Unraveling the relationship code in ISCOLE Portuguese children physical activity and sedentariness levels and patterns and obesity.

Unravelling the relationship code in ISCOLE Portuguese children physical activity and sedentariness levels and patterns and obesity Thayse Natacha Queiroz Ferreira Gomes 2015 Unravelling the relationship code in ISCOLE Portuguese children physical activity and sedentariness levels and patterns and obesity Dissertation written in order to achieve the PhD degree in Sports Science included in the doctoral course in Sports Science designed by the Center of Research, Education, Innovation and Intervention in Sport (CIFI2D), Faculty of Sport, University of Porto (Decree-Law nº 74/2006, March 24th), supervised by Professor Dr. José António Ribeiro Maia and cosupervised by Professor Dr. Peter Todd Katzmarzyk. Thayse Natacha Queiroz Ferreira Gomes Porto, June 2015 Cataloging Thayse Natacha Queiroz Ferreira Gomes IV CATALOGING Gomes, T.N.Q.F. (2015). Unravelling the relationship code in ISCOLE Portuguese children physical activity and sedentariness levels and patterns and obesity. Porto: Doctoral thesis in Sports Science. Faculty of Sport, University of Porto. Keywords: PHYSICAL ACTIVITY, SEDENTARINESS, OBESITY, CHILDREN, PORTUGAL, ISCOLE. Dedication Thayse Natacha Queiroz Ferreira Gomes V DEDICATION To mummy, Thacy and Polly… For your endless love, support and encouragement… For you I am where I am, for you I continue aim higher... I am because you are! To my dear friend Nanda... For being “my person”… Acknowledgements Thayse Natacha Queiroz Ferreira Gomes VII ACKNOWLEDGEMENTS A elaboração desta tese representa a conclusão de mais uma etapa em minha vida académica, e é com uma vaidade particular que vislumbro o fecho deste ciclo. Ao longo deste caminhar, sempre tive a certeza de que não caminhava sozinha... muitos foram aqueles, pessoas e instituições, que “caminharam” ao meu lado, deram forças para continuar, que me acolheram com carinho... Alguns estão ao meu lado desde o princípio desta jornada, outros conheci durante o percurso, mas todos contribuíram para a exequibilidade deste trabalho. Portanto, às pessoas e instituições que deramme suporte para concluir este trabalho, deixo meu sincero agradecimento, não obstante saiba que palavras não são suficientes e não expressarão toda a minha gratidão. E justamente por saber que não conseguirei expressar tudo o que gostaria, “abro parênteses” nesta tese, e faço a escrita destes agradecimentos em Português... minha língua materna, e onde sinto-me mais “confortável” para melhor exprimir o que sinto. Ao Professor Doutor José António Ribeiro Maia, orientador desta tese, agradeço por ter confiado na rapariga “ousada” que, um certo dia, apareceu em seu gabinete com sua “amiguinha do peito” e, como bem costuma referir, mal começara o Mestrado, já pensava no Doutoramento. Muito obrigada, Professor! Obrigada pela “prenda” oferecida na possibilidade de enveredar por este projeto que abracei “como meu” (ISCOLE). Obrigada pelas tantas horas despendidas a corrigir meus rascunhos, que por vezes julgava “prontos”. Obrigada por mostrar-me que, às vezes, o “simples” é o mais bonito...mas também por me permitir descobrir a beleza de desvendar o “complicado”. Obrigada pelo rigor académico-científico que mostrou-me, e mostra, em tudo o que faz. Obrigada por me tirar, tantas vezes, da minha “zona de conforto” e provocar-me a ir “mais longe, além, voar mais alto”. Obrigada pelas tantas “lições”, ensinamentos, não apenas académicos, mas pessoais. Obrigada pelo cuidado, pelo zelo...por sempre iniciar nossas conversas com uma simples, mas para mim relevante, pergunta: “está tudo bem contigo?”. Obrigada pelo Acknowledgements Thayse Natacha Queiroz Ferreira Gomes VIII silêncio que tantas vezes fez-me refletir... e por respeitar o meu silêncio tantas outras vezes. Obrigada por, não sendo “meu pai, ou meu amigo”, sempre preocupou-se como se tais fosse. Obrigada pela partilha, de conhecimento, do seu tempo (e quanto tempo!), do seu espaço... Obrigada por mostrar-me novos “mundos”, novas possibilidades... e por fazer-me ver, com “novos olhos”, o que, aparentemente, já tão bem conhecíamos... Levo comigo, tudo o que me ensinou, tudo o que, gentilmente, partilhou... e tentarei não esquecer de manter, sempre, o zanshin! Ao Professor Doutor Peter Katzmarzyk, co-orientador desta tese. Mesmo muito ocupado, mostrou-se sempre presente, encontrando tempo para correções e sugestões tão relevantes para melhorar a qualidade do trabalho. Obrigada pela partilha de conhecimentos e ideias, pelas conversas que tantas vezes deixaram “mais claro” o caminho a seguir, pelo rigor científico com que trabalha, pela oportunidade de visita ao Pennington (onde a “magia” acontece)... e pelos momentos informais, onde partilhou muito mais que “ciência”. Muito obrigada! À Faculdade de Desporto da Universidade do Porto (FADE-UP), na pessoa do respectivo Presidente do Conselho Diretivo, Professor Doutor Jorge Olímpio Bento, pelo acolhimento e apoio institucional, iniciado no Mestrado e que perdurou durante todo o Doutoramento. Estendo meus agradecimentos aos professores da FADE-UP que contribuíram, ao longo desta jornada, para minha formação acadêmica. Ao Professor Doutor António Manuel Fonseca, diretor do Programa Doutoral em Ciências do Desporto e Presidente do Conselho Científico da FADE-UP, agradeço todo o apoio ao longo destes anos de Doutoramento. Não foram poucas as vezes que precisei recorrer à sua ajuda, e em todas elas sempre respondeu com prontidão. Ao Pennington Biomedical Research Center, centro de investigação responsável pela coordenação do ISCOLE. Agradeço a toda a equipa pela forma sempre atenciosa, preocupada e rigorosa com que todos os nossos pedidos e questões foram tratados. O vosso suporte foi imprescindível para Acknowledgements Thayse Natacha Queiroz Ferreira Gomes IX que esta tese fosse realizada...afinal, não foram poucos os pedidos de bases de dados nos mais diferentes “formatos” e “estruturas”. Um agradecimento especial ao Tiago Barreira e Emily Mire pela ajuda a tratar/interpretar as informações da acelerometria; à Denise Lambert, sempre atenciosa às nossas solicitações e com uma boa disposição contagiante; e à Catherine Champagne, pela imprescindível ajuda na edição/correção do inglês. Ao Professor Doutor Gaston Beunen (in memorian), pela partilha de conhecimento e experiência. Sempre gentil, sempre atencioso...sempre com um sorriso. Ao Professor Doutor Donald Hedeker, pela colaboração nesta tese. Sempre muito atencioso, encontrou tempo para correções e sanar nossas (muitas) dúvidas. Esteve sempre disponível para atender nossos pedidos e nos dar “alguma luz”, quando por vezes nos encontrávamos perdidos. Que este tenha sido apenas o começo de uma parceria! Ao Professor Doutor Duarte Freitas, pelas aulas e conhecimentos partilhados ao longo destes anos, pela forma cuidada com que me recebeu em “sua escola”, pela oportunidade proporcionada em conhecer a Madeira e auxiliar em um dos vários projetos de investigação que pretende lá implementar. Que esta sua paixão pela investigação perdure! Ao Professor Doutor António Prista, pelo cuidado demonstrado a esse trabalho e a todos os outros desenvolvidos pelo Laboratório de Cineantropometria, bem como pela parceira com o nosso Laboratório que permitiu/permite a partilha de experiências bem como o envolvimento em projetos. Agradeço, particularmente, a oportunidade de ir a Maputo... uma experiência ímpar, que levarei sempre comigo, dado sua relevância para meu crescimento pessoal. Kanimambo! Ao Professor Doutor Rui Garganta, meu agradecimento pelo zelo, apoio e boa disposição. Foram muitos os momentos descontraídos, partilhados ao longo destes anos, no convívio quase que diário no Laboratório... e como souberam bem! Que sua boa disposição nunca o abandone, e que possa continuar a tratar de “coisas sérias” de forma tão descontraída e bem Acknowledgements Thayse Natacha Queiroz Ferreira Gomes XVI preocupação e cuidado foram sempre relevantes, sempre muito bem recebidos e sentidos. Vocês são especiais! Ao meu pai, Jailton Gomes... sei que mesmo “distante”, estás sempre a torcer por mim! À minha amada “voinha” (in memorian)... tão cedo partiste, mas deixaste em mim, muito de ti! Obrigada por todo teu amor, pela confiança, pela forma terna e direta com que agia... Foste a matriarca da família, e não foi fácil sê-lo! Obrigada pelas “visitas” constantes em terras lusitanas... estiveste sempre a olhar por mim! À “minha” Nandinha, àquela que foi muito mais que minha amiga... foi minha irmã, minha mãe, minha conselheira, minha família! Ter-te ao meu lado ao logo desta jornada tornou-a, sem dúvidas, mais doce, mais leve, mais colorida. Foste o ombro onde repousei minha cabeça e chorei; o abraço doce e envolvente que tantas vezes me acalmou; o sorriso amplo e sincero que me iluminou; a mão carinhosa e gentil que tantas vezes me auxiliou no caminhar; a palavra proferida que me incentivou a continuar ou que me corrigiu; o olhar orgulhoso que tantas vezes me surpreendeu; o silêncio que “falava” mais do que qualquer palavra... o fazer-se presente, mesmo quando entre nós um oceano existe a separar. Obrigada por teres estado comigo; por teres caminhado ao meu lado; por me permitir fazer parte da tua vida e por ser parte da minha... Este trabalho tem muito de ti! Não foram poucas as conversas e discussões sobre ele; não foram poucas minhas inquietações e questionamentos; não foram poucos os receios... mas estavas sempre lá, à disposição para me ouvir, para refletir junto comigo, para “pesquisar”, para aconselhar... Obrigada pelo teu apoio incondicional; pela tua confiança; por acreditar, sempre, na concretização deste trabalho... pelo amor que sempre me dedicaste, mesmo conhecendo tão bem meus defeitos! Carrego em mim muito de ti... Sou uma pessoa melhor porque tenho a ti! Às “mulheres da minha vida”, minha “mainha”, Thacy e Polly... que exemplos que são para mim! Obrigada por todo o amor incondicional que a mim dedicam; por todo o apoio e suporte; pelos sacrifícios que fizeram para Acknowledgements Thayse Natacha Queiroz Ferreira Gomes XVII que eu iniciasse um sonho há muito idealizado; por respeitarem minhas decisões, meu “silêncio”... por serem minha base, meu pilar. Ter-vos ao meu lado dá-me coragem para seguir em frente e não desistir! Peço desculpas pela minha ausência (que bem sei, é sentida!); por ter faltado a tantos momentos especiais... mas estejam certas que sempre estive convosco, em pensamento, em coração... afinal, para estar “junto” nem sempre faz-se necessário estar perto... e eu estou sempre junto a vós! Carrego-vos sempre comigo...! Tenho em mim, muito de todas três...! Se sou o que sou, é porque tenho a vós! Amovos! Table of Contents Thayse Natacha Queiroz Ferreira Gomes XIX TABLE OF CONTENTS DEDICATION ................................................................................................................................ V ACKNOWLEDGEMENTS ........................................................................................................... VII TABLE OF CONTENTS ............................................................................................................ XIX LIST OF TABLES ................................................................................................................... XXIII LIST OF FIGURES ................................................................................................................... XXV RESUMO ................................................................................................................................ XXVII ABSTRACT ............................................................................................................................. XXIX LIST OF SYMBOLS AND ABBREVIATIONS ........................................................................ XXXI CHAPTER I – GENERAL INTRODUCTION AND THESIS OUTLINE ......................................... 1 GENERAL INTRODUCTION ..................................................................................................... 3 THESIS OUTLINE ................................................................................................................... 13 REFERENCES ........................................................................................................................ 16 CHAPTER II – STUDY SAMPLE AND METHODS .................................................................... 29 STUDY SAMPLE AND METHODS .......................................................................................... 31 THE INTERNATIONAL STUDY OF CHILDHOOD OBESITY, LIFESTYLE AND THE ENVIRONMENT – ISCOLE ................................................................................................................................................. 31 ISCOLE – Portugal .............................................................................................................. 33 SAMPLE..................................................................................................................................... 34 PROCEDURES ............................................................................................................................ 35 Anthropometry and body composition ................................................................................. 35 Biological maturation ........................................................................................................... 36 Objective measured physical activity, sedentariness and sleep time ................................. 37 Diet and lifestyle information ............................................................................................... 38 Parental questionnaires....................................................................................................... 39 School environment ............................................................................................................. 40 Physical fitness .................................................................................................................... 41 Metabolic risk indicators ...................................................................................................... 42 Data management and control ............................................................................................ 43 Statistical analysis ............................................................................................................... 44 REFERENCES ........................................................................................................................ 45 CHAPTER III – RESEARCH PAPERS ....................................................................................... 49 PAPER I - CORRELATES OF SEDENTARY TIME IN CHILDREN: A MULTILEVEL MODELLING APPROACH .. 51 ABSTRACT ......................................................................................................................... 53 BACKGROUND ................................................................................................................... 55 METHODS .......................................................................................................................... 56 Sample ........................................................................................................................................... 56 Anthropometry ............................................................................................................................... 57 Family data .................................................................................................................................... 58 Sleep and sedentary time .............................................................................................................. 58 School environment ....................................................................................................................... 59 Data analysis ................................................................................................................................. 59 RESULTS ............................................................................................................................ 60 Table of Contents Thayse Natacha Queiroz Ferreira Gomes XX DISCUSSION ...................................................................................................................... 66 CONCLUSIONS .................................................................................................................. 70 ABBREVIATIONS ............................................................................................................... 70 COMPETING INTERESTS ................................................................................................. 71 AUTHOR CONTRIBUTIONS .............................................................................................. 71 ACKNOWLEDGEMENTS ................................................................................................... 71 REFERENCES .................................................................................................................... 72 PAPER II – WHY ARE CHILDREN DIFFERENT IN THEIR DAILY SEDENTARINESS? AN APPROACH BASED ON THE MIXED-EFFECTS LOCATION SCALE MODEL ........................................................................ 79 ABSTRACT ......................................................................................................................... 81 INTRODUCTION ................................................................................................................. 83 METHODS .......................................................................................................................... 84 Sample ........................................................................................................................................... 84 Anthropometry ............................................................................................................................... 85 Family data .................................................................................................................................... 86 Sedentary time and sedentary behaviour ...................................................................................... 86 Biological maturation...................................................................................................................... 87 Data analysis ................................................................................................................................. 87 RESULTS ............................................................................................................................ 88 DISCUSSION ...................................................................................................................... 93 CONCLUSIONS .................................................................................................................. 98 ACKNOWLEDGMENTS ...................................................................................................... 99 REFERENCES .................................................................................................................. 100 PAPER III – OVERWEIGHT AND OBESITY IN PORTUGUESE CHILDREN: PREVALENCE AND CORRELATES ............................................................................................................................................... 105 ABSTRACT ....................................................................................................................... 107 INTRODUCTION ............................................................................................................... 109 METHODS ........................................................................................................................ 110 Part I: Meta-analysis of obesity prevalence among Portuguese children ......................... 110 Part II: Correlates of childhood overweight and obesity .................................................... 111 Sample ......................................................................................................................................... 111 Anthropometry ............................................................................................................................. 112 Family data .................................................................................................................................. 112 Biological maturity ........................................................................................................................ 113 Nutritional and behavioural habits ................................................................................................ 113 Physical activity, sedentary time and sleep .................................................................................. 114 School environment ..................................................................................................................... 114 Statistical analysis ....................................................................................................................... 115 RESULTS .......................................................................................................................... 116 Prevalence of overweight/obesity among 9–11 year-old Portuguese children ............................. 116 Biological, behavioural and socio-demographic differences between normal-weight and overweight/obese children ........................................................................................................... 118 Individualand school-level correlates of BMI variation ............................................................... 119 DISCUSSION .................................................................................................................... 120 Prevalence of overweight/obesity in 9–11 year-old Portuguese children ..................................... 120 Biological, behavioural and socio-demographic differences between normal-weight and overweight/obese children ........................................................................................................... 123 Individualand school-level correlates of BMI variation ............................................................... 125 CONCLUSIONS ................................................................................................................ 128 ACKNOWLEDGMENTS .................................................................................................... 128 AUTHOR CONTRIBUTIONS ............................................................................................ 129 CONFLICTS OF INTEREST ............................................................................................. 129 REFERENCES .................................................................................................................. 130 Table of Contents Thayse Natacha Queiroz Ferreira Gomes XXI PAPER IV – “FAT-BUT-ACTIVE”: DOES PHYSICAL ACTIVITY PLAY A SIGNIFICANT ROLE IN METABOLIC SYNDROME RISK AMONG CHILDREN OF DIFFERENT BMI CATEGORIES? ........................................ 139 ABSTRACT ....................................................................................................................... 141 INTRODUCTION ............................................................................................................... 143 METHODS ........................................................................................................................ 144 Sample ......................................................................................................................................... 144 Anthropometry ............................................................................................................................. 145 Physical activity ........................................................................................................................... 145 Biological maturity ........................................................................................................................ 146 Metabolic syndrome ..................................................................................................................... 146 Data analysis ............................................................................................................................... 147 RESULTS .......................................................................................................................... 148 DISCUSSION .................................................................................................................... 149 CONCLUSIONS ................................................................................................................ 153 ACKNOWLEDGEMENTS ................................................................................................. 154 REFERENCES .................................................................................................................. 155 PAPER V – “ACTIVE AND STRONG”: PHYSICAL ACTIVITY, STRENGTH AND METABOLIC RISK IN CHILDREN ............................................................................................................................................... 161 ABSTRACT ....................................................................................................................... 163 BACKGROUND ................................................................................................................. 165 METHODS ........................................................................................................................ 166 Participants .................................................................................................................................. 166 Procedures .................................................................................................................................. 166 Anthropometry. ........................................................................................................................ 166 Physical activity ....................................................................................................................... 166 Static muscular strength .......................................................................................................... 167 Biological maturation ............................................................................................................... 167 Metabolic risk .......................................................................................................................... 167 Data analysis ............................................................................................................................... 