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PreAdolescent CardioMetabolic Associations and Correlates: PACMAC 

 

A thesis presented in partial fulfilment of the requirements for the degree of 

 

 

Doctor of Philosophy  

in 

School of Sport and Exercise 

 

at Massey University, Wellington, 

New Zealand 

 

Nicholas Castro 

 

2018 

  



 ii 

Abstract 

Cardiovascular disease is typically associated with adults; however, 

atherosclerosis often initiates during preadolescence and has been linked to 

cardiometabolic risk factors. Preceding cardiometabolic risk factors include lifestyle 

factors: body fatness, physical fitness, physical [in]activity, sedentary behaviour, 

nutrition, and sleep. No known study has comprehensively assessed simultaneous 

associations among lifestyle factors with cardiometabolic risk factors in preadolescent 

children. 

A multicentred cross-sectional study design was utilised to investigate lifestyle 

factor associations with cardiometabolic risk factors in a sample of 392 children aged 8 

to 10 years. Participants were recruited from primary schools located in the Wellington, 

Canterbury, and Otago regions in New Zealand. Data collection was carried out over 5 

days between 09:00 a.m. and 12:00 p.m. at each location. 

The first objective assessed the associations among physical fitness, physical 

[in]activity, sedentary behaviour, nutrition, and sleep with body fatness indicators (body 

fat percentage, fat mass index, body mass index, and waist-to-hip ratio). Results 

indicated nutrition independently associated with body fat percentage (p < 0.05), 

whereas cardiorespiratory fitness significantly associated with all four body fatness 

indicators (p < 0.05).  

The second objective assessed the associations among body fatness, physical 

fitness, physical [in]activity, sedentary behaviour, nutrition, and sleep with 

cardiometabolic factors (blood pressure, cholesterol, vascular, and carbohydrate-

metabolic). Results indicated body fat percentage associated with the blood pressure 

factor (p < 0.05); sedentary minutes, social jetlag, and Fruit and Vegetables pattern 

associated with the cholesterol factor (all p < 0.05); sedentary minutes and Processed 



 iii 

Food pattern associated with the vascular factor (both p < 0.05); and cardiovascular 

fitness (V̇O₂max) and handgrip strength associated with the carbohydrate-metabolic 

factor (both p < 0.001). Accordingly, body fatness, physical fitness, nutrition, and sleep 

all associated with at least one cardiometabolic factor.  

Cardiorespiratory fitness associated with cardiometabolic health and was the key 

finding in Objective 1; therefore, physical fitness may be the most important lifestyle 

factor. However, as nutrition, sleep, sedentary behaviour, and body fatness also 

associated with cardiometabolic health, it appears one specific lifestyle factor does not 

entirely explain cardiometabolic health in preadolescent children, and thus a multimodal 

approach for health is required for this population. 



 iv 

Preface 

The foundation for this research originated from my passion for working with 

children in afterschool programs and wanting to make a difference in their health and 

wellbeing. The PreAdolescent CardioMetabolic Associations and Correlates 

(PACMAC) study design came about due to the decline of children’s overall health in 

New Zealand and worldwide, indicating the need to examine the importance of lifestyle 

factors with cardiometabolic health. I believe improving the overall health and 

wellbeing of children will require a multifaceted approach; the PACMAC study 

represents the preliminary phase. 

Conceptualization 

Based on my experience working with children in various settings (academic, 

athletic, and social), along with researching outcomes from previous paediatric studies, 

it became clear which influential lifestyle factors should be included. In discussion with 

my supervisory team, we formatted a list of research questions and together determined 

a study design detailing the most productive, safe, reliable, and noninvasive way to 

collect data in a school setting. Based on the preferred study design, preadolescent 

children aged 8 to 10 years seemed to represent the most likely target age for reliable 

data, yet would be young enough (prepubertal) to minimise sex differences.  

Ethics 

One of my first PhD responsibilities was to attain ethical approval. I submitted 

my ethics application to the Massey University Human Ethics Committee. During the 

risk assessment process, the PACMAC study was categorised as high risk and invasive 

given that the methods included a standard finger prick procedure to collect biochemical 

markers and that the study population was preadolescent children. Therefore, I was 

required to seek approval from the New Zealand Health and Disability Ethics 



 v 

Committee (HDEC). I received approval from the HDEC on June 26, 2014 

(HDEC:14/CEN/83). 

Data Collection 

For the data collection process, I recruited each principal, teacher, participant, 

and parent or guardian that participated in this study. Additionally, I designed and 

formatted standard operating procedure guidelines for each study measurement, set up 

the data collection stations, and recruited and trained research assistants at each 

location. Furthermore, during the week of data collection in the schools I led activities 

that focused on the importance of being active, eating healthy, and getting the proper 

amount of sleep. School staff were very appreciative of all the health information I 

provided to the participants and their families in return for participating in the study.  

Data Analysis and Management 

Regarding the analysis and interpretation, each day I calculated and verified 

each participant’s measurement means (height, weight, waist circumference, hip 

circumference, blood pressure, augmentation index pressure, and pulse rate), and then 

transferred the calculated data from the data collection forms to an Excel spreadsheet. 

Next, I formatted and entered all the data into the SPSS program. For statistical analysis 

and interpretation, I was assisted by my supervisors, a mentor (Dr. Paula Skidmore), 

and a certified statistician.  

Writing 

The writing of this thesis is my work. I received extensive feedback from my 

supervisors while writing the literature review through multiple editing processes. The 

statistical analysis, results, and discussion sections I wrote after robust discussion of the 

results with my supervisors. 

  



 vi 

Publications 

The PACMAC study protocol has been already published and the following 

papers based on my research have been published or are in progress. My contribution to 

each paper is itemised in Table 1. 

Table 1 

Contributions to Publications 

Publications Conceptual-

ization 

Study 

Design 

Recruit-

ment 

Data 

Collection 

Data 

Analysis 

Writing 

Paper 1 X N/A N/A N/A N/A X 

Paper 2 X X X X X X 

Paper 3  X X X   

Paper 4  X X X   

Paper 5  X X X   

 

1. Castro, N., Lambrick, D. M., Faulkner, J., Lark, S., Williams, M. A., & Stoner, L. 

(2013). Decreasing the cardiovascular disease burden in Māori children: The 

interface of pathophysiology and cultural awareness. Journal of Atherosclerosis and 

Thrombosis, 20, 833-834. doi:10.5551/jat.20198 

2. Castro, N., Faulkner, J., Skidmore, P., Williams, M., Lambrick, D. M., Signal, L., . 

. . Stoner, L. (2014). Pre-adolescent cardio-metabolic associations and correlates: 

PACMAC methodology and study protocol. BMJ Open, 4(e005815), 1-9. 

doi:10.1136/bmjopen-2014-005815 

3. Stoner, L., Castro, N., Signal, L., Skidmore, P., Faulkner, J., Lark, S., … Harrex, H. 

(2018). Sleep and adiposity in preadolescent children: The importance of social 

jetlag. Childhood Obesity, 14, 158-164. doi:10.1089/chi.2017.0272 

  



 vii 

 

4. Stoner, L., Weatherall, M., Skidmore, P., Castro, N., Lark, S., Faulkner, J., & 

Williams, M. A. (2017). Cardiometabolic risk variables in preadolescent children: A 

factor analysis. Journal of the American Heart Association, 6(e007071). 

doi:10.1161/JAHA.117.007071 

5. Stoner, L., Castro, N., Skidmore, P., Lark, S., Faulkner, J., & Williams, M. A. 

(2018). Fitness and fatness are both associated with cardiometabolic health in 8-10-

year-old New Zealand children. Manuscript in preparation.  



 viii 

Acknowledgements 

This thesis would not have been possible without the support of many 

individuals and organizations, which cannot be done justice with a few words, however, 

I will endeavour to do so. My entire PhD was funded by Education New Zealand and 

Massey University, and this study would not have been possible without their support.  

Thank you to the staff and participants at the primary schools that participated in 

my study: Scots College, St. Patrick’s, Crofton Downs, St. Bernadette’s, Green Island, 

Holy Cross, Springston, Rāwhiti, and Yaldhurst. I really enjoyed data collection. 

Dr. Lee Stoner, from the first day I arrived in New Zealand you were there 

supporting and mentoring me, which has been essential to my growth and development 

during my PhD journey. So much appreciated, Lee. 

Dr. Sally Lark and Dr. James Faulkner, thanks for all the time you devoted to 

the PACMAC study. Sally, for keeping the team down-to-earth when things got hectic. 

James, for the time you took to counsel me and for the constructive criticism.  

Dr. Michael Hamlin, thanks for the V̇O₂max knowledge and for the support you 

and your family provided when I was collecting data in the Canterbury region. 

Dr. Paula Skidmore, thanks for mentoring me, for the support you provided 

when I was collecting data in the Otago region, and for all the principal component 

analysis work you did for the PACMAC study. 

To all my family and loved ones, I say thank you with all my heart for always 

believing in me and supporting this small-town boy from Blythe, California. 

To my partner Inge, thanks for always being encouraging and my biggest 

supporter. I could not have completed this PhD without your unconditional love.  

Ethical approval for this study was received from the regional HDEC 

(14/CEN/83).  



 ix 

List of Publications 

Castro, N., Faulkner, J., Skidmore, P., Williams, M., Lambrick, D. M., Signal, L., . . . 

Stoner, L. (2014). Pre-adolescent cardio-metabolic associations and correlates: 

PACMAC methodology and study protocol. BMJ Open, 4(e005815), 1-9. 

doi:10.1136/bmjopen-2014-005815 

Castro, N., Lambrick, D. M., Faulkner, J., Lark, S., Williams, M. A., & Stoner, L. 

(2013). Decreasing the cardiovascular disease burden in Māori children: The 

interface of pathophysiology and cultural awareness. Journal of Atherosclerosis 

and Thrombosis, 20, 833-834. doi:10.5551/jat.20198 

Stoner, L., Castro, N., Signal, L., Skidmore, P., Faulkner, J., Lark, S., Williams, M. A., 

Muller, D., & Harrex, H. (2018). Sleep and adiposity in preadolescent children: 

The importance of social jetlag. Childhood Obesity, 14, 158-164. 

doi:10.1089/chi.2017.0272 

Stoner, L., Weatherall, M., Skidmore, P., Castro, N., Lark, S., Faulkner, J., & Williams, 

M. A. (2017). Cardiometabolic risk variables in preadolescent children: A factor 

analysis. Journal of the American Heart Association, 6(e007071), 1-10. 

doi:10.1161/JAHA.117007071 

Manuscript in Preparation 

Stoner, L., Castro, N., Skidmore, P., Lark, S., Faulkner, J., & Williams, M. A. (2018). 

Fitness and fatness are both associated with cardiometabolic health in 8-10-year-

old New Zealand children.  

