Human Movement 13(2) 2012 - Akademia Wychowania Fizycznego

Transcription

University School of Physical Education in Wrocław
University School of Physical Education in Poznań
University School of Physical Education in Kraków
vol. 13, number 2 (June), 2012
University School of Physical Education in Wrocław (Akademia Wychowania Fizycznego we Wrocławiu)
University School of Physical Education in Poznań (Akademia Wychowania Fizycznego im. Eugeniusza Piaseckiego w Poznaniu)
University School of Physical Education in Kraków (Akademia Wychowania Fizycznego im. Bronisława Czecha w Krakowie)
Human Movement
quarterly
vol. 13, number 2 (June), 2012, pp. 91 – 194
Editor-in-Chief Alicja Rutkowska-Kucharska
University School of Physical Education, Wrocław, Poland
Associate Editor
Wiesław Osiński
University School of Physical Education, Poznań, Poland
Andrzej Klimek
University School of Physical Education, Kraków, Poland
Editorial Board
Tadeusz Bober
Jan Celichowski Lechosław B. Dworak Ewa Kałamacka Tadeusz Koszczyc
Stanisław Kowalik Juliusz Migasiewicz Edward Mleczko Łucja Pilaczyńska-Szcześniak
Zbigniew Szyguła Aleksander Tyka Marek Zatoń
Advisory Board
Wojtek J. Chodzko-Zajko
Charles B. Corbin Gudrun Doll-Tepper Józef Drabik
Kenneth Hardman
Andrew Hills
Zofia Ignasiak
Slobodan Jaric
Toivo Jurimae
Han C.G. Kemper Wojciech Lipoński
Gabriel Łasiński
Robert M. Malina Melinda M. Manore Philip E. Martin Joachim Mester Toshio Moritani
Andrzej Pawłucki John S. Raglin Roland Renson
Tadeusz Rychlewski
James F. Sallis James S. Skinner
Jerry R. Thomas
Karl Weber
Peter Weinberg
Marek Woźniewski
Guang Yue Wladimir M. Zatsiorsky Jerzy Żołądź
University of Illinois, Urbana, Illinois, USA
Arizona State University, East Mesa, Arizona, USA
Free University, Berlin, Germany
University School of Physical Education and Sport, Gdańsk, Poland
University of Worcester, Worcester, United Kingdom
Queensland University of Technology, Queensland, Australia
University of Delaware, Newark, Delaware, USA
University of Tartu, Tartu, Estonia
Vrije University, Amsterdam, The Netherlands
University of Texas, Austin, Texas, USA
Oregon State University, Corvallis, Oregon, USA
Iowa State University, Ames, Iowa, USA
German Sport University, Cologne, Germany
Kyoto University, Kyoto, Japan
Indiana University, Bloomington, Indiana, USA
Catholic University, Leuven, Belgium
San Diego State University, San Diego, California, USA
Indiana University, Bloomington, Indiana, USA
University of North Texas, Denton, Texas, USA
German Sport University, Cologne, Germany
Hamburg University, Hamburg, Germany
Cleveland Clinic Foundation, Cleveland, Ohio, USA
Pennsylvania State University, State College, Pennsylvania, USA
Translation: Michael Antkowiak, Tomasz Skirecki
Design: Agnieszka Nyklasz
Copy editor: Beata Irzykowska
Proofreading: Michael Antkowiak, Anna Miecznikowska
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Circulation: 200
HUMAN MOVEMENT
2012, vol. 13 (2)
contents
Christoph Alexander Rüst, Beat Knechtle, Irena Joleska, Patrizia Knechtle, Andrea Wirth,
Reinhard Imoberdorf, Oliver Senn, Thomas Rosemann
Is the prevalence of exercise-associated hyponatremia higher in female
than in male 100-km ultra-marathoners? ........................................................................................................... 94
Anderson S.C. Oliveira, Rogério B. Corvino, Mauro Gonçalves, Fabrizio Caputo, Benedito S. Denadai
Maximal isokinetic peak torque and EMG activity determined by shorter ranges of motion .......................102
Aleksandra Stachoń, Anna Burdukiewicz, Jadwiga Pietraszewska, Justyna Andrzejewska
Changes in body build of AWF students 1967–2008. Can a secular trend be observed?.................................109
Małgorzata Grabara
Analysis of body posture between young football players and their untrained peers.................................... 120
Krzysztof Buśko, Monika Lipińska
A comparative analysis of the anthropometric method and bioelectrical impedance analysis
on changes in body composition of female volleyball players during the 2010/2011 season..........................127
Bartłomiej Sokołowski, Maria Chrzanowska
Development of selected motor skills in boys and girls in relation to their rate of maturation –
a longitudinal study..............................................................................................................................................132
Edio Luiz Petroski, Diego Augusto Santos Silva, João Marcos Ferreira de Lima e Silva, Andreia Pelegrini
Health-related physical fitness and associated sociodemographic factors in adolescents
from a Brazilian state capital...............................................................................................................................139
José L. Arias
Does the modification of ball mass influence the types of attempted and successful shots
in youth basketball?..............................................................................................................................................147
Ryszard Panfil, Edward Superlak
The relationships between the effectiveness of team play and the sporting level of a team...........................152
Maciej Tomczak, Małgorzata Walczak, Grzegorz Bręczewski
Selected psychological determinants of sports results in senior fencers...........................................................161
Renata Myrna-Bekas, Małgorzata Kałwa, Tadeusz Stefaniak, Lesław Kulmatycki
Mood changes in individuals who regularly participate in various forms of physical activity.......................170
Tomasz Tasiemski, Maciej Wilski, Kamila Mędak
An assessment of athletic identity in blind and able-bodied tandem cyclists...................................................178
Ivo Jirásek, Emanuel Hurych
Pain and suffering in sport...................................................................................................................................185
Publishing guidelines – Regulamin publikowania prac........................................................................................ 190
93
HUMAN MOVEMENT
2012, vol. 13 (2), 94– 101
IS THE PREVALENCE OF EXERCISE-ASSOCIATED HYPONATREMIA
HIGHER IN FEMALE THAN IN MALE 100-KM ULTRA-MARATHONERS?
doi: 10.2478/v10038-012-0009-2
CHRISTOPH ALEXANDER RÜST 1, BEAT KNECHTLE 1, 2 *, IRENA JOLESKA 2 , PATRIZIA KNECHTLE 2 ,
ANDREA WIRTH 2 , REINHARD IMOBERDORF 3, OLIVER SENN 1, THOMAS ROSEMANN 1
1
Institute for General Practice and for Health Services Research, University of Zurich, Zurich, Switzerland
Gesundheitszentrum St. Gallen, St. Gallen, Switzerland
3
Klinik für Innere Medizin, Kantonsspital Winterthur, Winterthur, Switzerland
2
Abstract
Purpose. The prevalence of exercise-associated hyponatremia (EAH) has mainly been investigated in male endurance athletes.
The aim of the present study was to investigate the prevalence of EAH in female 100-km ultra-marathoners and to compare
them to male ultra-runners since females are considered more at risk of EAH. Methods. Changes in body mass, hematocrit,
[Na+] and [K+] levels in both plasma and urine, plasma volume, urine specific gravity, and the intake of energy, fluids and
electrolytes was determined in 24 male and 19 female 100-km ultra-marathoners. Results. Three male (11%) and one female
(5%) ultra-marathoners developed asymptomatic EAH. Body mass decreased, while plasma [Na+], plasma [K+] and hematocrit
remained stable in either gender. Plasma volume, urine specific gravity and the potassium-to-sodium ratio in urine increased
in either gender. In males, fluid intake was related to running speed (r = 0.50, p = 0.0081), but not to the change in body mass,
in post-race plasma [Na+], in the change in hematocrit and in the change in plasma volume. Also in males, the change in hematocrit was related to both the change in plasma [Na+] (r = 0.45, p = 0.0187) and the change in the potassium-to-sodium
ratio in urine (r = 0.39, p = 0.044). Sodium intake was neither related to post-race plasma [Na+] nor to the change in plasma
volume. Conclusions. The prevalence of EAH was not higher in female compared to male 100-km ultra-marathoners. Plasma
volume and plasma [Na+] were maintained and not related to fluid intake, most probably due to an activation of the reninangiotensin-aldosterone-system.
Key words: ultra-endurance, electrolyte disorder, fluid overload, sport nutrition
Introduction
Exercise-associated hyponatremia (EAH) is defined
as a serum sodium concentration of ([Na+]) < 135 mmol/L
and was described first in scientific literature in 1985
by Noakes et al. [1] in male ultra-marathoners in South
Africa as being due to “water intoxication”. EAH is a wellknown and a well-described fluid and electrolyte disorder in marathoners [2–8]. The prevalence of EAH varies
between 3% and 22% in marathoners depending upon
the number of studied athletes, their gender and fitness
level [2–6]. There is abundant literature about the pre
valence of EAH in marathoners [2, 4–7]. Studies investigating EAH in ultra-marathoners are rare, in which
exclusively male athletes have been investigated [9–11].
In marathoners presenting EAH, an association between
excessive fluid intake and both an increase in body
mass and a decrease in plasma sodium [Na+] has been
demonstrated [2, 5, 6, 12, 13]. In ultra-marathoners,
however, dehydration is a more common finding [14],
resulting in a decrease of body mass and an increase in
urine specific gravity [15, 16]. In cases of excessive fluid
* Corresponding author.
94
intake with fluid overload during endurance performance [17], we would also expect in ultra-runners a stable
or increased body mass [13, 17], a decrease in plasma
[Na+] [12, 13, 18], an increase in plasma volume [18] and
a decrease in hematocrit due to haemodilution [12].
Risk factors for fluid overload and subsequent EAH
are the female gender, a slow running pace and a high
frequency of fluid intake [2, 3, 19]. Following Noakes,
three independent mechanisms explain why some athletes develop EAH during and after prolonged exercise:
(i) overdrinking due to biological or psychological factors; (ii) an inappropriate secretion of antidiuretic
hormone (ADH), in particular, the failure to suppress
ADH-secretion in the face of an increase in total body
water; and (iii) a failure to mobilize Na+ from osmotically inactive sodium stores or the alternatively inappropriate osmotic inactivation of circulating Na+ [13].
Because the mechanisms causing factors (i) and (iii) are
unknown, it follows that the prevention of EAH requires
that athletes be encouraged to avoid overdrinking during
exercise [13]. Since ultra-marathoners run at a rather
slow pace [20, 21], they may be at an especially high
risk of fluid overload.
The aim of the present study was to investigate the
prevalence of EAH in both female and male ultramarathoners in the “100 km Lauf Biel” in Biel, Switzer-
HUMAN MOVEMENT
C.A. Rüst et al., Hyponatremia in ultra-marathoners
land. This race is the most famous 100-km ultra-mara
thon in Europe. The organizers offer a total of 17 aid
stations and the athletes may be accompanied by a cyclist
providing continuous fluid and nutrition support while
running. Since the female gender, a slow running pace
and excessive drinking behaviour [13, 19], combined
with a high frequency of fluid consumption [2, 13], are
considered as the main risk factors for fluid overload,
and subsequently developing EAH, we hypothesized that
(i) the prevalence of EAH would be higher in 100-km
ultra-marathoners compared to existing reports on ma
rathoners and that (ii) the prevalence of EAH would
be significantly higher in female than in male ultramarathoners.
Material and methods
After receiving approval by the Institutional Review
Board for use of Human Subjects of St. Gallen, Switzerland, all the participants of the 50th annual “100 km
Lauf Biel” in Biel, Switzerland in 2008 were contacted
via a separate newsletter, three months before the race,
where they were asked to participate in the current study.
Out of about 2,000 runners who were to start in the
race, 31 male and 19 female, non-professional, experienced ultra-runners agreed to take part in this study,
with all of them providing their informed written consent. The race began in the night of 13 to 14 June 2008,
with the runners beginning on 13 June at 10:00 p.m.
and had to finish the 100 km distance with a total
climb in altitude of 645 metres within a time limit of
21 hours. Two-thirds of the course was on asphalt
with the remaining third on unpaved roads. Throughout the 100 km there were 17 aid stations at intervals
of ~5 km that provided a variety of food and beverages.
The organizers offered isotonic sports drinks, tea, soup,
caffeinated drinks, water, bananas, oranges, energy bars
and bread. The athletes were allowed to be supported
by a cyclist in order to have access to food and clothing,
if necessary. At the start of the race the temperature
was 15° Celsius. During the night, the temperature
dropped to 8° Celsius and then rose to 18° Celsius the
next morning by 10:00 a.m. A maximum temperature
of 31°C was reached at 01:00 p.m. on 14 June 2008.
Out of the initial group of participants, twentyseven male and all female participants finished the
race within the 21 h time limit, with one male runner
finishing in the top three. Table 1 shows the age, anthropometric characteristics, training and pre-race experience of the subjects. Before the start of the race and
after arrival at the finish line, every participant underwent analysis to determine body mass, take blood
samples and be subject to urinary sampling. Body mass
was measured to the nearest 0.1 kg using an electronic
balance (Beurer, Germany) after voiding the urinary
bladder. The athletes were weighed pre- and post-race
in an identical manner in their running wear excluding
shoes. Samples of urine were collected for the determination of urine creatinine, urine [Na+], urine [K+] and
urine specific gravity. Urine specific gravity was analysed using a Clinitek Atlas® Automated Urine Chemistry Analyser (Siemens Healthcare Diagnostics, USA).
Creatinine in urinary samples was measured using
a COBAS INTEGRA® 800 (Roche Diagnostics, Switzer-
Table 1. Comparison of age, anthropometric characteristics, training and pre-race experience
between male and female subjects. Results are presented as mean (SD)
Male finishers (N = 27)
Age (years)
Body height (m)
Body mass (kg)
Body mass index (kg/m2)
Number of years participating in running (years)
Weekly distance ran (km)
Hours ran per week (h)
Number of weekly training units (n)
Minimal distance per week (km)
Maximal distance per week (km)
Distance per run training session (km)
Duration of run training sessions (min)
Mean speed of the training sessions (km/h)
Yearly running distance (km)
Yearly hours ran (h)
Number of finished marathons (n)
Personal best time in a marathon (min)
Number of finished 100 km ultra-marathons (n)
Personal best time in a 100 km ultra-marathon (min)
46.7 (8.0)
1.78 (0.06)
74.3 (10.2)
23.3 (2.2)
11.2 (8.4)
73.7 (28.7)
7.8 (3.2)
4.3 (1.5)
26.6 (21.4)
85.6 (56.7)
18.8 (12.8)
88.0 (24.9)
10.7 (1.5)
3,158.9 (1,568.1)
307.1 (171.5)
30.9 (38.5) (n = 27)
207.8 (31.3)
4.9 (6.9) (n = 18)
621.6 (250.2)
Female finishers (N = 19)
44.0 (10.4)
1.67 (0.09)**
61.0 (10.1)**
21.5 (2.3)*
10.3 (8.3)
66.3 (19.5)
6.9 (2.3)
4.0 (0.7)
29.4 (19.0)
74.0 (35.4)
14.9 (3.0)
87.9 (24.5)
9.5 (1.6)**
2,185.8 (924.1)
222.4 (80.8)
20.0 (14.3) (n = 17)
231.2 (20.4)**
2.8 (3.5) (n = 5)
831.8 (173.3)
* p < 0.05; ** p < 0.01
95
HUMAN MOVEMENT
land). Electrolytes in the urine samples were determined
using an ISE IL 943 Flame Photometer (GMI, Inc., USA).
[Na+] and [K+] in urine were normalised for creatinine
in urine. At the same time, blood was sampled to determine hematocrit, plasma [Na+] and plasma [K+] using
an i-STAT® 1 System (Abbott Laboratories, USA). The
changes in plasma volume were calculated according
Beaumont’s equation [22].
While running, the athletes consumed food and
drinks ad libitum and recorded their intake of fluid
and solid nutrition using paper and pencil at each aid
station. At every station, beverages and food were provided in same size portions. The ingestion of fluids,
electrolytes and solid food between pre- and post-race
measurements were determined by the reports of the
athletes using a food table [23]. Energy expenditure
was estimated using a stepwise calculation using body
mass, mean velocity and the time spent during performance [24].
Pre-race, the participants were asked to maintain
a comprehensive training diary consisting of their daily
workouts, their distance and duration in preparation
for the race. The training record consisted of the number
of training units showing duration, kilometres, pace,
the amount of kilometres ran per week, the amount of
hours ran, the minimal and maximal amount of kilometres ran per week as well as the running speed during
training in min/km were also recorded. Additionally,
they reported on the number of years they had actively
participated in running, the number of marathons and
100-km ultra-marathons they successfully completed
and the best times that were achieved in these races.
Following their arrival at the finish line, the subjects
were asked if they felt the symptoms of EAH [19].
Data are presented as mean and standard deviation
(SD). The measured parameters of both males and females were compared using the Kruskal-Wallis test.
The Student’s t-test was used to compare the parameters
before and after the race. Correlations in the changes in
the parameters during the race were evaluated using
the Pearson’s Product-Moment Correlational Analysis.
The significance level was set at p < 0.05.
Results
Three male (11%) and one female (5%) finishers
were diagnosed with asymptomatic EAH, where one
male and one female athlete showed post-race plasma
[Na+] of 131 mmol/L, and two male athletes were found
with plasma [Na+] of 134 mmol/L. Throughout the race,
females ran slower, consumed less energy, expended
less energy, ingested less fluid and less electrolytes than
the males (see Tab. 2). Body mass decreased (p < 0.01)
while plasma [Na+] and plasma [K+] remained unchanged
(p > 0.05) in either gender. For both genders, the decrease in body mass was found not to be related to an
energy deficit (p > 0.05). Also, the decrease in body mass
was not related to running speed (p > 0.05). Hematocrit
levels decreased non-significantly in the males (p > 0.05)
and significantly in the females (p < 0.05), plasma volume
increased by 5.5% in the males and by 6.4% in the
females. For both the males and the females, race time
was not correlated to post-race plasma [Na+] (p > 0.05).
In the three male athletes with EAH, body mass decreased by – 3.5 (1.2) kg.
Fluid intake was significantly and positively related
to the running speed of males (see Fig. 1), but not for
females (see Fig. 2). Running speed, however, was neither
related to post-race plasma [Na+] (p > 0.05) nor to the
change in plasma [Na+] (p > 0.05) in either gender. For
both males and females, there was no association between fluid intake and the following: the change in
body mass, post-race plasma [Na+], the change in hematocrit and the change in plasma volume (p > 0.05).
Sodium intake was not related to post-race plasma [Na+]
and potassium intake was not related to post-race plas-
Table 2. Comparison of race time, energy turnover, fluid and electrolyte intake and body mass
between male and female subjects. Results are presented as mean (SD)
Race time (min)
Running speed (km/h)
Energy intake (kcal)
Energy expenditure (kcal)
Energy balance (kcal)
Fluid intake (L/h)
Fluid intake (L/kg body mass)
Sodium intake (mg/h)
Potassium intake (mg/h)
Body mass pre-race (kg)
Body mass post-race (kg)
Body mass change (kg)
* p < 0.05, ** p < 0.01 (between genders); ## p < 0.01 (within gender)
96
689.9 (119.9)
8.9 (1.6)
758.5 (302.3)
7,424.5 (1,666.4)
–6,666.0 (1,648.5)
0.52 (0.18)
0.08 (0.02)
445 (471)
146 (176)
74.3 (10.2)
72.4 (10.1)
–1.9 (1.5)##
770.5 (103.4)*
7.9 (1.1)*
566.8 (229.3)*
6,198.2 (1,366.8)*
–5,631.4 (1,187.6)*
0.32 (0.11)**
0.21 (0.03)**
364 (250)**
62 (24)**
61.0 (10.1)**
59.6 (10.0)**
–1.4 (0.9)##
HUMAN MOVEMENT
Figure 1. Hourly fluid intake during the race
was significantly and positively related to running speed
in males (N = 27) (r = 0.50; p = 0.0081)
Figure 2. In females, fluid intake and running speed
had no association (N = 19) (r = 0.00, p = 0.98)
Table 3. Comparison of race performance and results obtained during the race between male and female subjects.
Results are presented as mean (SD)
Hematocrit pre-race (%)
Hematocrit post-race (%)
Hematocrit change (%)
Change in plasma volume (%)
Plasma sodium pre-race (mmol/L)
Plasma sodium post-race (mmol/L)
Plasma sodium change (mmol/L)
Plasma potassium pre-race (mmol/L)
Plasma potassium post-race (mmol/L)
Plasma potassium change (mmol/L)
Urine sodium/Creatinine pre-race (mmol/mmoL)
Urine sodium/Creatinine post-race (mmol/mmoL)
Urine sodium/Creatinine change (mmol/mmoL)
Urine potassium/Creatinine pre-race (mmol/mmoL)
Urine potassium/Creatinine post-race (mmol/mmoL)
Urine potassium/Creatinine change (mmol/mmoL)
Potassium-to-sodium ratio pre-race
Potassium-to-sodium ratio post-race
Potassium-to-sodium ratio change
Urine specific gravity pre-race (g/mL)
Urine specific gravity post-race (g/mL)
Urine specific gravity change (g/mL)
44.1 (2.8)
43.0 (2.9)
–1.1 (3.3)
+5.5 (13.9)
139.5 (1.4)
139.6 (3.8)
0.15 (4.13)
4.9 (0.7)
5.3 (1.0)
0.5 (1.2)
0.022 (0.009)
0.006 (0.004)
–0.016 (0.009)##
0.008 (0.006)
0.010 (0.004)
0.002 (0.006)
0.36 (0.19)
2.10 (1.12)
1.72 (1.15) ##
1.013 (0.008)
1.026 (0.005)
0.012 (0.007) ##
41.5 (2.4)**
40.3 (3.4)**
–1.2 (3.5)#
+6.4 (13.7)
138.4 (1.7)*
137.7 (2.3)*
–0.74 (2.23)
4.7 (0.5)
4.7 (0.6)*
0.05 (1.0)
0.031 (0.016)*
0.010 (0.002)
–0.027 (0.015)*##
0.011 (0.006)*
0.009 (0.006)
–0.002 (0.008)
0.39 (0.21)
2.25 (1.03)
1.86 (1.08)##
1.011 (0.007)
1.024 (0.004)
0.013 (0.007)##
* p < 0.05, ** p < 0.01 (between genders); # p < 0.05, ## p < 0.01 (within gender)
ma [K+] in either gender (p > 0.05). In males, the change
in plasma [Na+] was related to the change in hematocrit
(see Fig. 3). Urine specific gravity increased in both male
and female subjects (p < 0.01), urine [Na+] decreased
(p < 0.01) and urine [K+] remained unchanged (p > 0.05)
(see Tab. 3). The potassium-to-sodium ratio in urine increased in both males and females (p < 0.01). The change
in post-race potassium-to-sodium ratio in urine was significantly and positively related to the change in hematocrit in males (see Fig. 4), but not in females (see Fig. 5).
97
HUMAN MOVEMENT
Figure 3. The change in plasma [Na+] was significantly
and positively related to the change in hematocrit in males
(N = 27) (r = 0.48, p = 0.015)
Figure 4. The change in the post-race potassium-to-sodium
ratio in urine was significantly and positively related
to the change in hematocrit in males
(N = 27) (r = 0.32, p = 0.01)
The weekly running distance (r = –0.48, p = 0.0122),
the mean running speed during training (r = –0.52,
p = 0.0053), the personal best time in a marathon (r =
0.62, p = 0.0005) and the personal best time in a 100-km
ultra-marathon (r = 0.79, p = 0.0002) were related to
the achieved race time in the group of males. In females,
the training variables were not related to race time
(p > 0.05), however, the personal best time in a marathon (r = 0.59, p = 0.0136) and the personal best time
in a 100-km ultra-marathon (r = 0.82, p = 0.0091) were
associated with their race time.
Discussion
The aim of the present study was to investigate the
prevalence of EAH in both female and male ultramarathoners in a 100-km ultra-marathon. Since the
female gender, a slow running pace and excessive drinking behaviour with a high frequency of fluid consumption were considered as the main risk factors for
fluid overload, we hypothesized (i) that the prevalence
of EAH would be higher in 100-km ultra-marathoners
as based on the available reports on marathoners and
(ii) be especially higher in females than in male ultramarathoners. Three males (11%) and one female (5%)
developed asymptomatic EAH. The 11% prevalence of
EAH in the male ultra-marathoners was the same rate
as had been recently found in marathoners in the London Marathon [5]. The prevalence rates for EAH for
marathoners seem, however, to vary between 3% [6]
to 22% [3] depending upon weather and temperature
[6] and the fitness level of the subjects [3]. For the female
98
Figure 5. In females, the change in the post-race
potassium-to-sodium ratio in urine showed
no association with the change in hematocrit
(N = 19) (r = –0.01, p = 0.96)
ultra-marathoners, the 5% prevalence of EAH was considerably lower compared to the males.
Excessive fluid intake leading to fluid overload is
considered to be the most important risk factor for EAH
[2, 13, 19]. Fluid intake was significantly and positively
related to running speed for males (see Fig. 1), where
faster male athletes were drinking more compared to
slower ones. However, fluid intake was not associated
with the decrease in body mass, post-race plasma [Na+],
HUMAN MOVEMENT
the change in hematocrit and the change in plasma
volume. For fluid overload, fluid intake would have to
have been far greater and the athletes would have had
to gain weight as described by Speedy et al., where one
Ironman triathlete with EAH and who presented plasma
[Na+] < 130 mmol/L drank 16 L over the course of the
event and gained 2.5 kg in body mass [17]. The athletes
in this race, compared to a classical marathon, had the
opportunity to be supported by a cyclist. This cyclist
could carry food and drinks as well as additional clothing. We assume that the faster runners were followed by
a cyclist who provided fluids between the aid stations,
so they did not have to stop at each aid station to replenish their fluid level. However, the increased availability
of fluids did not lead to fluid overload and EAH.
Apart from the female gender, event inexperience
and a slow running pace are also considered as risk factor
for EAH [19]. In the present subjects, training volume
regarding the distance ran each year and the amount
of hours ran was not different between genders. The
female ultra-marathoners ran slower during training
and had a slower personal best marathon time; however, the personal best time in a 100-km ultra-marathon was not different between genders. Experienced
ultra-runners with a fast race time were obviously able
to consume rather large amounts of fluids so that neither dehydration nor fluid overload occurred. We assume that pre-race experience is an important factor
in preventing EAH in ultra-marathoners. In these subjects, weekly running distance, mean running speed
during training, personal best time in a marathon and
personal best time in a 100-km ultra-marathon were all
related to race time. Recent reports on 100-km ultramarathoners reported that pre-race experience such as
a high training volume in the distance ran in a week,
a fast running speed during training and a fast personal best time in a marathon were associated with race
time in a 100-km ultra-marathon [25–27]. A high training volume [25, 26] and a fast running speed while
training [25–27] were especially significant indicators
for a fast 100-km race time. We presume that these
subjects were both highly trained and highly experienced ultra-runners which might explain that the
prevalence of EAH was lower in these athletes compared to existing reports on marathoners.
The mean hourly fluid intake was 0.52 (0.18) L for
males and 0.32 (0.11) L for females, where males were
consuming more fluids compared to females. Faster male
runners drank more than slower runners (see Fig. 1),
whereas no association between running speed and
fluid intake existed in females (see Fig. 2). We speculated that a slower running pace during the race coupled with a frequent fluid intake would lead to fluid
overload and EAH. In contrast, the faster runners
drank more when compared to slower ones while running speed showed no association with either postrace plasma [Na+] or the change in plasma [Na+]. The
fact that no fluid overload occurred in the faster runners although they drank more might be explained by
a higher perspiration rate in these runners. We assume
that the rather low amount of fluids despite ad libitum
fluid consumption was responsible for the fact that no
fluid overload occurred. Although aid stations were provided every ~5 km and athletes could be followed by
a support crew to provide fluids, both male and female
athletes were found to not overdrink. In general, amounts
greater than 0.8 L per hour to 1.6 L per hour are recom
mended to maintain hydrated in performances lasting
1–3 h [25]. However, hourly amounts of ~0.5 L could also
lead to fluid overload and a decrease in serum [Na+]
concentration [12, 18]. Stuempfle et al. reported fluid
consumptions of 0.3 (0.1) L per hour in an ultra-distance race [12], and Speedy et al. described a mean hourly
fluid intake of 0.7 L in Ironman triathletes [18]. In both
studies, subjects developing EAH had evidence of fluid
overload despite a moderate fluid intake. Stuempfle et al.
concluded that the current recommendations for ultradistance athletes to consume 0.5 L to 1.0 L per hour
may be too high [12], and Speedy et al. summarised that
subjects developing EAH had evidence of fluid overload despite modest fluid intakes [18]. Therefore, recommendations for fluid intake, especially in ultra-endurance performances, should be adapted to take into
account these recent findings, where Gisolfi and Duchman have already recommended reducing hourly fluid
intake to 0.5 L to 1.0 L for endurance performances
lasting longer than 3 h [28]. Their recommendations
for fluid intake are as follows: a possible starting point
suggested for marathon runners (who are hydrated from
the outset) is they drink ad libitum from 0.4 L per hour
to 0.8 L per hour, with the higher rate suggested for
faster, heavier individuals competing in warm environments while the lower rate for the slower, lighter persons competing in cooler environments [29–32].
In a state of fluid overload, we would expect a stable
or rather increased body mass [19]. We found, however,
a significant decrease in body mass and a significant
increase in urine specific gravity in the studied ultrarunners. Since the energy deficit during the race was
not related to the change in body mass, the decrease
in body mass must be therefore associated with dehydration. In cases of dehydration resulting from ultramarathon running [14], body mass should decrease
and urine specific gravity should increase [15, 16]. Regarding our results, a loss of ~2.5% in body mass and an
increase in urine specific gravity to ~1.025 g/mL indicated severe dehydration, according to Kavouras [15].
Hematocrit decreased non-significantly in males and
significantly in females, plasma volume increased by
5.5% in males and by 6.4% in females. A transient expansion in plasma volume is reported after endurance
events [33]. The increase in plasma volume, however,
was not related to fluid intake. A possible explanation
for the increase in plasma volume could be a retention
99
HUMAN MOVEMENT
of [Na+] as a consequence of increased aldosterone activity since both fluid and sodium intake were not related
to post-race plasma [Na+] [12]. After intense exercise,
aldosterone increases and rises with growing exercise
intensity [34]. The activation of the renin-angiotensinaldosterone system (RAAS) leads to an enhanced retention of plasma [Na+] and water, consequently resulting
in an increase in plasma volume. An increased activity
in aldosterone should lead to an increase in plasma [Na+]
according to the findings of Wade et al. from a 20-day
500-km race [35]. We found, however, no change in
plasma [Na+] while urine [Na+] declined. The potassiumto-sodium ratio in urine was, however, increased. The
potassium-to-sodium ratio in urine is a physiological
reflection of the [K+] excretion in the distal tubulus
and when compared to [Na+] re-absorption as an estimate of the aldosterone activity in serum. We see the
increase in the potassium-to-sodium ratio in urine as
a stimulation of the RAAS. During the race, more urine
[K+] than urine [Na+] was excreted and a positive ratio
for urine [K+] to urine [Na+] suggests an increased activity of aldosterone. A recent study on male 100-km
ultra-marathoners showed a significant and positive
association between the change in aldosterone and both
the change in the potassium-to-sodium ratio in urine
and the post-race transtubular potassium gradient [36].
A potassium-to-sodium ratio in urine > 1.0 reflects
a contraction of the effective extra-cellular volume
leading to a hyperreninemic hyperaldosteronemia. Since
the change in hematocrit was positively related with
both the change in plasma [Na+] (see Fig. 3) and the
post-race potassium-to-sodium ratio in urine (see Fig. 4)
for males, we assume that both the change in hematocrit and the increase in plasma volume was due to an
increased activity of aldosterone and not due to fluid
intake. However, the decrease in hematocrit could also
be a result of intravascular hemolysis while running.
One limitation of this study is that we did not record the urine output of the athletes during the race.
Fluid balance might be estimated better with fluid intake and urine output. Future studies should include
fluid balance with an estimation of urines loss.
Conclusion
To summarize, the prevalence of EAH in these
100-km ultra-marathoners was not higher compared
to existing reports on marathoners and EAH was not
more frequent in female than in male ultra-marathoners. Although body mass decreased, plasma volume
and plasma [Na+] were maintained. Fluid intake showed
neither an association with the decrease in body mass,
nor with post-race plasma [Na+] and the increase in
plasma volume. We assume that the rather low fluid
intake was responsible for the low prevalence of EAH.
The potassium-to-sodium ratio in urine increased postrace to >1.0 and showed a significant and positive as100
sociation with the change in hematocrit. Maintained
fluid homeostasis in these ultra-runners was most pro
bably due to a stimulation of the RAAS. Future studies
investigating EAH in ultra-marathoners should determine the activity of aldosterone and include larger samples of female ultra-marathoners.
Acknowledgments
We wish to thank the organisers of ‘100 km Lauf Biel’ and
the athletes for their help in collecting data. In addition we
wish to thank Mary Miller from England who helped us
with translating this manuscript.
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Paper received by the Editors: March 12, 2011
Paper accepted for publication: December 5, 2011
Correspondence address
Beat Knechtle
Facharzt FMH für Allgemeinmedizin
Gesundheitszentrum St. Gallen
Vadianstrasse 26
9001 St. Gallen, Switzerland
e-mail: beat.knechtle@hispeed.ch
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2012, vol. 13 (2), 102– 108
MAXIMAL ISOKINETIC PEAK TORQUE AND EMG ACTIVITY
DETERMINED BY SHORTER RANGES OF MOTION
doi: 10.2478/v10038-012-0010-9
ANDERSON S.C. OLIVEIRA 1, 2 , ROGÉRIO B. CORVINO 3, 4, MAURO GONÇALVES 2 ,
FABRIZIO CAPUTO 3, 4, BENEDITO S. DENADAI 3 *
1
Biomechanics Laboratory, São Paulo State University, Rio Claro, Brazil
Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
3
Human Performance Laboratory, São Paulo State University, Rio Claro, Brazil
4
Center for Health and Sport Science, Santa Catarina State University, Florianópolis, Brazil
2
Abstract
Purpose. Isokinetic tests are often applied to assess muscular strength and EMG activity, however the specific ranges of motion
used in testing (fully flexed or extended positions) might be constrictive and/or be painful for patients with injuries or undergoing rehabilitation. The aim of this study was to examine the effects of different ranges of motion (RoM) when determining
maximal EMG during isokinetic knee flexion and extension with different types of contractions and velocities. Methods. Eighteen
males had EMG activity recorded on the vastus lateralis, vastus medialis, semitendinosus and biceps femoris muscles during
five maximal isokinetic concentric and eccentric contractions for the knee flexors and extensors at 60° · s–1 and 180° · s –1. The
root mean square of EMG was calculated at three different ranges of motion: (1) a full range of motion (90°–20° [0° = full knee
extension]); (2) a range of motion of 20° (between 60°–80° and 40°–60° for knee extension and flexion, respectively) and (3)
at a 10° interval around the angle where peak torque is produced. EMG measurements were statistically analyzed (ANOVA) to test
for the range of motion, contraction velocity and contraction speed effects. Coefficients of variation and Pearson’s correlation
coefficients were also calculated among the ranges of motion. Results. Predominantly similar (p > 0.05) and well-correlated
EMG results (r > 0.7, p 0.001) were found among the ranges of motion. However, a lower coefficient of variation was found for
the full range of motion, while the 10° interval around peak torque at 180° · s –1 had the highest coefficient, regardless of the
type of contraction. Conclusions. Shorter ranges of motion at around the peak torque angle provides a reliable indicator when
recording EMG activity during maximal isokinetic parameters. It may provide a safer alternative when testing patients with
injuries or undergoing rehabilitation.
Key words: torque, concentric, eccentric, knee extension, joint angle
Introduction
Maximal strength is currently one of the most important parameters tested in sports performance and
rehabilitation programs. Even modest sports associations have procedures for measuring maximal capacity
(using standard resistance training equipment), while
more sophisticated centers may make use of isokinetic
equipment (which provides constant velocity throughout
the entire range of motion). In addition, an evaluation
of muscular activity by use of electromyography [EMG]
during maximal effort provides more accurate results
and optimizes readings during testing and training [1, 2].
This isokinetic procedure usually requires a full range
of motion (RoM) from which the moment of maximal
torque is selected for analysis.
The optimized joint positions to produce torque are
~40–80° for knee extension [3–5] and 40–60° for knee
flexion [4, 5] (0° = full extension). However, existing
102
literature presents some divergent results on maximal
torque and joint positions: (1) the use of short RoMs
(partitions of 15° to 30° throughout the full RoM) provides similar results compared to full RoM at low and
moderate speeds, but also presents noteworthy inconsistencies [5, 6], while (2) other studies have verified
that the further from the optimized length-tension
joint position, the lower the maximal torque [3, 4, 7].
Despite these conflicting results, the use of isokinetic measurements are also used for rehabilitation
purposes, which itself presents a number of idiosyncrasies of joint RoM. Patients after trauma or with chronic
knee disease should avoid the use of full RoM during
such strength measurements [8], which can lead to complications in not allowing maximal torque to be determined as well as related EMG activity. Concerned about
how patients can adapt their neuromuscular system
during restricted training programs, Barak et al. [8]
found a transferability between the strength gains
from using a RoM of 30–60° knee extension to other
different RoMs (5°–30° and 60°–85°), which is useful
for injured/rehabilitation patients. Thus, at least for rehabilitation purposes, the most likely angles providing
maximal torque may be avoided.
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A.S.C. Oliveira et al., EMG at different ranges of knee motion
During isokinetic measurements both the contraction
type and speed are very important issues; it is well
known that concentric contractions show lower peak
torque than eccentric contractions [7, 9], with the EMG
activity for eccentric actions also being lower. Contraction speed, which affects generating peak torque, has
also been widely investigated, but EMG activity was
found not to follow the same pattern and shows no
changes among different contraction speeds [2, 7, 9].
Recent studies have assessed EMG activity during
maximal contractions at short RoMs [9, 10], which include the range corresponding to the optimal lengthtension relationship (i.e., the range that includes the
angle of peak torque). Sports performance and rehabilitation research could be benefited by using shorter
RoMs to evaluate maximal parameters, since knee joint
disorders may affect afferent information, especially
in the range of the injury [8]. It seems that by avoiding
larger RoM, EMG may be more accurately represented.
However there is no clear evidence in the reliability of
using shorter RoMs to determine EMG respective to
peak torque (PT) compared to the standard testing
procedure using Full RoM [6].
With this in mind, our main hypothesis was that
EMG during maximal isokinetic contractions, measured
in different RoMs, may differ between each other, since
different joint positions might reveal different muscle
activations. A second hypothesis was that the changes
verified between the different RoMs may be maintained when different contraction speeds are executed,
or even between different contractions types (eccentric
× concentric). The objective of the present study was
therefore to verify the differences in EMG activity during
maximal isokinetic contractions when measured by
different RoMs during concentric and eccentric actions
at both slow and moderate speeds.
Eighteen physically active, though not specifically
trained, males (mean ± SD: 22 ± 2 years old, height
179.1 ± 6.25 cm and weight 80.12 ± 9.56 kg) provided
their informed consent to participate in the study. All
subjects were healthy and free of cardiovascular, respiratory and neuromuscular disease. The study was approved
by the Institutional Research Ethics Committee.
The subjects were tested on two occasions. During
their first visit, all subjects were familiarized with the
maximal concentric and eccentric isokinetic contractions they were to perform (knee extension and flexion)
at speeds of 60° · s–1 and 180° · s–1 on an isokinetic dynamometer (Biodex System 3, Biodex Medical Systems,
USA). During the second test session, which took place
at least five days later, the (already familiarized) subjects returned to the laboratory to perform five maximal
concentric knee flexion and extension cycles at 60° · s–1
and 180° · s–1, and five maximal eccentric isokinetic
knee flexion and extension cycles at 60° · s–1 and 180° · s–1.
The order of concentric and eccentric contractions and
the contraction velocity was randomized.
During their familiarization session, the subjects
were fully instructed about the tasks they were to perform on the dynamometer. Prior to the test, they performed a standardized warm-up consisting of cycling
for 5 min at 70 Watts. After this, the subjects were positioned and allowed to perform the submaximal eccentric and isometric contractions at the tested velocity.
The subjects were instructed to work at maximal force
when performing knee extensions and flexions. The
order of the type of contraction during the familiarization and testing process was random, with 5–10 maximal contractions for knee flexion and extension at each
velocity (60° · s–1 and 180° · s–1). In order to standardize
the nomenclature, concentric contractions at 60° · s –1
and 180° · s–1 were named “CON-60” and “CON-180”,
respectively, for the knee flexors and extensors, while
the eccentric contractions at 60° · s–1 and 180° · s–1 were
named “ECC-60” and “ECC-180”, respectively, for the
knee flexors and extensors.
For both the familiarization and maximal test sessions, the subjects were placed in a sitting position
and securely strapped into the test chair. Extraneous
movement of the upper body was limited by two crossover shoulder harnesses and an abdominal belt. The
trunk/thigh angle was 85°. The axis of the dynamometer was lined up with the right knee flexion-extension
axis, and the lever arm was attached to the shank by
a strap. The subject was asked to relax his leg so that
passive determination of the effects of gravity on the
limb and lever arm could be carried out. The RoM for
the knee test was 70° for both concentric and eccentric
contractions (from 90° to 20° [0° = full extension]). To
ensure full extension, an anatomical 90° position was
determined by manual measurement using a goniometer.
All subjects were encouraged to give maximal effort
by both visual feedback and strong verbal encouragement when pushing the lever up, and then down, as
hard and as fast as possible during extension in the
eccentric contractions.
The isokinetic (torque, position and velocity) contractions were analyzed using specific algorithms created in MatLab software (The MathWorks Inc., USA).
