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SCIENTIFIC JOURNAL OF SPORT AND PHYSICAL EDUCATION
UDC 796.011
FACULTY OF SPORT AND
PHYSICAL EDUCATION
UNIVERSITY OF SARAJEVO
FACULTY OF SPORT AND
PHYSICAL EDUCATION
UNIVERSITY OF SARAJEVO
FACULTY OF SPORT AND
PHYSICAL EDUCATION
UNIVERSITY OF SARAJEVO
VOLUME 18 – ISSUE 1 – JUNE, 2016.
HOMO SPORTICUS ISSUE 1 2016
1
HOMO SPORTICUS ISSUE 1 2016
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ISSN: 1512 – 8822 (print)
ISSN: 1840 – 4324 (online)
UDC 796.011
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Faculty of Sports and Physical Education
University of Sarajevo
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Husnija Kajmović
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(BiH), Almir Mašala (BiH), Mensura Kudumović (BiH), Safet Kapo (BiH),
Siniša Kovač (BiH), Muhamed Tabaković (BiH), Asim Bradić (BiH),
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Contents
Ekrem Čolakhodžić, Izet Rađo,Nijaz Skender, Safet Kapo and Faris Rašidagić
DIFFERENCES IN THE DEVELOPMENTAL RATES OF VENTILATION CAPABILITIES
BETWEEN BOYS WHO PLAY FOOTBALL AND BOYS WHO DO NOT PLAY FOOTBALL
Jožef Šimenko and Damir Karpljuk
CAN SPECIAL JUDO FITNESS TEST BE USED TO DETECT ASYMMETRIES
IN MOVEMENT PATTERNS?
Omer Osmanović
IMPACT OF PUBLIC FINANCING FROM THE BUDGET OF BOSNIA AND HERCEGOVINA
ON AWARDING OLYMPIC MEDALS IN COMPARISON TO EU MEMEBERS AND EU
CANDIDATES
Adnan Ademović
DIFFERENCES IN THE QUANTITY AND INTENSITY OF PLAYING IN ELITE SOCCER
PLAYERS OF DIFFERENT POSITION IN THE GAME
Adriana Ljubojević, Saša Jovanović, Radomir Zrnićand Lejla Šebić
ZUMBA FITNESS CARDIO EXERCISE: THE EFFECTS ON BODY FAT MASS REDUCTION
OF WOMAN
Almir Mašala, Haris Alić, Damira Vranešić - Hadžimehmedović and Eldin Jelešković
ANALYSIS OF FACTORS OF A MARKET WHICH AFFECT USAGE OF BASIC
COMPLEMENTARY SPORT PRODUCTS THROUGH APPLIANCE OF FUNCTION
OF RESEARCH OF A MARKET
Instructions for autors
7
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18
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Volume 18 – Issue 1 – June, 2016.
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HOMO SPORTICUS ISSUE 1 2016
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IN MEMORIAM
1968. - 2016.
PhD Senad Turković, associate professor
Faculty of Sport and Physical Education University of Sarajevo
HOMO SPORTICUS ISSUE 1 2016
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Ekrem Čolakhodžić1, Izet Rađo2, Nijaz Skender3, Safet Kapo2 and Faris Rašidagić2
DIFFERENCES IN THE
DEVELOPMENTAL RATES OF
VENTILATION CAPABILITIES BETWEEN
BOYS WHO PLAY FOOTBALL AND
BOYS WHO DO NOT PLAY FOOTBALL
1 Teaching faculty University „Džemal Bijedić“ of Mostar, Bosnia and Herzegovina
2 Faculty of Sport and Physical Education University of Sarajevo, Bosnia and Herzegovina
3 Pedagogical faculty, University of Bihać, Bosnia and Herzegovina
Original scientic paper
UDC: 616.24-072: 796.332.015-055.15
Abstract
The aim of this research was to determine the differences in the rate of development of ventilation capabilities in 12 to 15-year-old boys
who systematically train football, and boys in the same age group who are not involved in any sports. The research included 40 football
players and 40 non-players, divided into chronological age groups U12, U13, U14, and U15. Ventilation capabilities are represented
with 10 parameters: forced vital capacity, forced vital capacity in percentage predicted, volume of air exhaled during the rst second,
volume of air exhaled during the rst second in percentage predicted, Tiffeneau index, percentage of the predicted Tiffeneau index,
forced expiratory ow, forced expiratory ow 25-75% of FVC, forced expiratory ow 75-85% of FVC, and maximum expiratory ow.
The difference between the arithmetic mean of groups and the development rate of the variables is determined by one-way analysis of
variance (ANOVA). Non-players have a higher and faster growth in value for most variables until the age of 13, followed by a gradual
stagnation and decline in values, while football players have a more balanced growth in values until the age of 13, and they experience
the maximum in development at 14 years of age, after which it begins to decline slightly. The obtained values conrm that boys who
are participating in systematic sport training have a continuous rate of development of pulmonary ventilation, and have greater capacity
values, as well as better airway patency.
Key words: spirometry, football, growth and development, training
Introduction
Developmental changes at the age of 12-15 are hetero-
geneous, and the development of organ systems does
not follow a homogenous pattern either, as this is a pe-
riod of rapid growth and development, with an increased
sensitivity to all types of stimuli. Unlike the mature age,
childhood and adolescence are periods of the most rapid
development of the lungs and lung capacities, as well as
the pulmonary ventilation. At this stage of development, the
increase in lung capacity, the number of alveoli, their size,
as well as the volume of the chest and muscle strength
are signicant (Gautlier & Zinman, 1983; Grivas, Burwell,
& Purdue, 1991; Quanjer et al., 2010).
During this period of child development, the inability to rap-
idly adapt to the specic functions of certain organ sys-
tems becomes prominent, and this developmental period
is susceptible and sensitive to changes which may occur
under the inuence of adequate training process and thus
move in a positive direction.
Active children of both sexes involved in physical activity
and sports training generally have a higher aerobic en-
durance and higher levels of a number of functional mo-
tor skills, especially endurance and running speed. This
nding is supported by many studies comparing active
and inactive children (Beunen et al.,1992; Mirwald et al.,
1981). Muscle activity is related to the transformation of
chemical energy into mechanical energy and heat, while
the energy for muscle contraction is delivered by the trans-
port system, which means that the increased activity of the
locomotor system is directly associated with an increased
activity of the internal organs. Lung activity is increased
due to an increased need for oxygen, and the increase in
blood circulation is necessary for the increased transport
of oxygen, so the heart must work faster and with more
power.
The lungs are developing rapidly, rate of respiration de-
creases and it becomes deeper, while the frequentional
HOMO SPORTICUS ISSUE 1 2016
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ability of the respiratory apparatus is gradually approach-
ing adult capabilities. As part of the further development
of the respiratory system, its functions are changing as
well. The vital lung capacity increases, and the value of
maximum pulmonary ventilation increases more rapidly in
girls, so that at the age of 12-13 it amounts to 69.8 - 80 l/
min in girls, and around 74.5 - 83.6 l/min in boys (Mišigoj-
-Duraković, 2008; Medved et al. 1989).
Absolute oxygen uptake (VO2max) in girls ranges from 0.97
l/min at the age of 8, and has a constant upward trend until
the age of 18, when it reaches the value of 2.15 l/min. In
boys, these values range from 1.12 l/min at the age of 8,
with a constant upward trend until the age of 18, when they
reach the value of 3.32 l/min (Medved et al., 1989). More
effective ventilation is observed in people who are much
better adapted to physical effort through systematic exer-
cise. Various sports differently affect the development of
respiratory function. Aerobic training has the greatest inu-
ence on the increase in vital capacity, while the anaerobic
stimuli most signicantly affect the increase in velocity of
the air (Diniz Da Silva, Bloomeld, & João, 2008).
The role of the respiratory system in sports activities is
related to the increased transport of oxygen and carbon
dioxide, as well as the maintenance of acid-base bal-
ance by controlling the concentration of carbon dioxide.
Ventilation increases and accelerates in order to facili-
tate a faster exchange of gases during physical activ-
ity, both pulmonary and alveolar. Similarly, the ow of
blood through the lesser circulatory system accelerates,
which leads to a faster exchange of gases. This change
and the response of the respiratory system to the ap-
propriate load is the same in children and adult athletes,
with the only difference being in the level of quantitative
expression.
Absolute values of ventilation parameters and their growth
rate from the ages of eight to eighteen continuously in-
crease in value (Mišigoj-Duraković, 2008; Stanojevic et
al., 2008). Forced vital capacity (FVC) and forced expir-
atory volume in one second (FEV1) in boys have a ten-
dency to increase continuously up to the age of 18 when
they reach three times the value they had at the age of 8.
Tiffeneau index in boys has a continuing decline between
the ages of 8 and 18. Forced ow in the middle of ex-
piration, between 25% and 75% of forced vital capacity,
and forced ow between 200ml and 1200ml, gradually
increase throughout the years, which is consistent with
their sensitivity to changes in the greater airways and de-
pendence on the effort exerted during exhalation (Miši-
goj-Duraković, 2008; Medved et al., 1989; Stanojevic et
al., 2008). Physical strains that require high respiratory
minute volume stimulate the growth and development of
the chest wall in young athletes, thus making the chest
wider, longer, and increasing its capacity. This is the man-
ner of developing “sport lungs” with greater air volume,
as well as greater blood volume and increased surface of
the alveoli.
Sport activity, including football practice, strengthens and
leads to hypertrophy of the respiratory muscles, and to a
more cost-effective breathing at lower frequencies, which
results in an increase in the patency of the airways, and an
increase in the ventilation function of the lungs. The aim
of this study is to determine the difference in the rate of
development of ventilation in boys who are involved in sys-
tematic football training and boys who are not.
Methods
Participants
Participants sample consisted of 80 participants from a
population of boys aged 12-15 from the city of Mostar
(Bosnia and Herzegovina). Participants were football play-
ers (junior football players of FC Velež Mostar, who trained
for at least three years), comprising four age categories
U12 (n = 10), U13 (n = 10), U14 (n = 10), U15 (n = 10),
and non-players of the same age (n = 40) who did not
engage in systematic sport activities, but have regularly
attended physical education classes at school: U12 (n =
10), U13 (n = 10), U14 (n = 10), U15 (n = 10).
Variables
Variables were represented by 10 parameters for assess-
ing the capability of ventilation and airway patency: FVC
(litre) - forced vital capacity, FVC% - forced vital capacity in
percentage predicted, FEV1(litre) - volume of air exhaled
during the rst second, FEV1% - volume of air exhaled dur-
ing the rst second in percentage predicted, FEV1/FVC -
Tiffeneau index, FEV1/FVC% - percentage of the predicted
Tiffeneau index, FEF (l/min) - forced expiratory ow, FEF25-
75 - forced expiratory ow 25-75% of FVC, FEF75-85- forced
expiratory ow 75-85% of FVC, and PEF (l/min) - maxi-
mum expiratory ow.
Procedures
Ventilation parameters were assessed using a spirometer
SPIROVIT SP1 (Schiller AG, Switzerland). Data on age,
height, and weight was collected in order to compare the
results with individual predictive values. Calibration of the
spirometer was performed based on external factors (room
temperature, air pressure, relative humidity), in order to ap-
ply the correction of the measured volume with regards to
standard conditions (BTPS standard) (Miller et al., 2005).
Statistical analysis
The results were processed using the software package
IBM SPSS 21.0 for Windows. One-way analysis of vari-
ance (ANOVA) was used to determine the difference be-
tween arithmetic means of groups, as well as the rate of
development of the observed variables. The development
rate of certain variables is shown graphically.
HOMO SPORTICUS ISSUE 1 2016
9
Results
Height and weight variables were assessed in order to de-
termine whether the sample of participants falls within the
parameters for the chronological age group, as well as to
compare the results with individual standards (predictive
values for age, body height, and weight of the participants).
Both groups have similar values, except for the nding that,
on average, children who are involved in the process of
training are taller and lighter (Table 1). In boys who are not
football players, the growth in height is accelerated starting
at the age of 12-13. After the age of 13, there is a slight
stagnation of growth, while the mass constantly increases
until the age of 14, when it starts decreasing in value. In
football players, the increase in height is continuous until
the age of 14, when it rapidly increases by about 10 cm
until the age of 15. Body mass in this group of boys experi-
ences a slight decline until the age of 13, when it gradually
increases until the age of 15. The height and weight of all
boys who participated in this study is at a level that corre-
sponds to their chronological age.
By comparing the values of arithmetic means of these
variables with the values of previous research (Malina,
1993; Mišigoj-Duraković, 2008), Čolakhodžić, Skender,
& Pistotnik, 2011; Śliwowski et al., 2011), we see that
these boys have similar values of arithmetic means of both
variables, with slightly increased values of body height in
football players. The values of basic descriptive param-
eters of ventilation capabilities shown in Table 1 (FVC,
FVC%,FVC1, FVC1%, Tiffeneau index, FEF25-75, FEF 75-85 ,
and PEF) ) show that both groups of boys experience an
increase in values of ventilation parameters until 13 years
of age, with non-players having a somewhat greater value
of the arithmetic means of ventilation parameters in com-
parison to football players. After the age of 13, there is a
mild stagnation in the values of parameters in the group
of non-players, and a sharp increase in these values in
the group of football players, when the latter experience
a continued increase in ventilation capacity of up to 117%
of the predicted value of FVC in the 14th year of life. After
the age of 14, FVC% of non-players declines to 98% of the
predicted value for this age, while it reaches the value of
109.40% in the group of football players. The value of the
Tiffeneau index decreases continuously from the age 12-
15 in non-players, and amounts to 103% of the predicted
value at the age of 15. This value is stable in the group of
football players, ranging from 105.20%, to 109% it meas-
ures at the age of 15. Similar values for 15-year-old foot-
ball players (Erceg, Jelaska, & Maleš, 2011). The values
of the variables FEF (forced expiratory ow) and PEF (peak
expiratory ow) showed a constant increase starting at the
age of 12-15 for both groups of subjects, but with higher
values in the group of players (FEF = 7.26 and = 8.40 PEF
at the age of 15).
Variables Group Footballers Non footballers
N Mean Std.Dev. N Mean Std.Dev.
AVIS U 12 10 156.10 6.78 10 158.80 8.67
U 13 10 166.40 8.59 10 163.70 9.20
U 14 10 165.50 13.12 10 167.20 8.14
U 15 10 173.10 7.78 10 176.60 6.25
AMAS U 12 10 42.70 8.16 10 47.30 7.36
U 13 10 54.10 10.69 10 46.20 8.03
U 14 10 61.40 15.49 10 54.50 9.21
U 15 10 59.50 12.28 10 59.70 5.65
FVC U 12 10 2.90 .52 10 2.98 .71
U 13 10 4.05 1.38 10 3.38 .83
U 14 10 4.26 1.25 10 4.67 1.18
U 15 10 4.47 1.11 10 5.18 1.04
FVC% U 12 10 92.90 15.68 10 91.10 17.38
U 13 10 103.70 23.42 10 93.10 15.15
U 14 10 104.10 20.93 10 117.50 24.95
U 15 10 98.00 14.61 10 109.40 21.55
FEV1 U 12 10 2.75 .51 10 2.83 .69
U 13 10 3.84 1.43 10 3.15 .82
U 14 10 3.92 1.27 10 4.47 1.06
U 15 10 4.03 .91 10 4.94 .98
FEV1% U 12 10 98.40 18.41 10 95.00 15.17
U 13 10 110.50 29.20 10 97.50 16.82
U 14 10 109.20 25.08 10 126.10 25.91
U 15 10 100.80 15.61 10 118.60 23.26
Table 1. Descriptive parameters of variables
HOMO SPORTICUS ISSUE 1 2016
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Analysis of variance (Table 2) tested whether groups of
participants belong to the same population, the F-test ex-
amined the statistical signicance of differences between
groups of variables and variables within the group, i.e. the
signicance of the difference between intergroup and intra-
group variance, and the growth rate of certain variables was
shown in graphs. If we examine the values of the F-test and
their statistical signicance among non-players, we can see
that most of the variables (FVC%, FEV1%, FEV1/FVC, FEV1/
FVC%, FEF, FEF25-75, FEF75-85, PEF) do not have a statisti-
cally signicant F-test value. These results clearly show that
there is no statistically signicant difference in the arithme-
tic means of these variables, i.e. that the participants in the
group practically belong to the same population.
Signicantly different variables between the groups are:
body height (AVIS), body weight (AMAS), forced vital ca-
pacity (FVC), and forced vital capacity in the rst second
(FVC1). In the group of football players, we notice that only
two variables, Tiffeneau index (FEV1/FVC) and percentage
of the predicted Tiffeneau index for this age group (FEV1/
FVC%), do not have a statistically signicant F-test value;
all other variables have the level of signicance of p=.01
or p=.05.