  



 x 

 

 Table of Contents 

Abstract ............................................................................................................................. ii 

Preface .............................................................................................................................. iv 

Acknowledgements ........................................................................................................ viii 

List of Publications .......................................................................................................... ix 

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

List of Figures ................................................................................................................ xvi 

List of Abbreviations .................................................................................................... xvii 

Definition of Terms ........................................................................................................ xix 

CHAPTER 1—INTRODUCTION ................................................................................... 1 

1.0 Circumstances and Statement of the Problem ............................................ 1 

1.1 Objectives of Research................................................................................ 2 

1.2 Outline of Thesis ......................................................................................... 3 

CHAPTER 2—LITERATURE REVIEW ........................................................................ 4 

2.0 Cardiometabolic Health .............................................................................. 4 

2.0.1 Background .......................................................................................... 4 

2.0.2 Evaluating Cardiometabolic Health ..................................................... 9 

Applied cardiometabolic health assessments .......................................... 15 

2.1 Body Fatness ............................................................................................. 15 

2.1.1 Background ........................................................................................ 15 

2.1.2 Evaluating Body Fatness .................................................................... 21 

Applied body fatness assessments .......................................................... 24 

2.2 Physical Fitness ......................................................................................... 25 

2.2.1 Background ........................................................................................ 25 

2.2.2 Evaluating Physical Fitness ............................................................... 29 

Applied physical fitness assessments ...................................................... 32 

2.3 Physical [In]activity and Sedentary Behaviour ......................................... 32 

2.3.1 Background ........................................................................................ 32 

Physical [in]activity physiological pathways to CVD ............................ 33 

Sedentary behaviour physiological pathways to CVD ........................... 35 

2.3.2 Evaluating Physical [In]activity and Sedentary Behaviour ............... 37 

Objective measures ................................................................................. 38 



 xi 

Subjective measures ................................................................................ 41 

Applied physical [in]activity and sedentary behaviour assessment ........ 43 

2.4 Nutrition .................................................................................................... 43 

2.4.1 Background ........................................................................................ 43 

Carbohydrate ........................................................................................... 44 

Protein ..................................................................................................... 50 

Lipids ...................................................................................................... 52 

Fruits and vegetables ............................................................................... 53 

2.4.2 Evaluating Nutrition ........................................................................... 55 

Applied nutritional assessment ............................................................... 56 

2.5 Sleep .......................................................................................................... 56 

2.5.1 Background ........................................................................................ 56 

2.5.2 Evaluating Sleep ................................................................................ 58 

Objective measures. ................................................................................ 58 

Self-report measures. .............................................................................. 60 

Applied sleep assessment. ....................................................................... 61 

2.6 Summary ................................................................................................... 61 

2.6.1 Applied Measures Analysis ............................................................... 62 

CHAPTER 3—METHODOLOGY ................................................................................ 64 

Pre-Adolescent Cardiometabolic Associations and Correlates:   

PACMAC Methodology and Study Protocol ................................................. 64 

3.0 PACMAC Study Protocol ......................................................................... 64 

3.0.1 Research Study Objectives................................................................. 66 

3.1 Methodology ............................................................................................. 66 

3.1.1 Study Design ...................................................................................... 66 

3.1.2 Study Participants and Recruitment ................................................... 67 

3.1.3 Cultural Sensitivity ............................................................................ 67 

3.2 Data Collection and Analysis .................................................................... 68 

3.2.1 Demographics .................................................................................... 68 

3.2.2 Cardiometabolic Health ..................................................................... 69 

3.2.3 Adiposity and Anthropometrics ......................................................... 69 

3.2.4 Physical Fitness .................................................................................. 69 

3.2.5 Physical Activity ................................................................................ 70 

3.2.6 Nutrition ............................................................................................. 70 



 xii 

3.2.7 Sleep ................................................................................................... 70 

3.3 Quality Control ......................................................................................... 71 

3.3.1 Development of Protocols .................................................................. 71 

3.3.2 Training of Research Assistants ......................................................... 71 

3.3.3 Data Collection .................................................................................. 73 

3.3.4 Data Cleaning ..................................................................................... 73 

3.4 Sample Size Calculations .......................................................................... 73 

3.5 Statistical Analysis .................................................................................... 74 

3.5.1 Principal Component Analysis .......................................................... 74 

Food frequency questionnaire principal components analysis ............... 75 

Cardiometabolic principal components analysis..................................... 78 

3.5.2 Univariate Analysis ............................................................................ 80 

3.5.3 Multivariate Analysis ......................................................................... 80 

3.6 Dissemination of Findings ........................................................................ 81 

3.7 Discussion ................................................................................................. 82 

CHAPTER 4—RESEARCH OBJECTIVE 1 ................................................................. 83 

Associations With Adiposity in Preadolescent Children: Fitness,  

Physical [In]activity, Sedentary Behaviour, Nutrition, and Sleep .................. 83 

4.0 Background ............................................................................................... 83 

4.1 Methodology ............................................................................................. 84 

4.1.1 Recruitment and Participants ............................................................. 85 

4.1.2 Study Design ...................................................................................... 85 

4.2 Data Collection and Analysis .................................................................... 88 

4.2.1 Demographics .................................................................................... 88 

4.2.2 Adiposity and Anthropometrics ......................................................... 88 

4.2.3 Physical Fitness .................................................................................. 89 

4.2.4 Physical [In]activity and Sedentary Behaviour .................................. 91 

4.2.5 Nutrition ............................................................................................. 92 

4.2.6 Sleep ................................................................................................... 93 

4.3 Statistical Analysis .................................................................................... 94 

4.4 Results ....................................................................................................... 95 

4.4.1 Univariate Models .............................................................................. 95 

4.4.2 Multivariate Models ........................................................................... 95 

4.5 Discussion ................................................................................................. 98 



 xiii 

4.5.1 Comparison With Other Studies ........................................................ 99 

4.5.2 Limitations and Strengths of This Study ......................................... 102 

4.5.3 Research Implications ...................................................................... 103 

4.6 Conclusion .............................................................................................. 104 

CHAPTER 5—RESEARCH OBJECTIVE 2 ............................................................... 105 

Associations with Cardiometabolic Health in Preadolescent Children:  

Fatness, Fitness, Physical [In]activity, Sedentary Behaviour, Nutrition,  

and Sleep ....................................................................................................... 105 

5.0 Background ............................................................................................. 105 

5.1 Methodology ........................................................................................... 106 

5.1.1 Recruitment and Participants ........................................................... 106 

5.1.2 Study Design .................................................................................... 108 

5.2 Data Collection and Analysis .................................................................. 109 

5.2.1 Demographics .................................................................................. 109 

5.2.2 Cardiometabolic Health  .................................................................. 110 

5.2.3 Adiposity and Anthropometrics  ...................................................... 111 

5.2.4 Physical Fitness  ............................................................................... 113 

5.2.5 Physical [In]activity and Sedentary Behaviour ................................ 114 

5.2.6 Nutrition ........................................................................................... 115 

5.2.7 Sleep  ................................................................................................ 116 

5.3 Statistical Analysis .................................................................................. 117 

5.4 Results ..................................................................................................... 118 

5.4.1 Cardiometabolic Factor Correlations and Analysis ......................... 118 

5.4.2 Univariate Models ............................................................................ 119 

5.4.3 Multivariate Models ......................................................................... 119 

5.5 Discussion ............................................................................................... 124 

5.5.1 Comparison With Other Studies ...................................................... 124 

5.5.2 Limitations and Strengths of This Study ......................................... 127 

5.5.3 Research Implications ...................................................................... 128 

5.6 Conclusion .............................................................................................. 129 

CHAPTER 6—GENERAL DISCUSSION AND CONCLUSIONS ........................... 130 

6.0 Summary of Findings .............................................................................. 130 

6.0.1 Objective 1 ....................................................................................... 130 

6.0.2 Objective 2 ....................................................................................... 134 



 xiv 

6.0.3 Implications ...................................................................................... 138 

6.0.4 Future Directions, Limitations, and Strengths ................................. 139 

6.1 Conclusion .............................................................................................. 140 

References ..................................................................................................................... 141 

Appendices   

Appendix A. PACMAC Questionnaire ........................................................ 195 

Appendix B. Standard Operating Protocols ................................................. 220 

Appendix C.  Study Invitation Letter ........................................................... 229 

Appendix D. Participant Paperwork ............................................................. 230 

Appendix E. Factor Loadings of Food Items Grouped into Three  

Identified Dietary Patterns in New Zealand Children ........................... 235 

  



 xv 

List of Tables 

 

Table 1. Contributions to Publications ............................................................................ vi 

Table 2. Sources and Functions of Key Adipokines ........................................................ 18 

Table 3. Physiological Benefits of Physical Fitness ....................................................... 27 

Table 4. Function of Micronutrients ............................................................................... 47 

Table 5. Standard Operating Protocol Foundations ...................................................... 72 

Table 6. Cardiometabolic Patterns ................................................................................. 79 

Table 7. Participant Data Description: Categorical Variables ........................................ 87 

Table 8. Participant Data Description: Continuous Variables ........................................ 91 

Table 9. Linear Association Between Body Fatness and Lifestyle Factors .................... 96 

Table 10. Participant Data Description: Categorical Variables .................................... 108 

Table 11. Participant Data Description: Continuous Variables .................................... 111 

Table 12. Linear Association Between Cardiometabolic Health and Lifestyle 

Behaviours ........................................................................................................ 121 

Table 13. Cardiometabolic Patterns ............................................................................. 123 

 

  



 xvi 

List of Figures 

 

Figure 1. Noncommunicable diseases (NCDs). ................................................................ 5 

Figure 2. The progression of atherosclerosis.. .................................................................. 6 

Figure 3. The progression of cardiovascular disease (CVD). ........................................... 9 

Figure 4. The role of inflammation and lipotoxicity on obesity ..................................... 16 

Figure 5. Body fatness assessments. ............................................................................... 21 

Figure 6. Dietary intake .................................................................................................. 46 

Figure 7. PACMAC measuring methods. ....................................................................... 63 

Figure 8. Study protocol. ................................................................................................ 66 

Figure 9. Component plots with factor diagrams from principal component analysis 

(PCA) with varimax orthogonal rotation. ........................................................... 76 

Figure 10. Scree plot of eigenvalues of the 21 food groups from the principal 

component analysis (PCA). ................................................................................ 77 

Figure 11. Participant recruitment. ................................................................................. 86 

Figure 12. Scree plot of eigenvalues of the 21 food groups from the principal 

component analysis (PCA). ................................................................................ 93 

Figure 13. V̇O₂max and body fat percent linear quadratic and cubic analysis. ................. 98 

Figure 14. Participant recruitment. ............................................................................... 107 

Figure 15. Scree plot of eigenvalues of the 21 food groups from principal component 

analysis (PCA). ................................................................................................. 116 

Figure 16. Component plots with factor diagrams from principal component analysis 

(PCA) with varimax orthogonal rotation. ......................................................... 120 

 