Torque and EMG measurements were collected throughout the whole ROM. From the isokinetic contractions,
peak torque and angle of peak torque (PTANG) were determined by using specific Matlab algorithms. Torque
curves were smoothed by use of a 10 Hz Butterworth
fourth-order zero-lag filter. After this, the contraction
with the highest peak torque from five individual efforts
was considered for further analysis. Peak torque was
taken in an averaged window of 10° around peak torque
[9]. The right leg was utilized for all test procedures.
EMG signals from the vastus lateralis (VL), vastus
medialis (VL), biceps femoris (BF) and semitendino103
HUMAN MOVEMENT
sus (ST) muscles were selected for analysis. The subjects
were prepared for the placement of the EMG electrodes
by having their skin shaven at each electrode site, which
was then cleaned carefully with an alcohol wipe and
lightly abraded. Two Medi-Trace Ag–Ag/Cl electrodes
(Covidean, USA), with a diameter of 2 cm and an interelectrode distance of 2 cm, were used per muscle and
placed according to procedure suggested by Hermens
et al. [11]. The ground (reference) electrodes were positioned on the tibia. To ensure that movement artefacts
were kept to a minimum, the electrodes and cables were
taped to the skin with surgical tape. The EMG activity
was recorded by an EMG System800 (EMG System, Brazil) at 2000 Hz with a signal amplification of 2000x.
Surface EMG signals were high pass filtered (20 Hz) and
low pass filtered (500 Hz), with the common mode
rejection ratio set to 80 dB. All EMG data was stored
together with the isokinetic measurements (torque, joint
position, velocity) on a computer disk. The EMG data
were low-pass filtered (15 Hz using a Butterworth fourthorder zero-lag filter), and the root mean square (RMS)
was calculated for three different RoMs from the original
signal: for the entire range of motion (FULLEMG), a RoM
of 20° (20EMG – 60–80° for knee extension, 40–60° for
knee flexion), and a RoM of 10° around peak torque
(10EMG). These RoMs were selected to verify the differences in EMG determined by different joint positions
(see Fig. 1 as an example).
Data are presented as mean ± standard deviation (SD)
for torque measurements and mean ± standard error
of mean (SEM) for EMG activity. A Shapiro-Wilks test
assessed the normality of distribution for torque (PT
and PTANG) and EMG (FULLEMG, 20EMG and 10EMG) mea
surements. The effects of the type of contraction and
the velocity on PT and PTANG were accessed by two-way
analysis of variance (two contraction types [concentric × eccentric], two velocities [60° · s–1 × 180° · s–1]) with
Tukey’s HSD post-hoc test, when applicable. For the
EMG measurements, the effects of the RoMs (FULLEMG,
20EMG and 10EMG) were assessed by a non-parametric
test, the Kruskal-Wallis Analysis of Variance. Differe
nces in EMG between concentric and eccentric contractions, and between 60° · s–1 and 180° · s–1 were accessed by the non-parametric Wilcoxon test. To assess
the relationships between the EMG measurements
(FULLEMG × 20EMG; FULLEMG × 10EMG and 20EMG × 10EMG),
Pearson’s correlation coefficient (r) was used. For all statistical tests, the significance level was set at p 0.05.
Results
For both knee extension and flexion the effects of
the type of contraction were analyzed, where concentric contractions were found to present lower peak
torque (PT) than eccentric contractions at 60° · s–1 and
180° · s–1 (p 0.01, Tab. 1). For PTANG, there were no differences in the contraction velocity, with the only con-
104
FLEXORS
Figure 1. Representative diagram of the different ranges
of motion used to calculate maximal EMG activity.
The example was extracted from a subject during
isokinetic knee extension at 60° · s –1, showing the torque
curve, EMG for the vastus lateralis (EMG VL) and the
vastus medialis (EMG VM). The peak torque (PT) was
achieved at 61° (marked by a thick vertical black line
and arrow). The more commonly-used method covered
the entire range of motion (FULLEMG), the second method
was calculated in a 20° fixed window (between 60°
and 80° for knee extension – marked between the dotted
vertical lines), and the third method considered peak
torque as a reference point, using 10° around this peak
torque (in this example from 56° to 66° – marked between
the thin solid vertical lines)
EXTENSORS
Table 1. Mean ± SD peak torque (PT) and the angle of
peak torque (PTANG) during maximal isokinetic concentric
contractions at 60° · s –1 (CON-60) and 180° · s –1
(CON-180), and eccentric contractions at 60° · s –1
(ECC-60) and 180° · s –1 (ECC-180)
PT (Nm)
PTANG (°)
PTANG
(min)
PTANG
(max)
234 ± 46*†
63.7 ± 4.6
54.4
73.6
CON-180 166.8 ± 38
63.1 ± 5.8
47.3
71.8
ECC-60
316.2 ± 72
70.7 ± 6.6
56.4
78.2
ECC-180
317.7 ± 61* 65.7 ± 10.2
39.6
81.2
CON-60
123.6 ± 22†
46.4 ± 9
29.7
61.9
CON-180
114 ± 24
55.3 ± 15
39.1
86.4
ECC-60
183 ± 31
41.2 ± 7
23.8
54.8
ECC-180
193 ± 34*
42.4 ± 12*
31.5
82.8
CON-60
*denotes significant difference in relation to CON-180
(p 0.05)
†
denotes significant difference in relation to EXC-60
(p 0.05)
HUMAN MOVEMENT
traction that showed differences was during knee flexion, with higher PTANG during CON-180 compared to
ECC-180 (p 0.05). The PTANG for knee extension and
flexion was predominantly reached within the defined
RoM of 20EMG. However, there were cases in which
PTANG was not achieved at this RoM.
In general, the different ranges of RoM had no effect in determining the EMG respective to PT for both
knee extension (Fig. 2) and flexion (Fig. 3), regardless
of the type of contraction and velocity. Except for the
VL muscle during CON-180, 20EMG was higher than
FULLEMG and 10EMG (p 0.05). However, the coefficient
of variation was frequently lower for FULLEMG in comparison to the other measurements (Tab. 2). Qualitative
comparisons between these coefficients of variation
showed higher variations for VM and ST muscles for
all EMG measurements. Correlations between the different EMG measurements presented generally good
to strong coefficients of correlation (p 0.05) for the
knee extensor muscles (Tab. 3). All muscles presented
good and strong correlations between FULLEMG × 20EMG
(p 0.05), regardless of the type of contraction and
velocity. Non-significant correlations (p 0.05) were
found only for the knee flexor muscles, between FULLEMG
× 10EMG and between 20EMG × 10EMG.
Concentric contractions at 60° · s–1 presented higher
EMG activity than eccentric contractions (p 0.05) for
all tested muscles, except for VL at 10EMG and for ST at
20EMG. Similarly, at 180° · s–1, concentric contractions
presented higher EMG readings than eccentric contractions (p 0.05) for all tested muscles except for ST
at 20EMG (Fig. 2 and 3). The contraction velocity affected the knee extensor muscles mainly at 10EMG. The
VL and VM muscles presented higher EMG activity
during 60° · s –1 in relation to 180° · s –1 (p 0.05) for
eccentric contractions at 10EMG (p 0.05) and for concentric contractions for VL (p 0.05). In addition, the
VL muscle also presented higher EMG activity at 60° · s–1
in relation to 180° · s –1 (p 0.05) for eccentric contractions at 20EMG (p 0.05). The contraction velocity had
minor effects on the EMG of the knee flexor muscles,
regardless of the RoM (Fig. 3). The only significant dif-
†denotes significant difference in relation to the full range of motion and 10°
(p 0.05)
* denotes significant difference in relation to eccentric contractions at the
same velocity (p 0.05)
‡denotes significant difference in relation to 180° · s –1 at the same type
of contraction (p 0.05)
* denotes significant difference in relation to the eccentric contractions
at the same velocity (p 0.05)
‡denotes significant difference in relation to 180° · s –1 at the same type of
contraction (p 0.05)
Figure 2. Mean (SEM) root mean square (RMS) for the
vastus lateralis (VL) and vastus medialis (VM) during
maximal isokinetic concentric contractions at 60° · s –1
(CON-60) and 180° · s –1 (CON-180), and eccentric
contractions at 60° · s –1 (ECC-60) and 180° · s –1 (ECC-180).
The RMS was calculated considering the full range of
motion (white bars), a fixed range of motion of 20°
(grey bars) and a fixed range of motion of 10° (black bars)
Figure 3. Mean (SEM) root mean square (RMS) for the
semitendinosus (ST) and the biceps femoris (BF) during
maximal isokinetic concentric contractions at 60° · s –1
(CON-60) and 180° · s –1 (CON-180), and eccentric
contractions at 60° · s –1 (ECC-60) and 180° · s –1 (ECC-180).
The RMS was calculated considering the full range of
motion (white bars), a fixed range of motion of 20°
(grey bars) and a fixed range of motion of 10° (black bars)
105
HUMAN MOVEMENT
Table 2. Coefficient of variation (CV) of the root mean square (RMS) for the vastus lateralis (VL), vastus medialis (VM),
semitendinosus (ST) and biceps femoris (BF) during maximal isokinetic concentric contractions at 60° · s –1 (CON-60)
and 180° · s –1 (CON-180), and eccentric contractions at 60° · s –1 (ECC-60) and 180° · s –1 (ECC-180). The RMS was calculated
considering the full range of motion (F), a fixed range of motion of 20° (20°) and a fixed range of motion of 10° (10°)
VL
CON-60
EXC-60
CON-180
EXC-180
VM
ST
BF
F
20°
10°
F
20°
10°
F
20°
10°
F
20°
10°
23%
25%
25%
28%
28%
38%
29%
39%
21%
30%
45%
38%
39%
48%
41%
46%
43%
51%
44%
52%
43%
46%
58%
60%
45%
31%
45%
39%
45%
36%
55%
51%
52%
43%
59%
40%
25%
22%
30%
29%
25%
23%
38%
53%
24%
31%
57%
46%
Table 3. Pearson’s correlation coefficient (r) for EMG RMS of the vastus lateralis, vastus medialis, semitendinosus and biceps
femoris muscles during maximal isokinetic concentric contractions at 60° · s –1 (CON-60) and 180° · s –1 (CON-180), and
eccentric contractions at 60° · s –1 (ECC-60) and 180° · s –1 (ECC-180). The correlation coefficient was calculated between the
full range of motion and a fixed range of motion of 20° (F × 20°), between the full range of motion and a fixed range
of motion of 10° (F × 10°) and between a fixed range of motion of 20° and a fixed range of motion of 10° (10° × 20°)
F × 20°
F × 10°
20° × 10°
F × 20°
F × 10°
20° × 10°
F × 20°
F × 10°
20° × 10°
0.87*
0.98*
0.97*
0.73*
0.72*
0.98*
0.92*
0.77*
0.59*
0.96*
0.92*
0.89*
0.76*
0.87*
0.88*
0.75*
0.84*
0.92*
0.69*
0.66*
0.69*
0.80*
0.85*
0.56†
0.77*
0.94*
0.96*
0.83*
0.38
0.61*
0.61*
0.45
0.34
0.73*
0.71*
0.42
0.74*
0.76*
0.87*
0.82*
0.39
0.74*
0.45
0.25
0.55*
0.69*
0.52†
0.05
† denotes significance at p
0.05, * denotes non-significant correlation at p
ference was for the ST during concentric contractions
at FULLEMG.
Discussion
The purpose of this study was to compare EMG activity at three different ranges of motion: (1) the more
commonly used full range of motion, (2) at a fixed
RoM of 20° at the point where PT is present (20EMG),
and (3) at a fixed RoM of 10° around the point where
PT was found (10EMG). We expected some differences in
EMG due to the changes in the RoM. For instance, if
the PTANG is found at 55° of knee extension, the EMG
related to PTANG is not included for 20EMG (between 60°
to 80°). This fact could cause differences between mea
surements, especially in relation to 10EMG, which always
contains the EMG for PTANG (within a range of 5° below
and 5° above peak torque). Contrary to our first hypothesis, no substantial differences were found among
FULLEMG, 20EMG and 10EMG except for only one measurement (see Results). In this way, the EMG respective
to the peak torque produced during isokinetic contractions may be successfully obtained regardless of the RoM
used, although caution needs to be exercised with res
pect to data variability.
In general, the subjects presented their PTANG within
the range that had been verified in previous studies [7]
106
Biceps femoris
20° × 10°
CON-60
ECC-60
CON-180
ECC-180
Semitendinosus
F × 10°
Vastus medialis
F × 20°
Vastus lateralis
0.01
and concurrent to the results expected for such torque
measurements. These expected results include higher
PT during eccentric contractions, higher PT under lower
velocity during concentric knee extension [7, 10], and
minimal effects of both contraction type and velocity
on PTANG [12]. However, ECC-180 presented higher
variability, caused in part by the complexity of performing faster isokinetic actions even after extensive
familiarization procedures [5]. Therefore, avoiding the
use of full RoM for eccentric contractions might provide
less reliable results since the variability is not only higher
but the probability that a given patient produces PT
outside this range is also high. However, further investigation is needed to confirm this theory.
Higher EMG activity during concentric contractions
is related to reduced input to the motor cortex and/or
increases in peripheral facilitation during eccentric contractions [12, 13]. In the same way, as was previously
verified [2, 14], there were minimal changes in EMG
related to movement velocity. This issue as of yet has no
consensus in the literature on the subject, primarily
because of the wide range of studied velocities [7].
With respect to the effects of RoM on EMG, motor
unit recruitment is increasingly impaired as it reaches
more extreme RoM positions (excessive flexion or extension), where EMG activity is decreased in order to
protect the knee joint against high toque [4, 7]. This
HUMAN MOVEMENT
fact has been corroborated by differences in the root
mean square (RMS) of EMG at selected RoMs (0–15°,
25–50°, 50–75° and 75–90°) [7]. Furthermore, Reichard
et al. [5] investigated isokinetic knee flexions and extensions at full RoM (0–90°) and shorter ranges (0–30°,
30–60° and 60–90°), where they verified the differences in PT concurrent to sporadic differences in EMG
activity among the various ranges. In addition, the intermediate RoM (30–60°) presented strong correlation
with full RoM for knee flexors and extensors in both
types of contractions. Concentric contractions also
presented correlation between 30–60° and full RoM.
The present study verified the similarities of these EMG
measurements, even with differences found between
EMG × RMS and the assessed RoMs.
The similar results observed with the RoMs may
indicate that EMG activity can be assessed by alternative
ranges of motion and not necessarily full RoM, which
permits the noninvasive, safe examination of athletes
and patients with injuries and/or debilitating conditions [5, 8]. For instance, 20EMG (which includes the pro
bable angle of peak torque) provided similar and highly
correlated EMG results when compared to FULLEMG
(r > 0.73, all p 0.01), regardless of the contraction velo
city. Although similar to FULLEMG, the results from 10EMG
did not correlate as strongly, especially at ECC-180 for
the knee flexors. Eccentric contractions present certain
particularities during neural drive [7], a higher recruitment prior to the onset of movement, and in the early
phase of the movement [13]. Inter-subject variation in
PTANG and the fact that isokinetic contractions demand
a high level of recruitment throughout the contraction
may explain the decreased correlation (but still significant) between FULLEMG and 10EMG. This shorter RoM
does not necessarily contain peak EMG, since there is no
direct relationship between torque and EMG measurements and increases in the contraction level [15]. In
addition, the EMG activity between the RoM related
to the PTANG and others ranges may be similar [5, 7].
These recorded similarities may be in part caused by
external errors, such as the variable volume of muscle
tissue immediately adjacent to the recording site and
the co-contraction of antagonist muscles [5], which
could cause considerable variability [6]. In order to improve the reliability of the torque and EMG measurements, a careful familiarization process was provided
to rather considerable sample size used in this study
(n = 18), nonetheless the coefficient of variation was
higher for EMG10 and EMG20 (see Tab. 2). Croisier et
al. [6] have verified EMG variability for full RoM and
shorter RoMs at 30° · s–1 as well as predominantly higher
values at 90° · s–1. In the present study, we found lower
variability for CON-60 and ECC-60, which corroborated previous studies’ results that moderate and fast
contraction velocities are determined by higher intersubject EMG variability.
Conclusion
In summary, the use of different RoMs did not affect the EMG results for both isokinetic knee flexion
and extension, regardless of the type of contraction
and velocity. Additionally, the strong correlation between FULLEMG and 20EMG suggest that short ranges of
motion (60°–80° for knee extension and 40°–60° for
knee flexion) can be used to determine the EMG activity respective to peak torque for both concentric
and eccentric actions. This would allow patients who are
injured or undergoing rehabilitation to avoid extreme
joint positions which may be painful and/or harmful.
However, attention needs to be paid to the fact that
the smaller RoM used, the higher the variation of EMG
data, mainly at moderate velocities.
Acknowledgements
We would like to thank FAPESP and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for their
financial support. Oliveira A.S. is currently supported by
a CAPES PhD fellowship.
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Paper received by the Editors: March 17, 2011
Benedito S. Denadai
Human Performance Laboratory
UNESP. Av. 24 A, 1515, Bela Vista – Rio Claro
– SP – Brazil – CEP – 13506-900
e-mail: bdenadai@rc.unesp.br
HUMAN MOVEMENT
2012, vol. 13 (2), 109– 119
CHANGES IN BODY BUILD OF AWF STUDENTS 1967–2008.
CAN A SECULAR TREND BE OBSERVED?
doi: 10.2478/v10038-012-0011-8
ALEKSANDRA STACHOŃ *, ANNA BURDUKIEWICZ, JADWIGA PIETRASZEWSKA,
JUSTYNA ANDRZEJEWSKA
Abstract
Purpose. Previous research on intergenerational changes in body build has focused on body height and mass. The aim of this
study was to determine both the direction and sexual dimorphism of secular changes in body build by using a sample population of students attending the University School of Physical Education (AWF) in Wrocław, Poland. Methods. The anthropometric data used in this study were collected every year from 1967 to 2008 and included a sample size of 4688 males and 3922
females. The subjects were analyzed for changes in somatotype by use of Sheldon’s method, as modified by Heath and Carter.
Basic statistical analysis for significance and post-hoc tests were used to analyze the data with Statistica 9.0 software. The data
were then converted in Excel 2003 into chart form to analyze the direction of changes. Results. Analysis of the successive
classes of male and female subjects during the 40-year period under study revealed a number of different directional changes
in the mean values of body height, mass and the level of body build components. Trend lines, calculated by the mean values
of five-year intervals, indicated an increasing tendency in both body height and mass in the two genders. Mesomorphy was
found to be the largest factor of body build composition of females and males. Throughout the entire analyzed period, the
endomorphy of males was significantly lower in comparison to females. In women, the level of fatness was similar to their
level of musculature, but during the last several years the observed level of muscle in the students exceeded their fatness level.
Ectomorphy happened to be the most stable component of both sexes. Conclusions. Analysis on the male and female sample
population revealed a constant increase in body mass and height in successive generations. In female subjects, intergenerational changes were found to be characterized by a decrease in endomorphy and an increase in ectomorphy, while the level of
mesomorphy remained at a similar level. In men, a secular trend was visible with an increase in mesomorphy, while the levels
of endomorphy and ectomorphy stayed constant.
Key words: somatotype, body components, body composition, body height and weight, secular trend, sexual dimorphism
Introduction
Secular changes in body height and mass, themselves important determinates of body build, have
been observed in the population of Poland [1–4] and
other European countries [5–9] and widely reported
in research. It should be emphasized that the changes
observed in successive generations featured both accelerating changes as well as drops in mean body
height and mass. The most important factors related
to the occurrence of these secular trends are attributed to the general progress of civilization and a rise in
living standards [10], where an improvement of living
conditions is related to an increase in body mass and
height. In addition, research has pointed to many socioeconomic factors as also playing a role, these being:
social class [11], income and education level [12], family size [13], urbanization level [14], place of residence
[15] and nourishment levels, illness, birth weight, etc.
Decreases in mean body mass and height are frequently
related to a population’s deterioration of living conditions, undernourishment and various forms of stress.
The majority of research conducted on intergenerational changes in body build focused on examining
body height and mass. A large amount of research data
published by, e.g., Proos [16], Loesch et al. [17], Fredriks et al. [18], was taken from health examinations
conducted on recruits or children. There are few studies
that have been conducted on adult subjects, especially
women, and even less have tackled the problem of intergenerational changes in body structure and the mutual
proportion of individual body build components. Hence,
the question arises: is such an intergenerational trend
also related to somatotype?
The aim of this study was to determine the intergenerational changes in body build of adult students
attending the University School of Physical Education
(AWF) in the city of Wrocław, Poland. Sheldon’s
method, as modified by Carter and Heath [19], was
applied in order to assess body build composition.
Analysis was focused on the directions of changes of
individual body build components and the sexual dimorphism of these changes.
109
HUMAN MOVEMENT
A. Stachoń et al., Secular changes in body build
The data used in this study were taken from the
anthropometric measurement of 4688 male and 3922
female AWF students, collected annually from 1967
to 2008 always in the same season – autumn. The disproportional number of males compared to females,
as well as the rather diversified number of individuals
in each student year, reflects the demographical structure of the school and students over the past forty
years. Cross-sectional study allowed for the selection
of subjects who were at least 19 when anthropometric
measurements were taken (it was assumed that at that
point in time their growth had stopped). The body
measurement results were then used to calculate the
somatotype components (characterized as the level of
endomorphy, mesomorphy or ectomorphy) as per Sheldon’s method, modified by Carter and Heath [19]. Endomorphy, as the somatotype component connected
with fatness, was based on skinfold thickness measurements (at the subscapular, abdominal and arm sites).
The level of mesomorphy, which reflects musculature,
was based on the circumference of the arm and calf,
as well as elbow and knee breadth. In both cases, in
order to assess the final results, it was necessary to consider the subject’s body height. Ectomorphy, which
reflects body leanness, was assessed on the basis of body
height and mass.
Although the individuals in charge of taking measurements changed over the forty-year period, all anthropological measurements were always taken by physical
anthropologists experienced in anthropometry. During
the whole period under study, the same type of equipment was used (GPM Anthropological Instruments,
Siber Hegner Machinery, Switzerland) with the instruments regularly calibrated. Body height was measured
with the use of a Martin-type anthropometer, while
body mass with an electronic weight scale (accurate to
0.1 kg). The breadth of the humerus and femur were
measured with the use of sliding caliper (accurate to
1 mm). A Holtain skinfold caliper was used to assess
skin thickness (accurate to 0.2 mm). The girth of the
arm and calf was measured with the use of anthropometric tape (accurate to 1 mm).
Analysis of mean body height and mass values and
the mean levels of body build components were calculated at one- and five-year intervals. The study subjects were divided into five-year groups (related to the
five subsequent years of research) as such a division
better presented intergenerational changes. The mean
age of the subsequent groups varied between 19.5–23.9
years. The raw data was then organized into chart
form by use of Microsoft Office’s Excel 2003, which
presented the direction of changes in the mean values
of body height, body mass and the three somatotype
components.
110
Basic statistical analysis was performed using Statistica 9.0 software, with the data tested for significant differences (Student’s t-test, ANOVA) as well as
with Scheffe’s post-hoc test. Changes in somatotypes
were estimated by the use of Somatotype Analysis of
Variance test (SANOVA) [19]. The significance level
was set at p 0.05.
Results
Analysis of the successive student years of males
and females during the 40-year period revealed numerous changes in the mean values of body height
and mass (Tab. 1, 2). The observed changes occurred
in different directions. Trend lines, determined by the
mean values of the five-year periods, indicated an increasing tendency for both genders in both body height
(Fig. 1, 2) and body mass (Fig. 3, 4).
The mean body height of female students in the
1970s did not exceed 164 cm. In the 1980s, these values
varied between 163–167 cm, while in the 1990s the
mean value was approx. 166 cm (Tab. 1). The differences in the mean values between some years reached
3 or 4 cm. Men were characterized with a higher body
height than women. The height of male subjects in the
1960s and 1970s was 173–174 cm, in the 1970s and
1980s 176–177 cm, in the 1990s between 178–179 cm,
and after the year 2000 average body height exceeded
180 cm (Tab. 2). Analysis of the mean values did not
reveal any significant differences in classes of around the
same timeframe, however, students separated by larger
time intervals did show significant differences (Tab. 3, 4).
In addition, the variability of the subsequent groups of
women seemed to decrease. The mean values of body
height reported in the latter (more recent) years were
found to be not statistically significant (Tab. 3).
The lowest mean body mass values of women during
the analyzed period were observed between the 1960s
and 1970s (approx. 55 kg). After the 1990s, the mean
values of body mass in female subjects were approx.
60 kg (Tab. 1). The trend line based on the mean values of the five-year intervals revealed a constant but
slight increase in female body mass in the successive
groups (Fig. 3). The body mass of male subjects was
higher by approximately a few kilograms. At the end
of the 1960s and in the 1970s, body mass ranged between 66–70 kg. In the 1980s, mean body mass values
did not exceed 72 kg, while in the 1990s they were in
the range of 72–74 kg. After the year 2000, the mean
body mass of men ranged between 75–77 kg (Tab. 2).
The diversity of mean body mass values observed between male and female students during the successive
years was similar to what was reported in body height.
Similarly, students who studied during the same period
did not reveal any significant differences in reference
to body mass while those separated by larger time intervals did show significant differences. However, the
N
40
51
54
78
46
73
131
157
103
128
40
110
132
115
127
47
126
148
72
132
154
154
133
29
61
39
52
96
103
79
55
55
69
57
91
78
157
134
167
96
153
Year
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
19.7
19.6
19.7
19.8
19.6
19.6
19.6
19.8
19.8
19.8
19.7
19.7
19.6
19.7
19.7
19.8
19.6
19.8
20.3
19.7
19.6
19.7
19.7
23.9
21.4
20.3
21.8
20.9
20.7
20.6
20.4
20.5
20.5
20.5
20.2
20.4
20.1
20.1
20.1
19.7
19.7
Mean
age
162.8
160.2
161.7
162.7
161.0
161.6
162.4
163.9
164.0
163.0
163.1
164.3
165.2
164.8
165.7
163.6
164.2
164.1
165.0
165.0
165.4
165.5
165.3
164.3
166.3
166.6
166.3
166.2
167.2
165.5
164.3
166.4
167.3
167.5
165.9
166.2
166.8
168.2
166.7
169.1
167.6
Mean
4.60
5.03
4.96
5.38
5.46
6.67
5.36
6.00
5.80
5.91
5.75
5.79
5.95
5.48
5.14
4.42
6.23
5.57
6.90
6.42
5.89
5.53
5.81
4.15
6.31
6.80
5.61
6.01
5.66
5.86
4.84
5.55
6.10
5.98
5.65
6.20
6.75
6.56
5.80
7.21
6.86
SD
Body height (cm)
57.3
53.8
54.7
55.3
54.1
55.5
55.9
56.3
56.3
56.2
54.6
56.0
57.0
57.8
57.9
56.7
55.7
59.2
56.3
56.5
56.7
57.9
57.2
56.4
59.7
60.2
58.2
57.6
59.7
59.7
57.7
58.9
59.5
59.8
59.4
57.5
58.6
60.0
58.3
60.7
59.3
Mean
5.71
6.77
6.04
7.20
5.09
7.05
7.04
6.57
6.38
7.56
6.05
6.11
6.95
6.20
6.16
5.50
6.50
6.46
6.89
7.06
6.91
6.71
7.17
4.89
6.54
7.94
6.12
10.85
6.62
7.64
7.43
7.06
7.74
7.77
7.44
5.43
7.26
7.39
7.18
7.56
7.71
SD
Body weight (kg)
4.4
4.0
4.0
2.9
4.0
3.7
2.9
4.1
3.5
4.0
3.2
3.9
3.3
3.4
2.6
3.8
3.8
4.3
3.3
3.4
3.7
4.1
3.4
2.9
3.1
3.9
2.2
2.8
3.2
3.4
2.8
3.0
3.1
3.1
3.8
3.3
3.3
3.4
3.4
3.6
3.2
Mean
1.10
1.21
0.91
0.78
0.87
0.84
0.91
1.14
0.86
1.08
0.78
0.90
0.82
0.69
0.95
0.91
0.93
0.88
0.98
0.81
0.84
1.11
0.81
0.83
0.72
0.82
0.76
0.88
0.83
0.88
0.82
0.74
0.90
0.99
1.14
0.91
0.97
1.09
1.14
1.06
0.93
SD
Endomorphy
3.6
3.9
3.8
3.8
3.8
3.7
3.8
3.9
3.8
3.6
3.6
3.8
3.7
4.0
3.8
3.9
3.9
4.4
3.9
3.3
3.3
3.4
3.4
4.0
4.0
3.9
4.0
3.9
3.7
3.8
3.7
3.9
3.4
3.6
3.8
3.2
3.9
3.8
3.9
3.9
3.8
Mean
0.89
0.93
0.92
0.92
0.84
0.88
0.81
0.99
0.98
0.96
0.89
1.05
1.01
0.91
0.96
1.01
0.91
1.05
1.07
1.08
0.86
0.96
1.03
1.00
0.97
1.16
1.07
1.10
1.02
0.97
1.12
0.97
1.09
0.96
0.98
1.01
1.00
1.05
1.05
1.14
1.09
SD
Mesomorphy
Table 1. Basic statistical data of the analyzed parameters for the female students in the years under study
2.4
2.6
2.7
2.8
2.7
2.5
2.6
2.8
2.8
2.7
2.9
2.9
2.9
2.7
2.8
2.7
3.0
2.3
3.0
3.0
3.0
2.8
2.9
2.9
2.6
2.9
2.9
2.8
2.8
2.5
2.7
2.8
2.9
2.9
2.6
3.0
2.9
2.9
3.0
3.0
3.0
Mean
0.99
1.00
0.99
0.97
0.93
0.95
0.89
1.01
0.95
1.09
0.89
1.09
0.99
0.93
0.91
1.00
0.96
1.02
1.17
0.89
0.97
1.05
1.16
1.16
0.91
0.98
1.02
1.02
1.00
1.00
1.12
1.01
1.09
1.04
1.06
1.03
0.96
0.98
1.00
1.13
1.12
SD
Ectomorphy
HUMAN MOVEMENT
111
112
N
65
66
61
90
46
57
183
110
146
146
64
122
139
105
134
78
166
186
129
140
133
157
179
157
56
72
99
58
78
56
56
84
99
123
121
124
197
181
143
116
70
96
Year
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
20.6
20.9
20.8
20.9
20.8
20.7
20.7
20.7
20.8
20.8
20.8
20.8
20.8
20.8
20.7
20.8
20.7
20.7
21.3
21.3
19.5
20.7
20.8
20.8
23.5
21.6
21.5
22.2
21.6
20.8
20.7
21.2
21.1
21.1
21.3
21.1
21.3
20.9
20.8
20.7
20.8
20.6
Mean
age
174.1
173.6
173.3
174.0
172.6
173.9
174.3
175.0
176.4
176.1
176.4
175.8
177.0
177.0
177.0
176.5
177.2
177.0
177.4
176.9
177.3
178.1
177.9
178.9
178.4
179.0
179.1
177.8
179.9
178.8
177.0
181.3
179.2
179.1
179.9
180.8
180.2
179.1
179.9
180.6
180.4
180.5
Mean
5.77
5.23
5.89
6.59
5.83
5.85
5.97
6.85
6.14
6.51
7.55
6.14
6.85
5.68
6.05
5.80
5.88
6.51
6.41
6.71
6.82
6.48
6.31
6.43
5.66
6.50
6.71
7.24
7.77
6.33
5.46
6.53
6.97
7.23
6.86
5.62
6.06
7.54
6.97
6.61
6.91
6.57
SD
Body height (cm)
67.3
68.0
67.5
69.6
66.2
67.8
67.6
69.3
70.3
69.8
69.7
68.8
70.3
70.8
70.3
69.9
70.2
72.5
70.5
71.2
69.3
70.2
72.3
72.6
74.1
74.0
74.0
72.9
73.6
73.8
72.1
76.3
76.3
77.1
75.7
76.0
75.4
75.0
75.2
75.3
76.0
75.6
Mean
6.60
6.30
7.06
7.29
7.16
8.33
7.12
7.94
7.13
8.23
7.47
8.45
7.98
7.86
7.48
7.31
7.06
7.99
7.41
7.61
8.00
7.71
8.36
8.92
8.96
7.94
8.15
7.34
7.91
8.03
6.41
9.96
11.25
9.26
10.70
8.52
8.02
10.21
8.72
8.58
10.40
8.36
SD
Body weight (kg)
2.8
2.6
2.6
2.4
2.8
2.7
2.2
2.9
2.6
2.9
2.0
2.7
2.4
2.3
2.0
2.6
2.8
3.0
2.3
2.2
2.3
2.3
2.7
2.4
2.4
2.2
2.3
2.3
2.3
2.6
2.2
2.3
2.4
2.6
2.4
2.7
2.6
2.4
2.6
2.9
2.6
2.3
Mean
0.80
0.78
0.77
0.63
0.77
0.70
0.84
0.96
0.75
0.92
0.49
0.83
0.78
0.66
0.60
0.81
0.90
0.79
0.81
0.71
0.67
0.86
0.88
0.70
0.84
0.72
0.68
0.77
0.79
0.89
0.61
0.81
1.02
1.08
1.06
1.08
0.90
0.99
1.02
1.11
1.00
0.74
SD
Endomorphy
4.6
4.8
4.6
5.0
4.6
4.4
4.9
5.2
4.8
4.6
4.6
4.7
4.8
4.9
4.7
4.6
4.7
5.0
4.8
4.3
4.5
4.5
4.6
4.6
4.9
4.9
5.2
5.1
5.1
4.9
5.0
4.7
5.2
5.0
5.0
4.8
4.2
5.2
4.9
5.0
5.2
5.0
Mean
1.02
0.94
1.00
0.89
1.05
0.99
0.99
1.10
0.99
1.01
1.05
1.04
1.16
0.98
1.01
0.94
1.13
1.08
0.94
1.11
0.93
0.97
1.12
1.12
1.11
1.04
1.05
0.91
1.24
1.07
1.01
1.14
1.12
1.07
1.25
1.13
1.15
1.30
1.20
1.20
1.23
1.06
SD
Mesomorphy
Table 2. Basic statistical data of the analyzed parameters for the male students in the years under study
2.8
2.6
2.6
2.4
2.7
2.7
2.8
2.7
2.8
2.8
2.9
2.9
2.9
2.8
2.9
2.8
2.9
2.6
2.9
2.7
3.1
3.0
2.7
2.9
2.6
2.7
2.7
2.5
2.7
2.6
2.6
2.8
2.4
2.3
2.6
2.7
2.7
2.5
2.7
2.8
2.7
2.7
Mean
0.86
0.88
0.88
0.79
0.98
1.06
1.00
0.91
0.94
0.98
1.20
0.93
1.03
1.05
0.92
0.88
1.03
0.96
0.97
0.83
0.93
1.01
1.01
1.12
0.97
0.86
0.94
0.79
1.19
0.88
0.93
1.02
0.94
0.96
1.07
0.91
1.00
1.05
1.06
1.06
1.14
0.93
SD
Ectomorphy
HUMAN MOVEMENT
HUMAN MOVEMENT
Figure 1. Mean body height for female students
in five-year periods with a trend line
Figure 2. Mean body height for male students
Figure 3. Mean body weight of female students
Figure 4. Mean body weight of male students in five-year
periods with a trend line
Table 3. Results of post-hoc testing for the mean height of female students (table contains p values, bold font indicates
statistically significant difference, p 0.05)
Years of examination
1967–1970
1967–1970
1971–1975
1976 –1980
1981–1985
1986–1990
1991–1995
1996–2000
2001–2005
2006–2008
0.7813
0.0027
0.0001
0.0000
0.0000
0.0000
0.0000
0.0000
1971–1975
1976–1980
0.7813
0.0027
0.1648
0.1648
0.0114
0.0000
0.0000
0.0000
0.0000
0.0000
0.9977
0.3164
0.0189
0.0011
0.0000
0.0000
1981–1985 1986–1990 1991–1995 1996–2000 2001–2005 2006–2008
0.0001
0.0114
0.9977
0.8627
0.1677
0.0288
0.0000
0.0000
0.0000
0.0000
0.3164
0.8627
0.9111
0.6556
0.0067
0.0000
0.0000
0.0000
0.0189
0.1677
0.9111
1.0000
0.8729
0.2639
0.0000
0.0000
0.0011
0.0288
0.6556
1.0000
0.9435
0.3432
0.0000
0.0000
0.0000
0.0000
0.0067
0.8729
0.9435
0.0000
0.0000
0.0000
0.0000
0.0000
0.2639
0.3432
0.9702
0.9702
Table 4. Results of post-hoc testing for the mean height of male students (table contains p values,
bold font indicates statistically significant difference, p 0.05)
1967–1970
1967–1970
1971–1975
1976 –1980
1981–1985
1986–1990
1991–1995
1996–2000
2001–2005
2006–2008
0.7746
0.0001
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
1971–1975
1976–1980
0.7746
0.0001
0.0279
0.0279
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.9303
0.0481
0.0001
0.0000
0.0000
0.0000
1981–1985 1986–1990 1991–1995 1996–2000 2001–2005 2006–2008
0.0000
0.0000
0.9303
0.7371
0.0149
0.0004
0.0000
0.0000
0.0000
0.0000
0.0481
0.7371
0.5857
0.1398
0.0000
0.0000
0.0000
0.0000
0.0001
0.0149
0.5857
0.9999
0.6219
0.2937
0.0000
0.0000
0.0000
0.0004
0.1398
0.9999
0.9199
0.5938
0.0000
0.0000
0.0000
0.0000
0.0000
0.6219
0.9199
0.0000
0.0000
0.0000
0.0000
0.0000
0.2937
0.5938
0.9916
0.9916
113
HUMAN MOVEMENT
Table 5. Results of post-hoc testing for the mean body mass of female students (table contains p values,
1967–1970
1967–1970
1971–1975
1976 –1980
1981–1985
1986–1990
1991–1995
1996–2000
2001–2005
2006–2008
0.9898
0.6191
0.0514
0.1538
0.0011
0.0000
0.0000
0.0000
1971–1975
1976–1980
0.9898
0.6191
0.9656
0.9656
0.1755
0.4574
0.0039
0.0000
0.0000
0.0000
0.9038
0.9933
0.1465
0.0002
0.0001
0.0001
1981–1985 1986–1990 1991–1995 1996–2000 2001–2005 2006–2008
0.0514
0.1755
0.9038
0.9999
0.8801
0.0673
0.0621
0.0428
0.1538
0.4574
0.9933
0.9999
0.5998
0.0090
0.0060
0.0043
0.0011
0.0039
0.1465
0.8801
0.5998
0.9746
0.9899
0.9676
0.0000
0.0000
0.0002
0.0673
0.0090
0.9746
1.0000
1.0000
0.0000
0.0000
0.0001
0.0621
0.0060
0.9899
1.0000
0.0000
0.0000
0.0001
0.0428
0.0043
0.9676
1.0000
1.0000
1.0000
Table 6. Results of post-hoc testing for the mean body mass of male students (table contains p values,
1967–1970
1967–1970
1971–1975
1976 –1980
1981–1985
1986–1990
1991–1995
1996–2000
2001–2005
2006–2008
1.0000
0.4827
0.0099
0.0008
0.0000
0.0000
0.0000
0.0000
1971–1975
1976–1980
1.0000
0.4827
0.5573
0.5573
0.0036
0.0001
0.0000
0.0000
0.0000
0.0000
0.8220
0.3753
0.0000
0.0000
0.0000
0.0000
1981–1985 1986–1990 1991–1995 1996–2000 2001–2005 2006–2008
0.0099
0.0036
0.8220
0.9995
0.0003
0.0000
0.0000
0.0000
0.0008
0.0001
0.3753
0.9995
0.0031
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0003
0.0031
0.2849
0.2921
0.4742
0.0000
0.0000
0.0000
0.0000
0.0000
0.2849
1.0000
1.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.2921
1.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.4742
1.0000
1.0000
1.0000
Figure 5. Mean level of somatotype components
of females from AWF Wrocław
Figure 6. Mean level of somatotype components
of males from AWF Wrocław
differences reported in male and female subjects in
the latter years were less discernible (Tab. 5, 6). In addition, it should be emphasized that the tendency of increased body mass was also probably affected by the
increase in body height.
Analysis on the successive classes of male and female subjects during the 40-year period revealed many
changes in the body build characteristics (endomorphy,
mesomorphy and ectomorphy) (Tab. 1, 2), with the observed changes occurring in different directions.
Analysis of the five-year periods revealed mesomorphy, which describes body musculature and skeletal
size, to have had the greatest effect on body build com-
position in females (Fig. 5) as well as in males (Fig. 6).
The level of endomorphy (fatness) in female students
was mostly similar to the level of musculature until the
end of the 1970s, but in separate student year classes,
body fatness was greater than musculature (Tab. 1).
The mean level of mesomorphy in women ranged between 3.2–4.4, while the mean level of endomorphy
was 2.2–4.4. After the 1980s, the observed level of
musculature of the AWF students exceeded their fatness level (Fig. 5). The distortion between these parameters could be most observed in the second half of
the 1980s. At that time, the level of musculature was
the lowest for the whole analyzed period.
