaged to provide a measure of screen time frequency.

Family and Environment Factors

The main caregiver provided information about their family’s demographic profile.

This information included the total family net income for the last 12 months. The

main caregiver also indicated their highest level of education currently completed.

The main caregiver also reported the total years of centre-based child care their child

had received up until 2011. From the postcode of the family, SES was determined

using the index of relative socioeconomic advantage and disadvantage Socio-

Economic Indices for Areas (SEIFA) from the Australian Bureau of Statistics (ABS)

2006 Census data 25

. The SEIFA includes measures of disadvantage (low income,

lack of post-school qualifications in people aged 15 years or older) and relative

advantage (e.g. tertiary education) of people residing in the area, with higher scores

indicating greater advantage with a corresponding lack of disadvantage. Parental

control over child choices e.g. bed timing and food eaten, was also measured. Six


questions were summed (highest score = 30), with higher scores indicative of greater

parental control over activities.

Statistical Analyses

Analyses were conducted to examine the relationship between sleep parameters,

child, family, and environmental factors for 1,111 children. All statistical analyses

were completed using the Statistical Packages for Social Sciences (SPSS) version

23.0 software. The purposeful selection method 26,27

was used to determine the sleep

parameters and control variables which had a significant effect on BMI z-score.

Correlation analysis identified the independent variables (both explanatory and

control) that were significantly associated with BMI z-score at the selected p-value

cut-off point of < .30. Then, an iterative process of variable selection was conducted.

Explanatory and control variables were entered into a General Linear Model (GLM)

with BMI z-score as the dependent variable. Variables were removed if they did not

have a “significant effect” on the model and were not a confounder. A significant

effect was defined as having a p <.10 and not having a significant influence over any

remaining variables within the model of greater than 15% 26,27

. After the process of

deleting and verifying the model, any variable that had been excluded was then

entered back into the model one at a time 26

. Any independent variables that had a

significant effect were retained in the model. This process provided the most

parsimonious model with the significant independent and control variables retained

and any extraneous variables removed. From the final GLM analyses, any significant

associations found between BMI z-score and the sleep parameters were examined

using post-hoc pairwise comparisons using Sidak correction for multiple

comparisons. Data were then stratified by gender and the models analysed separately

for boys (n = 576) and girls (n = 535), with the same process identified with each



sleep parameter and control variable examined for inclusion in the final GLM.

Planned post-hoc comparisons with Sidak corrections were utilised to examine any

significant differences between categorical sleep parameters and BMI z-score.

Results

Demographic information for the participating children and families are presented in

Table 2. Children were aged between 2.79 to 6.78 years and 1.9% of children were

identified as being of Aboriginal or Torres Strait Islander Origin. In this sample,

16.2% of the children were classified as overweight or obese 19

. Table 3 provides

information about the measured sleep parameters across the whole sample, and then

by gender. Boys woke up slightly earlier (t (1107.29) = -3.45, p = .001) and had

shorter night-sleep durations (t (1109) = -2.94, p = .003) than girls. Accordingly,

boys typically had an earlier sleep midpoint time than girls in this sample (χ2 (1,

1111) = 6.14, p = .013).

The result of the correlational analysis examining the relationship between sleep

parameters, control variables, and BMI z-score are reported in Table S1. The only

sleep parameter that was significantly associated with BMI z-score was night sleep

duration (r = -.07, p = .028). According to the purposeful selection methodology (p <

.30), napping frequency (r = .04, p = .154) was also included in the second, iterative

step of analyses. These analyses revealed that night sleep duration was the only sleep

parameter significantly associated with BMI z-score to be included in the final

model. The significant control variables identified for inclusion in the final model

were main caregiver education, parent control, inflexible temperament, and SEIFA.

The overall model was significant F (6,1070) = 5.27, p <.001; partial ɳ2 = .029

(Table 4). An effect size of .029 indicates a small effect of the model in accounting

for change in BMI z-score 28

. Night sleep duration remained a significant


independent predictor of BMI z-score, even after controlling for SEIFA, inflexible

temperament, parent control and main caregiver education. Post-hoc pairwise

comparisons using Sidak corrections indicated that there was a .22 mean unit

increase in BMI z-score for short sleepers (M = .77 ± .09) in comparison to normal

sleepers (M = .55 ± .04).

Separate analyses of gender found that for girls, there were no significant

correlations between any sleep parameters and BMI z-score (see Table S2 for

correlations). For boys (Table S3), sleep duration and napping frequency were

significantly associated with BMI z-score. Control variables identified to interact

with BMI z-score included; SEIFA, inflexible temperament, parent control, main

caregiver education, TV and PA frequency. The iterative process revealed that all

variables made a significant impact on the overall model. For example, although

SEIFA and PA frequency were not associated with BMI z-score, they did influence

the other variables within the model, thus all variables were included in the final

model

The overall model was significant F (11,548) = 2.61, p = .003; partial ɳ2 = .050

(Table 4). After adjusting for the covariates, night sleep duration and napping

frequency remained significant predictors of BMI z-score. Post-hoc pairwise

comparisons with Sidak corrections indicated that, male children classified as short

sleepers had a .27 mean unit increase in BMI z-score compared to normal sleepers.

In contrast, although there was a main effect of napping frequency, post-hoc

comparisons revealed that there were no significant differences between the napping

frequency groups on BMI z-score. However, Figure 1 illustrates a U-shaped pattern

between the four napping groups and BMI z-score observed in this sample of boys.



Figure 6.1. Mean BMI z-scores observed for boys in each of the napping frequency groups

after adjusting for night sleep duration, parent control, temperament and main caregiver

education.

Discussion

Poor sleep has been implicated in childhood weight gain, and has been suggested as a

potential modifiable mechanism to address unhealthy weight status. However, to

date, large scale studies examining the contribution of poor sleep to overweight and

obesity have primarily focussed on night-time sleep duration. This study aimed to

advance knowledge through investigation of the association of weight status with a

broader range of sleep parameters in a large sample of preschool aged children.

Consistent with previous research, our study identified a significant association

between shorter night-time sleep duration and increased BMI z-score 9. Children who


slept fewer than 10 hours per night had an increase of 0.22 BMI z-score units in

comparison to children getting more than 10 hours of sleep per night. This finding

lends support to the recently updated international recommendations that preschool

aged children achieve sleep durations of between 10 and 13 hours 29

. No other sleep

parameters were associated with weight status in this sample, including total sleep

duration, which is closely associated with night-sleep duration. The null finding may

reflect an interplay between night-sleep duration and napping, as evidenced in

previous research (see Thorpe et al., 2015).

Consistent with previous research, gender-specific differences of sleep parameters

were observed in the data 6,15

. After controlling for significant confounding variables,

night sleep duration and napping had independent effects on BMI z-score for boys.

In contrast, no sleep parameters were associated with BMI z-score for girls. Some

have hypothesised that such gender differences may be due to evolutionary adaptive

factors 6. Alternatively, gender-specific biological and/or social-environmental

differences may also play a role. In this study, boys had significantly shorter night-

sleep duration than did girls. Boys also had significantly earlier rise times than did

girls. These sleep patterns may reflect neurocognitive differences, for example

gender-specific differences in sleep architecture 31

. Alternatively, social-

environmental factors, such as differences in parental control and subsequent

parenting strategies between genders, may influence night sleep duration and other

sleep parameters 14,15

with subsequent effects on weight status.

The effect of napping and night sleep duration on BMI z-score across genders

warrants further investigation. All children in this study were recruited from licensed

childcare services, and previous research has shown that childcare sleep policies and



practices can affect both napping frequency 32

, and night-time sleep duration, both

concurrently and in the longer-term 33–35

. It may be that boys are particularly

susceptible to these practices.

After accounting for significant control variables, the amount of variance explained

in our models, though statistically significant, was low. In the full sample, the model

only explained 3% of variance in total BMI z-score. This may indicate that sleep

interacts with weight status in indirect ways, and that more complex modelling of

such interactions informed by developmental physiology may be needed.

Alternatively, this result may reflect a genuinely modest contribution of sleep to

overall weight status. The determinants of weight status are complex, multifaceted

and dynamic. Sleep parameters are one of a multiplicity of factors contributing to

weights status in our modern obesogenic environment. Further research examining

the associations between sleep duration, napping, and weight status in young children

is needed.

Strengths and Limitations

This study has a number of strengths. Firstly, it comprised a large population of

children aged between 3 and 6 years from a study which used stratified-random

sampling to capture a wide range of social and economic experiences of families

using ECEC services in Australia. Furthermore, BMI was directly measured by

trained researchers using standardised procedures, reducing the error that is

associated with parent-report. One of the major limitations of the study, however, is

that sleep parameters were based on parent report. Although parent-reported sleep

duration is a commonly employed method in large-scale studies and has been shown

to be correlated with actigraphic recorded sleep duration 36

, some research has


indicated that parents can overestimate sleep duration by more than one hour per

night 37

. Therefore, replication with objective measurement (such as actigraphy or

polysomnography) may be necessary to improve understanding of the relationships

between sleep parameters and weight status.

Conclusion

In conclusion, shortened sleep duration was associated with increased BMI z-score in

this Australian preschool sample. The results indicated that children sleeping less

than 10 hours per night had higher BMIz, which lends support to the current

international recommended sleep guidelines for children in this age group. In boys,

there was a significant independent effect of both night-sleep duration and napping

frequency on weight. It is recognised that sleep is important in early childhood, and

the potential for sleep to elicit better health outcomes remains.

Acknowledgments: The sampling derives from an Australian longitudinal study of

ECEC effectiveness, Effective Early Educational Experiences for Children (E4Kids).

E4Kids is a project of the Melbourne Graduate School of Education at The

University of Melbourne and is conducted in partnership with the Queensland

University of Technology. E4Kids is funded by the Australian Research Council

Linkage Projects Scheme (LP0990200), the Victorian Government Department of

Education and Early Childhood Development, and the Queensland Government

Department of Education and Training. E4Kids is conducted in academic

collaboration with the University of Toronto Scarborough, the Institute of Education

at the University of London and the Royal Children’s Hospital in Melbourne. The

E4Kids team would like to sincerely thank the ECEC services, directors,

teachers/staff, children and their families for their participation in this study.



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Table 6.1. Definition of the sleep parameters assessed in this study.

Sleep Parameter Description Variable

Type

Sleep onset Typical time that sleep commences. Continuous

Sleep offset Typical time that sleep ends.

Continuous

Night-sleep

duration

Calculated using the duration of time between

typical sleep onset and offset. Night-time sleep

duration was then categorised into long (>10

hours) and short sleepers (≤10 hours)

consistent with previous research and also with

the National Sleep Foundations recommended

10 hours of sleep for children in preschool age

range (Hirshkowitz et al., 2016).

Categorical

(2 levels)

Total-sleep

duration

Calculated by summing napping duration per

week with night time sleep duration. Nap

duration per week (Miller et al., 2014) is

calculated by multiplying nap duration by days

napped per week which is then divided by 7

days.

