Objectively measured sedentary behaviour and physical ...€¦ · Abstract Background: A...

Objectively measured sedentary

behaviour and physical activity in

relation to cardiorespiratory fitness in

Portuguese adolescents

Number of words: 16.172

De Brabanter Jolien and Platteau Yasmine Student number: 01610040 & 01512661

Promotor: Prof. dr. Benedicte Deforche

Copromotor: Prof. José Ribeiro

Tutor: dr. Dorien Simons

Master’s Dissertation submitted for obtaining the degree of Master of Science in Health Education and

Health Promotion

Academic year: 2017-2018

Objectively measured sedentary

behaviour and physical activity in

relation to cardiorespiratory fitness in

Portuguese adolescents

Number of words: 16.172

De Brabanter and Platteau Yasmine Student number: 01610040 & 01512661

Promotor: Prof. dr. Benedicte Deforche

Copromotor: Prof. dr. José Ribeiro

Tutor: dr. Dorien Simons

Master’s Dissertation submitted for obtaining the degree of Master of Science in Health Education and

Health Promotion

Academic year: 2017-2018

Abstract

Background: A significant part of adolescents do not meet the current guidelines for

sedentary behaviour and physical activity. Additionally, a continuous decrease in

cardiorespiratory fitness levels is observed. Since both sedentary behaviour and

physical activity are acknowledged as independent behaviours, more research is

necessary to fully understand their relationship with cardiorespiratory fitness.

Purpose: The main purpose of this master thesis was to explore the relationship

between the objectively measured combined variable of sedentary behaviour/ physical

activity with cardiorespiratory fitness in Portuguese adolescents.

Methods: This cross-sectional study, using data from the AFINA-te project, included

695 Portuguese adolescents (10-18 years). Both physical activity and sedentary

behaviour were assessed using accelerometers and dichotomized based on

respectively the guidelines for physical activity and the median. Afterwards, they were

grouped into the combined variable with the following categories: high

sedentary/inactive, low sedentary/inactive, high sedentary/active, low

sedentary/active. Cardiorespiratory fitness was assessed using the 20 m shuttle-run

test and dichotomized based on the FITNESSGRAM cutoff points. Binary logistic

regression models and a one-way ANOVA test were conducted.

Results: Adolescents who were high sedentary/active or low sedentary/active were

more likely to have a healthy cardiorespiratory fitness level in comparison to those who

were high sedentary/inactive.

Conclusion: Being active (i.e. MVPA) seems to be more important to increase

cardiorespiratory fitness in adolescents than being low sedentary. Low sedentary

levels may not be able to overcome the detrimental influence of low MVPA levels on

cardiorespiratory fitness.

Number of words master thesis: 16.172 (table of content, bibliography, figures and

attachments excluded)

Abstract

Achtergrond: Tegenwoordig halen heel wat adolescenten de aanbevelingen voor

sedentair gedrag en fysieke activiteit niet. Daarnaast werd er wereldwijd een continue

daling in cardiorespiratoire fitheid vastgesteld. Gezien sedentair gedrag en fysieke

activiteit erkend zijn als twee onafhankelijke gedragingen zijn, is meer onderzoek nodig

om hun relatie met cardiorespiratoire fitheid te begrijpen.

Doelstellingen: Het hoofddoel van deze thesis was om de relatie tussen de objectief

gemeten gecombineerde variabele van sedentair gedrag/fysieke activiteit

met cardiorespiratoire fitheid in Portugese adolescenten te onderzoeken.

Methode: Deze cross-sectionele studie, die gebruik maakt van data verzameld voor

het AFINA-te project, includeerde 695 Portugese adolescenten (10-18 jaar). Zowel

sedentair gedrag als fysieke activiteit werden gemeten door accelerometers en

gedichotomiseerd op basis van respectievelijk de aanbevelingen voor fysieke activiteit

en de mediaan. Nadien werden deze variabelen samengevoegd tot een

gecombineerde variabele met vier categorieën: hoog sedentair/inactief, laag

sedentair/inactief, hoog sedentair/actief en laag sedentair/actief. Cardiorespiratoire

fitheid werd geschat op basis van de resultaten van de 20 m shuttle-run test en

gedichotomiseerd via de FITNESSGRAM afkapwaarden. Binaire logistische

regressiemodellen en een one-way ANOVA test werden uitgevoerd.

Resultaten: Adolescenten die hoog sedentair/actief of laag sedentair/actief waren

hadden meer kans op een gezonde cardiorespiratoire fitheid, in vergelijking met

adolescenten die hoog sedentaire/inactief waren.

Conclusie: Fysiek actief zijn blijkt belangrijker in het verhogen van de

cardiorespiratoire fitheid dan weinig sedentair zijn. Lage niveaus van sedentair gedrag

zijn mogelijks niet in staat om de nadelige invloed van fysieke inactiviteit op

cardiorespiratoire fitheid te overwinnen bij adolescenten.

Aantal woorden: 16.172 (exclusief inhoudstafel, literatuurlijst, cijfermateriaal en bijlagen)

Table of contents

1 Introduction .......................................................................................................... 1

1.1 Problem analyses .................................................................................................... 1

1.2 Already existing knowledge ..................................................................................... 2

1.3 Selected approach .................................................................................................. 2

1.4 Construction of master thesis and division of task ................................................... 3

2 Literature review................................................................................................... 5

2.1 Adolescence............................................................................................................ 5

2.2 Physical activity ....................................................................................................... 6

2.2.1 Definition and guidelines of physical activity ..................................................... 6

2.2.2 Measurements ................................................................................................. 7

2.2.3 Physical activity and health .............................................................................. 9

2.2.4 Physical activity during adolescence ...............................................................10

2.3 Sedentary behaviour ..............................................................................................11

2.3.1 Definition and guidelines of sedentary behaviour ............................................11

2.3.2 Measurements ................................................................................................13

2.3.3 Sedentary behaviour and health ......................................................................14

2.3.4 Sedentary behaviour during adolescence ........................................................15

2.4 Cardiorespiratory fitness ........................................................................................17

2.4.1 Definition of cardiorespiratory fitness ...............................................................17

2.4.2 Measurements ................................................................................................17

2.4.3 Cardiorespiratory fitness and health ................................................................18

2.4.4 Cardiorespiratory fitness during adolescence ..................................................19

2.5 Relationship between physical activity, sedentary behaviour and cardiorespiratory

fitness….. ..........................................................................................................................20

2.5.1 Sedentary behaviour and cardiorespiratory fitness ..........................................20

2.5.2 Physical activity and cardiorespiratory fitness..................................................21

2.5.3 Combined relationship of physical activity, sedentary behaviour and

cardiorespiratory fitness ................................................................................................22

2.6 Problem analyses ...................................................................................................23

3 Research method ............................................................................................... 27

3.1 Design ....................................................................................................................27

3.2 The AFINA-te project ..............................................................................................27

3.3 Sampling ................................................................................................................28

3.4 Measurements .......................................................................................................28

3.4.1 Sociodemographic data ...................................................................................28

3.4.2 Anthropometric measurements .......................................................................28

3.4.3 Physical activity and sedentary behaviour .......................................................29

3.4.4 Cardiorespiratory fitness .................................................................................30

3.5 Statistical analyses .................................................................................................31

4 Results ............................................................................................................... 35

4.1 Descriptive statistics ...............................................................................................35

4.2 Statistical tests .......................................................................................................39

4.2.1 The relationship between sedentary behaviour and cardiorespiratory fitness ..39

4.2.2 The relationship between physical activity and cardiorespiratory fitness ..........39

4.2.3 The combined variable of sedentary behaviour and physical activity (MVPA) in

relationship to cardiorespiratory fitness. ........................................................................40

4.2.4 Comparison of cardiorespiratory fitness levels between categories of the

combined variable sedentary behaviour and physical activity (MVPA). ..........................41

5 Discussion .......................................................................................................... 43

6 Conclusion ......................................................................................................... 51

7 References ......................................................................................................... 53

8 Appendix ............................................................................................................ 71

List of figures

Figure 1. Accelerometers worn on the (a) wrist and (b) ankle (Lin, Gamble, Yang,

& Wang, 2012).

Figure 2. Accelerometers worn in the waist (ActiGraph, 2018).

Figure 3. Proportion (%) of adolescents achieving the recommended physical

activity guidelines during years of adolescence in 2006 and 2016

(Fernandes, 2018).

Figure 4. ActivPAL worn at the thigh (Byrom et al., 2016).

Figure 5. Logistic regression predicting belonging to the healthy zone or above for

CRF by the physical activity/ sedentary time group (Santos et al., 2014).

Figure 6. Normal distribution of VO2max.

List of tables

Table 1. Division tasks.

Table 2. Cutoff points for sedentary behaviour and physical activity intensity levels

(Evenson et al., 2008).

Table 3. Values and labels of the dependent and independent variables.

Table 4. Values and label of the combined variable (sedentary behaviour and

physical activity).

Table 5. Descriptive statistics of the 20-meter shuttle run test

Table 6. Estimated vo2max and mean time per day for sedentary behaviour and

physical activity for all participants and between the two categories of

cardiorespiratory fitness.

Table 7. Descriptive statistics of sample characteristics

Table 8. Unadjusted and adjusted model: sedentary behaviour in relation to

cardiorespiratory fitness (binary logistic regression)

Table 9. Unadjusted and adjusted model: physical activity in relation to

cardiorespiratory fitness (binary logistic regression)

Table 10. The combined variable of physical activity and sedentary behaviour in

relation to cardiorespiratory fitness

Table 11. Test of homogeneity of variances

Table 12. ANOVA

Table 13. Tukey Post Hoc Tests, Multiple comparisons

Table 14. Mean results and standard deviation (SD) of cardiorespiratory fitness by

combined groups of sedentary and moderate to vigorous physical activity

(MVPA)

Acknowledgement

The school year of 2017-2018 is one we will not easily forget. We got the chance to

write our master thesis in the beautiful city, Porto. Since you never write a thesis alone,

we would like to express our gratitude to a few people who helped us during this

process.

First of all, we would like to thank our promotor prof. Dr. Deforche. You created the

opportunity for us to go to Portugal after our application for Finland was withdrawn. We

really appreciate the effort you did. Furthermore, we would like to thank you for sharing

your expertise and for your patience during this process.

We would like to give a special word of thanks to our Portuguese copromotor Prof.

Ribeiro. Thank you for letting us participate in one of your projects and thank you for

making time in your busy schedule. Whenever we asked for your help, you gave us

advice, but also stimulated us to be more independent. We are grateful for your warm

welcome and your interest in our Erasmus experience. Muito obrigado por tudo.

Next, we want to thank our tutor dr. Simons for the quick and extensive feedback and

for stimulating us to make decisions when we kept doubting. We are grateful for your

listening ear and support when things got more difficult. Thank you.

A special word of thanks to our families and friends. Especially Eline and Hanne, our

fellow Erasmus colleagues, who stood by our side in Porto. Thank you for always

believing in us, for your never-ending patience and motivational speaking. When we

had a difficult time, we could share our concerns with you and this definitely meant a

lot to us..

Further, we would like to thank the PhD students and postdoctoral researchers of the

PhD room of the Faculty of Sports in Porto. You gave us the chance to work alongside

you, which was a strong motivator for us. Thank you for involving us in your research,

we certainly learned a lot. We also want to wish you the best of luck with your doctoral

theses and research. Muito obrigado e boa sorte.

Next, Yasmine would also like to thank the nursing home ‘Ter Beke’. Without their

support she would not have been able to go on this experience and write this master

thesis.

Last but not least, we would like to thank each other for their patience, knowledge and

perseverance. Writing this thesis did not only enrich our professional life but also our

personal life, thank you for the friendship.

Yasmine and Jolien

August 2018

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1 Introduction

1.1 Problem analyses

Since the 20th century, many changes occurred in the way of living which have led to

less physically active lifestyles and more sedentary lifestyles, especially among

adolescents. This because of the greater availability of television and TV programs,

new technologies, increased car use with the decrease of cycling and walking, etc.

(Biddle, Gorely, Marshall, Murdey, & Cameron, 2004). For a long time sedentary

behaviour and physical activity were considered each others opposites. However,

recent studies showed that sedentary behaviour and physical activity are both

independent behaviours, with their own influence on health (Santos et al., 2014 ;

O'Brien, Issartel, & Belton, 2018 ; Tremblay, Colley, Saunders, Healy, & Owen, 2010).

In addition to a decline in physical activity and an increase in sedentary behaviour,

Tomkinson and Olds (2007) also found a global decline in cardiorespiratory fitness

levels among adolescents since 1970. Between 2006 and 2008, 58% of Europese

adolescent girls and 61% of the boys had a healthy cardiorespiratory fitness level

(Ortega et al., 2011). Consequently, because of the increase in sedentary behaviour

and decrease in physical activity, accompanied with a decrease in cardiorespiratory

fitness levels, Santos et al. (2014) insinuated that both physical activity and sedentary

behaviour might influence cardiorespiratory fitness in adolescence. Alongside,

compelling evidence indicated that cardiorespiratory fitness is an important marker of

health, making this an important field of interest within the public health (Santos,

Marques, Minderico, Ekelund, & Sardinha, 2018).

As described in the previous paragraph, sedentary behaviour and physical activity are

both independent behaviours. Therefore, investigating if sedentary behaviour is related

to cardiorespiratory fitness and if physical activity is related to cardiorespiratory fitness

in adolescents is a contribution to evidence-based knowledge.

Adolescents can have an active and sedentary lifestyle at the same time. Therefore it

is important to explore if sedentary behaviour and physical activity combined are

related to cardiorespiratory fitness in adolescents (Martinez-Gomez et al., 2011 ;

Santos et al., 2018).

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1.2 Already existing knowledge

Literature about sedentary behaviour in relation to cardiorespiratory fitness,

independent of physical activity, is scarce. Furthermore, the literature that does exist

has contradictory results and used mostly subjective methods to measure sedentary

behaviour. This master thesis will contribute to the existing knowledge within this field

by using objective methods to measure sedentary behaviour.

Literature about physical activity in relation to cardiorespiratory fitness is more

coherent. A positive association between physical activity and cardiorespiratory fitness

is mostly found (Ekelund et al., 2007; Parikh & Stratton, 2011; Ruiz & Ortega, 2009).

Even though evidence is coherent, a shortage of literature measuring and comparing

the intensities of physical activity exist (Parikh & Stratton, 2011). This master thesis

will contribute with comparable evidence-based results about in which way MVPA is

related to cardiorespiratory fitness.

Literature about the combined variable of sedentary behaviour and physical activity in

relation to cardiorespiratory fitness is scarce. Literature that does exist has

contradictory results. Santos et al. (2014) results indicated a positive relationship

between the combined variable low sedentary behaviour and adequate levels of MVPA

with cardiorespiratory fitness. On the other hand, Denton et al. (2013) results indicated

that it is more important to focus on higher intensities of physical activity than on

sedentary behaviour in order to sustain or enhance cardiorespiratory fitness. This

master thesis will contribute with more evidence-based results. In so doing, more

coherent evidence will arise about the relationship of the combined variable sedentary

behaviour and physical activity with cardiorespiratory fitness.

1.3 Selected approach

To study the selected topic, a cross sectional design was used. The data used in this

master thesis came from the AFINA-te project. This is a longitudinal intervention study

to promote physical activity and nutritional knowledge in Portuguese adolescents (10

to 18 years old), conducted in northern Portugal (in the district of Porto).

The sociodemographic variables were collected through a self-administrated

questionnaire. The anthropometric measurements were collected in accordance with

the international standards for anthropometric assessment. Sedentary behaviour and

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physical activity were objectively measured using the actigraph GT3Xs. The cutoff

points used, in order determine the different physical activity intensity categories, were

developed by Evenson, Catellier, Gill, Ondrak, & McMurray (2008). Cardiorespiratory

fitness was measured with the 20 meter shuttle-run test (using the FITNESSGRAM

protocol). Through the Mahar equation (Mahar, Welk, Rowe, Crotts, & McIver, 2006)

the number of shuttles were converted into an estimated VO2max. Afterwards, the

estimated VO2max was categorized in three groups based on the age- and gender

specific cutoff points from the FITNESSGRAM (The Cooper Institute, 2010). In order

to perform the analyses, sedentary behaviour, physical activity and cardiorespiratory

fitness had to be dichotomized.

First, a binary logistic regression model was performed in order to explore the

relationship between sedentary behaviour and cardiorespiratory fitness as well as the

relationship between physical activity and cardiorespiratory fitness. Second, a binary

logistic regression model was again performed, this time in order to explore the

relationship between the combined variable sedentary behaviour and physical activity

with cardiorespiratory fitness. Finally, the One-Way ANOVA test was performed in

order to detect potential differences in the mean cardiorespiratory fitness levels

between the four possible combinations of sedentary behaviour and physical activity

(high sedentary/inactive, low sedentary/inactive, high sedentary/active, low

sedentary/active).

1.4 Construction of master thesis and division of task

The first part of this master dissertation includes the literature review. This part is based

on scientific publications and consist out of six subchapters. The first subchapter

contains a description of adolescence. The second subchapter contains a description

of physical activity. The third subchapter contains a description of sedentary behaviour.

The fourth subchapter contains a description of cardiorespiratory fitness. The fifth

subchapter contains a description of the already existing literature about the

relationship between sedentary behaviour, physical activity and cardiorespiratory

fitness. At last, the sixth subchapter contains the problem analyses with the research

questions and hypotheses.

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The second part includes the cross-sectional study. In chapter three the research

method was described and chapter four describes the results. In chapter five the

discussion was described. And chapter six finished with the conclusion.