168 RESULTS .......................................................................................................................... 168 DISCUSSION .................................................................................................................... 171 Limitations .................................................................................................................................... 172 Conclusion ................................................................................................................................... 173 IMPLICATIONS FOR SCHOOL HEALTH ......................................................................... 173 ACKNOWLEDGEMENTS ................................................................................................. 174 REFERENCES .................................................................................................................. 175 PAPER VI – RELATIONSHIP BETWEEN SEDENTARINESS AND MODERATE-TO-VIGOROUS PHYSICAL ACTIVITY IN YOUTH. A MULTIVARIATE MULTILEVEL STUDY ............................................................ 181 ABSTRACT ....................................................................................................................... 183 INTRODUCTION ............................................................................................................... 185 METHODS ........................................................................................................................ 186 Sample ......................................................................................................................................... 186 Outcome variables ....................................................................................................................... 187 Predictor variables ....................................................................................................................... 187 Child level ................................................................................................................................ 187 Anthropometry..................................................................................................................... 187 Biological maturation ........................................................................................................... 188 Sleep time ........................................................................................................................... 188 Family characteristics .......................................................................................................... 188 School level ............................................................................................................................. 189 Data analysis ............................................................................................................................... 189 RESULTS .......................................................................................................................... 190 DISCUSSION .................................................................................................................... 194 Table of Contents Thayse Natacha Queiroz Ferreira Gomes XXII PERSPECTIVE ................................................................................................................. 200 ACKNOWLEDGMENTS .................................................................................................... 200 REFERENCES .................................................................................................................. 201 PAPER VII – ARE BMI AND SEDENTARINESS CORRELATED? A MULTILEVEL STUDY IN CHILDREN ... 207 ABSTRACT ....................................................................................................................... 209 INTRODUCTION ............................................................................................................... 211 METHODS ........................................................................................................................ 212 Sample ......................................................................................................................................... 212 Outcome variables ....................................................................................................................... 212 Predictor variables ....................................................................................................................... 213 Child level ................................................................................................................................ 213 School level ............................................................................................................................. 214 Data analysis ............................................................................................................................... 214 RESULTS .......................................................................................................................... 215 DISCUSSION .................................................................................................................... 219 CONCLUSIONS ................................................................................................................ 222 ACKNOWLEDGEMENTS ................................................................................................. 222 REFERENCES .................................................................................................................. 224 CHAPTER IV – GENERAL OVERVIEW AND CONCLUSIONS .............................................. 231 GENERAL OVERVIEW ......................................................................................................... 233 LIMITATIONS ........................................................................................................................ 243 IMPLICATIONS AND OPPORTUNITIES FOR FUTURE RESEARCHES ............................. 245 Implications ....................................................................................................................... 245 Opportunities for future researches ................................................................................... 247 CONCLUSIONS .................................................................................................................... 249 REFERENCES ...................................................................................................................... 251 List of Tables Thayse Natacha Queiroz Ferreira Gomes XXIII LIST OF TABLES CHAPTER I – GENERAL INTRODUCTION AND THESIS OUTLINE Table 1. Thesis outline ........................................................................................................ 13 CHAPTER III – RESEARCH PAPERS PAPER I - CORRELATES OF SEDENTARY TIME IN CHILDREN: A MULTILEVEL MODELLING APPROACH Table 1. Descriptive statistics for variables at the child level (level-1) ............................... 61 Table 2. Descriptive statistics for variables at the school level (level-2) ............................ 61 Table 3. Results summary of hierarchical linear modelling for all sample: estimates, standard-errors, and p-values ............................................................................................. 63 Table 4. Summary of results of final model for two BMI groups (normal-weight and overweight/obese groups): estimates (standard-errors), and p-values .............................. 65 PAPER II - WHY ARE CHILDREN DIFFERENT IN THEIR DAILY SEDENTARINESS? AN APPROACH BASED ON THE MIXED-EFFECTS LOCATION SCALE MODEL Table 1. Descriptive characteristics of children ................................................................. 89 Table 2. Mean±standard deviation for daily sedentary time (hours·day-1) for boys and girls ............................................................................................................................................. 89 Table 3. Parameter estimates (±standard errors) of the four models ................................. 96 PAPER III - OVERWEIGHT AND OBESITY IN PORTUGUESE CHILDREN: PREVALENCE AND CORRELATES Table 1. Summary of overweight/obesity prevalence in 9–11 year-old Portuguese children used in the meta-analysis.................................................................................................. 117 Table 2. Biological, behavioural and socio-demographic trait differences between normalweight and overweight/obese children ............................................................................. 119 Table 3. Multilevel modelling results: regression estimates (β), standard-errors (SE), and p-values for children and school characteristics influencing BMI variation ...................... 122 PAPER IV - “FAT-BUT-ACTIVE”: DOES PHYSICAL ACTIVITY PLAY A SIGNIFICANT ROLE IN METABOLIC SYNDROME RISK AMONG CHILDREN OF DIFFERENT BMI CATEGORIES? Table 1. Descriptive statistics ........................................................................................... 148 Table 2. Differences in metabolic risk indicators and zMS across BMI-physical activity groups, controlling for sex and biological maturity ............................................................ 151 PAPER V - “ACTIVE AND STRONG”: PHYSICAL ACTIVITY, STRENGTH AND METABOLIC RISK IN CHILDREN Table 1. Descriptive statistics (means±standard deviation or percentage) ...................... 169 List of Tables Thayse Natacha Queiroz Ferreira Gomes XXIV Table 2. Differences in MR Indicators and zMR across PA-MS groups (mean±standard error) .................................................................................................................................. 170 PAPER VI - RELATIONSHIP BETWEEN SEDENTARINESS AND MODERATE-TO-VIGOROUS PHYSICAL ACTIVITY IN YOUTH. A MULTIVARIATE MULTILEVEL STUDY Table 1. Descriptive statistics for variables at the child level (level 1) .............................. 191 Table 2. Descriptive statistics for variables at the school level (level 2) ........................... 192 Table 3. Model 1 main results [parameter estimates, standard errors (SE) and deviance] for both Sed and MVPA..................................................................................................... 192 Table 4. Model 2 [parameter estimates, standard errors (SE) and deviance] including childlevel predictors for both Sed and MVPA .......................................................................... 193 Table 5. Model 3 [parameter estimates, standard errors (SE) and deviance] including childand school-level predictors for both Sed and MVPA ........................................................ 194 PAPER VII - ARE BMI AND SEDENTARINESS CORRELATED? A MULTILEVEL STUDY IN CHILDREN Table 1. Descriptive statistics for variables at the child and school level (level 1) ........... 217 Table 2. Null model main results [parameter estimates, standard errors (SE) and deviance] for both Sed and BMI ........................................................................................................ 217 Table 3. Results summary of modelling Sed and BMI: estimates¥(standard-errors) ........ 218 CHAPTER IV – GENERAL OVERVIEW AND CONCLUSIONS Table 1. Summary of the main conclusions of the papers ................................................ 233 List of Figures Thayse Natacha Queiroz Ferreira Gomes XXV LIST OF FIGURES CHAPTER I - GENERAL INTRODUCTION AND THESIS OUTLINE Figure 1. Ecological Model of Four Domains of Active Living, adapted from Sallis et al. (2006) .................................................................................................................................... 6 CHAPTER III – RESEARCH PAPERS PAPER II - WHY ARE CHILDREN DIFFERENT IN THEIR DAILY SEDENTARINESS? AN APPROACH BASED ON THE MIXED-EFFECTS LOCATION SCALE MODEL Fig 1. Time spent in sedentary behaviour over a week, for boys ...................................... 90 Fig 2. Time spent in sedentary behaviour over a week, for girls ........................................ 90 Fig 3. WS differences in sedentariness along a whole week, for 2 boys (up) and two girls (down), with same mean sedentariness time across the week .......................................... 91 PAPER III - OVERWEIGHT AND OBESITY IN PORTUGUESE CHILDREN: PREVALENCE AND CORRELATES Figure 1. Flow diagram of study selection for meta-analysis .......................................... 117 Figure 2. Meta-analysis results for boys, girls and both sexes combined ........................ 118 PAPER VI - RELATIONSHIP BETWEEN SEDENTARINESS AND MODERATE-TO-VIGOROUS PHYSICAL ACTIVITY IN YOUTH. A MULTIVARIATE MULTILEVEL STUDY Fig 1. Multivariate multilevel structure of outcome variables (Sed and MVPA) at level 1, nested within children at level 2, nested within schools at level 3 .................................... 190 List of Symbols and Abbreviations Thayse Natacha Queiroz Ferreira Gomes XXXII MIXREGLS Mixed-effects regression with location scale program mm Millimetre mmHg Millimetres of mercury MR Metabolic risk MS Metabolic Syndrome MS Muscular Strength MVPA Moderate-to-vigorous physical activity NA Normal-weight and physically active group NI Normal-weight and inactive group NW Normal-weight O/O Overweight/obese OA Overweight and physically active group OI Overweight and inactive group P50 50th percentile PA Physical activity PF Physical fitness PHV Peak height velocity SB Sedentary behaviour SBP Systolic blood pressure SD/std dev Standard deviation SE Standard error SED Sedentariness SES Socioeconomic status SPSS Statistical Package for the Social Sciences ST Sedentary time TRI Triglycerides WC Waist circumference WHO World Health Organization WINPEPI Programs for Epidemiologists for Windows WS Within-subject zMR Metabolic risk score zMS Metabolic syndrome score  Alpha (between-subject variance) List of Symbols and Abbreviations Thayse Natacha Queiroz Ferreira Gomes XXXIII β Beta (regression coefficient)  Delta (change) p p-value R2 Coefficient of determination  Rho (correlation coefficient)  Tau (within-subject variance) 2 Variance 2 Chi-squared % Percentage < Lower than ≤ Lower or equal than > Higher than ≥ Higher or equal than ± / + or - Plus or minus ≈ Approximately CHAPTER I General Introduction and Thesis Outline General Introduction and Thesis Outline Thayse Natacha Queiroz Ferreira Gomes 3 GENERAL INTRODUCTION It is generally accepted that the urbanization process observed in the last century, as a consequence of economic, social, cultural and behavioural changes, entailing epidemiological transitions, contributed to modify the population health status of almost nations of the world (Yusuf et al., 2001a, 2001b). This complex process promoted a higher life expectancy and the reduction of death by infectious diseases (World Health Organization, 2003), but it has also induced a more sedentary and inactive lifestyle with a marked reduction in physical activity levels (Hallal et al., 2012) and an increase in sedentariness (i.e. sitting) (Pate et al., 2011). Further, it also prompted the adoption of dietary patterns characterized by the consumption of high-energy dense foods (Rodriguez-Ramirez et al., 2011), leading to unparalleled increases in overweight/obesity incidence in the general population of all age ranges (Lobstein et al., 2004; Ng et al., 2014). In addition, and possibly as a consequence of this “new” urbanized lifestyle, a shift in the major causes of deaths from “traditional risk” (i.e. related to under-nutrition and poor sanitation) has been noted leading to a greater incidence of death by co-morbidities linked to sedentariness, inactive lifestyles and excess weight, so-called noncommunicable diseases, which became a leading global cause of death (World Health Organization, 2009, 2011). Moreover, physical inactivity and excess weight are ranked the fourth and fifth leading risks for mortality worldwide, being associated with an increased risk of chronic diseases such as heart disease and cancers (World Health Organization, 2009). The decreasing levels of physical activity, and the increasing prevalence of sedentariness and overweight/obesity in children and adolescents are of major concern, due to the fact that these traits are closely related to the development of chronic diseases in adulthood (Berenson & Srnivasan, 2005; Deshmukh-Taskar et al., 2006; Ortega et al., 2013). Although physical activity and sedentariness are two different behavioural constructs (Katzmarzyk, 2010; Pate et al., 2011), it has been suggested that their determinants might be General Introduction and Thesis Outline Thayse Natacha Queiroz Ferreira Gomes 4 similar (King et al., 2011; Pate et al., 2011; Uijtdewilligen et al., 2011), as they both arise from intrapersonal (biological, psychological, demographic), interpersonal (social, cultural), organizational, environmental (built, natural, social), and policy characteristics (Ferreira et al., 2007; Sallis et al., 2000; Van Der Horst et al., 2007). Though childhood obesity is a major public health problem across the world (de Onis et al., 2010; Ng et al., 2014), its individual expression is the result of a complex interaction of behavioural, biological and environmental factors (Kumanyika & Obarzanek, 2003; Damiani & Damiani, 2010). As such, recent investigations have focused their attention on unravelling the roles of physical activity and sedentariness in promoting excess weight in youth (Steinbeck, 2001), and further link these traits to their health. Adolescence is considered a unique time-window in human development (Steinberg & Morris, 2001), characterized by important changes in life, especially in terms of the interactions between individual lifestyles and environmental conditions. Since behaviours and different aspects of healthy/unhealthy statuses acquired in this period of life tend to track through adulthood (Malina, 2001; Singh et al., 2008), the promotion of healthy lifestyles and education on solid human values in early years are very important venues to also reduce the incidence of non-communicable diseases in adult life. There is evidence that up to 50% of obese adolescents may remain obese in adulthood (Steinbeck, 2001). For example, in the Bogalusa Heart Study, 22.5% of the participants who were overweight in childhood remained overweight in young adulthood, and only 2.3% of the overweight children became normalweight as adults (Deshmukh-Taskar et al., 2006). Similarly, data from the Fels Longitudinal Study showed a moderate-to-high prediction of adult body mass index (BMI) according to child/adolescent BMI (Guo & Chumlea, 1999; Guo et al., 2000; Guo et al., 2002); and The Physical Activity Longitudinal Study reported that over a 22 year period, about 83% of overweight youth remained overweight as adults, and that almost all healthy weight adults had been healthy weight youths (Herman et al., 2009). With respect to the tracking of physical activity and sedentary behaviour, results are not always conclusive (Herman et al., 2009; Telama, 2009), but a trend exists where adequately active youth General Introduction and Thesis Outline Thayse Natacha Queiroz Ferreira Gomes 5 become adequately active adults (Azevedo et al., 2007; Malina, 2001). Likewise, metabolic syndrome indicators, linked to low levels of physical activity, high levels of sedentariness and excess weight during childhood and adolescence, tend also to track well from childhood into adulthood (Camhi & Katzmarzyk, 2010; Eisenmann et al., 2004). These previous pieces of evidence reinforce the significant links between physical activity, sedentariness, overweight/obesity and several co-morbidities, requiring a better understanding of their interwoven net during early life (Andersen et al., 2006; Brambilla et al., 2011; Butte et al., 2005; Steele et al., 2008). However, the complex nature of these traits, whose predictors originate from different sources, requires the use of “substantive” models allowing the examination of their relationship from multiple layers of influence. A useful framework of thought and empirical research is the so-called Ecological Model of Human Development, initially developed by Urie Bronfenbrenner (Bronfenbrenner, 1977, 1979) and later adapted for the field of physical activity by Sallis, Owen and Fisher (2008). The focus of this model is on the subjectenvironment dyad, i.e., the investigation of individual development within the manifold environmental facets. Since active or sedentary behaviours occur in specific “places”, the use of the ecological model is well suited for research, acknowledges the identification of “place” characteristics that facilitate or impair their occurrence. The Ecological Model of Four Domains of Active Living (Sallis et al., 2006), includes several levels of interwoven influences, such as intrapersonal factors, perceived environment, behaviour, behaviour settings, and policy environment, and is an interesting approach to ground this purpose (Figure 1). This is also so because the higher order predictors, namely environmental and policy factors, have received less attention than individual ones when correlates of youth physical activity, sedentary behaviour and BMI are investigated and/or interventions are planned (Sallis et al., 2006; Story et al., 2008). Briefly, this model shows (Figure 1), at its centre, broad categories of intrapersonal variables, representing the individual. Individual environmental perception and objective aspects of the environment are distinguished, and both General Introduction and Thesis Outline Thayse Natacha Queiroz Ferreira Gomes 6 are likely to be important. Behaviour represents the interaction between the person and the environment, and is highlighted because this is the outcome of interest, and the four main domains of active living are presented: active recreation, active transport, occupational activities, and household activities. Behaviour settings are the places where the outcome variable occurs, being relevant to account both access to settings and specific characteristics. The policy environment can influence active living by different pathways, i.e., through the built environment, incentives, and programs. The socio-cultural environment, information environment, and natural environment are shown as cutting across the other levels (Sallis et al., 2006). Figure 1. Ecological Model of Four Domains of Active Living, adapted from Sallis et al. (2006). As such, it is relevant to investigate the role of variables coming from different levels of the ecological model on children’s physical activity, sedentariness and obesity, as well as the correlations among them. We will be Policy Environment Behavior Settings; Access & Characteristics Perceived Environment Behavior: Active Living Domains Intrapersonal Health care policies/ incentives Zoning codes Development regulations Transport investments & regulations Public Recreation investments Park policies Subsidized equipment Health care policies Zoning codes Home prices Housing-jobs balance Active Recreation Household Activities Occupational Activities Active Transport Information Environment Social Cultural Environment Natural Environment Neighborhood -ped/bike facilities -aesthetics -traffic safety Recreation Environment Home PA equipment Parks, trails, programs Private rec. facilities Community orgs Sports-amateur, pro Sedentary options Safety Attractiveness Comfort Accessibility Convenience Demographics Biological Psychological Family Situation Perceived Crime Interpersonal modeling, social support, partners for social activities Social climate, safety crime, clubs, teams, programs, norms, culture, social capital Advocacy by individuals & organizations Healthcare; counseling, info Mass media –news, ads Sports Informal discussions Media regulations Health sector policies Business practices Weather Topography Open space Air quality Neighborhood -walkability -ped/bike facilities -parking -transit -traffic Info during transport -safety signage -radio ads & news -billboards Workplace Environment Neighborhood walkability Parking Transit access Trail access Building design Stair design PA facilities & Programs School Environment Neighborhood walkability Ped/bike facilities Facilities PE program Walk to School program Transport policies Land use policies Zoning codes Development regulations Transport investments Traffic demand management Parking regulations Developer incentives Zoning codes Fire codes Building codes Parking regulations Transportation investments Health care policies School sitting policies PE policies & funding Facility access policies Facilities budget Safe Routes to School funding Home Environment PA equipment Gardens Stairs Electronic entertainment Labor-savings devices Policy Environment Behavior Settings; Access & Characteristics Perceived Environment Behavior: Active Living Domains Intrapersonal Health care policies/ incentives Zoning codes Development regulations Transport investments & regulations Public Recreation investments Park policies Subsidized equipment Health care policies Zoning codes Home prices Housing-jobs balance Active Recreation Household Activities Occupational Activities Active Transport Information Environment Social Cultural Environment Natural Environment Neighborhood -ped/bike facilities -aesthetics -traffic safety Recreation Environment Home PA equipment Parks, trails, programs Private rec. facilities Community orgs Sports-amateur, pro Sedentary options Safety Attractiveness Comfort Accessibility Convenience Demographics Biological Psychological Family Situation Perceived Crime Interpersonal modeling, social support, partners for social activities Social climate, safety crime, clubs, teams, programs, norms, culture, social capital Advocacy by individuals & organizations Healthcare; counseling, info Mass media –news, ads Sports Informal discussions Media regulations Health sector policies Business practices Weather Topography Open space Air quality Neighborhood -walkability -ped/bike facilities -parking -transit -traffic Info during transport -safety signage -radio ads & news -billboards Workplace Environment Neighborhood walkability Parking Transit access Trail access Building design Stair design PA facilities & Programs School Environment Neighborhood walkability Ped/bike facilities Facilities PE program Walk to School program Transport policies Land use policies Zoning codes Development regulations Transport investments Traffic demand management Parking regulations Developer incentives Zoning codes Fire codes Building codes Parking regulations Transportation investments Health care policies School sitting policies PE policies & funding Facility access policies Facilities budget Safe Routes to School funding Home