114
HUMAN MOVEMENT
Table 7. Results of post-hoc testing for the mean endomorphy values of female students (table contains p values,
1967–1970
1967–1970
1971–1975
1976 –1980
1981–1985
1986–1990
1991–1995
1996–2000
2001–2005
2006–2008
0.9964
0.9906
0.9218
1.0000
0.0000
0.0000
0.0516
0.1013
1971–1975
1976–1980
0.9964
0.9906
1.0000
1.0000
0.9994
0.9864
0.0000
0.0000
0.1091
0.2318
0.9999
0.9644
0.0000
0.0000
0.1589
0.3082
1981–1985 1986–1990 1991–1995 1996–2000 2001–2005 2006–2008
0.9218
0.9994
0.9999
0.7545
0.0000
0.0000
0.4687
0.6561
1.0000
0.9864
0.9644
0.7545
0.0000
0.0000
0.0012
0.0070
0.0000
0.0000
0.0000
0.0000
0.0000
0.8185
0.0000
0.0001
0.0000
0.0000
0.0000
0.0000
0.0000
0.8185
0.0558
0.0585
0.0516
0.1091
0.1589
0.4687
0.0012
0.0000
0.0558
0.1013
0.2318
0.3082
0.6561
0.0070
0.0001
0.0585
1.0000
1.0000
Table 8. Results of post-hoc testing for the mean mesomorphy values of female students (table contains p values,
1967–1970
1967–1970
1971–1975
1976 –1980
1981–1985
1986–1990
1991–1995
1996–2000
2001–2005
2006–2008
0.9998
1.0000
0.3251
0.0003
0.8813
0.9949
0.9984
0.9983
1971–1975
1976–1980
0.9998
1.0000
0.9891
0.9891
0.3769
0.0000
0.9700
0.7190
0.7590
1.0000
0.0203
0.0000
0.5541
0.9954
0.9988
0.9633
1981–1985 1986–1990 1991–1995 1996–2000 2001–2005 2006–2008
0.3251
0.3769
0.0203
0.0000
0.9989
0.0016
0.0008
0.6520
0.0003
0.0000
0.0000
0.0000
0.0000
0.0030
0.0001
0.0000
0.8813
0.9700
0.5541
0.9989
0.0000
0.1681
0.1741
0.9945
0.9949
0.7190
0.9954
0.0016
0.0030
0.1681
1.0000
0.6067
0.9984
0.7590
0.9988
0.0008
0.0001
0.1741
1.0000
0.9983
1.0000
0.9633
0.6520
0.0000
0.9945
0.6067
0.6448
0.6448
Table 9. Results of post-hoc testing for the mean ectomorphy values of female students (table contains p values,
1967–1970
1967–1970
1971–1975
1976 –1980
1981–1985
1986–1990
1991–1995
1996–2000
2001–2005
2006–2008
0.9999
0.7768
0.9943
0.1141
0.9156
0.9927
0.3668
0.0378
1971–1975
1976–1980
0.9999
0.7768
0.8648
0.8648
0.9999
0.0707
0.9746
0.9998
0.3681
0.0188
0.9887
0.9191
1.0000
0.9977
0.9984
0.6521
1981–1985 1986–1990 1991–1995 1996–2000 2001–2005 2006–2008
0.9943
0.9999
0.9887
0.2814
0.9991
1.0000
0.7333
0.0984
Ectomorphy happened to be the most stable somatotype component in women. The mean values of the successive student years ranged between 2.4–3.0 (Tab. 1).
These results indicate a greater tendency of the subjects
towards stockier and stronger body builds which was
caused by considerably higher levels of musculature
and fatness.
In comparison to other groups, analysis between the
female groups over the five-year periods revealed that
women examined in the second half of the 1980s and
in the 1990s displayed the greatest difference in their
0.1141
0.0707
0.9191
0.2814
0.9496
0.5211
0.9997
0.9997
0.9156
0.9746
1.0000
0.9991
0.9496
0.9998
0.9984
0.7616
0.9927
0.9998
0.9977
1.0000
0.5211
0.9998
0.8818
0.2425
0.3668
0.3681
0.9984
0.7333
0.9997
0.9984
0.8818
0.0378
0.0188
0.6521
0.0984
0.9997
0.7616
0.2425
0.9718
0.9718
endomorphy level (Tab. 7). Other groups did not show
significant differences in fatness. The greatest diffe
rences in musculature were observed between the
women examined in the 1980s and the rest of the female subjects (Tab. 8). In the case of body leanness,
no statistically significant differences were observed
in the female groups who studied at relatively similar
periods. However, a significant difference in body leanness was observed between women examined in 2006–
2008 and those who were tested in the 1960s and 1970s
(Tab. 9).
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HUMAN MOVEMENT
Table 10. Results of post-hoc testing for the mean mesomorphy values of male students (table contains p values,
1967–1970
1967–1970
1971–1975
1976 –1980
1981–1985
1986–1990
1991–1995
1996–2000
2001–2005
2006–2008
1.0000
1.0000
1.0000
0.1084
0.3732
0.8590
1.0000
0.3295
1971–1975
1976–1980
1.0000
1.0000
0.9913
0.9913
0.9956
0.0007
0.4150
0.9285
0.9997
0.3757
1.0000
0.0525
0.0404
0.3671
1.0000
0.0425
1981–1985 1986–1990 1991–1995 1996–2000 2001–2005 2006–2008
1.0000
0.9956
1.0000
0.0171
0.0422
0.3941
1.0000
0.0455
0.1084
0.0007
0.0525
0.0171
0.0000
0.0000
0.0031
0.0000
0.3732
0.4150
0.0404
0.0422
0.0000
0.9963
0.0800
1.0000
0.8590
0.9285
0.3671
0.3941
0.0000
0.9963
0.5507
0.9884
1.0000
0.9997
1.0000
1.0000
0.0031
0.0800
0.5507
0.3295
0.3757
0.0425
0.0455
0.0000
1.0000
0.9884
0.0823
0.0823
Table 11. Results of post-hoc testing for the mean endomorphy values of male students (table contains p values,
1967–1970
1967–1970
1971–1975
1976 –1980
1981–1985
1986–1990
1991–1995
1996–2000
2001–2005
2006–2008
1.0000
0.9995
1.0000
0.2421
0.0072
0.7766
0.9961
1.0000
1971–1975
1976–1980
1.0000
0.9995
1.0000
1.0000
0.9999
0.2448
0.0045
0.8825
1.0000
0.9991
0.9982
0.3711
0.0092
0.9461
1.0000
0.9949
1981–1985 1986–1990 1991–1995 1996–2000 2001–2005 2006–2008
1.0000
0.9999
0.9982
0.0258
0.0002
0.5326
0.9828
1.0000
0.2421
0.2448
0.3711
0.0258
0.7760
0.9992
0.4167
0.1279
0.0072
0.0045
0.0092
0.0002
0.7760
0.4971
0.0099
0.0026
0.7766
0.8825
0.9461
0.5326
0.9992
0.4971
0.9722
0.6231
0.9961
1.0000
1.0000
0.9828
0.4167
0.0099
0.9722
1.0000
0.9991
0.9949
1.0000
0.1279
0.0026
0.6231
0.9779
0.9779
Table 12. Results of post-hoc testing for the mean ectomorphy values of male students (table contains p values,
1967–1970
1967–1970
1971–1975
1976 –1980
1981–1985
1986–1990
1991–1995
1996–2000
2001–2005
2006–2008
0.8337
0.1539
0.3649
0.0465
0.9999
0.9982
0.9998
0.9358
1971–1975
1976–1980
0.8337
0.1539
0.9472
0.9472
0.9982
0.7621
0.9731
0.1355
0.9292
1.0000
0.9998
1.0000
0.3422
0.0010
0.1146
0.9769
1981–1985 1986–1990 1991–1995 1996–2000 2001–2005 2006–2008
0.3649
0.9982
0.9998
0.9897
0.6473
0.0063
0.3557
0.9992
Mean mesomorphy levels for male students were
found to be 4.2–5.2 (Tab. 2). Overall, the mean values
revealed a slight increase of musculature in AWF students, particularly since the 1990s. Accurate significant difference analysis between the separate groups
indicated that only the group tested in the second half
of the 1980s significantly differed in musculature
from the other groups (Tab. 10). The level of musculature in that group was lower than in the remainder of
the subjects.
116
0.0465
0.7621
1.0000
0.9897
0.1265
0.0000
0.0143
0.8959
0.9999
0.9731
0.3422
0.6473
0.1265
0.9231
1.0000
0.9937
0.9982
0.1355
0.0010
0.0063
0.0000
0.9231
0.7986
0.3987
0.9998
0.9292
0.1146
0.3557
0.0143
1.0000
0.7986
0.9358
1.0000
0.9769
0.9992
0.8959
0.9937
0.3987
0.9875
0.9875
Throughout the entire analyzed period, the fatness
of males was significantly lower in comparison to that
of females, with the level being 2.0–3.0 (Tab. 2, Fig. 6).
Only the group examined in the first half of the 1990s
displayed statistically significant differences in the
mean level of endomorphy (Tab. 11), as their level was
lower in comparison to other groups.
Ectomorphy was the most stable component both
in men and women. The mean values of leanness in men
ranged between 2.4–3.1 (Tab. 2). The lowest leanness
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level was observed in the group examined in the second half of the 1990s (Tab. 12, Fig. 6).
SANOVA test analysis of somatotype diversity showed
statistically significant changes in female body build.
In the seventies, female students were characterized as
endomorph-mesomorph, while in eighties as more
mesomorph-endomorph and, since the beginning of
nineties, they were found to be as a balanced category
of mesomorphy. The changes in body build of males
were smaller and statistically insignificant. The most
frequent category was the balanced mesomorph. In
eighties, the mesomorphic-ectomorphic somatotype
was observed.
Discussion
This study’s results paralleled the observed increase
in body height of the populations of Poland and other
European countries, as had been reported in a number
of studies [3–4, 20–24]. Tanner et al. [25] reported that
the increase in body height of successive generations
was mainly related to a lengthening of the lower limbs
with only slight change in sitting height. In successive
generations, body height increased in both male and
female students and the pace of growth was similar
for both genders. However, Ziółkowska-Łajp [4] found
contrary results: changes in body height over a 30-year
period were more pronounced and more dynamic in student males than in females. Eveleth and Tanner [26]
also claimed that men are more “ecosensitive”, that is,
male body height being more susceptible to environmental changes. Then again, Tanner et al. [25] and Kuh
et al. [11] stated that the contemporary trend of increased
body height is more observable in women than in men,
while Cole [9] confirmed a greater sexual dimorphism
in body height between the two genders. Other research
has proven that the pace of body height change in recent
years has decreased or even disappeared [8]. However,
this was not confirmed in this study, as the pace of
body growth throughout the entire examined period
was found to be rather constant.
Analysis of body mass also revealed a consistent increase in the successive generations of male and female
students. This finding was confirmed in the work done
by Ziółkowska-Łajp [4], Fredriks et al. [23] and Roelants
et al. [24]. In comparison to women, men displayed
a visible increase in body mass which was largely the
result of an increase in musculature (with the level of
fatness nearly unchanged). However, a decrease in fatness and a slight increase of leanness could be observed
in women. The existence of a secular trend in body
mass was also confirmed by Cole [27].
Mesomorphy, which describes body musculature,
had the greatest effect on the somatotypes of both male
and female students. This was probably related to the
specific character of the University School of Physical
Education. The level of male musculature was significantly higher than that of women. The level of musculature in women was constant while men displayed
a slight increase in musculature in more recent times.
The values of endomorphy (fatness) in women were
higher than in men, with the dimorphism of body build
composition being largely the result of differences in
the hormonal profile between both sexes, which is
connected with different evolutionary roles.
Analysis of somatotype in the five-year periods revealed that the students examined in the second half
of the 1980s displayed the lowest muscle level throughout the entire period under study (significantly lower in
comparison to other groups), while the groups of male
and female students examined in the 1990s displayed
significantly different levels of endomorphy in comparison to other groups. They were characterized by
the lowest level of fatness. Students who applied to the
University during this time were born during the postWWII baby boom. In order to be accepted by the University, they needed to pass physical fitness tests. The
increased number of applicants could have caused students with better performance capabilities to have a considerable advantage over others. In addition, many researchers also pointed to the influence of paragenetic
factors (during pregnancy and in infancy) on ontogenetic processes in later development of the body [28–30].
Furthermore, no sexual dimorphism was observed in
the leanness level; both male and female students displayed similar mean values of ectomorphy (2.4–3.1).
Such values indicate a tendency towards having a stocky
and strong body build due to developed muscles.
The data obtained in this study supports previous
conclusions and demonstrated the existence of secular
trends in body build. However, the detailed results
obtained by different authors in themselves slightly
differ, which confirms the fact that changes in body
proportion and constitution observed in many genera
tions are worth monitoring. Moreover, research evaluating body build composition in adults and ageing
people enables the control of undernourishment, malnourishment or eating disorders and facilitates the
monitoring of health conditions within a population.
Likewise, it enables the determination of links between
fat distribution and the risk of many diseases as well
as the death rate [31].
Conclusions
Different types of secular trends could be observed
in different somatic structures of AWF students. This is
reflected by the changing conditions of lifestyle as well
as different university admission criteria. The conclusions drawn from the research material were as follows:
117
HUMAN MOVEMENT
–Changes in the mean values of the analyzed parameters (body height, body mass, endomorphy,
ectomorphy, mesomorphy) between the examined
groups were in different directions for both males
and females.
– Analysis, on the basis of five-year intervals, indicated an increase in both body mass and height
in successive generations.
– Mesomorphy, which describes musculature, was
the somatotype component which was most do
minant both in male and female AWF students.
– Men displayed a higher level of mesomorphy in
comparison to women, while women were cha
racterized by a higher level of fatness.
– For female subjects, intergenerational changes
were found to compose of a decrease in endomorphy and an increase in ectomorphy while
mesomorphy remained at a similar level.
– For men, a secular trend is visible with an increase
in mesomorphy, whereas endomorphy and ectomorphy stayed constant.
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Paper received by the Editors: August 16, 2011
Aleksandra Stachoń
Zakład Antropologii Fizycznej
Akademia Wychowania Fizycznego
al. I.J. Paderewskiego 35, bud. P2
51-612 Wrocław, Poland
e-mail: olastachon@gmail.com
119
HUMAN MOVEMENT
2012, vol. 13 (2), 120– 126
ANALYSIS OF BODY POSTURE BETWEEN YOUNG FOOTBALL PLAYERS
AND THEIR UNTRAINED PEERS
doi: 10.2478/v10038-012-0012-7
MAŁGORZATA GRABARA
The Jerzy Kukuczka Academy of Physical Education, Katowice, Poland
Abstract
Purpose. The aim of this study was to analyze the body posture of young football players and their untrained peers. Methods.
A group of 73 football players and 78 untrained boys, all aged between 11 and 14 years, were studied by measuring body posture indices with computer posturography (the MORIE technique). Spinal angles and curvatures in the sagittal plane and body
posture asymmetry in the frontal and transversal plane were measured. Body height and mass and BMI were also determined.
Results. Compared to the untrained boys, the group of football players had lower BMI. The position of pelvis in the frontal plane
was more symmetrical (p < 0.001) in football players, but the alignment of the remaining measured parameters was similar
between the two groups except for the horizontal symmetry of the waist triangles (a higher incidence of symmetry in some ages
groups of football players) and the horizontal symmetry of the shoulder blades (a higher incidence of asymmetry in some ages
groups of football players). A postural symmetry index that was created for this study did not find any differentiation among
the studied groups. The spinal alignment of the football players featured a more flattened lumbar lordosis. Conclusion. Previously conducted studies on the body posture of young athletes are still not ample and complete, while the results do not clearly
indicate the development of posture when subjected to sports training.
Key words: body posture, football training, adolescent, asymmetry
Introduction
Physical exercise is an important component of
a healthy lifestyle for children and adolescents as well
as fulfilling the natural human need for physical activity.
However, special care should be given to young athletes
when they take part in strenuous physical training in
order to ensure their proper physical development.
Training for football, to a very large extent, develops
lower limb strength and increases the endurance capacity
of the muscular-ligamentous system. It was found that
a football player during just one match runs a distance
of over 10 km [1]. A too heavy training load can not only
contribute to the risk of injury but delay meniscus, tendon and joint recovery, which as a consequence could
lead to premature osteoarthritis. An estimated 5.5 million children and adolescents play football around the
world and as many as 28% of young players (aged 5–14
years) injure themselves [2].
In order to become successful in football, a player
ought to demonstrate a high level of speed, endurance,
strength and agility skills. For young players, these skills
share a significant correlation with growth and biolo
gical development, and as such, both body build [3] and
body posture play an important role. An individual’s
body posture is dependent on both varied ontogenetic
development and factors that stem from everyday life
[4], such as lifestyle as well as how much and what form
of physical activity is practiced. Correct body posture
is characterized by symmetry in both the frontal and
transverse places, the proper arrangement of various
120
body parts, and the proper arrangement of the spine
in the sagittal plane. Good posture also requires an
individual to have properly developed motor skills as
well as a well-functioning muscular and nervous system [4]. Various authors have studied the influence of
sports training on body posture, finding that there
exist both positive and negative aspects depending on
the exercise performed for a given sport [5–19], with
Zeyland-Malawka stating that “[…] physical activity
– depending on what form and its intensity or even
a lack of it can be incredibly important on the shaping
of the spine” [15, p. 87].
Football is dominated by mostly asymmetrical lower limb movement (such as using the dominant leg for
goal shots) [5] and symmetrical upper limb movement.
Although top players can effectively use both legs in
gameplay [20], the dominance of one leg over the other
has been observed in games played at the international
level [21]. Similar observations were reported by McLean
and Tumilty, who on the basis of their own research,
stated that most players feature leg dominance [22].
As such, the aim of this study was to focus on analyzing the body posture of young male football players
and comparing them against their peers who were not
physically active, with the following research questions:
1. Does the body posture of young football players
significantly differ from their untrained peers
in terms of body symmetry in the frontal and
transverse planes?
2.Does football training have any influence on
the formation of the spine in the sagittal plane?
HUMAN MOVEMENT
M. Grabara, Postural variables of boys practicing football
These research questions were taken into conside
ration with the following hypotheses:
1. Young males who practice football have a higher incidence rate of symmetrical body posture
than their untrained peers.
2. Football training does influence the formation
of the antero-posterior curvature of the spine.
The study included two groups of young males aged
11–14 years: one was a group of football players (N = 73)
and the other a control group of boys who did not participate in any form of sport (N = 78). Comparative ana
lysis was performed by splitting the study sample into
four age groups: 11-, 12-, 13- and 14-year-olds. The football players were recruited from a local football club in
Katowice, Poland, which had the following training
schedule: 11- and 12-year-olds trained three times a week
while 13- and 14-year-olds trained five times a week.
The training experience of the players was: two years for
the 11-year-olds, three years for the 12-year-olds, four
years for the 13-year-olds and five years for the 14-yearolds. Each practice session lasted 90 minutes, including
a 15–20 minute warm-up, with the players training and
practicing basic football skills, which included:
– increasing fitness potential according to the players’ biological rhythm development;
– comprehensive mastery of various technical skills;
– mastering both individual and team tactics;
– practically developing technical and tactical skills
during different game plays.
The subjects were assessed in early afternoon, with
the football players examined before their training
session in order to avoid the effects of postural muscle
fatigue, which could have affected correct body posture.
Body height was measured by a stadiometer (accurate
to 1 mm) and body mass was determined by a Tanita
electronic scale (accurate to 0.1 kg), with both measurements then used to calculate each subjects’ body mass
index (BMI).
Body posture was assessed by computer posturography (MORA 4, CQ Elektronik System, Poland) on
the basis of the Moiré Shadow Technique, which is an
objective, noninvasive method for screening body
posture [23]. This method is able to spatially register
an entire individual’s back in three dimensions with
a very short measurement time (about five seconds)
which helps avoid postural muscle fatigue [24]. Body
posture indices were marked on the vertebral processes (C7–S1), the anterior superior iliac spines (M1, M2),
and at the bottom corners of the scapulas (Ł1, Ł2) before measurements were taken. The subject was then
asked to stand with their back to the camera in a natu
ral (habitual) posture.
Body posture analysis in the frontal and lateral plane
included:
– The torso lateral inclination angle (KNT), determined by the deflection of the C7–S1 vertical line;
– The maximum deflection of the spinous process
from the C7–S1 (UK) (mm);
– The symmetry of the shoulders in relation to
each other (KLB) (mm);
– The symmetry of the shoulder blades based on
their height (UL) and depth (UB) differences as
well as the distance from the spine (OL) (mm);
Table 1. The criteria for assigning point values to the various postural elements of the body based on the synthetic index
of postural symmetry (WSyn)
Calculated
parameters
The number of points depending on the size of the deflection: provided in degrees (°)
for KNT and KPT, and in mm for the remaining parameters
<1
1–2
2.01–3
3.01–5
5.01–10
10.01–15
> 15
KNT
0
1
2
3
UK
0
1
2
3
4
KLB
0
1
2
3
UL
0
1
2
3
UB
0
1
2
3
OL
0
1
2
3
TT
0
1
2
3
TS
0
1
2
3
KNM
0
1
2
3
KSM
0
1
2
3
KPT
0
1
2
3
KNT – torso lateral inclination angle; UK – maximum deflection of the spinous process C7–S1; KLB – symmetry of the shoulders;
UL – height symmetry of the shoulder blades; UB – depth symmetry of the shoulder blades in the transverse plane;
OL – symmetry of the shoulder blades from the spine; TT – height symmetry of the waist triangles; TS – width symmetry
of the waist triangles; KNM – pelvic lateral inclination angle in the frontal plane; KSM – pelvic torsion angle in the transverse
plane; KPT – torso forward inclination angle.
121
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– The symmetry of the waist triangles’ height (TT)
and width (TS) (mm);
– The pelvic lateral inclination angle (KNM) in
the frontal plane and the pelvic torsion angle
(KSM) in the transverse plane (mm);
– The torso forward inclination angle (KPT).
The size of the deflection in the frontal and transverse planes is presented in absolute values as the size
of the deflection from a desired value of 0 (zero), but
ignoring the direction of this deviation. In order to
assess correct body posture by using symmetry as a deciding criterion, a synthetic index of postural symmetry (WSyn) was introduced to reflect the distribution
of each measured postural component in the frontal
and transverse planes by assigning point values to how
much each component deviated from perfect symmetry
(Tab. 1). For the shoulder blades (parameters UL, UB
and OL), the maximum number of points that could
be given was dependent on the one, most-asymmetrical
indicator. Similarly, this was also how the waist triangles were treated (TT, TS).
In the sagittal plane the following measurements
were taken:
– the C7–S1 line, drawn as a plumb-line indicating
the torso forward inclination angle (KPT), whose
size is presented as an absolute value
– the angular deviation from a plumb-line indicating the upper thoracic segment ( angle);
– the angular deviation from a plumb-line indicating the thoracolumbar segment ( angle);
– the angular deviation from a plumb-line indicating the lumbosacral segment ( angle).
Statistical analysis was performed using Statistica
ver. 9.0 software (Statsoft Inc., USA), which included calculating the means and standard deviations ( ± SD)
of the values as well as comparing the results of both
posture and other anthropometric parameters for the
studied young football players and their untrained
peers by means of the Student’s t-test. The significance
level was set at 5%. The values of those measurements
that were statistically significant were highlighted and
included the level of significance (p).
Results
The boys who played football did not differ from
their untrained peers in terms of body height, however, their body mass index (BMI) was found to be significantly lower in all of the studied age groups (Tab. 2).
Absolute values were used to present the parameters that characterize posture in Table 3 and the KPT
parameter in Table 4, otherwise the distance between
left and right symmetry would have been naturally
negative and positive, respectively. The differences between the boys who played football and their non-physically active peers in the frontal and transverse planes
were as follows:
– the pelvic lateral inclination angle (KNM) was
the only parameter to significantly differentiate
among all the age groups. The football players
were characterized by having an almost symme
trical pelvic lateral inclination angle;
– the pelvic torsion angle in the transverse plane
(KSM) in the group of 12-year-old football players
had significantly greater asymmetry in the shape
of their pelvis in the transverse plane, but this
value fit within the range of having moderate pel
vic asymmetry;
– the width symmetry of the waist triangles (TS)
was found to be more symmetrical in the group
of football players, with significant differences
found in both the 12- and 13-year-old groups of
trained and untrained boys;
Table 2. Mean values (± SD) of the anthropometric parameters of boys practicing football (PN) and their untrained peers (C)
Age group
Parameter
Body height (cm)
Body mass (kg)
BMI (kg/m2)
11-year-olds
12-year-olds
PN (N = 19)
C (N = 20)
PN (N = 22)
C (N = 24)
145.74 ± 7.34
36.51 ± 6.8*
17.09 ± 2.29**
147.73 ± 7.9
45.35 ± 13.49
20.48 ± 4.37
151.09 ± 7.07
40.09 ± 6.34
17.48 ±1.75*
150.77 ± 6.75
43.33 ± 7.79
18.98 ± 2.6
13-year-olds
Body height (cm)
Body mass (kg)
BMI (kg/m2)
PN (N = 17)
C (N = 20)
PN (N = 15)
C (N = 18)
156.18 ± 5.21
43.41 ± 4.86
17.71 ± 1.64*
156.65 ± 5.95
47.89 ± 9.41
19.41 ± 2.9
166.47 ± 9.51
52.33 ±10.17
18.69 ± 1.81*
166.72 ± 6.48
57.72 ± 8.03
20.76 ± 2.67
** significantly (p < 0.05) different from the control group
122
14-year-olds
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Table 3. Mean values (± SD) of the body posture parameters in the frontal and transverse planes
of boys practicing football (PN) and their untrained peers (C)
Age group
Parameters
KNT (°)
UK (mm)
KNM (mm)
KSM (mm)
TT (mm)
TS (mm)
UL (mm)
OL (mm)
UB (mm)
KLB (mm)
WSyn (pts.)
11-year-olds
12-year-olds
PN (N = 19)
C (N = 20)
PN (N = 22)
C (N = 24)
1.52 ± 0.9
3.98 ± 1.9
1.64 ± 2.23***
7.76 ± 5.43
8.18 ± 6.64
11.21 ± 8.32
7.98 ± 5.96
8.21 ± 6.64
15.79 ± 7.58
6.42 ± 5.27
10.11 ± 3.05
1.1 ± 0.87
4.46 ± 2.83
7.27 ± 5.21
7.27 ± 5.21
8.84 ± 9.86
12.74 ± 8.87
8.29 ± 7.74
12.24 ± 8.51
12.49 ± 10.3
5.11 ± 3.8
10.6 ± 3.35
0.94 ± 0.76
3.68 ± 2.4
1.56 ± 1.39***
9.89 ±4.06**
8.75 ± 5.95
5.72 ± 4.73**
5.84 ± 4.42
5.48 ± 4.14
18.36 ± 9.34**
4.04 ± 2.99
8.86 ± 2.56
0.86 ± 0.96
3.89 ± 2.06
6.24 ± 4.36
6.24 ± 4.36
7.06 ± 8.21
10.49 ± 6.09
7.11 ± 5.28
7.51 ± 5.46
10.77 ± 6.4
4.04 ± 3.76
9.42 ± 3.92
13-year-olds
KNT (°)
UK (mm)
KNM (mm)
KSM (mm)
TT (mm)
TS (mm)
UL (mm)
OL (mm)
UB (mm)
KLB (mm)
WSyn (pts.)
14-year-olds
PN (N = 17)
C (N = 20)
PN (N = 15)
C (N = 18)
1.15 ± 0.96
5.1 ± 3.01
2.36 ± 2.41***
7.56 ± 5.18
8.24 ± 3.8
7.09 ± 5.29*
8.13 ± 6.85
6.17 ± 4.24
15.71 ± 11.08
6.6 ± 6.15
9.65 ± 2.85
1.41 ± 1.1
4.45 ± 1.98
6.48 ± 3.42
6.48 ± 3.42
10.92 ± 7.09
11.08 ± 4.31
7.84 ± 4.73
6.65 ± 6.52
12.8 ± 8.86
7.33 ± 7
11.55 ± 3.05
1.18 ± 0.66
4.74 ± 3.79
2.18 ± 1.82***
10.08 ± 5.19
9.21 ± 7.14
10.83 ± 9.02
8.82 ± 6.52
9.9 ± 7.57
21.96 ± 9.65*
6.09 ± 3.74
10.27 ± 2.4
1.05 ± 0.68
4.56 ± 2.06
7.11 ± 4.97
7.11 ± 4.97
10.34 ± 7.37
12.65 ± 9.65
6.12 ± 4.97
7.47 ± 6.37
14.07 ± 7.75
6.12 ± 3.15
10.22 ± 2.82
KNT – torso lateral inclination angle; UK – maximum deflection of the spinous process C7–S1; KNM – pelvic lateral inclina
tion angle in the frontal plane; KSM – pelvic torsion angle in the transverse plane; TT – height symmetry of the waist triangles;
TS – width symmetry of the waist triangles; UL – height symmetry of the shoulder blades; UB – depth symmetry of the shoulder
blades in the transverse plane; OL – symmetry of the shoulder blades from the spine; KLB – symmetry of the shoulders;
WSyn – synthetic index of postural symmetry;
*** significantly (p < 0.05) different from the control group
*** significantly (p < 0.01) different form the control group
*** significantly (p < 0.001) different from the control group.
All mean values are significantly different from an expected value of zero.
– the depth symmetry of the shoulder blades (UB)
among the group of young football players was
found to be more frequent, with higher levels of
having protruding shoulder blades, in addition, significant differences were observed between trained and untrained 12- and 14-yearolds (Tab. 3).
A deflection of the spinous process line above 10 mm,
which points to spinal scoliosis, was found in 5.5% of
the football players and in 2.9% of the control (untrained) group, while a deflection of 5–10 mm was
observed in 22% of the football players and in 31% of
the control group.
Differences between the group of football players
and the untrained boys in the sagittal plane were as
follows:
– the shape of the lumbar lordosis: statistically significant differences for angle , angle and the
lordosis angle ( + ) occurred only in the group
of 11- and 14-year-old boys. A small value of
the lumbar lordosis angle was observed in football players (Tab. 4), while no differences were
found in the thoracic kyphosis angle among the
age groups.
123
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Table 4. Mean values (± SD) of the body posture parameters in the sagittal plane of boys practicing football (PN)
and their untrained peers (C)
Age group
Parameter
KPT (°)
angle (°)
angle (°)
angle (°)
Kyphosis angle ( + )
Lordosis angle ( + )
11-year-olds
12-year-olds
PN (N = 19)
C (N = 20)
PN (N = 22)
C (N = 24)
2.67 ± 2.07
12.92 ± 4.2
12.93 ± 2.82
12.02 ± 4.52**
25.86 ± 6.17
24.95 ± 5.12**
2.13 ± 1.6
14.94 ± 4.4
13.98 ± 3.16
16.94 ± 4.17
28.93 ± 5.99
30.92 ± 5.98
2.76 ± 2.47
13.6 ± 3.68
14.14 ± 3.29
12.4 ± 4.61
27.73 ± 4.57
26.53 ± 5.98
2.8 ± 1.97
12.88 ± 4.78
13.75 ± 2.61
13.77 ± 4.21
26.63 ± 6.21
27.52 ± 5.74
13-year-olds
KPT (°)
angle (°)
angle (°)
angle (°)
Kyphosis angle ( + )
Lordosis angle ( + )
14-year-olds
PN (N = 17)
C (N = 20)
PN (N = 15)
C (N = 18)
2.42 ± 2.19
14.44 ± 4.67
13.92 ± 2.37
13.28 ± 5.14
28.36 ± 5.46
27.19 ± 6.24
3.3 ± 1.82
14.54 ± 2.75
13.84 ± 3.34
14.14 ± 6.33
28.38 ± 4.58
27.98 ± 7.18
2.01 ± 1.54
14.51 ± 3.4
12.85 ± 2.77*
9.67 ± 3.13**
27.36 ± 5.16
22.52 ± 4.76**
2.19 ± 1.87
14.62 ± 3.75
14.7 ± 1.99
13.86 ± 5.03
29.32 ± 4.53
28.56 ± 5.36
KPT – torso forward inclination angle
** significantly (p < 0.01) different form the control group
Discussion
Increased physical activity of children and adolescents usually results in lower body mass and a lower
body mass index, which was confirmed by the anthro
pometric measurements of this study. In turn, low
physical activity promotes weight gains and obesity
[25]. The fact that football players have less body mass
than their peers was also reflected in other studies [3, 9].
Correct body posture, calculated on the basis of the
pelvic inclination angle in the frontal plane, was found
to be significantly greater in all the age groups of football players. However, this was the only measurement
of stance that differentiated among all of the age groups.
None of the remaining body measurements were found
to differentiate between the trained and untrained
groups, the only observed differences occurred between
individual age groups. In addition, the synthetic index
of postural symmetry found that the postural alignment of the various studied body parameters also did
not differentiate among the studied groups. Through
this index, it can be stated none of the groups had higher
levels of symmetrical posture, with football training
not significantly contributing to an improvement of
body posture in terms of its symmetry.
Nonetheless, different conclusions were reached by
other authors, who found that young male football
players [9] as well as boys in sports-oriented middle
schools [10] had better body posture. Other studies have
124
argued that athletes have larger values of pelvic asymmetry, which could be associated with the predominance of using certain muscle groups during training,
muscle groups which are responsible for the structure
of the pelvis [26]. This in turn may largely depend on
which sports discipline is practiced. For example, a higher
incidence of trunk asymmetry was commonly observed
in athletes who practiced sports that involved the asymmetric use of the shoulder, such as in handball players
[11]. The incidence of asymmetric posture among athletes who practice team sports had also been examined
by other authors [7]. Pietraszewska et al.’s research found
that left-sided scoliosis occurs in 29% of football players
[12]. It was noted in this study that the deflection in the
spinous processes, which may indicate a lateral curvature of the spine, occurred in slightly less than 30% of
the players, a value which was slightly less than in the
untrained group of boys. Thus, individuals who practice sports that feature elements of asymmetry can be
denoted with an asymmetric structure of various parts
of their body.
Assessing the shape of the spine is even more difficult due to its variability, the different methods of measuring the inclination of individual spine segments and
a lack of clearly defined assessment standards. In the
present study, different shapes of the spine in the sagittal
plane were observed in both studied groups. Generally,
body posture in the sagittal plane was characterized
by a smaller curvature of the lumbar lordosis, while no
HUMAN MOVEMENT
differences in the shape of the thoracic kyphosis were
found. A similar propensity of having a flattened lumbar
lordosis was also noted for 14- to 16-year-old athletes
[13]. However, other researchers have found that boys
who practice football were, on the whole, characterized
by a lower thoracic kyphosis and greater lumbar lordosis angles than their untrained peers [14].
This was not confirmed in the present study. In addition, research on the body posture of boys practicing
fencing when compared to their untrained peers found
no significant differences in the structure of anteriorposterior curvature of the spine [15]. Wojtys et al. [16]
noted a significant correlation in the shape of the spine
in the sagittal plane dependent on the duration of training (the longer the training period, the greater thoracic
kyphosis and lumbar lordosis angles), as was found
during measurements of individuals practicing different
sports. These authors did not find a relationship between the size of the anterior-posterior curvature with
age or sex [16]. Other studies confirmed a dependency
between the structure of the spinal curve in the sagittal
plane and the type of practiced sports. A greater curvature of the spine was noted in sprinters, medium- and
long-distance runners, and kendo and shot-put athletes,
while a smaller curvature of the spine characterized
football players, rugby players, swimmers and those
who did not practice any sport [17]. Zeyland-Malawka
[18] examined the posture of athletes from various disciplines (handball and hockey players, fencers, judokas,
weightlifters and skaters) and observed differences in
the shape of the spine in the sagittal plane, namely,
a larger lumbar lordosis angle in the groups mentioned
above when compared to an untrained group, and
a larger thoracic kyphosis angle in handball players and
fencers. The author proposed that sports training is
only one of several factors that affect the shape of the
spine in the sagittal plane [18]. Research by Lichote et al.
on athletes also confirmed a diversification in the formation of the curvature of the spine in the sagittal
plane. These authors concluded that the specificity of
sports training is one of the leading components that
can affect the shape of the anterior-posterior curvature
of the spine [19]. In addition to sports training, and as
was previously mentioned, body posture can also be
affected by a number of other factors. Studies on the
posture of 8- to 13-year-old children revealed a significantly different shape of the spine in the sagittal plane
depending on age and gender [27]. Other authors went
as far to report on the relationships between the size
of the anterior-posterior curvature on body build,
which indicates that height, weight and BMI can influence the size of the thoracic kyphosis and lumbar
lordosis [28–30].
In summary, previous studies on the body posture
of young athletes are still not ample and complete, while
the results do not clearly indicate the development of
posture when subjected to sports training. From the
results of the previously cited studies, the authors of this
study feel that more research needs to be performed
on young athletes over longer periods of time, which can
more definitely point to the impact of sports training
on body posture.
Conclusion
Through analyzing posture in terms of its symmetry, it was found that boys who practice football when
compared to their untrained peers are characterized
by a higher incidence of having the correct alignment
of the pelvis in the frontal plane. In some age groups
a higher incidence of having symmetry of the waist
triangles was observed, as well as a frequent incidence
of protruding shoulder blades and an asymmetry of
the pelvis in the transverse plane.
An assessment of posture in the sagittal plane noted
significant differences in the shape of the spine between
the football players and untrained boys only for the
lumbar lordosis and only in the group of 11- and 14-yearold boys, while the football players on the whole were
found with smaller lumbar lordosis. It is difficult to
confirm the original hypothesis of there being a connection between sports training and body posture, as
the differences were not observed in all of the studied
parameters as well not being significant in all of the
age groups.
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nal lower body postures in lateral sports relate to structural asymmetry of the pelvis? J Sci Med Sport, 2010, 13
(3), 360–364, doi: 10.1016/j.jsams.2009.02.010.
27. Olszewska E., Trzcińska D., Inclination of spine sections
in relation to the vertical axis of the body in 8–11 years old
children. Medycyna Sportowa, 2008, 24, 2 (6), 108–116.
28. Mauriciene V., Baciuliene K., Spine’s sagittal plane curves’
coherence with anthropometric parameters in schoolchildren. Education. Physical Training Sport, 2005, 3 (57),
25–29.
29. Grabara M., Pstrągowska D., Estimation of the body pos
ture in girls and boys related to their body mass index
(BMI). Medycyna Sportowa, 2008, 24, 4 (6), 231–239.
30. Lichota M., Spine shape in sagittal and frontal planes in
short-and-tall-statured children aged 13 years. Phys Educ
Sport, 2008, 52, 92–95, doi: 10.2478/v10030-008-0021-7.
Paper accepted for publication: February 16, 2012
Małgorzata Grabara
ul. Mikołowska 72a
40-065 Katowice, Poland
e-mail: m.grabara@awf.katowice.pl
HUMAN MOVEMENT
2012, vol. 13 (2), 127– 131
A COMPARATIVE ANALYSIS OF THE ANTHROPOMETRIC METHOD AND BIOELECTRICAL IMPEDANCE ANALYSIS ON CHANGES IN BODY COMPOSITION
OF FEMALE VOLLEYBALL PLAYERS DURING THE 2010/2011 SEASON
doi: 10.2478/v10038-012-0013-6
KRZYSZTOF BUŚKO *, MONIKA LIPIŃSKA
Józef Piłsudski University of Physical Education, Warszawa, Poland
Abstract
Purpose. The aim of this study was to observe the changes in body composition by using two measurement methods – anthro
pometric analysis and bioelectrical impedance analysis (BIA) – on a group female volleyball players and to compare the results
of both methods. Methods. Eleven female volleyball players participated in this study during the 2010/2011 season. Measurements
of body composition were performed with an electronic body composition analyzer (BIA method) adjusted for STANDARD
physical activity levels and then using the anthropometric method as per Piechaczek’s formula. Total lean body mass (LBM),
total body fat content (FAT) and body water content were measured. Measurements were taken before preseason training
(Measurement 0), one week before the end of preseason training (Measurement 1), after the first (Measurement 2) and the
second (Measurement 3) half of the competitive season and four weeks after the seasons’ playoffs during the offseason (Measurement 4). Additionally, during Measurement 4, body composition measured by the BIA method was adjusted for ATHLETIC
physical activity levels. Results. Body mass, lean body mass and body water content did not change throughout the analyzed
period. Body fat mass, as determined by BIA STANDARD, increased from 20.7 ± 5.3 kg (Measurement 0) to 22.2 ± 5.0 kg (Measurement 1) but subsequently decreased to 21.2 ± 5.7 kg (Measurement 2) and remained at this level until the end of the competitive
season. In the case of body fat as measured by the anthropometric method, a significant increase in fat was observed from 18.4
± 3.0 kg to 19.3 ± 3.4 kg and then from 19.5 ± 3.5 kg to 19.8 ± 3.6 kg. Analysis of LBM and FAT values found significant differences between the values obtained using the BIA method at the ATHLETIC physical activity level and the results registered
at the STANDARD level and those recorded by use of the anthropometric method. Conclusions. The results obtained using
the BIA method set at the STANDARD mode of physical activity and those by the anthropometric method did not significantly
differ. Significant correlation between the values obtained by the BIA method and anthropometric method was found.
Key words: body composition, anthropometric method, bioelectrical impedance, female volleyball players
Introduction
Assessing an individual’s body composition is particularly important especially in the case of athletes who
participate in sports that use weight categories (such
as wrestling, judo) [1]. In addition, such an assessment is
used to directly monitor the effects of physical activity
and/or nutrition (diet) on body composition. Body composition is an important factor of physical fitness for
volleyball teams, as excess body fat acts as ballast against
the body’s ability to perform a number of movements,
such as the vertical jump. However, most athletes have
their body composition measured only once [2–4]. Rarely
is this important tool used to monitor an athlete throughout an entire competitive or training season [5].
In addition, the various methods used to assess body
composition have themselves raised controversy and
debate. Studies carried out by various authors using different measurement methods have pointed to significant differences in the attained values of body composition [6, 7]. Currently, the most popular method for
determining body composition is through bioelectrical
impedance analysis (BIA), which is considered to be
simple, quick and noninvasive. Many authors [8–10]
found a significant correlation between body composition measured by BIA and those by either anthropometric or hydrometric analysis. However, most of these
studies were conducted on individuals who were not
physically active or on students of physical education
universities [10]. In addition, of those studies that attempted to tackle this issue, few were conducted on athletes, particularly female athletes [11, 12].
Therefore, the aim of this study was to observe the
changes of body composition by using two measurement methods, the anthropometric method as well as
bioelectrical impedance analysis, on a group of female
volleyball players during the 2010/2011 season and to
compare the results of both methods.
After receiving approval by the Senate Ethics Committee for Scientific Research of the Józef Piłsudski
University of Physical Education in Warsaw, eleven
Second Division volleyball players from the AZS AWF
Warszawa sports club were selected to participate in
the study. The physical characteristics of the subjects
(N = 11) were: age 21.6 ± 1.7 years, height 177.9 ± 4.6 cm,
127
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K. Buśko, M. Lipińska, Changes in body components
body mass 71.3 ± 6.6 kg, career length 8.6 ± 3.3 years.