  



 xvii 

List of Abbreviations 

AIx = Augmentation Index Pressure 

BIA = Bioelectrical Impedance Analysis 

BMI = Body Mass Index 

CVD = Cardiovascular Disease 

CDC = Centers for Disease Control and Prevention 

CBP = Central Blood Pressure 

CSHQ = Child Sleep Habits Questionnaire 

DBP = Diastolic Blood Pressure 

FMI = Fat Mass Index 

FMD = Flow-Mediated Dilation 

HbA1c = Glycosylated Haemoglobin  

HR = Heart Rate 

HRM = Heart Rate Monitors  

HDL-C = High Density Lipoproteins 

LDL-C = Low Density Lipoproteins 

20-MST = Maximal multistage 20-metre shuttle run test 

MOE = Ministry of Education 

MOH = Ministry of Health 

NCD(s) = Noncommunicable disease(s) 

OECD = Organization for Economic Co-operation and Development 

PEDALS FFQ = Physical Activity, Exercise, Diet, And Lifestyle Study Food 

Frequency Questionnaire 

PACMAC = PreAdolescent CardioMetabolic Associations and Correlates 

PCA = Principal Components Analysis 



 xviii 

PWA = Pulse Wave Analysis 

PWV = Pulse Wave Velocity 

SBP = Systolic Blood Pressure 

TC = Total Cholesterol 

TG = Triglycerides 

WC = Waist Circumference 

WHtR = Waist-to-Height Ratio 

WHR = Waist-to-Hip Ratio 

WHO = World Health Organization 

YPAQ = Youth Physical Activity Questionnaire 

  



 xix 

Definition of Terms 

Cardiometabolic health factors = Blood pressure, cholesterol, vascular, and 

carbohydrate-metabolic 

Cardiometabolic risk factors/cardiometabolic complications = obesity, hypertension, 

hyperglycaemia, and hyperlipidaemia 

Lifestyle factors/modifiable lifestyle factors = Body fatness, physical fitness, physical 

[in]activity, sedentary behaviour, nutrition, and sleep 

Physical activity = Active behaviours resulting in an increased energy expenditure 

Physical fitness = Cardiorespiratory fitness and muscular fitness 

Physical [in]activity = Physical activity and physical inactivity 

Physical inactivity = Achieving less than the recommended amount of daily physical 

activity 

Sedentary behaviour = Low energy expenditure for long periods of time 

V̇O₂max = Maximal oxygen uptake  

 



 1 

CHAPTER 1—INTRODUCTION 

1.0 Circumstances and Statement of the Problem 

Whereas cardiovascular disease (CVD) is typically associated with middle or old 

age, the atherosclerotic process often initiates early in childhood, and is occurring at an 

increasing rate (Bridger, 2009; Cote, Harris, Panagiotopoulos, Sandor, & Devlin, 2013; 

Dobashi, 2016; Sorensen et al., 1994; Stoner, Lambrick, Faulkner, & Young, 2013). In 

children, early onset of atherosclerosis has been linked to cardiometabolic risk factors such 

as obesity, and worldwide obesity statistics have nearly tripled since 1975 (World Health 

Organization [WHO], 2017c). Additionally, in 2016, over 340 million children and 

adolescents (aged 5 to 19 years) globally were overweight or obese (WHO, 2017c). 

Overweight and obese children are at increased risk of developing cardiometabolic 

complications, which could lead to noncommunicable diseases (NCDs; Anderson et al., 

2015; Centers for Disease Control and Prevention [CDC], 2015b; Ministry of Health 

[MOH], 2015b; Ogden et al., 2016; WHO, 2017b, 2017c), and NCDs kill 40 million people 

each year (WHO, 2017b). Furthermore, various lifestyle factors have shown associations 

with cardiometabolic risk factors; however, it is unknown which lifestyle factor(s) 

correlate(s) most strongly with cardiometabolic health.  

The prevalence of childhood obesity is a significant public health concern (S. A. 

Ali, Ali, Suhail, Tikmani, & Bano, 2016). In New Zealand, childhood obesity increased 

from 8% in 2006-07 to 12% in 2016-17 (MOH, 2015b), and in 2014, overweight and 

obesity prevalence in New Zealand children (aged 5 to 17 years) was the third highest 

worldwide (Anderson et al., 2017; Kelly & Swinburn, 2015; Organisation for Economic 

Co-Operation and Development [OECD], 2014). Although frequently cited causes of 

obesity include: lower than ideal physical fitness (Cohen et al., 2011; Czyż et al., 2017; 



 2 

Stratton et al., 2007), insufficient daily physical activity (O. Ali et al., 2014; Dumuid et al., 

2018; WHO, 2017d), sedentary behaviour (Carson, Hunter, et al., 2016; Carson, Tremblay, 

Chaput, & Chastin, 2016; Griffiths et al., 2016; Healy & Owen, 2010), unhealthy diet 

(Harrex et al., 2017; WHO, 2015, 2017a), and inadequate sleep (Davison et al., 2017; Sayin 

& Buyukinan, 2016; Skidmore et al., 2013), no known study has comprehensively assessed 

the simultaneous associations among these lifestyle factors with cardiometabolic health in 

preadolescent children. Consequently, uncertainty emerges as to which lifestyle factor(s) 

associate(s) more strongly with cardiometabolic risk factors, making it difficult to 

determine the ideal public health intervention and prevention strategy required for 

improving cardiometabolic health in children. 

Most public resources are aimed towards the fight against obesity; however, in 

children the lifestyle factor(s) that associate(s) more strongly with obesity have not been 

definitively identified. Therefore, additional research analysing the associations among 

lifestyle factors (body fatness, physical fitness, physical [in]activity, sedentary behaviour, 

nutrition, and sleep) with cardiometabolic health is essential. Overall, obesity is an outcome 

of a number of factors and addressing the disease is not the final goal; improving and 

maintaining cardiometabolic health is the primary objective. Findings from this study will 

add to the body of knowledge that informs health professionals and health policy writers 

regarding health and wellness of children and possibly make a positive impact on the 

deteriorating health of New Zealand children.  

1.1 Objectives of Research 

The overall aim of this thesis was to examine simultaneously the associations 

among lifestyle factors (body fatness, physical fitness, physical [in]activity, sedentary 

behaviour, nutrition, and sleep) with cardiometabolic health in New Zealand preadolescent 



 3 

children aged 8 to 10 years. Accordingly, the study was undertaken in primary schools 

across three regions in New Zealand (Wellington, Canterbury, and Otago) specifically to 

examine these questions: 

1. What are the associations among physical fitness, physical [in]activity, 

sedentary behaviour, nutrition, and sleep with body fatness in preadolescent 

children? (Objective 1, Chapter 4) 

2. What are the associations among body fatness, physical fitness, physical 

[in]activity, sedentary behaviour, nutrition, and sleep with cardiometabolic 

health in preadolescent children? (Objective 2, Chapter 5) 

1.2 Outline of Thesis 

This thesis is divided into six chapters and presented as a hybrid thesis displaying 

traits of thesis by publication and a traditional monograph approach. This first chapter 

(Chapter 1) introduces the thesis and presents the study rationale. Chapter 2 presents a 

review of the literature on cardiometabolic health, body fatness, physical fitness, physical 

[in]activity, sedentary behaviour, nutrition, and sleep. Chapter 3 is my study protocol 

paper, which was published in the BMJ Open under the title, “Pre-Adolescent 

Cardiometabolic Associations and Correlates: PACMAC Methodology and Study 

Protocol” (Castro et al., 2014). Chapter 4 (Objective 1) and Chapter 5 (Objective 2) were 

written as research papers and will be submitted for publication after the completion of my 

PhD. The final chapter (Chapter 6) summarises the findings, implications, future direction, 

and general conclusions of this thesis. It should be noted that Chapter 4 and Chapter 5 were 

written as research papers for publication and are based on similar data sets; therefore, the 

background and methodological sections of these two chapters will have some duplication.  



 4 

CHAPTER 2—LITERATURE REVIEW 

The following chapter provides an overview of the literature relating to 

cardiometabolic health and lifestyle factors: body fatness, physical fitness, physical 

[in]activity, sedentary behaviour, nutrition, and sleep. Many lifestyle factors contribute to 

cardiometabolic complications; however, uncertainty emerges as to which lifestyle factor(s) 

associate(s) more strongly with cardiometabolic risk factors in children. This literature 

review provides an overview of each of the listed lifestyle factors and cardiometabolic risk 

factors in children, followed by a rationale and evidence supporting the measuring methods 

applied in this study.  

2.0 Cardiometabolic Health  

2.0.1 Background 

Poor cardiometabolic health refers to a group of NCDs that encompass both cardiac 

and metabolic conditions. Moreover, cardiometabolic risk factors tend to cluster, creating 

complications that increase the probability of developing an NCD such as CVD (see Figure 

1; Ahrens et al., 2014; Bridger, 2009; Trandafir, Frasinariu, Corciovă, Boiculese, & 

Moscalu, 2017). Of all NCDs, CVD encompasses the highest mortality rates and annually 

more people die from CVD than any other cause (Frieden & Jaffe, 2018; Trandafir et al., 

2017; WHO, 2017a). Additionally, in 2015 it was projected that 17.7 million people would 

die from CVD, representing 31% of all global deaths (WHO, 2017a). While CVD is 

typically associated with middle or old age, the atherosclerotic process (see Figure 2) often 

initiates early in childhood, within the first and second decades of life, and is occurring at 

an increasing rate (Sorensen et al., 1994; Stoner et al., 2013; Trandafir et al., 2017).  

 



 5 

 

Figure 1. Noncommunicable diseases (NCDs). 

 

Vascular disease is a combination of vascular injury and vascular repair outcomes 

including: inflammation, oxidative stress, immune dysfunction, vasoconstriction 

(constriction of blood vessels), increased vascular permeability, and thrombosis, or clotting 

of the blood (Stein et al., 2008; Vanhoutte, Shimokawa, Tang, & Feletou, 2009). 

Collectively, vascular disease consists of circulatory system disorders such as endothelial 

dysfunction, which can lead to atherosclerosis (Stein et al., 2008).  

Atherosclerosis is a chronic inflammatory progress that initiates from built-up fatty 

substances inside the artery channels (Beauloye et al., 2007; Kenney, Wilmore, & Costill, 

2015). The fatty substances are referred to as plaque or atheroma and are made up of  

 

Non-Communicable Diseases (NCDs)

Cardiometabolic Risk 

Factors
• Obesity

• Hypertension

• Hyperglycaemia

• Hyperlipidaemia

Overview
• NCDs are also known as chronic diseases, which result 

from a combination of genetics, physiological, 

environmental, and lifestyle risk factors. 

Modifiable Lifestyle 

Factors
• Body fatness

• Physical fitness 

• Physical [in]activity

• Sedentary behaviour

• Nutrition

• Sleep

Main Types of NCD
• CVD 

• Type 2 Diabetes

• Cancers

• Chronic respiratory diseases 



 6 

 

Figure 2. The progression of atherosclerosis. Adapted from Vascular Diseases for 

the Non-Specialist: An Evidence-Based Guide by T. P. Navarro, A. Dardik, D. 

Junqueira, and L. Cisneros (Eds.), 2017, p. 37. Copyright 2017 by Springer.  