Environment PA equipment Gardens Stairs Electronic entertainment Labor-savings devices General Introduction and Thesis Outline Thayse Natacha Queiroz Ferreira Gomes 7 mostly interested in the roles of biological traits, family demographics and school context. Investigating the impact of lifestyle on childhood overweight/obesity, physical activity and sedentariness in a large study, involving countries from the major world regions (Eurasia & Africa, Europe, Latin America, North America, and Pacific) will provide high-impact results on the development of lifestyle interventions to reduce behavioural risks during childhood that can be culturally adapted for implementation around the world. The International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE) (Katzmarzyk et al., 2013) is a multi-national crosssectional study conducted in 12 countries (Australia, Brazil, Canada, China, Colombia, Finland, India, Kenya, Portugal, South Africa, United Kingdom, United States), with the purpose “to determine the relationship between lifestyle behaviours and obesity in a multi-national study of children, and to investigate the influence of higher-order characteristics such as behavioural settings, and the physical, social and policy environments, on the observed associations within and between countries” (p. 4). Using information from ISCOLE-Portugal, this thesis was developed aiming to unravel the relationship between physical activity, sedentariness and obesity, as well as their co-morbidities, in Portuguese children. The present doctoral thesis has its foundations on four main reasons: - The first one is grounded in the epidemiology of physical activity framework, which has a strong interest in studying predictors and correlates of physical activity and sedentary behaviour (Caspersen, 1989). Previous studies have suggested that children spend a considerable portion of their awake time in sedentary activities (Biddle et al., 2009; Pate et al., 2011), while their physical activity levels have decreased globally - more than 80% of adolescents aged 13-15 years do not comply with the daily recommended levels (Hallal et al., 2012). A study involving Portuguese children and youth showed that at ages 1011 yrs, 36% of them were considered sufficiently active; further, a progressive decrease was observed in this prevalence with age, where only 4% of youth aged 16-17 complied with the physical activity daily guidelines. Furthermore, General Introduction and Thesis Outline Thayse Natacha Queiroz Ferreira Gomes 14 Chapter III Paper III Overweight and Obesity in Portuguese Children: Prevalence and Correlates Purposes: to conduct a meta-analysis on overweight/obesity prevalence in 9 to 11 year old Portuguese children; to detect significant differences in behavioural characteristics among normal-weight and overweight/obese children; and to investigate the importance of individualand school-level correlates on variation in children’s BMI. Published in International Journal of Environmental Research and Public Health (2014), doi: 10.3390/ijerph111111398 Authors: Thayse Natacha Gomes; Peter T. Katzmarzyk; Fernanda Karina dos Santos; Michele Souza; Sara Pereira; José A. R. Maia. Paper IV “Fat-but-active”: Does physical activity play a significant role in metabolic syndrome risk among children of different BMI categories? Purpose: to explore the idea of “fat-but-active” by analysing differences in metabolic syndrome risk factors across distinct BMI and physical activity groups. Published in Journal of Diabetes and Metabolism (2014), doi: 10.4172/21556156.1000421 Authors: Thayse Natacha Gomes; Fernanda Karina dos Santos; Daniel Santos; Raquel Chaves; Michele Souza; Peter T. Katzmarzyk; José A. R. Maia Paper V “Active and strong”: physical activity, strength and metabolic risk in children Purpose: to explore the joint roles of physical activity and muscular strength on metabolic risk factors in children. Submitted Authors: Thayse Natacha Gomes; Peter T. Katzmarzyk; Fernanda Karina dos Santos; José A. R. Maia. Paper VI Relationship between sedentariness and moderate-to-vigorous physical activity in youth. A multivariate multilevel study Purpose: to jointly analyse moderate-to-vigorous physical activity and sedentariness as well as their correlates in children within their school contexts. Submitted Authors: Thayse Natacha Gomes; Donald Hedeker; Fernanda Karina dos Santos; Michele Souza; Daniel Santos; Sara Pereira; Peter T. Katzmarzyk; José A. R. Maia. General Introduction and Thesis Outline Thayse Natacha Queiroz Ferreira Gomes 15 Chapter III Paper VII Are BMI and sedentariness correlated? A multilevel study in children Purposes: to study the relationship between BMI and sedentariness in Portuguese children; and to investigate the importance of child and school correlates in BMI and sedentariness variation. Under reivew in Nutrients Authors: Thayse Natacha Gomes; Peter T. Katzmarzyk; Fenanda Karina dos Santos; Raquel Chaves; Daniel Santos; Sara Pereira; Catherine M. Champagne; Donald Hedeker; José A. R, Maia. Chapter IV General overview and conclusions General Introduction and Thesis Outline Thayse Natacha Queiroz Ferreira Gomes 16 REFERENCES Andersen, L. B., Harro, M., Sardinha, L. B., Froberg, K., Ekelund, U., Brage, S., et al. (2006). Physical activity and clustered cardiovascular risk in children: a cross-sectional study (The European Youth Heart Study). Lancet, 368(9532), 299-304. Anderssen, N., & Wold, B. (1992). Parental and peer influences on leisure-time physical activity in young adolescents. Res Q Exerc Sport, 63(4), 341-348. Atkin, A. J., Corder, K., Ekelund, U., Wijndaele, K., Griffin, S. J., & van Sluijs, E. M. (2013). Determinants of change in children's sedentary time. PLoS One, 8(6), e67627. doi: 10.1371/journal.pone.0067627 Azevedo, M. R., Araujo, C. L., Cozzensa da Silva, M., & Hallal, P. C. (2007). Tracking of physical activity from adolescence to adulthood: a population-based study. Rev Saude Publica, 41(1), 69-75. doi: S0034-89102007000100010 Beets, M. W., Cardinal, B. J., & Alderman, B. L. (2010). Parental social support and the physical activity-related behaviors of youth: a review. Health Educ Behav, 37(5), 621-644. doi: 10.1177/1090198110363884 Berenson, G. S., & Srnivasan, S. R. (2005). Cardiovascular risk factors in youth with implications for aging: the Bogalusa Heart Study. Neurobiol Aging, 26(3), 303-307. doi: 10.1016/j.neurobiolaging.2004.05.009 Biddle, S. J., Gorely, T., Marshall, S. J., & Cameron, N. (2009). The prevalence of sedentary behavior and physical activity in leisure time: A study of Scottish adolescents using ecological momentary assessment. Prev Med, 48(2), 151155. doi: 10.1016/j.ypmed.2008.10.025 Biddle, S. J., Gorely, T., & Stensel, D. J. (2004). Health-enhancing physical activity and sedentary behaviour in children and adolescents. J Sports Sci, 22(8), 679-701. doi: 10.1080/02640410410001712412 Bingham, D. D., Varela-Silva, M. I., Ferrão, M. M., Augusta, G., Mourão, M. I., Nogueira, H., et al. (2013). Socio-demographic and behavioral risk factors General Introduction and Thesis Outline Thayse Natacha Queiroz Ferreira Gomes 17 associated with the high prevalence of overweight and obesity in Portuguese children. Am J Hum Biol, 25(6), 733-742. doi: 10.1002/ajhb.22440 Braga-Tavares, H., & Fonseca, H. (2010). Prevalence of metabolic syndrome in a Portuguese obese adolescent population according to three different definitions. Eur J Pediatr, 169(8), 935-940. doi: 10.1007/s00431-010-1143-5 Brambilla, P., Pozzobon, G., & Pietrobelli, A. (2011). Physical activity as the main therapeutic tool for metabolic syndrome in childhood. Int J Obes (Lond), 35(1), 16-28. doi: 10.1038/ijo.2010.255 Brodersen, N. H., Steptoe, A., Boniface, D. R., & Wardle, J. (2007). Trends in physical activity and sedentary behaviour in adolescence: ethnic and socioeconomic differences. Br J Sports Med, 41(3), 140-144. doi: 10.1136/bjsm.2006.031138 Brodersen, N. H., Steptoe, A., Williamson, S., & Wardle, J. (2005). Sociodemographic, developmental, environmental, and psychological correlates of physical activity and sedentary behavior at age 11 to 12. Ann Behav Med, 29(1), 2-11. doi: 10.1207/s15324796abm2901_2 Bronfenbrenner, U. (1977). Toward an experimental ecology of human development. Am Psychol, 32, 512-531. Bronfenbrenner, U. (1979). The ecology of human development. Cambridge, Mass: Harvard University Press. Butte, N. F., Comuzzie, A. G., Cole, S. A., Mehta, N. R., Cai, G., Tejero, M., et al. (2005). Quantitative genetic analysis of the metabolic syndrome in Hispanic children. Pediatr Res, 58(6), 1243-1248. doi: 10.1203/01.pdr.0000185272.46705.18 Camhi, S. M., & Katzmarzyk, P. T. (2010). Tracking of cardiometabolic risk factor clustering from childhood to adulthood. Int J Pediatr Obes, 5(2), 122-129. doi: 10.3109/17477160903111763 Carvalhal, M. M., Padez, M. C., Moreira, P. A., & Rosado, V. M. (2007). Overweight and obesity related to activities in Portuguese children, 7-9 years. Eur J Public Health, 17(1), 42-46. General Introduction and Thesis Outline Thayse Natacha Queiroz Ferreira Gomes 18 Caspersen, C. J. (1989). Physical activity epidemiology: concepts, methods, and applications to exercise science. Exerc Sport Sci Rev, 17, 423-473. Chau, J. Y., Grunseit, A. C., Chey, T., Stamatakis, E., Brown, W. J., Matthews, C. E., et al. (2013). Daily sitting time and all-cause mortality: a meta-analysis. PLoS One, 8(11), e80000. doi: 10.1371/journal.pone.0080000 Cradock, A. L., Melly, S. J., Allen, J. G., Morris, J. S., & Gortmaker, S. L. (2007). Characteristics of school campuses and physical activity among youth. Am J Prev Med, 33(2), 106-113. doi: 10.1016/j.amepre.2007.04.009 Damiani, D., & Damiani, D. (2010). Avaliação da obesidade na infância e na adolescência. In M. C. Mancini, B. G. Neto, J. E. N. Salles, J. G. d. Lima & M. K. Carra (Eds.), Tratado de obesidade (pp. 203-2010). Itapevi, São Paulo: AC Farmacêutica. de Onis, M., Blossner, M., & Borghi, E. (2010). Global prevalence and trends of overweight and obesity among preschool children. Am J Clin Nutr, 92(5), 12571264. Deshmukh-Taskar, P., Nicklas, T. A., Morales, M., Yang, S. J., Zakeri, I., & Berenson, G. S. (2006). Tracking of overweight status from childhood to young adulthood: the Bogalusa Heart Study. Eur J Clin Nutr, 60(1), 48-57. doi: 10.1038/sj.ejcn.1602266 Diez-Roux, A. V. (2000). Multilevel analysis in public health research. Annu Rev Public Health, 21, 171-192. doi: 10.1146/annurev.publhealth.21.1.171 Duncan, C., Jones, K., & Moon, G. (1998). Context, composition and heterogeneity: using multilevel models in health research. Soc Sci Med, 46(1), 97-117. doi: S0277953697001482 Dunstan, D. W., Barr, E. L., Healy, G. N., Salmon, J., Shaw, J. E., Balkau, B., et al. (2010). Television viewing time and mortality: the Australian Diabetes, Obesity and Lifestyle Study (AusDiab). Circulation, 121(3), 384-391. doi: 10.1161/CIRCULATIONAHA.109.894824 Eisenmann, J. C., Katzmarzyk, P. T., Perusse, L., Tremblay, A., Despres, J. P., & Bouchard, C. (2005). Aerobic fitness, body mass index, and CVD risk factors General Introduction and Thesis Outline Thayse Natacha Queiroz Ferreira Gomes 19 among adolescents: the Quebec family study. Int J Obes (Lond), 29(9), 10771083. doi: 10.1038/sj.ijo.0802995 Eisenmann, J. C., Welk, G. J., Ihmels, M., & Dollman, J. (2007). Fatness, fitness, and cardiovascular disease risk factors in children and adolescents. Med Sci Sports Exerc, 39(8), 1251-1256. doi: 10.1249/MSS.0b013e318064c8b0 Eisenmann, J. C., Welk, G. J., Wickel, E. E., & Blair, S. N. (2004). Stability of variables associated with the metabolic syndrome from adolescence to adulthood: the Aerobics Center Longitudinal Study. Am J Hum Biol, 16(6), 690696. doi: 10.1002/ajhb.20079 Epstein, L. H., Paluch, R. A., Gordy, C. C., & Dorn, J. (2000). Decreasing sedentary behaviors in treating pediatric obesity. Arch Pediatr Adolesc Med, 154(3), 220-226. Epstein, L. H., Valoski, A. M., Vara, L. S., McCurley, J., Wisniewski, L., Kalarchian, M. A., et al. (1995). Effects of decreasing sedentary behavior and increasing activity on weight change in obese children. Health Psychol, 14(2), 109-115. Ferreira, I., Van der Horst, K., Wendel-Vos, W., Kremers, S., Van Lenthe, F. J., & Brug, J. (2007). Environmental correlates of physical activity in youth - a review and update. Obes Rev, 8(2), 129-154. doi: 10.1111/j.1467789X.2006.00264.x Gorely, T., Marshall, S. J., Biddle, S. J., & Cameron, N. (2007). Patterns of sedentary behaviour and physical activity among adolescents in the United Kingdom: Project STIL. J Behav Med, 30(6), 521-531. doi: 10.1007/s10865007-9126-3 Guo, S. S., & Chumlea, W. C. (1999). Tracking of body mass index in children in relation to overweight in adulthood. Am J Clin Nutr, 70(1), 145S-148S. Guo, S. S., Huang, C., Maynard, L. M., Demerath, E., Towne, B., Chumlea, W. C., et al. (2000). Body mass index during childhood, adolescence and young General Introduction and Thesis Outline Thayse Natacha Queiroz Ferreira Gomes 20 adulthood in relation to adult overweight and adiposity: the Fels Longitudinal Study. Int J Obes Relat Metab Disord, 24(12), 1628-1635. Guo, S. S., Wu, W., Chumlea, W. C., & Roche, A. F. (2002). Predicting overweight and obesity in adulthood from body mass index values in childhood and adolescence. Am J Clin Nutr, 76(3), 653-658. Hallal, P. C., Andersen, L. B., Bull, F. C., Guthold, R., Haskell, W., & Ekelund, U. (2012). Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet, 380(9838), 247-257. doi: 10.1016/S0140-6736(12)60646-1 Herman, K. M., Craig, C. L., Gauvin, L., & Katzmarzyk, P. T. (2009). Tracking of obesity and physical activity from childhood to adulthood: the Physical Activity Longitudinal Study. Int J Pediatr Obes, 4(4), 281-288. doi: 10.3109/17477160802596171 Jago, R., Baranowski, T., Baranowski, J. C., Thompson, D., & Greaves, K. A. (2005). BMI from 3-6 y of age is predicted by TV viewing and physical activity, not diet. Int J Obes (Lond), 29(6), 557-564. doi: 10.1038/sj.ijo.0802969 Katzmarzyk, P. T. (2010). Physical activity, sedentary behavior, and health: paradigm paralysis or paradigm shift? Diabetes, 59(11), 2717-2725. doi: 10.2337/db10-0822 Katzmarzyk, P. T., Barreira, T. V., Broyles, S. T., Champagne, C. M., Chaput, J. P., Fogelholm, M., et al. (2013). The International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE): design and methods. BMC Public Health, 13, 900. doi: 10.1186/1471-2458-13-900 Katzmarzyk, P. T., Church, T. S., Craig, C. L., & Bouchard, C. (2009). Sitting time and mortality from all causes, cardiovascular disease, and cancer. Med Sci Sports Exerc, 41(5), 998-1005. doi: 10.1249/MSS.0b013e3181930355 King, A. C., Parkinson, K. N., Adamson, A. J., Murray, L., Besson, H., Reilly, J. J., et al. (2011). Correlates of objectively measured physical activity and sedentary behaviour in English children. Eur J Public Health, 21(4), 424-431. doi: 10.1093/eurpub/ckq104 General Introduction and Thesis Outline Thayse Natacha Queiroz Ferreira Gomes 21 Kumanyika, S. K., & Obarzanek, E. (2003). Pathways to obesity prevention: report of a National Institutes of Health workshop. Obes Res, 11(10), 12631274. doi: 10.1038/oby.2003.172 Leech, R. M., McNaughton, S. A., & Timperio, A. (2014). The clustering of diet, physical activity and sedentary behavior in children and adolescents: a review. Int J Behav Nutr Phys Act, 11, 4. doi: 10.1186/1479-5868-11-4 Liao, Y., Liao, J., Durand, C. P., & Dunton, G. F. (2014). Which type of sedentary behaviour intervention is more effective at reducing body mass index in children? A meta-analytic review. Obes Rev, 15(3), 159-168. doi: 10.1111/obr.12112 Lobstein, T., Baur, L., & Uauy, R. (2004). Obesity in children and young people: a crisis in public health. Obes Rev, 5 Suppl 1, 4-104. doi: 10.1111/j.1467789X.2004.00133.x Maia, J. (2010). Santo Tirso comVida e com saúde. Porto. Malina, R. M. (2001). Physical activity and fitness: pathways from childhood to adulthood. Am J Hum Biol, 13(2), 162-172 Marshall, S. J., Biddle, S. J. H., Sallis, J. F., McKenzie, T. L., & Conway, T. L. (2002). Clustering of sedentary behaviors and physical activity among youth: A cross-national study. Pediatr Exerc Sci, 14(4), 401-417. Mota, J., Ribeiro, J., Santos, M. P., & Gomes, H. (2006). Obesity, physical activity, computer use, and TV viewing in Portuguese adolescents. Pediatr Exerc Sci, 17, 113-121. Mota, J., Silva, P., Aires, L., Santos, M. P., Oliveira, J., & Ribeiro, J. C. (2008). Differences in school-day patterns of daily physical activity in girls according to level of physical activity. J Phys Act Health, 5 Suppl 1, S90-97. Newton, R. L., Han, H., Sothern, M., Martin, C. K., Webber, L. S., & Williamson, D. A. (2011). Accelerometry measured ethnic differences in activity in rural adolescents. J Phys Act Health, 8(2), 287-295. General Introduction and Thesis Outline Thayse Natacha Queiroz Ferreira Gomes 22 Ng, M., Fleming, T., Robinson, M., Thomson, B., Graetz, N., Margono, C., et al. (2014). Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet, 384(9945), 766-781. doi: 10.1016/S0140-6736(14)60460-8 Olds, T., Maher, C., Zumin, S., Peneau, S., Lioret, S., Castetbon, K., et al. (2011). Evidence that the prevalence of childhood overweight is plateauing: data from nine countries. Int J Pediatr Obes, 6(5-6), 342-360. doi: 10.3109/17477166.2011.605895 Olds, T., Maher, C. A., Ridley, K., & Kittel, D. M. (2010). Descriptive epidemiology of screen and non-screen sedentary time in adolescents: a cross sectional study. Int J Behav Nutr Phys Act, 7, 92. doi: 10.1186/1479-5868-7-92 Ortega, F. B., Konstabel, K., Pasquali, E., Ruiz, J. R., Hurtig-Wennlof, A., Maestu, J., et al. (2013). Objectively measured physical activity and sedentary time during childhood, adolescence and young adulthood: a cohort study. PLoS One, 8(4), e60871. doi: 10.1371/journal.pone.0060871 Owen, N., Leslie, E., Salmon, J., & Fotheringham, M. J. (2000). Environmental determinants of physical activity and sedentary behavior. Exerc Sport Sci Rev, 28(4), 153-158. Padez, C., Fernandes, T., Mourão, I., Moreira, P., & Rosado, V. (2004). Prevalence of overweight and obesity in 7-9-year-old Portuguese children: trends in body mass index from 1970-2002. Am J Hum Biol, 16(6), 670-678. doi: 10.1002/ajhb.20080 Pate, R. R., Mitchell, J. A., Byun, W., & Dowda, M. (2011). Sedentary behaviour in youth. Br J Sports Med, 45(11), 906-913. doi: 10.1136/bjsports-2011-090192 Pulsford, R. M., Griew, P., Page, A. S., Cooper, A. R., & Hillsdon, M. M. (2013). Socioeconomic position and childhood sedentary time: evidence from the PEACH project. Int J Behav Nutr Phys Act, 10, 105. doi: 10.1186/1479-586810-105 General Introduction and Thesis Outline Thayse Natacha Queiroz Ferreira Gomes 23 Rabbee, N., & Betensky, R. A. (2004). Power calculations for familial aggregation studies. Genet Epidemiol, 26(4), 316-327. doi: 10.1002/gepi.10312 Ridgers, N. D., Stratton, G., Fairclough, S. J., & Twisk, J. W. (2007). Long-term effects of a playground markings and physical structures on children's recess physical activity levels. Prev Med, 44(5), 393-397. doi: 10.1016/j.ypmed.2007.01.009 Rodriguez-Ramirez, S., Mundo-Rosas, V., Garcia-Guerra, A., & Shamah-Levy, T. (2011). Dietary patterns are associated with overweight and obesity in Mexican school-age children. Arch Latinoam Nutr, 61(3), 270-278. Rosenberg, D. E., Sallis, J. F., Kerr, J., Maher, J., Norman, G. J., Durant, N., et al. (2010). Brief scales to assess physical activity and sedentary equipment in the home. Int J Behav Nutr Phys Act, 7, 10. doi: 10.1186/1479-5868-7-10 Rowlands, A. V., Gomersall, S. R., Tudor-Locke, C., Bassett, D. R., Kang, M., Fraysse, F., et al. (2015). Introducing novel approaches for examining the variability of individuals' physical activity. J Sports Sci, 33(5), 457-466. doi: 10.1080/02640414.2014.951067 Saland, J. M. (2007). Update on the metabolic syndrome in children. Curr Opin Pediatr, 19(2), 183-191. doi: 10.1097/MOP.0b013e3280208519 Sallis, J. F., Cervero, R. B., Ascher, W., Henderson, K. A., Kraft, M. K., & Kerr, J. (2006). An ecological approach to creating active living communities. Annu Rev Public Health, 27, 297-322. doi: 10.1146/annurev.publhealth.27.021405.102100 Sallis, J. F., Conway, T. L., Prochaska, J. J., McKenzie, T. L., Marshall, S. J., & Brown, M. (2001). The association of school environments with youth physical activity. Am J Public Health, 91(4), 618-620. Sallis, J. F., Owen, N., & Fisher, E. B. (2008). Ecological models of health behavior. In K. Glanz, B. K. Rimer & K. Viswanath (Eds.), Health behavior and health education: theory, research, and practice (4th ed., pp. 465-486). Hoboken: Jossey-Bass. Study Sample and Methods Thayse Natacha Queiroz Ferreira Gomes 31 STUDY SAMPLE AND METHODS The International Study of Childhood Obesity, Lifestyle and the Environment – ISCOLE The main purpose of ISCOLE, a cross-sectional study, is to “determine the relationship between lifestyle behaviours and obesity in a multi-national study of children, and to investigate the influence of higher-order characteristics such as behavioural settings, and the physical, social and policy environments, on the observed associations within and between countries” (Katzmarzyk et al., 2013, p. 2). The study hypothesizes that differences in the relationship between lifestyle behaviour and obesity will be different across countries and distinct environmental settings. It is expected that results coming from ISCOLE will provide relevant information that should be used in the development of interventions to address and prevent childhood obesity, suitable to be adapted for implementation around the world. The ISCOLE sample comes from 12 countries (Australia, Brazil, Canada, China, Colombia, Finland, India, Kenya, Portugal, South Africa, United Kingdom, and United States of America) from five geographic regions of the world (Europe, Africa, the Americas, South-Eastern Asia, Western Pacific). In each country, a random sample of at least 500 children (final projected total sample for at least 6000 children), gender balanced, aged 9-11 years (mean age of 10 years), from urban or suburban areas, was recruited. Children should be enrolled in schools which were stratified by socioeconomic status whenever possible. By design, the ISCOLE sampling frame was not representative of each country, but maximized socioeconomic status variation at each site. The Pennington Biomedical Research Center, in Baton Rouge, USA, is the ISCOLE Coordinating Center, responsible for the overall administration the study, and in each site there is a principal investigator, who is responsible for the all aspects of study implementation at the local level. The data collection was conducted during the school year, covering, whenever possible, different seasons, and started in September 2011. Each site Study Sample and Methods Thayse Natacha Queiroz Ferreira Gomes 32 should have completed their data collection across one school year (12 months). Objective and subjective information from distinct levels were obtained, as follows: - At the child level: age, anthropometry and body composition [height, weight, sitting height, waist circumference, mid-upper-arm circumference, body mass index (BMI), impedance, body fat]; somatic maturation (percentage of predicted adult stature and the maturity offset); accelerometry (physical activity, sedentary behaviour, steps count, sleep time); self-reported physical activity; outdoor time; television viewing and computer use; physical education class; active transport; motivation for and attitudes towards physical activity; food consumption (food frequency; eating in front of TV; frequency of eating breakfast; lunches at school and outside of the home; emotional eating); self-reported sleep duration and quality; self-rated health and well-being. - At the family level: ethnicity of participants; family health and socioeconomic factors; family structure; education level, and self-reported height and weight of biological parents; home social environment; home food environment; home physical activity environment. - At the neighbourhood level: neighbourhood social capital; neighbourhood food environment; neighbourhood physical activity environment; neighbourhood built environment. - At the school level: number of students; number of days students attend school during the academic year; school facilities; healthy eating and physical activity policies; extracurricular activities; frequency of physical education and breaks (recess); amount of class time mandated for physical education; promotion/support of active transportation; availability of healthy and unhealthy foods; directly-observed information pertaining to the school built and food environment (sports and play amenities, aesthetics), and competitive food environment (food environment of the area surrounding the school). All staff members were trained and certified by trained experts as competent to make the required measurements in regional training sessions Study Sample and Methods Thayse Natacha Queiroz Ferreira Gomes 33 organized by the ISCOLE Coordinating Center. The same standardized measurement protocols were used in all sites, and given that the main purpose of ISCOLE is to take into account the role of lifestyle behaviours and environmental characteristics on obesity, multilevel/random-effects models (children within schools, and schools within countries) are used in all major analyses. ISCOLE – Portugal In Portugal, the ISCOLE study was conducted by the Kinanthropometry Laboratory from the Faculty of Sport, University of Porto, coordinated by Prof. Dr. José António Ribeiro Maia (the ISCOLE Portugal principal investigator). The staff comprised 9 members (6 with master degree, 3 undergraduates in Physical Education and/or Sport Science). All staff members were trained and certified, and were involved full-time in the data collection, control and management of the study. A total of 23 schools, from the North of Portugal, were enrolled in the ISCOLE project. In each school, all 5th grade students were invited to take part in the study, and those aged 9-11 years were considered eligible. From those, parental or legal guardian consent was obtained, and approximately 30 to 40 children (50% of each sex), per school, were randomly selected. The response rate was 95.7%. Schools selection and inclusion in the project were done in a series of steps. Firstly, from a list provided by the North Regional Education Directory Board, eligible schools were selected. Since there is little variability in socioeconomic status at the school level in the Portuguese North Region, only public schools were selected; further, schools should be located in different regions and socioeconomic neighbourhoods. Secondly, selected schools were contacted and the project was presented to the Physical