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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
1
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
1
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).
2
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
3
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.
4
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
5
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
6
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
7
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)
8
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
9
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).
10
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
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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)