 

calcium, lipoproteins, cholesterol, inflammatory cells, fibrin, and other cellular waste 

substances, although low density lipoproteins (LDL-C) remain the most important 

component of atherosclerotic plaque accumulation (Navarro et al., 2017; Ross & Glomset, 

1976; Sorensen et al., 1994; Stoner, Lambrick, et al., 2014). The progression of 



 7 

atherosclerotic plaque buildup begins with LDL-C accumulation in the blood vessel intima 

(Navarro et al., 2017; Ross & Glomset, 1976; Sorensen et al., 1994; Stoner, Lambrick, et 

al., 2014; Tedgui & Mallat, 1999). This accumulated LDL-C becomes oxidised by free 

radicals and through glycation, which in turn initiates the recruitment of monocytes-

macrophages (Kenney et al., 2015; Tedgui & Mallat, 1999). Modified LDL-C consumed by 

macrophage scavenger receptors is transformed into foam cells (Navarro et al., 2017; 

Tedgui & Mallat, 1999). This process results in the formation of a fibrous cap containing 

smooth muscle cells, which permits stabilization of the plaque (Kenney et al., 2015; 

Navarro et al., 2017; Ross & Glomset, 1976; Sorensen et al., 1994; Stoner, Lambrick, et al., 

2014; Tedgui & Mallat, 1999). These substances accumulate over time within the artery 

walls, narrowing the lumen of the artery, which reduces blood flow through the vessel and 

thus influences/elevates blood pressure (Beauloye et al., 2007; Ross & Glomset, 1976). 

Collectively, restricted blood flow has a detrimental effect on the functioning of the heart, 

brain, kidneys, arms, legs, and pelvis (Ahrens et al., 2014; Bridger, 2009; Trandafir et al., 

2017). 

Endothelial dysfunction precedes atherosclerosis (Bonetti, Lerman, & Lerman, 

2003; Davignon & Ganz, 2004). Endothelial cells are a thin layer of cells that line the 

inside of blood vessels (Bonetti et al., 2003; Davignon & Ganz, 2004). These cells form a 

smooth protective layer between the blood circulating through the artery in the lumen and 

the vascular smooth muscle known as the endothelium (Bonetti et al., 2003; Davignon & 

Ganz, 2004; Kenney et al., 2015). When the endothelium is functioning properly, it 

produces molecules (nitric oxide, endothelin, and prostaglandin) that provide a protective 

barrier between toxic substances in the blood and the vessel wall, which in turn helps 

regulate vascular relaxation, blood clotting, and immune function, so that blood elements 



 8 

and the vascular smooth muscle remain normal (Bonetti et al., 2015; Davignon & Ganz, 

2004; Kenney et al., 2015). However, endothelial dysfunction occurs when endothelial 

cells become disrupted by oxidised free radicals, causing excessive oxidative stress 

(Davignon & Ganz, 2004; Galley & Webster, 2004; Sprague, Chesler, & Magness, 2010). 

The disrupted endothelial cells interfere with the normal release and regulation of nitric 

oxide (Bonetti et al., 2003; Charakida et al., 2005; Davignon & Ganz, 2004), which 

obstructs the endothelial cells’ ability to dilate properly, causing an imbalance between the 

narrowing and widening of blood vessels (Bonetti et al., 2003; Charakida et al., 2005; 

Davignon & Ganz, 2004; Galley & Webster, 2004). In addition, the amount of shear stress 

stimulus on the intima layer of vessel walls also plays a role in regulating nitric oxide 

(Davignon & Ganz, 2004; Galley & Webster, 2004; Sprague et al., 2010).  

Cardiometabolic risk factors that lead to endothelial dysfunction, then 

atherosclerosis, and subsequently to CVD include: excessive accumulation and storage of 

adipose tissue in the body (obesity), elevated levels of LDL-C and plasma homocysteine in 

the bloodstream (hyperlipidaemia), too much glucose in the bloodstream (hyperglycaemia), 

and excessive blood pressure on the artery wall (hypertension) exerting shear stress on the 

vulnerable endothelium (see Figure 3; Kenney et al., 2015; Ross & Glomset, 1976; 

Sorensen et al., 1994; Widmer & Lerman, 2014; WHO, 2017a, 2017b). Preceding 

cardiometabolic risk factors are modifiable lifestyle factors including but not limited to: 

excessive body fatness (Bridger, 2009; Trandafir et al., 2017), lower than ideal physical 

fitness (Cohen et al., 2011; Czyż et al., 2017; Stratton et al., 2007), insufficient daily 

physical activity (O. Ali et al., 2014; Dumuid et al., 2018; WHO, 2017d), sedentary 

behaviour (Carson, Hunter, et al., 2016; Carson, Tremblay, et al., 2016; Griffiths et al., 

2016; Healy & Owen, 2010), unhealthy diet (Harrex et al., 2017; WHO, 2015, 2017a), and 



 9 

inadequate sleep (Davison et al., 2017; Sayin & Buyukinan, 2016; Skidmore et al., 2013). 

However, no known study has examined simultaneously the associations among the listed 

lifestyle factors with cardiometabolic risk factors in preadolescent children. Consequently, 

uncertainty remains as to which lifestyle factor(s) associate more strongly with 

cardiometabolic risk factors in preadolescent children.  

 

Figure 3. The progression of cardiovascular disease (CVD). 

 

2.0.2 Evaluating Cardiometabolic Health 

In preadolescent children, cardiometabolic health can be assessed utilising a 

collection of approaches including: anthropometric indicators (for further information, see 

section 2.1.2 on evaluating body fatness), pulse wave analysis (PWA), pulse wave velocity 

(PWV), flow-mediated dilation (FMD), and cardiometabolic biochemical markers. The 

following paragraphs elaborate on the indicated cardiometabolic assessment methods and 



 10 

present evidence supporting the evaluating procedures chosen for this study over other 

approaches.  

Procedures for gauging blood pressure were chosen based on their noninvasive 

nature and the study’s setting. Blood pressure gauged from the upper arm brachial artery is 

termed peripheral blood pressure (diastolic DBP, systolic SBP), whereas central blood 

pressure (CBP) is gauged from the aorta, which is the largest artery through which the heart 

pumps blood (Hirata, Kawakami, & O'Rourke, 2006). Central blood pressure is the 

resistance the heart encounters while attempting to pump blood throughout the body. When 

CBP is elevated, the heart must work harder, which could lead to heart failure. As a result, 

CBP has been shown to predict hypertension and potential cardiometabolic complications 

(Hirata et al., 2006; Roman et al., 2007). Central blood pressure can be gauged 

noninvasively by utilising PWA and PWV.  

Pulse wave analysis assesses SBP, DBP, CBP, heart rate (HR), and augmentation 

index pressure (AIx; Stoner, Credeur, Dolbow, & Gater, 2015). The mentioned indicators 

are estimated by analysing a generated CBP waveform (Stoner et al., 2015). Initially, the 

pulse pressure wave is formed from the combination of the incident wave (systolic) and 

waves reflected back from the perimeter (diastolic; J. I. Davies & Struthers, 2003). Then 

this waveform is transmitted through the arterial wall, generating a corresponding aortic 

pulse pressure waveform (augmentation pressure), which can be measured and examined 

on a digital device (J. I. Davies & Struthers, 2003; Stoner et al., 2015). A study carried out 

on New Zealand children (aged 8 to 10 years; n = 57) found that PWA is an appropriate 

assessment of cardiometabolic risk in children (Stoner, Lambrick, et al., 2014). However, 

researchers analysed a small sample size in a laboratory setting; therefore, the results do not 

allow for accurate comparison to a study design with a sample size of 392 preadolescent 



 11 

children. Yet, PWA is deemed an appropriate assessment method as it investigates the 

arterial tree mechanisms, including CBP and arterial wave reflection (AIx), and elevated 

AIx has been identified as a CVD risk predictor (Stoner, Lambrick, et al., 2014). 

Furthermore, PWA is a simple, noninvasive, valid, and reliable measurement of 

cardiometabolic risk, particularly in large paediatric epidemiological studies (Hirata et al., 

2006; Stoner et al., 2013; Stoner, Lambrick, et al., 2014). However, to my knowledge, there 

are currently no standard PWA values for assessing a large group of preadolescent children 

in an uncontrolled field-based environment. Therefore, further paediatric research is 

necessary in evaluating PWA with other recognised techniques such as PWV and FMD 

with the intention of establishing reliable standards for applying PWA in preadolescent 

children (Stoner et al., 2013; Stoner, Lambrick, et al., 2014). 

Pulse wave velocity identifies functional changes in the large arteries by analysing 

the rate at which pressure waves transmit through the vessel (Hirata et al., 2006; 

McCloskey et al., 2014; Stoner et al., 2015). The velocity of the wave is determined by the 

time needed for the waveform to pass between two points a measured distance apart (pulse 

wave distance; McCloskey et al., 2014; Stoner et al., 2015). These readings are frequently 

obtained on the carotid and femoral arteries, which represent the aortoiliac pathway (Hirata 

et al., 2006; Reusz et al., 2010). Pulse wave velocity is the most widely accepted method to 

assess arterial stiffness in children and adolescents by the American Heart Association 

Atherosclerosis, Hypertension, and Obesity in Youth committee (Thurn et al., 2015; Urbina 

et al., 2009). A studied carried out on German children, adolescents, and young adults 

(aged 8 to 22 years; n = 1445) found that PWV can help identify arterial stiffness and 

predict cardiometabolic risk in children and adolescents (Elmenhorst et al., 2015). In this 

study, data were collected in a school setting from a predominantly Caucasian population; 



 12 

participants only rested for 5 to 10 minutes before blood pressure was assessed. If a 

measured blood pressure was elevated, the value was excluded (n = 578) from the analyses, 

thus the results from this study do not provide an accurate sample of blood pressure 

numbers collected in a school setting (Elmenhorst et al., 2015). Another study carried out 

on American children and adolescents (aged 4 to 18 years, n = 159) found that increased 

PWV was associated with body fatness and hypertension (Kulsum‐Mecci et al., 2017). 

Also in this study, the majority of the participants were Caucasian; therefore, the outcomes 

from these results may not allow a meaningful conclusion to be drawn for any other racial 

or ethnic group. Furthermore, it is necessary to point out a few limitations associated with 

most of the PWV data analysed in children including: the majority of the previous data 

examined were collected from Caucasian children (Elmenhorst et al., 2015; Kulsum‐Mecci 

et al., 2017; McCloskey et al., 2014; Thurn et al., 2015), various studies utilised dissimilar 

markers to assess PWV (e.g., some studies assessed carotid artery to radial artery while 

others assessed carotid artery to femoral artery; J. I. Davies & Struthers, 2003, Elmenhorst 

et al., 2015; McCloskey et al., 2014; Thurn et al., 2015), and to my knowledge, there are 

currently no standardised values for assessing children. Consequently, additional research 

is required to better understand the relationship among PWV and cardiometabolic risk 

factors in preadolescent children from all backgrounds, with the intention of establishing 

reliable standards for implementing PWV in paediatric research. 