Education Department coordinator, and if he/she agreed with the project implementation at the school, a presentation of ISCOLE for the Physical Education Department took place (purposes of the project, strategies for data collection, feedback about the Study Sample and Methods Thayse Natacha Queiroz Ferreira Gomes 34 collected information, and benefits for the implementation of the project to the school community). After approval by the Physical Education Department, the project was, in this order, presented to school Principal and Pedagogical Council, and Parental Council, and should be approved from all of them to be implemented. If a non-compliant school was found (no approval by at least one of these “groups”), it was replaced by the next school selected from the list. Thirdly, signed parental or legal guardian consents were sent to all 5th grade students and, as mentioned above, from those aged 9-11 years and with signed consent form, approximately 30-40 children were selected. Fourthly, a calendar with data procedure routines was developed and sent to each school. The data collection was obtained during a whole week per school, mostly during physical education classes. Fifthly, after the data collection, each school received a report containing the major results, and for each child enrolled in the project a report was sent to his/her parents/legal guardian containing information about anthropometry, body composition, physical activity and sedentariness levels, lifestyle, nutritional habits, and also metabolic risk indicators (blood analyses and blood pressure as explained below). Since it was of interest in Portugal to explore other research venues not comprised in the ISCOLE original project, and given that ISCOLE principal investigators were encouraged to develop ancillary studies that could enhance the scientific output of ISCOLE, information regarding children’s physical fitness levels and metabolic risk indicators [blood pressure, High Density Lipoprotein Cholesterol (HDL-C), glucose, triglycerides] were also obtained. All measurements proposed by ISCOLE original project were taken following the ISCOLE protocols (Katzmarzyk et al., 2013). The ISCOLE-Portugal and the ancillary study proposals were approved by the University of Porto ethics committee. Sample The sample for this thesis comprises 777 children (419 girls), aged 9-11 years, enrolled in 5th grade from 23 elementary schools from the North region Study Sample and Methods Thayse Natacha Queiroz Ferreira Gomes 35 of Portugal. All these 777 children were evaluated according to ISCOLE procedures. A sub-sample of 421 children (233 girls) accepted to participate in an ancillary study to understand the relationship between physical activity, physical fitness, weight status and metabolic risk. All data were collected from September 2011 to January 2013. Procedures The procedures described below correspond to all measurements made in the ISCOLE-Portugal project. Not all available information were used in this thesis. In addition, all procedures followed the protocol defined by the ISCOLE Coordinating Center, except for those variables belonging to the ancillary study. Anthropometry and body composition Height, sitting height, weight, waist circumference, and mid-upper-arm circumference were taken according to standardized ISCOLE procedures and instrumentation (Katzmarzyk et al., 2013). Height, sitting height and mid-upper-arm circumference were measured according to procedures described by Lohman et al (1988). For height and sitting height, children were without shoes, with heads positioned to the Frankfurt Plane, using a Seca 213 portable stadiometer rounding up to the nearest 0.1 cm (Hamburg, Germany). For height, children were fully erect, feet together, and the measurement was taken at the end of a deep inhalation, while for sitting height, children were seated on a table with legs hanging freely and arms resting on the thighs. Mid-upper-arm circumference was measured on the right arm, in the midway between the acromion and olecranon processes, with arm hanging loosely at the side of the body, using a non-elastic tape. Waist circumference measurement followed the procedures described by the World Health Organization (WHO) (2011), and were taken at the midway point Study Sample and Methods Thayse Natacha Queiroz Ferreira Gomes 36 between the lower rib margin and the iliac crest, at the end of a gentle expiration, using a non-elastic tape. Body weight, impedance and body fat were measured using a portable Tanita SC-240 Body Composition Analyzer scale (Arlington Heights, IL). Children were without shoes and socks, and wearing light clothes. All measurements were taken twice, and the average was used for analysis. A third measurement was obtained if the difference between the previous two measurements was outside the permissible range for each measurement and its replica: 0.5 cm for height, sitting height, waist circumference, and mid-upper-arm circumference; 0.5 kg for weight; and 2.0% for body fat. In this case, the closest two measurements were averaged and used for analysis. The BMI [weight(kg)/height(m)2], waist-to-height ratio, and sitting heightto-height ratio were computed. In addition, children were classified in their weight status using cut-points suggested by WHO (de Onis et al., 2007), International Obesity Task Force (Cole et al., 2000; Cole et al., 2007), and the US Centers for Disease Control and Prevention (Kuczmarski et al., 2002). Biological maturation Biological maturation was assessed by two methods: percentage of predicted adult height (Khamis & Roche, 1994), and the maturity offset (Mirwald et al., 2002). Using information from children’s chronological age, height, weight and mid-parent height (average of father’s and mother’s height), the final adult height of children was estimated; the closer to adult height children are, the more advanced in their somatic maturation. Regarding to maturity offset, the timing to peak height velocity (PHV) occurrence was estimated using information on sex, age, and physical growth characteristics (sitting height, leg length, height, and weight); a positive maturity offset expresses the number of years a child is beyond PHV, while a negative maturity offset means the number Study Sample and Methods Thayse Natacha Queiroz Ferreira Gomes 37 of years a child is before the PHV; a value of zero indicates that a child is experiencing the PHV. Objective measured physical activity, sedentariness and sleep time Actigraph GT3X+ accelerometers (ActiGraph, Pensacola, FL) were used to objectively estimate children’s physical activity, sedentariness and sleep time. Children were instructed to wear the accelerometer for at least 7 consecutive days (plus an initial familiarization day and a part of the final day), including two weekend days, 24 hours/day. Accelerometer data were divided into daytime activities and nocturnal sleep time using an automated algorithm (Barreira et al., 2014; Tudor-Locke et al., 2014). Any sequence of at least 20 consecutive minutes of zero activity counts during the awake period was considered as “non-wear time” (Barreira et al., 2014; Tudor-Locke et al., 2014). To be considered eligible, i.e. valid information, children had to have at least 4 days (with at least one weekend day) with a minimum of 10 hours of wear time per day. At the final day of data collection, accelerometers were returned to the ISCOLE staff, and the research team verified the data for completeness using the most recent version of the ActiLife software (version 5.6 or higher; ActiGraph, Pensacola, FL) available at the time. If a child was not considered eligible, ISCOLE staff could ask the child to wear the accelerometer again, for the same period or higher (up to a maximum of 14 days). Children received the accelerometers when anthropometric measurements were taken, and all instructions were given to them and a letter to their parent or legal guardian was sent, explaining the purpose of the device, and how children should use it. At the 10th day of monitoring, children returned the accelerometer for the ISCOLE staff (which went to the school with this purpose, as previous combined with children). Cut-points defined by Evenson et al (2008) and Treuth et al (2004), using 15 second epochs and 1 minute epochs, respectively, were used to define different activity phenotypes and sedentariness. The nocturnal sleep time was determined using a novel and fully-automated algorithm specially developed for Study Sample and Methods Thayse Natacha Queiroz Ferreira Gomes 38 use in ISCOLE and epidemiological studies employing a 24-hour waist-worn accelerometer protocol in children (Barreira et al., 2014; Tudor-Locke et al., 2014). Diet and lifestyle information A compilation of questions and measures obtained from several different sources was done and “The Diet and Lifestyle Questionnaire” was developed (Katzmarzyk et al., 2013). This questionnaire contains items related to dietary intake, physical activity, sedentary behaviour, and sleep, and was answered by all children, at school, on the same day that anthropometric measures were taken, under the supervision of at least one ISCOLE staff member, and all questionnaires were checked for completeness at the time of data collection. Concerning nutritional habits, the questionnaire provides information regarding to children’s food frequency, asking children about several different types of food consumed in a usual week, and also the consumption of different types of snacks while watching TV, which were exemplified by individual food items (not portion size). In addition, the questionnaire also provides information regarding breakfast consumption, lunches consumed at schools, and meals consumed prepared away from home. Subjective information about physical activity and sedentary behaviours was obtained by asking children about the amounts of time they spent in physical activity before/after school and during weekends, sedentary behaviours (namely time spent watching TV, using the computer, or playing video games, and the availability of TV in their bedroom), the number of physical education classes attended per week, and the method used to go to/from school (active or motorized). Sleep patterns, duration and quality were also obtained, as well as psychological constructs related to physical activity and dietary behaviour, such as motivation and self-efficacy for physical activity, and emotional eating, and health-related quality of life (health and mental well-being). Study Sample and Methods Thayse Natacha Queiroz Ferreira Gomes 39 Parental questionnaires Two questionnaires were answered by parents or legal guardians: “Demographics and Family Health Questionnaire” and “Neighbourhood and Home Environment” (Katzmarzyk et al., 2013). On the day of the anthropometry measurements, and after children answered the “Diet and Lifestyle Questionnaire” and received the accelerometer, they also received an envelope containing questionnaires that should be answered by their parents or legal guardians, as well as a letter explaining to parents or legal guardians about the relevance and procedures to answer the questionnaires; further, it also explained the use of the accelerometer by their child (as mentioned before). Children were instructed to give the envelope to their parent or legal guardian and return it, with all questionnaires answered, at the day where the accelerometer should be returned. After the receipt of the questionnaires, ISCOLE staff checked for completeness and possible mistakes during answering procedures, and if the existence of blank or doubled answers was observed, phone calls (whenever possible) were done to the parent or legal guardian to solve the problem. If it was not possible to contact the parent or the legal guardian, the respective questions were assigned as “missing information”. The “Demographics and Family Health Questionnaire” considered information on basic demographics, ethnicity, child’s birth date and weight, child history of breast feeding, home socioeconomic factors, biological parental height and weight. The “Neighbourhood and Home Environment Questionnaire” includes items related to neighbourhood social capital (collective efficacy and the degree to which persons in the neighbourhood know each other and engage socially), the home social environment (parental support for and modelling child physical activity), home and neighbourhood food environments (availability of healthy or unhealthy food and drink in the home, the availability and types of food stores in the neighbourhood), the home and neighbourhood physical activity environment and neighbourhood built environment (availability of electronics for the child’s Study Sample and Methods Thayse Natacha Queiroz Ferreira Gomes 46 Hedeker, D., & Nordgren, R. (2013). MIXREGLS: A Program for Mixed-Effects Location Scale Analysis. J Stat Softw, 52(12), 1-38. Katzmarzyk, P. T., Barreira, T. V., Broyles, S. T., Champagne, C. M., Chaput, J. P., Fogelholm, M., et al. (2013). The International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE): design and methods. BMC Public Health, 13, 900. doi: 10.1186/1471-2458-13-900 Khamis, H. J., & Roche, A. F. (1994). Predicting adult stature without using skeletal age: the Khamis-Roche method. Pediatrics, 94(4 Pt 1), 504-507. Kuczmarski, R. J., Ogden, C. L., Guo, S. S., Grummer-Strawn, L. M., Flegal, K. M., Mei, Z., et al. (2002). 2000 CDC Growth Charts for the United States: methods and development. Vital Health Stat 11(246), 1-190. LDX C. (2003). The accuracy and reproducibility of a rapid, fingerstick method for measuring a complete lipid profile is comparable to a reference laboratory method (b): Cholestec Corporation. Lohman, T., Roche, A., & Martorell, E. (Eds.). (1988). Anthropometric standardization reference manual. Champaign: Human Kinetics. Mirwald, R. L., Baxter-Jones, A. D., Bailey, D. A., & Beunen, G. P. (2002). An assessment of maturity from anthropometric measurements. Med Sci Sports Exerc, 34(4), 689-694. National High Blood Pressure Education Program Working Group on High Blood Pressure in Children and Adolescents. (2004). The fourth report on the diagnosis, evaluation, and treatment of high blood pressure in children and adolescents. Pediatrics, 114(2 Suppl 4th Report), 555-576. doi: 114/2/S2/555 Raudenbush, S. W., Bryk, A. S., Cheong, Y. F., & Congdon, R. T. (2011). HLM 7: Hierarchical Linear and Nonlinear Modeling. Lincolnwood, IL: Scientific Software International. Treuth, M. S., Schmitz, K., Catellier, D. J., McMurray, R. G., Murray, D. M., Almeida, M. J., et al. (2004). Defining accelerometer thresholds for activity intensities in adolescent girls. Med Sci Sports Exerc, 36(7), 1259-1266. doi: 00005768-200407000-00026 Study Sample and Methods Thayse Natacha Queiroz Ferreira Gomes 47 Tudor-Locke, C., Barreira, T. V., Schuna, J. M., Jr., Mire, E. F., & Katzmarzyk, P. T. (2014). Fully automated waist-worn accelerometer algorithm for detecting children's sleep-period time separate from 24-h physical activity or sedentary behaviors. Appl Physiol Nutr Metab, 39(1), 53-57. doi: 10.1139/apnm-20130173 Welk, G. J., & Meredith, M. D. (2008). Fitnessgram/Activitygram Reference. Dalas, TX: The Cooper Institute. World Health Organization. (2011). Waist circumference and waist-hip ratio: Report of a WHO expert consultation. Geneva: WHO. CHAPTER III Research Papers Paper I Correlates of sedentary time in children: a multilevel modelling approach Thayse Natacha Gomes1; Fernanda Karina dos Santos1,2; Daniel Santos1; Sara Pereira1; Raquel Chaves3; Peter T. Katzmarzyk4; José A. R. Maia1 1 CIFI2D, Kinanthropometry Lab, Faculty of Sport, University of Porto, Porto, Portugal 2 CAPES Foundation, Ministry of Education of Brazil, Brasília – DF, Brazil 3 Federal University of Technology – Paraná (UTFPR), Campus Curitiba, Curitiba-PR, Brazil 4 Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA, USA Published in BMC Public Health (2014) doi: 10.1186/1471-2458-14-890 Correlates of sedentary time in children: a multilevel modelling approach Thayse Natacha Queiroz Ferreira Gomes 53 ABSTRACT Background: Sedentary behaviour (SB) has been implicated as a potential risk factor for chronic disease. Since children spend most of their awake time in schools, this study aimed to identify individualand school-level correlates of sedentary time using a multilevel approach, and to determine if these correlates have a similar effect in normal-weight (NW) and overweight/obese (O/O) children. Methods: Sample comprised 686 Portuguese children aged 9-11 years from 23 schools that took part in the ISCOLE project. Actigraph GT3X+ accelerometers were used 24 hours/day for 7 days to assess sedentary time (daily minutes <100 counts/min); BMI was computed and WHO cut-points were used to classify subjects as NW or O/O. Sex, BMI, number of siblings, family income, computer use on school days, and sleep time on school days were used as individual-level correlates. At the school level, school size (number of students), percentage of students involved in sports or physical activity (PA) clubs, school promotion of active transportation, and students’ access to equipment outside school hours were used. All multilevel modelling analysis was done in SPSS, WINPEPI, and HLM. Results: School-level correlates explain ≈ 6.0% of the total variance in sedentary time. Results (β ± SE) showed that boys (-30.85 ± 5.23), children with more siblings (-8.56 ± 2.71) and those who sleep more (-17.78 ± 3.06) were less sedentary, while children with higher family income were more sedentary (4.32 ± 1.68). At the school level, no variable was significantly correlated with sedentary time. Among weight groups, variables related to sedentary time in NW were sex, sleep time and family income, while in O/O sex, number of siblings and sleep time were significant correlates. No school-level predictors were significantly associated in either of the weight groups. Conclusion: Notwithstanding the relevance of the school environment in the reduction of children’s sedentary time, individual and family characteristics played a more relevant role than the school context in this study. Keywords: sedentary behaviour; children; school; multilevel modelling. Correlates of sedentary time in children: a multilevel modelling approach Thayse Natacha Queiroz Ferreira Gomes 55 BACKGROUND Sedentariness is emerging as a potential risk factor for chronic disease [1-6]. For example, among adults, positive associations between sedentary behaviour (SB) such as sitting time and television viewing, and cardiovascular disease and adverse metabolic profiles have been reported [1-4]. In children, the link is also consistent between SB and increased prevalence of overweight/obesity [5], and an increase in metabolic risk factors [6]. Furthermore, systematic reviews have shown that screen time and overall sedentary time (objectively measured) track moderately during childhood and adolescence [7,8], which means that reducing their sedentary time may be a way to induce health benefits into adulthood [9]. Understanding the correlates of sedentary time may aid in developing preventive strategies [10]. Sedentary time may be best represented by a construct that is different from physical activity (PA) [11,12]; however, their determinants might be similar [11,13]. Recently, it has been proposed that ecological approaches may provide a sound basis for a better understanding of sedentary time [14]. These approaches examine interactions between the subject and multiple levels of influence across intrapersonal (biological, psychological), interpersonal (social, cultural), organizational, physical environment (built, natural), and policy (laws, rules, regulations, codes) domains [10]. As such, factors that influence sedentary time in children could be different in home, neighbourhood and school settings, emphasising the necessity to understand the setting-specific multilevel factors that influence this complex behaviour. Since children spend considerable time at school, this multifaceted environment could be an important venue for reducing their sedentary time. The school social and physical environments provide potential opportunities for children to avoid extended periods of sedentary time such as active transportation to and from school, large campus size or playground areas, sports equipment and sporting facilities, recess periods, lunch breaks, and physical education classes [15-19]. However, children spend most of their school time in sedentary activities [20]. The examination of school correlates of Correlates of sedentary time in children: a multilevel modelling approach Thayse Natacha Queiroz Ferreira Gomes 62 More than 90% of the schools have children engaged in sports participation or PA clubs, more than 75% of them promote active transportation among their students, and about 50% of them allow the students to have access to sports equipment outside of school hours. The mean number of students per school is 782 ± 309, ranging from 239 to 1589. Results of the null model, as well as for the other two models from the full sample, are presented in Table 3. Estimated variance at the school level suggests significant inter-individual differences across schools in sedentary time (χ2 = 67.32, p < 0.001). The estimated school-level effects from the intraclass correlation coefficient was 0.0609, meaning that ≈ 6.0% of the total variance in sedentary time among all children is explained by school effects, and 94% is explained by children’s distinct characteristics at their individual level. Also, the reliability estimate of 0.65 is an indicator of how well each school sample mean estimates the overall schools mean sedentary time parameter. Results from M1 related to individual-level predictors show that the sedentary time mean for a girl with a mean age of 10.5 years is 484 minutes·day-1. Boys, children with more siblings and those who sleep more are less sedentary, i.e. spend less time in sedentary activities (p < 0.05), but those with higher family income tend to be more sedentary (p = 0.013). No statistically significant associations were found for BMI and time spent using a computer on school days in mean sedentary time (p > 0.05). The reduction in the variance component at the children’s level allowed the estimation of the proportion (34.4%) of children’s characteristics explaining the inter-individual variance in sedentary time. Correlates of sedentary time in children: a multilevel modelling approach Thayse Natacha Queiroz Ferreira Gomes 63 Table 3. Results summary of hierarchical linear modelling for all sample: estimates, standard-errors, and p-values Parameters Null Model Model 1 Model 2 Estimates Standard Error p-value Estimates Standard Error p-value Estimates Standard Error p-value Intercept 467.02 4.53 <0.001 484.46 5.67 <0.001 491.70 13.05 <0.001 Sex -30.44 4.97 <0.001 -30.85 5.23 <0.001 BMI -0.22 0.69 0.752 1.06 1.85 0.566 BMI X Participation in sports or PA clubs -0.14 0.65 0.829 BMI X Promoting active transport -1.75 1.74 0.316 BMI X Access to equipment outside school hours 0.74 1.67 0.656 Number of siblings -8.50 2.67 0.002 -8.56 2.71 0.002 Family income 4.24 1.70 0.013 4.32 1.68 0.010 Computer using on school days 2.68 3.07 0.383 2.62 3.10 0.399 Sleep time -17.90 2.96 <0.001 -17.78 3.06 <0.001 School size -0.001 13.05 0.910 Participation in sports or PA clubs -1.81 3.78 0.637 Promoting active transport -2.33 8.53 0.787 Variance components: random effects School mean 309.50 202.89 190.05 Children level effect 4765.57 3854.52 3852.97 Model summary Deviance statistic 7781.240599 5551.69 5550.73 Number of estimated parameters 3 9 15 Correlates of sedentary time in children: a multilevel modelling approach Thayse Natacha Queiroz Ferreira Gomes 64 The final model, M2, investigated school effects as well as cross-level interactions. In this model, we assumed that the intercept parameter (sedentary time) varies at level 2. The mean sedentary time of a girl from a school where students are not involved in sports or PA clubs, and do not promote active transportation to school is 492 minutes·day-1. No significant associations were found for school size, percentage of students engaged in sports or PA clubs, or school promotion of active transportation. Similarly, cross-level interactions between BMI and school climate variables tested did not show any significant interaction. Table 4 shows the results for the two weight groups (NW and O/O). Since BMI was used to classify subjects in weight groups, this variable was excluded in these analyses, as well the cross-level interactions between BMI and school climate variables. Among NW children, significant associations were found for sex, sleep time and family income, where boys and children who sleep more are less sedentary (p < 0.05); those with higher family income have higher sedentary time (p = 0.008). For O/O, being a boy, children with more siblings and those who sleep more have a significantly lower mean sedentary time (p < 0.001). Similar to the overall sample, no significant associations were found between sedentary time and school variables in NW and O/O groups. Correlates of sedentary time in children: a multilevel modelling approach Thayse Natacha Queiroz Ferreira Gomes 65 Table 4. Summary of results of final model for two BMI groups (normal-weight and overweight/obese groups): estimates (standard-errors), and p-values Parameters Normal-weight (N = 340) Overweight/Obese (N = 272) Regression coefficients: fixed effects Estimates Standard Error p-value Estimates Standard Error p-value Intercept 483.23 13.35 <0.001 514.03 19.96 <0.001 Sex -32.93 8.45 <0.001 -29.12 7.33 <0.001 Number of siblings -4.25 4.48 0.344 -10.25 5.18 0.049 Family income 6.29 2.34 0.008 2.59 2.58 0.317 Computer using on school days 1.63 3.52 0.643 5.44 4.44 0.222 Sleep time -25.85 4.45 <0.001 -9.52 3.04 0.002 School size -0.02 0.01 0.105 0.01 0.01 0.502 Participation in sports or PA clubs -0.18 4.56 0.969 -6.20 5.75 0.294 Promoting active transport 7.67 8.85 0.397 -20.29 13.43 0.147 Variance components: random effects School mean 8.75 389.16 Children level effect 4043.96 3492.64 Model summary Deviance statistic 2919.962555 2223.24 Number of estimated parameters 11 11 Correlates of sedentary time in children: a multilevel modelling approach Thayse Natacha Queiroz Ferreira Gomes 66 DISCUSSION This study aimed to identify the magnitude of childand school-level correlates of sedentary time and to determine if their importance was similar in NW and O/O children using a multilevel modelling approach. At the child level, most of the variables included in the model were significantly linked to sedentary time. Sex differences in sedentary time are well documented [37], showing that girls spend more time in sedentary activities [11], which was