These results clearly show that there is a statistically signif-
icant difference in the arithmetic means of these variables
by groups of participants, i.e. that the groups of partici-
pants do not belong to the same population. Comparing the
results of the groups, a clear difference is observed in the
growth rate of the majority of variables, from which we can
conclude that playing football inevitably leads to the de-
velopment of functional or ventilation capabilities in boys.
Rates of growth and development of certain variables in
both groups, demonstrate that forced vital capacity (FVC)
in non-players has a growth curve slope with the highest
rate of increase from the age of 12-13, after which there is
a mild stagnation of this variable. The FEV1 variable, which
represents the forced expiratory volume of the participant
measured in the rst second and shown in litres (l), also
has a positive growth trend, especially in the period of 12-
13 years of age, when the rate of increase is the highest ad
amounts to 1.1 litres. After the age of 13, there is a sudden
decrease in this value and the curve has a gentler slope
until 15 years of age.
This is reected in the percentage value of the variable in
relation to the predicted value, and therefore the curve for
FEV1% experiences a sudden drop after the age of 14.
Tiffeneau index (FEV1/FVC) in boys who do not play foot-
ball has a constant relationship between the obtained and
predicted value from the age of 12-13, followed by a slight
decline in value until the age of 15, when it reaches 103%
of the predicted value. In the group of football players,
these values decrease between the ages of 12 and 13, af-
ter which they steadily increase until the age of 15, when
they reach 109% of the predicted value for that age group.
Forced expiratory ow (FEF) variable has the highest value
growth from the age of 12-13 in the group of non-players,
followed by a gradual decline in the value until 15 years
of age, while the largest growth in football players occurs
between 13 and 14 years of age (2.57), when it reaches
the maximum value (7.54).
This is followed by a mild stagnation of growth. These re-
sults clearly show that reduced sport activity or inactivity
FEV1/FVC U 12 10 94.52 5.22 10 95.21 6.69
U 13 10 93.96 7.55 10 93.20 4.65
U 14 10 92.01 7.93 10 95.00 3.36
U 15 10 91.10 6.13 10 95.44 4.30
FEV1/FVC% U 12 10 106.00 5.90 10 106.30 7.70
U 13 10 106.00 8.47 10 105.20 5.49
U 14 10 104.30 8.69 10 107.60 3.86
U 15 10 103.50 7.09 10 109.00 4.98
FEF U 12 10 4.71 1.29 10 4.48 1.23
U 13 10 6.20 2.02 10 4.97 1.14
U 14 10 6.11 1.60 10 7.54 2.52
U 15 10 6.07 1.03 10 7.26 1.54
FEF 25-75 U 12 10 4.18 1.26 10 3.45 .99
U 13 10 5.03 1.70 10 3.95 .86
U 14 10 4.87 2.05 10 6.12 1.78
U 15 10 4.72 1.02 10 6.08 1.36
FEF 75-85 U 12 10 2.87 1.11 10 2.04 .72
U 13 10 3.01 .99 10 2.42 .66
U 14 10 2.84 1.11 10 3.40 1.35
U 15 10 2.79 .72 10 3.60 .90
PEF U 12 10 5.16 1.42 10 5.03 1.28
U 13 10 6.86 2.68 10 5.37 1.21
U 14 10 6.89 2.14 10 8.64 2.95
U 15 10 6.83 1.36 10 8.40 1.84
HOMO SPORTICUS ISSUE 1 2016
11
leads to a decline in the air ow during expiration. Figure 8.
shows the growth rate of the variable that represents maxi-
mum expiratory ow (PEF). Predicted value of this variable
should have a continuous ow from the age of 12-15.
However, with regards to this variable as well, a positive
trend is observed only from the age of 12-13 in non-play-
ers, and 13-14 in football players, after which it begins to
stagnate and decline in value until the age of 15 (6.83 vs.
8.40 ).
Discussion
Results of this research show that participants have no ob-
structions in airway patency, as the indicators of ventilation
capabilities vary within a normal range. The development
rate of the main ventilation parameters for boys at this age
demonstrates a clear difference between the groups, which
is in favour of boys who play football and can certainly be
attributed to the sports activities and improved ventilation
capacity through systematic training. The difference is evi-
dent in nearly all variables, which undoubtedly proves that,
as children grow and develop, functional capabilities of
inactive children experience a gradual stagnation in com-
parison to children who practice football. If we compare
the values of the basic variables of ventilation - FVC, FVC1,
Tiffeneau index and FEF25-75 - with the results of previous
research (Mišigoj-Duraković, 2008; Ziaee et al., 2007),
we will see that our participants have increased values in
the group of players, while the boys who are not involved
in any sports activities have reduced values, but they fall
within the normal range for that age group.
The development trend of forced vital capacity (FVC) vari-
able in boys who play football has a different growth curve
slope compared to boys who do not play football. In the
group of football players, the highest growth occurs from
the age of 13-14, while the highest increase in value in the
group of non-players was between the ages of 12 and 13.
At the age of 14, we observe the highest value of the forced
Variables
Footballers Non footballers
df Mean
Square
F Sig. df Mean
Square
F Sig.
AVIS 3 489.09 5.54 .003 3 565.35 8.51 .000
36 88.18 36 66.38
39 39
AMAS 3 706.62 4.94 .006 3 404.15 6.85 .001
36 142.99 36 58.95
39 39
FVC 3 4.91 3.92 .016 3 10.88 11.74 .000
36 1.25 36 .92
39 39
FVC% 3 281.62 .77 .514 3 1635.42 4.04 .014
36 361.60 36 404.85
39 39
FEV1 3 3.53 2.95 .045 3 10,27 12.50 .000
36 1.19 36 .82
39 39
FEV1% 3 362.62 .70 .557 3 2374.86 5.50 .003
36 516.33 36 431.55
39 39
FEV1/FVC 3 25.93 .56 .644 3 10.49 .43 .729
36 46.24 36 24.10
39 39
FEV1/FVC% 3 15.76 .27 .846 3 26.95 .83 .484
36 58.12 36 32.33
39 39
FEF 3 5.04 2.14 .112 3 24.40 8.43 .000
36 2.35 36 2.89
39 39
FEF25-75 3 1.36 .55 .646 3 19.59 11.51 .000
36 2.44 36 1.70
39 39
FEF75-85 3 .08 .08 .968 3 5.72 6.32 .001
36 .99 36 .90
39 39
PEF 3 7.17 1.83 .159 3 37.02 9.69 .000
36 3.91 36 3.81
39 39
Table 2. One-way analysis of variance (ANOVA)
HOMO SPORTICUS ISSUE 1 2016
12
vital capacity in percentage predicted (FVC%) variable in
the group of football players, in the amount of 117% of the
predicted value, followed by a slight decline in the value
from the age 14 -15. Similar results were obtained by (Me-
dved et al., 1989); Erceg, Jelaska, & Maleš, 2011) for the
population of boys in Croatia, on a sample of senior play-
ers. Slightly lower values were obtained for the sample of
274 boys, aged from 11 to 16, taken from a population of
primary and secondary school students from Benin (Mes-
san et al., 2013). A similar look and slope of the curve is
observed for variables that determine forced vital capacity
in the rst second (FEV1, FEV1%), which clearly shows
that boys who systematically train, develop their ventila-
tion skills in a more consistent manner, achieve maximum
ventilation later than their counterparts, and have a higher
value of these parameters. The FEV1 (volume of air exhaled
during the rst second) variable has a positive growth trend
in the group of football players, especially in the period
from the age of 13-14, when the growth rate is the high-
est and amounts to 1.3 litres, after which the curve has a
somewhat gentler slope until the age of 15. In the group of
non-players, the highest growth is observed from the age
of 12 to 13, after which the value stagnates.
This is reected in the percentage values of this variable in
relation to the predicted value, and therefore the curve of
FEV% has the highest growth rate from the age of 13-14,
and experiences a sudden drop starting at the age of 14,
until the age of 15.
In terms of these variables as well, football players have
higher observed values and achieve the highest growth
slightly later than the boys who do not play football. Tiff-
eneau index (FEV1/FVC, FEV1/FVC%) in this group of par-
ticipants shows that participants who are 12-13 of age
have a constant ratio of the obtained and predicted value,
followed by a slight decline in value until the age of 15
when it amounts to 103% of the predicted value.
Unlike non-players, who have a sudden increase in the val-
ue from the age of 12-13, football players experience a
continuous growth rate with the largest increase in value
from the age of 13-14. After the age of 14, there is a slight
reduction in the curve slope of growth and decline of value
until the age of 15.
The growth curve slope of variables that dene forced ex-
piratory and maximum expiratory ow, i.e. the speed of
exhalation (FEF, PEF) shows similar inclinations as the pre-
vious variables, with the highest growth rate from the age
of 12-13 in non-players, and 13-14 for football players.
Conclusion
Based on the obtained results, it was determined that boys
who play football have a different rate of growth and devel-
opment of ventilation capabilities compared to boys who
do not play football. By comparing the value of the arithme-
tic means and the growth curves of variables, we noticed a
signicantly different slope and position of the point of ec-
tion of the curve. It was determined that non-players have
higher values and a faster growth of ventilation capabilities
until 13 years of age, followed by a gradual stagnation and
decline in value below 100% of the predicted value. Foot-
ball players have a balanced growth in values between the
ages of 12 and 15, with a steady slope of the growth and
development curve that is practically similar to the slope
of the curve of predicted development of the participants
in this age group, and achieves its maximum percentage
during the 14th year, after which it begins to gradually de-
cline to a value of 110% of the predicted value. It is evident
that both groups have values above 100%, which indicates
that they do not have obstructive disorders of pulmonary
ventilation. These values conrm the aim that there are dif-
ferences in the rate of growth and development of pulmo-
nary ventilation between boys who play sports and boys
who do not, and that these differences are in favour of the
former group. It is evident that boys who are engaged in
systematic sports activities have higher average values of
forced vital capacity, maximum speed exhalation, and total
pulmonary ventilation. These results can be attributed to
technology of training, because both groups belong to the
same population, and live in the same area, affected by the
same external factors.
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aspects of physical exercise. Zagreb: Kinesiology Faculty.
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Submitted: February 13, 2016
Accepted: June 02, 2016
Correspondence to:
Ekrem Čolakhodžić, PhD.
Teaching faculty University „Džemal Bijedić“ of Mostar
Bosnia and Herzegovina
E-mail: ekrem.colakhodzic@yahoo.com
HOMO SPORTICUS ISSUE 1 2016
14
Jožef Šimenko1 and Damir Karpljuk1
CAN SPECIAL JUDO FITNESS TEST BE
USED TO DETECT ASYMMETRIES IN
MOVEMENT PATTERNS?
¹Faculty of Sport, Department for combat sports, University of Ljubljana, Slovenia
Original scientic paper
UDC: 796.853.23.012
Abstract
Symmetry of movement actions expands sportsmens’ technical-tactical capacity and is considered as an element of greater versatility.
The aim of this study was to perform the Special Judo Fitness Test (SJFT) with young judokas on their dominant and non-dominant
side and to test whether SJFT could be used to asses’ differences in the movement patterns on the dominant and non-dominant body
side. The SJFT was used to compare movement patterns on the dominant and non-dominant body side on a sample of 9 young
judokas (age 13.78 ± 0.44, height 176 ± 3.8 cm, weight 74.2 ± 6.9 kg, years of training 7.78 ± 0.67). We used paired T-test and
set statistical signicance at p ≤ 0.05 to determine differences between movement symmetries and SJFT parameters. Main statistical
differences were found betwen performance of SJFT by both body sides 1) in second throwing phase of SJFT t(8) = 3.50, p = 0.008,
2) in total number of combined throws in SJFT t(8) = 2.83, p = 0.022, and 3) in nal SJFT INDEX t(8) = -2.57, p = 0.033. The
study presented with the results that SJFT could be used as a tool to detect movement asymmetries in judokas when performed on
both body sides and the importance of bilateral movement development of judokas. Also, the results of SJFT could be used to further
create normative values of SJFT for youth male judokas, since this would contribute to already known normative values of male and
female seniors and female youth categories.
Key words: judo, SJFT, youth sport, symmetry, functional laterality
Introduction
The leading movement coordination abilities of athletes
practicing combat sports are speed of adequate movement
reaction, ability to maintain balance and time and space ori-
entation. These abilities are, to a large extend, conditioned
genetically, but they are also subjected to evolution during
the training process (Markiewicz & Starosta, 2014). During
the process of training and normal motor developement
there is an occurance of laterality results in the dominance
of one side of the body over the other one (Bogdanowicz,
1992). In judo laterality is shown through different ghting
stances. In competitive judo lateral differences have per-
formance implications, where ghters with left-dominant
ghting stance are over-represented in competition results
from 1st-5th place when compared to results 7th or worse
(Tirp, Baker, Weigelt, & Schorer, 2014).
Judo is a complex sport that includes a large number of
factors, which affect the nal result of competitive perfor-
mance with intermittent high-intensity actions (Krstulović,
Sekulić, & Sertić, 2005; Dégoutte, Jouanel, & Filaire,
2003). For the throwing techniques to be effective, they
must be carried out at high speed, high power and with
motion control (Pulkkinen, 2001). One very important
factor for the minimum energy consumption in judo is the
effectiveness of implemented techniques. The pure tech-
nique execution is minimizing unwanted or unnecessary
movements, which unnecessarily increases energy con-
sumption during the ght. This feature is of great relevance
in judo, since the need for energy savings during the ght is
a key to success, especially during long and difcult ghts
(Drid, Trivić, & Tabakov, 2012).
The effectiveness of techniques and unnecessary move-
ments are less likely to appear in the judokas dominant
side, which is usually their right-side. This is because many
coaches are right-sided and young judokas receive more
instructions concerning the right side of the body during
regular trainings in their clubs. The transfer of movement
techniques from one side to the other increases athlete’s
effectiveness and enables him/her to win by taking the op-
ponent by surprise (Sterkowicz, Lech, & Blecharz, 2010).
Large, frequent one-sided training loads that inuences
athletes, and which are very frequent in judo during the
trainings of judoka’s special technique-tokui waza, may
also affect the body posture through different muscle pro-
portions (Šimenko & Vodičar, 2015).
Taking into consideration specic tehnical and motorical
aspects of judo, some specic tests have been developed,
of which the Special judo tness test (hereinafter SJFT) is
one of the mostly used evaluation tests in judo (Detanico
HOMO SPORTICUS ISSUE 1 2016
15
& Santos, 2012). This test asserts that the tested judoka
performs an ippon-seoi-nage throw in his/her dominant
side as fast as possible between two judokas (Sterkow-
icz, 1995). In literature, the SJFT test has been used only
once by Sogabe, Maehara, Sasaki, & Sterkowicz, (2015)
to evaluate the differences in dominant and non-dominand
body side. It was used for 9 male and 9 female Japanese
judokas, and it was observed that male judokas showed
signicantly better results in series A of SJFT with the do-
minant vs. non-dominant body side (p=0.030). Therefore,
the aim of this study was to test whether the SFJT could
be used also for young judokas, to assess the differences
in the movement patterns on their dominant and non-dom-
inant body sides.
Methods
Participants
the sample included nine young male judokas. Participants
of the study were 13.78 ± 0.44 years old, their height was
176 ± 3.8 cm, their weight was 74.2 ± 6.9 kg and only
one of them was left-hand dominant. They had been train-
ing judo for 7.78 ± 0.67 years. Eight of them had the 2.
kyu and one had the 1. kyu belt degree.
Sample of variables
Variables that were used for statistical analysis were
SJFT_15s_1 – rst part of 15 s SJFT, SJFT_30s_2 – sec-
ond 30 s part of SJFT, SJFT_30s_3 – third 30 s part of
SJFT, TT – total number of throws of rst, second and third
phase of SJFT, HR – heart rate immediately after 3rd phase
of SJFT, HR_1min – heart rate after 1 minute of SJFT, SJFT
INDEX – nal index of SJFT. All of variables were collected
for the test execution on dominant and on non-dominant
body side.