Continuous

Sleep midpoint Calculated as a proxy for circadian timing

(Martin and Eastman, 2002) using the midpoint

of the time between sleep onset and offset, then

transformed using a median split into late sleep

midpoint (>1:15am) and early sleep midpoint

(≤1:15am), this is consistent with previous

research (Thivel et al., 2015).

Categorical

(2 levels)

Napping

frequency

As a significant number (848; 76.7%) of

children who had ceased napping, the

distribution of napping duration per week was

extremely skewed. Therefore, children were

classified into four napping frequency groups:

Non-nappers; Incidental Nappers (<1 per

week); Transitioning nappers (1 – 3 days per

week); Consistent Nappers (4 – 7 days per

week).

Categorical

(4 levels)

Sleep problems The responses on the 2 sleep problem questions

were dichotomised into: “no problem/mild’

versus “moderate/severe”.

Categorical

(2 levels)


Table 6.2. Demographic Information of the 2011 E4Kids Sample included in the

final analysis.

Characteristic Descriptive Sample Size (n)

Child age in years, Mean (SD) 4.86 (0.63) 1111

Child gender (% Boys) 51.8 576

Child ever breastfed? (% Yes) 91.9 1024

Child is Aboriginal and/or Torres Strait Islander origin

(%) 1.9 1100

Child diagnosed as having intellectual disability or

development delay 6.2 1110

Child BMI z-score, Mean (SD) 0.55 (.96) 1111

Child weight status (IOTF):

Non-overweight 83.8 931

Overweight 12.7 141

Obese 3.5 39

Family Characteristics

Gender of Main Caregiver completing the survey in 2011

(% Female) 92.7 1106

Main Caregiver born in Australia (%) 80.4 1102

SEIFA (relative advantage and disadvantage), Mean (SD) 1036.15 (73.58) 1095

Main Caregiver Education

1100

High school or did not complete high school (%) 17.5 193

Technical certificate or diploma (%) 26.4 290

University bachelors or postgraduate degree (%) 56.1 617



Table 6.3. Information about the measured sleep parameters.

Whole

Sample Boys Girls Diffa

Sleep Parameter M (SD) M (SD) M (SD) t

Sleep Onset

19:47

(00:37)

19:47

(00:36)

19:48

(00:37)

-0.54

Sleep Offset

6:48

(00:37)

6:44

(00:39)

6:52

(00:35)

-3.45**

Night sleep duration (Hrs) 11.00 (.62) 10.96 (.63) 11.07 (.60) -2.94*

Total sleep duration (Hrs) 11.13 (.63) 11.10 (.64) 11.17 (.62) -1.94

% % χ2

Sleep Midpoint ≤1:15am 59.9 63.4 56.1 6.14*

Sleep problems

(moderate/severe) 3.9 3.6

4.1 0.16

Napping Frequency

1.02

Non-napper 65.2 64.1 66.3

Incidental 11.7 11.5 11.9

Transitioning 17.3 18.1 16.4

Consistent 5.9 6.3 5.5

aDiff refers to the difference between boys and girls on each of the sleep parameters

using independent-samples t-tests (t) and chi-square (χ2) analyses were appropriate.

*p <.05**p = .001


Table 6.4. General linear model of the significant sleep parameters effect on BMI z-

score with adjustment for significant control variables.

Variable β 95% CI ɳ2p p

Whole Sample (N = 1077)

Night Sleep Duration .222 .034 - .409 .005 .020

Main caregiver education .004 .094

School or did not finish schoola .047 -.116 - .209 .000 .572

Tertiary Study (i.e. Diploma)a .155 .015 - .296 .004 .030

Parent control .035 .014 - .056 .010 .001

Inflexible -.088 -.149 - .028 .008 .004

SEIFA -.001 -.001 - .000 .002 .122

Boys (N = 560)

Night Sleep Duration .268 .007 - .529 .007 .044

Napping Frequency .015 .045

Non napperb .011 -.344 - .366 .000 .950

Incidental napperb .297 -.119 - .712 .004 .162

Transitioning napperb .259 -.125 - .643 .003 .186

Main caregiver education .006 .187

School or did not finish school a .146 -.089 - .382 .003 .223

Tertiary Study (i.e. Diploma) a .173 -.028 - .374 .005 .091

Parent Control .036 .005 - .067 .009 .023

Inflexible -.061 -.147 - .026 .003 .169

SEIFA -.001 -.002 - .001 .001 .379

Screen time frequency -.041 -.094 - .012 .004 .126

PA frequency -.016 -.058 - .027 .001 .471

a The referent group for main caregiver education is parents with

bachelors/postgraduate degree.

bThe referent group for napping are consistent nappers



Supplementary Material

Chapter 6: Paper 2 – Beyond Duration: Investigating the association between sleep parameters and the weight status of children 111

* Correlation is significant at the p < 0.05 level ** Correlation is significant at the p < 0.01 level

Table S1. Correlations between BMI z-score, sleep parameters and control variables (N = 1,111)

Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

1 BMI z-score

2 Sleep midpoint .009

3 Sleep onset .019 .702**

4 Sleep offset -.010 .666** .503**

5 Sleep duration -.066* .001 -.296** .351**

6 Total sleep duration -.011 .000 -.389** .493** .547**

7 Napping frequency .043 .029 .137** -.086** -.195** .157**

8 Sleep problems .030 .045 .131** .007 -.085** -.122** -.017

Child Factors

9 Inflexible temperament -.084** -.084** -.130** -.024 .053 .083** -.051 -.113**

10 Discretionary food intake -.034 -.007 .022 -.038 -.051 -.075* -.006 -.025 -.025

11 Screen Time Frequency .001 .109** .132** -.002 -.112** -.103** .085** .025 -.083** .121**

12 PA Frequency -.012 -.071* -.067* -.006 .022 .063* .010 -.018 .106** .008 .036


13 Years centre based care .033 .000 .069* -.042 -.067* -.057 .105** -.022 -.012 -.022 .032 -.022

14 SEIFA -.065* -.122** -.116** -.085** .053 .021 -.016 -.058 -.025 .041 -.055 .060* .132**

15 Main caregiver education -.058 -.054 -.055 -.040 .049 .020 .037 -.055 .031 .079** -.036 .086** .146** .321**

16 Family net income -.042 -.086** -.089** -.131** -.008 -.035 .039 -.050 .014 .002 -.055 .034 .150** .429** .305**

17 Parent Control .087** -.096** -.149** -.039 .099** .101** -.017 .004 .069* .006 -.119** -.049 -.022 .020 .016 .028


Table S2. Correlations between BMI z-score, sleep parameters and control variables for girls (N = 535)

Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

1 BMI z-score



4 Sleep offset .041 .665** .507

**

5 Sleep duration -.023 -.105* -.381

** .225

**

6 Total sleep duration .018 -.039 -.436** .460

** .547

**

7 Napping frequency -.011 .033 .100* -.039 -.088

* .203

**

8 Sleep problems .040 .082 .237** .093

* -.109

* -.156

** .006

Child Factors

9 Inflexible temperament -.080 -.125** -.199

** -.062 .073 .114

** -.050 -.138

**

10 Discretionary food intake -.090* .008 .016 .002 -.014 -.036 -.043 -.029 .023

11 Frequency of TV watching .044 .128** .134

** -.002 -.098

* -.130

** .054 .054 -.083 .104

*

12 Frequency of physical activity .029 .011 -.036 .078 .091* .103

* -.019 -.054 .122

** -.012 .037


13 Years centre based care .040 -.034 .077 -.033 -.017 -.062 .097* -.007 .042 -.012 .062 .002

14 SEIFA -.087* -.114

** -.145

** -.089

* .089

* .050 -.021 -.077 -.088

* .103

* -.071 .025 .117

**

15 Main caregiver education -.032 -.063 -.060 -.065 .058 .022 .056 -.051 -.022 .066 -.046 .047 .175** .339

**

16 Family net income -.099* -.056 -.086 -.119

** .011 -.010 .056 -.050 -.009 .016 -.017 -.062 .154

** .404

** .289

**

17 Parent Control .072 -.063 -.088* .019 .124

** .090

* -.024 .009 .089

* -.030 -.140

** -.050 .018 -.027 .016 .008


Chapter 6: Paper 2 – Beyond Duration: Investigating the association between sleep parameters and the weight status of children 113

Table S3. Correlations between BMI z-score, sleep parameters and control variables for boys (N = 576)

Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

1 BMI z-score



4 Sleep offset -.035 .665** .504

**

5 Sleep duration -.088* .077 -.233

** .432

**

6 Total sleep duration -.026 .028 -.348** .516

** .548

**

7 Napping frequency .083* .030 .172

** -.119

** -.274

** .120

**

8 Sleep problems .024 .006 .023 -.072 -.068 -.092* -.038

Child Factors

9 Inflexible temperament -.076 -.058 -.072 -.008 .031 .050 -.049 -.092*

10 Discretionary food intake .016 -.023 .028 -.073 -.082* -.112

** .028 -.022 -.067

11 Frequency of TV watching -.047 .102* .134

** .013 -.117

** -.071 .110

** -.003 -.074 .139

**

12 Frequency of physical activity -.053 -.145** -.095

* -.069 -.029 .031 .035 .018 .097

* .027 .030


13 Years centre based care .021 .037 .062 -.044 -.100* -.048 .110

** -.036 -.055 -.030 -.002 -.048

14 SEIFA -.056 -.123** -.087

* -.072 .033 .002 -.015 -.038 .038 -.014 -.050 .088

* .143

**

15 Main caregiver education -.082 -.046 -.050 -.020 .043 .019 .019 -.060 .079 .092* -.028 .123

** .120

** .304

**

16 Family net income .005 -.114** -.091

* -.141

** -.022 -.056 .023 -.048 .038 -.011 -.093

* .126

** .147

** .454

** .322

**

17 Parent Control .087* -.116

** -.208

** -.071 .093

* .124

** -.015 .002 .065 .042 -.113

** -.054 -.068 .054 .016 .044


Chapter 7: Paper 3 - Environmental Light

Exposure is Associated with

Increased Body Mass in Children.



This paper is published: Pattinson, C.L., Allan, A.C., Staton, S.L., Thorpe, K.J., Smith,

S.S. (2016). Environmental Light Exposure is Associated with Increased Body Mass in

Children. PLOS ONE; IF = 3.23. PLOS One was the world’s first multidisciplinary Open Access

Journals. The journal’s remit is to publish studies that display high ethical standards and

rigorous scientific methodology. PLOS One is in the top quartile of journal rankings of the

following discipline areas – Medicine (miscellaneous), Biochemistry, Genetics and Molecular

Biology (miscellaneous), Agricultural and Biological Sciences (miscellaneous). Please note

that the following paper has been formatted in accordance with the requirements of the

journal. The following document is presented in the format of PLOS One as it was at final

submission before publication.


Ms Pattinson contributed to the conceptualisation and design of the study, performed

data collection; analysed and interpreted the data, and drafted the manuscript; Dr Allan

contributed to the analysis and interpretation of the data and contributed to the editing of

the manuscript. Dr Staton conceptualized and designed the study, developed study

measures, supervised and performed data collection, contributed to the analysis and

interpreted the data, and contributed to the drafting of the manuscript; Dr Smith

conceptualized and designed the study (as associate supervisor), contributed to

interpretation of data and critically reviewed the manuscript; Dr Thorpe conceptualized and

designed the study (as principle supervisor), supervised data collection, contributed to

interpretation of data and contributed to the drafting of the manuscript. All authors

approved the final manuscript as submitted.