confirmed in the present study. Van Stralen et al [38] studied the time devoted to sedentary activities at school in children aged 10-12 years from five European countries, and reported that girls spent a significant larger amount of school-time in sedentary activities (67%) than boys (63%, p < 0.0001), which can be related to differences in sex options for engagement in activities during recess time, with boys engaging more in competitive games while girls prefer socialising with friends [39]. Similarly, Verloigne et al [40] also found that girls spend more time in sedentary activities (511 minutes·day-1) than boys (478 minutes·day-1) taking into account the whole day, not only school time. Since in the present study children were monitored 24 hours·day-1, the sedentary time variable represents the entire day, not just sedentary time while at school. As such, in association with the explanation for the sex differences in sedentary time during school hours, it is also possible that these differences may be potentiated by dissimilarities in boys’ and girls’ leisure time activities. Since boys tend to devote more time in PA and/or in sports participation [41] during their leisure time, this behaviour may be relevant to decrease their sedentary time. The influence of siblings on children’s sedentariness is not clear. It has also been reported, in a longitudinal study, that children with more siblings exhibit smaller increases in objectively measured sedentary time [42]. On the other hand, Verligne et al [43] investigated the effect of an intervention program on 10-12 year old Belgian children’s total sedentary time, and reported that those with one or more siblings were less likely to reduce sedentary time after the intervention program. Further, Tandon et al [44] reported that children watched TV/DVD’s with siblings more days per week, on average, than they did Correlates of sedentary time in children: a multilevel modelling approach Thayse Natacha Queiroz Ferreira Gomes 67 PA’s, reinforcing a potentially positive influence of the sibling for sedentary behaviour. On the other hand, it was suggested that the presence of more children at home (i.e., more siblings) is highly related with more moderate-tovigorous PA overall and at home, and more sedentary time at home but less screen time [45]. We found a negative association between number of siblings and sedentary time in children, implying that the more siblings children have, the less sedentary they are. Since at this age there is a high peer influence in children behaviour [41], it is possible that those with less sedentary siblings tend to also become less sedentary. Sleep time was negatively associated with sedentary time, indicating that children that slept more spent less time in sedentary activities. Several studies have shown that SB may interfere with sleep [46-48], but the results are not conclusive. For example, Belgium students who spent more time in sedentary activities, such as watching TV, playing video games, and using the internet went to bed later, spending less time in bed on weekdays [47]. However, in Taiwanese adolescents [49] no association was found between the time they spent watching TV or using a computer and getting sufficient sleep. A positive association was found between family income and sedentary time, although the results from other studies have not always been clear about the magnitude and direction of this association [11]. For example, Olds et al [50] studied the socio-demographic correlates of SB in children aged 9-16 years, and found that children from higher SES reported greater engagement in nonscreen sedentary time (such as sitting or lying down), but those from lower SES spent more time in screen-based sedentary time (watching TV, playing videogames, using computer), and no significant difference across income bands was found for total sedentary time (sum of non-screen sedentary time and screen sedentary time). Similarly, Foley et al [51] reported that 10-18 years old adolescents from areas of lower deprivation (i.e., higher SES) tended to accumulate more total sedentary time, which was determined by the concomitant use of an accelerometer and a recall diary. Furthermore, Klitsie et al [52], also using an objective and subjective method to access sedentariness, reported that 9-10 year old children with higher SES spent more time in non- Correlates of sedentary time in children: a multilevel modelling approach Thayse Natacha Queiroz Ferreira Gomes 68 screen SB; however, those from low SES and those from high SES both had higher sedentary time than those of medium SES. Using an objective method to measure sedentary time, namely accelerometers, Steele et al [53] did not find any difference in sedentary time according to SES, while Atkin et al [42] reported an increase in sedentary time, after a one-year period, among children from higher SES. Our findings add to this body of evidence, and suggest that Portuguese children with higher family income have greater sedentary time than those with low family income. There is some prior evidence that children with a higher BMI are more sedentary, spending more time watching TV [54,55]. However, in the present study no significant association was found between sedentary time and BMI. Further, the interaction between BMI and school climate variable did not reveal a mediation effect of school characteristics on the role of BMI on sedentary time. However, TV watching was not specifically measured in the present study, and the relationship with BMI may differ across different sedentary behaviours. Schools offer extracurricular activities and policies that could potentially reduce sedentary time among students [15-19]. In this study, only 6.0% of the total variance in sedentary time was explained by school-level variables. It is known that schools with a larger campus size or playground areas provide more opportunities for their students to engage in PA during recess time, potentially decreasing their sedentary time [15,17,19]. In addition, athletic facilities such as school sports or PA clubs appear to be good opportunities to decrease sedentary time and increase PA in youth [56]. Moreover, active commuting to school is associated with higher PA levels among youth [57,58], and children who drive to/from school are less likely to achieve recommended levels of daily PA [59]. However, despite the suggestion that school context has the potential to reduce children’s sedentary time, in the present study we did not find such an association. Our study was potentially under-powered to identify school level effects, given the sample size of only 23 schools (versus a sample size of 686 children for individual-level correlates). Further, there was limited variance in some of the school-level variables measured in this study (i.e. more than 90% of the schools have children engaged in sports participation or PA clubs). Thus, Correlates of sedentary time in children: a multilevel modelling approach Thayse Natacha Queiroz Ferreira Gomes 69 a study with a larger sample size of school, and with greater variability among schools in the environmental variables, may be better suited to detect schoollevel correlates. When the analyses was stratified by body weight status, sex and sleep time were related to sedentary time in both NW and O/O groups; family income was only related to sedentary time in the NW group, while number of siblings was related to sedentary time in O/O; further, no school-level predictor was significantly associated with sedentary time in either group. Differences in individual-level sedentariness correlates among weight groups suggests that attention should be paid to weight status when implementing strategies to decrease sedentary time in children, such that the chosen activities should be easily and playfully performed by both NW and O/O children; additionally, body weight should not be a barrier to those children with higher weight. This study has several limitations and strengths. Firstly, as we did not study distinct SB’s (screen time, reading, listening to music, transportation to/from school, etc.), rather we focused on objectively determined overall sedentary time. Thus, it was not always possible to compare our results with previous studies that did not assess sedentary time objectively using accelerometry [11,60]. Secondly, the present sample comes from only one Portuguese region and its results do not necessarily generalize to all children. However, a comparison of the present sample characteristics with information available from the Portuguese population of the same age and gender was done. For example, in data not shown here, no differences were found in the prevalence of overweight/obesity [61], in the percentage of children attaining sufficient levels of PA [62], and SES distribution [63]. Thirdly, despite the evidence that moderate-to-vigorous PA attenuates the association between SB and health risk [64], we did not include this information as a covariate. Notwithstanding these limitations, the study has several important strengths: (1) the use of an objective method to estimate sedentary time; (2) the use of the accelerometer for 7 days; (3) inclusion of objective information regarding sleep time; (4) using standard methods and highly reliable data, and (5) the use of Correlates of sedentary time in children: a multilevel modelling approach Thayse Natacha Queiroz Ferreira Gomes 70 multilevel modelling to capture the complexity of nested information available at the child and school levels. CONCLUSIONS In summary, this study investigated the role of individualand schoollevel variables with children’s sedentary time within the multilevel modelling framework. School context explained 6.0% of the total variance in children’s sedentary time. At the individual level, sex, number of siblings, family income and sleep time explained 34.4% of the 94% of the variance fraction of the individual level. No significant association was found between sedentary time and BMI, as well as between sedentary time and school-level correlates. Notwithstanding the relevancy of school diversified environments to reduce sedentary time in children, enhancing their opportunities for being less sedentary in their awake time, requires further analysis with a more diversified list of markers than those explored in the present study. Furthermore, differences in sedentary time correlates among NW and O/O children suggest that different strategies may be needed to reduce sedentary time in these two groups. Moreover, given the association between sedentary time and health risks, future studies should be conducted using direct measures of total sedentary time, distinguishing different types of SB and examining different patterns in which sedentary time is accumulated. Furthermore, the use of an inclinometer, in association with the accelerometer, could be useful to provide information regarding postural changes. In addition, since sedentariness and PA are two distinct phenotypes, and being physically active does not imply being less sedentary, future studies should also investigate the relationship between these two variables on health risk factors, independently and in association. ABBREVIATIONS SB, Sedentary behaviour; PA, Physical activity; NW, Normal-weight; O/O, Overweight/obese; ISCOLE, International study of childhood obesity, Correlates of sedentary time in children: a multilevel modelling approach Thayse Natacha Queiroz Ferreira Gomes 71 lifestyle and the environment; BMI, Body mass index; WHO, World health organization; SES, Socioeconomic status. COMPETING INTERESTS The authors declare that they have no competing interests. AUTHOR CONTRIBUTIONS TNG collected the data, undertook the data analysis and interpretation, and led the writing of the article. FKS and DS collected the data and contributed to drafting the paper. SP and RC collected the data. PTK conceptualized and designed the study and contributed to drafting the paper. JM organized and supervised data collection and management, and contributed to drafting the paper. All authors read and approved the final manuscript. ACKNOWLEDGEMENTS We would like to thank Alessandra Borges, Pedro Gil Silva and Sofia Cachada for their role in data collection for the Portuguese site of ISCOLE, and the Coordinating Center of ISCOLE in Baton Rouge, Louisiana. We would also like to thank the study participants along with their parents, teachers and school principals for their involvement in the study. ISCOLE was funded by the CocaCola Company. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of this manuscript. Paper II Why are children different in their daily sedentariness? An approach based on the mixed-effects location scale model Thayse Natacha Gomes1; Donald Hedeker2; Fernanda Karina dos Santos3; Sara Pereira1; Peter T. Katzmarzyk4; José A. R. Maia1 . 1 CIFI2D, Kinanthropometry Lab, Faculty of Sport, University of Porto, Porto, Portugal 2 Department of Public Health Sciences, University of Chicago, Chicago, IL, USA 3 Department of Physical Education and Sports Science, CAV, Federal University of Pernambuco, Vitória de Santo Antão-PE, Brazil 4 Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA, USA Under review in PloS One Why are children different in their daily sedentariness? An approach based on the mixed-effects location scale model Thayse Natacha Queiroz Ferreira Gomes 81 ABSTRACT This study aimed to investigate the betweenand within-individual variability in sedentary time over seven days, using a mixed-effects location scale model. The sample comprised 686 Portuguese children (381 girls) aged 9-11 years, from 23 schools. Sedentary time was estimated by the Actigraph GT3X+ accelerometer, which was used 24 hours/day for 7 consecutive days; height, sitting height, and weight were measured, BMI was computed (WHO cut-points were used to classify subjects as normal-weight or overweight/obese), and maturity offset was estimated. Information regarding the home environment was obtained by questionnaire. Results revealed that: (i) children were more sedentary on Friday, but less so on Saturday and Sunday (compared to Monday), with significant variation betweenand within-subjects (betweensubject variance=0.800, within-subject variance=1.793, intra-subject correlation=0.308); (ii) there is a sex effect on sedentariness, with boys being less sedentary than girls (p<0.001), and the between-subject variance was 1.48 times larger for boys than girls; (iii) in terms of the within-subject variance, or erraticism, Tuesday, Wednesday and Friday have similar erraticism levels as Monday (Thursday has less, while Saturday and Sunday have more); in addition, girls (variance ratio=0.632, p<0.001), overweight/obese children (variance ratio=0.861, p=0.019), and those later mature (variance ratio=0.849, p=0.013) have less erraticism than their counterparts; (iv) the within-subject variance varied significantly across subjects (scale std dev=0.342±0.037, p<0.001); and (v) in the fixed part of the model, only biological maturation was positively related to sedentariness. This study demonstrated that there is significant betweenand within-subject variability in sedentariness across a whole week. This implies that a focus on intra-individual variability, instead of only on mean values, would provide relevant information towards a more complete map of children’s sedentary behaviour, which can be helpful when developing more efficient strategies to reduce sedentariness. Keywords: sedentariness; children; Portugal; ISCOLE; mixed-effects location scale model. Why are children different in their daily sedentariness? An approach based on the mixed-effects location scale model Thayse Natacha Queiroz Ferreira Gomes 83 INTRODUCTION The last years witnessed an augmented interest in monitoring and understanding sedentary behaviours [1], their correlates [2], and their relationships with health hazards and reduced quality of life [3,4]. There is now compelling evidence that children and adolescents spend a large proportion of their day in sedentary behaviours [5]. However, sedentary behaviour differs among youth according to their intrapersonal traits and interpersonal characteristics, as well as built and physical environmental factors [5,6,7]. For example, several studies have identified distinct clusters of youth based on their levels and patterns of sedentary behaviour alongside their physical activity levels [1,8,9]. Further, sex [10], age [11], and maturity status [12] have also been identified as correlates of sedentariness. Notwithstanding the intensified interest in having a more comprehensive understanding of patterns and correlates of sedentary behaviour [5,10,13,14], the available research has focused largely on mean differences [15], used sets of covariates in multiple regression models to predict sedentariness [2], or studied contextual inter-individual differences using multilevel models [16,17,18]. To our knowledge, exploring factors related to intra-individual variability in sedentary behaviour across days, which can help to better understand observed between-subject differences and effectiveness of interventions beyond mean changes, has never been addressed. Studying variability in intra-individual differences in daily behaviours is relevant to provide an understanding of patterns of sedentary behaviour over time [19,20]. Further, seven-day objective monitoring is an acceptable window to study different physical activity and sedentary behaviour expressions [21]. Thus, the purpose of the current study is to investigate the betweenand withinindividual variances in sedentariness over seven days of objective monitoring, in order to answer the following questions: (i) Is there a trend in children’s sedentary behaviour over an entire week? (ii) Is this trend similar in boys and girls? (iii) Is there appreciable variability in sedentary time across days? (iv) Does variability in sedentary behaviour differ for each subject or is its magnitude Why are children different in their daily sedentariness? An approach based on the mixed-effects location scale model Thayse Natacha Queiroz Ferreira Gomes 84 similar for all children? (v) Which variables are associated with variability in sedentary behaviour? To answer these questions, we used a mixed-effects location scale model [22,23] which allows both the mean and variance structures to be modelled in terms of covariates. METHODS Sample The sample of this study is part of the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE), a research project conducted in 12 countries from all major world regions. In short, ISCOLE aims to determine the relationship between lifestyle behaviours and obesity in a multi-national study of children, and to investigate the influence of higher-order characteristics such as behavioural settings, and the physical, social and policy environments, on the observed relationship within and between countries [24]. Since the purpose of ISCOLE was to study children with a mean age of 10 years, ranging from 9 to 11 years, our sample recruitment was only done in 5th grade students. A total of 777 5th grade Portuguese children (419 girls), aged 9-11 years, were assessed, and after the inclusion criteria (accelerometer valid data for at least 4 days, as described below), the final sample comprised 686 children (381 girls). These students belong to 23 schools from the metropolitan area of Porto, North of Portugal, which were selected from a list provided by the North Regional Education Directory Board, taking account their location (schools should be located in different socioeconomic neighbourhoods). After a first initial contact with a physical education teacher from each school, the project was presented to the physical education department. Following their approval, the project was then presented to the school principal as well as to the parental council; it was only after obtaining these approvals that the project was implemented in each school. All 5th grade children were invited to be part of ISCOLE; however, only children aged between 9 and 11 years were classified as “eligible” to be part at the project. From those “eligible” Why are children different in their daily sedentariness? An approach based on the mixed-effects location scale model Thayse Natacha Queiroz Ferreira Gomes 85 children, a sample of ≈30-40 children per school was randomly selected (50% for each sex). Non-response was negligible (response rate was 95.7%). Data were collected from September 2011 to January 2013. All assessments were done during a full week per school by trained personnel from the Kinanthropometry Laboratory of the Faculty of Sport (University of Porto) following certification from the ISCOLE Coordinating Center; the questionnaires were answered by each child, at their school, after anthropometric measures were taken, and under the supervision of at least one ISCOLE staff member. The study protocol was approved by the University of Porto ethics committee, as well as by the schools’ directorate councils. Written informed consent was obtained from parents or legal guardians of all children. All data collection and management activities were performed and monitored under rigorous quality control procedures, implemented by the ISCOLE Coordinating Center, as previously described in detail [24]. Anthropometry Height, sitting height, and weight measures were taken according to standardized ISCOLE procedures [24]. For height and sitting height, children were measured without shoes, with head positioned in the Frankfurt Plane, using a portable stadiometer (Seca 213, Hamburg, Germany); height was measured with children fully erect, feet together, and at the end of a deep inhalation, while sitting height was measured with children seated on a table with legs hanging freely and arms resting on the things. Leg length was computed by subtracting sitting height from standing height. Weight was determined using a portable Tanita SC-240 body composition analyzer (Arlington Heights, IL), with children wearing light clothes and without shoes or socks. Each child was measured twice and, when necessary, a third measurement was taken if the difference between the previous two was outside the permissible range for each measure and its replica (0.5 cm for height and sitting height; 0.5 kg for weight). The mean value of each measured variable was used for analysis. Why are children different in their daily sedentariness? An approach based on the mixed-effects location scale model Thayse Natacha Queiroz Ferreira Gomes 86 Body mass index (BMI) was calculated using the standard formula [weight(kg)/height(m)2], and subjects were classified into two groups (normalweight and overweight/obese) according to the cut-off points from the World Health Organization (WHO), based on BMI z-scores (normal-weight: <+1SD; overweight/obese; ≥+1SD) [25]. Family data Family information was obtained by a questionnaire completed by parents or legal guardians [see ISCOLE Demographic and Family Health Questionnaire [24]]. The questionnaire collected information on basic demographics, ethnicity, family health and socioeconomic factors, and was answered by parents/legal guardians during the same week their children were assessed at school. For the present study, we used information about media availability in the child’s bedroom. Media availability in the child’s bedroom was determined by asking parents if children had a computer or video game in their bedroom. The existence of TV in the child’s bedroom was informed by the children. Using information regarding media availability in the child’s bedroom (TV, computer or game), a “media bedroom” variable was computed to determine if there is, at least, one media available at children’s bedroom; so subjects were classified as “having media in bedroom” or “not having media in the bedroom”. Sedentary time and sedentary behaviour Actigraph GT3X+ accelerometers (ActiGraph, Pensacola, FL) were used to monitor sedentary time. Children wore the accelerometer at their waist on an elasticized belt, placed on the right mid-axillary line 24 hours/day, for at least 7 days, including 2 weekend days. To be eligible for this analysis, children had at least 4 days (from which at least one of them should be a weekend day) with a minimum of 10 hours of wear time per day; 686 children fulfilled this condition. Accelerometer information was divided into daytime activities and nocturnal sleep time using an automated algorithm, and any sequence of at least 20 Why are children different in their daily sedentariness? An approach based on the mixed-effects location scale model Thayse Natacha Queiroz Ferreira Gomes 87 consecutives minutes of zero activity counts during awake period was considered as non-wear time [26,27] Sedentariness is a multi-faceted characteristic that includes behaviour at work/school, at home, during transport, and in leisure-time including screentime, motorized transportation, and sitting (to read, talk, do homework, or listen to music) [28]. In the present study, sedentary time objectively measured by the accelerometer is our primary dependent variable, and is defined as equal to or less than 25 counts/15 seconds as advocated by Evenson et al. [29]. Further, information was also collected about children’s sedentary behaviour, by asking them about time spent watching TV during school days [ISCOLE Diet and Lifestyle Questionnaire [24]], and they were classified as ≤2hours/day or >2hours/day. Biological maturation Using information on sex, age, and physical growth characteristics (sitting height, leg length, stature and body mass), an estimate of biological maturity, namely somatic maturation, was obtained using the Mirwald et al. [30] maturity offset method. This method estimates, in decimal years, the status of the child relative to their age at peak height velocity (PHV) occurrence. A positive maturity offset expresses the number of years a child is beyond PHV; a negative maturity offset indicates the number of years before PHV. Data analysis All exploratory data analysis and descriptive statistics, as well t-tests, were done in SPSS 20, and Excel was used to plot differences in sedentariness trajectories and patterns of two boys and girls with similar mean sedentariness values. The specifics of the mixed-effects location scale model has been described elsewhere in great detail [22,23]. Briefly, the model for the sedentary measurement y, of child i (i=1, 2, 3, …, N subjects) on day j (j=1, 2, 3, …, ni days) is, Why are children different in their daily sedentariness? An approach based on the mixed-effects location scale model Thayse Natacha Queiroz Ferreira Gomes 94 examined volume and patterns of sedentary activities during different segments of the week in UK children aged 9-10 years, and Harrington et al. [33] found