Data collection
The SJFT consists of an Ippon-seoi-nage throwing tech-
nique that has to be executed as fast as possible and as
many times as possible between two partners (6 meters
apart from one another) in three time periods of 15, 30
and 30 s, with 10 s rest intervals (Detanico & Santos,
2012; Sterkowicz,1995). Training started with a regular
25-minute warmup. After that a demonstration of the SJFT
was performed. Judokas performed the rst test with their
dominant side, as instructed by Sterkowicz (1995). The
non-dominant side was tested the following day with the
same warmup to ensure adequate regeneration between
tests. Heart rate was monitored by Polar FT80 heart rate
monitor and polar H2 chest belt. Time was measured by
regular stopwatch. SJFT index was calculated according to
the following formulae: (heart rate at the test pulse P1 + 1
min after the test P2) / total number of throws in all three
series N (Sterkowicz, 1995).
(P1 + P2) / N = INDEX
From the equation it is evident that the lower the index, the
better test result was achieved (Drid, Trivić, & Tabakov, 2012).
Figure 1. Positioning on tatami in SJFT (Drid, Trivić, & Tabakov,
2012).
Figure 2. Ippon-seoi-nage throw (Drid, Trivić, & Tabakov, 2012).
Statistical analysis
Data was analyzed using the SPSS 21.0 software for Win-
dows. We used descriptive statistics to analyze our sample.
In order to determine the differences in movement sym-
metries and in SJFT parameters, we used paired T-test, for
which the statistical signicance was set at p ≤ 0.05.
Results
In Table 2 it is shown that there are statistical differences
in movement patterns on the dominant and non-dominant
body side, which resulted in the differences in nal SJFT
index (TT_D, TT_ND) t(8) = -2.57, p = 0.033. The main
differences were noted in 2nd part of the SJFT, where the
number of throws statistically differed between body sides
t(8) = 3.50, p = 0.008 and in total number of combined
throws in SJFT (TT_D, TT_ND) t(8) = 2.83, p = 0.022.
First variable of SJFT, which represents rst 15 s of throw-
ing (SJFT_15s_1_D, SJFT_15s_1_ND), was not statisti-
cally signicant t(8) = 2.00, p = 0.081, but it is very close
to the p ≤ 0.05 limit. The rest of the variables are not
statistically signicant.
Table 1. Descriptive statistisc of the sample
Variables N MEAN SD MIN MAX
AGE 9 13.78 .441 13 14
YEARS_OF_PRACTICE 9 7.78 .667 7 9
HEIGHT 9 171.49 8.70 158.50 184.80
WEIGHT 9 64.48 6.54 54.00 76.00
HOMO SPORTICUS ISSUE 1 2016
16
Discussion
Our ndings showed that the execution of Ippon-seoi-nage
throw in SJFT differed in judokas when it was performed in
the dominant and non-dominant body side. The test results
were signicantly better when the SJFT was performed in
the dominant side in the second 30 s part of the SJFT, in
the total number of throws and in the nal SJFT index. The
performance of SJFT compared between dominant and
non-dominant body side was also signicantly better, as
it was shown in results of the rst 15 s series of SJFT in
male Japanese Judokas (p=0.030), but was not statisti-
cally different in total number of throws and in the nal
SJFT index (Sogabe et al., 2015).
We can nd one potential reason for greater number of suc-
cessful throws on the dominant vs. non-dominant side in
high energy phosphate-PCr concentration in the main skel-
etal muscles that are involved in a given throw, i.e. higher
PCr concentration in the muscles of the dominate side. On
the one hand, Sogabe et al. (2015) said that difference in
PCr cannot be accountable for the observed difference be-
cause Zoladz et al. (2007) showed that PCr concentration,
as well as phosphorylation potential, was similar at rest
in dominant vs. non-dominant legs in athletes. However,
this may not apply for young athletes, since PCr and gly-
cogen concentrations increase with age, at least in boys
aged 11–15 years (Bergeron et al., 2015). Therefore, we
could assume that for our judokas’ PCr levels were lower,
and consequently, the possibility of higher PCr concentra-
tion in the muscles in the dominate side cannot be ruled
out in youth athletes. We could assume that if there is a
difference in movement patterns, there would also be a dif-
ference in intensity (i.e. in our case difference in heart rate
variables). But these heart rate variables did not statisti-
cally differ for performance in the dominant vs. non-domi-
nant side. Therefore, from a standpoint of physical exertion
movement patterns, the performance in the non-dominant
side of our judokas did not represent any additional intensi-
ty compared to the performance in dominant side. Hence,
the difference should be in the motor learning segment.
The possible explanation for perceived differences in the
SFJT index could be in higher frequent one-sided train-
ing loads in youth judokas, which are shown as greater
muscle efciency of throw movement on the dominant side
(Sogabe et al., 2015; Šimenko & Vodičar, 2015). Mikheev
et al. (2002) repor ted that high-skilled judokas showed
increased motor functions symmetr y in
a
r
m
and leg move-
ments. Therefore, when we are taking into consideration the
coaching process of young judo athletes, early
perfor-
mance
specialization is unacceptable (Sterkowicz et al.,
2010). Observations done by Hicks and Soames (2001),
of then eminent judo competitors such as A. Parisi, T.
Koga, J. Pawłowski, and U. Werbrouck showed that during
a contest they assumed the right or left stances and that
they were able to execute the same throws, symmetrically
to the right and the left side. Importance of evolving youth
judokas movement patterns to both body sides (Šimenko,
2012) and use of both left and right ghting stance (Adam,
Laskowski, & Smaruj, 2012) is there even greater from a
technical-tactical point of view.
Additionally, from a medical point of view, frequent on-
sided training could cause possible body asymmetries
that may lead to severe physical consequences making it
easier for the occurrence of injuries (Stradijot, Pittorru, &
Pinna, 2012). According to Šimenko and Karpljuk (2015),
it is necessary to carry out more SFJT tests on youth male
judokas to create separate normative values to enable opti-
mal development. Youth judokas cannot be compared with
adult judokas, and thus to compare their performance in
relation to the current senior male normative classication
table would be mistaken. Therefore, results of this study
could also be used in future to create normative values
of SJFT for youth male judo competitors, since they are
known for male and female seniors and female youth cate-
gories (Sterkowicz-Przybycien & Fukuda, 2014; Franchini,
Boscolo Del Vecchio, & Sterkowicz, 2009).
Conclusion
The study presents the usefulness of the SJFT as a tool
to detect movement asymmetries in youth judokas when
performed in both body sides. It highlights the importance
of bilateral movement development of youth judokas from
a technical-tactical point of view, as they will have stron-
Group
DOMINANT SIDE NON-DOMINANT SIDE 95% CI
Variables Mean SD Mean SD Lower Upper df t p
SJFT_15s_1 5.00 0.50 4.67 0.50 -0.05 0.72 8 2.00 .081
SJFT_30s_2 8.89 0.93 8.11 0.78 0.27 1.29 8 3.50 .008
SJFT_30s_3 8.22 0.67 8.00 0.50 -0.29 0.73 8 1.00 .347
TT 22.11 1.83 20.78 1.56 0.25 2.42 8 2.83 .022
HR 191.22 6.00 191.78 4.99 -2.96 1.85 8 -.53 .609
HR_1min 163.89 12.05 163.56 9.25 -8.79 9.46 8 .084 .935
SJFT INDEX 16.17 1.63 17.19 1.38 -1.93 -0.10 8 -2.57 .033
Table 2. Differences between selected morphological variables to determine body symmetries
p ≤ 0.05 / SJFT_15s_1 – rst part of SJFT, SJFT_30s_2 – second part of SJFT, SJFT_30s_3 – third part of SJFT, TT – total number of throws in rst, second
and third phase of SJFT, HR – heart rate immidieatly after 3rd phase of SJFT, HR_1min – heart rate after 1 minute of SJFT, SJFT INDEX – nal index of SJFT.
HOMO SPORTICUS ISSUE 1 2016
17
ger and wider foundations to develop themselves into elite
adult athletes. Also, the importance of future SJFT tests in
youth male categories are necessary, as they will serve in
creation of normative values of SJFT for youth male judo
competitors.
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Submitted: December 17, 2015
Accepted: June 11, 2016
Correspondence to:
Jožef Šimenko, Associate Expert
Faculty of Sport, Department for combat sports, University
of Ljubljana,
Gortanova ulica 22, 1000 Ljubljana, Slovenia
E-mail: jozef.simenko@fsp.uni-lj.si
HOMO SPORTICUS ISSUE 1 2016
18
Omer Osmanović1
IMPACT OF PUBLIC FINANCING
FROM THE BUDGET OF BOSNIA
AND HERZEGOVINA ON AWARDING
OLYMPIC MEDALS IN COMPARISON TO
EU MEMEBERS AND EU CANDIDATES
1PBS Bank Sarajevo, Bosnia and Herzegovina
Original scientic paper
UDC: 336.14:796 (497.6)
Abstract
The Olympic Committee and spor t organizations in Bosnia and Hercegovina function mainly as associations of citizens. Therefore,
information about nancial model and amount of nancing of programs they deal with are rather heterogeneous without exact data.
Adequate legal framework regulating sports is a starting basis for its efcient public nancing system. The goal of this research is to
determine to which extent the public nancing from the budget of Bosnia and Hercegovina impacts achievement of excellent sport
results, such as winning of the Olympic medal, in comparison to the EU countries and EU candidates. The research sample include
Bosnia and Herzegovina and eight EU member states (Austria, Germany, Slovenia, Denmark, the Czech Republic, Hungary, Croatia
and Sweden), as well as Serbia as the EU candidate country. The sample used involves: number of population, country budget, sport
grant, GDP, grant for sport per resident, grant for sport compared to the budget, Olympic medal per resident, Olympic medal compared
to the budget, Olympic medal compared to grant for sport, Olympic medal compared to GDP, all of those used as predictive indicators,
including the number of medals awarded at Summer Olympic Games, at Winter Olympic Games and total number of medals won at the
Olympic Games. This research includes period from 2008 to 2014. Regression analysis used here showed that the group of predictive
indicators, which involves the amount of budget and amount of assigned nancial means for sports, as well as the number of popula-
tion, has signicant impact on the number of the Olympic medals won. Based on the research results, it is recommended that Bosnia
and Herzegovina harmonizes its legal regulations which could provide efcient and effective nancing of sport, thus creating a positive
effect on achieving excellent sport results..
Key words: public administration, budget, grant for sport, excellent results
Introduction
Key problem in nancing of sports, beside low assignment
of funds, is unbalanced criteria system to be used for sport
nancing. In EU countries, which achieve excellent results,
there are sport systems in which legal and nancial legisla-
tion is precisely dened. Croatia, for instance, has a similar
state structure as B&H, having its line ministry nanced
sport at annual level in amount from 92,84% during 2010
to 99,12% in 2008. In this period, the Ministry of Science,
Education and Sports of the Republic of Croatia nanced
public demands in sport, which relate to functioning of
Croatian Olympic Board and national sport associations,
Croatian Special Olympics Board, Croatian Deaf Sports
Association, Croatian School Sports Federation and Croa-
tian Academic Sports Federation (Bronić et al., 2012). In
Croatia, criteria is very precisely set and it unambiguously
and clearly presents to whom the funds of budget pay-
ers are allocated, how they are controlled and what their
function is (Ministry of Science, Education and Sport of
the Republic of Croatia). Elite sport has quite often been
considered to be the main transmitter for articulating the
national pride and a tool to stimulate national cohesion and
correlation for increasing sports funds aiming at winning
more medals in order to rise national pride even more, (Van
Hilvoorde, Elling, & Stokvis, 2010).
Andreff & Szymanski (2006) have been researching a cor-
relation between the level of economic growth and sport
results made in large sport events such as the Olympics.
By researching the baselines for the Olympic success at
the state level (Bernard & Busse, 2004), they have also
researched the signicance of relation between population
and economic resources of a certain state in comparison
to the number of medals won. They found out that, al-
though on the margins, population and income per capita
create similar effects on winning the Olympic medals, thus
HOMO SPORTICUS ISSUE 1 2016
19
showing that there is a link between population and high
GDP per capita in terms of achieving excellent results. De
Bosscher et al., (2009) analyzed relation between politics
system in the EU countries in terms of elite sports, i.e.
incoming and ow of nancing and success achieved
in international competition, i.e. outcome. Since the EU
countries as well as the EU aspiring countries, including
Bosnia and Herzegovina, are not immune to the problem
that sports face in terms of nancing, regardless the level
of country development measured by national income per
capita or other relevant indicators, taking into consideration
as well as resolving of these issues require participation of
the community in whole (Bartoluci & Škorić, 2009:31). In
order to increase effectiveness and efciency, B&H pub-
lic administration needs to be changed thoroughly and it
should result in a better and higher quality public service.
While identifying population with sport results, state struc-
tures by directing nancial means, support sport organi-
zations, thus creating necessary triangle for improvement,
not only of sport system but also for achieving ideological
goals (Bartoluci & Perasović, 2008). Therefore, systematic
nancing of sport is of great signicance for every country
as well as for B&H as it conditions the quality, high number
and diversity of programs, availability of sports infrastruc-
ture, education of sport human resources, and nally it is
a precondition for making necessary impact on business
and sport results (European Commission, 2007a:27, ac-
cording to Bronić et al., 2012). A strong link is considered
to exist between nancial and sport success, having sport
managers thriving to achieve both maximum prot and
sport results with minimum investment made (Samagaio,
Couto, & Caiado, 2009).
The goal of this research is to determine the level of impact
of public nancing from the budget of Bosnia and Herze-
govina on achieving excellent sport results, i.e. winning of
the Olympic medals, when compared to the EU members
and the EU aspiring countries, but also to make relation to
budgetary funds assigned for sports as pragmatic as pos-
sible when compared to total budget of countries used in
this research.
Methods
Participants
Sampled population involve ten countries. Representa-
tion and relevance of samples are provided through re-
search which has been done using sample of Bosnia and
Herzegovina (state level, entities: the Federation of B&H,
Republic of Srpska and Brcko District), as well as eight
EU countries (Austria, Germany, Slovenia, Denmark, the
Czech Republic, Hungary, Croatia and Sweden) and Serbia
as EU candidate country.
Variables
Sample variable is made of a group of predictive indicators
and criteria variables as showed in Table 1.
R.B Abbrv. Variables Description
group of predictive indicators
1 NoP No of population Total No of population, in million
2 Budget budget Average national budget, period 2008-2014, in billion
3 GfS grant for sports Average sport grants of countries, period 2008-2014, in million
4 GDP Gross domestic product Average GDP, period 2008-2014, in thousands
5GfS/p.c grant for sport per capita Ratio between participation in grants for sports per capita
6 GfS/budget grant for sport compared to budget Ratio of allocation for sports grant from country budget
7OM/p.c Olympic medal per capita Ratio of Olympic medals won per capita
8 OM/budget Olympic medal compared to budget Ratio between Olympic medals and national budget
9 OM/GfS Olympic medal compared to grant for sport Ratio between Olympic medals won and sports grant
10 OM/GDP Olympic medal compared to GDP Ratio between Olympic medals won and GDP
Group criteria
11 OM/S Olympic medal SOG No of Olympic medals won on Summer OG 2008 and 2012
12 OM/W Olympic medal WOG No of Olympic medals won at Winter OG 2010 and 2014
13 OM Olympic medal Total Olympic medals won at OG 2008-2014.
Table 1. Summary of used variables
Variables chosen for the statistics were used to determine
the structure of public nancing of sports and should serve
to generate excellent results in sports.
Collection of data
Data used for this research, for Bosnia and Herzegovina, is
taken from ofcial web pages, from line ministries of Bos-
nia and Herzegovina (Ministry of Civil Affairs, Federal Min-
istry of Culture and Sports, Ministry of Family, Youth and
Sport of RS, District Brčko), and for Serbia from ofcial
web page of the Ministry of Family and Sports, as well as
from database used in research by Bronić et al. (2012) for
EU countries. A part of data has been collected from email
communication between the author of this research and
HOMO SPORTICUS ISSUE 1 2016
20
competent persons from the above mentioned state au-
thorities. All data which has been collected is documented
and sorted with a goal to implement this research.
Statistical analysis
Regression analysis and ratio of multiple correlation were
used to determine the impact of allocated public funds
on achieving excellent sport results at the Olympics, thus
showing the correlation between variables. The Pearson’s
correlation ratio was used as to determine correlation be-
tween variables in the EU countries and candidate countries
such as Serbia, based on the awarded Olympic medals.