The table below (table 1) shows the division of tasks between the two master students.

Table 1: Division tasks

Division of tasks

Written Read and modified

Abstract Jolien Yasmine

Introduction Yasmine Jolien

Foreword Jolien Yasmine

Literature review

2.1 Adolescence Jolien Yasmine

2.2 Physical activity Yasmine Jolien

2.3 Sedentary behaviour (2.3.1, 2.3.2 & 2.3.4)

Jolien Yasmine

(2.3.3) Yasmine Jolien

2.4 Cardiorespiratory fitness Jolien Yasmine

2.5 Relationships Yasmine Jolien

2.6 Problem analyses Yasmine Jolien

Research method (4.1, 4.2, 4.4 & 4.5) Jolien Yasmine

Research method (4.3) Yasmine Jolien

Results

5.1 Descriptive statistics Yasmine Jolien

5.1 Statistical tests

(5.1.1 & 5.1.2) Jolien Yasmine

(5.1.3 & 5.1.3.1) Yasmine Jolien

Discussion Jolien Yasmine

Conclusion Yasmine Jolien

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2 Literature review

2.1 Adolescence

In general, adolescence can be defined as ‘the time between the beginning of puberty

and the establishment of social independency’ (Steinberg, 2014). However, there is no

universal definition and the World Health Organization (WHO, 2018d) pointed out that

besides social independence, other changes in physical, psychological and

neurodevelopmental aspects must be taken into account in order to define

adolescence. Furthermore, the moment at which these changes occur, differs over

time and between individuals and cultures. All this explains the different age ranges

that have been used in literature to define adolescence (Curtis, 2015). However, for

scientific purposes, the chronological definition is the most convenient and most

commonly used way to define adolescence. The WHO (2018d) defines it as the time

between the 10th and the 19th year of life. When referring to adolescents in this thesis,

this definition will be used.

Adolescence is a period that is characterized by rapid and complex physical,

emotional, social and behavioural changes. The increase in height, weight, number

and volume of fat cells, changes in the location of fat, etc. are examples of physical

changes that occur during adolescence. Emotions also become stronger, empathy

develops and coping strategies emerge (Alberga, Sigal, Goldfield, Prud'homme, &

Kenny, 2012). The social changes include adopting more adult-like social roles which

accompanies the development of own values, a personal identity, autonomy and

independence (Alberga et al., 2012). During adolescence, peers start to play a more

significant role and have therefore a high influence on the adolescent’s decision-

making process and behaviour. Partly due to the presence of peers, adolescents

perform more risky behaviours then children and adults (Chein, Albert, O’Brien, Uckert,

Steinberg, 2011; O'Brien, Albert, Chein, & Steinberg, 2011).

Furthermore, adolescence is also the life stage where possible health-related

behaviours are established, behaviours that could possibly track into adulthood. The

term tracking refers to ‘the stability of a certain variable over time or the predictability

of a measurement early in life for the value of the same variable later in life’ (Twisk,

Kemper, & van Mechelen, 2000). Physical activity and sedentary behaviour are both

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examples of behaviours that track at a moderate level from adolescence into adulthood

and thus not only influence health in adolescence but also later in life (Alberga et al.

2012; Bay, Morton, & Vickers, 2016; Biddle, Pearson, Ross, & Braithwaite, 2010;

Sawyer et al., 2012). All those different types of changes that characterize the transition

into adulthood, makes adolescence a critical window for behavioural change,

intervention programs and thus public health in general (Alberga et al. 2012; Bay et al.,

2016; Sawyer et al., 2012).

2.2 Physical activity

2.2.1 Definition and guidelines of physical activity

According to Caspersen et al. (1985), physical activity can be defined as ‘any bodily

movement produced by skeletal muscles that requires energy expenditure’. This global

definition is still used by many researchers and organizations (e.g. WHO). Terms such

as exercise and physical activity are sometimes used interchangeably, although their

meaning is different. Physical activity is the performance of movement and exercise is

just one of its subcategories (Caspersen et al., 1985; World Health Organization,

2018b).

Recommendations exist about the amount of physical activity necessary to obtain

positive health effects. First of all, children and adolescents should be active on a daily

basis, which can be achieved by doing chores in the household, active transport

(walking and cycling), recreational activities (e.g. playing), planned exercises, physical

education lessons, etc. This can be done at school, at home or within the community

(Caspersen et al., 1985; Tremblay et al. 2011b; World Health Organization, 2018a).

The WHO (2018a) has also specific recommendations for physical activity in healthy

children and adolescents (5-17 years). Children and adolescents should, regardless of

race, ethnicity, socioeconomic status and gender, perform at least an accumulated 60

minutes of moderate-to-vigorous intensity physical activity (MVPA) a day. The biggest

part of these activities should be aerobic. Aerobic can be defined as “the rhythmical

contraction and relaxation of large muscle masses over an extended period of time”

(Dimeo, Fetscher, Lange, Mertelsmann, & Keul, 1997). Next to MVPA, children and

adolescents should also perform activities with vigorous intensity, including activities

that strengthen the bone and muscles, at least three times a week. Bone strengthening

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exercises can be turning, running, jumping and playing games (World Health

Organization, 2018a).

2.2.2 Measurements

Physical activity can be measured in an objective (direct) and subjective (indirect) way.

Objective methods include motion sensors, direct observation, heart rate monitors,

doubly labelled water method, etc. Subjective methods include questionnaires, diaries,

interviews, records and logs, etc. (Ainsworth, 2009; Sirard, & Pate, 2001).

Regarding the direct methods, two possible types of motion sensors can be used to

measure physical activity, namely accelerometers and pedometers (Ainsworth, 2009).

A pedometer is a small monitoring device that counts the number of steps with

acceptable accuracy. Evidence showed that they are reliable and have good validity

(Tudor-Locke, Ainsworth, Thompson, & Matthews, 2002). Despite the advantage of

the low cost, being able to provide behavioural and motivational feedback and the user-

friendliness, there are also some disadvantages. They tend to be less accurate when

used by people with altered step patterns and they are not able to measure non-

walking activities (e.g. cycling), energy expenditure and posture. Although the biggest

limitation is the fact that pedometers are not able to measure the duration, intensity,

type and frequency of physical activity (Ainsworth, Cahalin, Buman, & Ross, 2015 ;

Tudor-Locke et al., 2002; Ainsworth, 2009; Beighle, Pangrazi, & Vincent, 2001).

Besides pedometers, accelerometers are often used to measure physical activity

(Ainsworth, 2009). They provide reliable, practical, objective, valid and accurate data

for determining the intensity and amount of physical activity in adolescents (Reilly et

al., 2008; Ainsworth, 2009). They can be worn around the ankle, wrist or waist (see

figure 1 and 2). The preferred placement depends on the purpose and the type of the

accelerometer (Berlin, Storti, & Brach, 2006).

Figure 1: Accelerometers worn on the (a) wrist and (b) ankle Figure 2: Accelerometers worn in the waist

(Lin, Gamble, Yang, & Wang, 2012). (ActiGraph, 2018)

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The output of accelerometers is described in counts per unit time (epoch). If the amount

or intensity of the movements increases, so will the counts. These counts do not have

a biological meaning and must therefore be transformed into periods of physical activity

with a certain intensity (e.g. light/moderate/vigorous intensity physical activity). This is

possible, for example, through the use of cutoff points that have been developed in

laboratory research. They describe the linear or nonlinear relationship of the counts

with the energy expenditure (Reilly et al., 2008; Ainsworth, 2009; Tudor-Locke et al.,

2002; Trost, Loprinzi, Moore, & Pfeiffer, 2011).

The most frequently used accelerometer in research is the ActiGraph (MTI, Florida).

The ActiGraph is also the accelerometer that is most consistent and has the most high-

quality evidence that supports its use. It is found to be valid, reliable and feasible (Reilly

et al., 2008). The first ActiGraphs were uniaxial (e.g. The uniaxial ActiGraph 7164),

which means that only the counts from one axis, namely the vertical axis, were

measured. In 2008, the ActiGraph made an update with dual axes measurement

possibilities (e.g. GT1M), by adding the antero-posterior axis (Sasaki, Dinesh, &

Freedson, 2011). In 2009, the triaxial GT3X activity monitor was released which

measures accelerations in three axes. Next to the previous axises, it is also possible

to measure the mediolateral axis (Sasaki et al., 2011). Trost et al. (2011) also states

that the ActiGraph is the mostly used accelerometer in studies involving children and

adolescents.

Accelerometers solve some of the problems of subjective measurements (see below),

though they also have some disadvantages. They are more expensive and demand

additional software and hardware (this to calibrate, distill, import and analyze the

obtained data), accelerometers have a patented nature of numerous algorithms to

quantify physical activity, they lack the ability to put a specific range between sedentary

behaviour and light-intensity physical activity and they are unable to notice non-walking

activities (e.g. cycling) (Ainsworth et al., 2015 ; Tudor-Locke et al., 2002).

As for subjective methods, records and logs can be used. Records are mostly written

into a format of diary and provide extensive and detailed information. Because of their

administrative burden, this method is best suited for individual use by participants with

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high risk factors or when the information needs to be detailed (Ainsworth, 2009). Logs

contains checklists of specific activities and are filled out at the end of the day (with the

chance of having recall bias) or during short time periods (e.g. 15 minutes) during the

day (Ainsworth et al., 2015).

Besides the records and logs, questionnaires also exist. There are three types of

questionnaires that can be used in order to capture physical activity. First, there are

global questionnaires (e.g. (GPAQ) Global Physical Activity Questionnaire). These are

brief records about the participants global physical activity behaviour and do not

provide detailed information. Second, there are recall questionnaires (e.g. (PAR) seven

day physical activity recall). They are longer than the global questionnaires and asses

details about the time span, frequency and type of physical activity executed during

the last day, week or month. Third, there are quantitative histories (e.g. Historical

Leisure Activity Questionnaire). These questionnaires are long, and assess a detailed

description of the time span and frequency of the physical activities executed in the

past year or even lifetime (Ainsworth, 2009; Prince et al., 2008).

Self-reported methods are frequently used, because they are practical, they have low

cost, are generally accepted and have a low participation burden. Nevertheless, they

also have limitations such as the tendency to over- or underestimate the

intensity, frequency and amount of physical activity. They also have the issue of

response and recall bias (eg. inaccurate memory, social desirability), (Ainsworth et al.,

2015 ; Prince et al., 2008; Sallis & Saelens, 2000).

2.2.3 Physical activity and health

Being physically active has many health benefits in adolescence, such as a healthy

body weight, developing a healthy cardiovascular system (e.g. lower blood pressure,

advantageous serum lipids, …), developing thriving musculoskeletal tissues (e.g.

attain and preserve a suitable bone strength) and developing neuromuscular

consciousness (e.g. coordination) (Van Der Horst, Paw, Twisk, & Van Mechelen, 2007;

World Health Organization, 2018a). Moreover, Hallal, Victora, Azevedo and Wells

(2006) found that physical activity was beneficial in the reduction of chronic disease

incidence. Therefore, physically active adolescents had lower incidence in

hypertension, coronary heart disease, osteoporosis, type 2 diabetes and some cancers

(Hallal et al., 2006).

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Next to this, adolescents who are physically active enough also have higher degrees

of self-esteem and lower degrees of stress and anxiety. They are better in self-

expression, social integration and interaction (Van Der Horst et al., 2007; World Health

Organization, 2018a). Literature also found that these adolescents were healthier with

regard to other risk behaviours (e.g. avoiding drugs and alcohol). Furthermore, these

adolescents tend to have higher academic achievements (World Health Organization,

2018a).

As described above, physical activity is associated with many health benefits. Results

from experimental studies indicated, in high risk adolescents (e.g. obese adolescents),

that even minimal amounts of physical activity had health benefits. In observational

studies, the observed dose-response relation pointed out that the more physical active

the adolescent was, the greater the health benefits were (Janssen & LeBlanc, 2010).

In order to achieve considerable health benefits, the results of Janssen and LeBlanc

(2010) indicated that adolescents should at least engage in moderate intensity physical

activities. When they engaged in vigorous intensity physical activities, the health

benefits were even greater. The type of physical activity with the greatest health

benefits were aerobic, except in case of bone health, were influential weight bearing

activities were required (Janssen & LeBlanc, 2010).

2.2.4 Physical activity during adolescence

Nowadays, a significant part of the adolescents do not meet the current international

guidelines for physical activity (World Health Organization, 2018a). Hallal et al. (2012)

found, through self-reported measurements, that 80.3% of the adolescents worldwide

did not perform 60 minutes of MVPA a day and that boys were more active than girls.

The WHO (2016) found similar results concerning the European adolescents, namely

80% did not meet the current guidelines for physical activity. McMahon (2017) found

that only 10.7% of European adolescent girls and 17.9% of the boys met the guidelines

for physical activity, with boys being significantly more active than girls.

According to Portugal’s 2016 Report Card on physical activity in children and youth

(Mota, Silva, Raimundo, & Sardinha, 2016), measured through questionnaires, only

17% of the Portuguese girls and 34% of the boys (11-15 years old) met the guidelines

for physical activity in adolescents. The cross-national Health Behaviour in School-

11

aged Children (HBSC) study (Inchley, Currie, Jewel, Breda, & Barnekov, 2017) found

even lower values for Portuguese adolescents, also measured through questionnaires.

In 2014, only 8.9% of the girls met the recommendations, while 22.9% of the boys did.

These results showed that Portugal belongs to the lowest quartile of the 35 European

and Northern American countries that participated in the study (Inchley, Currie, Jewel,

Breda, & Barnekov, 2017).

The proportion of European adolescents meeting the guidelines for physical activity in

adolescents had however increased slightly since 2009-2010. Nonetheless, the

proportion of adolescents meeting the recommendations remained very low (World

Health Organization, 2016). In contrast to the results of the WHO (2016), the study of

Fernandes (2018) found a remarkable decrease in physical activity among the

Portuguese adolescents between 2006 and 2016 (see figure 3). During this period

there was an overall decline in physical activity of 10.8%. The decrease was also

greater in adolescents girls than in boys.

Figure 3: Proportion (%) of adolescents achieving the recommended physical activity guidelines during years of adolescence in 2006 and 2016 (Fernandes, 2018).

2.3 Sedentary behaviour

2.3.1 Definition and guidelines of sedentary behaviour

Definitions of sedentary behaviour have changed during the past decades but in order

to facilitate future research and the development of interventions and policies, the use

of standardized terminology is important (Tremblay et al., 2017). The Sedentary

Behaviour Research Network (SBRN, 2012) defines sedentary behaviour as “any

waking behaviour characterized by an energy expenditure 1.5 metabolic equivalents

(METs), while in a sitting, reclining or lying posture”. It should be emphasized that

sleeping is not considered a sedentary behaviour. The definition of the SBRN (2012)

can be used for toddlers, children, adolescents and adults, because the METs of

12

sedentary activities are similar for these age groups (Gao et al., 2016; Lau, Wang,

Acra, & Buchowski, 2016; Tremblay et al., 2017; Butte et al., 2018). The METs indicate

the ratio of the energy consumption during effort compared to the energy consumption

at rest. One MET can be defined as ‘the amount of oxygen consumed while sitting at

rest and is equal to 3.5 ml oxygen per kilogram body weight multiplied by the number

of minutes that the activity is performed’ (Jetté, Sidney, Blümchen, 1990).

Sedentary behaviour is not the same as physical inactivity (Owen, Healy, Matthews, &

Dunstan, 2010; Tremblay et al., 2017). The latter can be defined as ‘an insufficient

physical activity level to meet present physical activity recommendations’ (Lee et al.,

2012; Tremblay et al., 2017). For example, an adolescent can perform 60 minutes of

MVPA a day and thus be physically active, but still have a sedentary lifestyle. On the

other hand, an adolescent can be physically inactive without being sedentary.

In adolescence, different types of sedentary behaviour can be identified such as sitting

while watching television, playing video games and sitting at the computer. These are

all examples of sedentary screen time. When this behaviour is not related to school or

work it is called recreational sedentary screen time (Carson & Janssen, 2011;

Tremblay et al., 2017). Besides this, adolescents also engage in other types of

sedentary behaviour such as sitting while reading, sitting at school and during

motorized transport (Gorely, Biddle, Marshall, & Cameron, 2009).

Only recently, the first evidence-based guidelines for sedentary behaviour in children

(5-11 years) and adolescents (12-17 years) were released in Canada (Tremblay et al.,

2011c). According to these guidelines, all children and adolescents should limit their

total sedentary time on a daily basis and this regardless of ethnicity, race,

socioeconomic status and gender. Additionally, recreational screen time should not

exceed the maximum of two hours a day. Furthermore, sedentary time spend indoor

and during motorized transport should be reduced, just as prolonged sitting and this in

every context (family, community and school) (Tremblay et al., 2011c). All these

components of the Canadian recommendations are also present in the Australia’s

Physical Activity & Sedentary Behaviour Guidelines for Children and Young People

(Okely et al., 2012) and in the guidelines from the ‘Vlaams Instituut Gezond Leven’

(Vlaams Instituut Gezond Leven, 2015). According to the WHO (2015), Portugal does

13

not yet have guidelines for sedentary behaviour although they are being developed by

the Portuguese Institute of Sport and Youth. After searching the literature, no global

guidelines were found.

2.3.2 Measurements

In order to determine the relationship between sedentary behaviour and health, to plan

effective interventions and to formulate public health messages, accurate

measurement of sedentary behaviour is critical (Rosenberger, 2012). Due to the

multidimensional aspect of sedentary behaviour (volume, type and pattern),

researchers should select the method and measurement that fits with the aim and

extent of their study (Hardy et al. 2013; Byrom, Stratton, Mc Carthy, & Muehlhausen,

2016).