Flow-mediated dilation is the assessment of blood flow by examining endothelial 

(dys)function and quantifying the percentage increase in the brachial artery diameter in 

response to blood flow (Ras, Streppel, Draijer, & Zock, 2013; Stoner et al., 2015). By 

expression, FMD can be defined as the dilation of an artery following an increase in 

luminal blood flow and internal wall shear stress (Thijssen et al., 2011). A dysfunctional 



 13 

endothelium has been proven to precede and predict cardiometabolic complications and 

subsequently CVD (Ras et al., 2013; Stoner et al., 2015; Thijssen et al., 2011). A 

systematic review that examined 23 studies including 14,753 subjects (aged 40 to 80 years) 

across the globe, found that FMD can predict cardiometabolic risk factors and subsequently 

CVD (Ras et al., 2013). This study review analysed a large sample; however, all the 

participants were adults. Therefore, the results reported in this systematic review are not 

comparable to studies with samples of preadolescent children aged 8 to 10 years. A study 

carried out on obese German children (aged 11 to 16; n = 96) found that FMD is a 

consistent and significant marker of identifying early signs of CVD risk in obese children 

(Meyer, Kundt, Steiner, Schuff-Werner, & Kienast, 2006). However, in this study, the 

participants were already obese; therefore, the study participants did not represent a 

population of children of all body types. Additionally, a study carried out on children (aged 

6 to 17 years; n = 43) with chronic kidney disease found that children with chronic kidney 

disease had an increased occurrence of reduced FMD of the brachial artery (A. C. Wilson 

et al., 2008). In this study, the participants were not free of disease, so do not resemble a 

population of normative data. Flow-mediated dilation is highly valid and considered the 

gold standard assessment for endothelial function, however this assessment method is also 

highly technical, requires a skilled operator (Stoner et al., 2015), and is impractical for 

large paediatric studies. Additionally, there is limited research available analysing 

cardiometabolic function in healthy preadolescent children utilising FMD, and to my 

knowledge, there are currently no standardised values for this age group. Therefore, 

additional paediatric research is essential to enhance the knowledge surrounding FMD and 

cardiometabolic health and to establish FMD as a reliable assessment method for 

endothelial (dys)function in preadolescent children.  



 14 

Cardiometabolic biochemical markers are utilised to assess cardiometabolic risk 

factors associated with blood chemistry. Blood is collected utilising a venepuncture or 

finger prick. The venepuncture method punctures a vein with a needle, usually in the arm, 

whereas a finger prick pokes the tip of a finger drawing minimal blood. Collection of blood 

markers by venepuncture is invasive and impractical in preadolescent children, whereas 

blood marker collection by finger prick is simple, valid, reliable, and practical for large 

paediatric epidemiological studies. There is abundant paediatric research that validates high 

blood glucose levels (hyperglycaemia) and high levels of fat in the blood (hyperlipidaemia) 

as metabolic risk factors that predict cardiometabolic complications, and subsequent CVD 

into adulthood (Ahrens et al., 2014; Li et al., 2016; Sardinha et al., 2016; Trandafir et al., 

2017). A study review carried out on European children (aged 2 to 10 years; n = 18,745) 

found that biochemical markers can be a significant indicator of early signs of metabolic 

disease in children (Ahrens et al., 2014). Another study carried out on European children 

(aged 8 to 17 years; n = 4255) discovered that biochemical markers provide a more 

accurate and consistent evaluation of cardiometabolic risk in children when compared to 

anthropometric indicators (Sardinha et al., 2016). Additionally, a study carried out on obese 

Romanian children (aged 8 to 14 years; n = 78) using biochemical markers found that early 

identification and treatment of cardiometabolic risk is important to reduce and prevent 

CVD into adulthood (Trandafir et al., 2017). However in this study, the participants were 

obese, thus do not provide a standard example of children of all body types. 

Cardiometabolic blood markers are a significant aspect of examining and monitoring 

cardiometabolic health in children. However, additional research is essential to better 

understand the relationship among lifestyle factors with cardiometabolic biochemical 

markers in preadolescent children. 



 15 

Applied cardiometabolic health assessments. In this study, cardiometabolic 

health was assessed utilising PWA and cardiometabolic biochemical markers: SBP, DBP, 

CBP, HR, AIx, total cholesterol (TC), high density lipoproteins (HDL-C), LDL-C, 

triglycerides (TG), serum glucose, and glycosylated haemoglobin (HbA1c). For further 

information, see section 5.2 in Chapter 5. 

2.1 Body Fatness  

2.1.1 Background  

Childhood obesity continues to escalate as a global epidemic and a significant 

public health concern (Ajala, Mold, Boughton, Cooke, & Whyte, 2017; S. A. Ali et al., 

2016; Czyż et al., 2017; W. H. Dietz, 2004; Sayin & Buyukinan, 2016). Worldwide obesity 

statistics have nearly tripled since 1975 (WHO, 2017c). In 2016, over 340 million children 

and adolescents (aged 5 to 19 years) globally were overweight or obese (WHO, 2017c). 

Furthermore, in New Zealand, childhood obesity increased from 8% in 2006-07 to 12% in 

2016-17 (MOH, 2015b), and in 2014, overweight and obesity prevalence in New Zealand 

children was the third highest worldwide (Anderson et al., 2017; Kelly & Swinburn, 2015; 

OECD, 2014). 

 Obesity is a condition characterised by excessive accumulation and storage of 

adipose tissue in the body (S. A. Ali et al., 2016; WHO, 2017c). This adipose tissue 

accumulates when energy intake (nutrition) exceeds energy expenditure (physical activity 

and exercise), resulting in a positive energy balance over time (Romieu et al., 2017). There 

are two types of adipose tissue in the body: brown and white (DeClercq, Taylor, & 

Zahradka, 2008; Stoner, Gaffney, Wadsworth, & Page, 2014). Brown adipose tissue is 

involved in thermogenesis (DeClercq et al., 2008), whereas white adipose tissue is the main 

energy reservoir storing fat, hormones, and cytokines to be distributed to cells to help 



 16 

regulate metabolism and insulin effectiveness and to be utilised for energy when necessary 

(DeClercq et al., 2008; Redinger, 2007). The development and functioning of white adipose 

tissue is impacted by various cells (vascular cells, fibroblasts, lymphocytes, pre-adipocytes, 

and macrophages [Ouchi, Parker, Lugus, & Walsh, 2011; Wang & Nakayama, 2010]). As 

outlined in Figure 4, white adipose tissue is significant to cardiometabolic dysfunction as it 

initiates the pathological mechanisms associated with obesity, including lipotoxicity and 

inflammatory responses (DeClercq et al., 2008; Redinger, 2007).  

 

 

Figure 4. The role of inflammation and lipotoxicity on obesity. Adapted from “The 

Pathophysiology of Obesity and Its Clinical Manifestations,” by R. N. Redinger, 2007, 

Gastroenterology & Hepatology, 11, p. 856. Copyright 2007 by Gastro-Hep 

Communications. 

  



 17 

White adipose tissue executes several functions and has been identified as a key 

component of the metabolic system (Trayhurn & Wood, 2004). Along with the pivotal role 

of lipid storage, its major function is stockpiling various hormones and proteins referred to 

as adipokines (Ouchi et al., 2011; Trayhurn & Wood, 2004). Adipokines consist of billions 

of cells that perform pro- and anti-inflammatory actions such as assisting with body fatness 

regulation and the storage of triacylglycerol in fat deposits as energy reserves (Nakamura, 

Fuster, & Walsh, 2011; Ouchi et al., 2011; Redinger, 2007). However, abnormal 

production and/or secretion of adipokines can contribute to the pathogenesis of obesity 

related complications (Ouchi et al., 2011; Trayhurn & Wood, 2004). This occurs because 

the expansion of fat cells initiates an undesired molecular and cellular variation of the 

metabolic process, which triggers secretion of adipokines by white adipose tissue, which is 

identified as an inflammatory response as shown in Figure 4 (Ouchi et al., 2011; Redinger, 

2007; Wang & Nakayama, 2010). Adipocytes, macrophages, fibroblasts, lymphocytes, 

neutrophils, and precursor, foam, endothelial, and immune cells all contribute to the release 

of adipokines (Fasshauer & Blüher, 2015; Ouchi et al., 2011; Rabe, Lehrke, Parhofer, & 

Broedl, 2008). Table 2 provides a summary of the sources and functions of key adipokines 

(Ouchi et al., 2011; Rabe et al., 2008). The release of adipokines could lead to pathological 

cardiometabolic complications, which disrupt endothelial cells (thrombosis, plaque, 

atheroma), result in a dysfunctional endothelium, and ultimately lead to atherosclerotic 

disease (Ouchi et al., 2011; Redinger, 2007; Wang & Nakayama, 2010). For further 

information on endothelial dysfunction, see section 2.0.1 for background on 

cardiometabolic health. 

  



 18 

Table 2 

Sources and Functions of Key Adipokines 

Adipokine Primary source(s) Binding partner or 

receptor 

Function 

Adiponectin Adipocytes Adiponectin 

receptors 1 and 2, 

T-cadherin, 

calreticulin–CD91 

Insulin sensitiser 

Anti-inflammatory 

Leptin Adipocytes Leptin receptor Appetite control through the 

central nervous system 

Secreted 

frizzled-related 

protein 5 

Adipocytes WNT5a Suppression of pro-

inflammatory WNT 

signalling 

Resistin Peripheral blood 

mononuclear cells 

Unknown Promotes insulin resistance 

and inflammation through 

interleukin-6 and tumour 

necrosis factor secretion from 

macrophages 

Retinol binding 

protein 4 

Liver, adipocytes, 

macrophages 

Retinol (vitamin A), 

transthyretin 

Implicated in systemic 

insulin resistance 

Lipocalin 2 Adipocytes, 

macrophages 

Unknown Promotes insulin resistance 

and inflammation through 

tumour necrosis factor 

secretion from adipocytes 

Angiopoietin-like 

protein 2 

Adipocytes, other 

cells 

Unknown Local and vascular 

inflammation 

Tumour necrosis 

factor 

Stromal vascular 

fraction cells, 

adipocytes 

Tumour necrosis 

factor receptor 

Inflammation, antagonism of 

insulin signalling 

Interleukin-6 Adipocytes, stromal 

vascular fraction 

cells, liver, muscle 

Interleukin-6 

receptor 

Changes with source and 

target tissue 

Interleukin-18 Stromal vascular 

fraction cells 

Interleukin -18 

receptor, 

Interleukin-18 

binding protein 

Broad-spectrum 

inflammation 

Chemokine 

ligand 2 

Adipocytes, stromal 

vascular fraction 

cells 

Chemokine ligand 2 Monocyte recruitment 

Chemokine 

ligand 5 

Stromal vascular 

fraction cells 

(macrophages) 

Chemokine ligand 2 Antagonism of insulin 

signalling through the Janus 

kinase - signal transducer and 

activator of transcription 

pathway 

Nicotinamide 

phosphoribosyl 

transferase 

Adipocytes, 

macrophages, 

other cells 

Unknown Monocyte chemotactic 

activity 

 



 19 

Obesity is also associated with lipotoxicity, which is a metabolic disorder resulting 

from the accumulation of fat in nonadipose tissue (Engin, 2017). Lipotoxicity is the failure 

of the excessive buildup of fat in cell cytoplasm to combine into lipid droplets, which 

causes an increase of circulating fatty acids and may result in toxic levels of fatty acids 

within nonadipose tissue (Engin, 2017). Some previous research has indicated that storing 

fatty acid as TG inside fat cells protects against fatty acid toxicity. If not stored, free fatty 

acids would circulate unmonitored within the blood vessels and produce oxidative stress by 

spreading throughout the body (Engin, 2017). The excessive storage of fat that causes 

obesity ultimately triggers the release and circulation of fatty acids from lipolysis, 

which is stimulated by the supportive state of obesity. The discharge of free fatty acids then 

triggers lipotoxicity, creating oxidative stress to the cytoplasm of cells and mitochondria 

(Engin, 2017). This excessive accumulation of fat results in an imbalance between the 

amount of lipids produced and lipids utilised, which is responsible for the processes 

associated with cellular dysfunction and metabolic disease in many organs (e.g., heart, 

liver, pancreas) throughout the body (Redinger, 2007; Van Herpen & Schrauwen-

Hinderling, 2008). Adipose tissue also stores and releases active compounds such as free 

fatty acids into the blood circulation; consequently, elevated levels of free fatty acids are 

frequently associated with obesity (Boden, 2008; M. D. Jensen, 2006). Ordinarily, released 

free fatty acids travel to cells to be used as energy; however, when introduced in large 

amounts, they can increase internal glucose production rates and cause insulin resistance 

(Boden, 2008; M. D. Jensen, 2006).  