116 Chapter 7: Paper 3 - Environmental Light Exposure is Associated with Increased Body Mass in Children.

Environmental Light Exposure is Associated with Increased Body Mass in

Children.

Cassandra L. Pattinson1*, Alicia C. Allan

2, Sally L. Staton

1, Karen J. Thorpe

1, Simon S.

Smith1,2

*.

1Centre for Children’s Health Research, School of Psychology and Counselling, Institute for

Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove,

Queensland, Australia

2Centre for Accident Research and Road Safety – Queensland (CARRS-Q), Queensland

University of Technology, Kelvin Grove, Queensland, Australia

*Corresponding authors

Email: [email protected] (C.L.P.), [email protected] (S.S.S.).



Chapter 7: Paper 3 - Environmental Light Exposure is Associated with Increased Body Mass in Children. 117

Abstract

The timing, intensity, and duration of exposure to both artificial and natural

light have acute metabolic and physiological effects in mammals. Recent research in

human adults suggests exposure to moderate intensity light later in the day is

concurrently associated with increased body mass; however, no studies have

investigated the effect of light exposure on body mass in young children. We

examined objectively measured light exposure and body mass of 48 preschool-aged

children at baseline, and measured their body mass again 12 months later. At

baseline, moderate intensity light exposure earlier in the day was associated with

increased body mass index (BMI). Increased duration of light exposure at baseline

predicted increased BMI 12-months later, even after controlling for baseline sleep

duration, sleep timing, BMI, and activity. The findings identify that light exposure

may be a contributor to the obesogenic environment during early childhood.

Introduction

The contemporary child is exposed to greater daily duration and increased

variation in intensity, temporal distribution, and spectra of environmental light, than

children of any previous generation [1]. This is attributable to the use of artificial

lighting, and has paralleled global increases in the incidence of obesity [1, 2].

Coupled with the known physiological impacts of light on human physiology, this

raises a question; is light a factor in pediatric obesity?

It is estimated that 42 million children under the age of 5-years are classified

as overweight or obese globally [3], including 23% of children in developed

countries [4]. This is a significant clinical, public, and population health concern, as


pediatric obesity is associated with a multitude of negative psychosocial and health

sequela. Potential mechanisms that might be driving the global increase in obesity

include increased calorie intake, decreased physical activity, and more recently, short

sleep duration [5, 6], variable sleep timing [7], and gut flora [8]. However, attempts

to address these factors have not, as yet, led to effective and sustained change in the

prevalence and incidence of obesity [4]. Therefore, significant efforts are being made

to identify modifiable factors that contribute to weight gain and that constitute the

obesogenic environment. Recent evidence suggests that environmental light exposure

may be one such factor.

Light is the principal cue for circadian entrainment in all species [9].

Circadian processes drive physiological and behavioral mechanisms including sleep-

wake cycles [10], regulation of metabolism [11-13], emotion [14], and body mass

[15–17]. Through the adoption and use of artificial lighting, humans have

constructed a photoperiod that is malleable, creating an environment of relatively

dim days and bright nights [1, 18]. Manipulation of the timing, intensity, and

duration of light exposure to suit contemporary lifestyles has occurred with limited

consideration of its effects on health, behavioral, and environmental outcomes. An

understanding of these effects is only now beginning to emerge [1, 14, 18, 19].

Animal studies indicate that the timing and intensity of light exposure is

critical for metabolic functioning and weight status. Rodents exposed to continuous

white light, even at low levels, exhibited symptoms of metabolic syndrome,

increased adiposity, glucose intolerance [20, 21], and reduced sympathetic activity in

brown adipose tissue [22], independent of their caloric intake and locomotor activity.

Many of these symptoms are abolished when regular light-dark cycles are reinstated

[23]. Furthermore, studies of the natural environment indicate that increased artificial


light at night, both through direct illumination (e.g. structural, security, street, and

advertising lighting) and skyglow, affect the reproductive, migrative, and daily

movement behaviors of multiple plant and animal populations [18, 24-26]. The cost

of these changes are not yet fully understood. In adult humans, morning bright light

treatment has been shown to reduce body fat and appetite [27, 28], improve mood

[27], and modulate concentrations of the appetite regulating hormones; leptin and

ghrelin [29]. Commensurately, recent evidence shows that exposure to light of

moderate intensity (~500 lux) earlier in the day is associated with lower body mass,

independent of sleep timing, total sleep duration, and activity in adults [17]. Taken

together, these data indicate that the timing, duration and intensity of light exposure

has a potent role in metabolic and physiological functioning. Early childhood is a

pivotal time in the establishment of lifelong growth and adiposity trajectories [30].

However, to date, no studies have examined the effect of habitual light exposure on

body mass in children.

The present study investigated the relationship between timing, duration, and

intensity of light exposure and weight status of healthy, free-living children aged 3 to

5 years, both concurrently and longitudinally. Standardized independent

measurements of body mass index (BMI; kg/cm2) were taken for all participating

children at both time points. BMI measurements were transformed into age- and sex-

specific BMI z scores for each child [31]. It was hypothesized that, independent of

sleep midpoint (used as a proxy for sleep timing and circadian phase; [32]), sleep

duration and activity, timing and intensity of light exposure (earlier in the day) would

be associated with lower concurrent BMI z score. Further, it was hypothesized that

timing of light exposure at baseline, would be associated with BMI z score 12-

months later.


Materials and Methods

Ethics Statement

The study protocol was approved by Queensland University of Technology’s

Human Research Ethics Committee. Written informed consent was provided by

directors, teachers and the legal guardians of the children. Children gave their assent

to participate.

Study Design

Initially, 62 healthy pre-school children (32 Males (51.6%); M = 56.51

months, SD = 5.94, and Age Range: 39.0 – 74.0 months) were recruited for the 12

month study, from six long-day child care services in Brisbane, a capital city in

subtropical Australia. Participating child care services were recruited from a pool of

118 services participating in a pre-existing study [33]. All services were located in

high socio-economic status (SES) areas according to postcode (SEIFA [34]) and

were randomly selected to be approached to take part in the study. Lower SES is

associated with a range of sociodemographic factors which may impact upon child

health and development [35-37], as such services in high SES areas were specifically

targeted in the study design to control for some of these variations. Within each

childcare service, one room catering for children within the preschool age range (3 –

5 years) was targeted for recruitment. All children attending the target rooms were

invited to participate in the study. To avoid school holiday periods, the 14th

of

December, 2012 (final date of the school term in Queensland, Australia) was the

predefined study endpoint.

At baseline participating families were sent a 14-day sleep diary, parent

survey and Actiwatch 2 (MiniMitter Phillips) device. Actigraphs were worn by the


study child on the non-dominant wrist for 14 days. The sleep diary was completed

concurrently by parents who were asked to record their child’s sleep and wake times,

napping behaviour and any instances which the actigraph was removed from the

child’s wrist. The parent survey included demographic information and an 8-item

Food Frequency Questionnaire (FFQ), which asked parents to indicate, “In the last

24 hours, how often has the study child had the following foods?” with the following

trichotomous responses available “Not at all,” “Once,” or “More than once” [38].

Within the 14-day testing period, researchers visited each participating pre-school

classrooms on the same designated testing day each week (i.e. Tuesday, Wednesday,

or Thursday). Researchers observed the childcare routine and environment, including

sleep practices and behaviours, and on one visit measured each participant’s height

and weight. The baseline measurements were conducted in the Australian

spring/summer between October and December, 2012. During this period, in

Brisbane, Queensland (study location) the average sunrise occurred at 4:55am and

sunset occurred at 6:17pm.

Participating families were then contacted to participate again 12 months later

(follow-up). Accordingly, the follow-up period was between October and December,

2013. All participating families were sent a parent survey and a researcher visited the

family to collect the child’s height and weight measurements.

At baseline, complete data was obtained for 49 (79.03%) children. One

participant was excluded due to insufficient actigraphy data (< 2 days), giving a final

sample of 48 children at baseline. Of the 48 children who completed the baseline

measurements, 9 children did not complete the follow-up BMI measurements, giving

an attrition rate of 18.75%. No differences were found between those participants

who did and did not complete the study at either time point, for gender, age or BMI.


Measurement of light, activity and sleep

The Actiwatch devices measured children’s motor activity (range: 0.5 – 2G)

and white light luminance exposure (range: 5 – 100,000 lux) in 1 minute epochs for

14 days. The Actiwatch 2 has been calibrated to the international standard ISO-

10526 (CIE-S-005) and has been shown to measure illuminance of multiple white

light sources in agreement with a National Institute of Standards in Technology

(NIST)-traceable photometer [39]. Rest/sleep intervals were assessed by parent

reported sleep diaries and wrist actigraphy, using Actiware 5.2 software (Phillips

Respironics, Bend, Oregon 97701 USA). Sleep onset, offset and duration were

determined using the parent-reported sleep diary in conjunction with the actigraphy

data. Sleep onset was determined by using the exact diary time indicated by the

parent; unless the time indicated fell on an epoch determined as “sleep” (S) by

actigraphy then, sleep onset was operationalized as the last “wake” (W) epoch before

the first 3 consecutive S epochs. Sleep offset was determined by using the exact diary

time indicated by the parent; unless the time indicated fell on an epoch determined as

S then, the time was extended until the first W epoch, after the last 5 consecutive S

epochs. Data were cleaned using the Actiware 5.2 software which involved

excluding periods in which the parent indicated that the actigraph was removed from

the child’s wrist. Total sleep duration was calculated based on the mean duration of

all sleep periods (day and night) over the 14-day period. Sleep midpoint was

calculated based on the average of sleep onset and offset for the 14-day period. Sleep

midpoint was used as a proxy for circadian phase [32]. For a conservative estimate of

children’s motor activity and light exposure, any extended inactive periods (>5mins),

not recorded by the parents in the sleep diary, were excluded.


Light and activity analysis

Intensity, duration and timing of exposure to light were determined by using a

similar methodology to that described by Reid and colleagues [17]. Light and activity

data were collected and exported from Actiware 5.2 software in .csv format at a 1-

minute (epoch) resolution. Data was imported into RProject 2.11.1. and smoothed

using a 5-minute rolling average (to account for the finer measurement window used

compared to Reid and colleagues) [17], and then aggregated over 24-hours for each

participant. Any 24-hour periods with greater than 4 hours of excluded data were

considered invalid and subsequently excluded from further analyses. These aggregate

data allowed the calculation of time above threshold (TAT), and mean light timing

above threshold (MLiT) [17]. TAT is the average daily number of minutes (epochs)

spent above a given lux threshold. This captures both, intensity and duration of light

exposure. TAT intensity thresholds ranged from 10 to 3000 lux. MLiT [17] describes

the daily distribution of light exposure. Calculation of MLiT incorporates intensity

(lux threshold), duration (number of minutes above a threshold), and timing of

exposure (clock time of each minute above the threshold; 10 to 3000 lux). The

formula used to calculate MLIT was produced by Reid and colleagues [17], however

in this study j (minute of day) = 1, ..., 1440, as light exposure (lux) was measured at a

resolution of 1 minute epochs for 24 hours (24 x 60 = 1440mins). Also, k (day) = 1,

..., 14, as the children in this study wore the Actiwatch for 14 days. To illustrate,

throughout 24-hours across a 14 day period, a MLiT200

of 721minutes indicates that

the child’s light exposure above 200 lux was, on average, centred around 12pm (or

the 721st minute in the 1440 minute day from 12am). Representative examples of

individual light profiles of participating children are illustrated in Fig 1. The measure


of activity used in this analysis was the mean of each epoch of activity over 24 hours,

across the 14 days of recording.