Results
Results of this research are presented in cumulative values
and percentages. The research enables objective justica-
tion of investing of budget allocated funds for nancing of
sports. This research point to complexity of public nancing
system using budget at all levels of public administration in
Bosnia and Herzegovina. Nevertheless, analysis of existing
legislation in B&H showed that there is no differentiation be-
tween competencies in terms of nancing, so that in real life,
it is possible for sport organizations to be nanced from the
national budget of Bosnia and Herzegovina, the budget of
entities, budget of cantons and local administrations for the
same program activities. Analysis of sport system in B&H
showed that public nancing of sport is burdened by com-
plex bureaucracy and procedures thus calling for certain re-
forms to be made at all administrative levels. Table 2 shows
that funds allocated for sports by entities as well as the state
are rather the same when compared to total budget, whereas
the Republic of Srpska allocates double amount of money
for sport per capita compared to the Federation and the state
level. In terms of other countries, their funds for sports are
much higher than in B&H. Average number of population
which is required to achieve excellent sport results, such
as the Olympic medal, is 388,562. As shown in the Table
2, GDP per capita and amount of grant per capita can be
brought into correlation with all countries, save for Sweden,
which has signicantly lower amount of grant for sport per
capita compared to GDP per capita. It should be noted that
grant for sport compared to the budget is the highest in Ser-
bia whereas the lowest in B&H. Croatian sport results (15
medals in big international competitions such as the Olym-
pics), in period 2008 - 2012 (Milanović et al., 2005), speak
in favor of its competiveness. Research results unambigu-
ously show that economic growth of country measured by
amount of grant for sport and GDP signicantly inuences
achievement of excellent sport results expressed by the
number of the Olympic medals won. These research results
showed exactly that grant for sport at all level of B&H public
administration is considerably lower compared to those in
the EU countries, as well as the EU aspiring countries, thus
making it insufcient to generate success at the Olympics.
Country BS BUDZ GSP BDP GSP/
BS
GSP/
BUDZ BS/MOI BUDZ/
MOI
GSP/
MOI
BDP/
MOI
LJOI
2008/
2012
ZOI
2010/
2014
MOI
AUSTRIA 8.355 246,30 717 29.611 86,4 0,29% 232,083 6,84 19,92 822 3 33 36
GERMANY 82.002 2.270,80 5.091 27.619 62,1 0,22% 611,955 16,95 37,99 206 85 49 134
DENMARK 5.511 191,80 593 35.019 107,8 0,31% 344,438 11,99 37,06 2188 16 0 16
SWEDEN 9.256 322,30 662 35.099 72 0,21% 237,333 8,26 16,98 899 13 26 39
CZECH R. 10.468 86,00 164 8.282 15,6 0,19% 348,933 2,87 5,47 276 16 14 30
HUNGARY 10.031 67,90 248 6.756 24,8 0,36% 263,974 1,79 6,52 177 28 0 28
SLOVENIA 2.032 30,00 104 14.944 52 0,35% 101,600 1,50 5,20 747 9 11 20
CROATIA 4.284 15,30 44 10.297 10,15 0,28% 285,600 1,02 2,90 686 11 4 15
SERBIA 7.498 8,19 39 5.410 5,2 0,47% 1071,143 1,17 5,57 772 7 0 7
B&H 3.792 0,78 0,30 3.530 0,08 0,04% 0 0 0 0 0 0 0
Federation of B&H 2.372 1,02 0,76 3.740 0,32 0,07% 0 0 0 0 0 0 0
Republic of
Srpska 1.326 1,12 0,95 3.320 0,71 0,08% 0 0 0 0 0 0 0
District BRČKO 0,09 0,105 0,82 3.650 8,81 0,73% 0 0 0 0 0 0 0
Table 2. List of data base used for selected EU countries, EU aspirants and B&H
HOMO SPORTICUS ISSUE 1 2016
21
Table 3. shows that there is statistically signicant posi-
tive correlation of part of predictive set of variables (No of
population, total budget, grant for sport from budget) when
compared to criteria variable of excellent sport result pre-
sented in number of medals won at the Olympics (MOI)
with statistical reference of 0.01. Such results indicate that
countries having higher number of population, total coun-
try budget and nancial allocation for sports, do create a
prerequisite for achievement of excellent sport results, i.e.
winning of the Olympic medals.
Disperse diagram (Picture 1) shows there is a quantitative
matching between variations of shown data, for EU coun-
tries and Serbia. In fact, growth of budget and investment
in sports through grants increase the number of achieved
sport results, which can be seen in number of the Olympic
Pearson’s correlation ratio
NoP Budget GfS GDP/p.c GfS/p.c OM/p.c OM/budget OM/GfS OM/GDP OM
NoP 1
Budget ,977* 1
GfS ,985* ,985* 1
GDP/p.c ,285 ,356 ,427 1
GfS/p.c ,175 ,236 ,325 ,942* 1
OM/p.c ,338 ,253 ,276 -,077 -,144 1
OM/budget ,741 ,772 ,824* ,817 ,745 ,190 1
OM/GfS ,619 ,654 ,717 ,831* ,818* ,199 ,971* 1
OM/GDP -,284 -,241 -,166 ,625 ,701 ,094 ,353 ,517 1
OM ,963* ,933* ,971* ,438 ,327 ,228 ,792 ,661 -,230 1
Table 3. Pearson’s correlation ratio for EU countries and Serbia
medals. Arrangement of points is grouped rather linearly,
showing thus a linear, positive relation. Therefore, it makes
sense to examine existence and strength of linear relation
between samples. However, all points are not set within the
straight line as that would represent functional matching,
which is extremely rare.
While interpreting regression analysis results it is quite
often that there is statistically signicant multiple correla-
tion between predictive variables. However, in the case of
predictive indicators and criteria, it is possible to separate
statistically signicant individual variables, so that results
could be interpreted in general, by observing group of
predictive indicators separately, which means that certain
variables are more signicant and have impact on variable
of Olympic medals (awarded Olympic medals-AOM).
p < 0,01*
variable Model 1 ( OG) Model 2 (SOG) Model 3 (WOG)
BS
beta ratio. 0,963* 0,960* 0,990*
R,963a,960a,990a
R square ,928 ,922 ,980
Model 1 ( OG) Model 2 (SOG) Model 3 (WOG)
BUDZ
beta ratio. 0,933* 0,908* 0,994*
R,933a,909a,994a
R square ,871 ,825 ,987
Model 1 ( OG) Model 2 (SOG) Model 3 (WOG)
GSP
beta ratio. 0,971* 0,939* 0,995*
R,971a,939a,995
R square ,943 ,882 ,990
Figure 1. Relation between variables used for
data modeling (total budget, grant for sport, No
of population and No of Olympic medals).
Table 4. Presentation of parameters of regression
analysis of variables in the EU countries and
Serbia compared to variables of sport results at
the Olympics (OG) for 2008/2010/2012/2014,
Summer Olympics (SOG) 2008/2012. and Winter
Olympics (WOG) 2010/201.
p < 0.01*; BS – No of population, BEUR – total budget, GSP – granny for sport from the budget
HOMO SPORTICUS ISSUE 1 2016
22
Regression connection between predictive indicators and
criteria variable of AOM is very high, its statistical sig-
nicance being at 0.01, explaining very high level of par-
ticipation of joint dispersion. Here, it should be said that
Denmark, Hungary and Serbia were excluded from data
processing for Winter Olympics due to the fact they have
not been winning medals in the aforesaid period. All three
separate regression models conrmed that development of
elite sports expressed by the number of won Olympic med-
als depends on number of population, country’s economic
growth mirrored in the budget amount and level of alloca-
tion of funds for sports from the budget as well as GDP.
Analysis results showed that this model could be applied
on achieving sports success, i.e. Olympic medals, which
is to be very expected. This analysis clearly and unambigu-
ously shows to what extent nancial funds are linked to
excellent sport results.
Other variables, such as GDP per capita, grant for sport per
capita, also inuence development of elite sports, while re-
gression analysis conrms that economic development of
country to be viewed in terms of the budget and amount of
funds allocated for sports, as well as number of population,
statistically signicantly inuence excellent sports results
presented in this research by the number of Olympic medals.
Discussion
This research showed that set goal has been achieved.
And the goal was to determine to what extent the public -
nancing from B&H budget impacts achievement of excel-
lent sports results, i.e. Olympic medals, when compared to
EU members and EU aspirants. Previous researches
proved that success of a country in sports is directly linked
to economic resources available for such activities. Fac-
tors used to determine the amount of success of a devel-
oped country need not to be the same or as signicant as
those for developing countries. Nevertheless, there is a
lack of research in sports and organizational economy,
namely in developing countries (Manuel Luiz & Fadal,
2011). Andreff (2001) in his research showed a strong
connection between economic development of the country
and its sport development. Therefore, estimates made us-
ing “ordered-logit” model showed likelihood to win Olym-
pic medal by increasing GDP per capita and number of
population. Economic growth is the only fundamental for-
mula to ght underdeveloped sports. B&H Sports Law
which prescribed the way in which sport federations are
organized at state level introduced, for the rst time, legal
regulation, which precisely prescribes the system in which
sport is organized in B&H. The issue here is that existing
sport federations have not been reorganized at B&H level.
Such a situation might lead to the problem of nancing of
sport federations that have not fullled required legal provi-
sion and could not be co-nanced by a foreign country’s
administration since failure to apply this provision directly
leads to the violation of the law. (Rađo et al., 2010) Hetero-
geneousness of legal regulation is fundamental issue in
terms of budgeting of sports in B&H, as it has not exactly
dened the minimum of public nancing required for
sports, as well as not having a unique Ministry of Sports
and Culture at the state level, which is being reected
namely on nancing of 35 state sports federations and the
Olympic Committee. Although higher number of countries
invest large sums in sports in terms of competitions with
other countries, there are no clear proofs in which way
sport politics inuence international sport results. This pa-
per is an insight into important determining indicators that
can bring countries to signicant international sport re-
sults. The texts show that more than 50% of success vari-
ables at macro level are being out of reach of politicians’
control, unlike the middle level, which involves factors that
could be inuenced by sport politics. Empiric-based theory
involving factors of policies that determine elite sport suc-
cess has not been developed yet. (De Bosscher et al.,
2006). The lack of means for measuring competiveness is
obvious in terms of elite sports research and the texts
about impact of elite sports policies on international suc-
cess are mainly inadequately examined. All these is con-
nected to sport systems, sport complexity, and inuence
of policies‘ decisions (Green & Collins, 2008; Houlihan &
Green, 2008) as well as inexistence of standardized meth-
ods for making international comparison (Henry, 2007).
Sport, as public property, is nanced from public resourc-
es within public requirements. Financing of sports in B&H
is similar to European one and based on “mixed model of
nancing of sports” (Bartoluci, 2003.). This model in-
volves two nancing sources: nancing from budget (pub-
lic funding) and nancing from private activity (private
funding). Data shows link between investments in sports
with achieved results, although some countries use them
more efciently. Thus, for instance, Denmark has more
population than Slovenia, but in terms of investing in sports
per capita, Slovenia, although investing two time less in
sports, achieved better results. Total investments made in
sports in Slovenia recorded nominal increase by 2005 and
started gradually to decrease with regard to GDP due to
higher investments made in other social segments, namely
transportation infrastructure (Bednarik, Kolar & Jurak
(2010). This research analyzed correlation between realis-
tic GDP increase in EURO zone, direct foreign investments
in that zone, as well as rate of growth of population in the
same area (Alfaro et al., 2004; Basu & Chakraborty,
2003; Borensztein , De.Gregorio, & Lee, 1998; Trevino &
Upadhyaya, 2003). Investing in sports in the Czech Repub-
lic showed even better effects, with average investments in
sport per capita of EUR 15,6 per annum, having the Czech
national team won 30 Olympic medals, whereas Hungary
won 28 and had to invest in sport EUR 24,8 per capita per
annum. Andreff (2009) based his research results on
econometric testing, mainly on regression analysis. He
also takes GDP or GDP per capita and number of popula-
tion as important indicators to determine success at the
Olympic Games. Results from these researches indicate
the fact that public nancing segment solely does not nec-
essarily guarantee good sport results as only the main pil-
HOMO SPORTICUS ISSUE 1 2016
23
lar of sports nancing from the country budget was ana-
lyzed, and it should represent a factor to generate excellent
result in sport. Other factors that might have an impact on
nancial-sport result have not been included in this re-
search. In this context, it is shown that in spite having sig-
nicant economic and demographic factors, improvements
in these variables though governmental policies would
have limited effect on the number of awarded medals.
(Hoffmann, Ging, & Ramasamy, 2004). Other researches
assume that countries with higher standard measured by
GDP or GDP per capita may have higher amount of funds
allocated for elite sports ((Kuper & Sterken, 2005; Rob-
erts, 2006:2; Rathke & Woitek, 2007:1 acc.to Škorić,
2011). Research (Škorić & Hodak, 2011) showed that by
using regression analysis, economic growth of Croatia,
measured by changes in GDP, signicantly inuences num-
ber of registered participants in sport in a positive way. It is
obvious that ratio between number of awarded medals and
grant for sports per capita (GfS/per capita) is highest in
Germany followed by the Czech Republic, Croatia and Ser-
bia. Bosnia and Herzegovina does not have such a ratio
since it has never won any medal at the Olympics. Sport in
Bosnia and Herzegovina is still at low level and without
business climate for sport (Simović et al., 2010), as it
namely has not created facilitating circumstances for
those who would be willing to invest money. And that is the
main reason for stagnation and inability to progress. The
EU countries which put aside signicant amount of funds
from the budget for sports prove that nancial security for
potential investors in sport cannot go without proper su-
pervision and control of funds spending (Rađo et al.,
2010). Table 2 showing return ratio (medals) on funds in-
vested in sports and spor t activities per separate countries
also partially indicates that European countries having
much higher GDP per capita invest a lot more in develop-
ment and growth of sports. However, investment in per-
centage in respect to number of population is not signi-
cantly different from investments made by our neighbors
(Croatia and Serbia). In 2012. year, total income of all non-
governmental sport organizations was decreased for the
rst time in the last 10 years. Stagnation during this period
can be best seen in professional sports of NGOs due to
decrease in public income of app. 8% (Jurak et al., 2014).
This research proved that it could be possible to determine
even “the price of medal”. Thus, for sampled countries, in
average EUR 15.58 Mil should be invested from public
funds for each awarded medal. Croatia has invested funds
in sport most efciently, i.e. for each EUR 2.9 Mil invested
in sports Croatia won Olympic medal whereas Germany
and Denmark invested EUR 37 Mil in order to achieve the
same result. Research results point to connection between
sport and business success on one hand, and on predic-
tive variables of level of education of managers in football
clubs, on the other, which is considered to be an essential
potential of organizations. (Mašala et al., 2011) Financial
support by public sector need not necessarily to come
from increase of direct funds from the budget, but also
some other actions are possible, such as: to increase tax
reliefs for those investing in sport; to decrease VAT on
sport equipment (Škorić & Hodak, 2011) Participation of
budgetary sources of nancing in total amount of sports
requirements in the most developed countries of Europe
amount from 15 to 28% (Bartoluci, 2003). The practice is
to rank countries participating at the Olympic Games per
number of medals awarded. This ranking is based on abil-
ity of each country to win medal in comparison to available
funds (Lins et al., 2003). It should be highlighted that many
of the mentioned countries are traditionally focused on cer-
tain sports and are being awarded medals mainly in these
sport disciplines. This research offers new information for
more efcient and accurate setting of goals, and highlights
requirements of sport associations in terms of public funds
to provide for a better development of B&H sports.
Conclusion
All options of nancing in sports described here, either for
EU countries or for EU candidates cannot be compared by
simple comparison of certain elements as each of these in-
volve many factors making such systems unique, specic
and inapplicable to others. Each good practice used in Eu-
ropean countries, individually, would call for thorough anal-
ysis of effects, costs and possibility to implement those in
B&H legal system. Results of such research could help to
a large extent to state institutions, NGOs and sport workers
in rising awareness of importance of systematic nancing
of sports in B&H, as a good prerequisite for achievement
of excellent results in sport. These results showed clearly
there are certain issues in B&H in terms of distribution of
grant for sports at all levels in B&H. This applies both to
legal distribution that has not precisely dened minimum
required for public nancing of sports and to formal distri-
bution, i.e. inexistence of one Ministry of Sports and Cul-
ture at state level, which inuences namely systematic so-
lution for nancing of sports in B&H as whole. The Law on
Sports of B&H should provide that programs for public re-
quirements in sports, regardless their source of nancing,
(state, entities, cantons, local budget) are mutually recon-
ciled and linked to the main Criteria Rules for distribution of
nances for sports, as a strategic document. Making one
criteria system for nancing of sport programs, justied
nancing, as well as establishing of informational system
in sports, which would use known indicators to monitor re-
sults generated from invested funds, would result in much
better effects, and achievement of excellent results.
In order to see objectively the situation in nancing of sports
from the budget, here we found a link between structur-
ally and basically signicant segments, such as number of
population, amount of country budget, amount of grant for
sports, GDP, which statistically inuence excellent sport re-
sults to a large extent, achieved by winning Olympic medal.