higher levels of sedentary behaviour during weekdays when compared to weekend days among Irish female adolescents. This consistent pattern in sedentariness during the week days of children from different countries is governed, to some extent, by their school schedules and activities, which contribute to more sedentary behaviour among students. Harrington et al. [33] highlighted that during the period children spend at school they usually accumulate more sedentary time, implying that the school setting appears to impose sedentariness in children, especially promoting unbroken continuous periods of sitting. On the other hand, during the weekend, children have more opportunities to be physically active, spending less time in sedentary activities, such as sitting, reading or using the computer. The second question addressed the issue of sex differences in sedentary behaviour, and boys were not only less sedentary but also showed more heterogeneity in their sedentariness across the seven days. Sex differences on average levels of both physical activity and sedentariness have been previously reported showing that girls usually tend to be more sedentary [10,34], which can be related to their options for sedentary activities during their leisure time (such as reading, listening to music, socializing with friends), while boys tend to engage in more physically activities (such as sport participation, competitive games) [35]. Since sex differences were found in the children’s sedentary behaviour heterogeneity (Model 2, =0.390±0.147, p=0.008), this was further explored across all days as well as its difference between boys and girls (Model 3) using the novelty and flexibility of the mixed-effects location scale model [22,23] which allows the WS variance to be modelled in terms of regressors and also allows subjects to vary in their consistency/erraticism. Results showed significant WS variance across days, meaning that children do not have the same sedentary pattern across all days. To our knowledge this is the first time that the idea of BS and WS variance in sedentariness has been jointly explored. It is expected that children vary in their physical activity and sedentariness between and within Why are children different in their daily sedentariness? An approach based on the mixed-effects location scale model Thayse Natacha Queiroz Ferreira Gomes 95 days [36], and some studies have investigated the within-day variability in sedentariness in children and adolescents considering the school-time and outside school-time. For example, Harrington et al. [33] did not find differences in girls’ sedentary time between these two periods of the day, but reported that these adolescents tend to accumulate more sedentary bouts during school time. On the other hand, Steele et al. [32] reported period and sex differences in sedentary time during the day, with boys spending more time in sedentary activities out of school while no differences in sedentariness between “inschool” and “out-of-school” periods were observed in girls. However, these “trends” may not be similar in all children given that the analysis by Harrington et al. [33] and Steele et al. [32] were based on averages and/or percentages of the total time. We think that differences in children’s sedentariness may be more properly addressed by the modelling of the WS variance, which is not always taken into account. Previous research specifically addressing the issue of intra-individual differences, although in aging, reported by Hertzog and Nesselroade [37], clearly stated that using averages to describe changes is not always the best way to detect key features of developmental changes and/or short-term differences. Further, they showed that change can vary within a person over weeks, even when the time of the day and day of the week of testing is kept constant. Additionally, Epstein [38] pointed out that not everyone is equally predictable, highlighting that a significant WS variance exists and that it should be taken into account, independent of the outcome variable. Fig 3 highlights the WS differences in sedentariness along a whole week in two boys and two girls. Children with the same (or similar) mean sedentariness time show different sedentariness trajectories and patterns during the week, revealing that WS variance exists and that should be taken into account when studying correlates of sedentary behaviour. Why are children different in their daily sedentariness? An approach based on the mixed-effects location scale model Thayse Natacha Queiroz Ferreira Gomes 96 Table 3. Parameter estimates (±standard errors) of the four models Model parts Model 1 Model 2 Model 3 Model 4 ±SE ±SE ±SE ±SE Fixed part Intercept 9.243±0.062** 9.433±0.068** 9.451±0.066** 9.740±0.168** Tuesday 0.070±0.074ns 0.069±0.074ns 0.077±0.067ns 0.073±0.067ns Wednesday 0.004±0.074ns 0.005±0.074ns 0.008±0.068ns 0.006±0.068ns Thursday -0.064±0.073ns -0.064±0.073ns -0.058±0.065ns -0.064±0.065ns Friday 0.167±0.074** 0.167±0.074*** 0.184±0.071** 0.187±0.071** Saturday -0.261±0.073** -0.260±0.073** -0.265±0.076** -0.267±0.076** Sunday -0.267±0.074** -0.267±0.074** -0.294±0.077** -0.297±0.076** Sex -0.430±0.079** -0.475±0.079** -0.104±0.165ns BMI -0.151±0.090* Maturity Off 0.242±0.090** Time TV 0.183±0.126ns Media 0.072±0.095ns Between Subject (BS) variance Intercept -0.232±0.073** -0.482±0.104** -0.401±0.098** -0.436±0.099** Sex 0.390±0.147** 0.299±0.145** 0.330±0.146** Within Subject (WS) variance Intercept 0.584±0.022** 0.584±0.022** 0.330±0.071** 0.581±0.132** Tuesday -0.106±0.094ns -0.109±0.094ns Wednesday -0.023±0.092ns -0.024±0.092ns Thursday -0.199±0.093** -0.204±0.093** Friday 0.124±0.092ns 0.130±0.092ns Saturday 0.369±0.091** 0.370±0.091** Sunday 0.360±0.092** 0.356±0.092** Sex 0.199±0.055** 0.459±0.115** BMI -0.150±0.064** Maturity Off 0.164±0.066** Time TV -0.072±0.089ns Media 0.026±0.068ns Random location (mean) effect on WS variance Loc eff 0.086±0.031** 0.080±0.031** Random scale standard deviation Std dev 0.348±0.037** 0.342±0.037** Deviance (-2 Log L) 16892.843 16855.689 16712.490 16694.754 ns= non-significant; *<0.10; **<0.05; -2 Log L= -2 Log-likelihood The usual approach in population studies, taking into account only the mean values of the entire sample, is based on the assumption that results and the distribution of the variables at the population level somehow reflect withinperson processes, which allow the generalization from the population to the individual [39]. However, as we showed in Fig 2, results at the person level are different from those at the sample level, and as reported by Hamaker [39], when there are individual differences that cannot be ignored, it is necessary to “investigate to see what predicts them or what they predict” [39, p. 52]. This was the main drive of our last question (results in Model 4) addressing the simultaneous effects of sex, BMI categories, biological maturation, time Why are children different in their daily sedentariness? An approach based on the mixed-effects location scale model Thayse Natacha Queiroz Ferreira Gomes 97 watching TV, and electronic media in the bedroom in children mean differences (expressed in the fixed part of the model) and intra-individual variability (expressed in the WS variance) in sedentariness across the seven days. From the set of covariates tested, only biological maturation was significantly related to mean levels of sedentariness, indicating that more mature children tend to be more sedentary than their less mature peers. This result is in accordance with previous studies where this association was reported [12,40]. For example, Brodersen et al. [12] described that more advanced puberty was associated with greater sedentariness in youth. Similarly, Machado Rodrigues et al. [40] found that maturity status is a significant predictor of sedentariness, but only in boys. Since girls mature earlier than boys, it is possible that differences in maturation timing and tempo may also explain sex differences in sedentariness, as described in associations with physical activity and exercise [41,42,43], but this issue is still unclear [40]. From those variables related to the WS variance in sedentariness, sex (=0.459±0.115, p<0.001), BMI categories (=-0.150±0.064, p=0.019) and biological maturation (=0.164±0.066, p=0.013) showed significant effects on the variance in sedentariness, meaning that girls, overweight/obese children and those late in their maturation have a lower erraticism in their sedentariness. Further, this result reinforces the need for a careful study of WS variance. Since, in general, the covariates at WS level differ from those at BS level as well as from those at mean level of sedentariness, generalization of the results about covariates from the inter-individual level can extend to intra-individual variability, or considering erraticism a nuisance, may not be appropriate. In addition, as highlighted by Molenaar (cited by Hamaker [39]), this generalization from the population to the individual is only appropriate when the population moments (means, variances, and covariances) are identical to the corresponding within-person moments, which are not the case in almost situations. Some limitations in the present study should be discussed. Firstly, since children spend a substantial part of their awake time at school, the school environment can have a relevant role at WS variance in sedentariness; Why are children different in their daily sedentariness? An approach based on the mixed-effects location scale model Thayse Natacha Queiroz Ferreira Gomes 98 however, school context characteristics were not included in the model, and its effect on children’s sedentariness or physical activity is not always clear, given the large variability found in the school effects’ intraclass correlation going from ≈0.06 to ≈0.36 [18,44]. Secondly, it is possible that children vary in their sedentariness also within a day, due to their different surroundings (e.g., school, home, sports club), and studies of sedentariness variance during the day can offer relevant information about children’s patterns of sedentary behaviour. The use of ecological momentary assessment approaches may be highly useful in unravelling this issue [45,46]. Thirdly, the sample comes from only one Portuguese region, meaning that results cannot be generalized to other Portuguese children. However, in data not shown, similar results were observed in some sample characteristics between our sample and others from previous studies, namely in the prevalence of overweight/obesity [47] and socioeconomic status distribution [48]. In spite of these limitations, several strengths should be pointed out: (1) to our knowledge, this is the first study that explored WS variance in sedentariness, highlighting the relevance of understanding the BS variance, the WS variance as well as their possible predictors; (2) the use of an objective method to estimate sedentariness during a whole week; (3) the use of standard methods and reliable data; (4) and the use of the mixed-effects location scale model to study the complexity of BS and WS variance in sedentariness. CONCLUSIONS This study showed that children are significantly different in their sedentariness during the days of the week, and tend to be less sedentary during the weekend (suggesting that the school context may play a relevant role), and that sex difference exists regarding to sedentariness. Within-child consistency/erraticism showed high variability across days, meaning that children do not have the same sedentariness patterns along the week; further, sex, BMI, and biological maturation have significant effects on the sedentariness variance. Since results from betweenand within-child are not Why are children different in their daily sedentariness? An approach based on the mixed-effects location scale model Thayse Natacha Queiroz Ferreira Gomes 99 the same, namely in their correlates, this reinforces the need to a deeper investigation on intra-individual variability above and beyond the normative view of mean values and heterogeneity among subjects. In addition, results found at the inter-individual level do not generalize to intra-individual level. The approach used in the mixed-effects location scale model showed to be very important in providing detailed information for a better understanding of correlates that best explain intra-individual sedentariness consistency/erraticism. Taken together, these findings provide evidence that a more complete map of children’s patterns in sedentary behaviour will be highly important when designing intervention strategies to reduce their sedentariness and associated health hazards, and that within-child variance should not be neglected. ACKNOWLEDGMENTS We would like to thank Alessandra Borges, Pedro Gil Silva, and Sofia Cachada for their role in data collection for the Portuguese site of ISCOLE, and the Coordinating Center of ISCOLE in Baton Rouge, Louisiana. We would also like to thank the study participants along with their parents, teachers and school principals for their involvement in the study. Why are children different in their daily sedentariness? An approach based on the mixed-effects location scale model Thayse Natacha Queiroz Ferreira Gomes 100 REFERENCES 1. Gorely T, Marshall SJ, Biddle SJ, Cameron N. Patterns of sedentary behaviour and physical activity among adolescents in the United Kingdom: Project STIL. J Behav Med. 2007; 30: 521-531. 2. Atkin AJ, Corder K, Ekelund U, Wijndaele K, Griffin SJ, van Sluijs EM. Determinants of change in children's sedentary time. PLoS One. 2013; 8: e67627. 3. Vicente-Rodriguez G, Rey-Lopez JP, Martin-Matillas M, Moreno LA, Warnberg J, Redondo C, et al. Television watching, videogames, and excess of body fat in Spanish adolescents: the AVENA study. Nutrition. 2008; 24: 654662. 4. Steele RM, Brage S, Corder K, Wareham NJ, Ekelund U. Physical activity, cardiorespiratory fitness, and the metabolic syndrome in youth. J Appl Physiol. 2008; 105: 342-351. 5. Pate RR, Mitchell JA, Byun W, Dowda M. Sedentary behaviour in youth. Br J Sports Med. 2011; 45: 906-913. 6. Uijtdewilligen L, Nauta J, Singh AS, van Mechelen W, Twisk JW, van der Horst K, et al. Determinants of physical activity and sedentary behaviour in young people: a review and quality synthesis of prospective studies. Br J Sports Med. 2011; 45: 896-905. 7. Sallis JF, Prochaska JJ, Taylor WC. A review of correlates of physical activity of children and adolescents. Med Sci Sports Exerc. 2000; 32: 963-975. 8. Zabinski MF, Norman GJ, Sallis JF, Calfas KJ, Patrick K. Patterns of sedentary behavior among adolescents. Health Psychol. 2007; 26: 113-120. 9. Marshall SJ, Biddle SJH, Sallis JF, McKenzie TL, Conway TL. Clustering of sedentary behaviors and physical activity among youth: A cross-national study. Pediatr Exerc Sci. 2002; 14: 401-417. Why are children different in their daily sedentariness? An approach based on the mixed-effects location scale model Thayse Natacha Queiroz Ferreira Gomes 101 10. Van Der Horst K, Paw MJ, Twisk JW, Van Mechelen W. A brief review on correlates of physical activity and sedentariness in youth. Med Sci Sports Exerc. 2007; 39: 1241-1250. 11. Brodersen NH, Steptoe A, Boniface DR, Wardle J. Trends in physical activity and sedentary behaviour in adolescence: ethnic and socioeconomic differences. Br J Sports Med. 2007; 41: 140-144. 12. Brodersen NH, Steptoe A, Williamson S, Wardle J. Sociodemographic, developmental, environmental, and psychological correlates of physical activity and sedentary behavior at age 11 to 12. Ann Behav Med. 2005; 29: 2-11. 13. Marques A, Sallis JF, Martins J, Diniz J, Carreiro Da Costa F. Correlates of urban children's leisure-time physical activity and sedentary behaviors during school days. Am J Hum Biol. 2014. 14. Hinkley T, Salmon J, Okely AD, Trost SG. Correlates of sedentary behaviours in preschool children: a review. Int J Behav Nutr Phys Act. 2010; 7: 66. 15. Biddle SJ, Gorely T, Marshall SJ, Cameron N. The prevalence of sedentary behavior and physical activity in leisure time: A study of Scottish adolescents using ecological momentary assessment. Prev Med. 2009; 48: 151-155. 16. Ridgers ND, Timperio A, Crawford D, Salmon J. What factors are associated with adolescents' school break time physical activity and sedentary time? PLoS One. 2013; 8: e56838. 17. Leatherdale ST, Faulkner G, Arbour-Nicitopoulos K. School and student characteristics associated with screen-time sedentary behavior among students in grades 5-8, Ontario, Canada, 2007-2008. Prev Chronic Dis. 2010; 7: A128. 18. Gomes TN, dos Santos FK, Santos D, Pereira S, Chaves R, Katzmarzyk PT, et al. Correlates of sedentary time in children: a multilevel modelling approach. BMC Public Health. 2014; 14: 890. 19. Van Cauwenberghe E, Jones RA, Hinkley T, Crawford D, Okely AD. Patterns of physical activity and sedentary behaviour in preschool children. Int J Behav Nutr Phys Act. 2012; 9: 138. Why are children different in their daily sedentariness? An approach based on the mixed-effects location scale model Thayse Natacha Queiroz Ferreira Gomes 102 20. Telford RM, Telford RD, Cunningham RB, Cochrane T, Davey R, Waddington G. Longitudinal patterns of physical activity in children aged 8 to 12 years: the LOOK study. Int J Behav Nutr Phys Act. 2013; 10: 81. 21. Matthews CE, Hagstromer M, Pober DM, Bowles HR. Best practices for using physical activity monitors in population-based research. Med Sci Sports Exerc. 2012; 44: S68-76. 22. Hedeker D, Mermelstein RJ, Demirtas H. An application of a mixed-effects location scale model for analysis of Ecological Momentary Assessment (EMA) data. Biometrics. 2008; 64: 627-634. 23. Hedeker D, Mermelstein RJ, Demirtas H. Modeling between-subject and within-subject variances in ecological momentary assessment data using mixed-effects location scale models. Stat Med. 2012; 31: 3328-3336. 24. Katzmarzyk PT, Barreira TV, Broyles ST, Champagne CM, Chaput JP, Fogelholm M, et al. The International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE): design and methods. BMC Public Health. 2013; 13: 900. 25. de Onis M, Onyango AW, Borghi E, Siyam A, Nishida C, Siekmann J. Development of a WHO growth reference for school-aged children and adolescents. Bull World Health Organ. 2007; 85: 660-667. 26. Tudor-Locke C, Barreira TV, Schuna JM, Jr., Mire EF, Katzmarzyk PT. Fully automated waist-worn accelerometer algorithm for detecting children's sleepperiod time separate from 24-h physical activity or sedentary behaviors. Appl Physiol Nutr Metab. 2014; 39: 53-57. 27. Barreira TV, Schuna Jr JM, Mire EF, Katzmarzyk PT, Chaput J-P, Leduc G, et al. Identifying children's nocturnal sleep using 24-hour waist accelerometry. Med Sci Sports Exerc. 2014; in press. 28. Biddle S, Cavil N, Ekelund U, Gorely T, Griffiths M, Jago R, et al. (2010) Sedentary behaviour and obesity: Review of the current scientific evidence. Londo, UK: Department of Health. Department for Children, Schools and Families. Why are children different in their daily sedentariness? An approach based on the mixed-effects location scale model Thayse Natacha Queiroz Ferreira Gomes 103 29. Evenson KR, Catellier DJ, Gill K, Ondrak KS, McMurray RG. Calibration of two objective measures of physical activity for children. J Sports Sci. 2008; 26: 1557-1565. 30. Mirwald RL, Baxter-Jones AD, Bailey DA, Beunen GP. An assessment of maturity from anthropometric measurements. Med Sci Sports Exerc. 2002; 34: 689-694. 31. Carson V, Cliff DP, Janssen X, Okely AD. Longitudinal levels and bouts of sedentary time among adolescent girls. BMC Pediatr. 2013; 13: 173. 32. Steele RM, van Sluijs EM, Sharp SJ, Landsbaugh JR, Ekelund U, Griffin SJ. An investigation of patterns of children's sedentary and vigorous physical activity throughout the week. Int J Behav Nutr Phys Act. 2010; 7: 88. 33. Harrington DM, Dowd KP, Bourke AK, Donnelly AE. Cross-sectional analysis of levels and patterns of objectively measured sedentary time in adolescent females. Int J Behav Nutr Phys Act. 2011; 8: 120. 34. Verloigne M, Van Lippevelde W, Maes L, Yildirim M, Chinapaw M, Manios Y, et al. Levels of physical activity and sedentary time among 10to 12-year-old boys and girls across 5 European countries using accelerometers: an observational study within the ENERGY-project. Int J Behav Nutr Phys Act. 2012; 9: 34. 35. Blatchford P, Baines E, Pellegrini A. The social context of school playground games: Sex and ethnic differences, and changes over time after entry to junior school. Br J Dev Psychol. 2003; 21: 481-505. 36. Nilsson A, Anderssen SA, Andersen LB, Froberg K, Riddoch C, Sardinha LB, et al. Betweenand within-day variability in physical activity and inactivity in 9and 15-year-old European children. Scand J Med Sci Sports. 2009; 19: 1018. 37. Hertzog C, Nesselroade JR. Assessing psychological change in adulthood: an overview of methodological issues. Psychol Aging. 2003; 18: 639-657. 38. Epstein S. The stability of behavior: I. On predicting most of the people much of the time. J Pers Soc Psychol. 1979; 37: 1097-1126. Overweight and obesity in Portuguese children: prevalence and correlates Thayse Natacha Queiroz Ferreira Gomes 110 school, where healthy behaviours are learned [17], the school environment may promote healthy habits that positively affect children’s weight status [18]. In order to better understand the prevalence and correlates of overweight and obesity in Portuguese children, the present study aims to: (I) conduct a meta-analysis on overweight/obesity prevalence in 9 to 11 year old Portuguese children, (II) detect significant differences in behavioural characteristics among normal-weight and overweight/obese children; and (III) investigate the importance of individualand school-level correlates on variation in children’s BMI. METHODS To address this study’s aims, we present the methodology in two parts: Part I addresses the meta-analysis and Part 2 focuses on aims II and III. Part I: Meta-analysis of obesity prevalence among Portuguese children Between June and July 2014, an online search was conducted using Scopus, Pubmed and Scielo databases to find all available articles reporting overweight and obesity prevalence in Portuguese children using the following keywords: overweight, obese, obesity, children, youth, Portugal, Portuguese and their respective translation to Portuguese by the first author. In addition, another search was done at the Faculty of Sport, University of Porto central library, and Portuguese Statistics databases with the same keywords. Valid papers, or Health Directorate Reports, were included if they: (I) were published between January 2000 and May 2014 because International Obesity Task Force (IOTF) cut-points were first published in 2000; (II) sampled Portuguese children aged 9 to 11 years [to be in agreement with the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE) study sample age range, as mentioned below]; (III) used national samples; (IV) reported overweight/obesity prevalence; (V) used BMI to assess overweight/obesity; (VI) Overweight and obesity in Portuguese children: prevalence and correlates Thayse Natacha Queiroz Ferreira Gomes 111 used IOTF [19] cut points to define overweight and obesity; and (VII) were published in English or Portuguese. A meta-analysis of overweight/obesity prevalence was conducted using the Comprehensive Meta-Analysis v2.2.64 software [20]. Prevalence, 95% confidence intervals, Q-test and I2 statistic were computed according to algorithms implemented in the software. Further, the software was also used to assess effect size heterogeneity as advocated by Borenstein et al. [21] and Beretvas [22], and fixed and random effects models were used. Part II: Correlates of childhood overweight and obesity Sample The sample of the present study is part of ISCOLE, a research project conducted in 12 countries (Australia, Brazil, Canada, China, Colombia, Finland, India, Kenya, Portugal, South Africa, the United Kingdom, and the United States of America) from all major regions of the world. Its main aims are to determine the relationship between behaviours and obesity in a multi-national study of children aged 9 to 11 years, and to investigate the influence of higher-order characteristics such as behavioural settings, and physical, social and policy environments, on the observed relationship within and between countries [23]. Details regarding the ISCOLE study design and methodology were previously reported elsewhere by Katzmarzyk et al. [23]. The sample of the present study comprises 777 Portuguese children, aged 9 to 11 years, from 23 schools from the North of Portugal. In each school, after the project was approved by the Physical Education Department, school Principal and Parental Council, all 5th grade students were invited to enrol in ISCOLE, but only those children aged 9 to 11 years old were classified as eligible. From those, approximately 30 to 40 children per school were randomly selected (50% of each sex). Non-response was negligible (response rate was 95.7%), and missing information was at random (differences between subjects with missing information and those included were not statistically significant). The study protocol was approved by the University of Porto ethics committee, Overweight and obesity in Portuguese children: prevalence and correlates Thayse Natacha Queiroz Ferreira Gomes 112 as well as by schools’ directorate councils. Written informed consent was obtained from parents or legal guardians of all children. Anthropometry Height, sitting height and weight were measured according to standardized ISCOLE procedures and instrumentation [23]. Height was measured using a Seca 213 portable stadiometer (Hamburg, Germany) without shoes, with the head in the Frankfurt Plane, and sitting height was measured while seated on a table with legs hanging freely and arms resting on the thighs. Body mass was determined with a portable Tanita SC-240 scale (Arlington Heights, IL, USA), after all outer clothing, heavy pocket items and shoes were removed. Two measurements were taken on each child, and a third measurement was taken if the difference between the previous two was outside the permissible range for each measure and its replica (0.5 cm for height and sitting height, and 0.5 kg for weight). The mean value of each measured variable (closest two measurements) was used for analysis. BMI was computed using the standard formula [weight(kg)/height(m)2], and subjects were classified as normal-weight, overweight and obese according to the IOTF cut-off points suggested by Cole et al. [19]. Family data Information regarding family environmental characteristics was obtained from a questionnaire completed by parents or legal guardians (see ISCOLE Demographic and Family Health Questionnaire in Katzmarzyk et al. [23]). The questionnaire collected information on basic