Fig 1. Smoothed 7-day light exposure plots from three individual participants.

Light exposure data (measured in lux) were smoothed and shown on a logarithmic

scale for three representative children across 7 measurement days (plotted in hours).

The horizontal yellow shaded area represents a threshold of ≥200 lux. Points where

lux is registered as zero are not shown, as the log of zero is not defined.

BMI analysis

At both baseline and follow-up, height and weight were measured by trained

researchers using calibrated stadiometers (SECA Leicester Portable Height Measure)

and floor scales (HD-316, Wedderburn Scales; Tanita Corporation, Tokyo, Japan)

with subjects dressed in light clothing, and without shoes. Children were measured

twice and if measurements differed (weight >0.1kg; height >0.5cm) a third

measurement was taken by the researcher. The mean of the measurements were used.

Due to the high proportion of this sample being breast-fed at some point during

infancy (88.4%) and no significant developmental delays identified, the WHO

growth charts were utilised to calculate BMI and growth trajectories of the children

[31]. BMI measurements were transformed into sex- and age-specific z scores using

the WHO Anthro version 3.2.2 and AnthroPlus version 1.0.4.

Statistical analysis

All analyses were conducted using SPSS v. 22.0.0.0. Sensitivity analyses

were conducted to examine if any light variables were associated with BMI z score,

both at baseline and follow-up. Consistent with the procedures used by Reid et al.


[17], the light exposure variables with the highest correlation with BMI z score were

then used in all subsequent analyses. Bivariate correlations (Tables A and B in S1

File) were run to examine the association between BMI z score at baseline and at

follow-up with sleep midpoint, sleep duration, activity, diet variables, and the 24hr

light variables identified in sensitivity analysis: TAT and MLiT. Sleep midpoint was

not normally distributed so a log-linear transformation was conducted. Subsequently,

all analyses shown include the log transformed sleep midpoint variable. Nutrition

variables were available for a subset of children (n = 42). There was limited

variability in parent reporting of nutritional intake. Furthermore, none of the

nutritional intake items correlated with either BMI z score (baseline/follow-up) or the

light variables. As such the nutritional items were not included within the final

regression analyses. Multivariable linear regression models were then used to assess

the relationships between BMI z score at baseline with activity, sleep midpoint, and

sleep duration and the TAT and MLiT thresholds identified through bivariate

correlations. To assess the relationship between light at baseline and BMI z score at

follow-up another multivariable linear regression was conducted, adjusting for

baseline measures of BMI z score, activity, sleep midpoint and sleep duration.

Significance levels are indicated with asterisks: *p < 0.05; **p < 0.01; ***p < 0.001.

Results

Participant demographics

Participant demographic, BMI, BMI z score, sleep, activity and light

characteristics for both baseline and follow-up are described in Table 1. At baseline,

the average age of participating children was 4.76 years (SD = 4.94 months), with

52.1% of the sample being female. According to WHO percentiles 97.9% of


participants were classified as within the healthy weight range, with one participant

being classified as overweight/obese [31]. None of the participating children were

classified as underweight, at either time point, in this study. Parents of the

participating children predominantly identified themselves as Australian (67%).

Please refer to Table C (in S1 File) for further information regarding identified ethnic

group. Valid actigraphy recording ranged from 3 – 13 days (M = 10.7 days). The

average sleep onset time was 20:37 (SD = 00:39), sleep offset was 06:03 (SD =

00:36), sleep midpoint was 01:19 (SD = 00:34), and total sleep duration was 9.64

hours (SD = 31.19 minutes). MLiT200

centered on 12:37 (SD = 00:36) with a range

from 11:10 to 14:28. The average duration of time that participating children spent in

light above the 200 lux threshold was 3.43 hours (SD = 7.06 minutes). In

comparison, children spent 64 minutes (SD = 25.22 mins) above the 2500 lux

threshold. At follow-up, the mean age of participants was 5.74 years (SD = 5.12

months) and 51.3% of the sample were female. The majority of children (92.3%)

were classified as normal weight, two children (5.1%) classified as overweight and

one child (2.6%) classified as obese [31].

Table 7.1 Participant demographic, sleep, activity, and light characteristics at

baseline and follow-up.

Variable N Mean (SD)

Child Demographic Variables

Baseline 48

Age (months) 57.06 (4.94); range 45.90 – 64.66

Sex 25 F; 23 M

BMI 15.45 (1.10); range 13.61 – 18.79

BMI z –score 0.09 (.73)

Follow-up 39

Age (months) 68.87 (5.12); range 56.77 – 76.88

Sex 20 F; 19 M

BMI 15.63 (1.12); range 14.02 – 20.08

BMI z –score 0.20 (.68)

Actigraphy Variables 48

Days Actigraph Recorded 10.7 (2.03)

Activity (Mean Activity Count) 400.87 (58.25)

Sleep Midpoint (hh:mm) 01:19 (00:34)


Sleep Onset (hh:mm) 20:37 (00:39)

Sleep Offset (hh:mm) 06:03 (00:36)

Sleep Duration (minutes) 578.37 (31.19)

Mean TAT 10 LUX (minutes) 612.69 (67.24)






MLiT 10 LUX (hh:mm) 12:32 (00:25)

MLiT 100 LUX (hh:mm) 12:28 (00:30)

MLiT 200 LUX (hh:mm) 12:37 (00:36)

MLiT 500 LUX (hh:mm) 12:38 (00:43)

MLiT 1000 LUX (hh:mm) 12:32 (00:45)

MLiT 3000 LUX (hh:mm) 12:06 (00:52)

Light exposure profiles and baseline BMI z score

To determine which 24-hour light variables (MLiT/TAT), across the

thresholds of 10 – 3000 lux had the strongest association with baseline BMI z score,

sensitivity analyses were conducted (Fig 2, A and B). TAT2500

was identified as

having the strongest association with baseline BMI z score. This association was

positive, indicating that longer daily duration of light exposure above a threshold of

2500 lux was associated with higher BMI. This illuminance level would be

equivalent to outdoor lighting on an overcast day [40, 41]. MLiT was also

significantly associated with baseline BMI z score, with the strongest association

occurring at MLiT200

. Earlier light exposure above a threshold of 200 lux (MLiT200

)

was associated with higher BMI. Representations of early and later MLiT200

are

illustrated in Fig 3. Illumination of 200 lux is approximate to a typical home living

room or kitchen [42].

Fig 2. Sensitivity Analyses showing Pearson correlations between BMI z score

and a range of MLiT and TAT Light Thresholds (lux) at baseline and follow-up.

* indicates statistically significant at p < .05. (A) TAT thresholds of 2000 – 3000 lux

were significantly associated with BMI z score at baseline, with TAT2500

having the


strongest association (N = 48). (B) MLiT thresholds of 100 – 400 lux were

significantly associated with BMI z score at baseline, with the strongest association

at MLiT200

(N = 48). (C) TAT thresholds of 10 – 25 lux were significantly associated

with follow-up BMI z score, with the strongest association at TAT10

(N = 39).

Fig 3. Representative light exposure profiles (log linear lux) for two individual

participants with “Early” and “Late” light exposure.

The black dashed line depicts the mean light exposure of all participants across the

recording period (N = 48); the blue line depicts the light exposure of a participant

classified as having an “Early MLiT200

”; the red line depicts the light exposure of a

participant classified as having a “Late MLiT200

”. The horizontal line represents the

200 lux threshold.

To determine the effect of duration and timing of light exposure on BMI z

score, a hierarchical multivariable linear regression analysis was performed.

Specifically, we examined the effect of TAT2500

and MLiT200

on BMI z score, after

adjusting for activity, total sleep duration, and sleep midpoint (Table 2). The full

model accounted for 27.3% of the variance in baseline BMI z-score (F5,42 = 3.153, p

= .017, r2 = 0.273). In this model, later sleep midpoint was associated with increased

BMI z score (β = .363, p = .020). Although TAT2500

was correlated with BMI z

score, it was not a significant independent predictor when entered into the model.

However, MLiT200

did have a significant, independent effect on BMI z score (β = -

.419, p = .01). This result indicates that earlier exposure to moderate intensity light is

associated with increased concurrent BMI z score in preschool children, independent

of activity, total sleep duration, and sleep midpoint.

Table 2.


Table 7.2. Linear regression models predicting BMI z score at baseline and follow-up

(a) Baseline BMI z score (N = 48)

95% CI (b)

Predictors b (SE) β Lower Upper

Activity (M) .00 (.00) -.01 -.00 .00

Sleep Midpointa 1.36 (.57) .36* .22 2.50

Total Sleep

Duration

.00 (.00) .08 -.01 .01

TAT2500

.01 (.00) .24 -.00 .02

MLiT200

-.01 (.00) -.42* -.02 -.00

(b) Follow-up BMI z score (N = 39)

95% CI (b)

Predictors b (SE) β Lower Upper

Baseline BMI z

score

-3.69 (2.47) .62*** .39 .87

Activity (M) .00 (.00) .02 -.00 .00

Sleep Midpointa .64 (.42) .18 -.22 1.50

Total Sleep

Duration

-.00 (.00) -.08 -.01 .00

TAT10

.00 (.00) .40** .00 .01

*p < .05 **p < .01 ***p < .001 aSleep Midpoint has been log transformed


Light exposure at baseline predicts 12-month follow-up

BMI z score

After examining the association between light exposure and BMI z score at

baseline, we wanted to determine if baseline light exposure would predict BMI z

score 12-months later. We conducted a sensitivity analysis to establish if any of the

light variables at baseline had an association with BMI z score at follow-up, across

each threshold (10 – 3000 lux). No baseline MLiT variables were associated with

BMI z scores at follow-up. However, both TAT10

and TAT25

had a positive

association with BMI z score at follow-up (Fig 2C), with the strongest association

found for TAT10

. Illumination of 10 lux is approximate to a candlelit room.

To test the hypothesis that baseline light exposure predicts BMI z score at

follow-up (N = 39 children), a hierarchical multivariable regression analysis was

conducted. This model was adjusted for baseline measurements of BMI z score,

activity, sleep midpoint and total sleep duration (Table 2). The model significantly

predicted a striking 59% of the variance in BMI z score at follow-up (F5,34 = 9.501, p

< .001). Even after adjusting for baseline BMI z-score (β = .621, p < .001), TAT10

remained a significant and independent predictor (β = .400, p = .002) of BMI z score

12-months later. This result indicates that longer daily duration of light exposure

greater than 10 lux at baseline is associated with increased BMI 12-months later,

independent of baseline BMI, activity, sleep midpoint and total sleep duration.