The free fatty acids released from TG also obstruct lipogenesis, which impedes the 

clearance of serum triacylglycerol levels that then contributes to the development of 

hyperlipidaemia (Boden, 2008; Redinger, 2007). Additionally, the secretion of free fatty 



 20 

acids by endothelial lipoprotein lipase from accumulated serum TG within elevated 

lipoproteins causes lipotoxicity, resulting in insulin receptor dysfunction (M. D. Jensen, 

2006; Redinger, 2007). This subsequently results in insulin resistance, which can lead to 

hyperglycaemia (M. D. Jensen, 2006; Redinger, 2007). Furthermore, free fatty acids also 

reduce usage of insulin-stimulated muscle glucose, which can also lead to a 

hyperglycaemic state. (M. D. Jensen, 2006; Redinger, 2007). Collectively, these 

circumstances lead to a cascade of cardiometabolic pathophysiological complications such 

as sustained insulin resistance, hyperglycaemia, hyperlipidaemia, hypertension, and 

obesity, which can create a cluster effect and increase the risk of developing a 

dysfunctional endothelium and subsequently atherosclerosis (Galley & Webster, 2004; 

Redinger, 2007). 

Consequently, obesity has been identified as one of the most significant 

cardiometabolic risk factors in children (P. Dietz, Hoffmann, Lachtermann, & Simon, 

2012; Sayin & Buyukinan, 2016). Preceding cardiometabolic risk factors are modifiable 

lifestyle factors linked to childhood obesity prevalence, which include: lower than ideal 

physical fitness (Cohen et al., 2011; Czyż et al., 2017; Stratton et al., 2007), insufficient 

daily physical activity (O. Ali et al., 2014; Dumuid et al., 2018; WHO, 2017d), sedentary 

behaviour (Carson, Hunter, et al., 2016; Carson, Tremblay, et al., 2016; Griffiths et al., 

2016; Healy & Owen, 2010), unhealthy diet (Harrex et al., 2017; WHO, 2015, 2017a), and 

inadequate sleep (Davison et al., 2017; Sayin & Buyukinan, 2016; Skidmore et al., 2013). 

However, uncertainty exists as to whether body fatness or another lifestyle factor associates 

more strongly with cardiometabolic risk factors in preadolescent children.  



 21 

2.1.2 Evaluating Body Fatness  

Body fatness can be evaluated multiple ways; the most common methods include: 

bioelectrical impedance analysis (BIA), skinfold calliper, hydrostatic weighing, DEXA 

scan, and anthropometric indicators (see Figure 5). The following paragraphs elaborate on 

the field-based anthropometric indicators commonly implemented in assessing 

preadolescent children including: body mass index (BMI), waist-to-hip ratio (WHR), waist-

to-height ratio (WHtR), waist circumference (WC), and BIA. Also presented are current 

circumstances supporting the evaluating procedures applied in this study over other 

approaches (Gaya et al., 2017). 

 

Figure 5. Body fatness assessments. 

 

Assessing	
Body	Fatness

Anthropometric
(BMI, WHR, WHtR, WC)

PROS

• Practical 

• Easy to administer

• Cost and time efficient

CONS

• Variability of results 

dependent on measurer skill 

level

• No consistent cutoff points 

and guidelines for 

preadolescent children

Hydrostatic 

Weighing
PROS

• Considered the GOLD 

standard

• Very accurate

CONS

• Expensive

• Embarrassing (pool attire)

• Time consuming (offsite 

location)

• Impractical

BIA
PROS

• Practical 

• Easy to administer

• Cost and time efficient

CONS

• Variability of results 

dependent on degree of 

hydration  

• Testing environment (room 

temperature)

DEXA Scan
PROS

• Very accurate

• Identifies fat locations

CONS

• Expensive

• Time consuming (offsite 

location)

• Embarrassing (must go see a 

general practitioner) 

• Impractical

Skinfold 

Callipers
PROS

• Accurate (when skilled at 

measuring)

• Repeatable

CONS

• Variability of measurement 

(same exact spot needs to be 

measured each time) 

• Embarrassing (bare skin)

• Skilled professional required



 22 

Anthropometry refers to the study of human body measurements. Thus, 

anthropometric indicators assess the size, shape, and composition of the human body and 

are commonly applied to examine healthiness in children (Gaya et al., 2017). Body mass 

index is calculated as the ratio of body weight in kilograms divided by height squared in 

metres (kg/m²) and is the most common anthropometric indicator applied to determine total 

body composition in children (Ajala et al., 2017; Nambiar, Truby, Abbott, & Davies, 2009; 

Weber, Leonard, & Zemel, 2012). However, BMI has some limitations as the results are 

based on body weight and not differentiated by fat mass and muscle mass. Additionally, 

BMI does not account for fat distribution, whereas alternative anthropometric indicators 

(WHR, WHtR, WC) do (Javed et al., 2015; Sardinha et al., 2016). Furthermore, there are no 

consistent validated anthropometric cutoff points and guidelines (CDC, 2015a; 

International Obesity Task Force, 2015; WHO, 2011) for classifying body size and weight 

status in preadolescent children (MOH, 2015a). Still, there are advantages associated with 

anthropometric indicators including their being time efficient, noninvasive, and practical 

for large paediatric epidemiological studies. Studies carried out on Australian and New 

Zealand children (aged 5 to 17 years; n = 3597; n = 172) found that BMI was not an 

appropriate measurement of body fatness in children and adolescents (Nambiar et al., 2009; 

Rush, Puniani, Valencia, Davies, & Plank, 2003). However, one of the studies found 

anthropometric indicators (WHtR, WC) to be dependable gauges of body fatness because 

they account for body fat distribution (Nambiar et al., 2009). Moreover, various studies 

have revealed that BMI (Abarca-Gómez et al., 2017; O. Ali et al., 2014; N. S. O. Jensen, 

Camargo, & Bergamaschi, 2016), WHR (Roy & Sharma, 2016; Seidell, 2010), WHtR 

(Kahn, Imperatore, & Cheng, 2005; Savva et al., 2000), and WC (N. S. O. Jensen et al., 

2016; Savva et al., 2000) have all been shown to be appropriate preliminary indicators of 



 23 

body fatness in children (Sardinha et al., 2016). Nevertheless, uncertainty still remains as to 

the significance of BMI when compared to other anthropometric indicators (MOH, 2015a; 

Sardinha et al., 2016). Overall, BMI is an indicator for body size whereas the other 

anthropometric indicators (WHR, WHtR, WC) are gauges of body fat distribution 

(Nambiar, Hughes, & Davies, 2010), and collectively these anthropometric measurements 

are a good estimate of body fatness in paediatric research (O. Ali et al., 2014; Hara, Saitou, 

Iwata, Okada, & Harada, 2002; N. S. O. Jensen et al., 2016; Nambiar et al., 2010). 

Bioelectrical impedance analysis is utilised to estimate body composition, 

particularly the amount of body fat in comparison to lean body mass (Rush et al., 2003). 

Initially, the instrument sends an electric signal through the body, which estimates the 

amount of fluid and tissue (Kushner, 1992; Kyle et al., 2004a). When compared, lean tissue 

is mostly water whereas fatty issue has minimal water; therefore, the flow resistance of the 

electric current is utilised to calculate body fat and lean body mass (Kyle et al., 2004a). A 

study review carried out for 10 years on Brazilian children (aged 7 to 10 years; n = 47,726) 

found that BIA is a reliable indicator of body fatness when more accurate techniques such 

as the DEXA scan are not feasible (N. S. O. Jensen et al., 2016). One major limitation in 

this review was that various BIA devices were utilised across the 27 different studies, 

therefore, there is no consistency in the results when attempting to determine which BIA 

device is more reliable when measuring body fat percentage in children (N. S. O. Jensen et 

al., 2016). An additional study review carried out on German, Austrian, and Swiss children 

and adolescents (aged 3 to 16 years; n = 3327) found that BIA and BMI were similar with 

regard to association and reliability of cardiometabolic risk factors in overweight children 

and adolescents; however, this same study also indicated that BIA is the more appropriate 

fatness indicator for predicting CVD risk in overweight or obese children into their 



 24 

adolescent years as compared to BMI (Bohn et al., 2015). Conversely, some BIA research 

has shown inconsistencies with degree of hydration, warmth of testing environment, and 

reliability in severely obese individuals (Hendel, Gotfredsen, Højgaard, Andersen, & 

Hilsted, 1996; Kyle et al., 2004b) Ultimately, BIA is an effective assessment of body 

fatness as it differentiates between body fat and lean body mass. However, BIA does not 

account for the effects of height and body proportion (Weber et al., 2012), whereas other 

anthropometric indicators are gauges of body fat distribution (Rush et al., 2003). Therefore, 

BIA assessment used in conjunction with additional anthropometric measurements 

provides a more comprehensive estimation of body fatness in paediatric research. 

Although implemented less frequently in large paediatric epidemiological studies, 

clinical methods such as skinfold callipers, DEXA scan, and hydrostatic weighing are the 

most accurate assessments of body fatness based on reliability and validity when compared 

to anthropometric indicators (N. S. O. Jensen et al., 2016). However, there are also 

limitations associated with these clinical methods including that they are costly, time-

consuming (offsite location), potentially embarrassing, and require a skilled professional. 