demographics, ethnicity, family health and socioeconomic factors. For the present study, we used information regarding familial socioeconomic status (SES) and parental BMI. SES was defined according to annual family income, ranging from <€ 6,000 to ≥€ 42,000, and subjects were classified in two categories (<€23,999; ≥€24,000). Overweight and obesity in Portuguese children: prevalence and correlates Thayse Natacha Queiroz Ferreira Gomes 113 Biological maturity Using age, sex, sitting height, stature and body mass, a biological maturity estimate was obtained using the Mirwald et al. regression equations [24]. The set of equations, jointly labelled as maturity offset equations, estimates the timing to peak height velocity (PHV) occurrence. A positive (+) offset expresses the number of years a child is beyond PHV; a negative score (–) signifies the number of years a child is from PHV; a value of zero indicates that a child is presently experiencing his/her PHV. Nutritional and behavioural habits Information on diet and lifestyle was obtained from a questionnaire answered by each child [23], which includes questions about the frequency of consumption of different types of food in a typical week. Information related to fruits, vegetables, sweets, soft drinks and fast food consumption was assessed. Using principal components analysis, dietary scores were derived for each child from the children’s Food Frequency Questionnaire [23] food groups as input variables (excluding fruit juices), expressing children’s dietary patterns. Reported frequencies were converted into portions/week. Eigenvalues and a scree plot analysis were used as the criteria for deciding the number of components extracted. The two criteria led to similar conclusions, and two factors were chosen for each analysis. The components were then rotated with an orthogonal varimax transformation to force non-correlation of the components and to enhance the interpretation. The component scores computed for each subject for both dietary patterns were standardized to ensure normality. The two components were named “unhealthy food” (e.g., hamburgers, soft drink, fired food, etc.) and “healthy food” (e.g., vegetables and fruits). Time spent watching TV during the week was reported by children, and then categorized according to screen time recommendations (<2 hours/day and ≥2 hours/day). Children also reported whether or not if they had a TV available in their bedroom, as well as their main transportation method to/from school. Overweight and obesity in Portuguese children: prevalence and correlates Thayse Natacha Queiroz Ferreira Gomes 114 Physical activity, sedentary time and sleep Actigraph GT3X+ accelerometers (ActiGraph, Pensacola, FL, USA) were used to monitor physical activity, sedentary time and sleep. Children wore the accelerometer at their waist on an elasticized belt, placed on the right midaxillary line 24 hours/day, for at least seven days, including two weekend days. To be eligible for this analysis, children had at least four days (including at least one weekend day) with a minimum of 10 hours of wear time per day. From the original sample of 777 children, 686 children fulfilled this condition. Accelerometer information was divided into daytime activities and nocturnal sleep time using an automated algorithm [25,26]. Non-wear time during the awake period was defined as any sequence of at least 20 consecutive minutes of zero activity counts [26]. Different activity phenotypes were determined using cut-points developed by Evenson et al. [27]. For the present study, mean moderate-to-vigorous physical activity (MVPA) and mean sedentary time were used, which were defined as greater than or equal to 574 activity counts and less than or equal to 25 activity counts using 15 seconds epochs, respectively. The nocturnal sleep time for each participant was determined using a novel and fully-automated algorithm specifically developed for use in ISCOLE and other epidemiological studies employing a 24-hour waist-worn accelerometer protocol in children [25,26]. Mean sleep time across all days was used in the analyses. School environment Information concerning the school environment was obtained via a questionnaire (ISCOLE School Environment Questionnaire presented in Katzmarzyk et al. [23]) completed by the physical education teacher or the school principal. For the present study we primarily considered the following aspects of the school physical activity environment: the percentage of students participating in school sports or PA clubs; school promotion of active transportation (allowing children to bring their bicycles); student access to a Overweight and obesity in Portuguese children: prevalence and correlates Thayse Natacha Queiroz Ferreira Gomes 115 gymnasium during school hours and outside school hours; student access to playgrounds during school hours; student access to sports equipment outside of school time; student access to a cafeteria at school; student access to food and drink vending machines; and student access to fast food restaurant close to school. Statistical analysis Differences in means and frequencies of biological and behavioural characteristics between groups were computed using Student-t and χ2 tests. SPSS 20.0, and WinPeppi software [28] were used for these analyses. The extraction and identification of dietary patterns were performed in the SAS 9.3 (SAS Institute Inc., Cary, NC, USA, 2011). To answer aim III and given data dependency, students nested within schools, a multilevel approach was used and the analysis was done in SuperMix software [29] allowing a simultaneous estimation of all model parameters using maximum likelihood procedures. A series of hierarchical nested models were fitted to explain variation in children’s BMI using the Deviance statistic as a measurement of global fit [30]. Additionally, the relevance of predictors to explain variation in BMI was assessed with a pseudoR2 statistic, which is interpreted as a proportional reduction in variance for the parameter estimate resulting from the use of one model as compared to a previous one [30]. Modelling was done in a “stepwise” fashion as generally advocated [31,32]. Firstly, a null model (M0) was fitted to the data to compute the intraclass correlation coefficient to estimate the variance accounted for by the school effects in BMI. Secondly, using child-level BMI predictors (sex, biological maturity, mother and father BMI, TV/PC use during weekdays, having a TV in the bedroom, diet categories, time spent in MVPA, sedentariness and sleeping), Model 1 (M1) was fitted. Parental BMI, time spent in MVPA, sedentariness and sleeping were centred at the grand mean to facilitate the interpretation of parameter estimates. Thirdly, with the inclusion of school-level predictors, Model 2 (M2) was fitted. Statistical significance was set at p < 5%. Overweight and obesity in Portuguese children: prevalence and correlates Thayse Natacha Queiroz Ferreira Gomes 116 RESULTS Prevalence of overweight/obesity among 9–11 year-old Portuguese children Figure 1 presents a flow diagram illustrating the search process and the excluded studies in the meta-analysis. Only five studies fulfilled all inclusion criteria based on a close examination of abstracts and full texts. Further, this final list was checked by the first author against two recent systematic literature reviews concerning overweight and obesity in Portuguese children and adolescents [33,34]. Table 1 shows the available data using national samples. Studies were published between 2004 and 2013, and the sample sizes ranged from 405 to 3,584 subjects. The highest prevalence of overweight/obesity was for boys in 2009 [16], while girls in 2007 [35] had the lowest prevalence. A higher prevalence of overweight/obesity was found in boys in three [16,35,36] of the five studies, compared to girls. Taking boys and girls together, the prevalence of overweight/obesity ranged from 19% [36] to 35% [16] representing a moderate-to-high prevalence of Portuguese children with excess weight. Figure 2 presents Forrest plots, fixed and random effects prevalence estimates and their 95% confidence intervals for overweight/obesity across time in boys, girls, and both sexes together. Although there is considerable evidence for heterogeneity in the prevalences for boys (Q-test = 11.371, p = 0.023, I2 = 64.823), girls (Q-test = 56.564, p < 0.001, I2 = 92.928), and both sexes together (Q-test = 25.770, p < 0.001, I2 = 84.478), we nevertheless present fixed and random effects prevalence estimates (see Figure 2) although they are fairly similar. Across the time period, the prevalence estimate for overweight/obesity among boys is 0.306 (95%CI: 0.277–0.337), 0.284 (95%CI: 0.225–0.352) among girls and 0.303 (95%CI: 0.272–0.335) for both sexes. The funnel plot did not show evidence of publication bias; further, meta-regression analysis using study year as a moderator variable did not show any significant increase in overweight/obesity from 2002 till 2010 (boys, beta = 0.0096 ± 0.0132, p ≥ 0.05; Overweight and obesity in Portuguese children: prevalence and correlates Thayse Natacha Queiroz Ferreira Gomes 117 girls, beta = 0.0220 ± 0.0127, p ≥ 0.05; both sexes together, beta = 0.0178 ± 0.0916, p ≥ 0.05). Figure 1. Flow diagram of study selection for meta-analysis Table 1. Summary of overweight/obesity prevalence in 9–11 year-old Portuguese children used in the meta-analysis Study Study Year Age Range Sample Size Prevalence of Overweight/Obesity Boys Girls Total Padez et al. [37] 2002/2003 9.5 years 631 (317 boys; 314 girls) 29% 36% 33% Yngve et al. [35] 2003 11 years 1197 (552 boys; 645 girls) 27% 18% 22% DGS [16] 2008 11 years 405 (204 boys; 201 girls) 39% 31% 35% Sardinha [36] 2008 10 years 1001 (486 boys; 515 girls) 32% 28% 19% Bingham et al. [3] 2009/2010 9–10 years 3584 (1685 boys; 1899 girls) 31% 32% 31% Records identified through database and literature searching: n = 2736 n = 2736 Records after duplicates removed: n = 794 n = 794 Records excluded on basis of title and abstract: n = 588 n = 588 Potentially relevant articles: n = 206 n = 206 Records excluded with reasons (sample not comprising ages 9–11 years; not reporting overweight and/or obesity prevalence by age; not using IOTF cut-points): n = 201 n = 201 Studies included in the analysis: n = 5 n =5 Overweight and obesity in Portuguese children: prevalence and correlates Thayse Natacha Queiroz Ferreira Gomes 118 Figure 2. Meta-analysis results for boys, girls and both sexes combined Biological, behavioural and socio-demographic differences between normal-weight and overweight/obese children Descriptive data comparing differences between normal-weight and overweight/obese children in biological, behavioural and socio-demographic traits are presented in Table 2. Except for biological traits (height, weight, and parental BMI), no statistically significant differences (p > 0.05) were found in any of the other variables between the two groups. Overweight and obesity in Portuguese children: prevalence and correlates Thayse Natacha Queiroz Ferreira Gomes 119 Table 2. Biological, behavioural and socio-demographic trait differences between normal-weight and overweight/obese children Normal-weight (n = 417) Overweight/Obese (n = 269) t p-value Height (cm) 141.57 ± 6.39 145.73 ± 6.55 −8.403 <0.001 Weight (kg) 34.14 ± 4.59 47.79 ± 7.74 −28.584 <0.001 Biological maturity −2.07 ± 0.85 −1.69 ± 0.90 −5.639 <0.001 Unhealthy diet (z-scores) 0.005 ± 1.00 −0.10 ± 0.91 1.424 0.155 Healthy diet (z-scores) 0.04 ± 0.98 −0.03 ± 1.03 0.945 0.345 MVPA (min) 57.34 ± 22.48 55.20 ± 20.54 1.291 0.197 Sedentary time (min) 553.26 ± 61.92 549.68 ± 61.90 0.754 0.451 Sleep time (hours) 8.26 ± 0.83 8.26 ± 0.90 -0.027 0.979 Frequencies (%) 2 p-value SES 0.119 0.730 <€23,999 78.4 79.6 ≥€24,000 21.6 20.4 Mother BMI 11.744 0.001 Normal-weight 66.3 52.6 Overweight/obese 33.7 47.4 Father BMI 4.898 0.027 Normal-weight 41.3 32.2 Overweight/obese 58.7 67.8 Transport to/from school 0.339 0.561 Active 28.8 26.8 Non-active 71.2 73.2 TV bedroom 3.564 0.059 No 33.1 26.4 Yes 66.9 73.6 TV/school day 0.743 0.389 <2 hours 73.7 70.7 ≥2 hours 26.3 29.3 Individualand school-level correlates of BMI variation Results from M0, M1 and M2 are presented in Table 3. From M0, the intraclass correlation coefficient is 0.0216 [0.25/(0.25 + 11.30)], meaning that only 2.2% of the total variance in BMI among schoolchildren is at the school level. Results from M1 (child-level predictors) show that boys (β = 6.22, p < 0.001) have, on average, higher BMI than girls; more mature children had Are BMI and sedentariness correlated? A multilevel study in children Thayse Natacha Queiroz Ferreira Gomes 222 BMI and Sed; 3) one Portuguese regional population limits generalization, although overweight/obesity prevalence [52] and socioeconomic status distribution [53] compares with previous studies; 4) diet as a mediated variable in the relationship between Sed and BMI in multilevel models was not used; 5) since all children had at least 10 hours of awake wear time, with mean accelerometer use value of 15.17±0.86 hours, we did not adjust physical activity and Sed for wear time because this effect is not significant (data not shown). Strengths were: 1) multivariate multilevel analysis identifying relationships between Sed and BMI, with multilevel modelling to understand complex nested information at child and school levels; 2) objective methods to estimate Sed, MVPA and sleep time; 3) Standardized data collection methods; 4) reliable childand school-level information. CONCLUSIONS Sed and BMI were not significantly correlated, but MVPA was significantly associated with both. However, correlations were different and should be considered since strategies to reduce Sed or BMI may act through different pathways. Low variance at school level for both BMI and Sed reinforce suggestions that although children spend considerable awake time at school, individual variables play more relevant roles in differences between Sed and BMI than school. School policies promoting active and healthy habits play important roles in reducing sedentary time, making wise nutritional choices, and controlling body weight. ACKNOWLEDGEMENTS We would like to thank Alessandra Borges, Pedro Gil Silva, and Sofia Cachada for their role in data collection for the Portuguese site of ISCOLE, and the Coordinating Center of ISCOLE in Baton Rouge, Louisiana. We would also like to thank the study participants along with their parents, teachers and school principals for their involvement in the study. ISCOLE was funded by The Coca- Are BMI and sedentariness correlated? A multilevel study in children Thayse Natacha Queiroz Ferreira Gomes 223 Cola Company. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of this manuscript. Are BMI and sedentariness correlated? A multilevel study in children Thayse Natacha Queiroz Ferreira Gomes 224 REFERENCES 1. Ng, M.; Fleming, T.; Robinson, M.; Thomson, B.; Graetz, N.; Margono, C.; Mullany, E.C.; Biryukov, S.; Abbafati, C.; Abera, S.F., et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: A systematic analysis for the global burden of disease study 2013. Lancet 2014, 384, 766-781. 2. Huang, T.T.; Ball, G.D.; Franks, P.W. Metabolic syndrome in youth: Current issues and challenges. Appl Physiol Nutr Metab 2007, 32, 13-22. 3. Olds, T.; Maher, C.; Zumin, S.; Peneau, S.; Lioret, S.; Castetbon, K.; Bellisle; de Wilde, J.; Hohepa, M.; Maddison, R., et al. Evidence that the prevalence of childhood overweight is plateauing: Data from nine countries. Int J Pediatr Obes 2011, 6, 342-360. 4. Deshmukh-Taskar, P.; Nicklas, T.A.; Morales, M.; Yang, S.J.; Zakeri, I.; Berenson, G.S. Tracking of overweight status from childhood to young adulthood: The Bogalusa Heart Study. Eur J Clin Nutr 2006, 60, 48-57. 5. Berenson, G.S.; Srnivasan, S.R. Cardiovascular risk factors in youth with implications for aging: The Bogalusa Heart Study. Neurobiol Aging 2005, 26, 303-307. 6. Prentice-Dunn, H.; Prentice-Dunn, S. Physical activity, sedentary behavior, and childhood obesity: A review of cross-sectional studies. Psychol Health Med 2012, 17, 255-273. 7. Tremblay, M.S.; LeBlanc, A.G.; Kho, M.E.; Saunders, T.J.; Larouche, R.; Colley, R.C.; Goldfield, G.; Connor Gorber, S. Systematic review of sedentary behaviour and health indicators in school-aged children and youth. Int J Behav Nutr Phys Act 2011, 8, 98. 8. Pate, R.R.; Mitchell, J.A.; Byun, W.; Dowda, M. Sedentary behaviour in youth. Br J Sports Med 2011, 45, 906-913. Are BMI and sedentariness correlated? A multilevel study in children Thayse Natacha Queiroz Ferreira Gomes 225 9. Liao, Y.; Liao, J.; Durand, C.P.; Dunton, G.F. Which type of sedentary behaviour intervention is more effective at reducing body mass index in children? A meta-analytic review. Obes Rev 2014, 15, 159-168. 10. Epstein, L.H.; Paluch, R.A.; Gordy, C.C.; Dorn, J. Decreasing sedentary behaviors in treating pediatric obesity. Arch Pediatr Adolesc Med 2000, 154, 220-226. 11. Epstein, L.H.; Valoski, A.M.; Vara, L.S.; McCurley, J.; Wisniewski, L.; Kalarchian, M.A.; Klein, K.R.; Shrager, L.R. Effects of decreasing sedentary behavior and increasing activity on weight change in obese children. Health Psychol 1995, 14, 109-115. 12. Gupta, N.; Goel, K.; Shah, P.; Misra, A. Childhood obesity in developing countries: Epidemiology, determinants, and prevention. Endocr Rev 2012, 33, 48-70. 13. Van der Horst, K.; Paw, M.J.; Twisk, J.W.; Van Mechelen, W. A brief review on correlates of physical activity and sedentariness in youth. Med Sci Sports Exerc 2007, 39, 1241-1250. 14. Dunton, G.F.; Kaplan, J.; Wolch, J.; Jerrett, M.; Reynolds, K.D. Physical environmental correlates of childhood obesity: A systematic review. Obes Rev 2009, 10, 393-402. 15. Meigen, C.; Keller, A.; Gausche, R.; Kromeyer-Hauschild, K.; Bluher, S.; Kiess, W.; Keller, E. Secular trends in body mass index in German children and adolescents: A cross-sectional data analysis via crescent between 1999 and 2006. Metabolism 2008, 57, 934-939. 16. Bingham, D.D.; Varela-Silva, M.I.; Ferrao, M.M.; Augusta, G.; Mourao, M.I.; Nogueira, H.; Marques, V.R.; Padez, C. Socio-demographic and behavioral risk factors associated with the high prevalence of overweight and obesity in Portuguese children. Am J Hum Biol 2013, 25, 733-742. 17. Trinh, A.; Campbell, M.; Ukoumunne, O.C.; Gerner, B.; Wake, M. Physical activity and 3-year bmi change in overweight and obese children. Pediatrics 2013, 131, e470-e477. Are BMI and sedentariness correlated? A multilevel study in children Thayse Natacha Queiroz Ferreira Gomes 226 18. Raudsepp, L.; Neissaar, I.; Kull, M. Longitudinal stability of sedentary behaviors and physical activity during early adolescence. Pediatr Exerc Sci 2008, 20, 251-262. 19. Malina, R.M.; Bouchard, C.; Bar-Or, O. Growth, maturation and physical activiy. Human Kinetics: Champaign, 2004. 20. Van den Bulck, J. Television viewing, computer game playing, and internet use and self-reported time to bed and time out of bed in secondary-school children. Sleep 2004, 27, 101-104. 21. Patel, S.R.; Hu, F.B. Short sleep duration and weight gain: A systematic review. Obesity (Silver Spring) 2008, 16, 643-653. 22. Must, A.; Tybor, D.J. Physical activity and sedentary behavior: A review of longitudinal studies of weight and adiposity in youth. Int J Obes (Lond) 2005, 29 Suppl 2, S84-96. 23. Adachi-Mejia, A.M.; Longacre, M.R.; Gibson, J.J.; Beach, M.L.; TitusErnstoff, L.T.; Dalton, M.A. Children with a tv in their bedroom at higher risk for being overweight. Int J Obes 2007, 31, 644-651. 24. Delmas, C.; Platat, C.; Schweitzer, B.; Wagner, A.; Oujaa, M.; Simon, C. Association between television in bedroom and adiposity throughout adolescence. Obesity (Silver Spring) 2007, 15, 2495-2503. 25. Yannakoulia, M.; Brussee, S.E.; Drichoutis, A.C.; Kalea, A.Z.; Yiannakouris, N.; Matalas, A.L.; Klimis-Zacas, D. Food consumption patterns in mediterranean adolescents: Are there differences between overweight and normal-weight adolescents? J Nutr Educ Behav 2012, 44, 233-239. 26. Maier, I.B.; Ozel, Y.; Wagnerberger, S.; Bischoff, S.C.; Bergheim, I. Dietary pattern and leisure time activity of overweight and normal weight children in Germany: Sex-specific differences. Nutr J 2013, 12, 14. 27. Harrington, D.M.; Dowd, K.P.; Bourke, A.K.; Donnelly, A.E. Cross-sectional analysis of levels and patterns of objectively measured sedentary time in adolescent females. Int J Behav Nutr Phys Act 2011, 8, 120. Are BMI and sedentariness correlated? A multilevel study in children Thayse Natacha Queiroz Ferreira Gomes 227 28. Steele, R.M.; van Sluijs, E.M.; Sharp, S.J.; Landsbaugh, J.R.; Ekelund, U.; Griffin, S.J. An investigation of patterns of children's sedentary and vigorous physical activity throughout the week. Int J Behav Nutr Phys Act 2010, 7, 88. 29. Ridgers, N.D.; Stratton, G.; Fairclough, S.J.; Twisk, J.W. Long-term effects of a playground markings and physical structures on children's recess physical activity levels. Prev Med 2007, 44, 393-397. 30. Wechsler, H.; Devereaux, R.S.; Davis, M.; Collins, J. Using the school environment to promote physical activity and healthy eating. Prev Med 2000, 31, S121-S137. 31. Katzmarzyk, P.T.; Barreira, T.V.; Broyles, S.T.; Champagne, C.M.; Chaput, J.P.; Fogelholm, M.; Hu, G.; Johnson, W.D.; Kuriyan, R.; Kurpad, A., et al. The International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE): Design and methods. BMC Public Health 2013, 13, 900. 32. Tudor-Locke, C.; Barreira, T.V.; Schuna, J.M., Jr.; Mire, E.F.; Katzmarzyk, P.T. Fully automated waist-worn accelerometer algorithm for detecting children's sleep-period time separate from 24-h physical activity or sedentary behaviors. Appl Physiol Nutr Metab 2014, 39, 53-57. 33. Barreira, T.V.; Schuna Jr, J.M.; Mire, E.F.; Katzmarzyk, P.T.; Chaput, J.-P.; Leduc, G.; Tudor-Locke, C. Identifying children's nocturnal sleep using 24-hour waist accelerometry. Med Sci Sports Exerc 2014, in press. 34. Evenson, K.R.; Catellier, D.J.; Gill, K.; Ondrak, K.S.; McMurray, R.G. Calibration of two objective measures of physical activity for children. J Sports Sci 2008, 26, 1557-1565. 35. Mirwald, R.L.; Baxter-Jones, A.D.; Bailey, D.A.; Beunen, G.P. An assessment of maturity from anthropometric measurements. Med Sci Sports Exerc 2002, 34, 689-694. 36. Goldstein, H. Multilevel statistical models. 3rd ed.; Arnold: London, 2003. 37. Snijders, T.; Bosker, R. Multilevel analysis. An introduction to basic and advanced multilevel modeling. 2nd ed.; Sage Publications Ltd. Thousand Oaks: California, 2012. Are BMI and sedentariness correlated? A multilevel study in children Thayse Natacha Queiroz Ferreira Gomes 228 38. Hox, J. Multilevel analysis. Techniques and applications 2nd ed.; Routledge: New York, 2010. 39. Tabachnick, B.; Fidell, L. Using multivariate statistics. Pearson: New York, 2007. 40. Hedeker, D.; Gibbons, R.; du Toit, M.; Cheng, Y. Supermix for mixed effects models. Scientific Software International: Lincolnwood, IL, 2008. 41. Carandente, F.; Roveda, E.; Montaruli, A.; Pizzini, G. Nutrition, activity behavior and body constitution in primary school children. Biol. Sport 2009, 26, 349-367. 42. Janssen, I.; Katzmarzyk, P.T.; Boyce, W.F.; Vereecken, C.; Mulvihill, C.; Roberts, C.; Currie, C.; Pickett, W. Comparison of overweight and obesity prevalence in school-aged youth from 34 countries and their relationships with physical activity and dietary patterns. Obes Rev 2005, 6, 123-132. 43. Epstein, L.H.; Roemmich, J.N. Reducing sedentary behavior: Role in modifying physical activity. Exerc Sport Sci Rev 2001, 29, 103-108. 44. Butte, N.F.; Cai, G.; Cole, S.A.; Comuzzie, A.G. Viva la Familia Study: Genetic and environmental contributions to childhood obesity and its comorbidities in the Hispanic population. American Journal of Clinical Nutrition 2006, 84, 646-654; quiz 673-644. 45. Knutson, K.L.; Spiegel, K.; Penev, P.; Van Cauter, E. The metabolic consequences of sleep deprivation. Sleep Med Rev 2007, 11, 163-178. 46. Chen, M.Y.; Wang, E.K.; Jeng, Y.J. Adequate sleep among adolescents is positively associated with health status and health-related behaviors. BMC Public Health 2006, 6, 59. 47. Olds, T.; Ferrar, K.E.; Gomersall, S.R.; Maher, C.; Walters, J.L. The elasticity of time: Associations between physical activity and use of time in adolescents. Health Educ Behav 2012, 39, 732-736. Are BMI and sedentariness correlated? A multilevel study in children Thayse Natacha Queiroz Ferreira Gomes 229 48. Serra-Majem, L.; Aranceta Bartrina, J.; Perez-Rodrigo, C.; Ribas-Barba, L.; Delgado-Rubio, A. Prevalence and deteminants of obesity in spanish children and young people. Br J Nutr 2006, 96 Suppl 1, S67-S72. 49. Storey, K.E.; Forbes, L.E.; Fraser, S.N.; Spence, J.C.; Plotnikoff, R.C.; Raine, K.D.; McCargar, L.J. Adolescent weight status and related behavioural factors: Web survey of physical activity and nutrition. J Obes 2012, 2012, 342386. 50. Pallan, M.J.; Adab, P.; Sitch, A.J.; Aveyard, P. Are school physical activity characteristics associated with weight status in primary school children? A multilevel cross-sectional analysis of routine surveillance data. Arch Dis Child 2014, 99, 135-141. 51. Gomes, T.N.; dos Santos, F.K.; Santos, D.; Pereira, S.; Chaves, R.; Katzmarzyk, P.T.; Maia, J. Correlates of sedentary time in children: A multilevel modelling approach. BMC Public Health 2014, 14, 890. 52. Sardinha, L.B.; Santos, R.; Vale, S.; Silva, A.M.; Ferreira, J.P.; Raimundo, A.M.; Moreira, H.; Baptista, F.; Mota, J. Prevalence of overweight and obesity among Portuguese youth: A study in a representative sample of 10-18-year-old children and adolescents. Int J Pediatr Obes 2011, 6, e124-e128. 53. Fundação Francisco Manuel dos Santos. Pordata. 