Such a treatment of sports in B&H leads to occasional and
slight sport success (occurrences), and are mainly result of
talent and strong commitment of an individual and not the
result of systematic work of institutions.
HOMO SPORTICUS ISSUE 1 2016
24
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Submitted: April 13, 2015
Accepted: May 29, 2016
Correspondence to:
Omer Osmanović, MA
PBS Bank Sarajevo
Alipašina 6, 71 000 Sarajevo
Bosnia and Herzegovina
e-mail: omerosmanovic@gmail.com
HOMO SPORTICUS ISSUE 1 2016
26
Adnan Ademović¹
DIFFERENCES IN THE QUANTITY AND
INTENSITY OF PLAYING IN ELITE
SOCCER PLAYERS OF DIFFERENT
POSITION IN THE GAME
¹Teaching faculty of the University „Džemal Bijedić“ in Mostar, Bosnia and Herzegovina
Original scientic paper
UDC: 796.332.058
Abstract
The aim of this study was to determine differences in the amount and intensity of movement of top players in terms of the position in
the team, on a sample of 167 players who played all 90 minutes of the eighth nals of the World Cup 2010 in South African Republic.
By comparing the players’ position it was found that the mideld players, on average, most ran, (10952 ± 2123m), which is the case
and the amount of movement when the team in possession (4279 ± 1099m) and when the team is not in possession of the ball (4485
± 1010m). In relation to the intensity of the defensive players during a game on average the most time spent in the low intensity (83.86
± 2.92%), the activities of medium and high intensity most spent midelders (10.44 ± 1.47% of total distance traveled). The high-
est maximum average speed achieved attackers (24.24 ± 3.07 km / h). Canonical discriminant analysis obtained three discriminant
functions of which two have statistically signicant value (Canonical Cor. = 893, p = .00) and Canonical Cor. = 488; p = .00). The
highest correlation with the rst isolated discriminant function, which maximally different positions in the team (Wilks Lambda = 143;
p = .00), have all three variables that determine the intensities of movement, variables that determine the total length of the distance
traveled, distance covered in possession and the amount of players, a dominant position is mideld players. The second isolated dis-
criminant function that also statistically signicantly different position in the game (Wilks Lambda = 709; p = .00) variable determines
the amount of distance traveled without the ball, but the most dominant group are defenders, then midelders.
Key words: football, speed, intensity of movement, distance covered
Introduction
Top footballers from year to year progress in the quantity
and intensity of movement, which necessitates the need
for experts to determine what it characterizes prole play-
ers who are in the nals of major competitions. Modern
football is characterized by the fact that all players partici-
pate in defense and in the attack phase which requires a
lot of effort and energy requirements. Matches at the elite
level is characterized by activities that are dominated by
fast movement of players and a growing volume of mo-
tion activities in the basis of which are anaerobic-aerobic
tness. Thus developed skills players lead to the creation
of “shallow” formations in the IGI, where a large part of the
game all the players are in one part of the eld in the sum-
mary area of about forty meters. The tendency in modern
football is a multifunctional football player, usable at mul-
tiple positions that can do the tasks and in defense and in
the attack phase, which again carries a certain positional
specicity: goalkeeper, midelder, defender, side player
and the like. Research indicates that top football players
on average are aged between 25 and 27 years (Ekblom,
1994). The average height of the top players according to
exploring Bloomeld et al (2005) shows that the players
of the Bundesliga are the biggest 183 ± 6cm, four Eu-
ropean top league, the players of the Spanish La Liga are
the smallest 180 ± 6 cm. The average height, all play-
ers of the participants of the World Football Championship
2002 is 180.90 ± 6.13 cm and the average body weight
of 75.91 ± 6.38 kg. The greatest heights are goalkeep-
ers (186.42 ± 5,27cm), then they Defenders (181.87 ±
5,57cm), attacks (180.27 ± 5.76 cm) and at least aver-
age height (178.38 ± 5, 55 cm) and body mass (73.87
± 5.55 kg) were footballers midelder. As for players un-
der the age of positions, the oldest Goalkeepers (29.50 ±
4.34 years), and the youngest attackers (26.71 ± 3.82
years) (Đurašković and Associates, 2004). Average values
of age and height for all 368 participants at the European
Football Championship 2008 years was 27.58 ± 3.98
years and 182.97 ± 6,59cm. The highest average was
at goalkeeper (age 29.42 ± 4.76 years, height 189.06 ±
4.54 cm), and then the defense (age 27.79 ± 3.56; height
184.69 ± 5,43cm) and the offensive player (age 27.06 ±
3.98; height 182.60 ± 6.42 cm), and the lowest value of
height (179.02 ± 5.95 cm) and age (26.97 ± 3.83) was
recorded with midelders (Joksimovic et al, 2009). As for
HOMO SPORTICUS ISSUE 1 2016
27
the amount of movement, research has shown that the top
players on average cover a distance of 9 km -14km during
matches (Rienzi et al., 2000; Bloomeld et al., 2005; Bar-
ros et al., 2007; Di Salvo et al., 2007; Mohr, Krustrup and
Bangsbo, 2005; Rampinini et al., 2007; Lago et al., 2010).
If the football players moving at a constant rate, then the av-
erage speed of top players was between 6.5 km / h - 8 km
/ h corresponding legwork. Since it is constantly changing
speed in the game, it is necessary to the overall distance
covered to match dismember the categories in relation to
the speed (intensity) movement. So far dened catego-
ries of activities vary from author to author, therefore it is
very difcult to compare with each other. The latest results
obtained in the analysis of Champions League matches,
clearly suggest as a top footballer, on average, about 58%
of the time in the game carry standing (15%) and walking
(43%), about 30% of the time running around (7-14 km / h)
, about 8% of the time running at a moderate speed (15-19
km / h), about 3% of the time running at high speed (20-25
km / h), and only about 1% of the time sprinting at maxi-
mum speed. If these time percentages are converted into
distance traveled, then a professional player walk about 4
km, prancing about 4.5 km, running moderately fast about
1.8 km, runs fast about 0.7 km and 0.3 km sprint around.
To analyze their prole activity, so far the football players
on the eld usually shared on defense, midelders and at-
tackers. It is a known fact that midelders due role and re-
quirements of their positions, on average cross the longer
distance of defenders and attackers. Prole of competition
in this divided world-class players shows that the total dis-
tance by top defenders behind (about 1 km) for players
with other players’ positions, where the difference is most
visible in the distance traveled moderate and high inten-
sity running (speed greater than 14 km / h). Given that the
speed of about 14 km / h of running in which players reach
the so-called. anaerobic threshold, the central defenders
have less anaerobic demands of players at other positions
in the game. Comparing other positions it was observed
that midelders and defenders outer yarn about the same
distance, while the distance by attackers are between mid-
elders and central defenders. In addition to the distance
covered at a different pace, players in different positions
are different to other specic activities in the game. Most
of the information available on this section refers to the
classical division of players on the defenders, midelders
and attackers. Although there are signicant variations in
the data, it was observed that midelders perform signi-
cantly higher number of activities for the ball in relation
to players at other positions. During a game, top football
players on average quarterbacks 1200-1400 different ac-
tivities changes (mainly short-term), changing them every
4-6 seconds (Bangsbo, 1994; Mohr et al., 2005). Bangs-
bo, Mohr and Krustup (2005) determined that the mideld
players cover the total distance traveled and distance high-
intensity similar to the defenders and attackers, but Sprint
less. Earlier indicated studies have shown that players of
mideld cover greater distances during matches of de-
fenders and attackers. These differences are explained by
the development of the physical demands defenders and
attackersIt was noted that players at all positions on the
team have a signicant decrease in acts of high intensity
towards the end of the match, which proves that almost all
the players in the elite football exploit the maximum of their
physical abilities in the game (Bangsbo, 1994; Mohr et al.,
2003). Research Mohr & Bangsbo (2005) has shown that
footballers achieve a maximum speed in the game of 32
km / h. Activities that player performs during the soccer
game can be divided into two categories: things with the
ball and without the ball activities. Considering the total
number of players, as well as the dimensions of the court,
it is not surprising that the activities of individual players
without the ball waste on average over 95% of the effective
time of the game. Although the total physical work done
players includes a variety of activities, most of this work
is spent in walking and running at a different tempo and
different directions. Taking into account the above, as a
global indicator of the physical demands of football and the
total volume of work players used the total distance trav-
eled during the match and the intensity of labor. The aim is
to determine the amount and intensity of movement of top
players by position in the team for the World Cup in South
Africa 2010.
Methodology
Samples
Respondents in the survey consisted of 167 elite soccer
players who participated in the nals of the World Cup
football in South Africa 2010. The sample represents play-
ers who played all 90 minutes of the eighth nals champi-
onship and come from national teams: Urugvay, South Ko-
rea, USA, Ghana, Germany , England, Argentina, Mexico,
Netherlands, Slovakia, Brazil, Chile, Paragvay, Japan, Spain
and Portugal.
Sample of variables
Data were taken from the ofcial website of world football
federation (www.fa.com) presenting all the parameters of
situational efciency of all players of the participants of the
World Cup in 2010. To determine the amount and intensity
of movement of players are used the following variables:
age (years), body height (cm), the length of the distance
traveled (m), the length of the distance traveled in posses-
sion of the ball (m), the length of the distance traveled with-
out the ball (m) , the maximum recorded speed (km / h),
a low-intensity (%) - represents the time the player spent
in activities where speed is less than 10 km / h, medium
intensity activity (%) - represents the time the player has
been in activities where the speed is equal to or greater
than 10 km / h and less than 15 km / h, the activities of
high intensity (%) - represents the time the player spent in
activities where speed is equal to or greater than 15 km / h .
HOMO SPORTICUS ISSUE 1 2016
28
Data processing methods
For each applied variable was calculated by the arithmetic
mean and standard deviation, while the differences between
the different positions in the team were established by ca-
nonical discriminant analysis. The criterion for discriminat-
ing the strength of applied variables is Wilks’ lambda. In
this way were found discriminant functions and variables
on which are the most different positions in the team.
Results
Based on the results (Table 1), we can conclude that mod-
ern goalkeepers average age 28.25 ± 4.05 years, average
height 187.12 ± 5.96 cm and an average run across 4437
± 877m in the game. When the team is in possession of
the ball goalkeepers are ran, 1,621 ± 328m, when a team
is not in possession of the ball 1,601 ± 343m, while the
average maximum speed achieved was 17.18 ± 2.46 km
/ h. Due to the intensity of activity can be seen that the
goalkeepers during matches on average most of their time
spent in activities of low intensity and 97.75 ± 683%, in
the activities of medium intensity ± 1.25, 447% or a mini-
mum of ± 1, 632% are spent in activities of high intensity.
The average age of the corner points to the fact that the top
football goalkeeper station until late twenties. At the age
of thirty years, many top athletes nishing his successful
career, but in general terms today’s soccer goalkeepers,
they only then enjoyed their best. A large number of ap-
pearances and experience probably contributes a certain
psychological stability football goalkeeper, which in turn
affects their performance. Defensive players (n = 66)
who played all 90 minutes in the eighth nals of World Cup
2010 are an average age 27.71 ± 3.64 years, the aver-
age height of 180.57 ± 5,87cm and on average they ran,
10273.42 ± 1780 m. When the team in possession of the
ball defensive players ran, 3833 ± 843 m, and when the
team is not in possession of the ball 4276 ± 826 m, while
the average maximum speed was 22.88 ± 2.14 km / h.
It was noted that the defensive players during a game on
average most of their time spent in the low intensity and
83.86 ± 2.92%, in the activities of medium intensity 7.83
± 1.31% in activities of high intensity 8.33 ± 2, 04% of the
total duration of the match. The study included 52 mideld-
ers and based on the results we can say that modern mid-
elders average age 25.76 ± 3.78 years, average height
178.59 ± 5.84 cm, and an average of 10,952 ± 2123 run
across him during a game . When the team in possession
of the ball mideld players run across 4279 ± 1099m,
and when the team is not in possession of the ball 4485
± 1010m. The average top speed achieved was 23.54 ±
2.62 km / h. Due to the intensity of activity can be seen that
the mideld players during a game on average at least their
time in the low intensity and 80.07 ± 3.04%, in the activi-
ties of medium intensity 9.82 ± 1.79% in the high-intensity
activities implemented most time from all positions on the
team or 10.44 ± 1.47% of the total duration of the match.
Modern top strikers (n = 33) were, on average, the young-
est (25.42 ± 2.89 years), the average height of 179.12
± 6.38 cm and an average run across 9093 ± 1749 m
for 90 minutes. When the team in possession attackers
run across 3744 ± 748 m, and when the team is not in
possession of the ball 3425 ± 808 m. The maximum aver-
age speed you reach attackers was 24.24 ± 3.07 km / h.
Due to the intensity of activity, attackers during a game on
average most of their time in the low intensity and 83.15 ±
3.07%, in the activities of medium intensity 7.84 ± 1.43%
in activities of high intensity 9 ± 1.80 % of the total dura-
tion of the match.
Table 1. The values of arithmetic means and standard deviations of variables according to groups
Variable
Goalkeepers
(n=16)
Defenders
(n= 66)
Mideld
(n=52) Forward (n=33)
Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.
Age 28,25 4,05 27,71 3,644 25,76 3,78 25,42 2,89
Height (cm) 187,12 5,96 180,57 5,875 178,59 5,84 179,12 6,38
Distance covered (m) 4437,50 877,03 10273,42 1780,99 10952,00 2123,75 9093,03 1749,60
Distance covered in
possession of ball (m) 1621,87 328,35 3833,00 843,63 4279,32 1099,38 3744,42 748,31
Distance covered without
ball (m) 1601,50 343,02 4276,48 826,47 4485,55 1010,02 3425,39 808,79
Maximum speed (km/h) 17,188 2,46 22,88 2,14 23,54 2,62 24,24 3,07
Low-intensity (%) 97,75 ,68 83,86 2,92 80,07 3,04 83,15 3,07
Medium intensity activity
(%) 1,25 ,44 7,83 1,31 9,82 1,79 7,84 1,43
Activities of high intensity
(%) 1,00 ,63 8,33 2,04 10,44 1,47 9,00 1,80
HOMO SPORTICUS ISSUE 1 2016
29
In tables 2-6 were analyzed differences between groups di-
vided according to the players positions on the team, as well
as variables that are most different positions. The criterion
for discriminating the strength of applied variables was tz.
Wilks’ Lambda (Table 5), which is statistically signicant
at the 99% (Sig = .00). Determination of statistical signi-
cance of each discriminant variable was done on the ba-
sis of Bartlett’s chi-square test. Table 2 and Table 3 (boxes
M-test) was tested similarity matrix covariance between
the four groups, ie. positions on the team. We see that the
difference is statistically signicant covariance matrix (Sig
= .000) at the level of 99%, which is a condition that can
access the further procedure of canonical discriminant anal-
ysis. The results of discriminant analysis (Table 4) shows
that the obtained three discriminant functions of which two
have statistically signicant high value (Can. Correlation
=, 893 and Can. Correlation = 488), which indicates to
us that where the correlation data set based on which we
conducted discriminant analysis and the results of the dis-
criminant function. Based on Table 6 it can be seen that the
highest correlation with the rst isolated discriminant func-
tion, which maximally different positions in the team, with
all three variables that determine the intensities of move-
ment: a low-intensity (-, 852); medium intensity activities
(809); activities of high intensity (750), then the variables
that determine the total length of the distance traveled (493);
distance covered in possession of the ball (416) and the
amount of players (-, 198). These variables are most dif-
ferent groups of players which proves the value of Wilks’
lambda (, 143; p = .00). The predominant group of play-
ers in this discriminative Function (Table 7) are midelders
(1,449), followed by the players attacks (168), Defenders
(125) and the outskirts of the goalkeepers who are on the
negative side of the tool (-5.731). The second isolated dis-
criminant function that also signicant differences between
groups of players (Wilks Lambda = 709; p =, 000) dene
a variable amount of distance traveled without the ball (661)
and the most dominant group are defenders (466), followed
by midelders (, 064), goalkeepers (, 033) and at the end of
the attackers (-1.049) which are located on the negative pole
of this function. Third diskrimnativnu function that no statisti-
cally signicant (Wilks Lambda, 931; p =, 121) determine
the variables that determine the age of the players and the
average maximum speed, and the most dominant group are
midelders and goalkeepers, followed by attackers and de-
fenders by the negative pole this function.
Variable Wilks’
Lambda F df1 df2 Sig.