Sedentary behaviour can be measured in an objective (direct) and subjective (indirect)

way, both with their own advantages and disadvantages. Concerning the subjective

methods, questionnaires can be used. Questionnaires measuring sedentary behaviour

focus mainly on recreational screen time (TV watching, computer use and playing

video games), which is only a part of the total sedentary time (Loprinzi & Cardinal,

2011). Olds, Maher, Ridley and Kittel (2010) found that 60% of the total sedentary time

in adolescents consists of other sedentary activities than screen activities. Important

disadvantages of self-report measures are the consistent poor validity that has been

demonstrated, the recall bias and the social desirability bias (Atkin et al. 2012; Affuso

et al., 2016).

Accelerometers are increasingly used as a method to measure sedentary behaviour.

As seen in chapter 1.2.2, the data provided by accelerometers is most frequently

expressed in counts per minute (cpm). Different cutoff points for sedentary behaviours

have been published, although more and more evidence supports the use of the <100

cpm cutoff point for children, adolescents and adults (Fischer, Yildirim, Salmon, &

Chinapaw, 2012; Treuth et al., 2004; Trost et al., 2011). Accelerometers are unable to

detect differences between sitting, standing and lying, because the measured

acceleration will be equal for these three postures. According to the definition, standing

is a form of light-intensity physical activity while sitting and lying are sedentary

14

behaviours, this is why using accelerometers can lead to misclassification (Atkin et al.,

2012; Hart, Ainsworth, & Tudor-Locke, 2011)

Recently developed posture monitors or inclinometers (e.g. activPAL; see fig. 4), worn

at the thigh, appear to be able to assess body posture and postural changes. Although

these monitors show good validity and reliability in the small amount of yet available

studies, further research is necessary mainly in children and adolescence (Atkin et al.

2012; Hardy et al. 2013). Furthermore, triaxial accelerometers (e.g. GT3X) also have

an inclinometer function, which also has the ability to robustly detect the differences

between standing and sitting/lying, (Byrom et al., 2016). Although, multiple studies

(Alberto, Nathanael, Mathew, & Ainsworth, 2017; Kim, Barry, & Kang, 2015) found that

the ActivPAL, in comparison with the GT3X, is more accurate in measuring posture

and postural change.

Figure 4: ActivPAL worn at the thigh (Byrom et al., 2016)

Despite the advantages of objectively measured sedentary behaviour, inclinometers

and accelerometers are not able to collect contextual information about the recorded

sedentary activities, such as the distinction between sleeping and lying or recreational

and non-recreational sedentary behaviour (Atkin et al. 2012).

2.3.3 Sedentary behaviour and health

Independent of physical activities, sedentary behaviour (mainly measured through

objective measurements) is associated with an increase in several negative health

outcomes (Biddle, Pearson, & Salmon, 2018; Bermejo-Cantarero et al., 2017; Carson

et al., 2016). However, the relationship between sedentary behaviour (independent

from physical activity) and health outcomes is complex. It depends on the age group

and how sedentary behaviour is measured (De Rezende, Lopes, Rey-López, Matsudo,

& Do Carmo Luiz , 2014).

15

Sedentary behaviour, estimated primarily through self-reported television time, for

more than two hours a day is associated with an increase in all-cause mortality of 13%

(De Rezende et al., 2014).

Related unfavorable health outcomes, for children and adolescents aged between 5

and 17 years, are chronic diseases, poor psychosocial health (reduced scores of

prosocial behaviour and self-esteem), unfavourable body composition, decreased

academic accomplishments (although reading and doing homework appears to be

beneficial) and decreased physical fitness. Both objective (e.g. accelerometers) and

subjective measurements (e.g. questionnaires asking about television time, computer

use, etc.) were used in the reviews (Tremblay et al., 2011a; Carson et al., 2016).

Concerning chronic diseases, Katzmarzyk (2010) found that sedentary behaviour,

measured through television viewing and accelerometers, had a positive significant

association with cardiovascular disease, type 2 diabetes and obesity. However no

significant association was found with cancer.

Next to these unfavorable health outcomes, there is also evidence for the association

between sedentary behaviour and some mental health indicators in children and

adolescents aged between 5 to 18 years. This includes internalizing problems,

hyperactivity/ inattention problems, low perceived quality of life, low psychological well-

being and depression. These results were assessed using only subjective

measurements, e.g. screen-based sedentary activities (de Rezende et al., 2014;

Carson et al., 2016).

Recent studies showed that not only the age group and type of measurement for

sedentary behaviour are dependent for health risks, but also the way in which it is

accumulated (Chinapaw, de Niet, Verloigne, De Bourdeaudhuij, Brug, & Altenburg,

2014). Healy et al. (2008) found a beneficially association with body mass index, 2-

hour plasma glucose, waist circumference and triglycerides when increasing the

number of breaks in sedentary behaviour. However, this relationship was only found

in adults. Carson & Janssen (2011) found that breaks in sedentary behaviour did not

have a relationship with cardio-metabolic risk factors in children and adolescents.

2.3.4 Sedentary behaviour during adolescence

Since sedentary behaviour during adolescence is likely to track into adulthood, it is

important to observe and evaluate sedentary behaviour in adolescence (Biddle et al.,

16

2010; Lebacq et al., 2016). According to the review of Verloigne et al. (2016), a high

variation in the prevalence of sedentary behaviour can be found in literature, even

between articles of the same country. This variation is due to the use of different

outcome variables and assessment methods.

Concerning subjectively measured sedentary behaviour, the HBSC study (de Matos,

Simões, Camacho, & Reis, 2014) found through self-report that in 2014, 58.1% of the

Portuguese adolescents watched one to three hours of television on weekdays. On

weekend days, 46.9% of the Portuguese adolescents watched more than four hours

of television a day. Concerning the recreational time spent on the computer in the week

and weekend, almost 40% of the Portuguese adolescents spent between one and

three hours on the computer each day. On weekend days, 31.1% spent even more

than four hours a day on the computer. This suggests that a significant part of the

Portuguese adolescents exceeded the recommended maximum of two hours

recreational screen time a day. The Belgian National Food Consumption Survey (Bel,

De Ridder, Lebacq, Ost, & Teppers, 2016) examined, through self-reports, the amount

of screen time in Belgian adolescents. On week days, 54.9% of adolescents exceeded

the recommended maximum of two hours screen time a day. On weekend days, 83.9%

exceeded this maximum.

Concerning objectively measured sedentary behaviour, Spittaels et al. (2012) found

that Belgian adolescents spent 59% of the wear time of the accelerometers in

sedentary behaviour. This is similar to results found in studies in Canada and the

United States (Colley et al. 2011; Matthews et al. 2008). However, these are lower than

the averages reported by the HELENA-study (Ruiz et al., 2011). They found that

European adolescents (12-18 year) spent 71% of the wearing time in sedentary

behaviour, which corresponds to 9 hours a day. Furthermore, sedentary time rose as

the age increased. Another European study (Verloigne et al., 2012) found that

adolescent (10-12 years) girls spended 8.33 hours a day in sedentary behaviour and

boys 7.90 hours. These European averages are similar to the results of Portuguese

studies (Baptista, Silva, Santos, & Helena, 2011; Santos et al., 2014; Júdice et al.,

2017) that found that Portuguese adolescents engaged 9 to 10 hours a day in

sedentary behaviours. Mutiple studies (Ruiz et al., 2011; Santos et al., 2014; Verloigne

et al., 2012) found that adolescent girls were more sedentary than boys.

17

2.4 Cardiorespiratory fitness

2.4.1 Definition of cardiorespiratory fitness

Following Caspersen et al. (1985) cardiorespiratory fitness can be defined as ‘the

capacity of the cardiovascular and respiratory system to provide oxygen-rich blood to

the skeletal muscles and to eliminate its waste products during continuous exercise’.

Cardiorespiratory fitness is one of the health-related components of physical fitness,

along with muscular endurance, muscular strength, body composition and flexibility.

Beside this set of components, also the skill related components such as speed,

balance, coordination, etc. determine physical fitness (Caspersen et al., 1985; Corbin,

Pangrazi, & Franks, 2000).

In literature, the words physical fitness and physical activity are sometimes used

interchangeably, although they have a different meaning. Physical fitness is a feature

that one has to achieve, as opposed to physical activity that is just the performance of

movement. Different words have been used for the concept of cardiorespiratory fitness

such as cardiovascular fitness, cardiorespiratory endurance, aerobic

fitness/capacity/power, etc. For clarity, in this thesis, only the term cardiorespiratory

fitness will be used (Caspersen et al., 1985; World Health Organization, 2018b).

2.4.2 Measurements

Cardiorespiratory fitness can be objectively and accurately measured within a

laboratory setting and with laboratory tests. The golden standard for measuring

cardiorespiratory fitness is by determining the maximal aerobic power (VO2max), which

is expressed in liters of oxygen consumed per minute (l/min). It is the highest rate

attainable at which an individual is able to consume oxygen during continuous and

intensive exercise. This test is typically performed on a treadmill (Pate, Oria, &

Pillsbury, 2012). However, these tests are difficult to use in population-based studies,

because of the costs, time and instruments they require (Castro-Piñero et al., 2010).

Field-test can be an acceptable alternative. However, it must be taken into account

that these tests are depending on predictions and are sensitive to a certain error

(Castro-Piñero et al., 2010). Multiple reviews (Batista, Romanzini, Castro-Piñero, &

18

Vaz Ronque, 2017; Castro-Piñero et al., 2010) concluded that the 20 meter Shuttle-

run test, developed by Léger, Lambert, Goulet, Rowan and Dinelle (1984), had the

highest validity in estimating cardiorespiratory fitness in adolescents. Silva, Aires,

Mota, Oliveira and Ribeiro (2012) also found that this test was valid in Portuguese

adolescents. The test is easy to organize in schools for large groups of participants

and requires almost no equipment. The subjects have to run between two lines that

are 20 meters apart and pivot before the next audio signal, in order to complete a

shuttle successfully. The speed required to successfully complete the shuttles

increases throughout the test. When a subject does not successfully complete two

consecutive shuttles, the test is over (Léger, Mercier, Gadoury, & Lambert, 1988;

Batista et al., 2017).

Instead of objectively measuring the VO2max in a laboratory setting, the VO2max can

also be estimated based on the results of the 20m Shuttle-run test, using an equation.

Different equations exist, including different variables, to estimate the VO2max. There

is still no consistent evidence concerning the variables (e.g. age, gender, BMI, …) that

should be included in the equation or which equation is the most valid to estimate the

VO2max (Plowman & Meredith, 2013).

After estimating the VO2max, the age- and gender specific cutoff points of the

FITNESSGRAM (The Cooper Institute, 2017) can be used to categorize adolescents

in three zones, the ‘Needs Improvement - Health Risk Zone’, the ‘Needs Improvement

Zone’ and the ‘Healthy Fitness Zone’. Lobelo, Pate, Dowda, Liese and Ruiz (2009)

stated that using these cutoff points for adolescents is a valid way to discriminate

adolescents with a less and more favorable cardiovascular disease (CVD) profile.

Participants who were classified as unfit had a significant higher CVD risk score then

fit participants.

2.4.3 Cardiorespiratory fitness and health

The health-related components of physical fitness, especially cardiorespiratory fitness,

are important markers of health (Caspersen et al., 1985; Kaminsky et al., 2013).

Several reviews pointed out that higher levels of cardiorespiratory fitness in

adolescence are associated with a better cardiovascular profile. Fit adolescents seem

to have lower levels of CVD risk factors such as triglycerides, cholesterol, blood

pressure, abdominal adiposity, etc. Furthermore, it was also found that

19

cardiorespiratory fitness in adolescence is not only a predictor of CVD in adolescence

itself, but also later in life. A negative association was found between cardiorespiratory

fitness in adolescence and hypertension, ischemic heart disease, stroke, diabetes

mellitus type 2 and overall mortality later in life (Artero et al., 2011; Ortega et al., 2018;

Ortega, Ruiz, Castillo, & Sjöström, 2008; Ruiz et al., 2009).

Ortega et al. (2008) also explored the association between cardiorespiratory fitness

and mental health in adolescence. Although the evidence is rather scarce during this

life stage, some studies found that an improvement in cardiorespiratory fitness in

adolescence positively affected adolescents their self-esteem and depression status.

Esteban-Cornejo et al. (2017) also found a positive association between

cardiorespiratory fitness and the volume of some brain structures that are related to

better academic achievement.

2.4.4 Cardiorespiratory fitness during adolescence

A review of Lang, Tremblay, Léger, Olds and Tomkinson (2016) found that adolescents

from Southern European countries, including Portugal, had significant lower

cardiorespiratory fitness levels than adolescents from Central-Northern European

countries. This geographical difference was also found by Olds, Tomkinson, Léger and

Cazoria (2006) and Ortega et al. (2014). Both studies also found that boys had

significantly higher cardiorespiratory fitness levels than girls.

The HELENA-study (Ortega et al., 2011) assessed cardiorespiratory fitness levels

among adolescents out of nine European countries. Portugal did not take part in the

study. The study, using the cutoff values of the FITNESSGRAM (see chapter 1.4.2),

found that 58% of the adolescent girls and 61% of the adolescent boys had a healthy

cardiorespiratory fitness level. The difference between boys and girls was significant.

These European levels are similar to those that were found in the United States (Pate

et al., 2006). A Portuguese study (Santos et al., 2018) found that only 14.4% of the

Portuguese adolescent girls and 46.3% of the adolescent boys had healthy

cardiorespiratory fitness levels.

A meta-analysis of Tomkinson and Olds (2007) on more than 25 million children and

adolescents found a global and continuous decline in cardiorespiratory fitness levels

20

since 1970. Matton et al. (2007) observed a decline in Flemish adolescents. After

searching the literature no Portuguese studies were found that compared

cardiorespiratory fitness levels through time. Nevertheless, a Spanish study (Moliner-

Urdiales et al., 2010) found not a decrease, but an increase in cardiorespiratory levels

between 2001 and 2007.

2.5 Relationship between physical activity, sedentary behaviour and

cardiorespiratory fitness

2.5.1 Sedentary behaviour and cardiorespiratory fitness

Literature is scarce and inconsistent about the association between sedentary

behaviour and cardiorespiratory fitness independent of physical activity (van der Velde,

2017). Besides, most of the studies that observe this relationship are based on

subjectively measured sedentary behaviour. Lobelo et al. (2009) found that adolescent

girls who were sedentary for two or more hours (measured in the use of electronic

media), were more likely to have low levels of cardiorespiratory fitness. In the study of

Mitchell, Pate, & Blair (2012) a negative association had been found between

subjectively measured screen-based sedentary time and cardiorespiratory fitness.

Related, Vierola et al. (2016) found that children in developing countries increasingly

adopted a more sedentary lifestyle (and became less active), resulting in a decrease

of cardiorespiratory fitness levels in the past two decades. Vierola et al. (2016) found

these results using questionnaires.

Very little studies observed this relationship with objectively measured sedentary

behaviour. Denton et al. (2013) found that objectively measured sedentary behaviour

was not significantly associated with to cardiorespiratory fitness. Bai et al. (2016) also

found no significant association between screen time and cardiorespiratory fitness in

adolescents, independent of physical activity. This is in contrast to an older study of

Ekelund et al. (2007) that found a significant but relatively weak negative association

(r=-0.11) between objectively measured sedentary behaviour and cardiorespiratory

fitness. It has to be noted that physical activity was not taken into account as a possible

confounder. Kulinski et al. (2014) investigated the relationship between objective

measured sedentary behaviour and cardiorespiratory fitness, independent of physical

activity, and observed consistently that sedentary behaviour and cardiorespiratory

fitness had an inverse association. These results suggest that sedentary behaviour

21

may possibly act as an important determinant for cardiorespiratory fitness, and this

independent of physical activity levels. Although the population under study was

between 12 and 49 years old. Santos et al. (2014) found the same results in a

population aged 10 to 18 years. Namely, that adolescents with low objectively

measured sedentary behaviour had higher odds of having higher cardiorespiratory

fitness levels, independent of MVPA levels.

2.5.2 Physical activity and cardiorespiratory fitness

Physical activity is the key determinant of cardiorespiratory fitness. Their is consistent

evidence for a positive association between physical activity and cardiorespiratory

fitness in adolescents, especially when physical activity is defined as total bodily

movement. Bai et al. (2016) found, through self-reported measurements, that

adolescents who met the guideline for physical activity were more likely to have higher

cardiorespiratory fitness levels in comparison to adolescents who did not met the

guideline. A study of Ortega, Ruiz, Hurtig-Wennlöf and Sjöström (2008), using

objective measurements, found that performing 60 minutes of MVPA a day, and thus

meeting the guidelines, was associated with healthier levels of cardiorespiratory fitness

in adolescents. Boys who met the guidelines were eight times more likely to have high

cardiorespiratory fitness levels than those who did not met the guidelines. Girls who

met the guidelines were three times more likely to have high cardiorespiratory levels

than those who did not met the guidelines. These results were obtained after controlling

for sexual maturation and body fat. Adolescents who engaged in more than 40 minutes

of vigorous intensity physical activity a day also had better cardiorespiratory fitness

levels, measured with objective methods (Ekelund et al., 2007; Parikh & Stratton, 2011;

Ruiz & Ortega, 2009).