Additionally, they lack paediatric reference values and are impractical for large paediatric 

epidemiological field-based studies (Javed et al., 2015). In comparison, field-based 

assessments like BMI, WHR, WHtR, WC and BIA have shown evidence of effectiveness 

as pre-screening estimates of body fatness (N. S. O. Jensen et al., 2016; Sardinha et al., 

2016), and are practical for large paediatric epidemiological studies. 

Applied body fatness assessments. In this study, body fatness was assessed 

utilising: body fat (%), fat mass (kg), fat mass index (FMI, fat mass/m2), WHR, and BMI 

(kg/m2). To measure body fat (%) and fat mass (kg), BIA was utilised. To calculate the 

anthropometric indices (BMI and WHR), height, weight, WC, and hip circumference were 



 25 

measured using the WHO’s 2007 WC and WHR report (WHO, 2011). To calculate BMI, 

age and sex-specific BMI z scores were calculated using the WHO growth guidelines (De 

Onis et al., 2007; WHO, 2018a) and BMI values (overweight and nonoverweight) were 

categorised using the International Obesity Task Force’s (2015) sex and age-dependent 

cutoff points (Cole & Lobstein, 2012). For further information, see the data analysis 

sections in Chapters 4 and 5, sections 4.2 and 5.2, respectively.  

2.2 Physical Fitness  

2.2.1 Background  

The terms physical fitness, physical exercise, and physical activity are frequently 

used interchangeably in the field of health and wellness (Caspersen, Powell, & Christenson, 

1985). Physical activity is any bodily movement produced by skeletal muscles that leads to 

energy expenditure (Caspersen et al., 1985; Castillo Garzón, Ortega, & Ruiz, 2005). 

Physical exercise refers to physical activity or energetic movements that are planned, 

structured, efficient, and calculated in the sense that improving and/or sustaining physical 

fitness is the primary objective (Caspersen et al., 1985; Castillo Garzón et al., 2005). For 

further information on physical activity, see section 2.3.1 for background on physical 

[in]activity and sedentary behaviour. Physical fitness, on the other hand, is the ability to 

carry out tasks with vigour and alertness without undue fatigue to full capacity of 

physiological potential (Caspersen et al., 1985; Castillo Garzón et al., 2005). Physical 

fitness is reliant on cardiorespiratory and muscular fitness as both are significant 

components of physical fitness progression and sustainability (Ortega, Ruiz, Castillo, & 

Sjöström, 2008; Ruiz et al., 2016). Cardiorespiratory fitness is defined as the ability of the 

circulatory and respiratory systems to supply oxygen to exercising muscles during 

sustained physical activity (D. Lee, Artero, Sui, & Blair, 2010; Ruiz et al., 2016). Muscular 



 26 

fitness (strength and endurance) is the ability of the muscle to carry out exertion against 

resistance and to continue to perform physical movement without fatiguing (Froberg & 

Andersen, 2005; Ortega et al., 2008; J. J. Smith et al., 2014).  

Among adults, elevated levels of physical fitness and consistent engagement in 

physical activity have been shown to decrease the risk of developing cardiometabolic 

complications (LaMonte et al., 2005; Press, Freestone, & George, 2003; Schmidt, 

Magnussen, Rees, Dwyer, & Venn, 2016). In children, higher levels of physical fitness 

have been associated with healthier cardiometabolic profiles and labelled a significant 

marker of cardiometabolic functioning into adulthood (Cohen et al., 2011; Hamlin et al., 

2014; Howe et al., 2016; Leong et al., 2015; Stratton et al., 2007). Significant 

improvements in the physiological functioning of the body in response to consistent 

exercise and improved fitness levels include: increased HDL-C and insulin sensitivity, and 

reduced body weight, blood pressure, inflammation, and LDL-C levels (Lavie et al., 2015; 

Myers, 2003; D. L. Smith & Fernhall, 2011). For a complete list of the physiological 

benefits of physical fitness see Table 3 (Lavie et al., 2015; Myers, 2013). Ultimately, 

consistent physical activity and physical exercise have been shown to improve and sustain 

physical fitness levels, which contributes to multiple beneficial outcomes for the 

cardiovascular system, metabolism, and overall functioning of the body (LaMonte et al., 

2005; Press et al., 2003; Schmidt et al., 2016). 

Physical exercise produces a greater need to supply oxygen to the active tissues 

with the aim of maintaining the production of adenosine triphosphate required to support 

continued muscle contraction (Lavie et al., 2015; Myers, 2003; D. L. Smith & Fernhall, 

2011). Simultaneously, there is also the need to release built-up carbon dioxide created as a 

consequence of intensified cellular respiration (Lavie et al., 2015; Myers, 2003; D. L. 



 27 

Smith & Fernhall, 2011). In the initial cardiac response, consistent physical exercise 

increases whole body oxygen consumption in accordance with exercise intensity (Press et 

al., 2003; D. L. Smith & Fernhall, 2011). This upsurge in oxygen intake subsequently 

improves myocardial contraction and its electrical consistency, along with increasing stroke 

volume at rest and during exercise; this leads to a higher maximal cardiac output and 

oxygen extraction (Lavie et al., 2015; Myers, 2003; Press et al., 2003; D. L. Smith & 

Fernhall, 2011). These outcomes lead to significant improvements in the physiological 

functioning of the heart, which is evident both in a lower resting heart rate and at any given 

level of submaximal cardiac output (Lavie et al., 2015; Press et al., 2003).  

Table 3 

Physiological Benefits of Physical Fitness 

Decreases/Reductions  Increases/Improvements 

• Reduced blood pressure  

• Decreased myocardial oxygen demands 

• Reduced visceral adiposity 

• Reduced blood and plasma viscosity 

• Reduced systemic inflammation 

• Reduction in LDL-C 

• Reduction in TC 

• Reduction in glucose levels 

• Increased insulin sensitivity 

• Increased exercise tolerance 

• Improved heart rate variability 

• Improved endothelial function 

• Increased capillary density 

• Increased mitochondrial density 

• Improved sleep 

• Increased HDL-C 

• Maintenance of lean mass 

 

Regular cardiovascular workouts increase the HR; therefore, force of contraction 

increases while exercising, which enhances the heart’s ability to supply oxygen-rich blood 

to the muscles (D. L. Smith & Fernhall, 2011). Cardiac output is a product of stroke 

volume and HR, and stroke volume increases during exercise, which increases the rate of 

circulation (Lavie et al., 2015; D. L. Smith & Fernhall, 2011). In the course of exercising, 



 28 

there is a redistribution of cardiac output with a substantial percentage of output directed to 

the skin and skeletal muscles (80%) compared with being at rest (20%; Lavie et al., 2015; 

D. L. Smith & Fernhall, 2011). The percentage of cardiac output supplying the heart is the 

same; however, the overall amount of blood transported to the heart is greater during 

exercise compared to being at rest (Lavie et al., 2015; Myers, 2003; Press et al., 2003; D. L. 

Smith & Fernhall, 2011). As a result, during exercise, blood flow increases to the tissues 

that are extremely active and decreases to the tissues that are less active (e.g., visceral 

organs; Lavie et al., 2015; D. L. Smith & Fernhall, 2011). Consequently, this increased 

blood flow improves shear stress, and enhanced shear stress leads to increased nitric oxide 

production and bioavailability (Di Francescomarino, Sciartilli, Di Valerio, Di Baldassarre, 

& Gallina, 2009; Sherman, 2000). Additionally, regular physical activity and exercise 

lowers inflammatory factors such as plasma fibrinogen concentrations, c-reactive proteins, 

and white cell count (Di Francescomarino et al., 2009; Press et al., 2003; Sherman, 2000). 

For further information on the functioning of the endothelium and inflammatory responses, 

see section 2.0.1 for background on cardiometabolic health and section 2.1.1 on body 

fatness. 

Various metabolic adaptations occur in relation to exercise and post-exercise 

recovery, such as stimulation of lipid oxidation (Froberg & Andersen, 2005; Press et al., 

2003). Additionally, alterations in the transport of blood lipids include a higher ratio of 

HDL-C to LDL-C transferred and increased lipoprotein lipase activity (Froberg & 

Andersen, 2005; Press et al., 2003). The increase of this pancreatic enzyme enhances the 

usage of circulating triacylglycerol as energy, which increases the clearing of unwanted 

circulating lipids even at rest (Froberg & Andersen, 2005; Press et al., 2003). Furthermore, 

the activation of this enzyme also speeds up the conversion of the very-low-density protein 



 29 

to HDL-C (Froberg & Andersen, 2005; Press et al., 2003). For further information on 

lipoprotein activity, see section 2.4.1 for background on nutrition. 

Lastly, consistent exercise enhances sensitivity of the liver, skeletal muscle, and 

adipose tissue to the actions of insulin (Froberg & Andersen, 2005; Press et al., 2003). 

Subsequently, there are reductions in fasting insulin levels and the insulin response to 

glucose, which are associated with increases in the disposal rate for glucose (Froberg & 

Andersen, 2005; Press et al., 2003). For further information on insulin and glucose, see 

section 2.1.1 for background on body fatness and section 2.4.1 for background on nutrition.  

Physical fitness levels that do not meet the Cooper Institute (2014) FitnessGram® 

cutoff points for children (Howe et al., 2016; Zhu et al., 2010), along with insufficient daily 

physical activity per MOH (2015c) guidelines, sedentary behaviour (Cohen et al., 2011; 

Glynn, Emmett, & Rogers, 2005; Hjorth et al., 2013), unhealthy diet (Emmett & Jones, 

2015; Harrex et al., 2017; Howe et al., 2016), inadequate sleep (X. Chen, Beydoun, & 

Wang, 2008; Hjorth et al., 2013; Skidmore et al., 2013), and excessive body fatness 

(Bridger, 2009; Trandafir et al., 2017) have all been linked to cardiometabolic risk factors 

(Cohen et al., 2011; Hjorth et al., 2013; Stratton et al., 2007). However, uncertainty exists 

as to whether physical fitness or another lifestyle factor associates more strongly with 

cardiometabolic risk factors in preadolescent children.  

2.2.2 Evaluating Physical Fitness  

In preadolescent children, physical fitness (cardiorespiratory and muscular) is 

commonly assessed utilising V̇O₂max and muscular strength measuring methods. Frequently, 

V̇O₂max is estimated utilising the maximal multistage 20-metre shuttle run test (20-MST), 

whereas muscular strength is commonly assessed utilising a handgrip strength test. The 

following paragraphs elaborate on the indicated physical fitness assessment methods in 



 30 

addition to presenting evidence supporting the evaluating procedures applied in this study 

over other approaches.  

The most widely used indicator of cardiorespiratory fitness is maximal oxygen 

uptake (V̇O₂max; Noonan & Dean, 2000). V̇O₂max can be objectively assessed in a laboratory 

setting or by utilising field-based methods such as the 20-MST (Hamlin et al., 2014; 

Ramírez-Vélez, Silva-Moreno, et al., 2017). In a laboratory setting, V̇O₂max is assessed 

utilising a progressive run test or a cycle test to exhaustion (Hamlin et al., 2014; Ramírez-

Vélez, Silva-Moreno, et al., 2017). However, in preadolescent children there are several 

limitations associated with assessing V̇O₂max in a laboratory setting including cost, time, 

requirement for sophisticated equipment and trained technicians, and impracticality for 

large paediatric epidemiological studies. Alternatively, field-based tests like the 20-MST 

can be implemented to measure cardiorespiratory fitness (Hamlin et al., 2014).  