Age ,912 5,274 3 163 ,002
Height (cm) ,860 8,833 3 163 ,000
Distance covered (m) ,494 55,604 3 163 ,000
Distance covered in possession of ball (m) ,593 37,296 3 163 ,000
Distance covered without ball (m) ,500 54,322 3 163 ,000
Maximum speed (km/h) ,634 31,360 3 163 ,000
Low-intensity (%) ,258 156,162 3 163 ,000
Medium intensity activity (%) ,278 141,408 3 163 ,000
Activities of high intensity (%) ,309 121,678 3 163 ,000
Table 2. Testing equality of arithmetic means positions on the team
Box’s M 240,962
F
Approx. 4,906
df1 45
df2 39419,173
Sig. ,000
Table 3. Box’s M test
Function Eigenvalue % of Variance Cumulative % Canonical
Correlation
13,952 91,1 91,1 ,893
2,312 7,2 98,3 ,488
3,074 1,7 100,0 ,263
Table 4. Isolated canonical function
Test of Function(s) Wilks’ Lambda Chi-square df Sig.
1 through 3 ,143 309,919 27 ,000
2 through 3 ,709 54,754 16 ,000
3,931 11,434 7,121
Table 5. Wilks’ova Lambda
HOMO SPORTICUS ISSUE 1 2016
30
Discussion
The differences between players at different positions in
the game suggest that on average the oldest are goal-
keepers and the youngast are players attack. The great-
est heights are goalkeepers, then that footballers defense,
attack and at least average height are midelders. These
results are similar to previous results (Blomlda et al.,
2005; Đurašković et al., 2004; Joksimovic et al., 2009).
By comparing certain types of players in the average run
so meters during a match has been found that most run
across are mideld players (10952 ± 2123m), then they
are footballers defense, attack and at least average val-
ues at run on distances are goalkeepers. When the team in
possession of the ball during a game, the most run across
are mideld players (4279 ± 1099 m), then they are foot-
ballers defense, attack and minimum average values are
goalkeepers. By comparing certain types of players in the
values of the distance run so far when the team is not in
possession of the ball during a game most run across are
mideld players (4485 ± 1010m), then they are footballers
defense, attack and minimum average values are goal-
keepers. The values achieved the maximum speed during a
game, shows that the highest average speed of the attack-
ers were (24.24 ± 3.07 km / h), then they mideld play-
ers, defensive players, and that the lowest average realized
rate had goalkeepers as safe conditioned the specics and
requirements of the position in the team. Given the intensity
of the activity, it is possible to observe that most goalies
time (97.75 ± 683%) implemented in the activities of low
intensity, then the players attack, defensive players and a
minimum of time spent in activities of low intensity have
mideld players. By comparing certain types of players
due to the intensity of the activity, it is possible to observe
that midelders average (9.82 ± 1.79%) spend engaged
in activities of moderate intensity, then the players attack
(7.84 ± 1.43%) and defensive players (7.83 ± 1.31%),
which have approximately the same percentage of time
they spend engaged in the activities of at least moderate
intensity and time spent in activities of moderate intensity
have goalkeepers. Given the intensity of the activity has
been observed that midelders average of 10.44 ± 1.47%
distance traveled spend engaged in activities of high inten-
sity, then the players attack and defenders and the lowest
percentage in the high-intensity activities are goalkeepers.
Conclusion
Compared to similar previous studies, with the exception
of the position of goalkeeper, it is evident convergence
of positions on the team in relation to the amount of dis-
tance traveled, the intensities of movement and a maxi-
mum speed of movement which supports the thesis that
in modern football more proled polyvalent footballers
usable at multiple positions team. Discriminant analysis
conrmed statistically signicant differences in the amount
and intensity of movement between players’ position in the
Table 6. The matrix structure of isolated functions
Variable Function
123
Low-intensity (%) -,852(*) ,133 -,167
Medium intensity activity (%) ,809(*) ,036 ,464
Activities of high intensity (%) ,750(*) -,225 ,156
Distance covered (m) ,493(*) ,430 -,229
Distance covered in possession of ball (m) ,416(*) ,075 -,063
Age -,198(*) ,155 ,006
Distance covered without ball (m) ,466 ,661(*) -,223
Maximum speed (km/h) ,357 -,371 -,649(*)
Height (cm) -,092 ,399 -,432(*)
Table 7. Function group centroid
GRUPS Function
123
GOALKEEPERS -5,731 ,033 ,258
DEFENDERS ,125 ,466 -,243
MIDFIELD 1,499 ,064 ,342
FORWARD ,168 -1,049 -,179
HOMO SPORTICUS ISSUE 1 2016
31
team that are vital to the activities of medium and high in-
tensity movements and the total distance traveled during
the match. Top soccer players in certain positions are not
statistically signicant different in age and achieved maxi-
mum speed of movement. Based on the analysis of the
game, it is clear that training players at all positions should
focus on improving their ability to perform high-intensity
exercise and to recover quickly from periods of high-inten-
sity exercises. In relation to these results, the selection of
the football schools and training technology of younger age
groups should go in the direction of a multi-faceted motor
development, with the growing increase in the amount and
intensity of running in young players. Training technology
at earlier ages should not be much different to the posi-
tions on the team and sub-specialization to the positions
on the team should begin as late as possible. Normally you
should pay attention to the observed individual differences
and energy requirements among the players kind of work
and selection, gets the type of player who can make use
of the several positions and with better performance and
conditioning.
References
Bangsbo, J. (1994). The physiology of soccer: With spe-
cial reference to intense intermittent exercise. Acta Physi-
ologica Scandinavica, 619, 151.
Barros, R. M. L., Misuta, M. S., Menezes, R. P., Figueroa, P.
J., Moura, F. A., Cunha, S. A., et al. (2007). Analysis of the
distances covered by rst division Brazilian soccer play-
ers obtained with an automatic tracking method. Journal of
Science and Medicine in Sport, 6, 233-242.
Bloomeld, J. R., Polman, R. C. J., & O’Donoghue, P. G.
(2005). Effects of score-line on team strategies in FA Pre-
mier League soccer. Journal of Sports Sciences, 23, 192-
193.
Di Salvo, V., Benito, P. J., Calderon Montero, F. J., Di Salvo,
M., & Pigozzi, F. (2007). Activity prole of elite goalkeepers
during football match-play. Journal of Sports Medicine and
Physical Fitness, 48, 443-446.
Ekblom, B. (1994). Football (Soccer). Oxford. Blackwel
Scientic Publications.
Đurašković, R., Joksimović, A., & Joksimović, S. (2004).
Težinsko visinski pokazatelji fudbalera učesnika svetskog
prvenstva 2002 godine. Physical Education and Sport, 2,
(1): 13 – 24. University of Niš, Faculty of Physical Educa-
tion, Serbia.
Joksimović, A., Smajić, M., Molnar, S. & Stanković, D.
(2009). An analysis of anthropomorphological character-
istics of participants in the 2008 european football cham-
pionship. Beograd: Serb J Sports Sci 3 (2): 71-79.
Lago-Peñas, C., Rey, E., Lago-Ballesteros, J., Casais,
L., & Domínguez, E. (2009). Analysis of work-rate in soc-
cer according to playing positions. International Journal of
Performance Analysis in Sport, 9, (2): 218-227.
Lago, C., Casais, L., Dominguez, E., & Sampaio, J. (2010).
The effects of situational variables on distance covered at
various speeds in elite soccer, European Journal of Sport
Science, 10 (2): 103-109.
Mohr, M., Krustrup, P., & Bangsbo, J. (2003). Match per-
formance of high-standard soccer players with special
reference to development of fatigue. Journal of Sports Sci-
ences, 21, 519-528.
Mohr, M., Krustrup, P., & Bangsbo, J. (2005). Fatigue in
soccer: A brief review. Journal of Sports Sciences, 23,
593-599.
Rampinini, E., Coutts, A. J., Castagna, C., Sassi, R., & Im-
pellizzeri, F. M. (2007). Variation in top level soccer match
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1018 - 1024.
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Submitted: April 18, 2016
Accepted: June 21, 2016
Correspondence to:
Adnan Ademović, MA.
Teaching faculty of the University „Džemal Bijedić“ Mostar
Bosnia and Herzegovina
E-mail: aademovic@yahoo.com
HOMO SPORTICUS ISSUE 1 2016
32
Adriana Ljubojević1, Saša Jovanović1, Radomir Zrnić1 and Lejla Šebić2
ZUMBA FITNESS CARDIO EXERCISE:
THE EFFECTS ON BODY FAT MASS
REDUCTION OF WOMAN
1Faculty of Physical Education and Sport, University of Banjaluka, Bosnia and Herzegovina
2Faculty of Sport and Physical Education, University of Sarajevo, Bosnia and Herzegovina
Original scientic paper
UDC: 796.015.132:613.24-055.2
Abstract
The aim of this research was to determine the effects of Zumba tness model of exercise on reduction of women body fat mass. 45
women aged 25-35 participated in the conducted research. The Zumba tness program was estimated after eight weeks of exercise,
total 24 training sections. The effects of applied Zumba tness were analyzed by using Paired sample T test. The obtained result
showed that Zumba tness exercise achieved statistically signicant improvement in body weight loss (p=.01), body mass index
(p=.00), fat percentage (p=.01), fat mass (p=.00), right leg fat percentage (p=.01), left leg fat percentage (p=.00), right arm fat
percentage (p=.02), left arm fat percentage (p=.00) and trunk fat percentage (.03). The study results clearly indicated that the Zumba
tness exercise can be used as effective group tness exercise for total body fat mass reduction of women.
Keywords: Female, dance, training, transformation, exercise
Introduction
The popular Zumba tness is a new kind of dance work-
out, inspired by Latin American music and Latin American
dances. This group tness program exercise combines the
basic of dance merengue, salsa, samba, cumbia, reggea-
ton and other Latin American dances, uses basic aerobic
steps, but also enriches their composition of the other
dance like hip-hop, belly dancing, Indian, African dance,
etc (Hižnayova, 2013). It is fusion of basic principles of
aerobic interval training and strengthening exercises which
promote consumption of calories, improve cardiovascular
system and strength of the whole body (Perez and Green-
wood-Robinson, 2009).
The body fat mass reduction is the most common reason
for joining group tness programs. The researches conrm
that the implementation of various forms of group tness
program can contribute statistically signicant effects in
changes of body composition (Wong, et al., 2002; Dong-
es, Deufeld, & Drinkwater, 2010; Stasiulis, at al., 2010).
Especially, the latest researches separated dance aerobic
as the most effective group tness programto achieve im-
provements in motorical characteristics, functional abilities
and changes in body composition of women ( Luettgen, et
al.,2012; Donath, et al., 2013; Ljubojević, Jakovljević, &
Popržen, 2014). Motivating music and various dance steps
with different intensity of exercising in dance aerobic are
the main reason why all the participants are dedicated to
training. This is very important from the aspect of main-
taining interest for continuous exercise that provide the
achievement effects on body fat mass reduction. The aim
of this research is to reveal the effects of Zumba tness
program on body fat mass reduction of women.
Methods
Participants
The study was conducted on a sample of 45 women aged
25 to 35 who participated in all training sections during
eight weeks of implementation of Zumba tness program.
During this research they did not practice any other kind
of physical exercises continuing with a normal nutrition.
They were tested before and after the implementation of
Zumba tness program. All 24 Zumba trainings were per-
formed by a professor of physical education and sport
and licensed Zumba tness. Both instructors were with
long-term experience in coaching various groups of t-
ness programs.
Variables sample
The variables sample were: body weight (TM), body mass
index (BMI), fat percentage (FAT), fat mass (FATMASS),
right leg fat percentage (FATRL), left leg fat percentage
(FATLL), right arm fat percentage (FATRA), left arm fat per-
centage (FATLA) and trunk fat percentage (FATTR).
HOMO SPORTICUS ISSUE 1 2016
33
Zumba tness program
Zumba tness exercise was perfomed three times per
week (Monday, Wednesday, Friday) in the evening time
(19:00h-20:15h pm). Each Zumba training contained ba-
sic principles of Zumba excercise: warm-up (8-10 min-
utes), main part of the workout - Zumba party section (40-
50 minutes), cool down (5 minutes ) and streching (10
minutes) (Perez and Greenwood-Robinson, 2009). Warm
up contained basic dance steps (march, step touch, side
to side etc.) with gradually accelerating tempo of music,
without leaps and jumps (120-135 bpm). In the second
part of the warm-up the muscle toning exercises were
performed with soft intensity through dance variations,
slightly squats were allowed (tempo 125-140 bpm). The
main part of the Zumba training was performed with 8-10
original Zumba tness songs. The dance choreographies
and movements intensity was created in accordance
with tempo changing of music (tempo between 140-160
bpm). Each dance last 3-7 minutes, with pause 15-30
sec between dances. All Latin American dance choreog-
raphies (Merengue, Salsa, Samba, Belly dance, Chacha-
cha, Tango etc.) with their differences in character and
dynamic of the movement (Lukić, 2006) provide dosing
of exercise intensity.
Measurement description
All body parameters were measured by TANITA body
analyzer BC-418MA III. Examinees were tested in sports
equipment, barefoot, in woman’s tness pants and shirts.
During the data collection they were standing on the bot-
tom of body analyzer and held electrodes in both arms.
Data input contains body height and age, and for the test-
ing recreationist’s category were selected. After signal re-
ports the direct current goes throw the body and analyzed
necessary body parameters: body mass, body mass in-
dex, fat percentage, fat mass, right leg fat percentage, left
leg fat percentage, right arm fat percentage, left arm fat
percentage and trunk fat percentage.
Data analysis
Data gathered during this research were analyzed using
statistic programs for personal computers IBM SPSS 20.0.
for Windows. For analysis of basic statistic data and dis-
tribution of results on initial and nal measurement, basic
descriptive parameters have been calculated: arithmetic
mean, minimal value, maximal value and standard devia-
tion. The differences between initial and nal measurement
were analyzed using Paired sample T test. The
level of
sta-
tistical signicance was set at p<0.05.
Results
Descriptive statistics in Table 1 indicates changes of val-
ues in all testing parameters after eight weeks of Zumba
tness program.On average, the body mass index de-
creased for 0.37, the body mass was reduced for 0.77kg,
fat percentage reduced for 1.05%, fat mass was reduced
for 1.01kg, right leg fat percentage was reduced 1.04%,
left leg fat percentage was reduced 0.92%, right arm fat
percentage was reduced 0.86% , left arm fat percentage
was reduced for 1.25% and trunk fat percentage was re-
duced for 1.14%.
Table 1. Results of descriptive parameters of initial and nal
body composition measurement
Mean N Std.
Deviation
Std. Error
Mean
Pair 1 TMIni 66.85 45 8.30 2.39
TMFin 66.08 45 8.35 2.41
Pair 2 BMIni 23.15 45 2.85 0.82
BMIFin 22.78 45 2.73 0.78
Pair 3 FATIni 30.05 45 4.59 1.32
FATFin 29.00 45 4.08 1.17
Pair 4 FATMASSIni 20.38 45 5.49 1.58
FATMASSFin 19.37 45 4.90 1.41
Pair 5 FATRLIni 32.94 45 3.74 1.08
FATRLFin 31.90 45 3.72 1.07
Pair 6 FATLLIni 32.89 45 3.69 1.06
FATLLFin 31.97 45 3.65 1.05
Pair 7 FATRAIni 29.30 45 5.78 1.66
FATRAFin 28.44 45 5.06 1.46
Pair 8 FATTRIni 28.23 45 5.40 1.56
FATTRFin 27.09 45 4.64 1.34
Pair 9 FATLAIni 30.32 45 5.71 1.65
FATLAFin 29.07 45 5.18 1.49
Legend: TM-body mass initial; TMF- body mass nal; BMI-body
mass index initial; BMIF- body mass index nal, FAT-fat percent-
age initial; FATF-fat percentage nal; FATMASS- total fat mass
initial; FATMASSF-total fat mass nal; FATRL-fat percentage right
leg initial; FATRLF-fat percentage right leg nal; FATLL-fat per-
centage left leg initial; FATLLF-fat percentage left leg nal; FATRA-
fat percentage right arm initial; FATRAF-fat percentage right arm
nal; FATLA-fat percentage left arm initial; FATLAF-fat percent-
age left arm nal; FATTR-fat percentage trunk initial; FATTRF-fat
percentage trunk nal; Mean -means; Min - minimal value; Max
–-maximal value; StdDev. (Standard Deviation)-average deviation
of gained results from their arithmetic mean.