As described in chapter 1.1, evidence indicates that physical activity in adolescence is

a moderate predictor for physical activity in adulthood (Biddle et al., 2010). It has been

shown that the tracking of cardiorespiratory fitness from adolescence into adulthood is

stronger. The combined tracking of physical activity and cardiorespiratory fitness from

adolescence into adulthood has found to be low to moderate (Santos, 2014). This is

why, among others, it is important to improve physical activity and cardiorespiratory

fitness in adolescence (Twisk, Kemper, & Van Mechelen, 2000; Cleland, Ball,

Magnussen, Dwyer, & Venn, 2009).

22

2.5.3 Combined relationship of physical activity, sedentary behaviour and

cardiorespiratory fitness

Only few studies examined the combined relationship of sedentary behaviour and

physical activity with cardiorespiratory fitness in children and adolescents. The studies

that did investigate this are contradictory.

The study of Martinez-Gomez et al. (2011) found that girls who spend more than 69%

of their waking time sedentary had lower levels of cardiorespiratory fitness,

independent of BMI and other possible confounders. For boys, no significant threshold

was found. After taking physical activity into account, they found that the negative

relationship between sedentary time and cardiorespiratory fitness remained in

adolescent girls, who did not perform 60 minutes of MVPA a day, but disappeared in

girls who did meet the guidelines. These results suggested that the negative effect of

excessive sedentary behaviour might be attenuated by higher levels of physical

activity.

The results of Santos et al. (2014) indicated a positive association between the

combination of adequate levels of MVPA and low-sedentary behaviour with

cardiorespiratory fitness. Participants were more likely to be fit when they were high

active/low sedentary in comparison to participants that were low active/high sedentary.

The results (see fig. 5) also showed that participants who were high active/ low

sedentary and low active/ low sedentary had higher odds of being more fit than

participants that were low active/high sedentary, independent of accelerometer wear

time, gender, age and body mass index. Although the odds of adolescents who were

low active/ low sedentary were notable lower than the odds of adolescents who were

high active/ low sedentary (Santos et al., 2014). Santos et al. (2014) concluded that it

is important to discourage sedentary behaviour and promote physical activity, since

both contribute independently to cardiorespiratory fitness. However, the study of

Denton et al. (2013) disagrees with the results of Santos et al. (2014), since they

suggested that it is more important to focus on higher intensities of physical activity

and not on sedentary behaviour to sustain or enhance cardiorespiratory fitness. The

results of Bai et al. (2016) also disagree with these of Santos et al. (2014) since they

indicated that adolescents who met the guidelines for physical activity (measured

through subjective measurements) were more likely to have healthy cardiorespiratory

23

fitness levels, independent of subjectively measured screen time. They concluded that

only physical activity was strongly associated with cardiorespiratory fitness in

adolescents and not sedentary screen time.

figure 5: Logistic regression predicting belonging to the healthy zone or above for CRF by the physical activity/ sedentary time

group (Santos et al., 2014).

Santos et al. (2018) whereupon explored the reallocation of sedentary behaviour to

physical activity on children’s (aged 10 to 11 years) cardiorespiratory fitness. Their

results suggested that with reallocating 30 minutes of sedentary time to 30 minutes of

vigorous-intensity physical activity higher levels of cardiorespiratory fitness were

obtained. This effect was only noticeable with vigorous-intensity physical activity. On

the other hand, Kulinski et al. (2014) observed that the advantageous effects on

cardiorespiratory fitness of one-hour moderate-intensity physical activity was

equivalent to the negative effects on cardiorespiratory fitness of six to seven hours of

sitting. Santos et al. (2018) concluded that lowering sedentary time and increasing

physical activity at higher intensities are recommended in order to improve the

cardiorespiratory fitness levels and thereby positively influence adolescents’ health.

2.6 Problem analyses

High levels of cardiorespiratory fitness have been associated with health benefits in

adolescents (Kaminsky et al., 2013; Ortega et al., 2018; Santos et al., 2018). It can be

seen as an important health marker for all age groups (Ruiz & Ortega, 2009). Further,

a review of Lang et al. (2016) found that adolescents of Southern European countries

(including Portugal) have lower cardiorespiratory fitness levels than adolescents from

Central and Northern Europe. According to a Portuguese study (Santos et al., 2018),

only 14.4% of adolescent girls and 46.3% of adolescent boys have a healthy

cardiorespiratory fitness level.

24

Although cardiorespiratory fitness is determined by several non-changeable factors

(eg. age), Santos et al. (2014) indicated that both sedentary behaviour and physical

activity are also independent related with cardiorespiratory fitness (Santos et al., 2018).

Further, high sedentary behaviour and low physical activity have been associated with

negative health consequences. Although many interventions exist to promote physical

activity, the majority of Portuguese adolescents do not reach the global physical activity

recommendations of the WHO (World Health Organization, 2018c). The Global Health

Observatory (GHO) estimated that in 2010 only 13.3% of the Portuguese adolescents

(11 to 17 years old) met the physical activity recommendations (World Health

Organization, 2018c). As for sedentary behaviour, no international guidelines exist

about the amount of sedentary behaviour for adolescents. Though, the Canadian

Sedentary Behaviour Guidelines for Children (5 to 11 years) and Youth (12 to 17 years)

published recommendations for guidelines regarding sedentary time. In order to obtain

health benefits, adolescents their recreational screen time should be limited to two

hours a day (O'Brien et al., 2018). The HBSC study reported that 54.6% of the

Portuguese adolescents watched two or more hours television a day and 56% used

the computer for two or more hours a day (Inchley, Currie, Jewell, Breda, & Barnekow,

2017).

Literature about the relationship between sedentary behaviour and cardiorespiratory

fitness is rather scarce, particularly independent of physical activity (van der Velde,

2017). Literature that does exist has contradictory results and used mostly subjectively

measurements. The results of Martinez-Gomez et al. (2011) indicated that European

adolescent girls who spent ≥69% per day in sedentary behaviour, had lower levels of

cardiorespiratory fitness. The study of Mitchell et al. (2012) also found negative

association between sedentary time and cardiorespiratory fitness. Denton et al. (2013)

found that objectively measured sedentary behaviour was not significantly associated

with to cardiorespiratory fitness. Bai et al. (2016) also found no significant association

between screen time and cardiorespiratory fitness in adolescents, independent of

physical activity.

In order to obtain more coherent results, it is needed to explore the relationship

between objectively measured sedentary behaviour and cardiorespiratory fitness

(independent form physical activity), (Lobelo et al., 2009).

25

About the relationship between physical activity and cardiorespiratory fitness, more

coherent evidence exist. Most studies showed a consistent positive association

between physical activity and cardiorespiratory fitness in adolescents (Ekelund et al.,

2007; Parikh & Stratton, 2011; Ruiz & Ortega, 2009).

The combined relationship between sedentary behaviour and physical activity with

cardiorespiratory fitness in adolescents is a domain that still needs to be fully

investigated. The few existing studies showed contradictory results (Aires, Pratt,

Lobelo, Santos, Santos, & Mota, 2011; Santos et al., 2014).

To summarize, the aim of this master thesis is to explore the relationship between

objectively measured sedentary behaviour and cardiorespiratory fitness, independent

of physical activity. The relationship between objectively measured physical activity

(i.e. MVPA) and cardiorespiratory fitness (independent of sedentary behaviour),

independent of sedentary behaviour will also be explored in Portuguese adolescents.

At last, the combined relationship of sedentary behaviour and physical activity with

cardiorespiratory fitness will be explored in the Portuguese sample.

The following research questions will be answered:

1. Is objectively measured sedentary behaviour related to cardiorespiratory fitness

in Portuguese adolescents (10-18 years old)?

2. Is objectively measured physical activity (MVPA) related to cardiorespiratory

fitness in Portuguese adolescents (10-18 years old)?

3. Is the combined variable of objectively measured sedentary behaviour

and physical activity (MVPA) related to cardiorespiratory fitness levels of

Portuguese adolescents (10-18 years old)?

4. Is there a difference in mean cardiorespiratory fitness levels in Portuguese

adolescents between different combinations of combined physical activity levels

(MVPA) and sedentary behaviour levels?

26

Based on the analyzed literature, the following hypotheses can be formulated:

1. Sedentary behaviour will be significantly and negatively related to

cardiorespiratory fitness in Portuguese adolescents, even after controlling for

physical activity. Results will show that the Portuguese adolescents with low

sedentary behaviour will be more likely classified as fit in comparison to

adolescents with high sedentary behaviour.

2. Physical activity (MVPA) will be significantly and positively related to

cardiorespiratory fitness in Portuguese adolescents, even after controlling for

sedentary behaviour. Portuguese adolescents who reach the recommended

MVPA levels (World Health Organization, 2018a) will be more likely to be

classified as fit in comparison to adolescents who do not meet the

recommendation.

3. Concerning the combined variable of sedentary behaviour and physical activity

(MVPA), Portuguese adolescents that have low levels of sedentary behaviour

and high levels of physical activity (MVPA) will have a significant relation with

cardiorespiratory fitness in comparison to Portuguese adolescents with high

levels of sedentary behaviour and low levels of physical activity (MVPA).

4. There will be a significant difference in mean cardiorespiratory fitness levels

between the different possible combinations of sedentary behaviour with

physical activity. Adolescents who are low sedentary and active will have higher

significant cardiorespiratory fitness level in comparison to the other possible

combinations of sedentary behaviour and physical activity.

27

3 Research method

3.1 Design

This master thesis used a cross-sectional design. The used data in the present study

are original form the AFINA-te project (see 3.2).

3.2 The AFINA-te project

The data used in this master thesis was collected during the AFINA-te project study,

which was conducted in the district of Porto in Northern Portugal. AFINA-te stands for

‘Atividade Física e Informação Nutricional para Adolescentes’, translated as ‘Physical

Activity and Nutritional Information for Adolescents’. It is a longitudinal intervention

study in Portuguese adolescents (10 up to and including 18 years old) to promote

physical activity and nutritional knowledge. This AFINA-te study was ethically approved

by the Ethics Committee of the Faculty of Sports (University of Porto), the Portuguese

Foundation for Science and Technology and the regional section of the Ministry of

Education.

The aim of the AFINA-te project is fourfold. The first aim was to assess the nutritional

status, eating habits and physical activity in Portuguese adolescents. It is this cross-

sectional data that will be used in our master thesis. The second aim was to evaluated

nutritional knowledge. The third aim was to explore the relationship between nutritional

knowledge, eating habits, physical activity and nutritional status. The last aim was to

implement and evaluate an intervention about physical activity and nutritional

knowledge. This intervention still has to be conducted since the project is on hold. A

part of the sample will receive the intervention, which will last one school year (9

months) and involves the adolescents but also their parents, teachers and school.

Curricular and extracurricular activities will be organized. The students will attend three

lessons about physical activity and nutrition during school-time and will be able to

access a website after school. This website was created by nutritionists, exercise

specialists and a web programmer and aims to increase the knowledge and awareness

of the adolescents’ own habits through self-monitoring. The parents will also be invited

to join lectures about the same themes. Eventually, the effect of this intervention on

adolescents’ knowledge about physical activity and nutrition will be evaluated.

28

3.3 Sampling

The data used for the study was collected within the Porto area (Portugal) in several

middle and high schools (ages 10 up to and including 18 years). The participants

included in the study were enrolled in the second stage of basic education (10 to 12

years old), the third stage of basic education (12 to 15 years old) and the secondary

school (15 to 18 years old). These schools were selected through a convenience

sampling method. A total of 25 public schools within the Porto area were invited to

participate in the study, by email and mail. Six schools did not reply (24%), thirteen

schools rejected the invitation (52%) and six schools accepted to take part in the study

during the school year of 2015-2016 (24%).

The procedures used in the study followed the principles of the declaration of Helsinki.

The school (school authorities and directors), parents and adolescents received a

written description of the study. An informed consent was also obtained from all

adolescents and their legal guardians. Participants included adolescents (10-18 year),

who were properly enrolled in 5th to 12th grade classes, who agreed voluntarily to

participate in the study, who had parental written consent and who wore an

accelerometer on at least four days with a minimum of eight hours of data a day (three

weekdays and one weekend day). After applying the exclusion criteria and performing

the data cleaning, data of 695 participants (out of the six schools) was used in this

master thesis.

3.4 Measurements

3.4.1 Sociodemographic data

The sociodemographic variables used in the present study are age, gender and study

year/cycle. They were collected through a self-administered questionnaire, which was

obtained during school-time.

3.4.2 Anthropometric measurements

The variables height and weight were collected in accordance with the international

standards for anthropometric assessment (Stewart, Marfell-Jones, Olds, & Ridder,

2011). For measuring weight, the participants had to be lightly dressed (underwear and

t-shirt). The portable digital Tannita Innerscan BC 532 was used, which has an

29

accuracy of 100 grams. For measuring the body fat percentage, the same portable

scale was used to make bioimpedance analyzes while the adolescents were being

weighed.

For measuring height the participants had to be barefoot or wearing socks and stand

up straight against the SECA 217 portable stadiometer, which has an accuracy of 1

cm. BMI was calculated via the following formula: weight in kilogram/(height in

meters)². This variable was categorized as normal weight, overweight and obese,

using the international age- and gender specific cutoff points of Cole, Bellizzi, Flegal,

& Dietz (2000).

A non-metallic measuring tape was used for measuring waist circumference. The

measuring tape was placed in between the top of the iliac crest and the lowest piece

of the lowest rib (Graham et al., 2007). The circumference was measured at the end

of an expiration.

3.4.3 Physical activity and sedentary behaviour

Physical activity and sedentary behaviour were measured using a triaxial

accelerometer, the Actigraph GT3Xs. Although these triaxial accelerometers have an

inclinometer function, this was not used. All the participants and their parents were

informed about the use and purpose of the accelerometers with an information

brochure. During seven consecutive days, the adolescents had to wear an

accelerometer with an elastic band in the waistline, on the right side of the hip. They

were instructed to take off the device only while sleeping or doing water activities.

Furthermore, the adolescents had to fill in a physical activity diary during the days they

wore the accelerometer. They had to report all the classes they attended and all the

extracurricular activities they engaged in, including the related time of the day. They

also had to report the time they put the accelerometer on and off and the activities they

engaged in while the accelerometer was off (e.g. swimming). Next to this, some

questions had to be answered such as “Did you participate in physical education

classes and what day, time and kind of exercise was it?”, “Did you go to school by foot

or by bike? If the answer is yes, on which days were you walking or cycling to school?”

and “How many hours did you spend in sedentary behaviour? Write this down for every

day of the week.”.

30

The accelerometers collected raw data at 30 Hz. After the data-collection, the data was

analysed using the Actilife software program (version 6.9, Actigraph, Florida) and

converted and downloaded into 5-second epochs. In order to be included into the

study, participants needed to have accelerometer data of at least four days (including

one weekend day) with a minimum of eight hours of data each day. A period with ten

consecutive zeros was considered as a time where the accelerometer was not worn,

and this time period was therefore excluded from the analyses. The cutoff points

developed by Evenson et al. (2008) were used to determine the different physical

activity intensities based on the counts per minute (cpm), (see table 2).

Table 2: Cutoff points for sedentary behaviour and physical activity intensity levels (Evenson et al., 2008).

Counts/minute Physical activity intensity levels

0-100 Sedentary behaviour

101-2295 Light intensity physical activity

2296-4011 Moderate intensity physical activity

> 4012 Vigorous intensity physical activity

> 2296 Moderate-to-vigorous intensity physical activity

3.4.4 Cardiorespiratory fitness

Cardiorespiratory fitness was measured with the 20-meter Shuttle-Run Test which the

participants had to perform at their school. The FITNESSGRAM protocol was used

(The Cooper Institute, 2010), which is an international protocol used in schools and

research to measure the health-related components of physical fitness. The 20 meter

Shuttle-run test of the FITNESSGRAM protocol is also called the PACER test and is

derived from the original 20-meter Shuttle-run test designed by Leger et al. (1984),

(see 1.4.2). Participants had to wear their sports uniform of the school and appropriate

shoes in order to perform this test. The PACER test starts with an audio signal

indicating the participants to start running the clearly marked 20 meters and pivot. In

order to complete a shuttle successfully they have to do this before the next audio

signal. Every minute, the speed necessary to complete the shuttles was increased. At

the beginning of the test, the speed was 8.0 km/h, after one minute it was 9.0 km/h and

from then the speed increased every minute with 0.5 km/h. When a participant failed

31

two consecutive times to complete a shuttle before the next audio signal, his/her test

was over. The researcher guided the test and encouraged the participants to assure

they were making a maximum effort for the test. It should be mentioned that the

adolescents were familiar with this test because it is part of the curriculum of the

physical activity course in Portugal.

3.5 Statistical analyses

The statistical analyses were performed using the Statistical Package for the Social

Sciences (SPSS), version 25.0. The dataset was checked for missings or inaccurate

values, although such values were not found since the data set was already cleaned.

The statistical analyses include the descriptive statistics and the inferential statistics.

With regard to the descriptive statistics, the distribution of the variables was checked.

Variables were considered normally distributed if the skewness and kurtosis values

were between -1 and 1. If not, the variable was considered skewed. The normally

distributed variables are described by the mean and the standard deviation (SD) while

the skewed distributed variables are described by the median. With regard to the

inferential statistics, the null hypothesis was only rejected if the p-value was less than

0.05. A p-value higher than 0.05, but lower than 0.10, was considered borderline

significant. The null hypothesis was accepted if the results had a p-value higher than

0.10.