Discussion

This study is the first to investigate the relationship between the timing,

duration, and intensity of light exposure and the body mass of young children. We


found that daily environmental light exposure had a significant association with the

children’s body mass, both concurrently and longitudinally. Our findings are

consistent with those from studies conducted with animal models, and in adult

humans, which indicate that variations in light exposure may influence body mass

[17, 20-22, 27]. In the current study, earlier exposure to moderate levels of light was

associated with higher concurrent BMI. In clinical terms, for every hour earlier that

MLiT200

occurred during the day, there was a .6 unit increase in BMI. While this

degree of body mass gain may seem modest, it could indicate an early deviation in a

lifelong body mass trajectory. The direction of this relationship contrasts with those

reported for adults, where earlier light exposure was found to be associated with

decreased body mass [17]. The difference in the direction of these findings may

reflect variations in biological timing and threshold of exposure at which light exerts

an influence on physiological processes in young children. Consistent with this

interpretation, a recent study indicated that adolescents have a heightened sensitivity

to light exposure when compared to older adults [43]. Whilst the timing of light

exposure at baseline was not predictive of BMI 12-months later, the duration of light

exposure was. Specifically, longer duration of total light exposure at baseline was

predictive of higher BMI at follow-up. The increased use of electronic equipment

such as night lights, tablets, mobile phones, and televisions has been well-

documented for children 3 – 5 years [44, 45]. The current result may help us to better

understand findings of an association between this increased duration of screen use

and light in the bedroom and body mass in children.

Our findings provide evidence consistent with profound metabolic and

physiological effects of light on the human body [18, 19, 46–49]. These results are

especially striking when we consider that BMI in the first five years of life is


predictive of life-long body mass trajectories [30]. Unlike activity, dietary intake,

and sleep duration, light exposure is easily and directly manipulated; literally through

the flick of the switch. The current ubiquitous social, industrial, and culturally driven

manipulation of our environmental light may impact on body mass through three

very broad mechanisms that warrant exploration. Firstly, increased light duration

may provide insufficient dark, and insufficient metabolic ‘down time’, for normal

recuperative processes to occur. Indeed, depending on geographical location,

skyglow and other artificial light at night sources are increasing at rates of up to 20%

per year [50]. Children are increasingly exposed to broader spectral signatures and

more diverse intensity profiles of light [51]. Secondly, chronically increased daily

light duration may provide a biological signal analogous to endless summer days,

with the potential to amplify any seasonally-driven metabolic processes, such as

body mass acquisition [52, 53]. Alternatively, a child’s initial light state may

promote some mediating phenomena such as problematic behavior, physiological or

metabolic changes, which in turn, promote changes in BMI. One example of light

states interacting with physiological behavior is in the case of sleep. Multiple studies

document an association between short sleep duration and variability in sleep timing

with increased body mass in pediatric populations [5 – 7]. Thus, a confounding

relationship between sleep and light exposure is expected as sleep timing and

duration likely to influence the timing and duration of light exposure. In this study

sleep duration was not associated with either BMI or light exposure variables at

either time point. Although surprising, this finding is consistent with some research

conducted in the early childhood period [54, 55]. Furthermore, the null findings may

also be explained by our use of ambulatory recording versus parent report methods

used commonly in research reporting an association between sleep and body mass in


children (see review [56]). However, it is noted that sleep midpoint was associated

with timing of light exposure in this study. Further, in our model, later sleep

midpoint was a significant independent predictor of increased BMI z-score. This

indicates that timing of sleep and light exposure may be interacting to influence body

mass of children.

There are limitations to our study. We have not measured the spectral

signatures to which children are exposed, instead measuring light objectively in

ambient lux (lumens/m2, weighted to human perception of brightness) [57].

Throughout the day, wavelength composition varies and studies have shown that

spectral variations have very distinct impacts on different circadian, behavioral and

physiological responses [58]. As such, it is recommended that future studies use

devices that measure spectral power distribution, such as spectroradiometers [59].

Direct measurements of circadian phase and metabolic hormones were not

determined in this study. Future work should include measurement of circadian

phase and metabolic hormone variation of children to provide tests of the direct or

indirect path of associations found between light and body mass. For example,

timing of light exposure has been shown to affect expression of melatonin and shift

circadian phase [59, 60], which in turn impacts on hormones such as insulin [12, 61].

Additionally, the light intensities shown to be significantly associated with body

mass in this study need to be confirmed in a larger cohort of children. It is noted that

in this study, body mass was treated as a continuum, with only a small number of

children classified in the clinical range for overweight and obesity. As such future

research could investigate this association using children in the clinical range for

overweight and obesity. Although BMI has been shown to have good agreement with

body composition in children [62], future studies could consider the use of other


estimates of adiposity including; skin fold thickness, waist circumference, or dual

energy x-ray. Further research is needed to address these issues as well as the

mechanisms responsible for the association between light exposure and body mass in

children.

We live in a society of relatively dim days and bright nights [1, 16]. The

findings of this study suggest biologically inappropriately timed light exposure and

‘longer’ light periods, may be problematic for body mass of children. If light is in

fact a meaningful and distinct contributor to body mass and weight gain, then

quantification of light exposure could be included in clinical assessment protocols,

and even used routinely in pediatric assessments concerned with incipient obesity.

Furthermore, clinical prescription of ‘dark time', analogous to current light therapies,

could restitute a state of shorter, brighter days and longer dark nights, with resultant

increases in the amplitude of a child’s natural circadian rhythm. Indeed, inexpensive

consumer-grade wearables already collect similar data to that provided by

actigraphy, and individual tracking of habitual activity, sleep-wake patterns, and light

exposure, is already possible. The rapid acceptance and uptake of these devices

increases the potential for future effective and well-evaluated public health

interventions around light exposure. Likewise, ‘smart house’ applications already

allow control of artificial lighting in the home, school, and childcare environments,

and provide another potential point for intervention with public health implications.

By customizing our light environment, we have launched a global naturalistic

experiment, the effects of which are only just beginning to emerge. Our data provides

an impetus to investigate environmental light as a factor in the obesogenic

environment during human development. This may reveal new targets for pediatric

obesity intervention and prevention.


Acknowledgments

We would like to thank the participating families, services, teachers and

research staff who took part in this study. We also thank A. Zele for his comments.

Data availability statement: All data files are available from the Figshare

database (http://dx.doi.org/10.6084/m9.figshare.1609690).

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Supplementary Materials:

S1 File. Table A. Bi-variate correlations between Baseline BMI z-score (BMIz),

TAT, MLiT, sleep, and activity (N = 48). Table B. Bi-variate correlations between

Follow-up BMI z score (BMIz) and Baseline BMI z score, TAT, sleep, and activity

variables (N = 39). Table C. Proportion of parents in each identified ethnic group (N

= 42).


Figures

Figure 7.1. Smoothed 7-day light exposure plots from three individual participants


Figure 7.2 Sensitivity Analyses showing Pearson correlations between BMI z score

and a range of MLiT and TAT Light Thresholds (lux) at baseline and follow-up.


Figure 7.3 Representative light exposure profiles (log linear lux) for two individual

participants with “Early” and “Late” light exposure.


S1 File. Supporting Information (Online Supporting Information)

Table A. Bivariate correlations between baseline variables (N = 48).

1 2 3 4 5 6

1 BMIz at Baseline - .31* -.34* .13 .09 -.10 2 TAT2500 - -.36* -.23 .01 -.01 3 MLiT200 - .45** .10 .08 4 Sleep Midpointa - .15 -.13 5 Sleep Duration - -.14 6 Activity - Note: TAT

2500 is Time above threshold of 2500 lux; MLiT

200 is Mean Light above

Threshold of 200lux

*p < .05, **p < .01 aSleep Midpoint has been log transformed

Table B. Bivariate correlations between follow-up and baseline measures (N =

39).

1 2 3 4 5 6

1 BMIz at Follow-up

- .65*** .36* .21 -.07 -.04

2 BMIz at Baseline

- -.00 .22 .06 -.16

3 TAT10 - -.27 -.10 .10 4 Sleep

Midpointa - .04 -.05

5 Sleep Duration

- -.12

6 Activity - Note: TAT

2500 is Time above threshold of 2500 lux; MLiT

200 is Mean Light above

Threshold of 200lux

*p < .05, ***p < .001 aSleep Midpoint has been log transformed


Table C. Proportion of parents in each identified ethnic group (N = 42).

Proportion of Parents (%)

Oceania 66.7 North-East Asian 4.8 North African and Middle Eastern

2.4

Southern and Central Asian 2.4 North-West European 7.1 Southern and Eastern European

4.8

No Specific Ethnic Identification

11.9

Note: Ethnic group classification was identified in accordance to the Australian

Bureau of Statistics - Australian Standard Classification of Cultural and Ethnic

Groups (ASCCEG), 20111.

1Australian Bureau of Statistics. 1249.0 - Australian Standard Classification of Cultural and Ethnic

Groups (ASCCEG). 2011. Available online at:

http://www.abs.gov.au/ausstats/[email protected]/Latestproducts/1249.0Main%20Features12011?opendocume

nt&tabname=Summary&prodno=1249.0&issue=2011&num=&view=

http://www.abs.gov.au/ausstats/[email protected]/Latestproducts/1249.0Main%20Features12011?opendocument&tabname=Summary&prodno=1249.0&issue=2011&num=&view

http://www.abs.gov.au/ausstats/[email protected]/Latestproducts/1249.0Main%20Features12011?opendocument&tabname=Summary&prodno=1249.0&issue=2011&num=&view

Chapter 8: General Discussion 151

Chapter 8: General Discussion

Paediatric obesity remains a significant health issue in the 21st century. Given

the profound immediate and long-term, health-related, psychosocial, and economic

impacts of paediatric obesity, it is imperative that promising new approaches be

explored to aid current health strategy and policy. This research program aimed to

address the complex problem of childhood obesity by investigating modifiable

mechanisms proposed to influence body mass in children. As a result, this thesis has

questioned industrial, social, and biological processes associated with childhood

obesity.

8.1 SUMMARY OF KEY OUTCOMES

The original purpose of this thesis was to examine the effects of ECEC sleep

policies and practices on child health, namely body mass composition. However, as

with many PhD’s, the journey shifted due to the identification of key gaps, and new

scientific discoveries, during the program of research. Each of these factors are

discussed, alongside the subsequent response taken as part of the research program,

and a brief overview of the significant outcomes.

1. Gap identified: Research indicated that multiple methodologies are

currently in use to monitor and classify child growth. This has meant

significant variability in the reported overweight and obesity prevalence,

and significant difficulty with comparisons across studies.

Response: Paper 1 addressed this problem in two ways: 1) the application

of the three international growth standards to a large cohort of Australian

preschool children, and 2) examined the application of these standards to

report overweight and obesity status in Australian research.

Key Outcomes: This paper demonstrated significant differences in

prevalence estimates produced by each of the three commonly used

international growth standards in a population of preschool aged children.