Table 2 shows the effectiveness of applied Zumba tness
program on reduction in women fat mass. The reductions
of total body fat mass are reected in all observed param-
eters: body mass (p=.01), body mass index (p=.00), fat
percentage (p=.01),fat mass (p=.00), right leg fat per-
centage (p=.01), left leg fat percentage (p=.00), right arm
fat percentage(p=.02), left arm fat percentage (p=.00)
and trunk fat percentage(p=.03).
HOMO SPORTICUS ISSUE 1 2016
34
Discussion
Applied Zumba tness program, with its specic form of
fusion of aerobic exercise, body toning workout and Lat-
in American dances, has caused statistically signicant
changes on body fat mass reduction. When program was
constructed it was considered that intensity of exercising
can be changed according to previous adaptability. The in-
tensity of exercise is dosed by using toning sticks (Zumba
toning program) as well as the by changing character and
dynamic of the dance moves in presenting choreographies.
The results showed that reductions of total body fat mass
were almost equally distributed in all observed parts of the
body (legs, arm, and trunk). By observing these results,
it can be concluded that the intensity of exercise during
Zumba tness was in the aerobic level where the energy
is released by fat burning process. The Zumba choreogra-
phies were included synchronously upper and lower body
moves with congured intensity. Comparing Zumba tness
workout with tae-bo aerobics trainings Hižnayova (2013)
examined differences in the intensity of exercise. The re-
sults show that the intensity of exercise during the warm-
up and the main part of the training statistically signicant
increased in all 6 Zumba trainings compared to tae-bo aer-
obic trainings (p<0.05). Energy consumption (measured
in kilocalories) during all trainings was also signicantly
higher in favor of Zumba tness model exercise (p<0.01).
Similar results were obtained in the studies of Barene, et al.
(2013) who investigated the effects of twelve-week Zumba
tness program at the percentage of fat mass and the total
amount of fat mass. The results showed that the group
that exercised Zumba tness reduced total body fat mass
(-0.6kg; P<0.05) in comparison to the control group.
They concluded that Zumba tness training program out-
side of working hours can lead to certain health benets for
women employed in health care. The research by Sternli-
cht, Frisch, & Sumida (2013) focuses on a Zumba tness
related to use of chronic aerobic exercises for prevention
of diseases like obesity and heart disease in comparison
to running or cycling. The study shows that aerobic ex-
ercises are the best alternative which is used to maintain
healthy body weight and minimizing the risk of disease.
The study also shows that the tness workout was used
to evoke high amounts of caloric expenditure and appropri-
ate workout for health benets. The popularity of Zumba
tness exercise, among other things, is based on the va-
riety of Zumba programs (Zumba classic, Zumba gold,
Zumba toning, Zumba sentao, Zumba kids, etc.) in which
the complexity of dance choreographies and intensity of
exercise are adapted to age and the goals that wanted to
be achieved by practicing.
In particular, it should be stressed that the conducted re-
search on the effects of Zumba tness program in a rela-
tively short time period of eight weeks caused a signi-
cant reduction of body fat mass of women. So it can be
conrmed positive effect Zumba tness cardio exercise on
Paired Differences
Mean
Std.
Dev.
Std. Error
Mean
95% Condence
Interval of the
Difference
t df Sig.
Lower Upper
TMIni -TMFin 0.77 0.97 0.28 0.15 1.39 2.75 11 0.01
BMIni - BMIFin 0.36 0.30 0.08 0.17 0.56 4.15 11 0.00
FATIni-FATFin 1.05 1.20 0.34 0.28 1.81 3.00 11 0.01
FATMASSIni-FATMASSFin 1.00 0.92 0.26 0.42 1.59 3.78 11 0.00
FATRLIni-FATRLFin 1.04 1.21 0.35 0.26 1.81 2.96 11 0.01
FATLLIni-FATLLFin 0.91 0.94 0.27 0.31 1.51 3.35 11 0.00
FATRAIni-FATRAFin 0.86 1.16 0.33 0.12 1.60 2.58 11 0.02
FATLAIni-FATLAFin 1.25 1.31 0.38 0.41 2.08 3.28 11 0.00
FATTRIni-FATTRFin 1.14 1.69 0.48 0.06 2.21 2.33 11 0.03
Legend: TM-body mass initial; TMF- body mass nal; BMI-body mass index initial; BMIF- body mass index nal, FAT-fat percentage
initial; FATF-fat percentage nal; FATMASS- total fat mass initial; FATMASSF-total fat mass nal; FATRL-fat percentage right leg initial;
FATRLF-fat percentage right leg nal; FATLL-fat percentage left leg initial; FATLLF-fat percentage left leg nal; FATRA-fat percentage
right arm initial; FATRAF-fat percentage right arm nal; FATLA-fat percentage left arm initial; FATLAF-fat percentage left arm nal;
FATTR-fat percentage trunk initial; FATTRF-fat percentage trunk nal; Sig.-level of signicance
Table 2.The effects of Zumba tness program exercise on body fat mass reduction of women
HOMO SPORTICUS ISSUE 1 2016
35
body fat mass reduction of women. This is very important
if it is known that the most common motive for joining the
group tness programs is reduction of fat mass and body
weight loss. Further, the benets of practicing Zumba t-
ness are reected in its applicability to all ages and gender.
Especially is recommended for obese adolescents be-
cause it provides exercise and fun. It should be noted that
the researches about the effects of Zumba tness exercise
are very rare because the Zumba tness is the latest “hit”
that appeared in the domain of group tness exercise.
Conclusion
The aim of this research was to determine the effects of
Zumba tness model of exercise on reduction of women
body fat mass. The research of eight-week Zumba t-
ness program on women showed statistically signicant
effects on body fat mass reduction. The obtained result
showed that Zumba tness exercise achieved statistically
signicant improvement in body weight loss, body mass
index , fat percentage, fat mass, right leg fat percentage,
left leg fat percentage, right arm fat percentage, left arm
fat percentage and trunk fat percentage. Zumba tness for
its specic form of exercising can be applied for various
purposes: to improve motor skills, improve functional ca-
pacity, regulation of body weight and body composition,
but also for the different categories of trainees: beginners,
athletes, convalescents, children and adults. Its actual ef-
fects on different populations and for different purposes
are yet to be examined. This paper is a contribution to the
clarication of its actual effectiveness in the body fat mass
reduction of women in recreational exercise.
References
Barene, S., Krustrup, P., Jackman, S.R., Brekke, O.L., &
Holtermann, A. (2013). Do soccer and Zumba exercise
improve tness and indicators of health among female
hospital employees? A 12-week RCT.Scandinavian Journal
of Medicine & Science in Sportshttp://onlinelibrary.wiley.
com/doi/10.1111/sms.12138/pdf
Donath, L., Roth, R., Hohn, Y., Zahner, L., & Faude, O.
(2013). The Effects of Zumba training on cardiovascular
and neuromuscular function in female college students.
European Journal of Sport Science.http://www.ncbi.nlm.
nih.gov/pubmed
Donges ,C.E., Dufeld, R., and Drinkwater, EJ. (2010). Ef-
fects of resistance or aerobic exercise training on interleu-
kin-6, C-reactive protein, and body composition. Medicine
and Science in Sport and Exercise. 42(2):304-13.
Hižnayova, K. (2013). Exercise intensity during Zumba
tness and taebo aerobics. Journal of Human Sport and
Exercise. University of Alicante. 8(2):S228-S241.
Luettgen, M., Foster, C., Doberstein, S., Mikat, R., & Porcari,
J. (2012). Zumba(®): is the “tness-party” a good work-
out? Journal of Sports Science and Medicine. 11(2):357-
8.
Lukić, A. (2006). The relationship between motor skills
and performance efciency of the basic technique steps
in sport dance. Faculty of Physical Education and Sports.
University of Banja Luka. Unpublished master’s thesis.
Ljubojević, A., Jakovljević, V., & Popržen, M. (2014). The
effects of Zumba tness program on body composition
of woman. Sportlogia. Faculty of Physical Educstion and
Sport. University in Banja Luka.Simović, S. (Ed.). Vol.
10(1), str. 29-33. E-ISSN 1986-6119
Perez, B., & Greenwood-Robinson, M. (2009). Zum-
ba: Ditch the workout, join the party! The Zumba weight
loss program. United States of America: Maggie Green-
wood-Robinson
Stasiulis, A., Mockiene, A., Vizbaraite, D. & Mockus, P.
(2010). Aerobic exercise-induced changes in body com-
position and blood lipids in young women. Medicina
(Kaunas).46 (2):129-34.
Sternlicht, E., Frisch, F., & Sumida, K.D. (2013). Zumba Fit-
ness workouts: are they appropriate alternative to running
or cycling? Sport Sciences for Health. 9 (3):155
Wong, P.C., Chia, M.Y., Tsou, I.Y., Wansaicheong, G.K., Tan,
B., Wang, J.C., Tan, J., Kim, C.G., Boh, G., & Lim, D. (
2002). Effects of a 12-week exercise training programme
on aerobic tness, body composition, blood lipids and C-
reactive protein in adolescents with obesity.The American
Journal of Clinical Nutrition. 75 (5) :818-26.
Submitted: April 18, 2016
Accepted: June 20, 2016
Corresponding author:
PhD Adriana Ljubojevic, associate professor
Faculty of Physical Education and Sport
University of Banja Luka
1a Bulevar Vojvode Petra Bojovića
78 000 Banja Luka, Bosnia and Herzegovina
e-mail: adri.ljubojevic@gmail.com
HOMO SPORTICUS ISSUE 1 2016
36
Almir Mašala1, Haris Alić1, Damira Vranešić - Hadžimehmedović2 and Eldin Jelešković1
ANALYSIS OF FACTORS OF A MARKET
WHICH AFFECT USAGE OF BASIC
COMPLEMENTARY SPORT PRODUCTS
THROUGH APPLIANCE OF FUNCTION
OF RESEARCH OF A MARKET
1Faculty of Physical Education and Sport, University of Banjaluka, Bosnia and Herzegovina
2Sports club “Old Youg All Sports”, Sarajevo, Bosnia and Herzegovina
Original scientic paper
UDC: 005.21:796
Abstract
Appliance of function of marketing research include connecting consumers, clients and the public to the person in charge of market-
ing through information which is used for identication of marketing opportunities of successful sale and which is directly connected
to purchasing power of consumers. The aim of this research is related to assessment of market potential of basic complementary
sport product (sport and recreation) on the public market from the aspect of socio- demographic motivational characteristics and
the frequency and the way of using sport products as well as geographical distance of sport facilities. Analysis were conducted on
stratication sample subjects (n=450) of which (n=99) are women and (n=351) are men. Analysis shows that basic complementary
sport product (sport and recreation) are more frequently used by men, younger people, and single people and less by women, older
people and married people. Research results which referred to the motivation of subjects in participating in sport activities show that
there are two key motifs for subjects’ participating in sport activities and those are: Image and Profession. It can be concluded that
the subjects whose motifs are Image or Profession participate in sport activities and consume these non-materialistic sport products
more than others.
Keywords: socio-demographic characteristics, motivation, accessibility of sport acilities.
Introduction
Signicant potential of sport market in consuming basic
complementary sport product ( sport and recreation) is
primary based on socio- demographic characteristics as
well as geographic distance of sport objects and moti-
vational characteristics of the public (Mašala , 2003).
These statements are conrmed by authors (Beech &
Chadwick, 2010 who emphasise that the characteristics
which affect consumer’s choice of consuming certain
sports products are : age, gender, social status, edu-
cation, interests and family status. These demographic
characteristics and demographic factors can have crucial
impact on consumer’s behaviour because these selected
groups of consumers are psychologically identied to the
certain spor t product.
The successful appliance of function of research is uni-
maginable without well educated employees who posses
different specialities and who collaborate with each other
especially because of accelarating growth of informational
technologies. Therefore it is very important that these em-
ployees possess knowledge about: empirical connection
of market, characteristics of the products, channels of dis-
truibution, competition, methods of research (Malacko &
Rađo, 2005). And because of this managers use different
strategies to motivate their employees. Practice has shown
that the combination of well-known strategies is the best
in the process of work motivation and the dominant role
has the strategy which is the most adequate to the current
situation of organisation (Sadžak, Rađo, & Sadžak, 2015;
Malacko & Rađo, 2006).
Making good strategic decisions depends on well ed-
ucated sport and management team .That team is able
to identify precisely desired group of consumers and to
provide information support to the managers of a certain
products or services (Bartoluci & Škorić, 2009 ; Mašala
et al., 2013).
An important role in analysis of factors which inuence
the consummation of certain spor t products has so-
cio-economic environment such as purchasing power of
the consumers, level of development of sport in different
geographic areas mainly depends on economic strength
HOMO SPORTICUS ISSUE 1 2016
37
of a certain area rather than age and urbanisation of the
population of that area (Andreff, 2001; Redžepagić, 2013).
In order to recognize the importance of the mentioned in-
uences on objective and successful marketing concep-
tion of the market and business, Drucker states that it is
necessary to create scientically based market (Druck-
er, 2001).
Motivational element is important factor which is included
in analysis of market of sport products.
Maslow (2004) states and emphasises the importance
of the inuence of motivation on work activity, which is
explained through the fullment of human needs. Motiva-
cijski element je faktor koji se obavezno uzima u obzir pri
analizi tržišta sportskih proizvoda. In order to understand
a human being as an individual, Maslow states that ana-
lyzing of motivation should be directed to basic goals,
desires and needs. Following this thought Maslow insists
on the statement that there are certain stabile structures
of basic needs that are universal. These reasons point to
classication of the basic categories of human needs and
desires through a scale and in a following order: existential
needs, safety, social needs, need for appreciation and so-
cial status and need for self-initiation and activation. These
statements are not unexpected since the theory of sport in
its fundamental scientic areas and branches such econ-
omy, sport management, marketing and global leadership,
which are developed as relatively young scientic disci-
plines, are unquestionably related to almost every social
activities (Rađo & Sadžak, 2009).
The aim of this paper is based on assessment of market
potential of basic complementary sport product (sport and
recreation) on public market , from the aspect of socio-de-
mographic and motivational characteristics , frequency
and way of consummation of sport product, especially
taking into consideration geographic accessibility of sport
facilities.
Methods
Participants
Sample included 450 subjects of the general public. It
included (n=99) women and (n=351) men. The average
age of the sample subjects was 32,81 age. The average
age of women was 31,58 age, while the average age of
men was 33,16 age. The marriage status of sample sub-
jects was 218 single and 232 married. Socio-economic
status of the sample subjects was dened through their
monthly incomes. 35 of sample subjects didn’t have
any incomes, and 75 of sample subjects had monthly
incomes less than 500 KM, 223 of sample subjects had
monthly incomes from 501 to 1000 KM; 90 of sample
subjects had monthly incomers from 1001 to 1500 KM;
16 of sample subjects had monthly incomes from 1500
to 2000 KM and 7 of sample subjects had monthly in-
comes over 2000 KM.
Sample of variables
Sample variables consisted of 16 variables. Variables
of the research are divided on dependent and indepen-
dent variables or predictors. Dependent or criteria vari-
ables are: subject’s frequency in participating in sport
activities and time which sample subjects spend on
sport activities. There are three groups of variables of
independent or predictor variables. The rst group are
socio-demographic variables and those are: age, gen-
der, marriage status. The second group of predictors are
motivational factors and those are: tness (with means
0- not stated as a motive of participating in spor t activity
and means 1- stated as a motive of participating in spor t
activity) (2) Image (with means 0- not stated as a motive
of engaging in sport activity and means 1- stated as a
motive of engaging in sport activity) (3) Pleasure with
means 0- not stated as a motive of engaging in sport
activity and means 1- stated as a motive of engaging in
sport activity) (4) Profession (with means 0- not stated
as a motive for engaging in sport activity and means
1- stated as a motive of engaging in sport activity ), (5)
Social activities (with means 0- not stated as a motive
of engaging in sport activity and means 1- stated as a
motive of engaging in sport activity ) and (6) Other with
means 0- not stated as a motive of engaging in sport
activity and means 1- stated as a motive of engaging in
sport activity ).The third predictor refers to number and
distance of sport facilities and consists of the following
variables: ; (1)number of sports facilities (binary vari-
able with means 0 which notes that there are or aren’t
enough sport facilities in the area) (2) distance between
sport facilities and place of residence. Distance under
1000 m (binary variable with means 0 which notes that
sport facility is not on described distance and mean 1
which notes that the sport facilities are on the described
distance from the place of residence) (3) distance of
sport facilities from place of residence from 1000 to
2000 m (binary variable with means 0 which notes that
sport facilities are not on described distance and mean 1
which notes that the sport facilities are on the described
distance from the place of residence) and (4) distance
of sport facilities from place of residence over 2000m
(binary variable with means 0 which notes that sport
facility is not on described distance and mean 1 which
shows that the sport facilities are on the described dis-
tance from the place of residence).