In order to determine the cardiorespiratory fitness levels, the results from the 20 meter

Shuttle-Run test were used. More specifically, the number of completed shuttles was

converted into the estimated VO2max, through the use of an equation. Within this

thesis, the Mahar equation (Mahar et al., 2006) was used:

VO2max = 50.945 + (0.126 x number of laps) + (4.946 x gender) - (0.655 x BMI).

According to this equation, boys must be coded as 1 and girls as 0. The estimated

VO2max was than categorized into three groups based on the age- and gender specific

cutoff points of the FITNESSGRAM (The Cooper Institute, 2017; see appendix). The

first group was the ‘Healthy Fitness Zone’, the second group the ‘Needs Improvement

Zone’ and the third group the ‘Needs Improvement-Health Risk Zone’.

32

In order to answer the research questions, binary logistic regression models and a one-

way ANOVA test were conducted. Before performing the analyses, some variables

needed in the binary logistic regression models, were dichotomized and dummy coded

(see table 3). The mean time a day spent in sedentary behaviour was dichotomized

through ranking them according to their median by age and gender. Adolescents below

the median were described as low sedentary and adolescents above the median as

high sedentary. The mean time a day of MVPA was dichotomized based on the

international guidelines of the WHO for physical activity in adolescents (World Health

Organization, 2018a). More specific, the adolescents performing less than 60 minutes

MVPA a day were categorized as inactive, while the adolescents performing 60

minutes MVPA or more a day were categorized as active. The dependent variable, the

estimated VO2max with three categories, was dichotomized by combining the two

‘Needs Improvement Zones’.

Table 3: Values and labels of the dependent and independent variables.

Value Label

Sedentary behaviour

0 High sedentary (= reference category)

1 Low sedentary

MVPA 0 Inactive (<60 minutes a day, reference category)

1 Active (60 minutes a day)

Cardiorespiratory fitness (i.e. estimated VO2max)

0 Needs Improvement Zone

(= Needs Improvement Zone + Needs Improvement Health Risk Zone) (= reference category),

1 Healthy Fitness Zone

First, a binary logistic regression model was used to explore the relationship between

sedentary behaviour and cardiorespiratory fitness. Both an unadjusted and adjusted

model was used to explore this relationship. The unadjusted model only had sedentary

behaviour as independent variable, while the adjusted model also took physical activity

into consideration as a possible confounder.

33

Second, a binary logistic regression model was used to explore the relationship

between physical activity (i.e. MVPA) and cardiorespiratory fitness. Here too, an

unadjusted and adjusted model was used. In the unadjusted model only MVPA was

included as independent variable. In the adjusted model, sedentary behaviour was

included as possible confounder.

Concerning the variables of sedentary behaviour, physical activity and

cardiorespiratory fitness, respectively the ‘high sedentary’, ‘inactive’ and ‘Needs

Improvement Zone’ categories were used as reference categories. A statement will be

formulated about the other categories based on the 95% confidence interval (CI) and

the odds ratios (Exp(B)).

Third, a binary logistic regression model was used to explore the relationship between

the combined variable of sedentary behaviour/physical activity (MVPA) and

cardiorespiratory fitness. This combined variable was created through coding in SPSS

Syntax, by combining the dichotomous dummy coded variables of MVPA and

sedentary behaviour. The final variable had four categories (see table 4). When

performing the binary logistic regression, the ‘high sedentary behaviour - inactive’

category was used as reference category. As regards to the dependent variable (i.e.

cardiorespiratory fitness), the ‘Needs Improvement Zone’ was used as reference

category. A statement will be formulated about the other categories based on the 95%

confidence interval and odds ratios (Exp(B)).

Table 4: Values and label of the combined variable (sedentary behaviour and physical activity).

Value Label

Combined variable sedentary behaviour and physical activity

0 High sedentary – Inactive (= reference category)

1 Low sedentary - Inactive

2 High sedentary - Active

3 Low sedentary - Active

Fourth, a One-Way ANOVA (Analysis Of Variance) test was performed in order to

detect potential differences in the mean estimated VO2max (cardiorespiratory fitness)

between the four categories of the combined variable (see table 4). The continuous

variable of the estimated VO2max was used as the dependent variable. The combined

34

variable of sedentary behaviour and physical activity was used as the independent

variable. Before conducting this test, the conditions of homoscedasticity and

homogeneity were checked. Next, a potential significant difference between those four

groups was further explored doing pairwise comparisons using the Tukey post-hoc

test. When a significant difference was found between two groups, the mean estimated

VO2max and its standard deviation was reported in order to clarify the difference.

35

4 Results

4.1 Descriptive statistics

The total sample, adjusted for the exclusion criteria, consisted of 695 participants, with

a mean age of 13.15 years (SD: 2.44). Of the participants, 55.8% were girls and 44.2%

boys. The distribution of the participants in the different school years is described in

table 7.

The cardiorespiratory fitness levels were measured using the 20-meter shuttle run test.

The minimum and maximum number of the successfully completed shuttles within this

sample of adolescents is described in table 5. Furthermore, the table also also

describes the mean number of the successfully completed shuttles from all the

participants. In order to estimate the VO2max, the number of shuttles were transformed,

using the Mahar equation (Mahar et al., 2006). The minimum, maximum and mean

value of the estimated VO2max are described in table 7, for both the total sample as

for the two categories of the estimated VO2max (Needs Improvement Zone (NIZ) and

Healthy Fitness Zone (HFZ)).

Table 5: Descriptive statistics of the 20-meter shuttle run test

Mean ± SD number

Minimum completed shuttles

6

Maximum completed shuttles

11

Completed shuttles 32.97 ± 18.99

The estimated VO2max has a skewness of 0.12 and a kurtosis of -0.34. With both the

skewness and kurtosis being between -1 and 1, the estimated VO2max is normally

distributed. When looking at the histogram, this normal distribution is also shown (see

figure 6). This normal distribution means that the condition of normality was met and a

One-Way ANOVA, later in the analyses, can be performed.

36

Figure 6: Normal distribution of VO2max

Afterwards, the estimated VO2max was categorized based on the age- and gender

specific cutoff points of the FITNESSGRAM (The Cooper Institute, 2017, see

appendix). Table 7 presents the characteristics of the participants within the sample.

Absolute numbers and percentages are shown for the total sample as for the two

categories of the estimated VO2max.

Sedentary behaviour and physical activity were measured using accelerometers. In

agreement with the inclusion criteria, the minimum number of valid accelerometer wear

days was four and the maximum was seven, this with a median of 6.00 valid wear

days.

Table 6 describes, besides the estimated VO2max, also the mean time a day that the

participants spent in sedentary behaviour or other intensity levels of physical activity

(e.g. MVPA). These mean values are presented for the total sample size as well as for

the two categories of the estimated VO2max.

37

Table 6: Estimated vo2max and mean time per day for sedentary behaviour and physical activity for all participants and between the two categories of cardiorespiratory fitness.

Total sample NIZ HFZ

Estimated VO2max 43.28% ± 5.20 37.20% ± 2.57 45.38% ± 4.09

Estimated VO2max (minimum)

25.10% 25.10% 38.70%

Estimated VO2max (maximum)

56.7% 44.0% 56.70%

Sedentary behaviour

8.17h ± 1.43 8.36h ± 1.38 8.10h ± 2.20

Light intensity physical activity

4.40h ± 1.10 4.36h ± 1.06 4.42h ± 1.11

Moderate intensity physical activity

4.36h ± 1.06 27.22min ± 12.92

33.38min ± 15.1

Vigorous intensity physical activity

11.31min ± 21.57

12.30min ± 10.50

7.78min ± 9.11

MVPA 42.93min ± 21.57

35.00min ± 17.67

45.68min ± 22.13

Sedentary behaviour was dichotomized according to the median in two groups (see

table 3). In table 7, the proportion and absolute number of the total sample participants

being low sedentary or high sedentary is shown, as well as for the two categories of

the estimated VO2max. Besides, also the proportion and the absolute number of the

participants in the sample that met/did not met the international guidelines for physical

activity, is shown. Alongside, the proportion of the two groups of the estimated VO2max

were also shown.

The variables sedentary behaviour and physical activity were combined into four

categories (see table 4). Table 7 also shows the proportion of the participants

belonging to the four categories. Here too, the distinction between the two categories

of the estimated VO2max was made.

38

Table 7: Descriptive statistics of sample characteristics

Absolute number

Percentage (%)

CRF NI (%) | HFZ (%)

Sample

Total 695 100% 25.8% 74.2%

Boys 307 44.2% 7.2% 92.8%

Girls 388 55.8% 40.5% 59.5%

School cycle

Second cycle basic education (10-12 year)

349 50.2% 26.4% 73.6%

Third cycle basic education (12-15 year)

144 20.7% 28.5% 71.5%

Secondary school 202 29.1% 22.8% 77.2%

Estimated VO2max categorized

Healthy fitness zone 516 74.2%

Needs Improvement Zone 179 25.8%

Needs Improvement – health risk zone

74 10.6%

Needs Improvement Zone 105 15.1%

Meeting guidelines MVPA

< 60 minutes MVPA a day 561 80.7% 29.2% 70.8%

≥ 60 minutes MVPA a day 134 19.3% 11.2% 88.8%

Dichotomized sedentary behaviour

High sedentary 352 50.6% 29.0% 71.0%

Low sedentary 343 49.4% 22.4% 77.6%

Combined variable (sedentary behaviour/MVPA)

High sedentary/inactive 300 43.2% 32.3% 67.7%

Low sedentary/inactive 261 37.6% 25.7% 74.3%

High sedentary/active 52 7.5% 9.6% 90.4%

Low sedentary/active 82 11.8% 12.2% 87.8%

39

4.2 Statistical tests

4.2.1 The relationship between sedentary behaviour and cardiorespiratory fitness

In order to explore the relationship between sedentary behaviour and cardiorespiratory

fitness (i.e. cardiorespiratory fitness zones) a binary logistic regression was performed.

When interpreting the unadjusted model (see table 8), sedentary behaviour was

significantly related to cardiorespiratory fitness in Portuguese adolescents (95%CI:

1.00-1.99). Adolescents who were low sedentary (lower than the median) had 1.41

times higher odds of belonging to the Healthy Fitness Zone than adolescents who were

high sedentary. When taking MVPA into account (adjusted model, see table 8),

sedentary behaviour was no longer significantly related to cardiorespiratory fitness in

adolescents (95%CI: 0.92-1.85; OR: 1.30).

Table 8: Unadjusted and adjusted model: sedentary behaviour in relation to cardiorespiratory fitness (binary logistic regression)

Exp(B) 95% CI for Exp(B) Lower | Upper

Unadjusted model: Sedentary behaviour (high-low sedentary)

1.41

1.00

1.99

Adjusted model: Sedentary behaviour (high-low sedentary)

1.30

0.92

1.85

4.2.2 The relationship between physical activity and cardiorespiratory fitness

In order to explore the relationship between physical activity (i.e. MVPA) and

cardiorespiratory fitness (i.e. cardiorespiratory fitness zones), a binary logistic

regression was performed. As regards to the unadjusted model (see table 9), MVPA

was significantly related to cardiorespiratory fitness in Portuguese adolescents

(95%CI: 1.86-5.78). Adolescents who performed at least 60 minutes of MVPA a day

had 3.28 times higher odds of belonging to the Healthy Fitness Zone in comparison to

the adolescents who did not perform 60 minutes of MVPA a day. When taking

sedentary behaviour into account (adjusted model, see table 9), MVPA remains

significantly related to cardiorespiratory fitness (95% CI: 1.79-5.59). After taking

sedentary behaviour into account, adolescents who performed at least 60 minutes of

MVPA a day still had 3.16 times higher odds of belonging to the Healthy Fitness Zone

in comparison to the adolescents who did not perform 60 minutes of MVPA a day.

40

Table 9: Unadjusted and adjusted model: physical activity in relation to cardiorespiratory fitness (binary logistic regression)

Exp(B) 95% CI for Exp(B) Lower | Upper

Unadjusted model: MVPA (inactive-active)

3.28

1.86

5.78

Adjusted model: MVPA (inactive-active)

3.16

1.79

5.59

4.2.3 The combined variable of sedentary behaviour and physical activity (MVPA)

in relationship to cardiorespiratory fitness.

In order to explore the relationship between the combined variable of sedentary

behaviour and physical activity (i.e. MVPA) a binary logistic regression was conducted.

The odds ratio and 95% CI of the regression model are displayed in table 10.

Adolescents who were high sedentary and active had 4.49 times higher odds of

belonging to the Healthy Fitness Zone than adolescents who were high sedentary and

inactive (95%CI: 1.73 - 11.65). Since the one value of the null hypothesis is not lying

in the 95% CI, the result is significant.

Adolescents who were low sedentary and active had significantly 3.44 times higher

odds of belonging to the Healthy Fitness Zone in comparison to adolescents who were

high sedentary and inactive (95%CI : 1.70 - 6.96). Since the one value of the null

hypothesis is not lying in the 95% CI, the result is significant.

Adolescents who were low sedentary and inactive had 1.38 times higher odds of

belonging to the Healthy Fitness Zone than adolescents who were high sedentary and

inactive. However this result was only borderline significant (95%CI: 0.96 - 2.00).

Table 10: The combined variable of physical activity and sedentary behaviour in relation to cardiorespiratory fitness

Exp(B) 95%C.I. for Exp(B)

Lower | Upper

High sedentary - inactive (< 60 minutes MVPA a day) (reference category)

/ / /

Low sedentary - inactive 1.38 0.96 2.00

High sedentary - active (> 60 minutes MVPA a day) 4.49 1.73 11.65

Low sedentary - active 3.44 1.70 6.96

41

4.2.4 Comparison of cardiorespiratory fitness levels between categories of the

combined variable sedentary behaviour and physical activity (MVPA).

In order to further explore the relationship of the combined variable of sedentary

behaviour and physical activity with cardiorespiratory fitness, a One-Way ANOVA-test

was performed.

Since the estimated VO2max was normally distributed (see 4.1), the condition of

normality was met. The condition of homoscedasticity was also met, since no

significant difference was found in the variance between the groups (p>0.05; F: 1.42).

Table 11: Test of homogeneity of variances

Levene’s test P-value

Cardiorespiratory fitness level 1.42 0.23

A significant difference in cardiorespiratory fitness levels was found between the

different categories of the combined variable of sedentary behaviour and physical

activity (MVPA) (see table 12). In order to explore the significant differences between

the different categories, the Tukey post-hoc test was analyzed (see table 13). No

significant difference (p>0.05) was found in the mean estimated VO2max between the

high sedentary/inactive and low sedentary/inactive groups. There was also no

significant difference (p>0.05) found between the high sedentary/active and low

sedentary/active group.

However, significant differences (p<0.001) were found in the mean of the estimated

VO2max levels between the categories high sedentary/inactive and high sedentary/

active groups, the high sedentary/inactive and low sedentary/active groups, low

sedentary/ inactive and high sedentary/ active groups and between the low sedentary/

inactive and low sedentary/ active groups (see table 13).

Table 12: ANOVA

F-value P-value

ANOVA 21.51 <0.001

42

Table 13: Tukey Post Hoc Tests, Multiple comparisons

P-value

High Sedentary - inactive Low sedentary - inactive 0.70

High sedentary - active <0.001

Low sedentary - active <0.001

Low sedentary - inactive High sedentary - active <0.001

Low sedentary - active <0.001

High sedentary - active Low sedentary - active 0.77

Adolescents who were highly sedentary and inactive (mean: 42.34) had significant

lower estimated VO2max than Portuguese adolescents who were highly sedentary and

active (mean: 46.85) as well as adolescents who were low sedentary and active (mean:

45.99).

Adolescents who were low sedentary and inactive (mean: 42.79) had significant lower

estimated VO2max than adolescents who were highly sedentary and active as well as

adolescents who were low sedentary and active.

Table 14: Mean results and standard deviation (SD) of cardiorespiratory fitness by combined groups of sedentary and moderate to vigorous physical activity (MVPA)

Mean estimated VO2max ± SD

High Sedentary - inactive 42.33 ± 5.02

Low sedentary - inactive 42.79 ± 4.68

High sedentary - active 46.85 ± 5.38

Low sedentary - active 45.99 ± 5.42

43

5 Discussion

The aim of this thesis is threefold. First of all, the relationship between objectively

measured sedentary behaviour and cardiorespiratory fitness was explored in

Portuguese adolescents. This relationship was analyzed with and without taking

physical activity into account as a confounder. Second, the relationship between

objectively measured physical activity and cardiorespiratory fitness in Portuguese

adolescents was explored. Likewise, this relationship was analyzed with and without

taking sedentary behaviour into account as a possible confounder. Third, after

exploring these independent relationships, both variables (physical activity and

sedentary behaviour) were combined and the relationship between this combined

variable and cardiorespiratory fitness was explored. All these relationships were

analyzed through binary logistic regression models and a one-way ANOVA test. In this

discussion, the findings of this thesis will be discussed and compared with existing

literature. However, comparing the results of different studies is difficult because of the

different measuring methods of cardiorespiratory fitness, physical activity and/or

sedentary behaviour. After comparing the results, the limitations and strengths of this

study will also be discussed, followed by suggestions for further research within this

domain.

One of the findings within the present study was that sedentary behaviour was

significantly and negatively related to cardiorespiratory fitness in Portuguese

adolescents. However, after controlling for physical activity (i.e MVPA), sedentary

behaviour was no longer significantly related to cardiorespiratory fitness. Denton et al.

(2013) also explored the relationship between objectively measured sedentary

behaviour and cardiorespiratory fitness in British adolescents. No significant

association was found between sedentary behaviour and cardiorespiratory fitness.