The participants were informed about the purpose and
nature of the study and the possibility of withdrawing
at any moment in time. Written informed consent was
provided by all the participants prior to the experiment.
Measurements of body composition were carried out
using a Model TBF-300 body composition analyzer
(Tanita, Japan), set at the STANDARD setting for the
level of physical activity. Body composition by use of the
anthropometric method [13] was performed with three
skinfold measurements on the arm, abdomen and below
the shoulder with a caliper (SiberHegner, Switzerland).
Body composition was then estimated by use of Piechacz
ka’s method [14]. Total lean body mass (kg), total body
fat (kg), total body fat content (%, kg) and total body
water content (kg) were then calculated. Body height,
weight and skinfold thickness were measured with an
accuracy of 0.01 m, 0.1 kg and 0.001 m, respectively.
Generally, the total error of skinfold measurement
does not exceed 6% [3], while the total error by measurement of body composition does not exceed 3% [15]. In
this study, the maximum relative error of repeatability,
expressed as an indicator of variability, for skinfold
measurements ranged from 1.6% to 3.0% depending on
the skinfold, while analysis of fat content by the BIA
method (with two different available settings) was found
to be for BIA STANDARD 0.3% and for BIA ATHLETIC 0.6%.
The maximum relative error of repeatability was found
to be consistent with the results of Kutáč and Gajda [16].
Measurements of body composition during the 2010/
2011 season were taken before the start of preseason
training (Measurement 0), one week before the end of
preseason training (Measurement 1), after the first
(Measurement 2) and the second (Measurement 3)
half of the competitive season and four weeks after
the seasons’ playoffs during the offseason (Measurement 4). Additionally, during Measurement 4, body composition measured by the BIA method was adjusted
for an ATHLETIC level physical activity (for individuals
who intensively train for at least 10 hours a week and
have a resting heart rate below 60 bpm). Throughout
the entire study period the volleyball players did not
have a special diet or modify their dietary intake.
In order to verify the obtained results of each of the
measurements, one-way analysis of variance (ANOVA)
with repeated measures was used, while ANOVA/
MANOVA analysis was performed to compare the results of both of the measurement methods. The significance of differences among the obtained mean values
was evaluated by Tukey’s post-hoc test, with the relationships between the variables examined by use of
Pearson’s correlation coefficient. All statistical analysis
considered a p value of < 0.05 to be significant. All calculations were performed using STATISTICA™ software (v. 9.0, StatSoft, USA).
Results
The obtained results (mean ± SD) are presented in
Table 1. Body mass, body water content, lean body
mass (LBM) were found to not significantly change
during the period under analysis. Body fat mass (FAT),
as determined by BIA STANDARD, increased from 20.7 ±
5.3 kg (Measurement 0) to 22.2 ± 5.0 kg (Measurement 1) but subsequently decreased to 21.2 ± 5.7 kg
(Measurement 2) and remained at this level until the
end of the competitive season. Only the increase in
FAT between Measurement 0 and Measurement 1 was
found to be significant. In the case of body fat as
measured by the anthropometric-Piechaczka’s method
(ANT), a significant increase in fat was observed between Measurement 0 and Measurements 2, 3 and 4.
With the exception of Measurement 4, significant differences were observed between BIASTANDARD and the
anthropometric method (ANT) in all of the measurements for FAT (%), FAT (kg) and LBM (kg).
The results of Measurement 4, taken by the BIA
method at two different physical activity settings (BIASTA
Table 1. Changes in body tissue composition of the female volleyball players under study during the 2010/2011 season
Variables
Mass (kg)
BMI (kg/m2)
FAT BIA (%)
FAT BIA (kg)
LBM BIA (kg)
WaterBIA (kg)
FATANT (%)
FATANT (kg)
LBM ANT (kg)
a
Measurement 0
Measurement 1
Measurement 2
71.3± 6.6
22.5± 2.8
28.7± 4.8
20.7± 5.3
50.6± 1.8
37.0± 1.3
25.6± 1.9*
18.4± 3.0*
52.9± 3.7*
72.3± 6.2
22.9± 2.8
30.4± 4.3 a
22.2± 5.0 a
50.1± 2.0
36.7± 1.5
25.7± 2.4*
18.7± 3.3*
53.6± 3.1*
72.0± 6.9
22.8± 2.9
29.0± 5.2
21.2± 5.7
50.8± 2.0
37.2± 1.4
26.6± 2.4
19.3± 3.4 a*
53.6± 4.1*
Measurement 3
71.2± 6.7
22.4± 2.7
29.4± 4.6
21.2± 5.2
50.0± 2.0
36.6± 1.5
27.2± 2.7 ab*
19.5± 3.5 a*
51.7± 3.5 bc*
Measurement 4
71.7± 6.8
22.6± 2.8
29.7± 5.1
21.6± 5.6
50.1± 2.1
36.7± 1.6
27.4± 2.7 ab
19.8± 3.6 ab
52.0± 3.6 bc
the mean value is significantly different from Measurement 0
the mean value is significantly different from Measurement 1
c
the mean value is significantly different from Measurement 2; p < 0.05
*the mean value of this variable calculated by Piechaczka’s formula (i.e., the anthropometric method – ANT) is significantly
different from the mean value measured by the BIA method; p < 0.05
b
128
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Table 2. Subjects’ body composition as measured by
Piechaczka’s method (i.e., the anthropometric method –
ANT) and bioelectrical impedance analysis for two levels
of physical activity (STANDARD and ATHLETIC)
FAT (%)
FAT (kg)
LBM (kg)
Water (kg)
BIA ATH
BIA STA
ANT
22.0± 5.3
16.1± 5.3
55.6± 2.6
40.7± 1.9
29.7± 5.1 a
21.6± 5.6 a
50.1± 2.1 a
36.7± 1.6
27.4 ± 2.7 a
19.8± 3.6 a
52.0± 3.6 a
a
the mean value is significantly different from the values
measured by BIA ATH, p < 0.05
Figure 1. The linear relationship between fat tissue (kg)
measured by bioelectrical impedance analysis for two
levels of physical activity – STANDARD (BIA STA)
and ATHLETIC (BIA ATH) – and fat tissue (kg)
estimated by Piechaczka’s equation [14]
(anthropometric method – ANT)
for a standard level of physical activity and BIA ATH for
an athletic level of physical activity) as well as by the
anthropometric method (ANT), are shown in Table 2
and Figure 1. Body water content was not significantly
different between both physical activity levels of the
BIA method, although the difference was found to be
9.8%. Analysis of lean body mass and body fat values
found significant differences between the BIA method’s
ATHLETIC (BIA ATH) physical activity level versus those
values attained by the STANDARD (BIASTA) physical
activity level and the values estimated by the anthropometric method (ANT). By themselves, the results obtained by the BIA method’s STANDARD physical activity
level and those by the ANT were not significantly different. FAT (%) and FAT (kg) measured by BIA ATH significantly correlated to both BIA STA (r = 0.98, r = 0.99,
respectively) and the values obtained by ANT (r = 0.69,
r = 0.88, respectively). A significant relationship was
also found for FAT (%) and FAT (kg) between BIASTA
and ANT (r = 0.67, r = 0.90, respectively). In addition,
the variable LBM (kg) as found by BIAATH was also found
to have significant correlation between the values ob-
tained by BIASTA (r = 0.96) and ANT (r = 0.64). However, LBM (kg), was found to have no significant relationship between BIASTA and ANT (r = 0.60).
Discussion
Ascertaining an individual’s body composition is of
great practical importance in assessing the dynamic
changes of various body components during both recreational and sports training. As was found in literature, a number of studies focused on evaluating the
changes in both body mass and fat in athletes. Reilly [5]
suggested that football players accumulate fat during
the offseason and lose more body mass during preseason training than at the beginning of the competitive
season. In this study, an increase in body mass and fat
levels was observed during the preseason training period
and later a decrease in these values at the beginning of
the competitive season. Nonetheless, the fat values were
not significantly higher when compared to the measurements taken before the start of preseason training.
Literature on the subject also indicates that the body
fat percentage of female volleyball players to be in the
range of 11.7–27.1% [3, 17]. In Malousaris et al.’s study
[3], players from the A2 division were characterized by
a body fat content of 24.1 ± 2.6%. Depending on the
player’s position, the smallest body fat content was
among sweepers, at 21.4 ± 3.1%, while the largest body
fat content found among receivers, at 25.7 ± 3.4%. Although the body fat values obtained in our study seem
to be similar, body fat percentage in Malousaris et al. [3]
was calculated by Siri’s formula [18] while this study
used Piechaczka’s formula [14]. As such, when comparing the results from different authors, the various formulas for estimating body fat content should be taken
into account. For example, Durnin’s method, which was
used by Malousaris et al. [3], gives higher values of body
fat content than Piechaczka’s method. As a whole, the
reported BMI values for female volleyball players of different ages, different nationalities and different competi
tive levels oscillates between 20.5–22.5 kg/m2 [3, 19].
The results obtained in our study found an average BMI
value of 25.5 kg/m2 and was similar to what was found
by Gualdi-Russo and Zaccagni [19].
Many studies which have employed several measure
ment methods to assess body composition found different correlation coefficient values among the various
methods, with one of the reasons being the varying fat
content of the study subjects [8]. In our study, significant
correlation was found between the measurements obtained by two different methods as well as for those
measurements taken for two different physical activity
levels. However, simple regression analysis found that
smaller differences occur between the methods for periods of higher body fat values.
Another difficulty which arises when interpreting
the results from different authors stems from what ini129
HUMAN MOVEMENT
tial assumptions are considered about the study subjects’
physical activity levels, which leads to significant differences among the measured parameters. Thus, despite
a high reproducibility of results, the qualitative interpretation of certain findings at a given measurement
range can be fraught with uncertainties related to rather
subjective assumptions that determine physical fitness
levels. Although this study has shown a significant correlation in the measurements of both methods, the
average body fat percentage of the subjects obtained by
the bio-impedance method for an ATHLETIC level of
physical activity (22.0%) was significantly lower than
the STANDARD level (29.7%) and for body fat calculated by the anthropometric method (27.4%). As was
previously mentioned, researchers that deal with this
issue have no clear consensus as to the results that
both methods obtain.
Some authors [20, 21] state that bioelectrical impedance analysis overestimates body fat percentage,
others [20, 22] claim that it underestimates this value,
while even others [10] find that it provides accurate
results. This lack of consensus may result from the application of different research methods of body composition: anthropometric measurements that use skinfold
thickness were performed on different parts of the body
with different conversion formulas while bioelectrical
impedance analysis used various types of body composition analyzers. Besides this, rarely have studies been
conducted on top athletes. In this study, the BIA method
set at the ATHLETIC level of physical activity underestimated body fat content while overvaluing fat levels
when set at the STANDARD level when compared to
the results of the anthropometric method.
Conclusions
1. Throughout the 2010/2011 season, a significant
increase in body fat during preseason training period
was observed, which insignificantly decreased at the
beginning of the competitive season.
2. A qualitative interpretation of the results provided by BIA is subject to error due to rather subjective
assumptions that determine physical fitness levels as
well as any associated changes in fitness levels in the
event of a marked increase in workload intensity (e.g.,
during a training period)
3. Despite the high correlation of results obtained
by both methods (at Measurement 4), the mean values
of body composition were significantly different from
the rest of the measurements only when using the
ATHLETIC level of physical activity in the BIA method.
Hence, when monitoring the impact of exercise or diet
on body composition, it is not recommended to use
both methods interchangeably.
Acknowledgments
The study was supported by the Ministry of Science and
Higher Education (Grant No. AWF-Ds.-150).
130
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Paper received by the Editors: May 11, 2011
Krzysztof Buśko
Zakład Antropologii
Józefa Piłsudskiego
ul. Marymoncka 34
00-968 Warszawa 45, Poland
e-mail: krzysztof.busko@awf.edu.pl
131
HUMAN MOVEMENT
2012, vol. 13 (2), 132– 138
DEVELOPMENT OF SELECTED MOTOR SKILLS IN BOYS AND GIRLS
IN RELATION TO THEIR RATE OF MATURATION – A LONGITUDINAL STUDY
doi: 10.2478/v10038-012-0014-5
BARTŁOMIEJ SOKOŁOWSKI *, MARIA CHRZANOWSKA
Abstract
Purpose. The aim of this study was to examine the process of how motor skills were developed and shaped in boys and girls
in relation to their rate of maturation, based on the use of peak height velocity (PHV), which measures biological maturity.
Methods. This study made use of a longitudinal study researching the physical fitness of boys and girls from Kraków, Poland
during the years 1980–1990. From the original sample population, 296 boys and 196 girls were selected for further analysis.
Physical fitness tests were administered over the subsequent decade, measuring the following motor skills: speed, explosive
strength of the lower limbs, static strength of the right and left hand, agility, dynamic strength of the abdominal muscles,
static endurance of the upper limbs and shoulders, and flexibility. On the basis of the median and PHV age quartiles for both
sexes, the examined individuals were divided into two cohorts: early maturers and late maturers. The mean values and standard deviations of the physical fitness test results were calculated based on biological age. Afterwards, the means and standard
deviations of each tested motor skill of the early maturers were standardized into means and standard deviations of the late
maturers. Results. The motor skills best performed in all age groups and in both sexes by early maturers were in tests of
static strength of the hands. In the group of boys, early maturers in all age groups also performed the best in tests of speed and
explosive strength of the lower limbs. Late-maturing girls were positively differentiated in each age group in tests of static
strength of the upper limbs and shoulders, and in the dynamic strength of the abdominal muscles. Conclusions. The rate of
maturation was found to significantly influence the results of fitness tests, particularly in the case of boys.
Key words: children and teenagers, longitudinal study, rate of maturation, peak height velocity (PHV), motor skills
Introduction
The development of motor skills in humans is a subject of on-going interest for both researchers and practitioners of physical education and sport. Tests used to
determine various aspects of physical fitness focus on
individual as well as collective motor skills. Of particular interest is the period of adolescent maturity due
to the different rate and rhythm of how individual motor skills develop.
The development of motor skills has been studied in
children and teenagers practicing sport [1–4] as well
as how environmental [5, 6] and genetic [7] factors
influence motor development. In addition, the results
of studies have also been used to create a set of physical
development norms [8, 9]. Most conclusions were based
on the use of cross-sectional [10–12] or longitudinal
[13–18] studies, which used calendar age as a criterion
of maturity. However, biological age is a more objective benchmark as in this case the influence of somatic
development, providing more individual data as rate
of maturation, is taken into account when performing
various motor tasks. Frequently, biological maturity is
determined by peak height velocity (PHV), or the maxi
mum velocity in statural growth during adolescence.
132
The use of PHV has been widely applied in practice as
it allows for an individual look on the maturity process and its evaluation [8].
The aim of this study was to try to find out how
the rate of maturation, based on peak height velocity
(PHV), can influence motor skills, and particularly, how
the development of speed, strength (static, dynamic
and explosive), static endurance, flexibility and agility
progresses in boys and girls. The issue in question is
not novel, however, there are few studies which analyze this issue in a wider context by taking into consideration sex, age and a number of different motor
skills at the same time, and, what is even more important, basing their research on longitudinal studies carried out on the same group of subjects.
Throughout 1980–1990 the University School of
Physical Education in Kraków, Poland conducted an
interdisciplinary, longitudinal study on adolescents. It
included 820 students (460 boys and 360 girls) from
schools in Mistrzejowice, a district in Kraków, Poland.
The subjects were all urban schoolchildren and formed
a relatively homogeneous population, taking into consi
deration their socio-economic and cultural background.
The study measured the children’s physical characteristics, motor skills, mental growth and their ability
HUMAN MOVEMENT
B. Sokołowski, M. Chrzanowska, Motor abilities in relation to rate of maturation
to learn, with the results later presented in a few publications [10, 13, 14]. The motor skills tested in the study
used a modified version of the International Physical
Fitness Test, with the largest differences being that the
static strength of both hands was examined (not only
the dominant arm), long run was not performed, and
the bent-arm hang was used instead of pull-ups for
12-year-old and older boys in order to test static endurance of the upper limbs and shoulders. Altogether,
the modified physical fitness battery was used to assess
speed (50 m dash), the explosive strength of the lower
limbs (standing broad jump), the static strength of the
right and left hand (measured by a hand dynamometer),
running agility (4 × 10 m shuttle run), the dynamic
strength of the abdominal muscles (by the number of
sit-ups completed in 30 s), the relative strength of the
upper limbs and shoulders (bent-arm hang), and flexibility (sit-and-reach) throughout the subsequent years.
This study selected a sample size of 296 boys and
196 girls aged 8–16 years based on the large sample size
of these particular age groups as well as these pupils
participating in all physical fitness tests. The age of peak
height velocity was then determined based on the individual data for each of the studied children. The ave
rage age of maximal peak height velocity was then calculated, being 13.11 years for boys and 11.32 years for
girls (Tab. 1). The obtained average age of PHV in boys
and girls were found to be similar to the results published in other studies [9, 15, 18–20].
On the basis of the values of the median and quartiles of PHV age for both sexes, the examined were
then divided into two cohorts: early maturers and late
maturers. Accordingly, early or late-maturing boys and
girls constituted the I and IV distribution quartiles,
respectively, with average maturers in the middle.
Table 1. Average PHV age of the sample population
Boys
Girls
N
Mean (yrs)
Standard deviation
296
196
13.11
11.32
1.24
1.18
Table 2. Division of the sample population
into three cohorts
Cohorts
Age (yrs)
Boys
Mean
N
Age (yrs)
Girls
Mean
N
Earlymaturing
Normalmaturing
14.0
and more
14.42
74
Less
than 10.5
9.69
45
12.6
and more
12.69
54
11.31
97
Results
The differences in the obtained results between the
cohorts are illustrated by profiles of the standardized
differences for each of the examined motor skills. In
order to better present the results, the negative and
positive values of the standardized factors of the speed
and agility tests were replaced (as a shorter completion time signified a better score).
In each of the studied calendar ages, the early-maturing boys ran the 50 m distance faster than the late
maturers. Statistically significant differences appeared
in the period from those who were 11 to 16 years old
(the significance level was set at p 0.05). The greatest
differences were observed at the age of 14 and 15 years.
In girls aged 8 to 13 years the results of the early maturers were better and a statistically significant difference is observed at the age of 11. Late-maturing girls
presented better results at the ages of 14, 15 and 16
years (Fig. 1).
Early-maturing boys had better results in explosive
strength of the lower limbs for each calendar age with
significant differences found between the ages of 11–16
years. The girls did not present any consistent trends.
50m Dash
Latematuring
Less
12.5 – 13.99
than 12.5
11.45
13.30
74
148
10.5 – 12.5
The mean age and the numbers of subjects in each
cohort are presented in Table 2. The group of early maturers included boys who had their PHV before 12.5
years and the group of late maturers were those whose
PHV occurred after 13.99 years. Girls who had PHV
before the age of 10.5 years were recognized as early maturers and those with PHV after the age of 12.5 were
considered late maturers.
Next, the mean values and standard deviations were
calculated for the physical fitness test results among
the biological age cohorts for boys (Tab. 3) and girls
(Tab. 4), with the significance of differences was assessed with the Student’s t-test. Subsequently, the means
of each motor skill in the cohort of early maturers
were standardized among the means and standard deviations of the late maturers.
Figure 1. Profiles of standardized differences
in the test of speed
133
134
74
74
74
74
74
74
74
10
11
12
13
14
15
16
74
74
74
74
74
74
74
74
9
10
11
12
13
14
15
16
0.57
0.53
0.61
0.53
0.37
8.83
8.27
7.72
7.51
7.31
7.63
8.19
8.39
8.65
9.00
9.13
9.41
9.70
10.42
0.40
0.54
0.55
0.50
0.57
0.57
0.55
0.63
0.83
4.05
3.62
3.88
3.71
25.01
26.68
28.12
29.89
3.77
4.49
23.00
30.16
4.33
4.50
17.85
20.24
4.59
SD
14.88
Mean
Early maturing
30.08
29.28
26.96
24.99
23.46
22.46
20.77
16.39
14.73
Mean
3.62
3.51
3.47
3.62
3.78
2.80
3.79
3.43
3.96
SD
Late maturing
Sit-Ups (number of sit-ups in 30 sec)
0.58
0.61
0.61
0.79
9.03
9.38
9.62
10.30
SD
Mean
Mean
SD
Late maturing
Early maturing
23.03
22.38
20.02
18.59
15.62
18.38
15.73
15.55
20.19
SD
200.32
184.45
175.59
171.80
156.68
154.82
149.31
142.85
125.82
Mean
17.53
18.83
14.90
15.85
15.11
14.45
14.79
13.78
16.39
0.44
10.35
0.05
0.56
10.51
0.66
0.60
11.33
10.85
0.62
0.85
0.71
11.83
11.78
12.27
0.75
0.90
13.46
13.02
SD
Mean
Early maturing
SD
Late maturing
10.62
11.03
11.42
11.66
11.92
11.74
12.26
12.97
13.66
Mean
0.48
0.64
0.65
0.67
0.64
0.64
0.75
0.69
0.87
SD
Late maturing
4 × 10 m Shuttle Run (sec)
214.93
205.24
195.57
184.26
162.12
158.04
149.78
144.46
128.00
Mean
Early maturing
Standing Broad Jump (cm)
Bold font signifies that the mean is significantly different at p
74
8
N
74
9
Age
(yrs)
74
N
8
Age
(yrs)
50m Dash (sec)
3.00
3.51
14.81
17.26
27.03
25.03
22.49
16.93
14.11
14.53
16.02
14.67
13.15
13.79
14.15
12.81
10.88
12.43
10.45
8.90
SD
8.86
7.21
Mean
42.83
33.41
28.41
23.81
21.28
19.09
15.88
13.53
12.63
Mean
17.90
15.92
17.24
15.01
13.53
13.54
13.77
9.38
8.00
Mean
9.54
10.76
13.41
12.28
11.42
10.86
11.46
8.36
7.45
SD
Late maturing
7.54
6.52
5.79
4.06
3.72
3.15
3.09
2.59
2.53
SD
Late maturing
Bent Arm Hang (sec)
9.22
9.14
8.09
6.88
4.44
Early maturing
48.26
43.53
37.85
31.05
25.35
4.17
2.87
13.50
21.28
SD
Mean
Early maturing
Handgrip Test – right (kg)
65.95
55.51
54.55
51.22
50.22
50.53
50.85
49.87
48.32
Mean
40.18
30.31
25.85
22.05
20.14
17.27
14.64
12.07
11.80
Mean
9.51
6.20
6.84
6.13
5.88
5.21
4.95
4.58
5.13
SD
53.23
50.59
50.92
48.62
49.04
49.32
49.66
48.93
47.28
Mean
8.99
6.71
6.02
6.43
6.87
6.41
6.07
6.13
5.82
SD
Late maturing
7.17
6.61
4.67
3.53
3.64
2.96
3.15
2.52
2.57
SD
Late maturing
Sit-to-reach (cm)
7.47
7.40
7.51
6.74
4.05
3.46
3.75
2.66
3.00
SD
Early maturing
45.51
40.08
35.42
28.68
23.34
19.46
16.30
13.45
12.78
Mean
Early maturing
Handgrip Test – left (kg)
Table 3. Means and standard deviations of the physical ability scores for the biological age cohorts in the boy sample population
HUMAN MOVEMENT
45
45
45
45
45
45
45
10
11
12
13
14
15
16
45
45
45
45
45
45
45
45
9
10
11
12
13
14
15
16
0.68
8.69
54
54
54
54
54
54
54
54
54
8.51
8.60
8.48
8.98
9.26
9.49
9.92
10.32
10.95
3.77
2.77
4.28
4.25
3.99
21.96
22.00
23.11
23.80
23.78
5.30
5.07
18.38
21.42
4.46
16.02
54
54
54
54
54
54
54
54
25.39
24.91
24.96
23.17
22.46
21.44
19.91
17.56
SD
4.24
4.28
4.32
4.24
3.63
4.48
3.91
4.00
3.99
4.79
14.36
15.26
Mean
SD
Mean
0.65
0.52
0.61
0.60
0.63
0.65
0.62
0.69
Late maturing
54
SD
0.88
Early maturing
N
Sit-Ups (number of sit-ups in 30 sec)
0.65
0.55
0.60
8.64
8.55
8.87
0.60
0.61
9.28
9.15
0.76
1.02
1.02
9.92
10.30
10.84
Mean
N
Mean
SD
Late maturing
Early maturing
16.24
17.82
177.81
179.37
176.78
170.04
160.89
157.76
147.26
140.93
124.49
Mean
17.80
15.89
15.63
14.93
14.72
16.14
14.26
12.09
15.19
SD
Late maturing
0.05
11.75
11.79
11.88
12.16
12.26
12.15
12.96
13.38
14.01
Mean
0.65
0.73
0.76
0.80
0.75
0.76
0.86
0.85
1.26
SD
Early maturing
11.67
11.56
11.76
12.09
12.33
12.17
12.84
13.36
14.33
Mean
0.66
0.63
0.69
0.72
0.78
0.98
0.84
0.72
1.06
SD
Late maturing
4 × 10 m Shuttle Run (sec)
174.93
176.76
16.24
16.10
174.27
176.42
16.98
164.29
15.80
12.58
18.92
SD
15.13
147.27
139.64
124.53
Mean
Early maturing
Standing Broad Jump (cm)
160.93
Bold font signifies that the mean is significantly different at p
45
8
N
45
9
Age
(yrs)
45
N
8
Age
(yrs)
50m Dash (sec)
5.01
28.80
6.89
7.82
9.20
7.47
6.93
11.48
7.52
6.18
6.14
5.58
6.33
7.36
6.98
5.81
5.99
6.71
4.80
6.42
SD
Early maturing
Mean
32.00
26.85
25.67
22.17
19.63
16.63
13.54
11.63
11.28
Mean
12.81
12.56
12.80
12.19
12.17
11.89
12.80
8.56
8.92
Mean
13.58
13.31
13.07
11.55
11.80
11.16
11.44
8.53
8.40
SD
Late maturing
4.59
4.74
4.09
3.39
2.95
2.95
2.78
2.63
2.26
SD
Late maturing
Bent Arm Hang (sec)
5.45
4.94
27.73
32.40
4.62
4.06
25.07
22.91
3.30
2.92
14.58
19.07
3.14
2.54
11.93
12.02
SD
Mean
Early maturing
Handgrip Test – right (kg)
61.33
59.96
58.58
55.96
54.42
53.02
51.64
51.16
49.62
Mean
29.50
24.09
23.39
20.31
17.78
15.33
12.80
10.65
10.83
Mean
6.44
7.16
7.45
7.91
7.87
7.35
7.28
6.31
6.42
SD
59.30
58.83
57.76
56.17
55.30
54.34
53.38
52.44
50.25
Mean
5.97
4.79
4.86
5.24
5.36
5.52
5.64
5.07
5.13
SD
Late maturing
4.94
4.27
3.79
3.41
3.47
2.99
2.50
2.37
2.35
SD
Late maturing
Sit-to-reach (cm)
4.28
4.77
4.12
6.36
4.41
3.56
Early maturing
29.87
26.84
25.82
24.18
21.67
17.76
3.26
2.79
11.24
13.47
2.26
SD
11.40
Mean
Early maturing
Handgrip Test – left (kg)
Table 4. Means and standard deviations of the physical ability scores for the biological age cohorts in the girl sample population
HUMAN MOVEMENT
135
HUMAN MOVEMENT
Standing Broad Jump
Handgrip Test – right
Figure 2. Profiles of standardized differences in the test
of explosive strength of the lower limbs
of static strength of the right hand
Handgrip Test – left
of static strength of the left hand
Sit-ups
4 × 10 m Shuttle Run
in the test of agility
Bent Arm Hang
of dynamic strength of the abdominal muscles
of static endurance of the upper limbs and shoulders
At the age of 8, 10, and 14 there were no differences
between the groups. At the age of 11, 12, and 13 years
the early-maturing girls performed much better (Fig. 2).
The profiles illustrating the differences in strength
measured by a hand dynamometer were found to be
similar for both sexes, i.e., during the whole period
under observation the early-maturing boys and girls
performed better than the late maturers. The differences
increased in the younger ages and in boys aged 12–14
years and in girls aged 12–13 years were the greatest.
The differences became smaller in later years (Fig. 3, 4).
Early-maturing boys performed better in the shuttle
run test when aged 8, 12, 13, 14, 15 and 16 years, with
the differences being statistically significant. The differentiation in girls was found to be a complex issue.
Better results were obtained by the early-maturing
girls aged 8, 11 and 12 years, but at the ages of 9, 10,
and then from 13 to 16 years the cohort of the latematuring girls performed better (Fig. 5).
Profiles illustrating the differences in the number
of sit-ups from a lying position were found to be different between the sexes. Early-maturing boys obtained better results (statistically significant) than late
maturers. Only the ten-year-olds presented a slight
difference favoring late-maturing boys. Girls presented a contrary relationship: better results were found in
the cohort of late maturers. There was no significant
difference at the age of 11 (Fig. 6).
In the bent-arm hang test, the late maturing boys
obtained better results between 8 to 11 years, whereas
136
HUMAN MOVEMENT
Sit-to-reach
in the test of flexibility
from 12 to 16 years the early maturers presented significantly better abilities. Completely different results were
observed in girls: in all age groups the cohort of late maturers showed much better skill development (Fig. 7).
For the sit-to-reach test, higher flexibility was found
in the cohort of early maturing boys for each calendar
age. The greatest difference appeared at the age of 16.
Quite different profiles were noticed in girls at the age
of 8, 9, 10, 12, and 13 years, where better results were
obtained by the late maturing girls, while at the age of
14, 15, and 16 years early maturers performed better
with significant differences found at the age of 15 and 16
(Fig. 8).
Discussion
There is no doubt that there is a connection between
PHV and motor skills. Most studies found a stronger
relationship in boys than in girls between developmental age and fitness [20–23]. This phenomenon was
confirmed in the present study as the early maturing
boys obtained better results in most tests, specifically
in tests of speed, agility, static strength of the hands,
explosive strength of the lower limbs, dynamic strength
of the abdominal muscles and flexibility.
However, differences quickly grew with age. Only in
case of static endurance of the upper limbs and shoulders at the age of 8–12 years did the late maturers obtain better results. It is obvious that adolescents who
mature earlier have taller statures than those who mature later, therefore any motor skills dependent on body
height should be better developed in these individuals.
A number of studies [11, 19, 21, 24, 25] confirm this fact,
such as where tests of static strength indicate a rectili
near connection with body height or that taller individuals obtain better results in the standing broad jump.
Body proportion, in this case having longer lower limbs,
might be of crucial importance in the development of
motor skills.
In the present study the biological age differentiated
variously with the development of motor skills in girls.
The early maturers were characterized by more endo-
morphic and mesomorphic body types [12, 26, 27],
which should affect their fitness levels. In the case of
static strength of the hands, the observed differences
favored the early maturers. In addition, in the speed
tests the same cohort presented slightly better results.
Tests of explosive strength of the lower limbs, agility
and flexibility were found to not differentiate between
the early- and late-maturing girls. Significant differences favoring the late maturers were observed in the
test measuring the dynamic strength of the abdominal
muscles, and particularly, in the tests of static endurance of the upper limbs and shoulders. This might result
from a more leptosomatic body build of late-maturing
girls. It can be assumed that body weight also is an
important factor in these tests as they would be easier
for lighter individuals.
However, while the rate of maturation and somatic
features are important factors, Malina and Bouchard
believe that they do not determine sports performance
[28]. Early maturers achieve better results in motor
tests as somatic development is grounded in strength
and endurance development. These individuals are also
taller and heavier and have higher body mass in relation to height, which is more advantageous in most
motor tests when compared to normal- and late-maturing individuals. According to Łaska-Mierzejewska,
early maturers achieve rather short-term success in
children’s competitions over late maturers and in those
disciplines where body size counts [29]. However, they
later frequently give up sport or are outpaced by their
normal- or late-maturing peers who are characterized
by thinner body builds, which is more advantageous in
several sporting disciplines.
Data on sexual maturation and the ability to categorize pupils into groups of adolescent maturity may
make it easier to recognize gifted future athletes. Following Łaska-Mierzejewska’s juxtaposition [29], if a late
maturer achieves excellent results in fitness tests when
competing with similarly-aged peers, this may be used
as a form of talent identification and indicate an individual’s propensity towards sporting success. If an early
maturer achieves better results among their peers, this
rather may indicate only a temporary improvement in
physical fitness and motor skills.
Conclusions
1. At a younger age the rate of somatic development
does not affect motor development.
2. The rate of maturation significantly influences the
results of fitness tests and is particularly demonstrated
in boys, who achieved better results in most of the
analyzed motor skills.
3. The motor skills best performed in all age groups
and in both sexes by early maturers were in tests of
static strength of the hands. In the group of boys, early
maturers in all age groups also performed the best in
tests of speed and explosive strength of the lower limbs.
137
HUMAN MOVEMENT
4. The cohort of the late-maturing girls achieved
better results in all age groups in tests of static strength of
the upper limbs and shoulders, and of dynamic strength
of the abdominal muscles.
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motor skills in prepubertal boys. Biol Sport, 1996, 13
(4), 279–289.
26.Chrzanowska M., Gołąb S., Żarów R., Sobiecki J., Matusik S., Trends in body adiposity and overweight and
occurrence in children and youth in Krakow in the last
thirty years [in Polish]. Pediatria Polska, 2002, 2, 113–120.
27. Volver A., Viru A., Viru M., Improvement of motor abilities in pubertal girls. J Sports Med Phys Fitness, 2000,
40 (1), 17–25.
28. Malina R.M., Bouchard C., Growth maturation and phy
sical activity. Human Kinetics, Champaign 1991.
29. Łaska-Mierzejewska T., Anthropology in sport and physical education [in Polish]. COS, Warszawa 1999.
Paper received by the Editors: February 25, 2011
Paper accepted for publication: November 23, 2011
Bartłomiej Sokołowski
ul. Śląska 5/6
30-003 Kraków, Poland
e-mail: b.j.sokolowski@interia.pl
HUMAN MOVEMENT
2012, vol. 13 (2), 139– 146
HEALTH-RELATED PHYSICAL FITNESS AND ASSOCIATED
SOCIODEMOGRAPHIC FACTORS IN ADOLESCENTS
FROM A BRAZILIAN STATE CAPITAL
doi: 10.2478/v10038-012-0015-4
EDIO LUIZ PETROSKI 1, 4 *, DIEGO AUGUSTO SANTOS SILVA 1,
JOÃO MARCOS FERREIRA DE LIMA E SILVA 2 , ANDREIA PELEGRINI 3
1
Federal University of Santa Catarina, Post-Graduate Program in Physical Education, Sport Center, Florianópolis, SC. Brazil
Federal Institute of Education, Science and Technology of Ceará, Campus North Juazeiro, CE, Brazil
3
State University of Santa Catarina, Florianópolis, SC. Brazil
4
CAPES Foundation – grant BEX 0951/10-2
2
Abstract
Purpose. The aim of this study was to investigate the associations between health-related physical fitness and sociodemographic factors in students from a capital city of a Brazilian state. Methods. A cross-sectional study was conducted on 605
students aged 15 to 19 years. Sociodemographic data were collected, including gender, age, education level and family income,
and correlated to physical fitness levels. Results. The percentages of students with unhealthy body composition, unhealthy skeletal
muscle fitness and aerobic fitness levels were 23.8%, 34.4% and 30.5%, respectively. There was a trend for fewer male adolescents (OR: 0.65; IC95%: 0.42–0.98) to have unhealthy body composition. Students from lower socioeconomic families were
less likely to have musculoskeletal unfitness (OR = 0.60; IC95%: 0.41–0.89). In relation to aerobic fitness, male students
(OR = 3.86; IC95%: 2.67–5.58) and those aged 17–19 years (RO = 1.49; IC95%: 1.02–2.177) were more likely to be unfit. Conclusions. It is important to encourage young people to take part in sports and physical activities at moderate to vigorous intensities in order to improve their body composition, aerobic capacity and physical fitness.
Key words: physical fitness, physical exertion, students, health, motor activity, cross-sectional study, schoolchildren
Introduction
Studies show that low levels of health-related physi
cal fitness (HRPF) during adolescence are associated
with an increased risk of developing chronic degenerative diseases and a higher mortality risk in adulthood
[1]. One way of preventing these negative outcomes is
to regularly engage in a sufficient level of physical activity [2]. Despite this, research conducted in schools
reports elevated prevalence rates of little to no physical activity among adolescents [3–5].
Health-related physical fitness has been defined as
the capacity to take part in daily physical activities
without overexertion in combination with demonstrating a number of characteristics and abilities that
are associated with low risk of developing hypokinetic
diseases [6]. Therefore, an excellent level of HRPF does
not demand extreme levels of physical fitness nor does it
require individuals to perform similarly to athletes,
who generally perform exceptionally well in a range of
physical tests. The components of HRPF can be maintained at healthy levels through physical exercise directed at improving aerobic resistance, muscle strength,
muscle resistance and flexibility and lowering overall
body fat levels.
Motor test batteries used to assess the HRPF or
athletic fitness of children and adolescents are created
by combining several physical tests into one. Physical
fitness batteries are based either on standards of normality or on selected health criteria. Those that are based
on standards are compiled by testing a representative
population sample, with cut-offs then established in the
form of percentiles. Subjects that fall above or below
these percentiles are defined as having healthy or unhealthy levels of physical fitness.
Physical fitness batteries that are based on health
criteria offer the advantage that they identify physical
fitness levels that may expose a given person to certain
health risks. Lunardi and Petroski [7], for example,
investigated 11-year-old adolescents and found that elevated values of body composition (i.e. a body mass index
[BMI] 19.3 kg/m2) could be used as a diagnostic criterion for an unhealthy lipid profile, predicting elevated
triglycerides among females and elevated total cholesterol and LDL-cholesterol in males.
A large number of different factors have an influence
on HRPF, among the most important being sociodemo
graphic factors. For example, males have been shown to
have better fitness levels than females [8]. Santos et al. [9]
found that the likelihood of a girl having healthy HRPF,
on the basis of the four HRPF tests taken from the
Fitnessgram battery, is approximately 50% of that for
a boy. Luguetti et al. [10] studied schoolchildren aged
7 to 16 and observed that the proportion of them with
139
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E.L. Petroski et al., Health and physical fitness in adolescents
unhealthy HRPF was elevated at all ages and was more
common among girls.
Age and school year are also factors related to HRPF.
In Portugal, Lopes et al. [8] observed a tendency for
HRPF to reduce as chronological age increased. Another
sociodemographic factor that appears to modulate phy
sical fitness is economic status. Although still the subject of debate, adolescents from families with upper
socioeconomic status have more opportunities to take
part in physical activities and sports at gyms and sports
clubs and to engage in adventure activities because of
their increased ability to acquire sports equipment and
materials [11]. In contrast, although Dumith et al. [12]
did find a high prevalence of low physical fitness in
a sample of Brazilian schoolchildren, they did not find
any link with economic status.
With reference to the literature on the subject, it can
be observed that certain issues related to the health-related physical fitness of adolescents have not yet received
sufficient research attention. These include questions
such as whether males and females differ in terms of the
components of health-related physical fitness, whether
physical fitness is affected by an increase in age during
adolescence and whether there is a relationship between
the components of fitness and socioeconomic and demographic factors.
It is therefore evident that there is a need to investigate HRPF and the factors related to it in adolescents,
with an objective of identifying at-risk individuals early
enough in order to successfully intervene and change
their lifestyle while they are still adolescents. This is
primarily because adolescence is a critical period when
unhealthy behaviours and habits can be easily acquired
and continue to persist into adulthood.
Few studies have yet been undertaken in secondary
education to analyse HRPF and the factors associated
with it. The objective of this study is therefore to verify
associations between HRPF and sociodemographic
factors in adolescents from a Brazilian state capital.
The hypothesis being tested in this study is that sociodemographic factors determine components of HRPF in
Brazilian adolescents.
The study was approved by the Human Research
Ethics Committee at the Universidade Federal de Santa
Catarina (Protocol number 372/2006). This was a descriptive cross-sectional study conducted during the
second half of 2007 in the city of Florianópolis, which
is the state capital of Santa Catarina and is located in
southern Brazil. Brazil has 26 state capitals, one for each
of the 26 states in Brazil, plus the Distrito Federal in
which the Federal capital Brasilia is located. The state
capitals are urban centres and have the largest population concentrations in their respective states. The capi
tals also host the state governments, financial centres
140
and major industries and are responsible for a large
proportion of the states’ economies.
Sample selection was two-stage, first stratified by
geographic region and then clustered by school classes.
Part of the city of Florianópolis is located on the South
American mainland and part is on the island of Santa
Catarina. In stage one, the city was divided into five
geographical regions as: city centre, continental region,
east of the island, north of the island and south of the
island. The largest school in each region was selected
and then classes from that school were chosen by lots
until a sample representative of the geographic area
had been selected. In stage two, all adolescents in the
chosen classes who attended school on the day of data
collection were invited to take part.
Several different sample sizes were calculated as this
study was part of a larger project investigating a range
of different health-related outcomes. The sample used
for the analysis described here was calculated to study
the prevalence of physical unfitness. The calculation
procedure for finite populations was used to find the
minimum sample size for an estimated outcome preva
lence of 60% [13], an acceptable error of five percentage
points, a 95% confidence level and a design effect of 1.5,
and then the result was increased by 10% to account for
possible refusals and losses from the sample. On this
basis, a minimum of 591 schoolchildren were needed.
The characteristics of the sample process, which stipu
lated the inclusion of all individuals in each chosen
cluster, meant that the actual sample comprised 892
adolescents.
Eligibility criteria for participation in this investigation were as follows: enrolment in the state education
system, attendance on the day of data collection and
being between ages 15 to 19. The upper limit was based
on the World Health Organization [14] definition of
the end of adolescence and the subjects’ educational
context (they were all attending secondary education).