Instruments for data acquisition
An instrument of this research was a survey created by
the model of Lekert and Tersten’s scale from 1-3 and 1-7
degrees (Fajgelj, 2004, 2005). Creating a survey was spe-
cially done for this paper and the pattern used was a model
from a textbook Marketing research (Tihi, 1995).A sur-
vey is consisted of 23 questions. Only data which refer to
general socio-demographic data related to age and gender,
municipality, monthly incomes, profession and marriage
status are used in analysis.
HOMO SPORTICUS ISSUE 1 2016
38
Statistical analysis
Methods of data processing are based on statistical
process of Correlation analysis and Linear multiple
regression analysis with step-wize method which was
conducted based on statistical package SPSS 12.0 for.
Windows.
Results
The rst indicator - Frequency of subject’s engagement in
sport activities which included connection of independent
variables of motivational and socio-demographic charac-
teristics of a subject and accessibility, distance and num-
ber of sport facilities to the mentioned criteria variable.
Set Predictor correlation
coefcient
Level of
signicance p
Socio-demographic characteristics gender -0.188 <0.001
Age -0.320 <0.001
Marriage status -0.189 <0.001
Motivational characteristics Fitness 0.077 >0.05
Image 0.119 <0.05
Pleasure 0.061 >0.05
Profession 0.320 <0.001
Social activities 0.029 >0.05
Other -0.410 <0.001
Number and accessibility of sport
facilities in area of place of r
esidence
BSO 0.004 >0.05
Distance to 1000 m -0.085 >0.05
Distance from 1000 to 2000 m -0.016 >0.05
Distance over 2000 m 0.108 <0.05
Table 1. Correlation of socio-demographic characteristics, number and geographic distance of sport facilities from subject’s place
of residence in relation to frequency of subject’s engagement in sport activities
Table 1. shows that signicant number of predictors is sig-
nicantly connected to active engagement in sport activi-
ties. Individuals with motifs Image or Profession are more
involved in sport activities.
It is very interesting that the result of analysis which points
to the fact that distance of sport facilities over two kilometres
is signicantly related to the higher frequency of attendance.
The second indicator – Time which sample subjects spend
on sport activities. Correlation and regression analysis
with step-wize method show the correlation between inde-
pendent variables of motivational and socio-demograph-
ic characteristics, accessibility, distance, and number of
sport facilities to the mentioned criteria variable.
*Correlation is statistically signicant on a level lower than 5%
** Correlation is statistically signicant on a level lower than 1%
Set Predictor Correlation
coefcient
Level of signi-
cance p
Socio-demograophic characteristics Gender -0.230 <0.001
Age -0.250 <0.001
Marriage status -0.175 <0.001
Motivational characteristics Fitness -0.003 0.952
Image 0.118 <0.05
Pleasure 0.060 >0.05
Profession 0.355 <0.001
Social activities -0.003 >0.05
Other -0.295 <0.001
Number and accessibility of sport facilities
in the area of place residence
BSO -0.022 >0.05
Distance to 1000 m -0.173 <0.001
Distance from 1000 to 2000 m 0.066 >0.05
Distance iver 2000 m 0.120 <0.05
Table 2. Correlation of socio-demographic characteristic, number, and geographic distance of sports facilities from the place of
residence, to the time subject spends on sport activities
*Correlation is statistically signicant on a level lower than 5%
** Correlation is statistically signicant on a level lower than 1%
HOMO SPORTICUS ISSUE 1 2016
39
Results of table 2 show that signicant number of pre-
dictors statistically correlates to the time spent on sport
activities. It is noted that variables,: gender, age, marriage
status signicantly (in a negative way) correlate to the time
spent on sports activities. Women, married people and
older people spend less time practicing sport activities
while, men, single people and younger people spend more
time practicing sport activities.
Variables of motivational characteristics like Image and
Profession and Other reasons for doing sports are in statis-
tically signicant correlation to the Time spent on sport ac-
tivities. Other as a motif for engaging in sport activities is
negatively correlated to the Time spent on sport activities.
Cluster of variables of number and distance of sport facili-
ties shows that there are only two variables signicantly re-
lated to the Time spent on sport activities. The rst variable
(Distance up to 1km) is correlated in a negative way, which
indicates that subjects who have sport facilities within
1km range spend less time practicing sport activities. The
second variable is the Distance over 2km and it correlates
positively with the time spent practicing sport activities.
Table 3. Demonstration of coefcient mean of multiple
correlations and multiple determinations in examining model of
predication of Frequency in engaging in sport activities
Model R R2Determination
coefcient
Std. Error of
parameter
1 0.411 0.169 0.167 1.469
2 0.499 0.249 0.246 1.398
3 0.545 0.297 0.292 1.355
4 0.578 0.334 0.328 1.320
5 0.586 0.343 0.335 1.312
Results in table 3 show of coefcient of multiple correlations
and multiple determinations. Predictor structure of the rst
model (around 16,7%) and the second model (24,6%) ex-
plain variations among subjects in terms of their frequency
in engaging in sport activities. This percentage is increased
in the third model to 29,2% , in fourth 32,5% and 33,5% in
the fth model. Variance analysis (ANOVA) shows that all
models are statistically signicant on a level lower than 1%
(F=90,861 & p=0.000 for the rst model, F=74,048 &
p=0.000 for the second model, F=62,524 and p=0.000
for the third model, F=55,648 and p=0.000 for the fourth
model and F=46,222 & p=0.000 for the fth model).1
The rst predictor variable which is the base of structures
of all models is the answer Other as motive of practicing or
non-practicing sport activities. All models, in which struc-
tures is this independent variable, show negative regression
coefcients which are statistically signicant on a level low-
er than 1%. The common thing for all ve models is that
subjects that state variable Other as a reason for engaging
in sport activities are less engaged in spor t in relation to the
subjects that do not state this variable. Second predictor
variable which refers to the professional sport engagement
is a part of model of prediction from 2 to 5. This predictor
variable in all models shows positive regression coefcients
which are statistically signicant on levels lower than 1%.
Subjects, who are engaged in sport as a profession, engage
in sport activities for 2.273 units more frequently than the
subjects who don’t do sports as a profession. The third pre-
dictor variable is Age which is included in the third, fourth
and fth model of prediction. This variable, in all models,
shows negative regression coefcients which are statisti-
cally signicant on levels lower than 1%.
1ANOVA, which is used in regression analysis reffers to analysis of regression variance and error variance F-scale is a scale of
regression variance and error variance and both variances are included in strucuture of coefcient of multiple correlation. So when
we say that we are examiming the signicance of a model we are actually examining signicance of coefecient of multiple correlation
. this methodological procedure is uused in all other casesin which regression analysis is used.
Model Predictor Non-standard coefcient Standard
beta-coefcient
t-test p
B St. error B
1Constant 4.26 0.07 - 56.40 <0.001
Reason-Other -1.81 0.19 -0.41 -9.53 <0.001
2Constant 4.14 0.07 - 56.22 <0.001
Reason- Other -1.69 0.18 -0.38 -9.34 <0.001
Proffessional 2.27 0.32 0.28 6.90 <0.001
3Constant 5.49 0.25 - 21.42 <0.001
Reason- Other -1.51 0.17 -0.34 -8.46 <0.001
Proffessional 2.10 0.32 0.26 6.55 <0.001
Age -0.04 0.00 -0.22 -5.46 <0.001
4Constant 5.75 0.25 - 22.54 <0.001
Reason-Other -1.41 0.17 -0.32 -8.03 <0.001
Proffesional 2.22 0.31 0.27 7.09 <0.001
Age -0.04 0.00 -0.24 -6.04 <0.001
Gender -0.76 0.15 -0.19 -4.99 <0.001
5Constant 5.68 0.25 - 22.31 <0.001
Reason- Other -1.36 0.17 -0.31 -7.76 <0.001
Proffessional 2.27 0.31 0.28 7.28 <0.001
Age -0.04 0.00 -0.23 -5.99 <0.001
Gender -0.78 0.15 -0.20 -5.13 <0.001
Image 0.66 0.27 0.09 2.45 <0.05
Table 4. Means and levels of signicance of regression coefcients of certain predictors in model of prediction of frequency in
engaging in sport activities
HOMO SPORTICUS ISSUE 1 2016
40
Third model of prediction shows that if subject is one year
older frequency of sport activities is reduced and if the
subject is one year younger frequency of sport activities
is increased. The fourth and the fth model show that if
the subject is one year older frequency of sport activities
is reduced.
The fourth predictor variable Gender is included in structure
of fourth and the fth model of prediction of frequency of
engagement in sport activities. Its regression coefcient is
negative and statistically signicant on levels lower than 1%
in both models. In both models of prediction male subjects
are related to more frequent participation in sport activities.
The fourth and the fth model show that the male subjects
participate in sport activities more frequently than the fe-
male subjects. The fth predictor variable which refers to
Image as a motive for participating in a spor t activities is
included in the fth model of prediction of frequency of
participation in sports activities. This variable shows pos-
itive regression coefcient which is statistically signicant
on a level lower than 5 %.The fth model of prediction
shows that if the Image is the motive for participation in
sports activities then the frequency of the participation in
sport activities is more frequent in both men and women.
The following method analyzes correlation between inde-
pendent variables of motivational and socio-demographic
characteristics, distance, accessibility of sport facilities and
dependent variables Time spent on sport activities (table
2). Cluster of variables which refer to socio-demographic
characteristics show that all three variables: Gender, Age,
and Marriage status signicantly correlate in a negative way
with time spent on sport activities. Females, married and
older subjects spend less time practicing sport activities,
while males, single and younger subjects spend more time
practicing sport activities. Cluster of variables of motiva-
tional characteristic of subjects participating in sport activ-
ities show that variables which refer to Image, Profession
and Other reasons, have statistically signicant correlation
to the time spent on sport activities. The cluster of Variables
of number and distance of sport facilities shows that only
two variables are signicantly correlated to the time spent
on sport activities and those are: the variable Distance up
to 1km and variable Distance over 2 km. Results show that
sample subjects who have sport facilities 2 km away from
their homes spend more time participating in spor t activi-
ties than other subjects.
Table 5 shows that coefcients of multiple correlation and
multiple determination of predictor structures of the rst
model includes about 12,6% of variation among subjects
in time spent on sport activities. The second model in-
cludes 19,6 and the third around 24,9% variations among
subjects in time spent on sport activities. This percentage
of variations is 28,3% in the forth model and 29,9% in the
fth model of prediction.
ANOVA analysis shows that all mentioned models of pre-
diction are on statistically signicant level lower than 1%.
(F=64.451 & p=0.000 for the rst model, F=54,523 &
p=0.000 for the second model, F=49,052 &p=0.000
for the third model , F=43.856 & p=0.000 for the fourth
model & F=37.756 & p=0.000 for the fth model). That
means that all isolated models of prediction perform better
Table 5. Demonstration of means of coefcient of multiple correlation and multiple determination in examining models of prediction
of time spent on sport activities
Model R R2Coefcient of
determination
Std. Error of
parameter
1 0.355 0.126 0.124 1.057
2 0.443 0.196 0.193 1.014
3 0.499 0.249 0.243 0.982
4 0.532 0.283 0.277 0.960
5 0.547 0.299 0.291 0.951
predictions of means of time spent on sport activities in
relation to the generated predictions.
The rst predictor variable which refers to Profession as
a reason for participating in spor t activities is included in
structure of ve isolated models of prediction of time spent
on sport activities. Regression coefcient of this predictor
is positive and statistically signicant on a level lower than
1 % in all models of prediction. Results pf all ve models
show that Profession in sport has signicantly increased
effects of time spent on sport activities in relation to other
subjects. The second predictor variable; Other, which is in-
cluded in structure of second, third, fourth, and fth model
of has negative regression coefcient and statistically sig-
nicant on level lower than 1%. Subjects whose answer
that their motive of their participation in spor t activities is
Other time spent in sport activities is less in relation to the
subjects who don’t have this motive. The third predictor
variable Gender is included in structure of model 3, 4 and
5. Regression coefcient of this predictor variable in each
model is negative and statistically signicant on level low-
er than 1%. If the subject is female then one spends less
time on sport activities. The fourth predictor variable is Age
which is included in structure of fourth and the fth isolat-
ed model of prediction of time spent on sport activities.
Both model of prediction regression coefcients of this
independent variable are negative statistically signicant
HOMO SPORTICUS ISSUE 1 2016
41
on a level lower than 1% The fourth model of prediction
shows that if a person is one year older time spent on sport
activities is reduced. It’s the same case with the fth mod-
el. The fth predictor variable refers to distance of sport
facilities less than 1000 m and they spend less time on
sport activities.
Discussion
Analysis results conrmed that consuming basic comple-
mentary sport products (sport and recreation) inuenced
by socio-demographic characteristics of general public,
distance and number of sport facilities and motivation-
al characteristics of the subjects. results are conrmed
based on two key indicators of high statistical signicance.
(indicators : frequency of participation in sport activities
and time spent on sport activities)
The rst part of analysis refers to frequency of participation
in sport activities and time spent on spor t activities which
indicate that men, young people, single people participate
more frequently in sport activities in relation to women, old-
er people and married people. Analysis show that females,
married and older subjects spend less time practicing sport
activities, while males, single and young subjects spend
more time practicing sport activities. If subjects state the
Image or Profession as their motifs for practicing sport ac-
tivities then they spend more time practicing sport activities
in relation to the other subjects. Subjects whose answer is
that their motive of their participation in spor t activities is
Other participate less in sport activities than other subjects.
Also if analyze the distance of sport facilities we’ll nd that
subjects who have sport facilities within 1km range spend
less time practicing sport activities while subjects who have
sport facilities 2 km away from their homes spend more
time participating in spor t activities. Reasons for this are:
motivation, culture, or infrastructure factors which creates
need for consummation of sport and recreation.
Conclusion
In the area of scientic and objectively justied market-
ing analysis it is discovered that tjhere are several groups
of potential consumers which are related to: market of
non-users (women, older people and married people),
market of users of products and services of competition
(subjects who have sport facilities up to 1km located from
their homes) market of real users (men, younger people
and single people. Also two main motifs are determined
for using basic complementary product and those are the
individuals with motif Image and Looks or individuals who
are professional athletes. This research conrms appliance
of function of marketing and the main goal of the research
which objectivise market potential of basic complementa-
ry product (sport and recreation) on the market, from the
aspect of socio-demographic, motivational characteristics,
frequency of consummation of sports products and geo-
graphical accessibility of the sport facilities is gained.
Model Predictors Non-standard coefcient Standard beta-
coefcient
t-test p
B St. Eror B
1 Constant 2.01 0.05 - 39.48 <0.001
Professional 1.98 0.24 0.35 8.02 <0.001
2 Constant 2.14 0.05 - 40.12 <0.001
Professional 1.85 0.23 0.33 7.75 <0.001
Other -0.82 0.13 -0.26 -6.25 <0.001
3 Constant 2.26 0.05 - 40.36 <0.001
Professional 1.96 0.23 0.35 8.46 <0.001
Other -0.75 0.12 -0.24 -5.84 <0.001
Gender -0.62 0.11 -0.23 -5.55 <0.001
4 Constant 3.09 0.18 - 16.65 <0.001
Professional 1.86 0.22 0.33 8.19 <0.001
Other -0.63 0.12 -0.20 -4.96 <0.001
Gender -0.67 0.11 -0.24 -6.07 <0.001
Age -0.02 0.00 -0.19 -4.63 <0.001
5 Constant 3.15 0.18 - 17.07 <0.001
Professional 1.86 0.22 0.33 8.26 <0.001
Other -0.61 0.12 -0.19 -4.82 <0.001
Gender -0.66 0.11 -0.24 -6.02 <0.001
Age -0.02 0.00 -0.18 -4.43 <0.001
Distance to1000 m -0.28 0.09 -0.12 -3.14 <0.001
Table 6. Demonstration of means and level of signicance of regression of coefcient of certain predictors in model of prediction of
time spent on sport activities
HOMO SPORTICUS ISSUE 1 2016
42
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Submitted: September 13, 2015
Accepted: May 21, 2016
Correspondence to:
Almir Mašala, PhD.
Faculty of Sport and Physical Education University
of Sarajevo, Bosnia and Herzegovina
E-mail: almir.masala@gmail.com
HOMO SPORTICUS ISSUE 1 2016
43
HOMO SPORTICUS ISSUE 1 2016
44
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