In the absence of Australian-specific growth norms, care needs to be taken

152 Chapter 8: General Discussion

when selecting growth standard for screening or assessing children in both

research and clinical settings.

Further, this research indicated that the majority of Australian researchers

have been utilising the IOTF growth standards, however there were some

variations in the literature. In the absence of Australian-specific growth

norms, it was recommended that no matter the choice of standard used, raw

height, and weight data should be published to ensure comparability across

studies and internationally.

2. Gap identified: A significant gap in knowledge was identified regarding

the specific aspects of sleep that may interplay with weight. Furthermore,

variations between studies in the age of children and in the methods used to

measure sleep suggested the need to further explore the associations

between sleep parameters and weight status in young children.

Response: Paper 2 examined a broader range of sleep parameters proposed

to influence weight status in young children.

Key Outcomes: Paper 2 contributed to existing research indicating that

short sleep duration is associated with increased BMI z-score in young

children. This paper also showed that for males, short sleep duration and

frequency of napping were independent predictors of BMI. Including

significant control variables such as parent control, child temperament

(inflexibility/reactivity) and main caregiver education, the models explained

a relatively small proportion of the variance in overall BMI z-score. This

indicates that, whilst important, further exploration is needed to account for

the complexity of overweight with investigation of factors that advance

beyond calorie intake and expenditure. Furthermore, longitudinal analysis

may be important to investigate if there are longer term interactions and/or

indirect relationships between sleep parameters and weight status in these

children.

3. Scientific discovery: Due to emerging discoveries in sleep and circadian

research, a potential association between sleep, light exposure, and weight

status was investigated.


Response: Paper 3 included objective measurement of sleep, activity and

light exposure to determine if the timing and intensity of light exposure has

an effect on weight status in young children.

Key Outcomes: Paper 3 provided the first published investigation of the

effect of environmental light exposure and sleep (duration and midpoint) on

child weight status. The cross-sectional analysis showed that, after adjusting

for confounders of activity and sleep, earlier exposure to light above 200lux

was associated with higher BMI in children. Twelve-month follow-up data

showed that more overall light exposure at baseline was associated with

increased body mass. This effect was found even after controlling for

baseline BMI. This was a novel finding and provided new potential

pathways for future research (see section 8.4).

8.2 SIGNIFICANCE OF KEY OUTCOMES

The published papers and research within this thesis provide several significant

contributions to knowledge about weight status and basic sleep and circadian

research, including:

1. Documentation of the differences found when applying growth standards to a

single cohort of Australian children.

2. The first systematic review of usage of growth references commonly used in

preschool children in Australia.

3. Contribution to the body of research suggesting that sleep duration is important

for weight status in young children

4. Provided evidence that napping frequency is important for weight status in

male children

5. The first evidence that timing and intensity of light exposure is a contributing

factor to child weight status.

8.3 STRENGTHS AND LIMITATIONS OF THIS RESEARCH PROGRAM

The strengths and limitations of each paper have been addressed in the

respective discussions of the papers, as such, to avoid repetition this section will

focus on the overall strengths and limitations of the program of research.


This body of work has a number of strengths. Firstly, the three papers have

addressed complex questions using a number of study designs and research

methodologies. This has included tracking large child cohorts (>2000 children),

standard observation techniques (including development and use of the SOME), and

parent survey, as well as studies employing physiological (actigraphy) and direct

child anthropometric measurement. This allowed a trajectory within the research

program from the breadth of a large cohort sample (papers 1 & 2) down to the fine-

grained focus of sleep and light exposure using objective measurement of actigraphy

(paper 3). The E4Kids cohort employed in studies 1 and 2 has particular strength in

terms of population representation. The sampling frame was purposefully drawn to

capture a representative and diverse sample of all children attending child care in

Australia (Tayler et al., 2016). With over 1 million Australian children attending

licensed ECEC services in the year prior to school (Karvelas, 2013), this indicates

that the findings of this research program may generalise to the population. The

sampling also takes account for important covariates such as SES. The E4kids and

Sleep in Childcare Studies are of naturalistic environments with free-living healthy

children. As such we are able to provide an idea of the influence of sleep and light

exposure in an ecologically valid context.

There are several limitations to this work. First, measurement of nutrition and

dietary intake was based on parent report using an adapted version of a 9-item food

frequency questionnaire (Irwin & King, 2008). Although used previously in the

Longitudinal Study of Australian Children (LSAC), the measure evidenced limited

variability across the cohort. As such, more sensitive measures may be necessary in

future studies to account for the potential influence of energy consumption on weight

status in these children.

BMI and BMI z-score were used throughout this thesis to classify children as

overweight and obese. As a measure of body composition, BMI and BMI z-score are

widely used in large-cohort studies and have been shown to have good association

with fat mass and some cardiometabolic risk factors such as blood pressure in young

children (Eisenmann et al., 2004; Sijtsma et al., 2014). However, BMI has been

shown to significantly underestimate body fatness during the adiposity rebound in

comparison to dual energy x-ray (Eisenmann et al., 2004). Furthermore, this study

was conducted in children between the ages of 3 and 5 years, which correspond to


the time of adiposity rebound; the point in childhood where BMI begins to increase

from its nadir (Taylor et al., 2005). Therefore, replication in other populations and

longitudinal analyses of children is necessary to ensure that the same patterns of

associations between weight status, sleep and light exposure persist over time.

Finally, it is important to note that the study designs of both the E4Kids and

Sleep in Childcare data are observational in nature. Paper 1 and 2 present cross-

sectional findings from the E4Kids study and paper 3 presents 12-month follow-up

data using the Sleep in Childcare Study data. Specifically, for papers 2 and 3, the

observational nature of this research means that causality cannot be inferred. As

such, this research program provides an imperative for experimental studies to

answer the questions raised in both papers 2 and 3.

8.4 IMPLICATIONS AND FUTURE DIRECTIONS FOR RESEARCH

This research program has presented a number of novel findings in the context

of defining and combating childhood obesity. However, there is an urgent need to

further investigate the relationship between weight status, sleep, and light exposure.

To assist in further developing the findings of this thesis, a research agenda,

alongside key directions for future research, as well as a new conceptual framework

and theoretical advancement derived from the findings are presented.

8.4.1 Research Agenda

As per the recommendations of paper 1, there is an imperative for more

sophisticated measures to identify growth trajectories predictive of long term

pathology. In the interim, paper 1 provides a rationale for growth standard selection,

whilst encouraging researchers to publish their raw anthropometric data to allow for

comparison and further developing our understanding of the obesity problem.

Furthermore, an extension of the systematic review in paper 1 to expand our

knowledge on BMI standard usage beyond Australian populations is also

recommended.

From paper 2, it is recommended that future sleep research in young children

incorporate napping measurement in any analysis of sleep and weight status. The

early childhood is a period of significant maturation and change in sleep-wake

patterns (Jenni & Carskadon, 2007), however, our understanding of this period and


the effects of napping are still limited (Akacem et al., 2015). Measurement of sleep

and napping in this age group is made difficult by the types of care, both parental and

non-parental that children in this age group typically receive (OECD, 2016). Thus,

measurement of the effects of parental, non-parental and the interaction between

these types of care on sleep and health are needed. This includes investigations of the

significance of ECEC practices and policy. The importance of ECEC services on

child health and development is well acknowledged however, further information

and documentation about sleep practices and policies and the effects of these on

napping frequency, night time sleep duration and health are recommended.

Finally, further elucidation of the role of light exposure, sleep and

understanding of circadian timing in young children is urgently required. Future

experimental studies analogous to those being conducted in both rodent and adult

human populations may help bridge our understanding of the role of light in body

mass gain in young children.

8.4.2 Further exploration into the effect of light exposure on young children on

both sleep and weight status

Paper 3 served to raise significant questions about the inputs of the physical

environment and circadian systems to child weight status. Further investigation and

replication is needed. Currently, there are approximately 200 children aged between

birth and 3 years taking part of a study of sleep policy and practices in childcare (the

candidate is a CI on this grant, listed on page ix). Alongside observations of the

childcare environment, children older than 12 months of age are asked to wear an

actigraph for 2 weeks. Furthermore, measurements of height, weight and waist

circumference are also being conducted by trained research staff. As such replication

and extension of paper 3 to this younger cohort of children will be conducted.

Paper 3 indicates that light exposure may play a direct role in weight status of

children. As such, future research should also aim to examine metabolic hormone

regulation, especially in response to light exposure in humans. Melatonin, which has

been shown to be directly influenced by timing and intensity of light exposure

(Lockley et al., 2003), and has also been implicated in weight and metabolic

regulation. Melatonin administered to rats suppressed body fat, nocturnal leptin and

diurnal insulin secretion (Puchalski, Green, & Rasmussen, 2003; Wolden-Hanson et


al., 2000). Furthermore, melatonin levels have been associated with insulin resistance

(Peschke, Bähr, & Mühlbauer, 2013). In a sample of elderly adults, Obayashi and

colleagues (2012) showed that ALAN was associated with suppression of melatonin,

increased body mass and weight circumference, as well as impaired lipid secretion.

However, as evidenced from the results of paper 3, there may be variations in the

intensity and biological timing of light exposure which may exert an influence on

physiological processes in young children in comparison to adults (Pattinson, Allan,

Staton, Thorpe, & Smith, 2016). Recent research has shown that there is considerable

individual variability in melatonin onset time in toddlers (LeBourgeois et al., 2013).

Furthermore, napping exerts an influence on this variability (Akacem et al., 2015),

potentially through delaying the sleep onset of these young children, which in turn

promotes greater exposure to ALAN. Longitudinal research which tracks children

throughout toddlerhood and into early childhood, the period when napping typically

ceases, and which incorporates objective sleep, light, activity and hormonal

measurement, is necessary to examine these effects further.

8.4.3 Should light be added to the WHO list of obesogenic factors?

The WHO were recently seeking consultation on a draft implementation plan

as part of the Commission on Ending Childhood Obesity. Currently, it contains

recommendations for PA, nutrition, and family functioning. However, missing from

the list of obesogenic factors is light exposure. Although, research in the preschool

period is just emerging, the field is not new (see, Rybnikova, Haim, & Portnov,

2016; Wyse et al., 2014). The findings from this PhD only further bolster current

literature which indicates that light exposure has a profound effect on metabolic and

physiological functioning in humans. Shift work, with associated circadian

disruption, has already been recognised by the WHO’s International Agency for

Research on Cancer (IARC; WHO) as a probable carcinogen to humans (Adams &

World Health Organization, 2013; International Agency for Research on Cancer,

2007), with light exposure as the proposed causal mechanism (S. Davis et al., 2001),

although this continues to be debated.