The exclusion criteria were: (a) age greater than 19 years;
(b) pregnancy; (c) failure to provide a free and informed consent form signed either by their parents or
guardians, in the case of minors, or by the adolescents
themselves if over the age of 18. Students were defined
as refusals if they did not wish to take part in the
study or as sample losses if they did not answer the
entire questionnaire.
Subjects were given a minimum of 3 days’ notice
prior to data collection. On the day on which they were
informed of the study, they were also provided the
free and informed consent forms and the test procedures were explained to them. The tests were aimed at
being administered during physical education classes,
although this was not always possible.
Socioeconomic status was classified using a questionnaire produced by the Brazilian Association of Market
Research Companies (ABEP – Associação Brasileira de
Empresas de Pesquisa, 2003) [15]. In this questionnaire
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the answers are converted into scores using a points
system and the results divide the Brazilian population
into economic classes according to their spending
power. The ABEP criteria result in five classes, from
“A” to “E”, in descending order of spending power.
The questionnaire covers the head of the family’s educational level, the number of domestic servants the
family employs and nine items related to consumer
durables that the family possesses. After preliminary
analysis of frequency distributions, for the purposes of
this study, socioeconomic status was collapsed down to
three classes: high “A”; medium “B” and low “C+D+E”.
The components of HRPF were measured using
tests, instruments and standards recommended by the
Canadian Physical Activity, Fitness & Lifestyle Approach Protocol [16], which is based on health-related
criteria. The entire examination analysed three main
components, with the first being body composition in
terms of (a) body mass index (BMI), (b) waist circumference (WC) and (c) the sum of five skin folds – 5Sk
(triceps, biceps, subscapular, iliac crest and medial calf).
The second measured component was skeletal muscle
fitness in terms of (a) flexibility – by a modified “sit-toreach” test, (b) handgrip strength test – using a dynamometer to measure each hand alternately, with two
attempts per hand (the best score for each hand were
added together to provide a global score), (c) lower limb
strength – the best score from three attempts at the
standing jump test (with the following formula used
to calculate the final result: Lower Limb Strength (W)
= [60.7 × height of jump (cm)] + [45.3 × body weight (kg)]
– 2055), (d) press ups – continued up to exhaustion, with
males supporting themselves with their toes and females
supporting themselves with their knees, (e) partial situps – (for one-minute) the speed of this test was set by
a metronome adjusted to 50 beats per minute, and (f)
back extension – the back was extended and the trunk
supported for 3 minutes.
The final third component was aerobic fitness, measured by a (a) bench test, performed using a stepped
ergometer which the subject had to ascend and descend
at the speed demanded by the test protocol, indicated
by a metronome. The test returned a score for aerobic
fitness according to the following equation: aerobic
fitness score = 10 × [17.2 + (1.29 × O2 consumption) –
(0.09 × body weight in kg) – (0.18 × age in years).
Scores for the components of the body composition domain (BMI, WC and 5Sk), the skeletal muscle
fitness domain (flexibility, muscle strength/resistance)
and the aerobic fitness domain were generated from the
combined results of each test [16]. Each component
was then classified into one of five categories based on
the scores’ implications for health: (1) “must improve”,
(2) “regular”, (3) “good”, (4) “very good” or (5) “excellent”. For the purposes of this study, the five HRPF
categories were collapsed down to “healthy” (categories 3, 4 and 5) or “unhealthy” (categories 1 and 2).
Fieldwork was carried out by a team of four professors and postgraduates studying for master’s degrees
and doctorates in Physical Education. These assessors
were trained in all of the procedures in advance in
order to standardize data collection. Each team member was made responsible for data collection related to
one test in order to avoid interobserver variability,
thereby increasing the reliability of data. Furthermore,
a pilot study was conducted at a school that was not
part of the definitive sample in order to test the used
instruments and general logistics in the field. Anthropometric measurements were taken by anthropometrists
who had been trained and certified by the International
Society for the Advancement of Kinanthropometry
(ISAK). All of them had a Technical Error of Measurement within acceptable limits for anthropometric
measurements [16].
Descriptive statistics were used to analyse the data.
Both crude and adjusted binary logistic regression mo
dels were used to test for associations between physical
unfitness (the dependent variable) and sociodemographic
variables. The adjusted model was constructed using
the stepwise method, based on likelihood ratios, to calculate adjusted odds ratios (with 95% confidence intervals), starting from a model including those variables
which had a p value of 0.20 or less in the crude analysis.
Statistical analysis was performed using SPSS statistical
analysis software (SPSS ver. 15.0, IBM, USA) to calculate
odds ratios and their respective 95% confidence intervals.
Results
From the total number of adolescents in class on
the day of data collection, 21.74% refused to take the
physical tests, 8.41% were considered sample losses and
2% were excluded because they were outside the age
range. The final sample taken under consideration
comprised of 605 secondary education students with
a mean age of 16.1 years (standard deviation = 1.0).
It can be observed from Table 1 that the majority
of the sample were female, aged 15 to 16, were in the
eleventh grade and had middle socioeconomic status.
Table 1 illustrates the prevalence rates of adolescents
with unhealthy HRPF levels. In this sample, 23.8%
(95%CI: 20.3–27.2) were unhealthy in terms of body
composition, 34.4% (95%CI: 30.5–38.1) were unhealthy for skeletal muscle fitness and 30.5% (95%CI:
26.8–34.2) were unhealthy in terms of their aerobic
fitness. Table 1 lists the prevalence of adolescents who
did not meet the recommended level of each component of health-related physical fitness for each of the
analysed sociodemographic variables.
Crude logistic regression analysis indicated that
male adolescents and twelfth-grade students were less
likely to have unhealthy body composition. For skeletal
muscle fitness, the subsets least likely to be unhealthy
were in the twelfth grade and had lower economic sta141
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Table 1. Distribution of the sample by sociodemographic variables and prevalence of failure to achieve the levels recommended
for good health in the components of health-related physical fitness, broken down by the sociodemographic variables
Health-related physical fitness: Unhealthy
Sample
distribution
body composition
skeletal muscle fitness
aerobic fitness
N (%)
% (95% CI)
% (95% CI)
% (95% CI)
605
23.8 (20.3–27.2)
34.4 (30.5–38.1)
30.5 (26.8–34.2)
gender
Female
Male
388 (64.1)
217 (35.9)
26.5 (22.1–30.9)
18.8 (13.6–24.1)
33.8 (29.0–38.4)
35.4 (29.1–41.9)
20.1 (16.0–24.1)
49.3 (42.5–56.1)
age (years)
15–16
17–19
383 (63.3)
222 (36.7)
24.8 (20.4–29.1)
22.1 (16.6–27.5)
36.2 (31.4–41.1)
31.1 (24.9–37.2)
27.2 (22.7–31.7)
36.1 (29.6–42.4)
grade
10th
11th
12th
180 (29.8)
259 (42.8)
166 (27.4)
22.7 (16.5–28.9)
30.5 (24.8–36.1)
14.4 (9.1–19.8)
33.8 (26.9–40.8)
38.6 (32.6–44.5)
28.3 (21.3–35.2)
29.2 (22.4–35.9)
28.2 (22.6–33.7)
35.3 (27.9–42.7)
socioeconomic status
Upper
Middle
Lower
43 (7.1)
374 (61.8)
188 (31.1)
25.5 (11.9–39.1)
22.4 (18.2–26.7)
26.0 (19.7–32.3)
32.5 (17.9–47.1)
38.2 (33.2–43.1)
27.1 (20.7–33.5)
34.9 (20.0–49.7)
32.3 (27.5–37.1)
25.8 (19.4–32.1)
Total
95%CI – confidence interval
Table 2. Crude logistic regression analysis for health-related physical fitness against the sociodemographic variables
body composition
†
OR (95% CI)
†
aerobic fitness
OR (95% CI)†
OR (95% CI)
gender
Female
Male
1
0.64 (0.42–0.96)**
1
1.08 (0.76–1.53)
1
3.88 (2.69–.59)**
age (years)
15–16
17–19
1
0.86 (0.58–1.27)
1
0.79 (0.56–1.13)*
1
1.50 (1.05–.15)**
grade
10th
11th
12th
0.67 (0.43–1.04)
1
0.38 (0.23–0.63)**
0.82 (0.55–1.21)
1
0.63 (0.41–.96)**
1.05 (0.69–1.60)
1
1.39 (0.91–2.12)
Upper
Middle
Lower
1.18 (0.57–2.45)
1
1.21 (0.81–1.82)
0.78 (0.40–1.53)
1
0.60 (0.41–.88)**
1.12 (0.58–2.18)
1
0.73 (0.49–1.08)
OR – odds ratio; 95%CI – confidence interval; † Crude logistic regression analysis; * p
tus. Male subjects were more likely to have poor aerobic fitness than females (Tab. 2).
The adjusted logistic regression model indicated that
male adolescents (OR: 0.65; 95% CI: 0.42–0.98) and
twelfth-grade students (OR: 0.39; 95% CI: 0.23–0.64)
were less likely to have unhealthy body composition
142
0.20; ** p
0.05
than females in the eleventh grade (Tab. 3). Adolescents with lower socioeconomic status were less likely
(OR: 0.60; 95% CI: 0.41–0.89) to have skeletal muscle
unfitness, compared with those from middle-class families. With regard to aerobic resistance, male schoolchildren (OR: 3.86; 95% CI: 2.67–5.58), and those aged
HUMAN MOVEMENT
Table 3. Adjusted logistic regression model for the health-related physical fitness components
against the sociodemographic variables
body composition
†
OR (95%CI)
gender
Female
Male
age (years)
15–16
17–19
grade
10th
11th
12th
Upper
Middle
Lower
OR (95%CI)
†
aerobic fitness
OR (95%CI) †
1
0.65 (0.42–0.98)**
##
1
3.86 (2.67–5.58)**
##
1
0.92 (0.59–1.42)
1
1.49 (1.02–2.17)**
0.69 (0.44–1.07)
1
0.39 (0.23–0.64)**
0.81 (0.54–1.22)
1
0.66 (0.41–1.47)
##
##
0.75 (0.38–1.48)
1
0.60 (0.41–0.89)**
##
OR – odds ratio; CI – confidence interval
†
logistic regression model adjusted for those values that had a Wald test p value 0.20 in the crude analysis (Tab. 2)
## variables omitted from the multivariate model because their Wald test p values were greater than 0.20
** p 0.05
17 to 19 years (OR: 1.49; 95% CI: 1.02–2.17) were more
likely to have a low level of aerobic fitness than females
and adolescents aged 15–16 years.
Discussion
This study found worryingly high prevalence rates
of low HRPF levels among adolescents. Additionally,
male adolescents and those in the thirteenth-grade were
less likely to have unhealthy body composition than
females and those in the twelfth-grade. Furthermore,
schoolchildren with lower socioeconomic status were
less likely to have skeletal muscle unfitness than those
with middle socioeconomic status. Male schoolchildren
and older children were more likely to have low aerobic
fitness than their female and younger peers.
Glaner [17], investigated HRPF in adolescents from
urban and rural areas of the Brazilian states of Rio
Grande do Sul and Santa Catarina and found a prevalence of unhealthy HRPF in the order of 90% for components of body composition and skeletal muscle fitness, and found that unfitness was more prevalent
among urban dwellers, providing evidence of the influence the physical environment exerts on physical
fitness. The present study was conducted in Florianopolis, which is the city with the highest Human Development Index of all the state capitals in Brazil. This
is an urban area in which the behaviour of young people
is becoming ever more sedentary, possibly combined
with limited access to leisure facilities and adoption of
a lifestyle with hypokinetic characteristics.
Body composition is considered to be part of HRPF
and unhealthy body composition is associated with diseases such as obesity, diabetes and arterial hypertension [18]. Overweight adolescents have a greater probability of high blood pressure (PR = 1.95 to 2.03) and
hypertension (PR = 4.22 to 4.60) with relation to their
underweight/normal weight peers [19].
It is therefore apparent that public health promotion
is in need of strategies that can raise adolescents’ awareness and encourage them to strive for healthy body composition if health problems associated with obesity
are to be avoided in adulthood. One way of combating
and preventing these problems is by including additional programs at school, during physical education
classes, as shown by Farias et al. [20]. These authors
conducted a study in the city of Porto Velho, Roraima,
Brazil, in which a program of guided physical activity
was included in physical dducation classes for one academic year. The intervention group exhibited stable subscapular skin folds, BMI, body fat percentage and fat
mass; and significant reductions in triceps skin folds
and abdominal circumference. In contrast, the control
group had increases in BMI, triceps skin folds, abdo
minal circumference and fat mass.
Skeletal muscle fitness was assessed using a series
of physical tests making it possible to conduct a more
detailed analysis of HRPF, which is one of the Canadian
143
HUMAN MOVEMENT
system’s strong points. One in three of the adolescents
studied here had low musculoskeletal physical fitness,
which is commonly associated with functional limitations and an increased risk of sarcopenia and osteoporosis in later life. One prospective study investigated
whether flexibility, strength, muscle resistance and phy
sical activity predict bone and joint diseases and conditions. After 25 years of study, the authors concluded
that adolescents who scored low in the skeletal component had a 96 times greater chance of lombalgia,
postural problems and joint conditions than people
with high scores [21].
The divergence between different motor test batteries can be observed by comparing the prevalence of
skeletal muscle fitness found in this study with the
prevalence rates reported by others. Ronque et al. [22]
analysed the motor performance of upper socioecono
mic status children from the Brazilian city of Londrina
using the AAHPERD battery and found that more than
80% of the studied schoolchildren did not meet the
minimum criteria for good health. The results of this
study demonstrated that the adolescents with upper
socioeconomic status had higher scores for the skeletal
muscle component than those with lower socioeconomic status. Studies in literature that have investigated
upper status [22] and lower status adolescents [23] reported that the prevalence of low scores was higher
among lower socioeconomic status children.
Another important component of HRPF that has
a significant impact on individuals’ health is aerobic
fitness. The high prevalence of adolescents with low
levels of aerobic fitness observed in this study is of great
concern because poor aerobic fitness is associated with
lipid profile imbalances, metabolic disorders and an
increased risk of obesity. After a 9-month case-control
study of schoolchildren from Madison, Wisconsin (USA),
it was shown that a school-based physical fitness program was capable of improving aerobic capacity. These
results were associated with reductions in body fat and
improvements in insulin resistance [24]. A school-based
epidemiological study conducted in the south of Brazil
observed that adolescents of both genders with low levels
of aerobic fitness have a 4.10 times greater chance of
abdominal obesity than their peers with healthy levels
of aerobic fitness [25].
The present study found that gender was associated
with aerobic fitness, indicating that male adolescents
have an almost four times greater chance of poor aerobic
fitness than females. Although comparison of aerobic
fitness observations is complicated by the great diversity in methodologies and cut-off points employed by
different studies, the study results are in line with the
literature. Vasques et al. [26] used the Fitnessgram to
investigate the aerobic fitness of adolescents and found
that 68% of the boys and 37.8% of the girls did not reach
the minimum levels recommended for good health.
Similar results have been reported by Pelegrini et al. [27],
144
who applied the AAHPERD criteria and observed low
physical fitness levels, representing health risk in terms
of cardiorespiratory fitness (boys: 80.8%; girls: 77.6%,
p < 0.001). Notwithstanding, those results are in opposition to the results of other studies in the literature,
which have indicated that males are fitter [21].
In the present study, adolescents aged 17 to 19 had
a 1.49 times lower chance of attaining a healthy score
than the 15 to 16 age group. Malina [28] conducted an
extensive systematic review to quantify secular changes
in the aerobic fitness of children and adolescents. Among
other results, they found that, according to 33 studies
from 27 different countries, over the last 45 years the
aerobic fitness of young people all over the world has
been decreasing by 0.36% per year. Furthermore, it was
also found that after puberty this loss intensifies as age
increases, above all because of the inactive lifestyle that
is ever more pervasive among adolescents.
Despite the scientific evidence illustrating the relationship between physical activity and health, young
Brazilians are adopting ever more inactive or low-activity lifestyles. Silva et al. [3] conducted a survey in
the capital city of a state in the northeast of Brazil and
found that more than 75% of adolescents were inactive.
Ceschini et al. [4] reported that 62.5% of the children
attending secondary education in the city of São Paulo
engaged in little or no physical activity. Another recent
study reported that one in every four adolescents in
a south administrative region of Brazil were inactive or
underactive [5]. As was shown in a recent systematic
review, estimates of this magnitude have been made
in many different countries [2].
The present study did not compare relative and absolute oxygen consumption, but restricted the analysis
to the proportion of a sample of schoolchildren who
met the minimum criteria for good health, using lower
cut-off points for females than for males [16]. Prospective studies that take direct assessments of aerobic fitness into account are needed before further conclusions
can be drawn. In addition, this study does suffer from
certain limitations. The sample was drawn exclusively
from state-funded secondary schools. Therefore other
sectors of the educational system, such as municipal
schools, private schools and technical schools were not
assessed. The conclusions arrived here are thus only
related to the population studied. Another limitation is
the number of children who refused to take the physical tests, which is evidence of a lack of motivation to
exercise on the part of the participants and may have
affected the results.
The findings reported in this study should serve as
a starting point for future investigations conducted in
other locations using the Canadian physical fitness
battery, which includes a greater number of tests and
measures for diagnosing HRPF levels and should therefore offer more specific results for these variables. Notwithstanding, further debate is needed in order to create
HUMAN MOVEMENT
intervention strategies aimed at encouraging school
populations to engage in sufficient physical activity to
maintain good health.
Conclusion
This study concluded that a low proportion of adolescent children in Florianopolis scored well for HRPF.
Approximately one in every four children had an unhealthy body composition, and two in every three had
low levels of aerobic and musculoskeletal fitness.
Analysis of the associations between physical fitness and socioeconomic and demographic factors suggests that: (a) the proportion of adolescents with poor
cardiorespiratory fitness was greater among males than
among females; (b) that the prevalence of cardiorespiratory unfitness is greater among older male adolescents
(aged 17 to 19 years) than among younger male adolescents; (c) that the proportion of adolescents with unhealthy body composition is greater among females and
among those nearing the end of their school education;
(d) and that more students from middle socioeconomic
status families than from lower socioeconomic status
families are classed as having musculoskeletal unfitness.
It is therefore recommended that adolescents be encouraged to take part in moderate to vigorous intensity physical activities and sports in order to improve their
aerobic capacity and increase their physical strength.
Acknowledgments
The authors of this study wish to thank the CAPES Foundation for their support (grant BEX 0951/10-2).
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Edio Luiz Petroski
Universidade Federal de Santa Catarina
Campus Universitário – Trindade,
Caixa Postal 476
Núcleo de Pesquisa em Cineantropometria
e Desempenho Humano
CEP 88,040-900. Florianópolis, SC, Brazil
e-mail: petroski@cds.ufsc.br
HUMAN MOVEMENT
2012, vol. 13 (2), 147– 151
DOES THE MODIFICATION OF BALL MASS INFLUENCE THE TYPES
OF ATTEMPTED AND SUCCESSFUL SHOTS IN YOUTH BASKETBALL?
doi: 10.2478/v10038-012-0016-3
JOSÉ L. ARIAS
Department of Physical Activity and Sport Sciences, Catholic University St. Anthony of Murcia, Murcia, Spain
Abstract
Purpose. This study aimed at determining: (a) whether the effect of modifying ball mass allowed youth basketball players to
attempt a greater number of lay-ups and hook shots during real games, and (b) whether the modification affected successful
shots. Methods. Fifty-four boys from six basketball teams, aged between 10–11 years, participated in the study. The independent
variable was ball mass and the dependent variable was the attempted and successful type of shots (set and jump shot, lay-ups,
and hook shot). We established three situations in which four games were played with each of the following balls: (a) a regulation
ball, (b) a ball of smaller mass (440 g), and (c) a ball of greater mass (540 g). Four observers were trained (intra- and inter-observer
reliability > 0.96). Four observers recorded the data utilizing a systematized register from observation of the game videos.
Results. A higher percentage of lay-ups were attempted with the 440-g ball in comparison with the regulation ball (U = 227906,
p = 0.01, ES = 0.152) and with the 540-g ball (U = 218614, p = 0.01, ES = 0.160). A higher percentage of lay-ups were successful
with the 440-g ball in comparison to the 540-g ball (U = 223080, p = 0.02, ES = 0.210). Conclusions. Only attempted lay-ups
increased with the 440-g ball in comparison to the regulation ball, but the percentage of the rest of kinds of attempted shots
and successful shots were similar when comparing the modified balls to the regulation ball.
Key words: children, rule modification, game analysis, team sport, teaching sport
Introduction
Shooting ability is very important in youth basketball for three reasons: (a) it is the technique that directly
leads to scoring points, (b) it is the favorite technique
of young basketball players [1], and (c) it is one of the
aspects of basketball that is the most fun for children
and provides them with the most satisfaction [2]. In
the game of basketball, young basketball players need
to use different types of shots in order to adjust their
strategy to in-game situations. The problem is that most
shots are quite standard in nature (i.e., the set-shot
and jump-shot) in youth basketball [3–5]. Coaches
and teachers need to constantly develop strategies and
think up ways to solve the problem of efficient scoring
through shooting.
Motor praxeology has conceptualized each sport as
a motor system [6] with its own internal logic, which
are the relationship dynamics between players and the
structural elements of the sport as defined by a set of
rules. The rules determine four types of participant relationships that cause game action to emerge: (a) with
other participants, (b) within the game space, (c) with
the manipulated equipment, and (d) the way in which
players should adjust to game time. Consequently, each
motor system has its own internal logic that causes
players to carry out certain game actions in order to play
the game. If any rule is changed, no matter how inconsequential, such as what kind of ball is used in the game,
the game actions themselves may change.
Children normally lack the strength and physical
characteristics required to efficiently perform in shooting situations [7–10]. For example, several studies that
analyzed the effects of ball dimension on shooting indicated that a dimensionally smaller ball allowed for
better shot technique [9] or did not impair it [10], suited
children’s preferences [9], and increased shot effectiveness [8, 9] or did not impair it [7, 10]. The above studies
that utilized shooting test procedures found that changes
in ball mass may improve shot performance and other
ball handling skills. However, little attention has been
given to the effect of modifying ball mass on shots
performed during real games in youth basketball.
Piñar [5] analyzed the effect of introducing various
rule modifications on the types of shots, among other
variables, used in basketball in order to study the variability of this behavior. Piñar modified several rules
(court size, the free-throw line, the three-point line, game
duration, and the number of players) and found no differences in the lay-ups performed by each player (11.7%
vs. 11.2%), but differences were found for standard and
hook shots (12.3% vs. 15.8%) after introducing the
modified variables. Arias et al. compared the effects of
two shapes of the three-point line, among other variables,
on the types of shots thrown. The results showed an
increase in standard (35% vs. 40.5%) and hook shots
(0.2% vs. 1%) when the three-point line was outlined
by the free-throw lane [3].
The standard shot (e.g., the set-shot and jump shot)
is the most frequently used type of shot in basketball
147
HUMAN MOVEMENT
J.L. Arias, Ball mass and type of shot
[3, 5, 9] and is characterized by: (a) not requiring one
to move towards the basket and (b) where the shooting hand is placed behind and slightly underneath the
ball, while the non-shooting hand balances the ball
from the side. On the other hand, lay-ups are the most
effective way to score, as well as to be charged with
a personal foul [11, 12]. This type of shot decreases energy
demands, allows for more control, is less affected by
aerodynamic variables, and requires less ball spin [13].
The hook shot is difficult to perform [14] because it
involves a lateral placement of the body with regard to
the basket and an overhead shot. However, this type
of shot allows one to protect the ball and one’s performance in the presence of close opponents [15].
Therefore, considering the types of shots used in basketball, the objectives of this study were to determine:
(a) whether the effect of modifying ball mass allowed
participants to complete a greater number of lay-ups and
hook shots during gameplay, and (b) whether such
a modification affected the success of these shots. The
first hypothesis of this study was that a reduction in
ball mass would improve game play, where each type
of attempted shot would become more successful with
a ball of increasingly lower mass. The second hypothesis
was that each type of attempted shot would be less
successful with a ball of higher mass. The rationale of
these hypotheses was that the dominating use of a ball
in sport makes it one of the most important pieces of
equipment in team sports. It is therefore very likely
that players’ technical-tactical shooting pattern would
also change due to the modifications made to such an
important component of the game [16].
Fifty-four boys, all aged between 10–11 years (M =
10.63 ± 0.55), were selected from six youth basketball
teams to participate in this study. All belonged to official, federated teams for 2.52 ± 0.75 years and practiced each week for a total of 5.03 ± 0.80 hours. The
selection of the teams and players was deliberate, as
these teams fulfilled the following inclusion criteria:
(a) that the team participated in all scheduled season
games, (b) that the team consisted of the same players
in all the games, and (c) that the selected teams from the
league competed at the highest playing level, based on
the opinions of coaches, and that its players were mostly
homogeneous in age, previous experience and playing
level. The goal of the study was only communicated to
the sports director of each team, but not to the coaches
or the players so that this information would not affect
the way they played. The parents of the participants and
the coaches completed a consent form to participate in
the study. In addition, this study was approved by the
Research Ethics Committee of the University (CEI 22-540).
The different basketballs that were to be tested were
based on the more extreme proposals on ball mass from
148
research on the subject. For a ball of lower mass, we
selected a mass slightly less than the 467.76 g ball proposed by Satern et al. [10], weight 440 g, 69–71 cm.
For a ball of higher mass, we chose a ball with a mass
in between the suggested weights of other researchers.
Chase et al. [7] proposed a mass of 538.65 g, while
Isaacs and Karpman [8], Regimbal et al. [9], and Satern
et al. [10] proposed a ball with a mass of 552.8 g. This
ball was to be 540 g, 69–71 cm.
It was decided that all of the participating teams
would play in real basketball games differing only in
the mass of the used basketball, whether a regulation
ball (485 g, 69–71 cm), the ball of smaller mass (440 g,
69–71 cm), and the ball of greater mass (540 g, 69–71 cm).
A three-day tournament was organized consisting of
12 games in which the six participating teams were
randomly matched. Each day, the teams played between
one and two games. The game ball for each game was
also randomly chosen. Between all the teams, four
games were played with each ball. Each team played
a minimum of one game and a maximum of two games
with each ball. The tournament was organized on
a weekend one week after the team finished a compe
titive playing stage, with the players later continuing
to compete the weekend after the tournament. They
were used to participating in tournaments similar to
the one organized in the present study, which were not
competitive in nature.
One month before the study was to begin, the coaches
were informed that they would be playing in a tournament: (a) with balls provided by the organizing committee, (b) where the games would be previously staged,
(c) in which all the participants would receive a diploma,
and (d) they would have to respect the inclusion criteria
(stated above) as well as the requisites of inter-sessional
consistency. The requirements were: (a) the players were
always the same ones on each team, (b) the participants
played all the games on identical courts (28 × 15 m),
(c) rest interval between games was a minimum of one
hour, (d) each game consisted of four 10-minute periods,
(e) the participants warmed up with a ball that was similar to the game ball, (f) individual defense was compulsory, (g) the height of the baskets was 2.60 m, (h) the
balls were the same in texture, color, circumference and
bounce, and (i) the games followed the same game rules.
A group of six experts (three researchers specialized
in basketball and three coaches with experience coaching 9–11 year-old basketball players) delimited and
defined the variables and their categories. The variable
was the type of shot. The experts defined it as the way
in which a player shoots. They studied the presence or
absence of motion (traveling), arm movements, hand
technique and shooting performance. As a result, the
following categories of shots were chosen: (a) standard
shot attempts and successful standard shots, (b) lay-up
attempts and successful lay-ups, and (c) hook shot attempts and successful hook shots. The categories were
HUMAN MOVEMENT
exhaustive and mutually exclusive [17]. The categories
were coded using a numeric system to facilitate their
register. Four observers were trained in the types of
shoots that were to be registered until they accumulated a minimum of 20 hours of experience. Intra- and
inter-observer reliability was later calculated by use of
Cohen’s kappa coefficient; inter-observer reliability
reached values between 0.96 and 1 and intra-observer
reliability was 1.
In accordance with Isaacs and Karpman [8], as well
as with basketball regulations, the properties of the ball
that were controlled were: (a) mass, (b) circumference,
and (c) bounce height. Three collaborators monitored
this half an hour before and after each game. They
followed a protocol that was adapted by Crisco et al.
[18]. This consisted of taking three measurements of
each property and calculating the mean. To monitor
bounce, the collaborators let the ball fall from a height
of 1.80 m (measured at the bottom of the ball) and
measured the height the ball reached after bouncing
(at the top of the ball). The measurements were taken
by recording the height and extrapolating them to the
calibration mark by use of a video camera (Everio Full
HD-GZ-HD7, JVC, Japan) connected to a computer
(Acer Aspire 3630, Acer Inc., Taiwan). The image was
then analyzed by video processing software (Virtual
Dub 1.6.15), where measurements with a horizontal
component were eliminated.
Two assistants recorded the games with a video ca
mera (Everio Full HD-GZ-HD7, JVC, Japan). The camera
was located transversally to the basketball court on the
opposite side from the scoring table. It was placed on
a tripod which could be rotated if necessary five meters off the ground, two meters from the sideline and
aimed at the center of the court in order to record the
entire game. As a general rule for recording, the camera
was to always film the player with the ball, the court
and the basket, in addition to the rest of the players.
A group of four observers analyzed the data utilizing
a systematized register developed for the examination
of the game videos [17].
The register technique used in the study was to code
the examined variables on the registry instrument [17],
with the main unit of analysis being each attack phase
(i.e., the percentage of each type of shot related to the
number of attack phases with each ball). In order to
increase observation reliability, the observers used a protocol of observing each attack phase twice at real speed.
If necessary, the observers examined each attack phase
at a speed of 25 frames per second. The observers regis
tered the numeric code that corresponded to each cate
gory, with each observer observing and registering
three games. The total sample size consisted of 2,100
attack phases from the 12 games, of which 736 corres
ponded to the four games played with the regulation
ball (485 g), 660 for the four games played with the ball
of smaller mass (440 g), and 704 for the four games
played with the ball of greater mass (540 g).
Statistical analysis of the data was performed with
SPSS v. 17.0 for Windows (SPSS, Inc., USA). We conducted descriptive analyses to measure the frequency
and percentage of the type of shots taken and how
successful they were. We assessed the normality of the
data by the Kolmogorov-Smirnov test, which indicated
that the data were non-parametric. The Kruskal-Wallis’ H test was used to assess in which categories there
were significant differences and post-hoc comparisons were performed with the Mann-Whitney U test
to determine in which balls did these differences occur.
Statistical significance was set at p 0.05. The effect
sizes (ES) for significant differences in the type of shot
among different ball masses were also determined.
Results
Table 1 shows statistically significant differences for
the attempted, 2(2, N = 2,100) = 8.448, p 0.01, and
successful lay-ups, 2(2, N = 2,100) = 5.724, p 0.05.
A higher percentage of lay-ups were attempted with
the 440-g ball in comparison to the regulation ball
(U = 227906, p 0.01, ES = 0.152) and to the 540-g
ball (U = 218614, p 0.01, ES = 0.160). A higher percentage of lay-ups were successful with the 440-g ball
in comparison to the 540-g ball (U = 223080, p 0.05,
ES = 0.210). Although there were statistically significant differences for the lay-ups, but not for the standard and hook shots, the differences were low in practical terms.
Discussion
The objectives of this study were to determine: (a)
whether the effect of modifying ball mass allowed the
study participants to attempt a greater number of layups and hook shots, during real gameplay, and (b)
whether this modification affected successful shots.
The results do not completely confirm our original
Table 1. Frequencies, percentages, and significant
differences of the means of the compared variables
Type of ball
Type of shot
440 g
N
Standard attempt
284
Successful standard 126
Lay-up attempt
203*
Successful lay-up
90**
Hook attempt
9
Successful hook
2
%
Regulation
N
43.1 310
19.1 105
30.8 181
13.6
79
1.4
9
0.3
1
%
42.1
14.9
24.6
10.7
1.2
0.1
540 g
N
%
296 42
107 14.5
175 24.9
68
9.7
5 0.7
2 0.3
**signifies the comparison of the 440-g ball to the regulation ball and to the 540-g ball, p 0.01
**signifies the comparison of the 440-g ball to the 540-g
ball, p 0.05
149
HUMAN MOVEMENT
hypotheses. The amount of attempted lay-ups increased
with the 440-g ball in comparison to both the regulation ball and the 540-g ball. The amount of successful
lay-ups increased with the 440-g ball in comparison
to the 540-g ball. Neither of the types of attempted
and successful shots decreased with the 540-g ball in
comparison to the regulation ball. There was a similar
percentage of both attempted and successful standard
and hook shots with all three balls. In practical terms,
the differences were non-significant according to the
results of the effect size. Despite the fact that the ball
is one of the most important pieces of equipment that
stands as the defining characteristic of most team
sports, the effect of its modification in the case of basketball was subservient to the players’ physical characteris
tics and personal interpretation [16].
After analyzing 431 basketball shots taken by children under 12 years of age, Ibáñez et al. [14] found that
27.5% of attempted shots were lay-ups and 58.7% were
standard shots. Arias et al. [3] showed that 21.3% of
attempted shots were lay-ups. In the present study with
the 440-g ball, the percentage of attempted lay-ups
was higher while the percentage of attempted standard
shots was similar. Along with the rest of the analyzed
literature, this reaffirms that the 440-g ball allows for
a higher percentage of lay-ups. This variation was posi
tive as the lay-up is one of the least frequent types of
shots and should be actively promoted in youth basketball [1, 14, 15].
According to Wissel [15], the reason that the number of attempted lay-ups can increase during a game is
that more situations arise in which there are no opponents to hinder progress towards the basket. When
compared to the way they perform with the other
balls, this means that a game with a 440-g ball could
facilitate children’s spatial advantages over their opponent, which would allow them to shoot with a lay-up.
Research has found that children direct their attention
towards the interpretational aspects of the game when
their physical conditions are suitable or, inversely, when
the game conditions are adapted to them [19]. A reduction of ball mass may have allowed the studied basketball players to focus more on aspects about the interpretation of the game instead of focusing on aspects
related solely to handling the basketball. This result
seems to be in accordance with studies consulted on
facilitating ball handling by reducing its mass [20, 21].
Neither the amount of attempted nor successful layups decreased with the 540-g ball in comparison to the
regulation ball. According to the reasoning stated above,
the attempted and successful lay-ups should have both
decreased when the participants played with the heavier
ball in comparison to them playing with the regulation ball. However, this did not occur, as other studies
have found that only an increase in ball circumference
can impair the quality of the players’ handling [20, 22].
In this study, we maintained the ball circumference
and only modified the mass. Thus, the increase in mass
150
of the 540-g ball in regard to the regulation ball did not
particularly hinder the participants from generating
more advantageous situations over their opponents.
The amount of attempted and successful standard
and hook shots were similar with all three balls. These
results may be related to three arguments. First, these
shots are difficult to perform [14]. The standard shot demands more leg strength and better coordination [15].
Theoretically, this kind of shot increases the chance of
success because it allows for a higher height of ball release [23–25]. However, it is usually the least successful
shot due to the conditions basketball players play in [11].
The difficulty of the hook shot resides in the required
lateral position in regard to the basket and in the required overhead shot [14]. The hook shot is recommended
for throws from very close positions and with nearby
opponents as it allows one to protect the ball [15]. Such
a shot requires a high level of skill in these situations
with numerous opponents aggressively defending their
basket. Second, the standard shot is generally used more
frequently and the hook shot is used more rarely [5, 9,
14, 26]. Third, and due to the above stated reasons, the
players’ shooting patterns with regard to the predominant and non-predominant shots used in the game
seems to be so well established that it was not affected
by a short-term modification in ball mass.
The modification of ball mass did not produce a cri
tical fluctuation strong enough to cause behavior change.
That is, ball mass was not a sufficiently large enough
stimulus to cause the number of standard and hook
shots to change. However, just because the hook shot
is not used very frequently does not mean that it is not
important and should not be practiced [1, 5, 9, 15].
These results reveal the need for more research in looking for other modifications in basketball that could
lead to an increase in hook shots.
Conclusion
The present study provides evidence about the effect
of modifying ball mass on variables exhibited during
real gameplay in youth basketball. The results show
that only the amount of attempted lay-ups increased
with the 440-g ball in comparison to the regulation ball.
The percentage of the rest of the types of attempted
and successful shots were similar when comparing
both the modified balls to the regulation ball. Nonetheless, youth basketball should promote lay-ups and
hook shots so that adolescent players can practice the
different kinds of shots necessary to successfully compete at higher levels. Modifications that lead to an improvement of these aspects of the game are very important in such a complex sport. In this study, a ball of
lower mass led to an increase of attempted lay-ups.
However, this ball did not increase the number of attempted or successful hook shots. This result reveals
the need to study other modifications that could improve youth gameplay.
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This study has several limitations: (a) only boys were
studied, (b) anthropometric characteristics, biological
age, strength, heart rate, perceived exertion, and skill
level were not controlled, (c) nutrition characteristics
and hydration level were not tested, and (d) a description of game situations in which the particular shots
were used was not made. These conditions may limit
a more simplified explanation of the results and restrict
them only to participants with similar characteristics
to those in this study. Moreover, these results should be
analyzed with precaution due to the data on effect size.
All of these shortcomings should be taken into conside
ration in future studies.
The results exemplify how modifying a relationship
between players and the equipment they use produces
changes in game actions. This supports the need to
further analyze what changes in gameplay occur after
a modification is introduced and how these changes
interact with a given component of the motor system.
The conceptualization of team sports as motor systems
allows us to consider and facilitate such analysis.
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Paper received by the Editors: June 29, 2011
José L. Arias
C/ La Labor, 1
Jumilla (30520)
Murcia, España
e-mail: jlae84@hotmail.com
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2012, vol. 13 (2), 152– 160
THE RELATIONSHIPS BETWEEN THE EFFECTIVENESS OF TEAM PLAY
AND THE SPORTING LEVEL OF A TEAM
doi: 10.2478/v10038-012-0017-2
RYSZARD PANFIL, EDWARD SUPERLAK *
Abstract
Purpose. This study aimed at determining what relationships exist between the determinants of team play efficiency (coope
ration) for creating scoring situations in volleyball and the sporting level of a team. These relationships take into consideration
the number of players who cooperate within a team and the speed at which sets are performed. Methods. The study gathered
observational data on how three leading male volleyball teams, namely Brazil, Russia and Serbia, played in the semi-finals of
the 2010 World League, finishing first, second and fourth, respectively. The research tool was a self-made data registration sheet
which included the description of the chosen variables, namely the type of plays used and how sets were formed, and the criteria for their quantification. The collected results were presented as tables and indexes. An interdisciplinary interpretation of
the results was conducted, which included a qualitative identification of the dependencies that existed between the chosen
variables. Results. The efficiency of team cooperation when solving situational problems in the offense was not significantly
different in the analysed teams. The prevailing activities during the game were those performed in the 2nd tempo, i.e. team play
requiring the coordination and synchronization of three players. Players’ activity as well as the performance efficiency of the
sets they performed in the 2nd tempo increased along with an increase in the sporting efficiency of the examined teams. In the
case of sets performed in the 3rd tempo, activity and the efficiency of coordination declined along with the growth of sports efficiency. The suggested indexes of the employment and application of synergy enable us to determine its level within the teams
which differ in regards to sports efficiency. A diagnostic value of the index of synergy usage, which is determined by the efficiency
of sets in various paces, is higher than a diagnostic value of the index that is determined by the activity of team play. Conclusions. Nowadays, sport teams, especially those which play volleyball at the highest world level, are characterized by a high level
of dynamic organization of players’ activities, which is manifested in the game with the high efficiency of team play when solving
situational problems in the offense, particularly with the use of two or three players conducted in the 1st and 2nd tempos.
Key words: team game, team play, sets, efficiency, synergy
Introduction
The factors determining sports efficiency or sporting level are established within the context of sporting
skills by analysing the results obtained in qualified competition. In the case of team sports where the skills
needed in competition are determined by pro-team,
personality-related, intellectual and motor dispositions,
an analysis of the determinants of sports efficiency is
particularly complex [1–4].
A review of the numerous studies available on sports
efficiency in team sports indicates that the factors that
have had an effect on sporting level are interpreted in
the Cartesian way and analysed separately. In some
studies motor skills were determined as the most important factors, as in Spieszny and Żak [5], Latash [6],
Stępiński et al. [7]. The role of fitness abilities were
examined, among others, by Hoffman et al. [8], Bittenham [9], Klocek and Żak [10]. Coordination abilities
were regarded as particularly important by, among
others, Ljach and Waśkiewicz [11]. To a broader extent,
152
other factors determining efficiency in team sports were
considered, such as players’ mental dispositions, both
personality-related [12, 13] and intellectual [14, 15].
On the other hand, both in practice and in research,
analysis on players’ efficiency is restricted to individual
assessments without any attempts to calculate the meaning of the team context. Such an approach is presented
by, among others, Dembiński [16], Beal [17], Huciński
and Tymiński [18]. Here, team efficiency is regarded as
a set of individual aspects of efficiency rather than a set
of mutually dependent individual efficiency levels. It is
believed that assessments obtained in such a method
are not entirely objective because they do not take into
consideration the mutual interaction of the players,
that is, the influences and relations which have an essential influence on efficiency, as far as team play is
concerned.
A sports team is an sum exceeding its parts, one cannot perceive players in isolation from one other. An
explanation of a sports team’s actions requires an integral approach that treats the team of players as a dynamic system. Such a description is necessary in order
to take into account the social and material-based relationships and influences, which can be better known
as synergy, that occur in such a system [2, 19–22].
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R. Panfil, E. Superlak, The relationships between the effectiveness of team play and the sporting level of a team
For example, in sports competition, the football and
volleyball teams representing Poland in international
competitions were perceived by their coaches in such
a synergic way (Kazimierz Górski for the football team
and Hubert Wagner for the volleyball team). When
time came to select players for the national teams
these coaches selected the best two- or three-player
groups they saw that worked effectively in local teams,
not just single individuals, to be put on the roster. In
this way the positive effects of two-person and threeperson synergies were introduced to the new team.