However it should be noted that they did not include physical activity as a confounder.

Since the present study did find a significant relationship between sedentary behaviour

and cardiorespiratory fitness (without taking physical activity into account) the results

of Denton et al. (2013) differ from the results of the present study. The results of the

study of Santos et al. (2014), which also examined the relationship between sedentary

behaviour, physical activity and cardiorespiratory fitness in Portuguese adolescents,

were different. They did find that objectively measured sedentary behaviour was

44

negatively related to cardiorespiratory fitness even after taking physical activity into

account. Possible explanations for the difference in results of the present study and

the one of Santos et al. (2014) is the use of a different equation to estimated VO2max

and the different statistical test that was used to explore this relationship (binary versus

linear regression).

Another finding of the present study was that physical activity (i.e. MVPA) was

significantly and positively related to cardiorespiratory fitness, even after controlling for

sedentary behaviour. These results are in line with the finding of Santos et al. (2014),

Ortega et al. (2008) and Parikh and Stratton (2011). They also found that physical

activity was significantly and positively related to cardiorespiratory fitness in a

Portuguese and European sample of adolescents. These findings emphasize the

importance of including physical activity into prevention programs, since its confirmed

positive relationship with cardiorespiratory fitness in Portuguese adolescents.

Results of the binary logistic regression model with the combined variable of sedentary

behaviour and MVPA, found that high sedentary/active and low sedentary/active

adolescents were more likely to have healthy cardiorespiratory fitness levels, in

comparison to adolescents who were high sedentary and inactive. Adolescents who

were low sedentary/inactive also were more likely to have healthy cardiorespiratory

fitness levels compared to adolescents who were high sedentary/inactive, although it

has to be noted that this relationship was only borderline significantly. When comparing

these results to the study of Santos et al. (2014), which also studied the combined

relationship in Portuguese adolescents, some differences were found. Santos et al.

(2014) found that only adolescents who were low sedentary/active or low

sedentary/inactive were more likely to have healthy cardiorespiratory fitness levels in

comparison to adolescents who were high sedentary/inactive. With these findings, they

concluded that being active, and thus meeting the guidelines for MVPA, may not be

able to overcome the adverse influence of high sedentary behaviour on

cardiorespiratory fitness. However, the findings of the present study rather suggest that

low sedentary behaviour is not able to overcome the adverse influence of being

inactive on cardiorespiratory fitness levels. Again, methodologic differences such as

the use of different equations to estimate cardiorespiratory fitness must considered

while comparing these results. When comparing the results of the present study to the

results of Bai et al. (2016) who also explored this combined relationship of sedentary

45

behaviour and physical activity with cardiorespiratory fitness, tough in American

adolescents, more similarities can be found. Adolescents who were inactive,

independent from the level of sedentary behaviour, were more likely to have lower

cardiorespiratory fitness levels in comparison to adolescents who were active and low

sedentary. In other words, being active as an adolescent seems more important in

maximizing cardiorespiratory fitness than being low sedentary. The HELENA-study

(Martinez-Gomez et al., 2011) which explored this relationship in a European sample

of adolescents, found that being sedentary was significantly and negatively related to

cardiorespiratory fitness levels in inactive adolescent girls. Although this influence

disappeared when the adolescent girls were active instead of inactive. The present

study also found that adolescents who were low sedentary/inactive were more likely to

have healthier cardiorespiratory fitness levels than adolescents who were highly

sedentary/inactive, although this finding was only borderline significantly.

A comparison of the mean estimated VO2max between the four categories of the

combined variable of sedentary behaviour (low/high) and physical activity

(active/inactive) was obtained by performing a one-way ANOVA test. A significant

difference in the mean cardiorespiratory fitness levels was found between the four

categories. The pairwise comparisons showed that adolescents who were active had

a significantly higher cardiorespiratory fitness levels, regardless of being low or highly

sedentary, than adolescents who were inactive, also regardless of being low or highly

sedentary. There was no significant difference in the mean cardiorespiratory fitness

level between the adolescents that were active, despite having different sedentary

behaviour levels (low/high). The same applied for adolescents being inactive. In other

words, regardless of sedentary behaviour, adolescents who were active had

significantly higher cardiorespiratory fitness levels than adolescents who were inactive.

The study of Santos et al. (2014) found the same significant and not-significant

differences. These results suggest that low sedentary behaviour is not able to

overcome the detrimental effect of being inactive on the cardiorespiratory fitness levels

in adolescents. Despite the similar results of the present study with the study of Santos

et al. (2014), the HELENA-study (Martinez-Gomez et al., 2011) found different results.

They found that adolescents girls who belonged to the low sedentary/inactive category

had significant higher levels than girls being high sedentary/inactive. Although the

significant difference in mean cardiorespiratory fitness levels estimated was rather

46

small (± 1.5 ml/kg/min). Just as in the present study, no significant difference was found

in the estimated VO2max between adolescents belonging to ‘low sedentary/active’ and

‘high sedentary/active’ group. Again, methodological differences such as the used

equation, the method used to dichotomize and the fact that the HELENA-study

(Martinez-Gomez et al., 2011) conducted the analyses for boys and girls separately,

must be taken into account. The suggested gender effect of excessive sedentariness

on cardiorespiratory fitness can possibly be explained by the way of gaining muscle

mass. In inactive girls, light intensity physical activity may play an important role in

gaining muscle and thus having higher cardiorespiratory fitness levels.

An additional finding of this study is that only 25.8% of the Portuguese adolescents

had unhealthy cardiorespiratory fitness levels. These results are similar to the 22.4%

found by the study of Santos et al. (2014). Although, the European HELENA-study also

using the same gender and age specific cutoff points of the FITNESSGRAM (The

Cooper Institute, 2017), classified 62.6% of the European adolescents as fit. A part of

this difference can be possibly explained by the difference in version of the

FITNESSGRAM cutoff points. The present study used a more recent version then the

HELENA-study. Another possible explanation is that Portugal was not included into the

HELENA-study and that Portugal has shown to have lower cardiorespiratory fitness

levels than European averages (Santos et al. 2018).

When interpreting the results of the present study, some limitations should be taken

into account. First of all, sedentary behaviour and physical activity were measured

using the GT3Xs triaxial accelerometer, which was worn at the hip. Even though there

is an inclinometer function present on this device, it was not used to collect the data of

the present study. As a consequence, the distinction between standing (light-intensity

physical activity) and lying/sitting (sedentary behaviour) could not be made, resulting

in a possible misclassification between light-intensity physical activity and sedentary

behaviour. Standing will be most likely wrongly included into the sedentary behaviours

category, since no acceleration was measured, which can lead to an overestimation of

sedentary behaviour (Atkin et al., 2012; Hart et al., 2011). For this reason only MVPA,

and not also light-intensity physical activity, was considered as an indicator for physical

activity within the present study. Another consequence of not using the inclinometer

function is that sedentary behaviour patterns (prolonged bouts, sitting breaks, …) could

47

not be examined with sufficient accuracy and therefore only the total sedentary time

was used as an indicator for sedentary behaviour. Exploring the relationship between

sedentary behaviour patterns and cardiorespiratory fitness in adolescence can be an

important field of interest for future research.

A second limitation of the present study is that the data derived from the

accelerometers was not adjusted for non-wear activities (e.g. swimming). Despite

literature (De meester, De Bourdeaudhuij, Deforche, Ottevaere, & Cardon, 2011)

pointing out a significant difference between the physical activity levels in adolescents

when including or not including non-wear activities (e.g. collected through diaries).

Even though these non-wear activities were collected through diaries within the

present study, the overall time spent in MVPA was not adjusted for these activities.

This may have led to an underestimation of the time spent in MVPA.

A third limitation of the present study is that cardiorespiratory fitness was not measured

according to the golden standard, namely measuring the objective VO2max through a

treadmill exercise. Nevertheless, the VO2max was estimated through an equation

based on the results of the 20 meter Shuttle-run test. Despite not being the golden

standard, the 20 meter shuttle run test has shown good validity in multiple reviews for

measuring cardiorespiratory fitness in adolescents (Batista et al., 2017; Castro-Piñero

et al., 2010). Silva et al. (2012) found that this test was also valid in Portuguese

adolescents. Nevertheless the use of such equations can lead to certain errors

(Moreira et al., 2011). A lot of researchers developed different equations, including

different variables which can lead to different outcomes in the estimated VO2max. As

a consequence, comparison between studies, using different equations, is difficult.

There is still no consistency in the evidence about which equation has the best validity

to estimate the VO2max. Within the present study, the Mahar equation (Mahar et al.

2006) was used, which has shown good validity (r = 0.66) and cross-validity (r = 0.69)

(Mahar, Guerieri, Hanna, & Kemble, 2011). However, it should be noted that this

equation is not yet validated in a Portuguese sample.

A fourth limitation concerns is the cross-sectional design of the present study. Since

only longitudinal designs are appropriate to make cause-effect implications, no

causality between the objectively measured sedentary behaviour/physical activity and

48

cardiorespiratory fitness could be determined. Further research should verify the

results of the present study using a longitudinal design.

A fifth limitation of the present study is that the participating schools were enrolled

through a convenience sampling method. Since the sample was not recruited at

random, reservations concerning the representativeness of the sample and

generalizability of the results must be made. Only public schools within the Porto region

were included, which makes it difficult to generalize the results to all Portuguese

adolescents.

A last limitation of the present study is that besides physical activity and sedentary

behaviour, no other confounders were included into the study. When exploring the

relationship between on the one hand physical activity and cardiorespiratory fitness

and on the other hand sedentary behaviour and cardiorespiratory fitness, respectively

sedentary behaviour and physical activity were included into the binary logistic

regression models as confounders. When exploring the combined relationship

between sedentary behaviour/physical activity and cardiorespiratory fitness, no

possible confounders were included into the analyses. However, know confounders

such as age, gender and BMI) were used in the Mahar equation (Mahar et al., 2006)

to estimate the VO2max based on the results of the 20 meter shuttle-run test. It has to

be noted that other known confounders such as the maturation status of the participant

(e.g. Tanner score) or parental influences were not included as a confounders or

included into the equation.

Beside these limitations, the study also has its strengths. First of all, a relatively large

sample was used within the present study. More precisely, a total of 695 participants

met the inclusion criteria and were therefore included into the analyses.

A second strength of the present study is the use of accelerometers for measuring

sedentary behaviour and physical activity. Despite not being able to detect the context

in which the measured behaviours are performed, accelerometers show good validity

in measuring sedentary behaviour and certainly in measuring physical activity in

adolescents. They resolve some of the problems/disadvantages of subjective

measurement, such as response bias and recall bias. Another strength of this study is

the use of the cutoff points of Evenson et al. (2008) to discriminate sedentary behaviour

49

and the different intensity levels of physical activity. These cutoff points have shown to

have good validity in an adolescent sample (Trost et al., 2011).

It can be concluded that the results of the present study further build upon the evidence

that physical activity plays a key role in maximizing cardiorespiratory fitness in

adolescents. Therefore, future intervention programmes that want to promote

cardiorespiratory fitness in adolescence, should focus on increasing the number of

adolescents meeting the present guidelines for physical activity. The results of the

present study contribute to the inconsistency in literature about the role of sedentary

behaviour in maximizing cardiorespiratory fitness, further research within this field is

necessary. In addition, further research should also focus on clarifying the

inconsistency in the results between the few studies exploring the relationship between

the combined variable of sedentary behaviour/physical activity and cardiorespiratory

fitness in adolescents.

Longitudinal studies can create clarification about the not yet well understood

relationship between these variables. Furthermore, future research should also take

confounders into account. Variables such as age, gender, body fat percentage, BMI

and maturation status (e.g. Tanner score) are known confounders and should be

included in order to explore this relationship more clearly.

Furthermore, sedentary behaviour is a relatively recent and complex behaviour mainly

because of its multidimensional aspect. It can be interesting for further research to

measure not only sedentary behaviour through subjective or objective methods, but

use a combination of both. In this way the objective data can be supplemented with

contextual information derived from self-reports (Healy et al., 2011). In addition,

measuring sedentary behaviour with inclinometers (e.g. ActivPAL) can create the

possibility to also explore the relationship with sedentary behaviour patterns (e.g.

prolonged bouts and breaks) instead of the mean sedentary time a day.

In the present study, the mean time spent in sedentary behaviour a day is categorized

based on the median. To the best of our knowledge, no international guidelines exist

concerning a cutoff value for excessive objectively measured sedentary behaviour in

adolescents. The only widely accepted guidelines in adolescents are about limiting

recreational screen time (Martinez-Gomez et al., 2011; Tremblay et al., 2011c).

50

Thereby, future research should focus on the development of meaningful cutoff points

for excessive objectively measured sedentary behaviour since this will facilitate

research within the domain of sedentary behaviour and its influence on health.

Concerning physical activity, it can also be interesting to not only consider MVPA as

an indicator for physical activity, but also light-intensity physical activity, since the study

of Martinez-Gomez et al. (2011) emphasized its possible important role in adolescent

girls. Despite of the fact that higher intensity levels of physical activity have been

proven to have stronger health benefits (Janssen & Leblanc, 2010), a lot of adolescents

do not meet the WHO guideline of MVPA. Thus promoting light intensity physical

activity can possibly be more successful than promoting MVPA (World Health

Organization, 2018a). Exploring the effect of substituting sedentary activity by light

intensity physical activity on cardiorespiratory fitness in adolescents can be interesting

for future research (Tremblay, Esliger, Tremblay, & Colley, 2007).

51

6 Conclusion

Today, European adolescents have increased opportunities to be more sedentary and

less physically active, which could result in lower cardiorespiratory fitness levels and

therefore in a worse health. Research should focus on this relationship (both combined

and independent of each other) in order to comprehend their complex relationship and

provide coherent insights within the domain of public health (Bai et al., 2016).

Especially in Portuguese adolescents examining the relationship between sedentary

behaviour and physical activity (both independent and combined) with

cardiorespiratory fitness added value to public health. The majority of Portuguese

adolescents do not reach the global recommendations of the WHO on physical activity.

A significant part of the Portuguese adolescents also exceed the recommended

maximum of two hours recreational screen time a day (de Matos et al., 2014).

According to the Portuguese study of Santos et al. (2018), only 14.4% of the

adolescent girls and 46.3% of the adolescent boys had healthy cardiorespiratory

fitness levels.

First, this master dissertation investigated the relationship of physical activity (MVPA)

with cardiorespiratory fitness. This master thesis adds to the consistent evidence for a

positive relationship with cardiorespiratory fitness.

Second, this master dissertation investigated the relationship of sedentary behaviour

with cardiorespiratory fitness. Studies about sedentary behaviour are still scarce and

inconsistent. In this master thesis, results show a negative relation between sedentary

behaviour and cardiorespiratory fitness. Although, when taking MVPA into account,

sedentary behaviour had no longer a significant relationship with cardiorespiratory

fitness.

At last, the relationship between the combined variable of sedentary

behaviour/physical activity with cardiorespiratory fitness was investigated. Literature

up till now is scarce and results inconsistent. In this master thesis the combined

variable sedentary behaviour and physical activity (MVPA) had a significant

relationship with cardiorespiratory fitness. Portuguese adolescents who are low

sedentary and active, high sedentary and active or low sedentary and inactive had

52

higher odds of being fit than Portuguese adolescents that were high sedentary and

inactive.

The results of the One-Way ANOVA showed that the mean cardiorespiratory fitness

levels tend to be higher with adolescents who are active, and therefore meet the

international guidelines of the WHO (2018a), independent of sedentary behaviour.

In the end, this master thesis concludes that, when wanting to improve the

cardiorespiratory fitness levels of adolescents, the focus should be on promoting

MVPA (to the point where adolescents meet the WHO guidelines of physical activity).

MVPA is therefore an important aspect within public health. Furthermore, research

investigating the relationship and clarifying the role of sedentary behaviour in

maximizing cardiorespiratory fitness levels in adolescents is necessary to formulate

appropriate health messages.

53

7 References

ActiGraph, LLC. (2018). actigraph wGT3X-BT. Retrieved on the 18th of May 2018 from

https://www.actigraphcorp.com/actigraph-wgt3x-bt/

Affuso, O., Stevens, J., Catellier, D., McMurray, R.G., Ward, D.S., Lytle, L., Sothern,

M.S., & Young, D.R. Validity of self-reported leisure time sedentary behavior in

adolescents. Journal of Negative Results in BioMedicine, 10,(2). Retrieved on the 15th

of July 2018 from https://dx.doi.org/10.1186%2F1477-5751-10-2

Alberga, A.S., Sigal, R.J., Goldfield, G., Prud’homme, D., Kenny, D.P. (2012).

Overweight and obese teenagers: why is adolescence a critical period? Pediatr Obes,

7(4), 261-273.

Alberto, F. P., Nathanael., A.M., Mathew, B., & Ainsworth, B.E. (2017). Wearable

monitors criterion validity for energy expenditure in sedentary and light activities.

Journal of Sport and Health Sciences, 6(1), 103-110.

Ainsworth, B.E. (2009). How do I measure physical activity in my patients?

Questionnaires and objective methods. British journal of sports medicine, 43(1), 6-9.

Ainsworth, B., Cahalin, L., Buman, M., & Ross, R. (2015). The current state of physical

activity assessment tools. Progress in cardiovascular diseases, 57(4), 387-395.

Aires, L., Pratt, M., Lobelo, F., Santos, R.M., Santos, M.P., Mota, J. (2011).

Associations of cardiorespiratory fitness in children and adolescents with physical

activity, active commuting to school, and screen time. Journal of Physical Activity and

Health, 8(2), 198-205.