8.4.4 Theoretical and conceptual advancement

The results of this PhD highlight the need to incorporate light into the

theoretical and conceptual framework of obesity. There have been three mechanisms


proposed to underlie the association between light and increased weight status; 1)

disruption to hormone regulation (e.g. melatonin and leptin), 2) increased light

means reciprocal insufficient darkness and decreases metabolic downtime for

recuperative processes to occur, and 3) increased light amplifies seasonally-driven

metabolic processes. Animal and human research has shown that physical activity,

body temperature and metabolism can change in accordance with the seasons

(Ebling, 2014; Quiles, de Oliveira, Tonon, & Hidalgo, 2016). Seasonality is indicated

to the body primarily by the length of the light/dark cycle (Gaston et al., 2014;

Stevens & Rea, 2001). Longer light periods, may send a biological signal analogous

to an “endless summer” (i.e. extended virtual day length), meaning that seasonally-

driven metabolic processes such as body mass acquisition may remain relatively

invariant, and may promote weight gain. The findings of paper 2 and 3 indicate that

sleep is also important to the risk of increased weight status. As such, the sleep-light

exposure conceptual framework combines the learnings from this thesis (Figure 8.1).

This integrates our knowledge about EST, sleep, and now light exposure, and the

ways in which these factors may work to affect body composition in young children.

The ecological factors recognise that the child lives within, and affects, their family

and environment around them. These ecological factors exert an influence on the

sleep and light exposure of the child. Sleep and light also interact with each other and

within the circadian system. When there is a disruption to this intricate system there

may well be an effect on child weight status. The mechanisms which have been

proposed to underlie the association between sleep, light exposure and weight status

are also indicated.


Figure 8.1. Proposed sleep–light exposure conceptual framework emerging from the

thesis.

8.5 CONCLUDING STATEMENT

Obesity in early childhood remains a significant public health concern, both in

Australia and internationally. Early childhood is a critical period for the development

of long-term sleep, health, and wellbeing trajectories. This program of research

presented 3 papers, each of which focussed on elucidating the problem of paediatric

obesity in preschool aged (3 to 5 year old) children. With research indicating

significant health, psychological and fiscal benefits of early intervention on weight

status, this thesis provides new learnings and directions for exploration. The findings

and potential new paths derived from this research program are relevant to

researchers, healthcare professionals and policy makers in implementation of

intervention and preventative strategies to fight against paediatric obesity.

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Appendices 195

Appendices

Appendix A

Highlighted Published Abstracts from the PhD Research Program

Reference:

Pattinson, C., Staton, S., Thorpe, K., & Smith, S. (2016). Naptime practices in

childcare are associated with body mass of preschool children. SLEEP 2016, the 30th

Annual Meeting of the Associated Professional Sleep Societies, Denver, CO. SLEEP.


Abstract:

Introduction: Sleep and napping undergo substantial transitions in the early

childhood period due to a range of genetic and environmental influences. Childcare

settings are one environment which has shown significant potential for impact and

intervention on children sleep patterns. Many childcare environments feature a nap

time as part of their curriculum and mandated nap periods, in which children are

required to lie on their beds without alternative activities are a feature in the majority

of these services. This study aimed to determine the effects of childcare nap time

practices (flexible vs mandatory) on children’s body mass and activity.

Methods: 62 children (30 females; mean age = 4.76 years ± .49; ages 3.28-6.18

years) were recruited from six childcare services in Brisbane, Australia. Children’s

sleep, activity and light exposure were measured via Actigraphy for 14 days. Each

child’s height (cm) and weight (kg) were measured objectively for BMI z-score

calculations according to the World Health Organization growth charts. Services

were classified as either having flexible (< 45mins of time spent on bed without

alternative activity; n = 19 children attended these centres) or mandated (> 45mins

spent without alternative activity; n = 43 children attended these centres) nap

practices.

Results: Preliminary data analyses indicated that mandated nap time practices were

associated with increased napping (r = -4.09, p = .005) and increased body mass

index (r = -.268, p = .035). Day-to-day variability calculated as the mean referenced

variation in sleep duration and wake after sleep onset will also be discussed.

Conclusion: Though preliminary, these findings suggest that mandated childcare nap

time practices are associated with increased body mass. This finding indicates an

impetus to further investigate the effect of childcare nap time practices on children’s

sleep and health.

Support (If Any): Financial Markets Foundation for Children Grant (2012-213)

196 Appendices

Reference:

Smith, S. S., Pattinson, C. L., Thorpe, K. J., Irvine, S. S., Wihardjo, K., & Staton S.

L. (2016). Early childhood educator’s experiences with sleep. SLEEP 2016, the 30th

Annual Meeting of the Associated Professional Sleep Societies, Denver, CO. SLEEP.


Abstract:

Introduction: Over 80% of children in developed countries attend childcare at some

point before school entry. The childcare environment very often provides for

sleep/rest. This early environment therefore plays an important role in the shaping of

children’s sleep behaviours. The aim of this study was to understand the beliefs and

experiences of educators working in Early Childhood Education services regarding

children’s sleep.

Methods: 250 educators working within Australian childcare settings completed an

on-line survey, with items assessing beliefs, attitudes, practices, and understanding

around sleep for young children. This sample included educators from long day care

(50%), kindergarten (30%) and family day care (10%). The average age of children

attending these services was between 2.2-4.9 years.

Results: Of the educators, 208 (83%) indicated that their service currently had a

scheduled sleep or rest time during the day (range = 15-180 minutes; M

= 89 minutes, SD = 43 minutes). 73% of educators indicated that catering for

children’s individual sleep needs were a little challenging (42%), somewhat

challenging (22%) or very/extremely challenging (9%). The educators reported that

they had received varying amounts of information/formal education regarding the

sleep needs of children. 58% of educators indicated that they had received ‘a lot of

information’ about safe sleeping guidelines (i.e. SIDS/SUDI), with 7% indicating

they had received ‘no information’ at all. Approximately 32% of respondents

indicated that they had received ‘no information’ about the typical sleep patterns for

children. 84% of educators indicated that they would be interested in receiving more

information about sleep and sleep practices for children.

Conclusion: These data demonstrate the need for more training and information for

childcare educators about sleep and sleep practices for young children. Childcare

provides a point of opportunity for the promotion of good sleep, and it is vital that we

improve our education and training of the childcare workforce.

Support (If Any): This research was conducted with the support of the Queensland

Government’s Department of Education and Training.

Appendices 197

Reference:

Pattinson, C., Allan, A., Staton, S., Thorpe, K., Smith, S. (2015). Physiological

consequences of light exposure in preschool children.

Sleep and Biological Rhythms. Vol. 13, Supplement S1, p08.

Queensland University of Technology, Brisbane, Queensland, Australia

Abstract:

Introduction: Light is recognised as the principal cue for circadian entrainment in all

species. Through the use of artificial lighting, humans have constructed a malleable

photoperiod, creating an environment of relatively dim days and bright nights.

Manipulation of the timing, intensity, and duration of light exposure to suit

contemporary lifestyles has occurred with limited consideration of its effects on

health. Recent research in human adults suggests that later peak exposure to

moderate intensity light is associated with increased body mass; however, the effect

of light exposure on the body mass of children is unknown. This study aimed to

determine the effects of sleep, activity, and light exposure on children’s body mass

index (BMI). Methods: Data were collected from 48 children (25 females; mean age

= 58 months ±4.9; ages 45.90–64.66 months) recruited from six childcare services in

Brisbane, Australia. Children’s sleep, activity, and light exposure were measured via

Actigraphy for 14 days. Each child’s height (cm) and weight (kg) were measured

objectively and BMI z-score calculated. Results: Cross-sectional analyses of baseline

data showed that higher BMI z-scores were associated with longer duration of light

exposure above a threshold of 2500 lux (r = .31, p < .05), and earlier exposure to

light above 200 lux (r = -.34, p < .05). Sleep midpoint was also positively associated

with the timing of light exposure above 200 lux (r = .45, p < .01). Linear regression

adjusting for activity, total sleep duration, and sleep midpoint, indicated that the

duration of light exposure above 2500 lux did not contribute significant variance.

However, earlier peak exposure to light above 200 lux (β = -.419, p = .01)

independently predicted increased BMI z-score (R2 = .273, p = .017). In this model,

later sleep midpoint was also associated with increased BMI z score (β = .363, P =

.020). Conclusion: This study is the first to show that exposure to moderate levels of

light earlier in the day is associated with increased BMI in children, independent of

their sleep-wake and activity behaviour. This result may reflect variations in the

biological timing and intensity at which light exerts an influence on physiological

processes in young children. Light appears to be an important element in the

obesogenic environment in children. The possible mechanisms and implications for

future research are discussed.

198 Appendices

Reference:

Pattinson, C., Allan, A., Thorpe, K., Staton, S., Smith, S. (2015). Dim light duration

predicts body mass index of young children. SLEEP 2015, the 29th Annual Meeting

of the Associated Professional Sleep Societies, Seattle, WA. SLEEP. Vol. 38,

Abstract Supplement, pA28.


Abstract:

Introduction: A potential role for light exposure in appetite, sleep and weight

regulation is currently emerging. This study aimed to determine the effects of sleep,

activity and light exposure on children’s body mass index (BMI) at baseline and at

12-month follow-up.

Methods: Data was collected from 48 children (25 females; mean age = 57.06

months ±4.90; ages 45.90–64.66 months) recruited from six childcare services in

Brisbane, Australia. Children’s sleep, activity and light exposure were measured via

Actigraphy for 14 days. Each child’s height (cm) and weight (kg) were measured

objectively for BMI z-score calculations. At 12 month follow-up, parent survey and

objective BMI measurements were conducted; 40 (83.33%) children participated.

Results: Cross-sectional analyses of baseline data showed higher BMI z-scores were

associated with longer duration of light exposure above a threshold of 2500 lux (r =

.31, p < .05), and earlier exposure to light above 200 lux (r = -.34, p < .05). Linear

regression adjusting for activity, total sleep duration, and sleep midpoint, indicated

duration of light exposure above 2500 lux did not contribute significant variance

however, earlier timing of light exposure above 200 lux (β = -.419, p = .01)

independently predicted increased BMI z-score (R2 = .273, p = .017). At 12-month

follow-up, duration of light exposure >10 lux was a significant independent predictor

of BMI z-score (β = .409, p = .001) after adjusting for Baseline measures of BMI z-

score and sleep midpoint, and accounted for 58.3% of the variance in BMI z-score (p

< .001).

Conclusion: Exposure to dim levels of light can influence children’s body mass both

concurrently and at 12 months post-exposure, independent of sleep and activity.

While mechanisms remain unclear, these data suggest that light should be considered

as a factor in studies of weight gain and obesity in children.

Support (If Any): Financial Markets Foundation for Children Grant (2012-213)

Appendices 199

Reference:

St Pierre, L., Staton, S. L., Pattinson, C. L., Thorpe, K. J., & Smith, S., (2015).

Sleep deprivation and recovery in an expedition adventure race. SLEEP 2015, the

29th Annual Meeting of the Associated Professional Sleep Societies, Seattle, WA.

SLEEP. Vol. 38, Abstract Supplement, pA131. 2015:

Abstract:

Introduction: Sleep deprivation, defined as either suboptimal, fragmented or a

complete lack of sleep, has significant consequences for cognitive function, attention

and operant memory along with a vast array of other health implications. Whilst

there have been a number of well documented cases of prolonged sleep deprivation

within controlled studies, the consequences of sleep deprivation as they pertain to

athletic performance and recovery from endurance sports, in particular adventure

racing, remain largely uncharacterised. Expedition adventure racing is a multi-

disciplinary team sport involving wilderness navigation with races anywhere up to

two weeks in length. As the clock does not stop during a race, teams will normally

push through all hours, often forgoing sleep completely with sleep deprivation

perceived as staple consequence of the sport. This pilot study provides the first

objective data of sleep patterns in the period leading up to, during and following an

expedition adventure race.