2013. CHAPTER IV General Overview and Conclusions General Overview and Conclusions Thayse Natacha Queiroz Ferreira Gomes 238 explore this relationship in our data. Finally, available data indicate that boys usually have higher BMI than girls (Serra-Majem et al., 2006); early maturing children are taller and heavier than their latter-maturing peers (Malina et al., 2004); children with TV in bedroom are at greater risk of overweight/obesity (Adachi-Mejia et al., 2007; Delmas et al., 2007); normal-weight children tend to consume less fat and high calorie beverages than their overweight peers (Storey et al., 2012); and there is familial aggregation in BMI, where parental BMI can be related to offspring BMI (Fuentes et al., 2002; Hu et al., 2013). This is the trend in the Portuguese children we studied. Since children spend a large amount of their awake time at school, it is expected that the school environment and policies could act in concert to promote active and healthy habits which could lead to increases in physical activity levels, decreases in sedentariness, and help in reducing/controlling body weight (Cradock et al., 2007; Ridgers et al., 2007; Sallis et al., 2001; Wechsler et al., 2000). Furthermore, given the known role of the environment on the manifestation of our main outcomes, and the urge to investigate children in their living contexts, the school environment was our prime target as a major element in children’s physical activity, sedentariness, and BMI. Surprisingly, the explained variance by different markers of the school environment, in each of these traits was low, from 1.5% (for BMI) to 6.0% (for sedentariness). In addition, only in sedentariness we were able to find significant school-level correlates, but not for physical activity or BMI, suggesting that individual correlates are more important, namely biological and behavioural, and the family environment. Notwithstanding the fact that most of the time children are at school is spent in sedentary behaviours (such as sitting, reading, talking), which can partially explain the higher sedentariness variance at the school level, it is more likely that the options they make during their leisure time regarding physical activity (be active or be sedentary), and food consumption at home or with parents, have more impact on their activity/sedentariness and weight gain. The second “cluster” of issues (papers 4 and 5) tackled by this thesis addressed the complexities of the relationship between physical activity, General Overview and Conclusions Thayse Natacha Queiroz Ferreira Gomes 239 physical fitness and obesity in the expression of metabolic risk in children and adolescents. This link has previously been studied and these variables seem to be closely related to the development of metabolic abnormalities (Saland, 2007; Steele et al., 2008; Tailor et al., 2010), although there is no consensus if they act separately or in concert (Bridger, 2009; Ruiz & Ortega, 2009; SteeneJohannessen et al., 2009). Since our results showed a high prevalence of low physical activity levels, high overweight/obesity and sedentary time, we tried to explore the joint role of these traits on the expression of metabolic risk factors. When obesity and physical activity were analysed together in the expression of metabolic risk factors, we found that “normal-weight and active” children had a better metabolic profile than those with “overweight/obesity and inactive”. However, physical activity did not attenuate the negative role of excess weight on the development of metabolic risk, since significant differences were only observed between BMI groups (normal-weight vs overweight/obese) but not within BMI groups (active vs inactive). This reinforces the significant role of excess weight in the development of metabolic abnormalities, and that obesity may be more highly associated with metabolic disorders than lower physical activity. Moreover, when the joint role of physical activity and muscular strength on metabolic risk was studied, children classified as “active and with high muscular strength” had better metabolic profile than those classified as “inactive and with low muscular strength”. In this case, muscular strength seems to attenuate the negative role of low physical activity levels in the development of metabolic risk, since significant differences within physical activity groups (high muscular strength vs low muscular strength) were observed. Children classified as having high muscular strength, independently of their physical activity levels, showed a better metabolic profile than their peers classified as having low muscular strength. The role of physical activity on metabolic syndrome in the paediatric population has been partly credited to be either independent of other factors or mediated by adiposity (Ekelund et al., 2007; Guinhouya et al., 2011). In addition, available data concerning the putative role of muscular strength on metabolic risk in youth showed an association between high muscular strength General Overview and Conclusions Thayse Natacha Queiroz Ferreira Gomes 240 with a better metabolic profile (Steene-Johannessen et al., 2009), but we did not find evidence to suggest that it acts in conjunction with physical activity or adiposity. On the other hand, regarding adiposity, it has been pointed out as a stronger predictor of metabolic risk in children when compared to physical activity or physical fitness (Eisenmann, 2007; Ekelund et al., 2006). Taking together, our results suggest that there is a relationship between these variables, which should be considered when planning strategies to reduce the negative impact of excess weight on youth health. In this case, increasing physical activity and muscular strength in children having excess weight is a relevant strategy to reduce their metabolic risk, since these variables could act in mediating/reducing the undesirable effect of each other. Especially in the school context, this information should be of relevance for school principals, councils and physical education teachers when designing and implementing their annual plans to increase children’s physical activity and physical fitness by both structured and non-structured physical activities/play. Finally, we tackled the commonality of correlates for physical activity, sedentariness and BMI. Our third and last results’ “cluster” is related to the papers aiming to determine if physical activity, sedentariness and BMI are correlated, or not, with each other (papers 6 and 7). To the best of our knowledge, these are the first studies where these variables were jointly analysed. Regarding the correlation between physical activity and sedentariness, a negative and significant correlation between them was found, meaning that increasing one, the other decreases, and vice-versa. Although these behaviours are expressed as different constructs, and that they can co-occur in the same person (Leech et al., 2014; Marshall et al., 2002), it has also been suggested that increasing time spent in sedentariness leads to decreases in physical activity levels (Tammelin et al., 2007). It is possible that the negative correlation observed may be related to the fact that day is limited to 24 h, and increasing time spending in one activity, implies in the reduction of time available to be engaged in other activity. General Overview and Conclusions Thayse Natacha Queiroz Ferreira Gomes 241 Moreover, the correlation between sedentariness and BMI was not significant, meaning that the current idea that increasing time in sedentariness leads to increases in BMI may not be necessarily true or it may be mediated by another biological or behavioural variable. In this context, it has been suggested that food intake can be a significant mediator of the relationship between children’s BMI and TV viewing (Epstein et al., 1995; Fuller-Tyszkiewicz et al., 2012), since when watching TV children are more likely to consume snacks and beverages (Carandente et al., 2009). We did not address this hypothesis in our study, but we can only speculate about it. However, the possibility of the existence of mediating factors in this relationship, namely food consumption, showed to be relevant for parents and educators roles, which can act to help children to change habits related to their food choices. Taking all these results together (from the three clusters), it seems obvious that using the ecological model to better unravel the importance of links between children’s physical activity, sedentariness and BMI was a relevant choice - the main assumption of the ecological model is that behaviours are influenced by various factors from intrapersonal, interpersonal, organizational, community and public policy levels (Sallis et al., 2008). In addition, since alterations in one level of the ecological model can affect the other levels and may influence the outcomes, directly or indirectly (Bronfenbrenner, 1979), it is important to understand how all ecological environments act in concert to regulate children’s physical activity, sedentariness and obesity, and thus the use of multilevel models are the most adequate approach to generate this knowledge. Apart from the significance of the ecological model, it has inherent weaknesses: (1) the lack of specificity about the most important hypothesized influence; (2) the lack of information about “how the broader levels of influence operate or how variables interact across levels” (Sallis et al., 2008, p. 480), and (3) its silence about putative mechanisms that express and regulate such variety in behaviours in children, adolescents and adults (i.e., across the lifespan). For example, we were not able to investigate interactions across levels or even the degree of relevance of them in each level not because we General Overview and Conclusions Thayse Natacha Queiroz Ferreira Gomes 242 could not do it, but because we felt a substantial lack of thinking/hypothesizing about them. Nonetheless, the ecological model is a consistent and highly regarded approach to persons in their contexts, especially when intervention strategies are planned to improve child health (decreasing sedentariness and overweight/obese prevalence, and increasing physical activity levels), because interventions tend to be more effective when operating at different levels (Sallis et al., 2008). As clearly noted by Sallis et al. (2008, p. 482) the challenge for researchers is “to be creative and persistent in using ecological models to generate evidence on the roles of behavioural influences at multiple levels, and on the effectiveness of multi-level interventions on health behaviors, and to translate that evidence into improved health.” General Overview and Conclusions Thayse Natacha Queiroz Ferreira Gomes 243 LIMITATIONS Notwithstanding the relevance of the present results, this thesis has limitations that should be addressed. The first limitation is related to the study design. Cross-sectional studies do not allow causal interpretations of the links among physical activity, sedentariness and BMI. However, the recent years witnessed a plethora of multi-national studies (Moreno et al., 2008; Riddoch et al., 2005) having a cross-sectional design, and even so they provided relevant information in terms of policy making, public health and education. It would be an enormous task to conduct a longitudinal study involving 12 countries, although this idea was also in the minds of the coordinating group at Pennington Biomedical Research Center. The second limitation concerns the sample size and the fact that it comes from only one Portuguese region, which does not allow the generalization to all Portuguese children. Yet, in the very beginning, it was very clear to all sites around the world that it was not a purpose of the ISCOLE to have representative data of each country, but to gather multi-country information from different levels, increasing its heterogeneity as well as power. However, since in the Portuguese context schools are quite similar in their environments and policies, the use of only 23 schools may have limited the identification of the school roles on children physical activity, sedentariness and BMI. The third limitation concerns the fact that sedentariness is a very complex trait, usually involving a wide array of behaviours. The objective measure of sedentariness (accelerometry) we employed does not provide extensive information about the types, frequency and duration of activities children are involved with. Although a questionnaire was also applied to all children, it may not provide sensible information about all sedentary behaviours children are involved with or even the moment when they occurred. The use of direct observation of children’s activities, or the use of diaries, would be of relevance, although it would probably be unfeasible with the present sample size across all the sites. General Overview and Conclusions Thayse Natacha Queiroz Ferreira Gomes 244 The fourth limitation concerns the fact that we circumscribed our sample age to 9-11 years which does not allow any inference to adolescence or early childhood nor was this intended. The fifth limitation concerns the small sample size in the ancillary study. However, costs related to analytical data (blood analysis) prohibited a larger sample size. Further, we did not explore the relationship of other physical fitness components with metabolic risk, as this would probably provide “interesting” results. General Overview and Conclusions Thayse Natacha Queiroz Ferreira Gomes 245 IMPLICATIONS AND OPPORTUNITIES FOR FUTURE RESEARCHES Implications We are convinced that the results of this thesis have educational as well as policy making implications concerning physical activity/exercise/sports participation, sedentariness and obesity. Firstly, we showed that at 10 years of age a large proportion of Portuguese children have excess weight, do not comply with the physical activity guidelines, and spend great portions of their day in sedentary activities. This information calls for families, schools and public health authorities’ attention/concern in order to change this scenario. The first call is towards schools, since the school context is also an important agent to promote/develop policies to increase children’s physical activity/physical exercise/sports participation to reduce their time in sedentary behaviours and thus help in weight control; further, physical education classes should be more interesting/challenging providing ample opportunities for individual success in all tasks, and playground equipment should always be available for children to use. Secondly, although the relationship between physical activity, sedentariness and BMI have been well explored, we showed that they have different determinants and this is relevant information that should be taken into account when planning intervention strategies to promote active and healthy lifestyles in children. Thirdly, children differ in their sedentariness from each other, as well as in their patterns, meaning that individual differences have to be considered when designing intervention programs to reduce sedentariness. Fourthly, the family environment plays an important role on children’s behaviour and health. With respect to overweight/obesity, we found that children whose parents have higher BMI also tend to have high BMI, i.e., there is familiality in this trait. This calls for careful paediatrician interventions so that General Overview and Conclusions Thayse Natacha Queiroz Ferreira Gomes 246 they pay closer attention to these children in order to prevent their future obesity status. Fifthly, the Portuguese school environment does not seem to explain a large proportion of children’s physical activity, sedentariness, and BMI variance, and several of their correlates did not show any significant association. However, schools have prominent roles on children physical activity, sedentariness and obesity, by developing and promoting active and healthy lifestyles helping them to make “better” choices in the use of their time outside school as well as in their nutritional choices. Sixthly, physical activity, physical fitness and BMI are associated with children’s metabolic risk, acting separately or in association with each other. Obesity appears to be the strongest correlate of metabolic risk, reinforcing the need to reduce this risk during childhood, and also in later life. Further, high levels of physical activity and high levels of muscular strength were also associated with decreasing metabolic risk. Given that muscular strength attenuates the negative role of low levels of physical activity on metabolic risk, this information should be carefully used by paediatricians and physical education teachers. Seventhly, and lastly, physical education teachers, school authorities, paediatricians and health care professionals have to have in mind that physical activity and sedentariness, although correlated, are not different sides of the same coin, but they co-occur in the same child, i.e., a given child can spend sufficient time in moderate-to-vigorous physical activity but, at the same time, he/she can also spend a high proportion of his/her time in sedentary activities. Since both behaviours seem to be related to overweight/obesity development in children, as well as in their co-morbidities, interventions should be designed to act in both behaviours. In addition, since BMI and sedentariness are probably not correlated, suggesting that their link may be mediated by other covariates, this calls again to school policies regarding children healthy eating habits. General Overview and Conclusions Thayse Natacha Queiroz Ferreira Gomes 247 Opportunities for future researches From the large set of data collected in the ISCOLE-Portugal, as well as in its ancillary study, only a small portion was explored. Further, we have not yet considered the joint analysis with other European countries (UK and Finland), or Brazil, and less so with the other 11 countries of the ISCOLE project. Below we provide a short list of questions that will be addressed in a near future considering only the Portuguese data set: Questions related to the prevalence and correlates of physical activity, sedentariness and overweight/obesity:  What is the proportion of time children are physically active, considering their total physical activity? Does it vary substantially within subjects? If so, which are the main predictors and why?  Are children different in their daily physical activity? What is the variance withinand between-subjects in physical activity over a week?  Do moderate-to-vigorous physical activity and sedentariness vary along the day? Is there a pattern in this variation? Is this pattern different between school-days and weekend day? Which variables are associated with these patterns?  Does the neighbourhood environment play a significant role in children’s physical activity, sedentariness and BMI?  At the home environment, is parental support a significant predictor of children’s physical activity and sedentariness? Does the availability of sports equipment explain the expression of children’s physical activity and sedentariness? Does the quantity and quality of food availability act as suitable predictors of children’s BMI? Questions related to the relationship between physical activity, physical fitness, obesity and its co-morbidities:  Can physical fitness, namely muscular strength, attenuate the role of obesity in the expression of metabolic risk? General Overview and Conclusions Thayse Natacha Queiroz Ferreira Gomes 254 Hallal, P. C., Andersen, L. B., Bull, F. C., Guthold, R., Haskell, W., & Ekelund, U. (2012). Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet, 380(9838), 247-257. doi: 10.1016/S0140-6736(12)60646-1 Hu, Y., He, L., Wu, Y., Ma, G., & Li, L. (2013). Familial correlation and aggregation of body mass index and blood pressure in Chinese Han population. BMC Public Health, 13(1). doi: 10.1186/1471-2458-13-686 Janssen, I., Katzmarzyk, P. T., Boyce, W. F., Vereecken, C., Mulvihill, C., Roberts, C., et al. (2005). Comparison of overweight and obesity prevalence in school-aged youth from 34 countries and their relationships with physical activity and dietary patterns. Obes Rev, 6(2), 123-132. doi: 10.1111/j.1467789X.2005.00176.x Leech, R. M., McNaughton, S. A., & Timperio, A. (2014). The clustering of diet, physical activity and sedentary behavior in children and adolescents: a review. Int J Behav Nutr Phys Act, 11, 4. doi: 10.1186/1479-5868-11-4 Lobstein, T., Baur, L., & Uauy, R. (2004). Obesity in children and young people: a crisis in public health. Obes Rev, 5 Suppl 1, 4-104. doi: 10.1111/j.1467789X.2004.00133.x Malina, R. M., Bouchard, C., & Bar-Or, O. (Eds.). (2004). Growth, Maturation and Physical Activiy. Champaign: Human Kinetics. Marshall, S. J., Biddle, S. J. H., Sallis, J. F., McKenzie, T. L., & Conway, T. L. (2002). Clustering of sedentary behaviors and physical activity among youth: A cross-national study. Pediatr Exerc Sci, 14(4), 401-417. Moreno, L. A., De Henauw, S., Gonzalez-Gross, M., Kersting, M., Molnar, D., Gottrand, F., et al. (2008). Design and implementation of the Healthy Lifestyle in Europe by Nutrition in Adolescence Cross-Sectional Study. Int J Obes (Lond), 32 Suppl 5, S4-11. doi: 10.1038/ijo.2008.177 Ng, M., Fleming, T., Robinson, M., Thomson, B., Graetz, N., Margono, C., et al. (2014). Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: a systematic analysis for the Global General Overview and Conclusions Thayse Natacha Queiroz Ferreira Gomes 255 Burden of Disease Study 2013. Lancet, 384(9945), 766-781. doi: 10.1016/S0140-6736(14)60460-8 Nilsson, A., Andersen, L. B., Ommundsen, Y., Froberg, K., Sardinha, L. B., Piehl-Aulin, K., et al. (2009). Correlates of objectively assessed physical activity and sedentary time in children: a cross-sectional study (The European Youth Heart Study). BMC Public Health, 9, 322. doi: 10.1186/1471-2458-9-322 Olds, T., Ferrar, K. E., Gomersall, S. R., Maher, C., & Walters, J. L. (2012). The Elasticity of Time: Associations Between Physical Activity and Use of Time in Adolescents. Health Educ Behav, 39(6), 732-736. doi: 10.1177/1090198111429822 Ortega, F. B., Konstabel, K., Pasquali, E., Ruiz, J. R., Hurtig-Wennlof, A., Maestu, J., et al. (2013). Objectively measured physical activity and sedentary time during childhood, adolescence and young adulthood: a cohort study. PLoS One, 8(4), e60871. doi: 10.1371/journal.pone.0060871 Pate, R. R., Mitchell, J. A., Byun, W., & Dowda, M. (2011). Sedentary behaviour in youth. Br J Sports Med, 45(11), 906-913. doi: 10.1136/bjsports-2011-090192 Patel, S. R., & Hu, F. B. (2008). Short sleep duration and weight gain: a systematic review. Obesity (Silver Spring), 16(3), 643-653. doi: 10.1038/oby.2007.118 Riddoch, C., Edwards, D., Page, A., Froberg, K., Anderssen, S., Wedderkopp, N., et al. (2005). The European Youth Heart Study - cardiovascular disease risk factors in children: rationale, aims, study design and validation of methods. J Phys Act Health, 2, 115-129. Ridgers, N. D., Stratton, G., Fairclough, S. J., & Twisk, J. W. (2007). Long-term effects of a playground markings and physical structures on children's recess physical activity levels. Prev Med, 44(5), 393-397. doi: 10.1016/j.ypmed.2007.01.009 Ruiz, J., & Ortega, F. (2009). Physical activity and cardiovascular disease risk factors in children and adolescents. Curr Cardiovasc Risk Rep, 3(4), 281-287. doi: 10.1007/s12170-009-0043-6 General Overview and Conclusions Thayse Natacha Queiroz Ferreira Gomes 256 Saland, J. M. (2007). Update on the metabolic syndrome in children. Curr Opin Pediatr, 19(2), 183-191. doi: 10.1097/MOP.0b013e3280208519 Sallis, J. F., Conway, T. L., Prochaska, J. J., McKenzie, T. L., Marshall, S. J., & Brown, M. (2001). The association of school environments with youth physical activity. Am J Public Health, 91(4), 618-620. Sallis, J. F., Owen, N., & Fisher, E. B. (2008). Ecological models of health behavior. In K. Glanz, B. K. Rimer & K. Viswanath (Eds.), Health behavior and health education: theory, research, and practice (4th ed., pp. 465-486). Hoboken: Jossey-Bass. Seabra, A. F., Mendonça, D. M., Thomis, M. A., Anjos, L. A., & Maia, J. A. (2008). Biological and socio-cultural determinants of physical activity in adolescents. Cad Saude Publica, 24(4), 721-736. Serra-Majem, L., Aranceta Bartrina, J., Perez-Rodrigo, C., Ribas-Barba, L., & Delgado-Rubio, A. (2006). Prevalence and deteminants of obesity in Spanish children and young people. Br J Nutr, 96 Suppl 1, S67-S72. doi: S0007114506002376 Steele, R. M., Brage, S., Corder, K., Wareham, N. J., & Ekelund, U. (2008). Physical activity, cardiorespiratory fitness, and the metabolic syndrome in youth. J Appl Physiol, 105(1), 342-351. doi: 10.1152/japplphysiol.00072.2008 Steene-Johannessen, J., Anderssen, S. A., Kolle, E., & Andersen, L. B. (2009). Low muscle fitness is associated with metabolic risk in youth. Med Sci Sports Exerc, 41(7), 1361-1367. doi: 10.1249/MSS.0b013e31819aaae5 Stone, M. R., Stevens, D., & Faulkner, G. E. (2013). Maintaining recommended sleep throughout the week is associated with increased physical activity in children. Prev Med, 56(2), 112-117. doi: 10.1016/j.ypmed.2012.11.015 Storey, K. E., Forbes, L. E., Fraser, S. N., Spence, J. C., Plotnikoff, R. C., Raine, K. D., et al. (2012). Adolescent weight status and related behavioural factors: web survey of physical activity and nutrition. J Obes, 2012, 342386. doi: 10.1155/2012/342386 General Overview and Conclusions Thayse Natacha Queiroz Ferreira Gomes 257 Tailor, A. M., Peeters, P. H. M., Norat, T., Vineis, P., & Romaguera, D. (2010). An update on the prevalence of the metabolic syndrome in children and adolescents. Int J Pediatr Obes, 5(3), 202-213. doi: 10.3109/17477160903281079 Tammelin, T., Ekelund, U., Remes, J., & Nayha, S. (2007). Physical activity and sedentary behaviors among Finnish youth. Med Sci Sports Exerc, 39(7), 10671074. doi: 10.1249/mss.0b13e318058a603 Tandon, P., Grow, H. M., Couch, S., Glanz, K., Sallis, J. F., Frank, L. D., et al. (2014). Physical and social home environment in relation to children's overall and home-based physical activity and sedentary time. Prev Med, 66C, 39-44. doi: 10.1016/j.ypmed.2014.05.019 Tandon, P., Zhou, C., Sallis, J. F., Cain, K. L., Frank, L. D., & Saelens, B. E. (2012). Home environment relationships with children's physical activity, sedentary time, and screen time by socioeconomic status. Int J Behav Nutr Phys Act, 9, 88. doi: 10.1186/1479-5868-9-88 Van den Bulck, J. (2004). Television viewing, computer game playing, and internet use and self-reported time to bed and time out of bed in secondaryschool children. Sleep, 27(1), 101-104. Van der Horst, K., Oenema, A., Ferreira, I., Wendel-Vos, W., Giskes, K., van Lenthe, F., et al. (2007). A systematic review of environmental correlates of obesity-related dietary behaviors in youth. Health Educ Res, 22(2), 203-226. doi:10.1093/her/cyl069 Van der Horst, K., Paw, M. J., Twisk, J. W., & Van Mechelen, W. (2007). A brief review on correlates of physical activity and sedentariness in youth. Med Sci Sports Exerc, 39(8), 1241-1250. doi: 10.1249/mss.0b013e318059bf35 Wechsler, H., Devereaux, R. S., Davis, M., & Collins, J. (2000). Using the school environment to promote physical activity and healthy eating. Prev Med, 31(2), S121-S137. Yngve, A., De Bourdeaudhuij, I., Wolf, A., Grjibovski, A., Brug, J., Due, P., et al. (2008). Differences in prevalence of overweight and stunting in 11-year olds General Overview and Conclusions Thayse Natacha Queiroz Ferreira Gomes 258 across Europe: The Pro Children Study. Eur J Public Health, 18(2), 126-130. doi: 10.1093/eurpub/ckm099 Yusuf, S., Reddy, S., Ounpuu, S., & Anand, S. (2001). Global burden of cardiovascular diseases: part I: general considerations, the epidemiologic transition, risk factors, and impact of urbanization. Circulation, 104(22), 27462753.