These relationships were further developed by their
training and in their parent clubs. The teams later
showed high sports efficiency, being that they treated
the teams in a synergic way as systems of cooperating
sub-subjects, i.e. as two-player and three-player groups.
This was opposed to an the individualized perception
of players (which is unfortunately common), which
treats the team as set of subjects with individual skills.
The characteristic feature of present-day sports
teams, in particular those which play volleyball, is the
high degree of organization needed in the dynamic play
and actions taken by each of the players. Panfil [2],
defining the game of sport, states that it is composed
of both the surprising and anticipated plays and actions of a team cooperating with its offensive, counteroffensive and defensive elements. These elements are
performed by the all of the participants of the game
aimed at achieving a number of individual, common
and opposing goals based on the accepted rules of the
game (regulations and strategy). In the context of this
study, the skills in resolving game situations are systems (sequences) of actions taken by the players with
respect to goals of the game, that is, the deliberate sequences of actions in two-player and three-player groups
and played at various tempos. In the case of volleyball,
it is game rules that determine the dominant influence
of the effects of team play between two or three players
in offensive, counter-offensive or defensive systems on
the achieved results.
The organizational relations of a sports team are
determined by spatial dependencies, that is, by the type
of coordinated actions taken by its team members, and
time dependencies, as the degree of synchronization
of these actions. Team play carried out at the highest
level of synchronization and coordination introduces
new qualities to the team, allowing for the effective
realization of the goals in the game. The level of synchronization and coordination of team play in the game
depends on selecting the time, place and method of play
of a given player with respect to the time, place and
method of play of his/her teammates [22].
Therefore, the logical basis for analysing the efficiency of team play would be a five-factor calculation of
the performed actions which are absolutely dependent
on each other, these factors (marked by the symbols g,
p, c, s and w) are:
g – the cooperating players identified in two-player
or three-player sets
p– the space available to effectively play, defined
by situational factors
c – the goal of coordinating team play
s – the way of executing the sets
w– the result of coordinated team play
On the basis of this predicate, we can define the
two-player and three-player sets used by the offense of
a volleyball team, and find:
g – stands for the type of sets executed in the game
p– are factors determining the space available to
play in, including organizational and infrastructural factors
c – the goal is to create scoring situations by the
offense
s – is the way of synchronizing and coordinating
team play
w– the result, which points to the level of team
play efficiency
Identifying the synergy level in the two-player and
three-player offense sets was also carried out on the
basis of the following different factors:
a) the number of players participating directly in
the sets
– from 2 to 3
b) the goal of the play, i.e. creating scoring situations
c) the type of synchronization in the sets:
– advancing (active),
– following (reactive),
d) the type of coordination that takes advantage
of the available space in the sets:
– gaining (changing) the position of the players,
– maintaining the same position by the players,
e) the type of pass used in the play:
– short (fast, underhand),
– long (slow, overhead).
All of the presented determinants differentiate the
levels of synergy in the sets used in creating scoring
situations in volleyball [21].
A high level of synergy in the sets creates a new value
and known sometimes as the so-called winning and
fast sets, characterized by: the participation of three
players, gained game space, changes in the occupied
position, performing fast passes and is considered an
advancing (active) set. Examples of these sets in volleyball are: fast sets performed in the “second tempo”, e.g.
the shifted short, short from the back (the so-called
pipe). An average level of synergy in sets (creating an
added value quality) are groups of sets characterized
by the participation of two players who change positions by the player who is the hitter and making short
advancing passes. The examples of the sets in volleyball are playing the ball in the so-called “first tempo”
in the attacking line. A low level of synergy is shown in
the so-called maintaining, “slow” sets. These sets are
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characterized by the participation of two players, they
maintain their occupied positions, they perform a long,
high pass and is synchronized sequentially. Examples
of these sets in volleyball are a high pass to a player in
zones II or IV in the so-called “third tempo”.
Based on the presented circumstances, the objective
of this study were formulated and research questions
postulated to better analyse the problem. The goal of
the study was to define the relationships between the
determinants of team play efficiency in creating scoring
situations in volleyball and the sporting level, considered
within the context of the number of teammates and
“tempo” at which they performed the sets.
1. Are there any differences in team play efficiency
for teams presenting high but differentiated sports
efficiency?
2. To what extent is the activity of team play in the
two-player and three-player sets connected with
team efficiency in volleyball?
3. Did the studied teams differ with respect to the
use of offense sets played in the first, second and
third tempo?
4. To what extent does the level of synergy in twoplayer and three-player sets differentiate team
efficiency in volleyball?
The inspirations for the development of this method
stemmed from both practical and methodological circumstances. From a practical point of view, there is no
method of objectively identifying exceptional cases
(phenomena, processes, conditions) that take into consideration a number of pragmatic issues. The inspiration for the methodological influence came from the
scientific method used in economic, social psychology
or pedagogical science known as the case study, or the
analysis of an individual case.
Therefore, the proposed method is an original research procedure taking into account the situational and
system-based dimensions of the study, a pragmatic perception of the research and in particular the obtained
results, and their interdisciplinary dimension. This
method allows for the analysis of complex entities (subjects) which can be the subject of interest in any number
of applied sciences, e.g. medicine, economy, pedagogy or
sport, and regarded as exceptional, that is, distinguished
by unique progression or regression or regarded as unique
in the sense of being ideal or regressive. The uniqueness of the examined object may have a quantifiable dimension, e.g. a uniqueness in size, speed, stability or
changeability, as well an effective dimension or a quality
dimension, e.g. uniqueness of organization, skills, competence or attractiveness.
154
The structure of the research procedure
1. Justification for the uniqueness of the selected
case studies (a complex subject or object)
1.1. Research instruments:
– choosing a unique object by experts and
justifying the choice
– fulfilling the objective criteria of uniqueness
2.Choosing and describing hypothetic (variable)
factors allowing the identification of the uniqueness of the examined phenomenon:
a) choosing and describing the variables, e.g. actions, behaviours, dispositions,
b) choosing and describing the criteria for analysing the variables, e.g. skills, efficiency
– choosing the variables and criteria by experts
–choosing the variables and criteria by
brainstorming
– describing the chosen variables and criteria allowing their quantification
3. Quantitative identification of the variables based
on the accepted criteria
– observing the factors, e.g. use of a recording sheet
– basic statistical calculations
– formalisations of the results, e.g. in the
form of tables, figures or indices
4. The interdisciplinary interpretation of the results, based on the knowledge, imagination and
intuition of the researchers by the qualitative
identification of the dependencies between the
variables and the uniqueness of the similarities
and differences with respect to the accepted criteria (by taking into consideration average or
unique cases)
– interdisciplinary interpretation
– formalisation of dependencies or relationships
5.Formulating practical directives which allow
for the:
– rationalization of the actions of the unique object under examination and those similar to it
– systematic ordering of the knowledge about
this unique phenomena
For this study, the data collected for analysis came
from the three leading men’s volleyball teams, namely:
Brazil, Russia and Serbia. The players from these teams
competed with each other in the semi-finals of the
2010 World League, taking 1st, 2nd and 4th place, respectively (see Tab. 1).
Quantification of the observed differences of the
teams’ sporting level included their direct competition
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Table 1. The differences in the sporting level
of the studied teams
National team
Brazil
Russia
Serbia
5
3
1
Number of points scores
in direct competition
Set ratio (won : lost)
Place and points in FIVB
ranking in 2010 [23]
6:3
st
1 place
247.5
points
4:3
2
nd
place
185
points
2:6
th
4 place
167.75
points
and the use of synergy in the sets, determined on the
basis of the ratio of the number of individual types of
actions.
Thus, the synergies application index was defined
(Wss), calculated on the basis of comparing the activities (A) of the groups in performing the various sets as
the sum of the ratios of the number of completed sets
with a lower level of complexity to the number of sets
with a higher level of complexity, divided by 3:
Wss =
and world ranking, which confirmed the primacy of
the team from Brazil.
Based on the number of players, it was found that the
majority of plays were composed of two-player sets –
with the playmaker and the central player, and threeplayer sets – the playmaker, central player (simulating
an attacking action) and the hitter actually hitting the
ball from a specific position on the court – left attack,
right attack, right defence or central defence. Selinger
[24] proposed a classification of the methods of creating sets by use of a time basis, which is defined by
using the term “offense tempo”, namely:
– the first offense tempo, a low set, in which the
height of the ball is set from 30 to 60 cm
– the second offense tempo, a middle set, in which
the height of the ball is set from 60 to 120 cm
– the third offense tempo, a high set, which allows any number of actions by the hitter
Taking into consideration the number of players,
the tempo of carrying out the attack sets and the spatial
organization of the players in comparison to one other, a classification of the most frequently sets used in
the men’s world volleyball was conducted (see Tab. 2).
The criteria which took into account the specific
features of two-player and three-player sets and created
a scoring situation were used in the analysis and interpretation of the obtained results. These criteria include:
the activity, its efficiency and the reliability of team play
as described by Panfil [2].
Thus the action in performing the sets was absolutely
dependent on each other in order to create a scoring
situation. It was determined based on the number of
sets performed at various tempos by two or three players
regardless of their result. On the other hand, efficiency
was determined in a non-graded manner based on eva
luating the conformity of the result with the executed
goal. Hence, the determinant of team play efficiency was
the number of sets which ended with a scoring a point by
the team. Reliability was based on the index value calculated from the ratio of the number of effective sets to
all executed sets, i.e. the sum of effective, ineffective and
counter-effective sets.
By synthesizing the significance of team play efficiency in creating a scoring situation, two indices
were constructed to assess the scope of the application
III tempo
+
II tempo
I tempo
+
II tempo
III tempo
:3
I tempo
The second index which defined the importance of
synergies was the index of using synergies (Wws), was
based on the comparison of efficiency (S) of the teams
in performing various sets as the sum of the ratios of
the number of effectively completed sets with a lower
level of complexity to the number of effective sets with
a higher level of complexity, divided by 3.
Wws =
Effectiveness, III tempo
Effectiveness, II tempo
+
Effectiveness, I tempo
Effectiveness, II tempo
+
Effectiveness, III tempo
Effectiveness, I tempo
Results
Analysis of the results began with comparing the
examined teams with respect to their efficiency, including the activity, efficiency and reliability of coordinated team play in the performed sets. Then, the efficiency of the teams was compared to the coordinated
team play tempo. As a result, a rational attempt to determine the significance of synergies for the efficiency
of volleyball was created.
The results presented in Table 3 do not significantly
differentiate the individual teams. The values and indices shown, illustrating the scope of performing the
two-player and three-player sets performed at various
tempos, could on the other hand constitute a model
mapping high sports efficiency and may be a reference
point for formulating new design models for volleyball teams.
The results from the evaluation of efficiency are
presented in Table 4, including the mean values of activity, efficiency and the reliability of sets performed
at various tempos, and indicate a relationship between
the accepted criteria and the efficiency of the examined teams, such as:
– the slower tempo, the lower reliability of the set,
as the reliability of sets performed at the 3rd tempo
was 0.32, in the 2nd tempo – 0.48, and in the 1st
tempo – 0.61
– the ratio of the number of sets performed in each
of the tempos was respectively: 1st tempo – 21%,
2nd tempo – 48% and 3rd tempo – 31%, and the
ratio of the number of effective sets performed at
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:3
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Table 2. The classification of sets used by professional men’s volleyball teams
Type of play
Attack in two-player
sets at the first tempo
Description of the types of play
short, attack by the middle
player in the first tempo
sit uated approx.
1 m before the playmaker
short from the back, attack
hort shifted, attack of the
of the middle player at the first middle player at the first tempo
tempo situated approx.
situated approx.
1 m behind the playmaker
3 m in front of the playmaker
left attack – short, execution
of the attack from the left side
of the attack field, the middle
player pretends to hit a short
attack, or
left attack – short from the back, execution of the attack
from the left side of the attack field, the middle player
pretends a short attack from the back
Attack in three-player
sets at the second
tempo – left attack
left attack – short shifted,
execution of the attack from
the left side of the attack field,
the middle player simulates
a short shifted attack
sets at the second
tempo – right attack
right attack – short, execution
of the attack from the right side
of the attack field, the middle
player pretends to hit
a short attack, or
right attack – short from the back, execution of the attack
from the right side of the attack field, the middle player
pretends to hit short attack from the back
right attack – short shifted,
the right side of the attack field,
the middle player pretends
short attack
sets at the second
tempo – attack by the
right defence
right defence attack – short,
the right side of the defence
field, the middle player pretends
to hit a short attack or
attack right defence – short
shifted, execution of the attack
from the right side of the
attack field, the middle player
pretends to hit a short attack
156
attack right defence – short from the back, execution
of the attack from the right side of the defence field,
the middle player pretends to hit a short attack
HUMAN MOVEMENT
Attack in the threeplayer sets at the second
tempo – attack by the
defence middle player,
the so-called pipe play
pipe behind the playmaker –
pipe in front of the playmaker pipe in front of the playmaker
short, execution of the attack
– short shifted, execution
– short, execution of the attack
from the middle of the defence of the attack from the middle from the middle of the defence
field behind the playmaker,
of the defence field between
field on the left side of the
the middle play and playmaker,
middle player, the middle
short attack, or
player pretends to hit short
to hit short shifted attack
attack
pipe behind the playmaker –
short shifted, execution of the
from the middle of the defence
field behind the playmaker,
to hit a short shifted attack
Attack after setting the
ball at the third tempo
in two-player team play
execution of the attack after setting of the ball by the playmaker or other player:
– from the left side of the attack field, or
– right side of the attack field, or
– middle of the defence field, or
– right side of the defence field
each tempo was, respectively: 1st tempo – 28%,
2nd tempo – 50% and 3rd tempo – 22%
– both the activity and the efficiency were dominated by the sets performed in the 2nd tempo,
constituting 50% of all attempts
– the activity of the sets performed in the 3rd tempo
was slightly higher than the number of the effective ones and was 31% to 22%, respectively
We then analysed the differences of the activity,
efficiency and reliability of performing the two-player
and three-player sets at various tempos by the volleyball teams. A comparison of the values presented in
Table 5 illustrates the activity, efficiency, as well as the
indices values of coordination reliability of sets played
at various tempos allows us to define certain trends of
actions determining sports efficiency in volleyball, including:
– the activity and efficiency of the sets in the 2nd
tempo increased with the growth of efficiency
of the examined teams
– however, for the sets performed in the 3rd tempo,
we observe the contrary, where the activity and
efficiency of team play decreased with an increase
in efficiency
It is worth nothing that the Brazilian team, ranked
as the most effective, played to the smallest extent in
the in the 1st tempo and achieved a high reliability.
Analysis of the importance of synergies
in terms of the examined teams’ sports efficiency
In order to summarise the results in terms of the
efficiency of team play in creating a scoring situation
in volleyball, the indices used to assess the use of synergies in sets were analysed. They were analysed by
determining the basis of the ratio of the number of
individual types of actions performed at various tempos by each team.
The index of applying synergies (Wss) was calculated by comparing the activity (A) of the teams in
performing the various sets, and was:
Brazil Wss =
27 18 27
+
+
: 3 = 0.75
61 61 18
Russia Wss =
30 26 30
+
+
: 3 = 0.76
49 49 26
157
HUMAN MOVEMENT
Table 3. The efficiency of team play for the examined teams
Effectiveness criteria
Team name
team play activity (number)
team play efficiency (number)
team play reliability (index)
106
105
118
109
48
53
51
50
0.45
0.50
0.43
0.46
Brazil
Russia
Serbia
Mean value
Table 4. The efficiency of team play in sets performed at various attack tempos
Effectiveness criteria
Tempo of team play
in the set
team play activity
number
1
2
3
S
team play efficiency
%
23
52
34
109
number
21
48
31
100
14
25
11
50
team play reliability
%
index
28
50
22
100
0.61
0.48
0.32
–
Table 5. The efficiency of sets at various tempos performed by the teams
Team name
Team play
tempo
Brazil
activity
1
2
3
Serbia Wss =
18
61
27
Russia
efficiency reliability
11
31
6
0.65
0.52
0.24
activity
26
49
30
45 26 45
+
+
: 3 = 1.08
47 47 26
The indices of using synergies (Wws) were determined by similar principles, by adding up the numbers of the effective sets performed at various tempos,
finding:
Brazil Wws =
6
11
6
+
+
: 3 = 0.36
31 31 11
Russia Wws =
10 16 10
+
+
: 3 = 0.53
27 27 16
Serbia Wws =
18 15 18
+
+
: 3 = 1.01
18 16 15
Interpreting the indices values of the application
(Wss) and utilization (Wws) of synergies finds that the
increase in efficiency leads to an increase in the level
of synergies used in creation scoring situations in volleyball. This is confirmed by the indices values of both
activity and efficiency and the diagnostic ability of
158
Serbia
16
27
10
0.63
0.57
0.35
activity
26
47
45
15
18
18
0.65
0.43
0.31
the synergy utilization index, determined by the efficiency of the sets at various tempos, and is higher than
the diagnostic ability of the application index determined using the activity of team play.
The fact that the values of both the synergy application (Wss) and utilization (Wws) indices approached
zero indicates the ever greater application and use of
three-player sets performed in the 2nd tempo within
the game space. On the other side of the spectrum an
opposite trend was found with the values approaching
one or exceeding it by the teams with lower efficiency
(see the Serbian team), which indicates the decreasing
importance of less complex sets performed in the 1st
tempo, and in particular in the 3rd tempo.
Discussion
By recognizing the results of other studies seeking
to identify the efficiency of a sports team, understood
as the components of efficiency by players that are
relatively isolated from one other, a research procedure
was proposed that took into consideration a number
of aspects, such as the system-based and situational
dimension of the game, a pragmatic perception of the
HUMAN MOVEMENT
research, and in particular the obtained results and
their interdisciplinary dimension. The scope and type
of the research presented in this study takes an innovative approach with entirely different perception of
team play than the one found in above-mentioned studies and produces an original research methodology.
A sports team is treated by Panfil [21] as an integral and dynamic system in which we can distinguish
social and material influences and relationship which
appear in various levels of synergy, all of which were
different among each of the competing teams.
The dominant importance of synchronization and
coordination forces the players to treat working together as a quality of its own, as it is not possible to
identify the size of an individual’s contribution to that
of players cooperating together to achieve a common
result. The action components are so interdependent
that we can perceive the joint effort of the players as
a separate entity. Therefore, looking for a higher efficiency of an action or, on the contrary, registering its
low efficiency, the synchronization and coordination
of actions should be analysed together rather than by
their individual dimension. Thus, the efficiency of
fast play depends to the same extent on the playmaker
as well as the hitters. The perceived level of team play by
synchronization and coordination facilitates analysis
of team play in sets and its variants as the actual causes
of skilful team play, which lie in the coordination or synchronizations of actions, rather than in the actions
themselves.
In more effective teams, actions taken in the 2nd tempo that required the coordination and synchronizations of team play by three players, are central. On the
other side, the number of sets performed in the 1st and
3rd tempos are similar, but their reliability is twice as
high in the case of the 1st tempo (0.61 to 0.32). This implies a greater ability to effectively anticipate the actions performed in the 3rd tempo and a higher level of
surprise of actions performed in the 1st tempo (a fast play
with two-player sets).
The increasing trend of growth in the activity and
efficiency for the sets in the 2nd tempo may indicate
a decrease in the importance of the skills needed to
play sets in the 3rd tempo because they are considered
classic sets, commonly used and performed rather
slowly by making high passes, which makes them easy
to anticipate.
The synergic perception of team play efficiency, as
accepted by the players, according to the opinion of Panfil [21], is favourable for creating added value in the
team and by affecting team efficiency, i.e. task-focused
coherence and, consequently, on emotional coherence.
The players, accepting joint responsibility for team cooperation, improve their own and their teammates’
sense and consciousness of the interdependence of the
tasks of the game by a final team result, i.e. winning or
losing. A sense of shared responsibility for the results
also develops and strengthens the emotional coherence and material group conflicts in the team, which
stem from the so-called basic composition of the twoplayer and three-player sets.
It appears as if the proposed indices for evaluating
synergies could become a diagnostic criterion for the
differences of efficiency in team sports. In practicing
sports, when assessing the efficiency of actions, players
are usually treated as separate subjects. Even if any sets
are differentiated when carrying out an assessment on
the efficiency of executing these sets, each player is evaluated individually, ignoring the dependencies between
their actions which form two-player or three-player
wholes.
Ignoring the joint responsibility for the result
achieved by a specific team play by pointing to only
a select group of player, as well as evaluating their performance by a specific action on the efficiency of another
player who could not have influenced its result, such
as when one player assists another, only confuses the
players, makes self-evaluation more difficult, and reduces motivation in general as well as in playing together. That is why an assessment on the efficiency of
two-player and three-player cooperation should concern itself with the level of synergies, and, in particular,
synchronization and coordination.
It should be noted that although the creation of a scoring situation is undoubtedly the most basic component
of volleyball, others, such as serving, playing in a block
and the mistakes made by opposing team are also important components of the game which can influence
efficiency. Therefore, the formulation of clear conclusions could only be possible after analysing the abovementioned game components.
Conclusions
The results obtained in the course of this analysis
allow us to answer the formulated research questions
on what specific determinants exist in playing volleyball at the championship level.
1. The efficiency of team play (i.e. activity, efficiency and reliability) in resolving offense plays,
considered due to the scope of performing certain sets with two and three players, performed
at various tempos, did not significantly differentiate in the studied teams.
2.Sets performed at the 2nd tempo were the most
dominant in the more effective volleyball teams
and were carried out by a group of three players.
The highest reliability was observed in the interaction of two players who carried out sets at
the 1st offense tempo. The activity of the players
and the efficiency of using the sets performed at
the 2nd tempo increased with an increase in the
efficiency of the examined teams, while for sets
performed at the 3rd tempo, the activity and effi159
HUMAN MOVEMENT
ciency of team play decreased with an increase
in the efficiency.
3. It was observed that the Brazilian team, at the
top of the world ranking, is characterized by
significant activity in three-player cooperation
at the 2nd tempo, with the significantly lowest
number of sets performed at the 1st tempo. On the
other side, the team from Serbia, with the lowest
sporting level among the competitors, performed
mostly two-player attacks at the 3rd tempo. Currently, these types of sets in the game of volleyball are regarded as routine plays used more in
critical situations, and hold only one team play
variant and are not very surprising plays to their
opponent.
4. The suggested indexes of applying and using sy
nergy make it possible to determine the level of
synergy of a team in regards to their sporting
efficiency. Index values coming close to zero indicate the greater application and utilization of
three-player sets performed at the 2nd tempo by
more effective teams (the Brazilian team in this
case). On the other hand, an opposing trend was
found with values coming close to one or exce
eding it by the teams with lower efficiency (the
team from Serbia) and indicates the decreasing
importance of less complex sets performed at
the 1st tempo, and in particular at the 3rd tempo.
The comparison of these two indices allows us
to state that a diagnostic ability of the synergy
utilization index, determining the efficiency of
the plays performed at various tempos, is higher
than the diagnostic ability of the play utilization
index, which determines team cooperation.
References
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Superlak E. (eds.), Personal dispositions towards sports
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Wrocław 2003, 73–79.
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as a point guard in basketball [in Polish]. Roczniki Naukowe AWF w Gdańsku, 2001, 10, 39–54.
19. Pszczołowski T., Synergy and its place in the theory of
organisation [in Polish]. Prakseologia, 1973, 3–4, 42–54.
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Paper received by the Editors: June 26, 2011
Edward Superlak
Zakład Treningu w Grach Zespołowych
ul. Mickiewicza 58, bud. P5
e-mail: edward.superlak@awf.wroc.pl
HUMAN MOVEMENT
2012, vol. 13 (2), 161– 169
SELECTED PSYCHOLOGICAL DETERMINANTS OF SPORTS RESULTS
IN SENIOR FENCERS
doi: 10.2478/v10038-012-0018-1
MACIEJ TOMCZAK *, MAŁGORZATA WALCZAK, GRZEGORZ BRĘCZEWSKI
Abstract
Purpose. The aim of the present study was to determine the correlations between the strength of the stimulation process,
mobility of nervous processes, achievement motivation and sports results of fencers in the senior age category. Methods. The
sample was comprised of 38 senior fencers (19 men and 19 women) aged 21–32 years. The strength of the stimulation process
and mobility of nervous processes were assessed with the Strelau and Zawadzki PTS inventory. The subjects’ achievement mo
tivation was measured with the achievement motivation questionnaire by Widerszal-Bazyl. Results. The study revealed that
senior fencers (women, in particular) featured higher mean levels of the examined psychological determinants, i.e., strength
of the stimulation process, mobility of nervous processes and achievement motivation than the general population in the same
age range. Strong and statistically significant correlations were found between all the studied psychological determinants and
sports results among the male fencers in the sample. In the group of female fencers none of the correlations were statistically
significant. Conclusions. The results might provoke reflection on the role of coaches and, consequently, may have implications
for the cooperation between coaches and athletes, indicating that the individual psychological differences of fencers should be
considered during training. The coach may also decide on the necessity for considering on the reported diversification within
the gender or age domain, and the need to consider such psychological properties as temperamental characteristics or the
level of achievement motivation.
Key words: psychology, temperamental features, achievement motivation, fencing
Introduction
Psychological research is often an integral component of the multidimensional process of athletic training. The results of psychological studies not only enable
the development of theoretical frameworks describing
and explaining the structure of a number of psychological factors, but also on the determinants of sports
results. They indicate the potential guidelines for future research. Psychological studies are carried out on
various populations of athletes, whose variability depends on the type of sport, age category, length of
competitive experience and sports level. Therefore the
structure of the determinants of sports results is usually different in each study group. This variability is
related to a great extent to the factors of general and
specific physical fitness and well as to psychical fitness that can determine sporting success in different
age groups.
Taking into considering the psychological determinants of sporting success, three basic groups of factors
affecting the range of variability in athletes are often
enumerated: temperament, motivation and cognition
[1, 2]. Their role is crucial as competitors usually dis-
play a similar level of physical fitness in senior categories, i.e., in already a selected population.
A factor often associated with a fencers’ effective
action is undoubtedly his or her psychical resistance,
whose counterpart in an individual’s temperament is
having a strong stimulation process, i.e., the ability of
the nervous system to endure long-term or short-term
intensive effort or arousal. Individuals with a high level
of strength of the stimulation process can act effectively in heavily stimulating conditions, such as stressinducing conditions, and feature low levels of affective disorders and changes in effectiveness under heavy
or long-term stimulation. They also feature lower susceptibility to fatigue, do not need long rest, act efficiently in unplanned situations, experience less emotional tension and emotional dysregulation. Another
important temperamental factor determining fencers’
effectiveness is the mobility of nervous processes, i.e.,
the speed and adequacy of responses to quick and unexpected conditions in fencing [3], or in other words,
one’s adjustment speed to new situations.
Achievement motivation, which is manifested by
the willingness to achieve the best possible results, is
particularly important in fencing. Achievement motivation is often understood as the tendency to pursue
standards of perfection via competition [4]. It is related to actions pursuing ever higher standards without
any external rewards. Persons with high achievement
161
HUMAN MOVEMENT
M. Tomczak et al., Psychological factors of results in fencing
motivation are able to take risks, achieve their goals with
ingenious instrumental actions, display a high level of
time utilization skills, organize their life according to
the standards they pursue and want to demonstrate
their own effectiveness in actions [5–7].
The structure of relationships among temperamental
and motivational factors is often complex and multidimensional. The aim of the present study was to determine
correlations between the strength of the stimulation
process, the mobility of nervous processes, achievement motivation and the sports results of fencers in
the senior age category. The strength of the stimulation process, the mobility of nervous processes and
achievement motivation were considered as the main
independent variables, whereas the sports result in
fencing was treated as the dependent variable and the
subjects’ age as the extraneous variable. For the study,
the following research hypotheses were formulated:
1. Higher strength of the stimulation process positively affects the sports results of senior fencers.
2. Higher mobility of nervous processes positively
affects the sports results of senior fencers.
3. Higher achievement motivation positively affects
the sports results of senior fencers.
4. The structure of correlations between the studied
psychological determinants and fencers’ sports
results is different in male and female fencers.
The sample comprised of 38 competitive fencers
(19 men and 19 women) aged 21–32 years selected by
way of purposive sampling. The fencers represented
all three fencing disciplines: sabre (13 individuals: six
women and seven men), épée (13 people: seven women
and six men) and foil (12 people: six women and six
men). Out of the entire research group, 24 people
(ca. 63% of the researched group, 12 women and 12
men) were part of the very narrow group of fenders who
were Polish national representatives in their fencing
disciplines. The study was conducted on those fencers
who, having equal chances in the cup rankings (each of
the studied fencers had participated in the same number of the Polish Cups and Poland National Championships) were all ranked on the list of the Polish Fencing
Federation. Each of the fencers had also participated in
at least two international championships. The subjects
were selected with regard to their age, gender and sports
results in the senior fencing age category. The selection
was made with reference to the independent variables
(age, gender) to reflect the population of senior fencers
in the best way possible.
The strength of the stimulation process and the
mobility of nervous processes (as independent variables) were measured with the Pavlovian Temperament
Survey (PTS) constructed by Strelau and Zawadzki [3],
which contained 57 items in three sets corresponding
162
to the individual properties of the nervous system:
strength of the stimulation process, strength of inhibition and mobility of nervous processes. Each property
was examined in 19 items and each subject gave his or
her answers on a four-point scale. Although the subjects fulfilled the entire inventory, only the items referring to the strength of the stimulation process and
the mobility of nervous processes were taken into consideration. Each property was then scored from 19 to
79 points [3].
Fencers’ achievement motivation was measured with
the 20-item Achievement Motivation Inventory designed by Widerszal-Bazyl [5]. The total score for this
property was between 20 and 100 points. Each subject
chose one out of three or five answers in each inventory item.
The senior fencers’ sports results were taken from
the annual ranking lists of the Polish Fencing Association. Each fencer under study was given a number corresponding to his or her place on the ranking list.
The normal data distribution was checked with the
Shapiro-Wilk’s test; the homogeneity of variance was
checked with Leaven’s F-test. The Student’s t-test (for
one sample) was also used to verify whether the studied properties in the studied group of fencers differed
significantly from the general population in the same
age brackets (arithmetic means from the normalized
inventories – arithmetic means from the general popu
lation). The standardized effect (SE) was determined
by estimating the differences between the mean values
of the psychological determinants in the study group
and in the general population, expressed in units of
population standard deviation. In addition, the values
were provided as a standardized sten scale (a “StandardTen” point scale).
To compare the studied properties in the male and
female fencers the Student’s t-test for independent
groups was applied. The strength and direction of the
correlations between the studied determinants and the
fencers’ sports results was measured with Pearson’s
coefficient of correlation. In order to explain the variance of the sports results for all independent variables,
a model of multiple regression was used [8], where
a stepwise forward selection regression method was
employed. Additionally, the best subsets of the predictors of the sports results were obtained based on the
Mallows’ Cp criterion that determines the degree of
fitness of a potential model to the data (the lower the
Mallows’ Cp, the better fit of the model).
Results
First, the subjects’ descriptive statistics were provided and then the sample was compared with the
general population in the same age range with regard
to the studied determinants: strength of the stimulation
process, mobility of nervous processes and achieve-
HUMAN MOVEMENT
t (W – M)
Sten
SE
t ( – GP)
SD
Gender
N = 38
Table 1. Descriptive statistics and comparison
of independent variables between senior fencers and
general population: strength of stimulation process,
mobility of nervous processes and achievement motivation
SSP
8
7
–
0.88
MNP
W
57.68 8.25 3.07** 0.77
M
56.37 8.10 2.04
0.48
M + W 57.03 8.09 3.66*** 0.63
7
6
–
0.50
AM
69.32 6.53 6.22*** 1.43
8
M
66.16 8.10
0.76
7
7.43 6.41*** 1.04
–
M+W
67.74
3.31**
N = 19
SSP
MNP
AM
W
55.26 9.70 5.65*** 1.49
M
52.79 7.33 2.97** 0.66
M + W 54.02 8.57 5.65*** 1.06
W
Table 2. Matrix of correlations between the variables
in the group of female fencers
SR
Source: authors’ own study
**p < 0.01; ***p < 0.001
SSP – strength of stimulation process
MNP – mobility of nervous processes
AM – achievement motivation
– arithmetic mean
SD – standard deviation
SE
–standardized effect computed using Cohen’s formula
(sample mean – population mean/population standard deviation)
Sten – results presented on a 1 to 10 Sten scale
t (W – M)– Student’s t-test value (male and female fencers)
t ( – GP) – Student’s t-test value (senior fencers and general
population)
ment motivation (Tab. 1). Correlations between the
variables were presented (Tab. 2, 3) and the multiple
regression model was used in reference to the sports
results (Tab. 4–8).
Since no significant differences within the scope of
the studied variables as well as the correlation structures were observed between the fencing disciplines
(sabre, épée and foil), a common analysis was performed
for the fencers regardless of the discipline. The participants, however, were analyzed separately in regards to
the basic division criterion, gender.
The data from Table 1 show that the senior fencers
had a significantly higher mean level of strength of the
stimulation process than the general population at the
same age. The standardized effect value was also high
at (SE = 1.06), particularly among the female fencers
(SE = 1.49; t = 5.65; p 0.001; a high score: a sten score
of 8), although the difference in the male group of fencers
was also statistically significant at p 0.01 (t = 2.97)
with SE = 0.66 (a score at the beginning of a high
score band: a sten of 7).
The female fencers had also a higher level of mobility
of nervous processes than the general population (t =
3.07; p 0.01), with a relatively high standardized ef-
MNP
AM
SR
1.000
–
–
–
–
–
–
0.836
p = 0.000
1.000
–
–
–
–
0.209
p = 0.389
0.029
p = 0.907
1.000
–
–
0.334
p = 0.162
0.125
p = 0.608
0.324
p = 0.175
1.000
Table 3. Matrix of correlations between the variables
in the group of male fencers
N = 19
1.32
SSP
SSP
MNP
AM
SR
SSP
MNP
AM
SR
1.000
–
–
–
–
–
–
0.678
p = 0.001
1.000
–
–
–
–
0.457
p = 0.049
0.595
p = 0.007
1.000
–
.–
0.601
p = 0.006
0.581
p = 0.009
0.525
p = 0.021
1.000
The correlations between the psychological variables and the
sports results are in bold
SSP – strength of stimulation process
MNP– mobility of nervous processes
AM – achievement motivation
SR – sports result
fect value of 0.77 (a score at the beginning of a high
scores band: a sten of 7). The t-test value in the male fencers was statistically non-significant, and the standardized
effect for the male fencers was 0.48 (an average score:
a sten of 6).
The studied fencers also featured significantly higher
achievement motivation than the general population
with a high standardized effect (1.04). The standardized
effect was particularly high in female fencers (SE = 1.43;
t = 6.22; p 0.001; a high score: a sten of 8) and lower
but still relatively high in male fencers (SE = 0.76; t =
3.31; p 0.01; a score at the beginning of a high score
band: a sten of 7).
The subjects’ gender had no significant effect on
the studied psychological determinants. None of the
obtained t-test values was of statistical significance at
p 0.05.
The distribution of fencers’ results was significantly
different from that of the general population. Fencers
featured significantly higher levels of the examined
psychological properties. Also the female fencers constituted a more select group than men in terms of the
studied properties – the standardized effects and sten
values for each psychological determinants were higher
in women. The mobility of nervous processes featured
lower standardized effect values than the two other
psychological properties. Following these calculations
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the relationships between the variables were analyzed
(Tab. 2, 3).
The data from Table 2 revealed that no correlation
between the psychological determinants and sports
results in the group of women was statistically significant at p 0.05. In the group of male fencers (Tab. 3),
however, the correlations were positive, statistically
significant and relatively strong between: the strength
of the stimulation process and sports results (r = 0.60,
p 0.01), the mobility of nervous processes and sports
results (r = 0.58; p 0.01), and between achievement
motivation and sports results (r = 0.52; p 0.05). The
study revealed a totally different structure of correlations in the male and female fencers.
Next, the stepwise forward selection regression was
employed for sports results in the whole group of participants (Tab. 4).
Introduced in the first step, the strength of the stimulation process accounted for ca. 21% of variance in
sports results in fencing. Achievement motivation, which
was introduced in the second step, increased the explanation of variance by ca. 10%. Adding another predictor in the next step did not increase the R 2 significantly; however, it decreased the adjusted R 2 , which
points to a fall in the quality of the model. The obtained
model with two predictors reached the assumed statistical significance (F = 7.808; p 0.01) and explained
about 27% of variance in sports results (based on the
adjusted coefficient). This model with two predictors
(standardized coefficients: SSP: =0.34; p 0.05 and
AM: = 0.34; p 0.05) had the best goodness of fit as
based on the Mallows’ Cp criterion in the light of other potential models. For this model the Cp (Cp = 1.03)
was the smallest (model no. 1: Tab. 5).
Table 4. A stepwise forward regression model for the dependent variable sports results for the whole studied group
R2
R2
Adj. R 2
Step
Predictors
Beta (p-value)
St. err. Beta
1
SSP
0.456(0.004)
0.148
0.208
0.208
0.186
9.462(0.004)
2
SSP
AM
0.342(0.028)
0.337(0.031)
0.149
0.149
0.100
0.308
0.269
7.808(0.002)
SSP
AM
G
0.344(0.030)
0.342(0.033)
–0.026(0.858)
0.152
0.154
0.146
0.001
0.308
0.248
5.072(0.005)
SSP
AM
G
MNP
0.351(0.132)
0.343(0.037)
–0.027(0.859)
–0.001(0.966)
0.227
0.158
0.149
0.227
0.000
0.308
0.225
3.693(0.014)
3
4
F(p-value)
The model that best fits the data in the context of other models based on the adjusted R– squared (the model with two predictors) is in bold
SSP – strength of stimulation process, MNP – mobility of nervous processes, AM – achievement motivation, G – gender
Table 5. The selection of the model that best fits the data as based on the Mallows’ Cp criterion. The models are arranged
in ascending order from 1 to 10, where 1 indicates the best fitting model and 10 indicated the worst fitting model
The best subset selection method in regression (standardized regression coefficients for each model)
Model no.
1
2
3
4
5
6
7
8
9
10
Mallows’ Cp
Predictors
(amount)
1.033
3.002
3.032
3.384
3.829
3.964
5.000
5.383
5.749
5.782
2
3
3
2
1
1
4
3
2
2
SSP
0.342
0.344
0.347
MNP
–0.007
0.248
AM
0.337
0.342
0.338
0.367
G
–0.026
0.456
0.351
0.408
0.451
–0.010
0.248
0.063
0.453
0.343
0.368
–0.027
–0.020
0.031
The model that best fits the data in the context of other models based on the Mallows’ Cp Criterion (the model with two
predictors) is put in bold
SSP – strength of stimulation process, MNP – mobility of nervous processes, AM – achievement motivation, G – gender
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Table 6. A stepwise forward regression model for the dependent variable sports results in the female fencers
R2
Adj. R 2
F(p-value)
0.111
0.111
0.060
2.139 (0.162)
0.411
0.411
0.080
0.191
0.089
1.887 (0.184)
0.441
0.241
0.431
0.036
0.227
0.073
1.470 (0.263)
Step
Predictors
Beta (p-value)
St. err. Beta
1
SSP
0.334 (0.162)
0.228
2
SSP
AM
0.764 (0.081)
–0.513 (0.229)
3
SSP
AM
MNP
0.638 (0.168)
0.202 (0.414)
–0.414 (0.352)
R2
SSP – strength of stimulation process, MNP – mobility of nervous processes, AM – achievement motivation
Table 7. A stepwise forward regression model for the dependent variable sports results in the male fencers
R2
Adj. R 2
F(p-value)
0.361
0.361
0.323
9.606 (0.006)
0.210
0.210
0.080
0.441
0.371
6.304 (0.009)
0.260
0.238
0.288
0.015
0.456
0.348
4.201 (0.024)
Step
Predictors
Beta (p-value)
St. err. Beta
1
SSP
0.601 (0.006)
0.194
2
SSP
AM
0.456 (0.045)
0.317 (0.151)
3
SSP
AM
MNP
0.358 (0.189)
0.248 (0.313)
0.191 (0.518)
R2
The model that best fits the data in the context of other models based on the adjusted R- squared (the model with two predictors) is in bold
SSP – strength of stimulation process, MNP – mobility of nervous processes, AM – achievement motivation
Table 8. The selection of the model that best fits the data as based on the Mallows’ Cp criterion. The models are arranged
in ascending order from 1 to 7, where 1 indicates the best fitting model and 7 indicated the worst fitting model
Model no.
1
2
3
4
5
6
7
The best subset selection method in regression (standardized regression coefficients for each model)
Mallows’ Cp
2.439
2.638
3.091
3.270
3.893
4.000
4.978
Predictors (amount)
2
1
2
1
2
3
1
SSP
0.456
0.600
0.382
0.357
MNP
AM
0.317
0.322
0.581
0.416
0.190
0.278
0.248
0.525
The model that best fits the data in the context of other models based on the Mallows’ Cp Criterion (the model with two
predictors) is in bold
SSP– strength of stimulation process, MNP – mobility of nervous processes, AM – achievement motivation
Next, the contribution of the particular predictors
to the dependent variable in the group of women was
analyzed (Tab. 6).
None of the predictors that were introduced (and,
consequently, none of the obtained models in the group
of women) reached the assumed statistical significance.
Next, the contribution of the predictors was analyzed
in the group of men (Tab. 7).
Introduced in the first step, the strength of the stimulation process explained ca. 36% of variance in the
fencers’ sports results. Achievement motivation, which
was introduced in the second step, increased the explanation of variance by ca. 8%. Adding another predictor in the next step did not increase the R 2 significantly; however, it decreased the adjusted R 2 , which
points to a fall in the quality of the model. The obtained model with two predictors reached the assumed
statistical significance (F = 6.304, p 0.01) and explained about 37% of variance in sports results (based
on the adjusted). This model with two predictors (stan
dardized coefficients: SSP: = 0.456; p 0.05 and AM:
= 0.317; p 0.151) had the best goodness of fit (as165
HUMAN MOVEMENT
sessed as based on the Mallows’ Cp criterion) in the context of other potential models (model no. 1: Cp = 2.439)
(Tab. 8).