In children, the 20-MST is one of the most common field-based tests utilised to 

assess cardiorespiratory fitness, specifically V̇O₂max (Hamlin et al., 2014; Melo et al., 2011). 

The 20-MST consists of participants running between two lines set 20-metres apart with a 

beginning speed of 8.5 km/h-1, which increases by 0.5 km/h-1 at every completed level 

(Hamlin et al., 2014; Howe et al., 2016; Léger & Lambert, 1982). In accordance with the 

Cooper Institute (2014) FitnessGram® cutoff points, a “healthy cardiorespiratory fitness 

zone” is reported if girls achieve a V̇O₂max equal to or greater than 39 and boys achieve a 

V̇O₂max equal to or greater than 42 (Howe et al., 2016; Zhu et al., 2010). A V̇O₂max below 

those cutoff points is categorised as “needs improvement fitness zone” for both sexes 

(Cooper Institute, 2014; Howe et al., 2016; Zhu et al., 2010). A study carried out on New 

Zealand children (aged 8 to 13 years; n = 53) determined 20-MST is a consistent field-



 31 

based assessment of cardiorespiratory fitness (Hamlin et al., 2014). The most significant 

limitation associated with this study was all the participants were from one school and 

similar in age, body size, and socioeconomic status, therefore only representing a limited 

sample from the population of New Zealand children. Nonetheless, further studies have 

confirmed the 20-MST as a valid and reliable field-based test for cardiorespiratory fitness 

in children (Hamlin et al., 2014; Howe et al., 2016; Léger & Lambert, 1982), and the most 

commonly utilised cardiorespiratory fitness assessment method in large paediatric 

epidemiological studies (Ortega et al., 2008).  

A handgrip dynamometer is utilised to measure handgrip strength, which is an 

indicator of muscular fitness (Leong et al., 2015). The handgrip strength test involves being 

seated with shoulders adducted and neutrally rotated, elbow flexed to 90 degrees, and wrist 

in a neutral position. Participants squeeze the dynamometer handle as hard as possible for 

three or more seconds, providing a score for each hand. In accordance with the Camry 

EH101 manual, participants’ handgrip strength is characterised as weak, normal, or strong 

based on the sex-specific cutoff points. A study carried out on Columbian children and 

adolescents (aged 10 to 17 years; n = 1950) utilising a handgrip strength dynamometer 

found that muscular strength had a direct correlation with cardiometabolic risk (Ramírez-

Vélez, Peña-Ibagon, et al., 2017) However, in this study, the majority of the participants 

were teenagers. Teenagers will have larger hands as compared to preadolescents, and as 

hand size strongly influences handgrip strength results (Ramírez-Vélez, Peña-Ibagon, et al., 

2017), the results from this study are not comparable to a study where the participants are 

preadolescent children aged 8 to 10 years. Another study carried out on European 

adolescents (aged 12 to 17 years; n = 1053) utilising a handgrip strength dynamometer 

suggested that enhancing muscular strength could reduce cardiometabolic risk in children; 



 32 

however, hand size, sex, ethnicity, and maturity must be accounted for when analysing the 

beneficial effects muscular strength has on reducing cardiometabolic risk in children of 

different ages (Jimenéz-Pavón et al., 2011).  

Applied physical fitness assessments. The 20-MST and handgrip strength tests 

have been shown to be reliable indicators of cardiorespiratory fitness and muscular fitness. 

Additionally, the 20-MST and the handgrip strength test are easy to administer, time 

efficient, noninvasive, popular in school settings, and practical for large paediatric 

epidemiological studies. Therefore, in this study, cardiorespiratory fitness was measured 

utilising the 20-MST and muscular fitness was measured utilising the handgrip strength 

test. For further information, see the data analysis sections in Chapters 4 and 5, sections 4.2 

and 5.2, respectively. 

2.3 Physical [In]activity and Sedentary Behaviour 

2.3.1 Background 

 More than 80% of the world's adolescent population is not sufficiently active 

(WHO, 2017d, 2018b). Furthermore, 1.6 million deaths annually can be attributed to 

insufficient physical activity (WHO, 2017d, 2018b). Over the last four decades, 

accumulated evidence indicates physical inactivity and/or sedentary behaviour in 

individuals considerably increases the risk of developing coronary heart disease when 

compared to individuals who have lived an active lifestyle (Da Silva et al., 2018; Ding et 

al., 2016; I. M. Lee et al., 2012; Liu & Manson, 2001; Pate et al., 1995; Press et al., 2003; 

Warren et al., 2010). Physical inactivity is one of the major modifiable risk factors of 

coronary heart disease (WHO, 2017d, 2018b).  

In the past, the terms physical inactivity and sedentary behaviour have been used 

interchangeably. However, being physically inactive signifies not meeting daily physical 



 33 

activity guidelines, whereas being sedentary refers to very low energy expenditure 

behaviour for an extended period of time (González, Fuentes, & Márquez, 2017; Van der 

Ploeg & Hillsdon, 2017). Thus, physical inactivity and sedentary behaviour are dissimilar 

concepts with different definitions, each with its specific health hazards, and therefore need 

to be addressed independently (González et al., 2017; Van der Ploeg & Hillsdon, 2017).  

Physical [in]activity physiological pathways to CVD. The MOH (2015c) physical 

activity guidelines for children and young people aged 5 to 17 years state that an 

accumulation of at least 1 hour a day of moderate-to-vigorous physical activity, with 

incorporation of vigorous-intensity activities and muscle and bone strengthening activities 

at least 3 days a week, is the minimal requirement. Physical activity is characterised by the 

United States’ National Institutes of Health (2016) as any bodily movement produced by 

skeletal muscle resulting in an increased energy expenditure, including aerobic activities 

(e.g., bicycling and dancing), muscle strengthening activities (e.g., gymnastics and push-

ups), bone strengthening activities (e.g., jumping rope and skipping), and stretching (e.g., 

daily stretching and yoga; Caspersen et al., 1985; González et al., 2017). To determine the 

intensity of physical activity the metabolic equivalent method can be applied. Therefore, 

physical activity intensities are categorised as light-intensity (< 3 metabolic equivalents), 

moderate-intensity (3-6 metabolic equivalents), and vigorous-intensity (> 6 metabolic 

equivalents; Caspersen et al., 1985; González et al., 2017; Sheldrick, Tyler, Mackintosh, & 

Stratton, 2018).  

In children, moderate-to-vigorous physical activity has been shown to have a 

positive effect on multiple systems (Faigenbaum & Myer, 2012; Lieberman, 2013; Stoner, 

Matheson, Hamlin, & Skidmore, 2016). This includes the neuromuscular (promoting 

skeletal muscle fibre growth), musculoskeletal (straightening and thickening bones), and 



 34 

cardiovascular systems (the arterial system becoming more elastic; Faigenbaum & Myer, 

2012; Lieberman, 2013; Stoner, Matheson, et al., 2016). Moreover, adequate levels of 

physical activity have been shown to have a positive association with cognition, which is 

linked to motor skills and locomotor movements such as walking, running, throwing, 

lifting, skipping, etc. (Chaddock-Heyman et al., 2013; Sibley & Etnier, 2003). Cognitive 

growth, along with motor skill and movement development, sets the foundation for being 

active and exercising to become fit and sustain a healthy fitness level into adulthood 

(Chaddock-Heyman et al., 2013; Sibley & Etnier, 2003). In adults, moderate-to-vigorous 

physical activity has been shown to improve physical fitness levels and has a direct effect 

on peak physical fitness levels (Booth, Roberts, & Laye, 2012; Nocon et al., 2008; Press et 

al., 2003). Furthermore, sustained high levels of physical fitness have multiple positive 

effects on cardiometabolic conditions such as hypertension, hyperglycaemia, 

hyperlipidaemia, and obesity (Booth et al., 2012; Nocon et al., 2008; Press et al., 2003). For 

further information on physiological benefits of physical fitness, see section 2.2.1 for 

background on physical fitness. Overall, moderate-to-vigorous physical activity is 

associated with multiple progressive cardiometabolic outcomes (González et al., 2017; Van 

der Ploeg & Hillsdon, 2017). In contrast, light-intensity physical activity (<3 metabolic 

equivalents) has not been shown to improve physical fitness levels (González et al., 2017; 

Van der Ploeg & Hillsdon, 2017). 

Physical inactivity is defined as achieving less than the recommended amount of 

moderate and vigorous activity on a daily basis per physical activity guidelines (MOH, 

2015c). There are detrimental consequences for not meeting the daily physical activity 

recommendations. In children, not meeting the daily moderate-to-vigorous activity 

requirements could result in detrimental outcomes for biological development of cognitive 



 35 

functioning and the neuromuscular, musculoskeletal, and cardiovascular systems 

(Faigenbaum & Myer, 2012; Lieberman, 2013; Sibley & Etnier, 2003). In adults, physical 

inactivity has been shown to have a direct correlation with type 2 diabetes and obesity 

(González et al., 2017; Van der Ploeg & Hillsdon, 2017). Obesity also has been shown to 

have positive correlations with additional cardiometabolic complications such as 

hypertension, hyperglycaemia, and hyperlipidaemia (Booth et al., 2012; Nocon et al., 2008; 

Press et al., 2003). For further information on physiological effects of obesity, see section 

2.1.1 for background on body fatness. Clearly, physical inactivity is a determinant for 

overall health and wellness (González et al., 2017; Van der Ploeg & Hillsdon, 2017). 

Furthermore, recent evidence has shown that both physical inactivity and sedentary 

behaviour contribute to the global burden of chronic diseases (González et al., 2017; Van 

der Ploeg & Hillsdon, 2017). Therefore, although physical inactivity and sedentary 

behaviour are dissimilar concepts, both need to be addressed; however, this should be done 

independent of the other (González et al., 2017; Van der Ploeg & Hillsdon, 2017).  

Sedentary behaviour physiological pathways to CVD. Countries like New 

Zealand and Canada have characterised sedentary behaviour as a major risk factor of CVD 

in addition to developing and implementing sedentary lifestyle recommendations 

(Canadian Society for Exercise Physiology [CSEP], n.d.; MOH 2015a, 2015c, 2017; 

Tremblay et al., 2011). The recommendations state that school-aged and young people 

(aged 5 to 17 years) should not have more than 2 hours per day of recreational screen time 

and should limit sitting for extended periods (CSEP, n.d.; MOH 2015a, 2015c, 2017; 

Tremblay et al., 2011). Sedentary behaviour is characterised as any waking behaviour 

characterised by an energy expenditure below or equal to 1.5 metabolic equivalents for 

long periods of time including: sitting, lying, reading, doing homework, playing video 



 36 

games, or operating an electronic device while stationary (MOH, 2015a; Van der Ploeg & 

Hillsdon, 2017). At present in children there is minimal re