Artero, E.G., Lee, D.C., Ruiz, J.R., Sui, X., Ortega, F.B., Church, T.S., … & Blair, S.N.

(2011). A prospective study of muscular strength and all-cause mortality in men with

hypertension. J Am Coll Cardiol, 57(18), 1831-1837.

54

Atkin, A.J., Gorely, T., Clemes, S.A., Yates, T., Edwardson, C., Brage, S., Salmon, J.,

Marshall, S.J., & Biddle, S.J. (2012). Methods of measurement in epidemiology:

sedentary behaviour. Int J Epidemiol, 41(5), 1460-1471.

Bai, Y., Chen, S., Laurson, K.R., Kim, Y, Saint-Maurice, P.F., & Welk, G.J. (2016). The

association of youth physical activity and screen time with fatness and fitness: the 2012

NHANES National Youth Fitness Survey. PLoS ON, 11(1). Retrieved on 21st of july

2018 from

http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0148038&type=pr

intable

Baptista, F., Santos, D.A., Silva A.M., Mota, J., Santos, R., Vale, S., … Sardinha, L.B.

(2011) Prevalence of the Portuguese population attaining sufficient physical activity.

Med Sci Sports Exerc, 44(3), 466-473.

Batista, M.B., Romanzini, C.L., Castro-Piñero, J., & Vaz Ronque E.R. (2017) Validity

of field tests to estimate cardiorespiratory fitness in children and adolescents: a

systematic review. Rev Paul Pediatr, 35(2), 222-233.

Bay, J.L., Morton, S.M., & Vickers, M.H. (2016). Realizing the Potential of Adolescence

to Prevent Transgenerational Conditioning of Noncommunicable Disease Risk: Multi-

Sectoral Design Frameworks. Healthcare (Basel), 4(3). Retrieved on the 19th of March

2018 from https://doi.org/10.3390/healthcare4030039

Beighle, A., Pangrazi, R.P., & Vincent, S.D. (2001). Pedometers, physical activity, and

accountability. Journal of Physical Education, Recreation & Dance, 72(9), 16-19.

Bel, S., De Ridder, K., Lebacq, T., Ost, C., & Teppers, E. (2016). Lichaamsbeweging

en sedentair gedrag, voedselconsumptiepeiling 2014-2015. Retrieved on the 10th of

july 2018 from https://fcs.wiv-

isp.be/nl/Gedeelde%20%20documenten/NEDERLANDS/PA_NL.pdf

Berlin, J. E., Storti, K.L., & Brach, J.S. (2006). Using activity monitors to measure

physical activity in free-living conditions. Physical Therapy, 86(8), 1137-1145.

55

Bermejo-Cantarero, A., Alvarez-Bueno, C., Martinez-Vizcaino, V., Garcia-Hermoso,

A., Torres-Costoso, A.I., & Sanchez-Lopez, M. (2017). Association between physical

activity, sedentary behavior, and fitness with health related quality of life in healthy

children and adolescents: A protocol for a systematic review and meta-analysis.

Medicine (Baltimore), 96(12), e6407-6412.

Biddle, S.J., Gorely, T., Marshall, S.J., Murdey, I., & Cameron, N. (2004). Physical

activity and sedentary behaviours in youth: issues and controversies. The journal of

the Royal Society for the Promotion of Health, 124(1), 29-33.

Biddle, S.J. Pearson, N. Ros, G.M., & Braithwaite, R. (2010) Tracking of sedentary

behaviours of young people: a systematic review. Prev Med, 51(5), 345-351.

Biddle, S. J. H., Pearson, N., & Salmon, J. (2018). Sedentary behaviors and adiposity

in young people: causality and conceptual model. Exercise and sport sciences reviews,

46(1), 18-25.

Butte, N.F., Watson, K. B., Ridley, K., Zakeri, I.F., McMurray, R.G., Pfeiffer, K.A.,

Crouter, S.E., Hermann, S.D., Bassett, D.R., Long, A., Berhane, Z., Trost, S.G.,

Ainsworth, B.E., Berrigan, D., Fulton, J.E. (2018). A Youth Compendium of Physical

Activities: Activity Codes and Metabolic Intensities. Medicine and Science in Sports

and Exercise, 50(2), 246–256.

Byrom, B., Stratton, G., Carthy, Mc., & Muehlhausen, W. (2016). Objective

measurement of sedentary behaviour using accelerometers. Int J obes, 40(11), 1809-

1812.

Carson, V., Hunter, S., Kuzik, N., Gray, C.E., Poitras, V.J., Chaput, J.-P., … & Connor

Gorber, S. (2016). Systematic review of sedentary behaviour and health indicators in

school-aged children and youth: an update. Applied Physiology, Nutrition, and

Metabolism, 41(6), 240-265.

56

Carson, V., & Janssen, I. (2011). Volume, patterns, and types of sedentary behavior

and cardio-metabolic health in children and adolescents: a cross-sectional study. BMC

Public Health, 11, 274.

Caspersen, C.J., Powell, K.E., Christenson, G.M. (1985). Physical activity, exercise,

and physical fitness: definitions and distinctions for health-related research. Public

health reports, 100(2), 126-131.

Castro-Piñero, J., Artero, E.G., España-Romero, V., Ortega, F.B., Sjöström, M., Suni,

J., & Ruiz, J.R. (2010). Criterion-related validity of field-based fitness tests in youth: a

systematic review. Br J Sports Med, 44(3), 934-943.

Chein, J., Albert, D., O’Brien, L., Uckert, K., & Steinberg, L. (2011). Peers increase

adolescent risk taking by enhancing activity in the brain’s reward circuitry. Dev Sci,

14(2), 1-10.

Chinapaw, M.J., de Niet, M., Verloigne, M., De Bourdeaudhuij, I., Brug, J., & Altenburg,

T.M. (2014). From sedentary time to sedentary patterns: accelerometer data reduction

decisions in youth. PLOS One, 9(11), 1-6.

Cleland, V.J., Ball, K., Magnussen, C., Dwyer, T., & Venn, A. (2009). Socioeconomic

position and the tracking of physical activity and cardiorespiratory fitness from

childhood to adulthood. American journal of epidemiology, 170(9), 1069-1077.

Cole, T.J., Bellizzi, M.C., Flegal, K.M., & Dietz, W.H. (2000). Establishing a standard

definition for child overweight and obesity worldwide: international survey. BMJ,

320(7244), 1240-1246.

Colley, R.C., Garriguet, D., Janssen, I., Craig, C.L., Clarke, J., & Tremblay, M.S.

(2011). Physical activity of Canadian children and youth: Accelerometer results from

the 2007-2009 Canadian Health Measures Survey. Health Rep, 22(11), 15-23.

57

The Cooper Institute. (2004). FINTESSGRAMM test administration manual, third

edition. Retrieved on the 20th of march 2018 from https://pftdata.org/files/hfz-

standards.pdf

The Cooper Insitute. (2017). FITNESSGRAM: Healthy Fitness Zone Charts. Retrieved

on the 20th of march 2018 from https://pftdata.org/files/hfz-standards.pdf

Corbin, C.B., Pangrazi, R.P., & Frank B.D. (2000). Definitions: health, fitness, and

physical activity. Research Digest, 3(9), 1-8.

Cristi-Montero, C. (2017). An integrative methodology for classifying physical activity

level in apparently healthy populations for use in public health. Rev Panam Salud

Publica, 41. Retrieved on 8th of august 2018 from

http://iris.paho.org/xmlui/bitstream/handle/123456789/34507/v41e1612017.pdf?sequ

ence=1

Curtis, C. (2015) Defining Adolescence. Journal of Adolescent and Family Health 7(2),

1-40.

De Matos, M.G., Simões, C., Camacho, I., & Reis, M. (2014). A saúde dos

adolescentes Portugueses em tempos de recessão, estudo HBSC. Retrieved on the

3th of July 2018 from

http://aventurasocial.com/arquivo/1437158618_RELATORIO%20HBSC%202014e.p

df

De Meester, F., De Bourdeaudhuij, I., Deforche, B., Ottevaere, C., & Cardon, G.

(2011). Measuring physical activity using accelerometry in 13–15-year-old

adolescents: the importance of including non-wear activities. Public Health Nutrition,

14(12), 2124-2133.

Denton, S.J., Trenell, M. I., Plötz, T., Savory, L. A., Bailey, D. P., & Kerr, C. J. (2013).

Cardiorespiratory fitness is associated with hard and light intensity physical activity but

not time spent sedentary in 10–14 year old schoolchildren: the HAPPY study. PLOS

One, 8(4), e61073-e61080.

58

De Rezende, L.F.M., Lopes, M.R., Rey-López, J.P., Matsudo, V.K.R., & Do Carmo

Luiz, O. (2014). Sedentary behavior and health outcomes: an overview of systematic

reviews. PLOS One, 9(8), e105620-105627.

Dimeo, F., Fetscher, S., Lange, W., Mertelsmann, R., & Keul, J. (1997). Effects of

aerobic exercise on the physical performance and incidence of treatment-related

complications after high-dose chemotherapy. Blood, 90(9), 3390-3394.

Ekelund, U., Anderssen, S.A., Froberg, K., Sardinha, L.B., Andersen, L.B., Brage, S.,

& European Youth Heart Study Group. (2007). Independent associations of physical

activity and cardiorespiratory fitness with metabolic risk factors in children: the

European youth heart study. Diabetologia, 50(9), 1832-1840.

Esteban-Cornejo, I., Cadenas-Sanchez, C., Contreras-Rodriguez, O., Verdejo-

Roman, J., Mora-Gonzalez, J., Migueles, J.H., … & Ortega, F.B. (2017). A whole brain

volumetric approach in overweight/obese children: Examining the association with

different physical fitness components and academic performance. The ActiveBrains

project. Neuroimage, 159, 346-354.

Evenson, K.R., Catellier, D.J., Gill, K., Ondrak, K.S., & McMurray, R.G. (2008).

Calibration of two objective measures of physical activity for children. J Sports Sci, 26,

1557–1565.

Fernandes, H. M. (2018). Physical activity levels in Portuguese adolescents: A 10-year

trend analysis (2006–2016). Journal of Science and Medicine in Sport, 21(2), 185-189.

Fischer, C., Yildirim, M., Salmon, J., & Chinapaw, J.M. Comparing different

accelerometer cut-points for sedentary time in children. Pediatr Exerc Sci. 24(2), 220-

228.

Gao Y., Sun, H., Zhuang, J., Zhang, J., Ransdell, L., Zhu Z, Wang, S. (2016) Metabolic

Equivalents of Selected Sedentary and Physical Activities in Chinese Youth. J Phys

Act Health, 13(6), 48–52.

59

Gorely, T., Biddle, S.J., Marshall, S.J., Cameron, N. (2009). The prevalence of leisure

time sedentary behaviour and physical activity in adolescent boys: An ecological

momentary assessment approach. Int. J. Pediatr. Obes., 4(4), 289–298.

Hallal, P.C., Victora, C.G., Azevedo, M.R., & Wells, J.C.K. (2006). Adolescent physical

activity and health. Sports medicine, 36(12), 1019-1030.

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.

Hardy, L.L., Hills, A.P., Timperio, A., Cliff, D., Lubans, D., Morgan, P.J., Taylor, B.J., &

Brown, H. (2013). A hitchhiker’s guide to assessing sedentary behaviour among young

people: deciding what method to use. J Sci Med Sport, 16(1), 28-35.

Hart, T.L., Ainsworth, B.E., Tudor-Locke, C. (2011). Objective and subjective

measures of sedentary behavior and physical activity. Med Sci Sports Exerc, 43(3),

449-156.

Healy, G.N., Dunstan, D.W., Salmon, J., Cerin, E., Shaw, J.E., Zimmet, P.Z., & Owen,

N. (2008). Breaks in sedentary time: beneficial associations with metabolic risk.

Diabetes care, 31(4), 661-666.

Healy, G.N., Clark, B.K., Winkler, E.A., Gardiner, P.A., Bronw, W.J, & Matthews, C.E.

(2011). Measurements of adult’ sedentary time in population-based studies. Am J Prev

Med, 41(2), 216-227.

Hurtig-Wennlöf, A., Ruiz, J.R., Harro, M., & Sjöström. (2007). Cardiorespiratory fitness

relates more strongly than physical activity to cardiovascular disease risk factors in

healthy children and adolescents: the European Youth Heart Study. Eur J Cardiovasc

Prev Rehabil, 14(4), 575-581.

60

Inchley, J., Currie, D., Jewell, J., Breda, J., & Barnekow, V. (2017). Adolescent obesity

and related behaviours: trends and inequalities in the WHO European Region [report].

Retrieved on the 26th of February 2018 from

http://www.euro.who.int/__data/assets/pdf_file/0019/339211/WHO_ObesityReport_2

017_v3.pdf

Janssen, I. & LeBlanc, A.G. (2010). Systematic review of the health benefits of physical

activity and fitness in school-aged children and youth. International Journal of

Behavioral Nutrition and Physical Activity, 7(1), 40-56.

Jetté, M., Sidney, K., & Blümchen, G. (1990). Metabolic equivalents (METS) in exercise

testing, exercise prescription, and evaluation of functional capacity. Clinical

Cardiology, 13(8), 555-565.

Júdice, P.B., Silva, A.M., Berria, J., Petroski, E.L., Ekelund, U., & Sardinha, L.B.

(2017). Sedentary patterns, physical activity and health-related physical fitness in

youth: a cross-sectional study. International Journal of Behavioral Nutrition and

Physical Activity, 14(1), 25.

Kaminsky, L.A., Arena, R., Beckie, T.M., Brubaker, P.H., Church, T.S., Franklin, B.A.,

… Williams, M.A. (2013). The importance of cardiorespiratory fitness in the United

States: the need for a national registry: a policy statement from the American Heart

Association. Circulation, 127(5), 652-662.

Kang, M., & Rowe, D.A. (2015). Issues and challenges in sedentary behavior

measurement. Measurement in physical education and exercise science, 19(3), 105-

115.

Katzmarzyk, P.T. (2010). Physical activity, sedentary behavior, and health: paradigm

paralysis or paradigm shift? Diabetes, 59(11), 2717-2725.

Mota, J., Coelho-e-Silva, M.J., Raimundo, A.M., & Sardinha, L.B. (2016). Results From

Portugal's 2016 Report Card on Physical Activity for Children and Youth. Journal of

physical activity & health, 13(11), 242-245.

61

Kim, Y., Barry, V.W., & Kang, M. (2015). Validation of the Actigraph GT3X and

activPAL accelerometers for the assessment of sedentary behaviour. Measurement in

physical education and exercise science, 19(3), 125-137.

Kulinski, J.P., Khera, A., Ayers, C.R., Das, S.R., de Lemos, J.A., Blair, S.N., & Berry,

J.D. (2014). Association between cardiorespiratory fitness and accelerometer-derived

physical activity and sedentary time in the general population. Mayo Clin Proc, 89(8),

1063-1071.

Lang, J.J., Tremblay, M.S., Léger, L., Olds, T., & Tomkinson, G.R. (2016). International

variability in 20 m shuttle run performance in children and youth: who are the fittest

from a 50-country comparison? A systematic literature review with pooling of

aggregate results. Br J Sports Med, 54. Retrieved on the 29th of June 2018 from

http://dx.doi.org/10.1136/bjsports-2016-096224

Lau, M., Wang, L., Acra, S., Buchowski, M.S. (2016). Energy Expenditure of Common

Sedentary Activities in Youth. J Phys Act Health, 13(6),17–20.

Lebacq, T., Ost, C., Bel, S., Brocatus, L., Teppers, E., Cuypers, K., Tafforeau, J.

(2016). Physical activity and sedentary behaviour in Belgium: design, methods and

expected outcomes. Arch Public Health, 75, 1-12. Retrievd on the 14th of May 2018

from

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066291/pdf/13690_2016_Article_156

.pdf

Lee, I.M., Shiroma, E.J., Lobelo, F., Puska, P., Blair, S.N., Katzmarzyk, P.T. (2012).

Lancet Physical Activity Series Working Group. Effect of physical inactivity on major

non-communicable diseases worldwide: an analysis of burden of disease and life

expectancy. Lancet, 380(9838), 219–29.

Léger, L.A., Lambert, J., Goulet, A., Rowan, C., & Dinelle, Y. (1984). Aerobic capacity

of 6 to 17-year-old Quebecois--20 meter shuttle run test with 1 minute stages. Can J

Appl Sport Sci, 9(2), 64-69.

62

Léger, L.A., Mercier, D., Gadoury, C., & Lambert, J. (1988). The multistage 20 meter

shuttle run test for aerobic fitness. Journal of Sports Sciences, 6, 93-101.

Lin, C.-W., Gamble, J., Yang, Y.-T., & Wang, J.-S. (2012). Estimating the influence of

chronotype and social zeitgebers on circadian rhythms using an accelerometer-based

sensor network. International Conference on Biomedical and Health Informatics, 285-

288.

Lobelo, F., Pate, R.R., Dowda, M., Liese, A.D., & Ruiz, J.R. (2009). Validity of

cardiorespiratory fitness criterion-referenced standards for adolescents. Med Sci

Sports Exerc. 41(6), 1222-1229.

Loprinzi, P.D., & Cardinal B.J. (2011). Measuring children’s physical activity and

sedentary behaviors. Journal of Exercise Science & Fitness, 9(1), 15-23.

Mahar, M.T., Welk, G.J., Rowe, D.A., Crotts, D.J., & McIver, K.L. (2006). Development

and validation of a regression model to estimate VO2peak from PACER 20-m shuttle

run performance. J Phys Act & Hlth, 3(2), 34 –46.