Multiple regression analysis showed that the psychological determinants explained many more variances
of sports results in men than in women.
Discussion
The senior fencers under study featured higher mean
levels of psychological properties, i.e., strength of the
stimulation process, mobility of nervous processes and
achievement motivation, than the general population
in the same age category. Particularly significant differences between the fencers and the general population
were found in the group of female fencers featuring high
standardized effect values, e.g., 1.49 (sten of 8) for the
strength of the stimulation process and 1.43 (sten of 8)
for achievement motivation. This can be an indication
of the fencers’ selectivity due to the studied determinants
and could be also related to the length of fencing training and the competition experience of senior fencers,
who in order to comply with the heavy demands of
fencing must display a specific profile of psychological
properties. The group of senior fencers could have
stemmed from some process of natural selection, which
eliminated individuals with less suitable psychological
properties. The obtained results are partly consistent
with the findings from previous psychological studies
conducted on competitive athletes. Dissertations on the
psychological studies conducted on fencers are sparse,
which is one of the reasons why the results from this
study found a frame of reference not only in the studies
on fencers but also on other competitive athletes. Thus,
for instance, Williams et al. [9] obtained somewhat diffe
rent results and revealed that National Fencing Women’s
Foil Team showed an average level of anxiety (a strong
negative correlate of strength of stimulation process).
However, results similar to those obtained in our research were reported by Bandach [10] who conducted
studies on National Fencing Men’s Team in the age
range of 17–36. Bandach’s research participants revealed,
among other things, a high resistance to risk situations
and a larger acceptance towards adapting to one’s surroundings (positive, strong correlate of strength of stimulation process and mobility of nervous process). These
findings are quite consistent with those obtained by
Gracz and Tomczak [11], who conducted their studies
on fencers (treating all three fencing disciplines as one)
aged 14–32 years (41% of whom were the members of
National Fencing Team in their age group). The fencers,
inter alia, showed low emotional reactivity (a negative
correlate of strength of stimulation process), a quite
high level of mobility of nervous processes and slightly
above average level of achievement motivation. Again,
comparatively similar results were obtained by Tom
czak [12] in the studies investigating adolescent fencers
166
who showed low emotional reactivity (a negative strong
correlate of strength of stimulation process), relatively
high briskness (a positive strong correlate of mobility
of nervous processes) and high achievement motivation. In turn, relatively similar results were reported by
Bukowska and Zgadzaj [13], who examined a popula
tion of football players and chess players. Their studies
revealed that the subjects displayed significantly higher
levels of strength of the stimulation process than the
general population. Moreover, the footballers also featured a higher level of mobility of nervous processes than
the general population, whereas the chess players displayed a higher level of inhibition strength. Similar results were obtained in the studies carried out by O’Sul
livan et al. [14], where young (student) competitors of
team sports (footballers, hockey players, etc.) featured
a significantly lower level of neuroticism and anxiety
(negative correlates of strength of the stimulation process) when compared with the university population.
The findings from these studies, however, are not always
so clear-cut. A comprehensive study in Korea compared
athletes and non-athletes (mean age 17 and 36, respectively) and demonstrated that athletes and persons
leading physically-inactive lifestyles did not differ with
respect to novelty seeking (a demand for stimulation),
harm avoidance, reward dependence, and persistence.
On the other hand, the levels of both trait and state
anxiety were found to be lower in the population of
non-athletes than athletes [15]. Furthermore, similar
results were obtained in the studies carried out by Malinauskas [16], in which rowers and middle-distance
runners (both groups were comprised of members and
candidates of the Lithuanian national team) featured
a relatively high level of neuroticism (a negative correlate of strength of the stimulation process).
The obtained high values of the psychological determinants should come as no surprise. The strength
of the stimulation process, identified with the strength
of the nervous system, is crucial in sport, both in
long-duration training (taking place in highly stimulating situations) and actual fencing combat. On the
other hand, the mobility of nervous processes, implying the speed of adjustment to new situations is highly
significant in fencing, in which the situation on the
strip changes frequently and rapidly. Achievement
motivation is important in every task situation, not
only in competitive sport [17–20].
The formulated research hypotheses which (1, 2, 3)
assumed positive correlations between the psycholo
gical variables and sports results in senior fencing were
confirmed in the male group. The initial regression analysis employed on the whole studied group of fencers
pointed to two significant predictors: the strength of
stimulation process (introduced in the first step) accounting for 21% of the variance in the sports results
and achievement motivation (introduced in the second
step) then increasing the explanation of variance by
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ca. 10%. However, a detailed analysis of the relationships within each gender revealed that these factors
were of great importance in the group of men, where
the noted correlations were relatively strong: r = 0.60
(p 0.01) for strength of the stimulation process and
sports results, r = 0.58 (p 0.01) for mobility of nervous
processes and sports results, and r = 0.52 (p 0.05) for
achievement motivation and sports results. These results were confirmed by the regression model that was
employed. These variables accounted for about 37% of
the variances of sports results (37% based on the adjusted R2, 44% based on R2). Introduced in the first step,
the strength of stimulation process was of great importance in this model and explained 36% of the sports
results. Less significant, however, for achieving a better
fitting model was achievement motivation (introduced
in the second step), which increased the explanation of
variance by ca. 8%.
These results party overlap with the results obtained
by Borysiuk [21, 22], who also showed that the strength
of simulation process is a significant predictor of the
sports level in senior male fencers (however, in the
studies conducted by Borysiuk this predictor turned
out to be also significant in the group of senior female
fencers). He also showed that the strength of simulation process was significant in the junior female fencers,
but not in the junior male fencers and no significant
relationships between the mobility of nervous processes
and fencers’ sports level [21] were observed, which is
comparative to the obtained results, since in the light
of the regression model, mobility did not turn out to
be a significant predictor after the strength of simulation process and achievement motivation had been
introduced to the model. However, no direct relationships of emotional reactivity (a negative correlate of
strength of stimulation process), briskness and achievement motivation with sports results of adolescent fencers
were found in the study conducted by Tomczak [12].
The obtained results were also partly in accord with
the results from Rychta’s study [23] that examined
a group of fencers within the age range of 14–32 years,
and revealed a statistically significant average correlation between the mobility of nervous processes and
the fencers’ sports level. The obtained results confirm the
view expressed by Czajkowski [24, 25] as to the importance of the strength of the nervous system and achievement motivation for action effectiveness in fencing, in
particular in the senior stage [21]. The results also confirm the great importance of psychological factors in
fencing within the context of other important variables from various groups, such as physical, physiological
and motor factors, and factors of special fitness, which
were pointed out by Roi and Bianchedi [26].
Thus, the fourth hypothesis that assumed that the
structure of correlations between the studied psychological determinants and the sports results of senior
fencers was different in men and women was in fact
confirmed. However, in the group of female fencers the
correlations were lower and did not reach the assumed
level of statistical significance (0.05). Likewise, the regression model did not obtain the assumed statistical
level of significance and showed that the predictors
accounted for a small part of variance in sports results.
On the whole, when compared with the general population, women turned out to be better selected than men
in terms of the studied psychological factors. However,
the relationships between these factors and the results
in the female group are lower than in the male group.
Possible explanations for these results could be the different approaches by men and women towards rivalry
and competition in general. Men are usually more disposed towards direct rivalry, fighting and in making
comparisons of different determinants than women.
This attitude could be explained by the different tasks
(in family, at work) and social roles that had been fulfilled by the two genders for thousands of years. In men,
these tasks required more intensive rivalry and fighting
for material and non-material resources. On the other
hand women’s tasks involved less direct rivalry and
more focus on upbringing children and household duties [27, 28]. Women have also taken an less active part
in wars and conflicts, and it needs mentioning that
fencing is an exemplification of real combat. It might be
then that women engaged in fencing, as a sport involving long-term direct competition and fighting (which
shows that this activity suits them well) are better selected than men in terms of certain psychological determinants than men who are more directly competitive by nature. Beside the differences between the senior
female fencers and the general population, and the
group of male fencers and the general population, no
other significant differences in the examined psychological properties were noted between the two genders;
whereas men from the general population usually display higher strength of the stimulation process and mobility of nervous processes. Thus it can be stated that
female fencers display a “male” temperament in terms
of the levels of the studied psychological determinants.
Thus, female senior fencers can be expected to feature weaker correlations with their sports results. In
fact, due to the greater selection, they had significantly
fewer low and very low results than men, which could
have been related to low and very low sports results.
In this group, however, having fulfilled a certain required criterion, e.g., a high level of strength of the
nervous system, other factors can also determine the
level of specific fitness, such as intuition. On the other
hand, in the less selected group of male fencers, still
featuring low levels of studied determinants, correlation coefficients can be high, as they can be related with
low sports results or at least could impede the achievement of high sports results.
Another explanation could be related to the other
determinants of sports results, such as motor fitness,
167
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which could play a greater role in male fencers whose
combat often depends on the factor of speed. In female
fencers there could be some factors that are more individual than statistical, e.g., a high level of intuition can
compensate for a lower level of a certain skill. If that
is so, then a low variance of motor fitness (low standard
deviation) could have been noted in the male group of
fencers and the fitness results could have been distri
buted to the right of the arithmetic mean. However,
this group would not have featured strong correlations
between motor fitness and sports results, rather other
correlations would have been found between other psychological properties and sports results.
Thus after having met a certain criterion of motor
fitness in a population where all members feature a simi
larly high level of this criterion, it ceases to differentiate
this population in terms of their effectiveness. More
significant here may be other variables, for example,
the “psychical fitness” examined in the present study.
Conclusions
1. Senior fencers had higher mean levels of the
studied psychological properties, i.e., strength of the
stimulation process, mobility of nervous processes and
achievement motivation, than the general population
in the same age range. It could be an indication of the
high selectiveness of fencers in terms of the studied
psychological determinants. The female fencers constitute a better selected group than male fencers in all
studied psychological determinants of sports results.
2. A relatively strong correlation can be noted between the male senior fencers’ strength of the stimulation process, mobility of nervous processes and achievement motivation and their sports results. It can be
illustrative of the tendency to achieve higher sports results by men with a higher level of strength of the sti
mulation process, mobility of nervous processes and
achievement motivation than men with lower levels
of these properties.
3. No statistically significant correlations were noted
between the strength of the stimulation process, mobility of nervous processes, achievement motivation
and sports results in the studied senior female fencers.
4. The obtained results from the present study conducted in order to identify the main determinants of
sports success in fencing may have far-reaching implications for the interactions between coaches and athletes. Coaching should recognize the individual psychological differences among fencers and should reflect
the effects of gender and age diversification on psychological properties such as temperamental characteristics or the level of achievement motivation.
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Paper received by the Editors: May 31, 2010
Maciej Tomczak
Zakład Psychologii
ul. Królowej Jadwigi 27/39
61-871 Poznań, Poland
e-mail: maciejtomczak5@gmail.com
169
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2012, vol. 13 (2), 170– 177
MOOD CHANGES IN INDIVIDUALS WHO REGULARLY PARTICIPATE
IN VARIOUS FORMS OF PHYSICAL ACTIVITY
doi: 10.2478/v10038-012-0019-0
RENATA MYRNA-BEKAS 1, MAŁGORZATA KAŁWA 2 *
TADEUSZ STEFANIAK 2 , LESŁAW KULMATYCKI 2
1
2
State Vocational College in Legnica, Poland
Abstract
Purpose. A number of mental and physical benefits arise from leading an active lifestyle. Many forms of therapies make use
of physical activity to reinforce rehabilitation as well as improve the condition of the body and mind. It is in this way that an
individual can improve their well-being through cleansing the body of negative emotions and seek inner harmony, which is
one of the most important features of mental health. However, the question arises whether all forms of physical activity improve
the emotional state of an individual in the same way. A qualitative change in mood may be in fact related to the methodical
factors present in physical activity (the type of exercise, the training method or its intensity and frequency) but also an instructor’s personality, the age and gender of the participant as well as their physical fitness and motor skills, the subject’s current
social and mental state, environmental factors or other factors related to everyday life such as work, family, etc. The aim of this
study was to determine the changes in mood of physically active and highly fit people, aged 22–25 years, after various forms of
physical activity and with different training methodologies. Methods. The Mood Adjective Check List (UMACL) was adminis
tered to 84 students before and after completing a course in a number of physical activities. Statistical methods were then
applied to the results to measure the size of the differences and for any statistical significance. Results. The results found that
regardless of the form of physical activity or class duration, there was a positive change in the mood of participants. Differences
in size of the changes, when compared to the forms of physical activity and gender, were not found. Conclusions. The improvement
in mood of fit and regularly physically active adults is observed regardless what form of physical activity is practiced.
Key words: physical activity, fitness, mood
Introduction
The physical and psychological well-being of humans is dictated by many factors, key among them
lifestyle and a number of different factors that compose everyday life. It is not without reason that physical
activity plays a large role here, as it is the only one of
numerous human needs that plays a role in maintaining
both physical and mental health [1, 2]. In many therapies, physical activity is used to support not only the
healing process but also improve both body and spirit
[3–6]. It is in this way that an individual can improve
their well-being through cleansing the body of negative
emotions and seek inner harmony, which is one of the
most important features of mental health [7, 8].
An additional factor that can also have a positive
impact on mental health is music. The hedonic experience one can experience from moving to the rhythm
of music was known already from antiquity. Plato himself (427-347 BC) makes mention of this, where “[…]
gymnastics is for the body and music for the mind” [in
9, p. 25]. Nonetheless, the combination of music and
170
physical activity as a form of training (not including dancing) appeared only in the second half of the twentieth
century. This itself is a part of the popular culture revolution that is occurring before our eyes, which includes
physical culture, and is to a large extent conditioned by
geopolitical, sociocultural and technological changes.
As a result of the dynamic changes in sport, new and
more attractive forms of exercise are arising that utilize
all of the aspects present in physical culture. Also evolving is our outlook on what it means to be healthy and
on psychophysical well-being. One way of measuring
this condition can be mood, although as unreliable as
it may be, it can serve as an authoritative parameter for
judging mental health [10]. In literature there are many
well-known scientific reports that point to physical
activity’s influence in changing mood [11–14]. There is
clear scientific evidence that shows a link between physi
cal activity and improved mood and well-being, whether
young, healthy and physically active women [15], middleaged women [16] or women during menopause [17].
In addition, the therapeutic and preventive nature
of physical activity was confirmed as a form of rehabili
tation, in restoring physical health as well as controlling
mental needs and well-being [18, 19]. Also studied were
the psychological effects of physical training, including
a significant reduction in stress, depression and anger
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R. Myrna-Bekas et al., Influence of exercise on mood change
after physical exercise [2, 20]. One of the scientific papers that stands out in this regard is LeUnes’ study
[21], in which the author presents 57 scientific papers
on this subject. Significant changes in mood and emotion are noted among athletes who take part in intensive exercise whether as a part of training or athletic
competition [21–24]. A large group of scientists versed
in physical therapy stress the value of physical activity
when dealing with anxiety, depression or pessimistic behavior [5, 25–28]. However, the question arises whether
the mood of a healthy and abled individual is significantly affected depending not only on the form of phy
sical activity but also its intensity. Also interesting is the
axiological aspect that this question poses. A qualitative change in mood may be in fact related to the methodical factors present in physical activity in addition to the known influence of various psychosocial,
environmental and sociocultural factors or components of everyday life [29–31].
Therefore the aim of this study is to determine what
changes in mood occur after selected forms of physical activity, varied by types of exercise and different
training methods, in a sample group of 22 to 25 year
old individuals who are physically active and in excellent physical shape. Hypothetically, it was assumed
that the mood of healthy and regularly exercising individuals would improve, but with different levels of
improvement depending on the type of exercise as
well as the volume and intensity of the workload.
The study included 84 students aged 22–25 years
(47 women and 37 men) who study physical education at
the University School of Physical Education in Wroclaw
or the State Vocational College in Legnica, Poland. The
study was conducted before the subjects began taking
part in their selected forms of physical activity and
again after in order to assess the change in mood by use
of the UWIST Mood Adjective Checklist (UMACL) by
Matthews, Chamberlain and Jones, as adapted by Go
ryńska [32]. The authors of this scale determined mood
as a prolonged core emotion, consisting of three dimensions:
a) TH – the hedonistic tone: moods associated with
the intensity of feeling pleasure-displeasure
b) PN – tense arousal: moods described as the emotions between being nervous-relaxed
c) PE – energetic arousal: moods oscillating between
feeling energetic-tired
The use of this concept assumes that good mood is
viewed as having high TH and low PN. The authors
pointed out that certain difficulties may arise from
interpreting energetic arousal moods (PE). However, it
was accepted that high or moderate values of this dimension are desired. The UMACL scale measures mood
by the use of 29 adjectives which describe the various
types of moods that make up the three dimensions.
The respondents indicate their current state of mood
by use of a four-point scale (definitely yes, rather yes,
rather not, definitely not) for each of the moods. Each
of the measured dimensions of mood obtained a raw
value on the UMACL scale, with the values of TH and
PE ranked in the 10–40 range, while PN was ranked
between 9–36. The obtained raw values were then
transferred to a sten scale as a standardized scoring
system for age and sex which was developed by the
UMACL’s authors [32]. The sten scale allows for the
interpretation of the results, where an average value falls
between 5 and 6 on the sten scale and is described as
having a “neutral” mood. Values below 5 indicate that
the test parameter is below average (which can be, e.g.,
a feeling of uncertainty, physical discomfort, a kind of
nervousness or excitement), while a value above 6 is
considered above average and suggests an increased
mood state characterizing the evaluated dimension.
Statistical analysis was performed with the use of
nonparametric tests. Differences in the changes in mood
before and after physical activity were evaluated by the
Wilcoxon signed ranks test, while evaluation of the
differences in the change of mood, depending on the
type of course and sex, was performed by univariate
analysis of variance (Kruskal-Wallis test). All calculations and graphs were obtained with the use of Statistica ver. 9.0 software (Statsoft, USA). The significance
level was set at p 0.05.
The size of the individual components of mood
(TH, PE and PN) was evaluated immediately (using
unpublished software created by this author) before
and after each physical activity class. The forms of phy
sical activity that the sample group took part in were
as follows:
Boxing – is a form of combat sport in which opponents use their gloved fists to delivery blows above
the belt line as well as avoiding punches to their own
head and body. Participants are required to have high
strength-endurance levels and produce highly dynamic acyclic and asymmetrical movements. Effectively
preparing for boxing depends mainly on coordinating
such neurophysiological factors such as intramuscular
and intermuscular coordination, the high speed delivery
of a single move, response time (especially to visual-motor stimuli) and high levels of tactical skills. Energy
production is alternatively provided by both aerobic
and anaerobic processes. The course which the students took part in and which evaluated the change in
mood, was to teach and improve simple punches (in
the up and down direction) and two types of defense
moves – one by capturing the blow and the other by
use of an open block. In the final part of the course
these fighting techniques were integrated in the socalled simple fighting style. The main emphasis was
on dynamic strength of the upper limbs and neck, eyehand coordination (by forming choice reaction time).
171
HUMAN MOVEMENT
Twelve men took part in the course, with each class
lasting 75 min. Intensity: moderate, high and submaximal. Training method: variable intermittent. Teaching
method: stationary.
Bodybuilding (Strength Training) – a set of weight
training exercises using free and stationary weights
designed to develop and display one’s physique. The
essence of bodybuilding is training to body to have
a symmetrical muscular figure and encompasses a competitive aspect. Those who take part in bodybuilding
are required to be knowledgeable in resistance exercises as well as be focused with full involvement of
their psychomotor skills during training. Energy production comes from high-energy phosphate transfer
during anaerobic metabolism where the main energy
substrate is phosphocreatine. However, depending on
both the goal and methods, another energy source
can also be muscle glycogen. The predominant bodybuilding method is cyclical in nature and requires
strict observance of the general principles of strength
training [33]. The bodybuilding course in this study
was aimed at teaching introductory methods by the
adaptive method for an overall increase in muscle
mass. The classes included moderately heavy training
of all of the main muscle groups (three series of 8–12
repetitions). The class was composed of a mixed group
(N = 24; 12 men, 12 women) with each class lasting
90 min. The main emphasis was in training until failure of all limb flexors and extensors and the trunk as
well as intramuscular and intermuscular coordination. Intensity: average and large. Training method:
repetitive. Teaching method: circuit training.
TBC (Total Body Conditioning) – is a form of complex aerobic training aimed at uniformly stimulating
the entire body. TBC classes usually consist of exercises set to a predetermined rhythm, where participants need to focus their attention on their starting
position before each exercise. Mirroring proper technique and being fully concentrated minimizes the risk
of injury as well as increases health gains. The structure of each class is dependent upon the instructor,
but normally consists of six parts (an overall warm-up,
a focused warm-up, the target exercise, a relief exercise,
a cool down and then a relaxing part) and lasts 60
minutes. TBC can be performed with free weights up
to 5 kg and can include gymnastics and music components. Since a participant does not need to be specially
prepared to take part in the classes, TBC can be used
for individuals regardless of their physical condition.
The class structure also allows individuals to prepare
for more intensive workouts by multiple repetitions of
a specific set of exercises. The purpose of the TBC
course within this study was to stimulate the body by
reaching a heart rate of 150 bpm and to complete three
series of exercises for four parts of the body at a variable
heart rate of 80–140 bpm, with the rhythm set by using
music. The main training emphasis was improving
172
strength endurance of the shoulder girdle muscles, the
flexors and extensors of the lower limbs, the abdominal
muscles and the back as well as auditory-motor coordination. This physical activity class had a mixed group
of participants (N = 24; 11 men and 13 women). Class
time: 60 min. Intensity: moderate and high. Training
method: continuously variable. Teaching method: instructional drill.
Hi Lo Combo (Dance Aerobics) – is a group of rhythmic exercises involving the use of techniques from
both dance and gymnastics which are choreographed by
each participant during classes to create an individual
gymnastic and dance routine. More advanced forms
of Hi Lo Combo or Dance Aerobic are choreographed
together, although each individual has their own different style, to create a group act. Depending on the type
of accompanying music, there are many variations of
dance aerobics (funky aerobics, street dance, hip-hop
aerobics, Latin, salsa, afro, etc.). Choreography classes
are based primarily on dance steps performed as marches
in various directions using body rotation, in which a participant stays in constant rhythm to the music. The
need for constant coordination leads to an improvement in both rhythmic and spatial orientation. Due to
the analytical and rather comprehensive method of
teaching dance aerobics, motor memory plays a large
role in proper execution. TBC training primarily burns
glycogen and fat as its main source of energy conversion. In addition, the average beat per minute during
dance aerobic is around 130–134 bpm. The aim of the
course, as a part of this study to evaluate changes in
mood, was for participants to create their own form
of individual choreography and perform it in full. The
main emphasis was on long-term endurance of all
parts of the body and auditory-motor coordination.
Twelve women attended the course, with each class
lasting 70 min. Intensity: moderate and low. Training
method: continuously variable. Teaching method: instructional drill.
Step aerobics – is a form of gymnastics set to music
with a fixed or variable tempo and performed on
a 100 × 15 × 30 cm step. The workout is dependent on
how advanced the group is (time, tempo, step height,
the use of additional equipment as well as its weight
and elasticity, breaks, etc.). The parts of the body that
are exercised in step aerobics are the front group of
muscles of the lower limbs and the lower back, while
the highest risk of injury is with the knees and ankles.
Aerobics methodology recommends exercising in both
high and low positions in order to activate the entire
body, which can effectively improve the strength endurance of the lower limbs, the front and rear trunk
muscles as well as auditory-motor coordination. The
main source of energy during step aerobics is glycogen
and fat in aerobic metabolism. The aim of the course in
this study was to perform 8–12 series of 16–24 repe
titions of a specific training exercise with increasingly
HUMAN MOVEMENT
levels of difficulty for three parts of the body with
a steady heart rate of 120–130 bpm. The main emphasis was in improving strength endurance of the front
muscle groups of the lower limbs as well as the flexors
and extensors of the trunk and sharpening intermuscular coordination. Twelve women participated in this
course. Intensity: average and high. Training method:
continuously uniform and variable. Teaching method:
instructional drill.
for PN. It can therefore be concluded that the mood of
the participants clearly improved after physical activity.
A better mood after exercising (especially with music)
was found in women, while for men a marked improvement in mood was found after boxing. The large dispersion of the results as well as the significantly higher
level of differences in the mood indicators in women
suggests that women show slightly greater volatility in
mood after physical activity.
Slight fluctuations in the changes of mood were
observed depending on the form of physical activity
(Fig. 2), however, these were largely negligible with no
significant differences found among the various parameters in connection with an improvement in mood
after physical exercise (Tab. 2). Taking into consideration both men and women who took part in various
physical activities, no significant differences in mood
change were observed between genders (Fig. 3). In addition, it was found that neither the intensively nor
the duration of the class had any significant effect on
the differences in improving mood in both women
and men.
Results
Analysis on the mood changes of the tested students found that the indicator values specific for good
mood (TH and PE) significantly increased after all
forms of physical exercise. This went in parallel with
a drop in value of the PN dimension (p 0.05) (Fig. 1, 2,
Tab. 1). Before exercise the mood values of the subjects were 5 or 6 on the sten scale for TH and PN and
at a sten score of 4–5 for PN; after exercise the values
on the whole changed, with an increase to 7 or 8 on
the sten scale for TH and PE with a decrease down to 3
WOMEN
MEN
Sten
TH_1
TH_2
PN_1
PN_2
PE_1
PE_2
12
10
8
6
4
2
0
-2
TH_1
TH_2 PN_1 PN_2 PE_1
PE_2
TH_1
TH_2 PN_1 PN_2 PE_1
Mood dimensions
PE_2
Figure 1. The range of the each
mood dimension for men
and women before physical
exercise (1) and after (2)
Mood dimensions
Median; Box: 25%x-75%; Plot: concentration of non-deviating values
Mood dimensions: TH – hedonistic tone; PN – tense arousal; PE – energetic arousal
Table 1. The significance of differences between mood before and after specific forms of physical activity
as based on the Wilcoxon matched pairs test
Men
Measurements
1_2
TBC
N
TH_1 & TH_2
PN_1 & PN_2
PE _1 & PE _ 2
Z
Women
Weight training
p
N
Z
p
Boxing
N
Z
TBC
p
N
Z
Weight training
p
N
Z
p
Hi-Lo Combo
N
Z
p
Step aerobics
N
Z
p
13 1.083 0.097 11 2.934 0.003 12 3.059 0.002 10 2.803 0.005 10 2.141 0.032 12 2.393 0.017 12 2.393 0.094
13 2.236 0.025 10 2.191 0.028 11 2.934 0.003 11 2.934 0.003 12 2.510 0.012 12 0.078 0.937 12 0.078 0.043
12 2.432 0.072 9 1.896 0.058 12 2.903 0.004 11 2.801 0.005 11 2.089 0.037 12 3.059 0.002 12 3.059 0.002
Bold signifies values at the adopted level of significance: p 0.05.
173
HUMAN MOVEMENT
MEN
sten
*
WOMEN
sten
**
*
*
***
**
***
***
***
*
**
**
*
*
*
*
*
***
***
**
Figure 2. The size of the differences
of both men’s and women’s
improvement in mood after physical
exercise as assessed by the Wilcoxon
test including a test for significance
* p 0.05; ** p 0.01; *** p 0.001
Median; Box: 25%–75%; Plot: concentration of non-deviating values
Table 2. Analysis of the statistical significance of the changes in mood after various forms of physical exercise
using the Kruskal-Wallis one-way analysis of variance by ranks (ANOVA)
Grouping variable
Difference TH; H
(4. N = 84) = 2.51;
p = 0.6411
Participants
Difference PN; H
(4. N = 84) = 7.47;
p = 0.1126
Difference PE; H
(4. N = 84) = 2.34;
p = 0.6732
Physical activity
N
Sum rank
Mean rank
Sum rank
Mean rank
Sum rank
Mean rank
TBC
Weight training
Step aerobics
Boxing
Hi-Lo
24
24
12
12
12
949.00
1028.00
441.50
594.50
557.00
39.54
42.83
36.79
49.54
46.42
1150.50
1166.50
375.00
486.50
391.50
47.94
48.60
31.25
40.54
32.63
1079.50
896.00
592.50
504.00
498.00
44.98
37.33
49.38
42.00
41.50
10
8
6
Sten values
4
2
0
-2
-4
-6
-8
-10
MEN
r_TH;
Deviation;
r_PN;
r_PE
Median; Box: 25%-75%; Plot: concentration of non-deviating values
WOMEN
Kruskal-Wallis Test:
TH: KW-H(1;84) = 1,8768; p = 0,1707
PN: KW-H(1;84) = 2,3997; p = 0,1214
PE: KW-H(1;84) = 0,0082; p = 0,9279
Figure 3. Comparison of the range differences of the changes in the mood parameters of men and women
174
HUMAN MOVEMENT
Discussion
Research on the various factors that can influence
changes in mood are very popular in recent literature
[1–6, 28, 34]. There is no doubt that recreational phy
sical exercise has a positive impact on both the direction and magnitude of the various components of mood
in humans [12–19, 25, 35]. Research found that there is
an improvement in mood even after a single workout,
as was shown by Guszkowska [14, 36] in an experiment
on the so-called acute effects of exercise as well as the
effects of regular exercise in a month. A group of Ame
rican researchers [37] confirmed this finding, where
they attempted to assess the impact of multiple factors
on an improvement in mood, focused on physical acti
vity and an individual’s external environment. By ana
lyzing a large sample group of more than 1200 people
of different age groups and different mental states, the
group came to the conclusion that mood and self-esteem improved already after five minutes of physical
exercise in a green environment such as a park. The health
changes were particularly pronounced among young
people and the mentally-ill. Moreover, the positive impact of being physically active in a natural environment
was even further amplified when exercising in a body
of water. Similar conclusions were reached by Thompson-Coon et al. [30] who conducted a comprehensive
review of literature on the subject from the following
sources: Medline, Embase, PsychInfo, GreenFILE, SportDISCUS, the Cochrane Library, the Science Citation Index Expanded, the Social Sciences Citation Index, the
Arts and Humanities Citation Index, the Conference
Proceedings Citation Index – Sciences, and BIOSIS. It
was concluded that exercising in fresh air in a natural
environment had a number of beneficial effects on
health. This literature review found promising effects
in training in a natural environment, effects which
were not found when conducting the same activity indoors. Another important factor that had a substantial
positive effect on mood and emotion during exercise was
music. The therapeutic and hedonistic influence of music
when exercising has been known for years [9, 38, 39].
This study attempted to assess the impact of different forms of physical exercise on changes in mood in
young and active men and women. The physical activities the students took part in varied in intensity, volume
and equipment and some also included music. The results of this study verified the hypothesis about the
direction of mood changes and confirmed the findings
of previous research by Lane and Lovejoy [12], and Pio
trowski-Całki and Guszkowska [28], who also pointed
to an improvement in mental health during and after
a series of physical exercises at moderate intensity. Not
confirmed was the assumption that there would be differences in the size of the three mood dimensions depending on sex and the form of physical activity. This
was similar to what Matthews et al. found [40], who
also received inconclusive results on the size of mood
differences for both sexes. Their study emphasized that
an improvement or decline, as well as the dynamics of
the indicators mood, is dependent on a number of psychophysiological factors. Nonetheless, they verified
previous research which assessed the impact of various
factors on human mood [1-10]. Guszkowska and Sio
nek [35] also addressed the influence of a training program on mood changes by searching for the relationships between a 12-week aerobics course and mood
changes in correlation with certain personality traits.
Among a number of conclusions, it was stated that the
three months of exercise reduced the level of anxiety,
improved self-efficacy and optimism.
In the Polish adaptation of the UMACL scale mood
was defined as an “[…] affective experience of moderate
duration (at least a few minutes) unrelated to the subject
or related to a quasi-object that compromises of three
dimensions of core emotion: a hedonistic tone, one of
tense arousal and one of energetic arousal” [32, p. 7].
The above description indicates how mood is strongly
conditioned by context and dependent on the surrounding circumstances or changes in stress one is subject to.
Moreover, this state is determined by environment,
social factors and personality traits [2, 31]. Watson [8]
also described how an individual’s daily and personal
dependencies influence changes in mood. Extensive
research was also conducted by Scully et al. [14] in
searching for factors that change human mood. They
determined that the relationships that exist between
one’s physical condition and depression, anxiety, response to stress, mood, self-esteem, assessing one’s own
body and premenstrual syndrome. In addition, resear
chers from Chicago under the direction of Reid et al.
[42] successfully found the relationship between exercise and an improved quality of life and mood in people
suffering from chronic insomnia. The above study
highlighted the complexity of issues that exist when
evaluating human well-being.
In view of the above, this study was conducted by
taking into account the above factors by providing the
right conditions in order to eliminate as much interference as possible. Special attention was paid to the res
pondents attending their classes, fully understanding the
purpose of the study and knowing how to complete
their mood assessments as well as ensuring anonymity
and limiting the participation of psychologists in the
study. Goryńska [32] allowed her study subjects to use
their names or be anonymous; however, she found significant differences in all three mood dimensions of
those who opted to take part in the test using their
names. According to the author, “[…] named vs. anony
mous individuals (who had a free choice) had strongly
differentiated results. The results showed that anonymous individuals had higher tense arousal (PN) but
lower hedonistic tone (TH) and tense arousal (PE) levels”
[32, p. 53]. Due to the fact the participation in this
175
HUMAN MOVEMENT
study was anonymous, there appears to be a possibility
that the results could be in fact more varied in the size
of mood change for each form of physical activity if the
respondents had provided their personal names.
The results of this study suggest that further analysis
is needed in this area, even though that research has
been carried out for years on what factors determine
an improvement in mood. Also interesting are the dependencies of changes in mood and emotion with age,
which was demonstrated by Larsen and Diener [43]
and Goryńska [32]. There also exists evidence that
physical activity can have a negative impact on an individual’s mental state [44]. Therefore, it is felt that
the search for the various relationships between phy
sical exercise and the psychological reactions of people
ought to continue.
Conclusion
An improvement in mood, characterized by high
values of the hedonistic tone and energetic arousal
and the decrease in the tense arousal mood dimensions, was found to occur in physically active young
men and women regardless of the physical activity
they took part in.
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Małgorzata Kałwa
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e-mail: mkalwa@awf.wroc.pl;
e-mail: katedra.ds@awf.wroc.pl
177
HUMAN MOVEMENT
2012, vol. 13 (2), 178– 184
AN ASSESSMENT OF ATHLETIC IDENTITY IN BLIND
AND ABLE-BODIED TANDEM CYCLISTS
doi: 10.2478/v10038-012-0020-7
TOMASZ TASIEMSKI *, MACIEJ WILSKI, KAMILA MĘDAK
Abstract
Purpose. The purpose of this study was to determine the athletic identity (AI) of blind and able-bodied tandem cyclists
and explore its relationship to selected variables. An additional objective of this study was to analyze the reliability of
the seven-item Athletic Identity Measurement Scale (AIMS) for tandem cyclists. Methods. The participants (N = 50)
completed measures of AI, variables characterizing their loss of vision (degree and time of loss) and variables characterizing
their sports level (the number of hours of training per week and sports experience). Results. The AI level of able-bodied
tandem captains is significantly higher than the level in visually impaired athletes. Blind tandem cyclists were found to
be a fairly homogeneous group according to AI. There were no differences in AI and the degree and time of vision loss,
the number of hours of training per week and when a cycling license was received. Psychometric analysis showed that
AIMS is a reliable and consistent research tool in the evaluation of AI of tandem cyclists. Conclusions. The findings
suggest that there is a need to increase the involvement of blind cyclists in the sport as well as their responsibility for
sports results.
Key words: athletic identity, blind athlete, tandem cycling
Introduction
Among the numerous contributing elements that
determine sports performance, psychological factors
are increasingly being taken under consideration. Particular emphasis is placed on the search for personality
variables that could point to athletic predisposition in
sports performance as well as optimizing psychophysi
cal potential during competition. One of the variables
that has received some focus in contemporary literature is athletic identity (AI).
Brewer et al. [1] defined AI as the degree in which
an individual identifies him/herself with the role of
an athlete. They saw it as a structure containing both
cognitive and social elements. The cognitive element
allows an individual to clarify information, cope in
different situations and influence behavior, while the
social element is considered to relate to the perception
we hold of other persons and existing social roles. In
such a context the notion of AI serves to explain the
manifestations of human activity in relation to athletes
and non-athletes [1–3]. The above-mentioned authors
created a ten-item scale in 1993 named the Athletic
Identity Measurement Scale (AIMS) [1] for just this
purpose. An updated, shortened seven-item version was
proposed by Brewer and Cornelius in 2001 [4], which
is currently used in clinical studies on AI.
178
The strength of AI in athletes was discovered to vary
depending on sports experience as well as the success
or failure rate in sport itself [3]. A strong positive correlation was found among AI and health and physical
fitness [5], self-esteem [6], better social relationships
and self-confidence [7], participation in physical activity [8], better sporting success and a larger network of
social contacts [3]. On the other hand, other studies
pointed to a strong positive correlation between AI and
post-injury depression [9], a lack in athletic career maturity [10], and increased susceptibility to the use of
drugs in sport [2].
Previous studies found differences in AI between
men and women as well as between athletes and nonathletes in both able-bodied [4] and disabled [11] individuals. Positive correlations were also documented
between AI and the positive perception of one’s physical abilities in children with visual impairments [12]
and between AI and the level of the quality of life for
athletes with cerebral palsy [13]. One specific case
found that disabled athletes who were preparing for
the Paralympic Games in alpine skiing were characterized by AI levels similar to their non-disabled peers
competing at a similar level [14]. Research conducted
by Lantz and Schroeder [15] established that AI is positively correlated to “masculinity” and negatively correlated to “femininity”. It was also discovered that the
level of AI increases with time even in individuals
who were no longer actively involved in sport [15].
Moreover, a study on persons who suffered spinal
cord injury demonstrated that AI is positively corre-
HUMAN MOVEMENT
T. Tasiemski et al., Athletic identity in blind cyclists
lated with the amount of time spent playing sports
[11], and that individuals who took part in team sports
featured better psychological adaptation to life after
suffering injury than those who practiced individual
sports [16].
Most of the research on AI by use of the AIMS scale
has been conducted on both able-bodied and disabled
athletes as well as individuals who do not practice
sport [3, 11, 17]. A perusal of the available literature
found no research on AI in disabled athletes whose
sports ability and sporting success are determined not
just individually but by working together with another
individual, as is the case with blind cyclists practicing
tandem cycling.
Tandem cycling is a specific form of sport that is
available for disabled persons, where a cyclist with reduced vision rides together with an able-bodied cyclist
(the captain) on a tandem bicycle. Both riders pedal the
drive train (equally providing pedaling power); therefore, success in tandem bicycle races depends equally
on both riders and in their ability to work together.
However, it is not known whether this is in fact the
case as riding tactics are solely in the hands of the ablebodied captain, who can evaluate their standing in relation to the other racers during competition. Furthermore, athletes who are visually impaired or blind are
not always recognized by society as “real athletes” [12],
which may affect their self-image and change the way
in how they perceive their AI in comparison to their
able-bodied captains, themselves being individuals who
have frequently finished their sports career in ablebodied competition.
In view of the above, the main aim of this study was
to answer the following questions:
1. To what extent do blind cyclists and their ablebodied captains identify themselves with the
role of an athlete?
2. Are there any differences between them in this
regard?
3. Is there a relationship between AI level and the
variables that quantify the visual impairment
a rider has (the degree of the disability and when
it occurred) and AI and the variables that cha
racterize the sports level (number of hours of
training and the amount of professional cycling
experience since receiving a cycling license) of
tandem cyclists?
An additional objective was to also analyze the relia
bility of the seven-item AIMS scale for tandem cyclists.
The study involved 50 athletes, which represented
81% of all cyclists who practiced competitive tandem
cycling in Poland. They belonged to sports clubs for the
visually impaired from the cities of Olsztyn, Warsaw,
Lublin and Poznan. All of the participants held a com-
petitive cycling license from the Polish Cycling Federation and a valid medical fitness certificate, allowing
them to compete in national and international races.
In addition, many of the tandem cyclists were world
champions and regularly won medals at major cycling
events, including the Paralympic Games.
The collected demographic data found that the majority of the respondents were male (72%). The average
age was 37 years (SD = 10.2) for the disabled athletes
and 33 years for the captains (SD = 7.4). The disabled
athletes were in possession of a cycling license for, on
average, 5.4 years; the captains had a cycling license
for, on average, 11.5 years. More than half (56%) of the
disabled cyclists suffered from congenital blindness or
visual impairment, with the remaining 44% of participants had an acquired visual disability due to disease or
injury. Over 60% of the disabled cyclists were in a stable
relationship, 44% held a university degree and more
than half of the cyclists (56%) were employed. Within
the group of able-bodied cyclists, the captains, threequarters of this group were in a stable relationship, 56%
held a university degree and 76% were employed.
The study used an anonymous questionnaire, which
consisted of two parts:
1. A personal questionnaire
It contained questions on demographic data such
as age, sex, education level, job and marital status. Additional questions asked which position
the respondent rode in tandem, when the loss of
vision took place and its degree for those who
were visually impaired, the numbers of hours spent
on training per week, and the year when a competitive cycling license was obtained.