Methods: Four participants (3 male, 1 female) comprising a single team at the 2014

GODZone Adventure Race in New Zealand collected activity and light exposure data

via actigraphy over a 10 day pre-race, 5 day race and 2 weeks post-race period. Data

was analysed to determine objective 24-hour sleep/wake parameters, physical

activity intensity, and ambient light levels.

Results: The longest period of wakefulness observed was 40 hours and 28 minutes,

with total sleep time averaging 3 hours per day across the 5 days or racing.

Individual differences were observed in post-race recovery despite the original

degree of sleep deprivation effectively being determined by a tethered group

decision.

Conclusion: The current study represents a real world sleep deprivation model where

sleep loss, performance goals and risk management are self-regulated. Findings of

this pilot study indicate that adventure racers form an excellent novel ecological

model for examining the relationship between sleep deprivation, performance and

recovery.

200 Appendices

Reference:

Smith, S. S., Neil, E. H., Thorpe, K. J., Pattinson, C. L., & Staton, S. L. (2015).

Characteristics of children who do not nap in childcare. SLEEP 2015, the 29th

Annual Meeting of the Associated Professional Sleep Societies, Seattle, WA.


Abstract:

Introduction: Over eighty percent of children aged 3 to 6 years in developed

economies attend early childhood education and care services (including daycare and

kindergartens) in the years prior to school. A scheduled naptime is a common feature

of most of these environments. However, not all children are able to sleep during

these times. Some of these children have been identified in the literature as ‘problem

nappers’, not only because they do not get to sleep but also because they may present

with behavioural difficulties during the scheduled naptime. The characteristics of

children who do not nap in childcare are not known.

Methods: To differentiate ‘problem nappers’ from those that either sleep or lie

quietly during naptime, typical napping behaviour was obtained through educator

report for 143 children aged 3 to 6 years. Parents completed standardized behavioural

and temperament questionnaires for children. A test of cognitive ability, the

Woodcock Johnson III Brief Intellectual Ability test, was administered to each child.

Results: Results indicated that children who have difficulty lying quietly during

naptime sleep were significantly older than those who did nap (mean 2.9 months),

performed significantly better on neurocognitive tests, and had significantly shorter

night time sleep duration.

Conclusion: These data suggest a mismatch between children’s neurocognitive

development, including their requirement for daytime sleep, and the practice of

scheduling naptimes in early childhood education and care settings. Further research

is needed to inform recommendations for sleep practices in childcare centers that

best suit individual needs.

Appendices 201

Reference:

Staton, S., Smith, S., Hurst, C., Pattinson, C., & Thorpe, K. (2015). Group napping

patterns in relation to duration of mandatory naptimes in childcare settings. SLEEP

2015, the 29th Annual Meeting of the Associated Professional Sleep Societies,

Seattle, WA. SLEEP. Vol. 38, Abstract Supplement, pA28.

Abstract:

Introduction: Naptime is a routine feature within many childcare settings and may

include a mandatory period in which all children are required to lie down without

alternate activity permitted. This study aimed to examine the relationship between

variation in duration of mandatory naptimes for preschool aged children (3–6 years)

and children’s sleep patterns within these settings.

Methods: An observation study of a community sample of 113 pre-school rooms

attended by 2114 preschool aged children was undertaken. Within each childcare

room sleep practices and children’s sleep patterns were observed using a standard

protocol. Observations were conducted within in the second semester of the

education year. Counts of the number of children asleep were coded in 10-minute

intervals. Poisson mixed effect regression models were conducted to map the

patterns of the number of children asleep and latency to sleep onset in rooms with

different durations of mandatory naptime, whilst controlling for potential confounds

of age range, socio-economic status, childcare quality, childcare type and nap start

times.

Results: Three-quarters of childcare settings implemented a mandatory naptime with

considerable variation in duration (15–145 minutes). Compared to rooms with ≤ 30

minutes of mandatory naptime, there was a two-fold increase in the proportion of

children napping within rooms with 31–60 minutes of mandatory naptime, and a

four-fold increase for those in rooms with > 60 minutes mandatory naptime. Napping

patterns across mandatory naptime groups were similar; increased duration of

mandatory naptime was associated with increased napping prevalence, but not the

time to nap onset.

Conclusion: The results of the current study suggest that mandatory naptimes are

associated with an increase in napping prevalence, but not sleep onset time, within

childcare rooms. Future studies should examine the influence of other child and

childcare sleep characteristics, including routines, noise and teacher strategies on

children’s sleep patterns within these settings.

Support (If Any): This study was funded via a grant from the Institute of Health and

Biomedical Innovation at Queensland University of Technology. The E4Kids study,

from which the sample is derived, is funded by the Australian Research Council

Linkage Projects Scheme, the Victorian Government Department of Education and

Early Childhood Development, and the Queensland Government Department of

Education and Training.

202 Appendices

Reference:


Australian children: The effects of day, night and total sleep. 26th

Annual Meeting of

the Australasian Sleep Association Conference, Perth, Australia.

Published in: Sleep and Biological Rhythms, Vol. 12, Supplement 1, p72.


Abstract:

Introduction: Sleep is a cornerstone of physical and mental health. Strong and

consistent associations between paediatric sleep quality (duration, variation, and

timing) and obesity have been documented, but the underlying mechanisms of this

association are not understood. Napping, night-sleep, timing and sleep duration have

each been shown to have an influence over different cognitive and hormonal

processes. As such, identifying the sleep parameters which underpins the association

between sleep and weight status in children, will lead us closer to the underlying

mechanisms that may be involved. This study aimed to clarify the role of each sleep

parameter on the relative risk of paediatric overweight/obesity. Method: We present

data from the E4Kids study, a large, longitudinal study of Australian children.

Parents (N = 1,095) reported on their child’s typical sleep patterns, including bed and

wake times, as well as their napping frequency and duration. Parents also provided

demographic information, alongside child, family and environmental characteristics

that have been shown to influence both sleep and obesity outcomes for young

children. Anthropometric data (height, weight and waist circumference) for each

child was collected directly by fieldworkers using WHO standard protocols. Body

mass index (BMI) was calculated using the Centre for Disease Control’s (CDC) sex-

specific BMI-for-age SAS statistical program. CDC guidelines were used to classify

children into: average, overweight (≥85th percentile) and obese categories (≥95th

percentile). Results: Logistic regression analysis was used to determine the odds ratio

of being overweight/obese in relation to the following sleep parameters: total sleep

duration, bed-time, wake-time, napping duration, napping frequency and ratio of

day:night sleep duration. Each analysis controlled for child factors (age, gender,

temperament, perinatal adversity), family factors (parent age, parental control, SES

and education) and environmental factors (childcare attendance, media use and

family stress). Discussion: Our results provide an insight into the sleep parameters

that influence a child’s subsequent risk of overweight/obesity. By identifying the

sleep parameters that are most influential in the association between sleep and

obesity we are one step closer to isolating the mechanism by which sleep may be

associated to child weight status.

Appendices 203

Reference:

Marriott, A., Staton, S., Thorpe, K., Pattinson, C., Smith, S. (2013) How do current

sleep practices in Early Childhood Education and Care settings reflect current

knowledge about good sleep habits and environments? Sleep Down Under 2013

River of Dreams: 25th

ASM of the Australian Sleep Association and Australian Sleep

Technologies Association, Brisbane, Queensland. Published in Sleep and Biological

Rhythms, Vol. 11, Supplement 2, p15-16. DOI: 10.1111/sbr.12028.

Queensland University of Technology, Brisbane, Australia

Abstract:

Introduction: Sleep is an essential component of the physiological restoration of the

body. Poor sleep is linked to negative effects on not only physiological wellbeing,

but psychological health and cognitive functioning as well. The study of sleep

practice and environments is generally acknowledged to cover three domains; the

immediate environment of the sleeper, the behaviour and practices that precede sleep

and activities undertaken during the day that may impact on the quality of sleep. The

regulation of these variables ensures effective and continuous sleep that is seen as

being of benefit to the individual. Although there is a substantial body of research in

the literature on the sleep practices and environments of specific populations, there is

very little information on sleep practices and environments for children in a general

sample and no information about the use of sleep practices and environments to

assist in day time sleep for young children. Nap time, sleep or rest periods are

currently a curriculum component of many early childhood education and care

(ECEC) settings in Australia. This study focuses on the sleep practices and habits

that facilitate quality sleep and the practices that surround day time napping or sleep

in ECEC settings. The data for this paper comes from an Australian study on the

sleep practices and children’s sleep patterns in ECEC settings. Methods:

Observations of full sleep/rest periods using a structured observation protocol were

conducted in 118 kindergarten and long-day care centres in Brisbane. This study

investigates the observational data to qualitatively explore what is happening in

ECEC settings during rest/nap time and how this relates to positive sleep practices

and environments. Results: Our results suggest that practices in many centres do not

provide sleep environments that are conducive to positive sleep experiences, with

particular problems relating to abrupt transitions into the sleep period and negative

characteristics of the immediate sleep environment including noise levels, disruptive

activity and negative emotional tone. Preliminary results suggest the need to assess

the impact of current practices on children and review provisions in ECEC sleep

environments.

204 Appendices

Appendix B

Queensland University of Technology Thesis by Published Papers Guidelines

Introduction

1 In 2000, QUT adopted an additional model for presentation of PhD theses, called Thesis by Publication. Hence QUT now recognises three types of PhD thesis, with the others being the Traditional Monograph Thesis and Thesis by Creative Works. Students in the Faculty of Health can submit their thesis either by monograph or publication.

Thesis by Publication Regulations

2 The QUT PhD Regulations 14.1.1 and 14.1.2 state:

The Queensland University of Technology permits the presentation of theses for the degree of Doctor of Philosophy in the format of published and/or submitted papers where such papers have been published, accepted or submitted during the period of candidature. Papers submitted as a PhD thesis must be closely related in terms of subject matter and form a cohesive research narrative.

3 In addition to the guidelines set down by QUT, the following guidelines should be

addressed by students enrolled in the Faculty of Health intending to submit their thesis by publication.

8 For thesis by publication, PhD Regulation 14.2.1 to 14.2.3 states:

14.2.1 The thesis may be comprised of published papers, manuscripts accepted for publication, manuscripts submitted for publication or under review. 14.2.2 The minimum number of papers and/or manuscripts is normally three. At least one paper must have been published, accepted, or be undergoing revision following refereeing. For the Faculty of Health, one paper must have been published or accepted. 14.2.3 Where the papers have multiple authorship, the candidate must be the principal author on a least two of the three papers and have written permission from the co-authors.

9 Although published and available in reprint format, it is required that an electronic version of the article is re-formatted, eg, to a WORD document, to simplify production and enhance presentation and ease of reading with the other chapters. When the article has been reformatted there should be a footnote containing a full citation of the published paper.