Mahar, M.T., Guerieri, A.M., Hanna, M.S., Kemble, C.D. (2011). Estimation of aerobic

fitness from 20-m multistage shuttle run test performance. Am J Prev Med, 41(4), 117-

123.

Matos M.G., Simões, C., Tomé, G., Silva, M., Gaspar, T., & Diniz, J.A. (2006).

Indicadores de saúde dos adolescentes Portugueses. Retrieved on the 24th of July

2018 from http://www.fmh.utl.pt/aventurasocial/pdf/Indicadores_de_Saude.pdf

Matthews, C.E., Chen, K.Y., Freedson, P.S., Buchowski, M.S., Beech, B.M., Pate,

R.R., & Troiano, R.P. (2008). Amount of time spent in sedentary behaviors in the

United States, 2003-2004. Am J Epidemiol, 167(7), 875-881.

63

Matton, L. Duvigneaud, N., Wijndaele, K., Philippaerts, R., Duquet, W., Beunen, G., …

Lefevre, J. (2007). Secular trends in anthropometric characteristics, physical fitness,

physical activity and biological maturation in Flemish adolescents between 1969 and

2005. Am J Hum Biol. 19(3), 345-357.

McMahon, E.M., Corcoran, P., O’Regan, G., Keeley, H., Cannon, M., Carli, V., …

Apter, A. (2017). Physical activity in European adolescents and associations with

anxiety, depression and well-being. European child & adolescent psychiatry, 26(1),

111-122.

Mitchell, J.A., Pate, R.R., & Blair, S.N. (2012). Screen-based sedentary behavior and

cardiorespiratory fitness from age 11 to 13. Medicine and Science in Sports and

Exercise, 44(7), 1302-1309.

Moliner-Urdiales, D., Ruiz, J.R., Ortega, F.B., Jiménez-Pavón, D., Vicente-Rodriguez,

G., Rey-López J.P., … Moreno, L.A. (2010). Secular trends in health-related physical

fitness in Spanish adolescents: the AVENA and HELENA-studies. J Sci Med Sport.

13(6), 584-588.

Moreira, C., Santos, R., Ruiz, J.R., Vale, S., Soares-Miranda, L., Marques, A.I., Mota,

J. (2011). Comparison of different VO(2max) equations in the ability to discriminate the

metabolic risk in Portuguese adolescents. J Sci Med Sport, 14(1), 79-84.

O’Brien, L., Albert, D., Chein, J., & Steinberg, L. (2011). Adolescents prefer more

immediate rewards when in the presence of their peers. Journal of Research on

Adolescence, 21, 747-753.

O'Brien, W., Issartel, J., & Belton, S. (2018). Relationship between Physical Activity,

Screen Time and Weight Status among Young Adolescents. Sports, 6(3), 1-11.

Okely, T., Salmon, J., Vella, S., Cliff, D. Timperio, A., Tremblay, M., … Marino, N.

(2012). A systematic review to update the Australian sedentary behaviour guidelines

for children and young people. Retrieved on the 29th of June 2018 from

64

http://www.health.gov.au/internet/main/publishing.nsf/content/3768EA4DC0BF11D0C

A257BF0001ED77E/$File/SR-ASBGCYP.pdf

Olds, T.S., 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. Retrieved on the 20th of March 2018 from

https://doi.org/10.1186/1479-5868-7-92

Olds, T., Tomkinson, G., Léger, L., & Cazoria, G. (2006). Worldwide variation in the

performance of children and adolescents: an analysis of 109 studies of the 20-m shuttle

run test in 37 countries. J Sports Sci, 24(10), 1025-1038.

Ortega, F.B., Ruiz, J.R., Castillo, M.J., & Sjöström, M. (2008). Physical fitness in

childhood and adolescence: a powerful marker of health. Int J Obes (Lond), 32(1), 1-

11.

Ortega, F.B., Ruiz, J.R., Hurtig-Wennlöf, A., & Sjöström, M. (2008). Physically active

adolescents are more likely to have a healthier cardiovascular fitness level

independently of their adiposity status. The European Youth Heart Study. Rev Esp

Cardiol, 61(2), 123–129.

Ortega, F.B., Artero, E.G., Ruiz, J.R., España-Romero, V., Jiménez-Pavón, D.,

Vincente-Rodriguz, G., … Castillo, M.J. (2011). Physical fitness levels among

European adolescents: the HELENA study. Br J Sports Med, 45, 20-29.

Ortega, F.B., Ruiz, J.R., Labayen, I., Martínez-Gómez, D., Vincente-Rodriguez, G.,

Cuenca-García, M., … & Castillo, M.J. (2014). Health Inequalities in Urban

Adolescents: Role of Physical Activity, Diet, and Genetics. Pediatrics, 133(4), 884-895

Ortega, F.B., Cadenas-Sanchez, C., Lee, D., Ruiz, J.R., Blair, S.N., & Sui, X. (2018).

Fitness and Fatness as Health Markers through the Lifespan: An Overview of Current

Knowledge. Progress in preventive medicine, 3(2). Retrieved on the 27th of June 2018

from doi: 10.1097/pp9.0000000000000013

65

Owen, N., Healy, G.N., Matthews, C.E., Dunstan, D.W. (2010). Too much sitting: the

population health science of sedentary behavior. Exerc Sport Sci Rev, 38, 105-113.

Parikh, T., & Stratton, G. (2011). Influence of intensity of physical activity on adiposity

and cardiorespiratory fitness in 5–18 year olds. Sports medicine, 41(6), 477-488.

Pate, R.R, Wang, C.Y, Dowda, M., Farrell, S.W., & O’Neill, J.R. (2006).

Cardiorespiratory fitness levels among US youth 12 to 19 years of age: findings from

the 1999-2002 National Health and Nutrition Examination Survey. Arch Pediatr

Adolesc Med, 160(10), 1005–12.

Pate, R.R., Oria, M., Pillsbury, L. (2012). Fitness Measures and Health Outcomes in

Youth. Health-Related Fitness Measures for Youth: Cardiorespiratory Endurance.

Washington (DC): National Academia Press.

Plowman, S.A., & Meredith, M.D. (2013). FITNESSGRAM ® /ACTIVITYGRAM ®

Reference Guide (4th Edition). Retrieved on the 10th of April 2018 from

https://www.cooperinstitute.org/vault/2440/web/files/662.pdf

Prince, S.A., Adamo, K.B., Hamel, M.E., Hardt, J., Gorber, S.C., & Tremblay, M.

(2008). A comparison of direct versus self-report measures for assessing physical

activity in adults: a systematic review. International Journal of Behavioral Nutrition and

Physical Activity, 5(1), 56-80.

Reilly, J.J., Penpraze, V., Hislop, J., Davies, G., Grant, S., & Paton, J.Y. (2008).

Objective measurement of physical activity and sedentary behaviour: review with new

data. Archives of disease in childhood, 93(7), 614-619.

Rosenberger, M. (2012). Sedentary behavior: Target for change, challenge

to assess. International Journal of Obesity, 2(1), 26–S29.

Ruiz, J.R., Castro-Piñero, J., Atero, E.G., Ortega, F.B., Sjöström, M., Suni, J., &

Castillo, M.J. (2009). Predictive validity of health-related fitness in youth: a systematic

review. Br J Sports Med, 43(1), 909-923.

66

Ruiz, J.R., & Ortega, F.B. (2009). Physical activity and cardiovascular disease risk

factors in children and adolescents. Current cardiovascular risk reports, 3(4), 281-287.

Ruiz, J.R., Ortega, F.B., Martinez-Gómez, D., Labayen, I., Moreno, L.A., De

Bourdeaudhuij, I., … & Sjöström, M. (2011). Objectively measured physical activity and

sedentary time in European adolescents: the HELENA-study. Am J Epidemiol, 174(2),

173-184.

Sallis, J.F., & Saelens, B.E. (2000). Assessment of physical activity by self-report:

status, limitations, and future directions. Research quarterly for exercise and sport,

71(2), 1-14.

Santos, R., Mota, J., Okely, A.D., Pratt, M., Moreira, C., Coelho-e-Silva, M.J., … &

Sardinha, L.B. (2014). The independent associations of sedentary behaviour and

physical activity on cardiorespiratory fitness. British Journal of Sports Medicine, 48,

1508-1512.

Santos, F.K., Prista, A., Gomes, T.N., Santos, D., Damasceno, A., Madeira, A., K., ...

Maia, J.A. (2015). Body mass index, cardiorespiratory fitness and cardiometabolic risk

factors in youth from Portugal and Mozambique. Int J Obes, 39(10), 1467-1474.

Santos, D.A., Marques, A., Minderico, C.S., Ekelund, U., & Sardinha, L. B. (2018). A

cross-sectional and prospective analyse of reallocating sedentary time to physical

activity on children's cardiorespiratory fitness. Journal of sports sciences, 36(15), 1720-

1726.

Sasaki, J.E., John, D., & Freedson, P.S. (2011). Validation and comparison of

ActiGraph activity monitors. Journal of Science and Medicine in Sport, 14(5), 411-416.

Sawyer S.M., Afifi, R.A., Bearlinger, L.H., Blakemore S., Dick, B., Ezeh, A.C., & Patton,

G.C. (2012). Adolescence: a foundation for future health. Lancet, 379, 1630-1640.

67

Sedentary Behaviour Research Network. 2012. Standardized use of the terms

“sedentary” and “sedentary behaviours”. Appl Physiol Nutr Metab. 37, 540–542.

Sedentary Behaviour Research Network. (2012). Standardized use of the terms

“sedentary” and “sedentary behaviours”. Appl Physiol Nutr Metab. 37(3), 540–542.

Silva, G., Aires, L., Mota, J., Oliveira, J, & Ribeiro, J.C. (2012). Normative and Criterion-

Related Standards for Shuttle Run Performance in Youth. Pediatric Exercise Science,

24, 157-169.

Sirard, J. R., & Pate, R.R. (2001). Physical activity assessment in children and

adolescents. Sports medicine, 31(6), 439-454.

Spittaels, H., Van Cauwenberghe, E., Verbestel, V., De Meester, F., Van Dyck, D.,

Verloigne, M., Haerens L., Deforche, B., Cardon, G., De Bourdeaudhuij, I. (2012).

Objectively measured sedentary time and physical activity time across the lifespan: a

cross-sectional study in four age groups. Int J Behav Nutr Phys Act. Retrieved on the

15th of June 2018 from https://doi.org/10.1186/1479-5868-9-149

Steinberg, L. (2014). Age of opportunity: Lessons from the new science of

adolescence. Boston: Houghton Mifflin Harcourt.

Tomkinson, G., & Olds, T. (2007). Secular changes in pediatric aerobic fitness test

performance: the global picture. Med Sport Sci 2007, 50, 46–66.

Tremblay, M.S, Esliger, D.W., Tremblay, A., & Colley, R. (2007). Incidental movement,

lifestyle-embedded activity and sleep: new frontiers in physical activity assessment.

Can J Public Health, 95, 208-2017

Tremblay, M.S., Colley, R.C., Saunders, T.J., Healy, G.N., & Owen, N. (2010).

Physiological and health implications of a sedentary lifestyle. Appl Physiol Nutr Metab,

35(6), 725-740.

68

Tremblay, M.S., LeBlanc, A.G., Kho, M.E., Saunders, T.J., Larouche, R., Colley, R.C.,

… & Gorber, S.C. (2011a). Systematic review of sedentary behaviour and health

indicators in school-aged children and youth. International Journal of Behavioral

Nutrition and Physical Activity, 8(1), 98-120.

Tremblay, M. S., Warburton, D.E., Janssen, I., Paterson, D.H., Latimer, A.E., Rhodes,

R.E., … & Zehr, L. (2011b). New Canadian physical activity guidelines. Applied

Physiology, Nutrition, and Metabolism, 36(1), 36-46.

Tremblay, M.S., LeBlanc, A.G., Janssen, I., Kho, M.E., Hicks, A., Murumets, K., Colley,

R.C., Duggan, M. (2011c). Canadian Sedentary Behaviour Guidelines for Children and

Youth. Applied Physiology, Nutrition, and Metabolism, 36, 59-64.

Tremblay, M.S., Aubert, S., Barnes J.D., Saunders, T.J., Carso;n, V., Latimer-Cheung,

A.E., Chastin, S.F.M., Altenburg, T.M., & Chinapaw, M.J.M. (2017). Sedentary

Behaviour Research Network (SBRN) - Terminology Consensus Project process and

outcome. International Journal of Behavioral Nutrition and Physical Activity, 14(1), 1-

17. Retrieved on the 29th of June 2018 from

https://ijbnpa.biomedcentral.com/articles/10.1186/s12966-017-0525-8

Treuth, M.S., Schmits, K., Catellier, D.J., McMurray, R.G., Murray, D.M., Almeida, M.J.,

… Pate, R. (2004). Defining accelerometer thresholds for activity intensities in

adolescent girls. Med Sci Sports Exerc., 35(7), 1259-1266.

Trost, S.G., Loprinzi, P.D., Moore, R., & Pfeiffer, K.A. (2011). Comparison of

accelerometer cut points for predicting activity intensity in youth. Medicine and Science

in Sports and Exercise, 43(7), 1360-1368.

Tudor-Locke, C.E., Ainsworth, B.E., Thompson, R.W., & Matthews, C.E. (2002).

Comparison of pedometer and accelerometer measures of free-living physical activity.

Medicine and Science in Sports and Exercise, 34(12), 2045-2051.

69

Twisk, J.W., Kemper, H.C., & Van Mechelen, W. (2000). Tracking of activity and fitness

and the relationship with cardiovascular disease risk factors. Medicine & Science in

Sports & Exercise, 32(8), 1455-1461.

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. Medicine & Science in

Sports & Exercise, 39(8), 1241-1250.

van der Velde, J.H. (2017). Two examples of patterns of sedentary behaviour.

Retrieved on the 27th of June 2018 from

https://cris.maastrichtuniversity.nl/portal/files/16726282/c5820_embargo.pdf

Verloigne, M., Loyen, A., Van Hecke, L., Lakerveld, J., Hendriksen, I., De

Bourdheaudhuij, I., … van der Ploeg, H. (2016). Variation in population levels of

sedentary time in European children and adolescents according to cross-European

studies: a systematic literature review within DEDIPAC. Int J Behav Phys Act.

Retrieved on the 22th of July 2018 from https://doi.org/10.1186/s12966-016-0395-5

Verloigne M., Van Lippevelde, W., Maes, L., Yildirim, M., Chinapaw, M., Manios, Y.,

Androutsos, O., Kovács, E., Bringolf-Isler B., Brug, J., & De Bourdeaudhuij, I. (2012).

Levels of physical activity and sedentary time among 10- to 12-year-old boys and girls

across 5 European countries using accelerometers: an observational study within the

ENERGY-project. International Journal of Behavioral Nutrition and Physical Activity.

Retrieved on the 11th of June 2018 from https://doi.org/10.1186/1479-5868-9-34

Vierola, A., Suominen, A.L., Lindi, V., Viitasalo, A., Ikävalko, T., Lintu, N, … & Lakka,

T.A. (2016). Associations of Sedentary Behavior, Physical Activity, Cardiorespiratory

Fitness, and Body Fat Content with Pain Conditions in Children: The Physical Activity

and Nutrition in Children Study. Journal of Pain, 17(7), 845-853.

Vlaams Instituut Gezond Leven, Van Acker, R., & De Meester, F. (2015). Langdurig

zitten: dé uitdaging van de 21ste eeuw. Syntheserapport als actuele onderbouw voor

de factsheet sedentair gedrag. Retireved on the 29th of June 2018 from

70

https://www.gezondleven.be/files/beweging/sedentair-

gedrag_SYNTHESERAPPORT1.pdf

Welk, G.J., Corbin, C.B., & Dale, D. (2000). Measurement issues in the assessment of

physical activity in children. Research quarterly for exercise and sport, 71(2), 59-73.

World Health Organization. (2015). Portugal Physical Activity Factsheet. Retrieved on

the 16th of April 2018 from file:///H:/Downloads/i023277%20(1).pdf

World Health Organization. (2016). Physical activity in adolescence [factsheet].

Retrieved on the 16th of April 2018 from

http://www.euro.who.int/__data/assets/pdf_file/0018/303480/HBSC-

No.7_factsheet_Physical.pdf?ua=1

World Health Organization. (2018a). Physical activity and young people [factsheet].

Retrieved on the 3th of May 2018 from

http://www.who.int/dietphysicalactivity/factsheet_young_people/en/

World Health Organization. (2018b). Physical Activity. Retrieved on the 25th of April

2018 from http://www.who.int/dietphysicalactivity/pa/en/

World Health Organization. (2018c). Portugal Physical Activity [factsheet]. Retrieved

on the 25th of April 2018 from

http://www.euro.who.int/__data/assets/pdf_file/0004/288121/PORTUGAL-Physical-

Activity-Factsheet.pdf?ua=1

World Health Organization. (2018d). Adolescent development. Retrieved on the 19th

of March 2018 via

http://www.who.int/maternal_child_adolescent/topics/adolescence/development/en/

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8 Appendix

Attachment 1: approval Medical Ethics Committee (Jolien De Brabanter)

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Attachment 2: approval Medical Ethics Committee (Yasmine Platteau)

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Attachment 3: Aga and gender specific cutoff points FINTESSGRAM (The Cooper

Institute, 2017)

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Attachment 4: Informed consent adolescents

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Attachment 5: Age and gender specific cutoff points for BMI (Cole et al., 2000)

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