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
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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)
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
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.
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
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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).
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6 Conclusion
Today, European adolescents have increased opportunities to be more sedentary and
less physically active, which could result in lower cardiorespiratory fitness levels and
therefore in a worse health. Research should focus on this relationship (both combined
and independent of each other) in order to comprehend their complex relationship and
provide coherent insights within the domain of public health (Bai et al., 2016).
Especially in Portuguese adolescents examining the relationship between sedentary
behaviour and physical activity (both independent and combined) with
cardiorespiratory fitness added value to public health. The majority of Portuguese
adolescents do not reach the global recommendations of the WHO on physical activity.
A significant part of the Portuguese adolescents also exceed the recommended
maximum of two hours recreational screen time a day (de Matos et al., 2014).
According to the Portuguese study of Santos et al. (2018), only 14.4% of the
adolescent girls and 46.3% of the adolescent boys had healthy cardiorespiratory
fitness levels.
First, this master dissertation investigated the relationship of physical activity (MVPA)
with cardiorespiratory fitness. This master thesis adds to the consistent evidence for a
positive relationship with cardiorespiratory fitness.
Second, this master dissertation investigated the relationship of sedentary behaviour
with cardiorespiratory fitness. Studies about sedentary behaviour are still scarce and
inconsistent. In this master thesis, results show a negative relation between sedentary
behaviour and cardiorespiratory fitness. Although, when taking MVPA into account,
sedentary behaviour had no longer a significant relationship with cardiorespiratory
fitness.
At last, the relationship between the combined variable of sedentary
behaviour/physical activity with cardiorespiratory fitness was investigated. Literature
up till now is scarce and results inconsistent. In this master thesis the combined
variable sedentary behaviour and physical activity (MVPA) had a significant
relationship with cardiorespiratory fitness. Portuguese adolescents who are low
sedentary and active, high sedentary and active or low sedentary and inactive had
52
higher odds of being fit than Portuguese adolescents that were high sedentary and
inactive.
The results of the One-Way ANOVA showed that the mean cardiorespiratory fitness
levels tend to be higher with adolescents who are active, and therefore meet the
international guidelines of the WHO (2018a), independent of sedentary behaviour.
In the end, this master thesis concludes that, when wanting to improve the
cardiorespiratory fitness levels of adolescents, the focus should be on promoting
MVPA (to the point where adolescents meet the WHO guidelines of physical activity).
MVPA is therefore an important aspect within public health. Furthermore, research
investigating the relationship and clarifying the role of sedentary behaviour in
maximizing cardiorespiratory fitness levels in adolescents is necessary to formulate
appropriate health messages.
53
7 References
ActiGraph, LLC. (2018). actigraph wGT3X-BT. Retrieved on the 18th of May 2018 from