2. The AIMS scale
An assessment of AI was conducted by use of
the seven-item AIMS scale [4]. The scale is multidimensional and measures three dimensions
of AI: social identity (items #1 and #2), exclusivity (items #3 through #5) and negative affectivity (items #6 and #7). Social identity relates
to the strength of how an athlete views him/
herself as occupying the role of an athlete. Exclusivity is the degree to which an individual
draws their identity from the role of an athlete,
while at the same time identifying themselves
to a lesser extent with other life roles (e.g., a student, a friend, an employee). Finally, negative
affectivity relates to negative emotional reactions that are the result of a pause in training or
competition due to injury, illness or retirement
from sports. The respondents expressed their
opinions on a seven-point Likert scale, where “1”
signifies that an individual “strongly disagrees”
with an opinion while “7” signifies that they
“strongly agree”. The sum of all the points determined the level of AI, with a higher score re
presenting a stronger identity to the role of an
179
HUMAN MOVEMENT
athlete. Although the AIMS scale was developed
for an able-bodied populations [1], it has been
widely used in studies on persons with disabilities [11, 13, 16, 18, 19]. Previous analysis on the
psychometric properties of the seven-point AIMS
scale found high internal consistency (Cronbach = 0.81 to 0.83) in studies on able-bodied
individuals [1, 4], and also in studies conducted
on individuals with spinal cord injuries ( = 0.90)
[16]. The AIMS scale used in this study had been
translated into Polish by Tasiemski [20] in a scien
tific monograph, and the accuracy of the translation was verified by a sworn English translator.
All of the demographic data were subjected to descriptive analysis, including their statistical frequency
(N) and percent (%). The scores from the AIMS scale
were presented as arithmetic mean ( ) for each of the
seven items and also provided their standard deviation
(SD). The reliability of the AIMS scale was analyzed
by using Cronbach’s alpha coefficient ( ). Due to the
lack of a normal distribution of the variables as well as
the presence of heterogeneity of variance, the nonpara
metric Mann-Whitney U-test (Z) was used to assess the
differences of AI between the disabled athletes and captains as well as the potential differences of AI among
the disabled athletes themselves due to the a number
of intermediate variables: the degree of the visual disability (blind or visually impaired), the time when vi-
sion was lost (whether acquired or from birth) and the
number of hours of training per week (9–12 and 13–16
hours). Finally, Spearman’s rank correlation coefficient (rs) was used to analyze the differentiation of AI
based on when the rider received their competitive cycling license. In addition, in the division of the respon
dents in terms of the degree of impaired vision, indivi
duals who lost their sight before the age of three (who
therefore had a lack of visual memory) were included
into the group of blind cyclists. The able-bodied athletes
were merged with regard to gender, as well as athletes
with disabilities, due to lack of significant differences
in AI between male and female tandem cyclists. All
analysis was performed using analytic software SPSS
Statistics ver. 14.0 (IBM, USA).
Results
Before detailed statistical analysis was performed,
an assessment of the research tool’s reliability was made.
It found that the reliability of the AIMS scale for tandem cyclists was high ( = 0.87), with the reliability of
the scale for each of the items presented in Table 1.
The AI level of the visually impaired ( = 24.84;
range: 7–49) and able-bodied ( = 36.40; range: 7–49)
cyclists was found to be statistically significant (Z =
–4.461, p 0.01). Significant differences were also found
between both groups with respect to six opinions found
Table 1. The reliability of the AIMS scale after removing the individual scale items
Item.
after removing the scale items
AIMS scale
1.
2.
3.
4.
5.
6.
7.
tandem cyclists
I consider myself an athlete
I have many goals related to sports
Most of my friends are athletes
Sport is the most important aspect of my life
I spend more time thinking about sports than anything else
I feel bad about myself when I do poorly in sports
I would be very depressed if I were injured and could not compete in sport
0.85
0.85
0.87
0.84
0.85
0.84
0.88
Table 2. The mean values for each opinion on the AIMS scale
Item. AIMS scale
Disabled cyclists
(N = 25)
Captains
(N = 25)
SD
1.
2.
3.
4.
5.
6.
7.
I spend more time thinking about sports than anything else
I would be very depressed if I were injured and could not compete in sport
* p 0.05
180
4.16
4.48
1.88
2.88
2.56
3.56
5.32
1.86
1.58
1.05
1.74
1.66
1.81
1.55
5.56
6.28
2.72
5.36
4.04
5.92
6.52
U-test
SD
Z
1.19
0.74
0.84
1.25
1.31
0.86
0.51
–2.800
–4.073*
–3.051*
–4.366*
–3.206*
–4.540*
–3.174*
HUMAN MOVEMENT
ber of hours of training per week (Z = –0.63, ns) and
the period of holding a cyclist license (rs = 0.03, ns).
Table 3. The results of the AIMS scale in relation
to the degree of visual disability
Degree of visual disability
Blind
Visually impaired
Disabled
cyclists
N
%
13
12
52
48
AIMS scale
SD
26.00 8.09
23.53 7.93
to when vision began to fail
Time when vision failed
From birth
Later in life
Disabled
cyclists
N
%
14
11
56
44
AIMS scale
SD
24.45 8.50
25.14 7.56
to the number of hours of training per week
Number of hours
of training per week
9–12 hours
13–16 hours
Disabled
cyclists
N
%
17
8
68
32
AIMS scale
SD
21.63 4.50
26.35 8.84
on the AIMS scale (with the exception of item #1). The
group of able-bodied captains scored higher than the
disabled cyclists (Tab. 2).
No significant (ns) differences were found in the AI
level of the disabled cyclists (Z = –0.82; ns) in terms of
the degree they had a visual disability (Tab. 3).
In addition, no significant differences were found in
the AI level of the disabled cyclists (Z = –0.11; ns) with
respect to when their vision began to fail (Tab. 4).
All of the disabled athletes were found to train from 9
to 16 hours per week and were therefore, for the purposes
of the study, divided into two further subgroups: cyclists
who trained intensively (9–12 hours per week) and very
intensively (13–16 hours per week). Nonetheless, the
number of hours spent on training tandem cycling each
week did not significantly differentiate (Z = –1.17; ns)
the AI level of the disabled athletes (Tab. 5).
Furthermore, no significant relationships (rs = 0.18; ns)
were found between the length of time since receiving
a cyclist license ( = 5.4 years, SD = 3.92) and the AI
level of the disabled tandem cyclists. Additio
nally,
analysis was also performed on the group of able-bodied
captains and the relationships between the number of
hours spent training per week and the period when
they received their cycling license. Similar to the visually impaired cyclists, no significant differences and relationships were found among these variables: the num-
Discussion
The main objective of this study was to question to
what extent do visually impaired cyclists and their
non-disabled captains identify themselves with the role
of an athlete and whether there were any differences
among these two groups. Psychometric analysis, which
revealed that the AIMS scale is a reliable and consistent
research tool in the evaluation of AI in tandem cyclists.
Studies conducted on the AI of disabled athletes have
suggested the level of athletic involvement in sports is
a more important factor influencing AI than the functional status of the athlete [14]. The results obtained in
this study do not support this hypothesis. Despite the
equal involvement of both cyclists, the visually impaired
athletes showed lower levels of AI in relation to their
able-bodied captains. Moreover, the level of involvement
as measured by the number of hours of training per week
was proved to have no connection with AI among both
the visually impaired and able-bodied cyclists. Several
factors may have contributed to such an outcome.
First, of considerable importance may be the type
of disability the athlete is afflicted with. For example,
blind individuals are characterized by having persona
lities that include qualities such as being introverted,
passive, withdrawn and lacking social skills [21, 22].
These are emotional characteristics that develop over
time and are directly caused by a blind person’s experience with the restrictions they face in the outside
world. On the other hand, AI is associated with such
qualities such as a willingness to compete [23], higher
levels of social competence [3], a positive self-image,
increased self-confidence and a willingness to take on
challenges [24]. A comparison of these two types of personalities naturally points to them being opposites.
The specific personality characteristics of blind indivi
duals can influence the way AI develops in their mind.
This area undoubtedly requires far more in-depth ana
lysis and research aimed at assessing the relationship
between AI and the personality factors associated with
different types of disabilities. This study attempted to
introduce such a research component through the division of the subjects in the degree and time when vision was lost. Based on available literature, we assumed
that there were difference in the functioning of individuals who were completely blind and those who featured some vision loss, as well as those who were blind
from birth or those who later lost their vision. Each
group features a slightly different adaptation process
to the afflicting disability and, therefore, could lead to
different cognitive and emotional consequences [25].
However, the results of this study found no relationship between these variables and AI.
Another factor that may influence the differences in
the results of both groups is the specificity of tandem
181
HUMAN MOVEMENT
cycling, in which decisions are made mostly by the ablebodied captains. By having the ability to better assess
a given situation, captains determine to a greater extent
the tactics used in a race and thus carry greater responsibility in its outcome. Naturally, the greater responsibility an individual has, the better they identify themselves with the activity they are performing (in this
case the role of an athlete). It can also be assumed that
the goal for most blind athletes is not the achievement
of sporting success. Based on results of studies conducted on individuals with musculoskeletal disorders
[20], one can also assume that persons with visual impairments treat sport as an opportunity to meet new
friends, put value in their lives, overcome their own
weaknesses and fears, and a way to travel and see the
world. A contradiction to this was the fact that the
group of captains evaluated each of the items from the
AIMS scale higher than the disabled cyclists, which
allows us to conclude that captains are more involved
in direct competition, are more ambitious and place
more emphasis and planning in cycling as a sport that
is an integral part of their lives.
A third way to explain the discrepancy of the results
between both groups may be the effect of the time which
elapsed since receiving a cyclist license. Almost all of the
captains received their licenses significantly earlier than
the visually impaired cyclists. This points to the fact
that most captains had more competitive experience
before they began riding in tandem. Previous research
postulates that persons who were physically active for
longer periods of time were more likely to have stronger
AI [3, 26]. This suggests that the length of one’s competitive career needs to be taken into account when
interpreting the results of AI. On the other hand, this
study found no relationships between the length of time
of having a cycling license to AI in both the able-bodied
and visually impaired athletes. Similar results were also
obtained by Groff and Zabriskie [14], who examined
the relationship between career length and AI in disabled skiers. Undoubtedly, the problem of athlete senio
rity and the large discrepancy in findings requires further analysis in larger and more diverse populations.
Due the lack of available research, both domestic and
foreign, on the AI of blind athletes, the results of this
study were compared (Tab. 6) to the results obtained on
a group of disabled swimmers with cerebral palsy, amputation of the lower limbs, and after spinal cord injury;
on a group of les autres individuals [19]; and adults
without disabilities (athletes and non-athletes) [4].
As the table shows, the visually impaired tandem
cyclists are characterized by low levels of AI, not only
in comparison to their able-bodied captains, but also
when compared to disabled athletes of other sports.
This is a puzzling result, especially when one considers
that the athletes in this study are professionals, who
successfully compete at the international level. This
may confirm the hypothesis that the specificity of the
sport does have an effect on AI, where in this case the
non-uniform distribution of responsibility in tandem
cycling plays a large role in sporting outcome.
Furthermore, the captains in this study received the
highest scores in five items from the seven-item scale.
This demonstrates the high level of AI in able-bodied
tandem cyclists when compared to the rest of the athletes. The group of captains scored significantly lower
only when questioned about social identity, which could
be partially explained by the low popularity of tandem
cycling. The overall high level of AI in captains could
support the thesis that this is in part due to the increased
responsibility they hold in tandem cycling.
The results presented in this study could provide
a number of important suggestions for coaches of tandem cycling. Every coach knows that the outcome of
a team depends on the concentrated effort of all of its
members; this is largely conditioned by having a common goal. The results of this study suggest that the goals
for tandem cyclists may be somewhat different, where
captains better identify themselves with the role of an
athlete than visually impaired cyclists. It seems that
a good way at increasing the level of competition would
Table 6. The mean values for each item in the AIMS scale in comparison to other studies
Item.
1.
2.
3.
4.
5.
6.
7.
182
AIMS scale
I spend more time thinking about sports
than anything else
I would be very depressed if I were injured
and could not compete in sport
Disabled cyclists
Captains
(own research) (own research)
Martin et al.
[19]
Brewer,
Cornelius [4]
4.2
4.5
1.9
2.9
5.6
6.3
2.7
5.4
5.9
6.0
4.3
4.5
5.7
5.4
5.0
4.0
2.6
4.0
3.4
3.7
3.6
5.9
4.5
5.0
5.3
6.5
5.9
4.8
HUMAN MOVEMENT
be to encourage blind riders to become more involved
in both training and competition. In such a context,
a central factor would be increasing the importance of
blind athlete’s decisions made during a race, which
would then increase their level of responsibility in the
outcome of the race.
The study featured here not only answered the formulated research questions, but also provides a starting
point for future research in this area. Uncovering the
factors responsible for lower AI levels in blind athletes
appears to be particularly necessary when considering
the overall small number of studies carried out the
physical activity of disabled individuals. Of particular
interest can be data on the AI of blind athletes who
are more directly responsible for their results in competition, such as blind runners (a guide only provides
directional input) or athletes who play goalball (with
no guides).
A limitation of this study was the small sample size of
athletes (N = 50), as well as the fact that all of the subjects were from a single country. This does significantly
reduce the possibility of providing wide-ranging conclusions, though it should be noted that this study was
conducted on 81% of all competing tandem cyclists
from Poland. Nonetheless, future studies ought to enlarge the scope of its research by including the results of
tandem cyclists from other countries. In addition, comparative analysis that takes into account a wider range
of sports and different types of disabilities should be
conducted, as it cannot be ruled out that they have a different impact on the AI of disabled athletes.
Conclusions
The following conclusions can be stated based on
the results of this study:
1. The AI level of tandem captains is significantly
higher than the AI level of visually impaired tandem cyclists.
2. Visually impaired tandem cyclists are a fairly
homogeneous group with respect to AI. There
were no differences in AI and the degree and time
of vision loss, the number of hours of training
per week or the length of time since receiving
a cycling license.
3. Psychometric analysis revealed that AIMS is a reliable and consistent research tool in the evaluation of AI in tandem cyclists.
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176–188.
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74–82.
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Edu Res, 1994, 1 (1), 87–99.
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21. MacCuspie P.A., The social acceptance and interaction
of visually impaired children in integrated settings. In:
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velopment of social skills by blind and visually impaired
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Paper received by the Editors: November 23, 2011
Paper accepted for publication: March 1, 2012
Tomasz Tasiemski
Katedra Kultury Fizycznej Osób Niepełnosprawnych
ul. Królowej Jadwigi 27/39
61-871 Poznań, Poland
e-mail: tashma@poczta.onet.pl
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2012, vol. 13 (2), 185– 189
PAIN AND SUFFERING IN SPORT
doi: 10.2478/v10038-012-0021-6
IVO JIRÁSEK 1 *, EMANUEL HURYCH 2
1
2
Faculty of Physical Culture, Palacký University Olomouc, Czech Republic
College of Polytechnics, Jihlava, Czech Republic
Abstract
Pain is an authentic part of humanity. This text deals with the topic of pain within the context of sports. It compares the agon
of war to the agon of sports. Here, pain is considered as a physical phenomenon, as a cultural and social construct as well as
a meaningful phenomenon. Another issue addressed in this paper is how pain is presented as an authentic component of performing sports. A loss of authenticity in sports is mentioned in connection with the prevalence of injuries. Special attention is
paid to the topic of death, which is understood as being a part of the horizon of pain. The last part of the article focuses on
crises in professional sports and asks about the meaning of pain and suffering in sports.
Key words: pain, suffering, agon, authenticity, death
What is sports?
Is it really composed of every intentional form of
movement activity? This is the most common usage of
this term. We can recollect a discussion within Journal
of the Philosophy of Sports about the triangle of sports
– game – play and how the relationship between these
notions was searched for in terms of different hierarchy and meanings. Of course, such an answer could
not have been definitely explained. We instead argue
from a different point of view, where sports are a part
of movement culture [1]. This social-cultural subsystem
includes, besides sports education, movement recreation, movement therapy and movement art. Tangible
movement activity, such as lifting one’s knee, belongs
to a different subsystem according to the definition of
this activity. Therefore, the logic of movement education
is in the development of personality. The rejuvenative
and relaxing aspects of movement places it as a part of
movement recreation. The valuation of health plays
a key role in movement therapy while the value of beauty
is the main feature in movement art. What is the situation with sports? From this point of view sports has
two basic components: one being the push for maximum
achievement and the other of the struggle for victory
in strictly organized competitive conditions.
Agon – a transcultural component of society
The process of challenge, interpersonal rivalry, and
the endeavor to achieve victory is clearly recognized
in any society. This can be seen in any feudal society
of medieval Europe, in the samurais of Japan or in the
Native-Americans of North America. Furthermore, the
individual endeavor to become exceptional, perfect, personally honored or superior is reached by accepting
higher levels of hazardous competition and is seen in
every society. This common social principle is known as
an “agon motive” [2]. Agon is the ancient Greek word
expressing competition, encounter, and contest. Ancient
Greeks had another name for struggle, “polemos”, which
is understood as competition being a form of war. Within such a context it is possible to find some common as
well as singular features concerning sports and war [3].
Hence, there are parallels between sports and war; sports
can be considered as “a secondary war”, a symbol of
real agon. Naturally, it is not surprising that the original
martial agon was transformed into sports agon. Full
agon is fixated not just by the goal to beat a rival but
in killing one’s opponent. Whereas sports agon is connected with temporal sequels, which negate the risk of
death but uphold the value of victory at a higher level
than the one of life.
Agonal behavior is kept and cultivated in a sports
context where it reaches a high form of competitions
and games. Adding to this is: “in war, rule exists wholly
as a result of victory; in sports victory exists as a result of
rule” [4, p. 31]. The transformation of agon and its role
in society are not obvious. Some concepts such as pseudoagonal or post-agonal are used for the transmission of
real agon into its present symbolic form. The wish to
fight, to find the best, is a constitutive aspect of personality and society. This agon motive has a different
shape in various historical and cultural environments,
and it is seen in some form in every culture. Throughout
the centuries, real antagonism was substituted by symbolic modalities. The most important part from these
symbolic forms of the agon motive is sports.
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I. Jirásek, E. Hurych, Pain and suffering in sport
Pain – an authentic part of war agon
In any case, it is necessary to present the topic of pain
within a war agon. We can find even a brutal description of pain in battle, for example, in ancient Greek
texts. Let us look at some examples from Homer’s Iliad. We apologize for using a Czech translation of this
Greek epic, and we understand that the reader can
find a definitely better quality translation in a more
strict poetic context. However, the meanings of these
words would be the same:
– He stubbed a pike into his brow, and penetrated
into the entrails of bones.
– The pike cut his tongue across the back set of
his teeth.
– The blade of huge shaft enmeshed in his innards
from below.
– A wounded person rattled with effort and he
falls out from the elegant vehicle.
– He interposed into the middle of throat, till
painful peak throughout penetrated his nape.
We can suppose that similar or even greater levels
of pain are an essential part of every war agon, including
such contemporary war conflicts as in Afghanistan or
in the terrorist attacks that occur all over the world. Is
this pain a part of sports agon as well?
Forms of pain and suffering in sports
We have already said that there are some differences
between sports and war agons. However, the question
is if the pain in sports and war are one and the same.
Before we can answer this question we should look at
where pain in sports originates. We can argue four
different principles on the purpose of pain in sports:
1. A sign of one’s training regime and lifestyle.
2. A sign of maximum exertion to reach top achievement.
3. A sign of a substantial component of a specific
sport.
4. A demonstration of lost sports authenticity (disease, injury).
Before we describe these principles (as meaningful
levels where we can meet pain in the sports agon), we
have to review a few possible approaches in the study
of pain.
Possible approaches in the study
of pain in sports
These approaches come from Loland’s [5] division
and could be very interesting and useful. The first possibility is a scientific (medical) approach where pain is
understood as a physical phenomenon. The second one
is constructivist. Pain is not an independent phenome
non but a product of a social and cultural context. The
third approach is phenomenological, which takes into
186
consideration subjective experiential qualities. Now we
can look at each of these approaches in more detail.
Pain as a physical phenomenon
This concept, which is common in our society, originated from the thinking of the philosopher René Des
cartes. With his philosophy we can interconnect his
well-known theory on the division of the human being
into two separate substances: the body and the mind.
The body is characteristic by it occupying a position in
space, it is res extensa. Contrarily, the mind is a completely different substance in which we cannot think
about space, it is thinking itself, it is res cogitans.
Unfortunately, neither Descartes nor his followers
solved the connection between these two substances.
And to this day it is the most important problem in the
Cartesian way of thinking. That is why the body is
understood only as separate physical object in a deterministic world. In such a world every situation and every
activity could be causally explained. In spite of the fact
the problem of res extensa and res cogitans is still not
solved, this understanding of the body is a basic paradigm in a scientific approach. Pain is the process that
informs, through sensation, the cognitive parts of an
organism (the brain) about the dangers of physical events
– disease or injury.
Pain as a cultural and social construction
Besides the natural scientific concept, we can be inspired by sociology and social and cultural anthropology.
In this approach, pain is not only a physical phenomenon in a deterministic order of causality (the process
of cause and result) but the consequence and product of
historical, cultural and social conditions. This anthropological point of view better respects these natural
and cultural differences than the formerly mentioned
Cartesian concept. The difference in perceiving what
pain is and what are its borders differs from society to
society in various cultural aspects. Every specific society
predestinates what pain is and how to overcome it. As
an example we can mention indigenous populations
and their initiative rituals and symbols of journey and
“crossing over”.
Pain as a meaningful phenomenon
Phenomenological philosophy is connected firstly
with Edmund Husserl. He developed the philosophy
of Franz Brentano and his emphasis of the medieval
concept of intentionality. It is based on the thought
that our consciousness is always focused on the subject of our interest; it cannot be free of thought. This
specific method of phenomenology is based on phenomenological reduction, which makes it possible for us
to progress from appearances and phenomena toward
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their concrete substance. It is a method for understanding existential topics, an area of meaning and purpose
where the key role is played by experience. However, this
does not mean this is subjectivism! Husserl’s thoughts
are compatible with transcendental subjectivity. What
phenomenological thought offers is the ability to enrich
the study of pain in sports as a reflection of intentional
consciousness. So the experienced phenomena refer to
the meaning of pain. We can hardly find any other way
of philosophy which allows for a better understanding
of the field of meaning, including the meaning of pain
in sports.
Pain and suffering:
a training regime and a way of living
Based on the above review of the different approaches
on the study of pain, we can go back to the four forms
of pain which were defined earlier as the basic areas of
pain in sports. The first sphere where we can meet
pain in sports is in one’s training regime and lifestyle.
What is notable at first sight is the strict training regime of top athletes, which can be compared to the
monastic way of life in a friary. Asceticism, which is interconnected with the lifestyle of athletes, makes them
to give up many of the gratifying activities and pleasures
common for ordinary people. The extreme example is
fakeers, who connect asceticism with the willful experience of pain. Such a punishing lifestyle pushes motor
efficiency to its biological limits. When we reach the
natural limits of our human capabilities, increasing our
performance is possible through the use of technology
or doping. However, reaching one’s best achievements in
sport is connected nonetheless with pain and suffering.
Pain and suffering:
maximum exertion to reach top achievement
A sports champion who devoted his/her life to top
achievement is an example of the over-fulfillment of
animality to transcendence levels. Exhaustion and attrition can stem from the practical results of the pursuit
after maximum output. On the other hand, sports can
become a form of ecstasy from the everydayness of life
as a form of spiritual overreaching. This way of trans
cendence, which cannot occur without presence of pain,
could be seen as a ritual or a document of the ritualistic
character of sports [6]. The parallel between sports
and spirituality can be even seen in the fact that sports
are “becoming inadvertently one of the forms of natural
religion” [7, p. 42].
Pain and suffering:
an authentic part of the sports experience
Pain is an authentic part of the sports experience,
not only in sports where it is anticipated, such as in
boxing, wrestling, martial arts or other power sports,
but also in non-contact sports. We can often find pain
in sports situations where it was not even intentional.
A ball hitting the face of player could be one of many
examples. Even though pain is an essential part of such
situations in sports, to overcome it is expression of will
winning over corporeality (transcendence). A difference
between pain and suffering can also be seen. This difference does not just stand for a more physical character
of pain and a mental basis of suffering; it is connected
with its consequences. Heidegger [8] speaks about two
main modes of attunement: anxiety and fear. Fear is
a part of the present existence which endangers a human
in some way, which Heidegger considers to be an inauthentic way of attunement. On the other hand, an
xiety does not have its own object. Heidegger uses the
attunement of anxiety as the means in how to take
the human element out of captivity in the objective
givenness of the world. We can see a similar (not the
same) relationship (such as between fear and anxiety)
in the case of pain and suffering. While pain is generally connected with a loss of authenticity, suffering
could be an authentic part of physical activity in some
specific situations.
Pain and suffering:
a loss of sports authenticity
We have mentioned situations where pain is an authentic part of the sports experience. However, we
should not neglect pain experienced in sports which
proves a loss of its authenticity. Such a modus of pain
is a sign of the crisis in sports, which focuses more on
results rather than on consequences [3]. (The difference
is much more remarkable in the Czech language, where
the word “results” is “výsledky” and the word “důsledky”
means “consequences”.) This substitution of consequence for results is done by a preference for the present
over the future, where a bigger emphasis on results has
a higher cost on health or honor. And it specifically
leads to phenomena which are generally criticized:
victory at any cost, the discrimination of unsuccessful
athletes, the exaggeration and overemphasis of winners
and records, a rise in the levels of aggression, cheating, etc. That could be considered as a cause of the rise
in doping, injuries, and these all lead to pain. Therefore,
we are witnesses to a historical metamorphosis from
a sacral, religious relationship of competition and achievement to the depersonalization of human beings (an
instrumentalization of human body and its output).
The horizon of pain: death
An analysis of pain in sports is not possible without at least taking a short glace at the anthropological
mind-body problem and its ontological dimension. From
many different approaches we can pay witness to Hei187
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degger’s fundamental ontology. In this type of thought
the human being (Dasein) is defined as “a being towards
death.” Unlike animals, we are conscious of our finiteness, about the impassable horizon that looms ahead of
us, about the legacy we leave which nobody can take
over in our place. The specific feature of humanity is its
temporality. The past is the thrownness (Geworfenheit) while future is the schedule. It means we can project (schedule) our possibilities. The future is open to
our activities (in contrary to the past). As it was said,
Heidegger distinguishes between two ways of being
authentic and inauthentic. The authentic way of being
is life where the human being is him/herself when he/
she fully executes the possibilities of self-knowledge
and self-realization. Contrarily, the inauthentic way
of being is characterized by succumbing to the impersonal “the They” (das Man). This means that personality succumbs to the dictation of fashion, the mention in a crowd, because “it is being done”, “it is being
worn” and so on. When we know about our death as
the final horizon, we can schedule our possibilities
with a higher level of discretion; it allows us to have
a chance at living life more authentically.
Death and sports
In this context the question of death as a consequence of sports activity is more relevant. Could it be
the sign of authenticity? There are many occasions in
sports where death is present: parachuting, BASE jumping, free-style climbing, full-contact sports etc. What
are extreme (high-risk, or adrenaline) sports from the
point of view authenticity? What are people looking for
through such experiences? Is it still the overcoming of
obstacles? Is it just for excitement? Could it be an easier
ingress to death?
Is there any desire for the clear and definite confidence of the “love of life” phenomenon, known from
stories describing extreme physical achievements in
punishing conditions (such as the fictional characters
of Jack London’s stories about the Gold Rush in Alaska,
or the often controversial behavior of real mountaineers in extreme situations)? There, pain can be taken as
the reason (not in the masochistic context) why these
risky activities are performed. Or perhaps, more often,
it can present the acceptable consequence which naturally goes along with doing them. To be able to accept
pain, and even the danger of death, can enable some
people to uncover the pure phenomenon of “love of
life.” Death presents in these cases the horizon of pain
as well as the horizon of life.
Limits of professional sports:
death at the playing field
Contemporarily, we can meet the fact of death not
only in extreme sports but in more and more sports
188
which were considered to be quite safe. For instance,
within a period of ten days four soccer players between
the ages of 16 to 31 years died during matches due to
a heart attack. However, death is not just a sign of overexertion during a game. Death can also arise in the
training process. A very important point connected
with this increase in the number of mortalities in sports
it is not just the old or weak who die, but the young
and strong who are the top athletes and well-trained.
What does this mean? Generally, it is a sign of a crisis
in sports connected with the loss of the authentic way
of being, as we have said before. It is represented by
the fact that sports are valued more than life.
Crisis in professional sports
We just saw death in sports as result of crisis in sports.
What are the purposes of this crisis? Hägele [9] defines
three basic features:
– the professionalization of sports structures connected with its oligarchization and bureaucratization,
– its commercialization, meaning the connection
of sports to commerce, business and the marketplace,
– its politicization, which is primarily visible in
the Olympic Games.
These signs of crisis belong in a sports setting, but
they influence the whole of society as well. Hogenová
even sees sports as an indoctrinating system, which
“holds one in the most manipulative roles in today’s
world” [10, p. 19].
Meaning of pain and suffering in sports
By connecting Heidegger’s idea of authenticity
and the phenomenon of pain and suffering can ask
about the sense the role of pain plays in sports. Accepting this point of view, the act of performing sports
cannot be more important than health or even life.
When we are trying to live authentically this means we
are looking for the meaning of our being, where sports
can be only the means, not the goal, of our being. The
possibilities of an authentic existence via the world of
sports can be well-seen in the sphere of the Paralympic movement and sports played by disabled persons.
Nowadays, the Paralympic Games are the elite sporting event for athletes with a disability, where the athletes’ achievements are emphasized over their form of
disability. Undoubtedly, sports and physical movement
can help disabled athletes to live an ordinary life, an
authentic life.
Sports as the possibility of an authentic existence
offers the development of self-knowledge and self-realization through the realization of values like courage,
fairness, respect to others, perfectionism and personal
transcendence, among others.
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References
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injury in sport. Routledge, London 2006, 49–62.
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(ed.), Aktuelle Probleme der Sportphilosophie – Topical
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Philosophy [in Czech]. Karolinum, Praha 1999, 18–21.
Paper received by the Editors: October 6, 2010
Paper accepted for publication: May 6, 2011
Ivo Jirásek
Faculty of Physical Culture
Palacký University Olomouc
Tř. Míru 115
771 11 Olomouc, Czech Republic
e-mail: ivo.jirasek@upol.cz
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(terms)
6. The third page should contain:
– The title of the article
– The main text
7. The main body of text in empirical research articles should be divided into the following sections:
4.Strona tytułowa powinna zawierać:
– tytuł pracy w języku polskim i angielskim;
– skrócony tytuł artykułu w języku angielskim (do 40 zna
ków ze spacjami), który zostanie umieszczony w żywej
paginie;
–imię i nazwisko autora (autorów) z afiliacją zapisaną
wg następującego schematu:
– nazwę uczelni, nazwę miejscowości, nazwę kraju, np.
Akademia Wychowania Fizycznego, Wrocław, Polska;
– adres do korespondencji (imię i nazwisko autora, jego
adres, e-mail oraz numer telefonu).
5. Następna strona powinna zawierać:
– tytuł artykułu;
– streszczenie w języku angielskim (około 250 wyrazów)
składające się z następujących części: Purpose, Methods,
Results, Conclusion;
–słowa kluczowe w języku angielskim (3–6) – ze słownika i w stylu MeSH.
6. Trzecia strona powinna zawierać:
– tytuł artykułu;
– tekst główny.
7. Tekst główny pracy empirycznej należy podzielić na następujące części:
Introduction
The introduction prefaces the reader on the article’s subject, describes its purpose, states a hypothesis, and mentions
any existing research (literature review)
Wstęp
We wstępie należy wprowadzić czytelnika w tematykę
artykułu, opisać cel pracy oraz podać hipotezy, stan badań
(przegląd literatury).
This section is to clearly describe the research material
(if human subjects took part in the experiment, include
their number, age, gender and other necessary information), discuss the conditions, time and methods of the research as well identifying any equipment used (providing
the manufacturer’s name and address). Measurements and
procedures need to be provided in sufficient detail in order
to allow for their reproducibility. If a method is being used
for the first time, it needs to be described in detail to show
its validity and reliability (reproducibility). If modifying
existing methods, describe what was changed as well as
justify the need for the modifications. All experiments using human subjects must obtain the approval of an appropriate ethnical committee by the author in any undertaken
research (the manuscript must include a copy of the approval document). Statistical methods should be described
in such a way that they can be easily determined if they are
correct. Authors of comparative research articles should
also include their methods for finding materials, selection
methods, etc.
Materiał i metody
W tej części należy dokładnie przedstawić materiał badawczy (jeśli w eksperymencie biorą udział ludzie, należy
podać ich liczbę, wiek, płeć oraz inne charakterystyczne
cechy), omówić warunki, czas i metody prowadzenia badań
oraz opisać wykorzystaną aparaturę (z podaniem nazwy
wytwórni i jej adresu). Sposób wykonywania pomiarów
musi być przedstawiony na tyle dokładnie, aby inne osoby
mogły je powtórzyć. Jeżeli metoda jest zastosowana pierwszy raz, należy ją opisać szczególnie precyzyjnie, przedstawiając jej trafność i rzetelność (powtarzalność). Modyfikując uznane już metody, trzeba omówić, na czym polegają
zmiany, oraz uzasadnić konieczność ich wprowadzenia.
Gdy w eksperymencie biorą udział ludzie, konieczne jest
uzyskanie zgody komisji etycznej na wykorzystanie w nim
zaproponowanych przez autora metod (do maszynopisu
należy dołączyć kopię odpowiedniego dokumentu). Metody statystyczne powinny być tak opisane, aby można było
bez problemu stwierdzić, czy są one poprawne. Autor pracy
przeglądowej powinien również podać metody poszukiwania materiałów, metody selekcji itp.
Results
The results should be presented both logically and consistently, as well as be closely tied with the data found in
tables and figures.
Wyniki
Przedstawienie wyników powinno być logiczne i spójne
oraz ściśle powiązane z danymi zamieszczonymi w tabelach i na rycinach.
Discussion
Here the author should create a discussion of the obtained results, referring to the results found in other literature (besides those mentioned in the introduction), as well
as emphasizing new and important aspects of their work.
Dyskusja
W tym punkcie, stanowiącym omówienie wyników,
autor powinien odnieść uzyskane wyniki do danych z literatury (innych niż omówione we wstępie), podkreślając
nowe i znaczące aspekty swojej pracy.
Conclusion
In presenting any conclusions, it is important to remember
the original purpose of the research and the stated hypotheses,
Wnioski
Przedstawiając wnioski, należy pamiętać o celu pracy
oraz postawionych hipotezach, a także unikać stwierdzeń
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HUMAN MOVEMENT
and avoid any vague statements or those not based on the
results of their research. If new hypotheses are put forward,
they must be clearly stated.
ogólnikowych i niepopartych wynikami własnych badań.
Stawiając nowe hipotezy, trzeba to wyraźnie zaznaczyć.
Acknowledgements
The author may mention any people or institutions that
helped the author in preparing the manuscript, or that provided support through financial or technical means.
Podziękowania
Można wymienić osoby lub instytucje, które pomogły
autorowi w przygotowaniu pracy bądź wsparły go finansowo lub technicznie.
Bibliography
The bibliography should be composed of the article’s citations and be arranged and numbered in the order in which
they appear in the text, not alphabetically. Referenced sources
from literature should indicate the page number and enclose it in square brackets, e.g., Bouchard et al. [23].
The total number of bibliographic references (those found
only in research databases such as SPORTDiscus, Medline)
should not exceed 30 for empirical research papers (citing
a maximum of two books); there is no limit for comparative research papers. There are no restrictions in referencing
unpublished work.
Bibliografia
Bibliografię należy uporządkować i ponumerować według
kolejności cytowania publikacji w tekście, a nie alfabetycznie. Odwołanie do piśmiennictwa należy oznaczyć w tekście
numerem i ująć go w nawias kwadratowy, np. Bouchard
et al. [23].
Bibliografia (powołania zawarte tylko w bazach danych,
np. SPORTDiscus, Medline) powinna się składać najwyżej
z 30 pozycji (dopuszcza się powołanie na 2 publikacje książ
kowe), z wyjątkiem prac przeglądowych. Niewskazane jest
cytowanie prac nieopublikowanych.
Citing journal articles
Bibliographic citations of journal articles should include:
the author’s (or authors’) surname, first name initial, article title, abbreviated journal title, year, volume or number,
page number, doi, for example:
Opis bibliograficzny artykułu z czasopisma
Opis bibliograficzny artykułu powinien zawierać: naz
wisko autora (autorów), inicjał imienia, tytuł artykułu, tytuł czasopisma w przyjętym skrócie, rok wydania, tom lub
numer, strony, numer doi, np.
Tchórzewski D., Jaworski J., Bujas P., Influence of long-lasting balancing on unstable surface on changes in balance.
Hum Mov, 2010, 11 (2), 144–152, doi: 10.2478/v10038-0100022-2.
Tchórzewski D., Jaworski J., Bujas P., Influence of long-lasting balancing on unstable surface on changes in balance.
Hum Mov, 2010, 11 (2), 144–152, doi: 10.2478/v10038-0100022-2.
If there are six or less authors, all the names should be
mentioned; if there are seven or more, give the first six and
then use the abbreviation “et al.”
If the title of the article is in a language other than English, the author should translate the title into English, and
then in square brackets indicate the original language; the
journal title should be left in its native name, for example:
Gdy autorami artykułu jest sześć lub mniej osób, należy
wymienić wszystkie nazwiska, jeżeli jest ich siedem i więcej,
należy podać sześć pierwszych i zastosować skrót „et al.”.
Tytuł artykułu w języku innym niż angielski autor powinien przetłumaczyć na język angielski, a w nawiasie kwadratowym podać język oryginału, tytuł czasopisma należy
zostawić w oryginalnym brzmieniu, np.
Jaskólska A., Bogucka M., Świstak R., Jaskólski A., Mechanisms, symptoms and after-effects of delayed muscle soreness (DOMS) [in Polish]. Med Sport, 2002, 4, 189–201.
Jaskólska A., Bogucka M., Świstak R., Jaskólski A., Mechanisms, symptoms and after-effects of delayed muscle soreness (DOMS) [in Polish]. Med Sport, 2002, 4, 189–201.
The author’s research should only take into consideration articles published in English.
W pracy powinny być uwzględnianie tylko artykuły publikowane ze streszczeniem angielskim.
Citing books
Bibliographic citations of books should include: the author (or authors’) or editor’s (or editors’) surname, first
name initial, book title translated into English, publisher,
place and year of publication, for example:
Opis bibliograficzny książki
Opis bibliograficzny książki powinien zawierać: nazwisko autora (autorów) lub redaktora (redaktorów), inicjał imienia, tytuł pracy przetłumaczony na język angielski, wydawcę,
miejsce i rok wydania, np.
Osiński W., Anthropomotoric [in Polish]. AWF, Poznań 2001.
Osiński W., Anthropomotoric [in Polish]. AWF, Poznań 2001.
Heinemann K. (ed.), Sport clubs in various European countries. Karl Hofmann, Schorndorf 1999.
Heinemann K. (ed.), Sport clubs in various European countries. Karl Hofmann, Schorndorf 1999.
Bibliographic citations of an article within a book should
include: the author’s (or authors’) surname, first name initial, article title, book author (or authors’) or editor’s (or
editors’) surname, first name initial, book title, publisher,
place and year of publication, paga number, for example:
Opis bibliograficzny rozdziału w książce powinien zawie
rać: nazwisko autora (autorów), inicjał imienia, tytuł roz
działu, nazwisko autora (autorów) lub redaktora (redaktorów), inicjał imienia, tytuł pracy, wydawcę, miejsce i rok wydania, strony, np.
McKirnan M.D., Froelicher V.F., General principles of exercise testing. In: Skinner J.S. (ed.), Exercise testing and
exercise prescription for special cases. Lea & Febiger, Philadelphia 1993, 3–28.
McKirnan M.D., Froelicher V.F., General principles of exercise testing. In: Skinner J.S. (ed.), Exercise testing and
exercise prescription for special cases. Lea & Febiger, Philadelphia 1993, 3–28.
192
HUMAN MOVEMENT
Citing conference materials
Citing conference materials (found only in international
research databases such as SPORTDiscus) should include:
the author’s (or authors’) surname, first name initial, article title, conference author’s (or authors’) or editor’s (or editor’s) surname, first name initial, conference title, publisher,
place and year of publication, page number, for example:
Opis bibliograficzny materiałów zjazdowych
Opis bibliograficzny materiałów zjazdowych (umieszczanych tylko w międzynarodowych bazach danych, np.
SPORTDiscus) powinien zawierać: nazwisko autora (autorów), inicjał imienia, tytuł, nazwisko autora (autorów) lub
redaktora (redaktorów), tytuł pracy, wydawcę, miejsce i rok
wydania, strony, np.
Rodriguez F.A., Moreno D., Keskinen K.L., Validity of a twodistance simplified testing method for determining critical swimming velocity. In: Chatard J.C. (ed.), Biomechanics and Medicine in Swimming IX, Proceedings of the IXth
World Symposium on Biomechanics and Medicine in Swimming. Université de St. Etienne, St. Etienne 2003, 385–390.
Rodriguez F.A., Moreno D., Keskinen K.L., Validity of a twodistance simplified testing method for determining critical swimming velocity. In: Chatard J.C. (ed.), Biomechanics and Medicine in Swimming IX, Proceedings of the IXth
World Symposium on Biomechanics and Medicine in Swimming. Université de St. Etienne, St. Etienne 2003, 385–390.
Citing articles in electronic format
Citing articles in electronic format should include: author’s (or authors’) surname, first name initial, article title,
abbreviated journal title, journal volume or number, year of
publication, website address where it is available, doi number,
for example:
Opis bibliograficzny artykułu w formie elektronicznej
Opis bibliograficzny artykułu w formie elektronicznej
powinien zawierać: nazwisko autora (autorów), inicjał imienia, tytuł artykułu, tytuł czasopisma w przyjętym skrócie,
tom lub numer, rok wydania, adres strony, na której jest
dostępny, numer doi, np.
Donsmark M., Langfort J., Ploug T., Holm C., Enevoldsen L.H., Stallknech B. et al., Hormone-sensitive lipase
(HSL) expression and regulation by epinephrine and exercise in skeletal muscle. Eur J Sport Sci, 2 (6), 2002. Available
from: URL: http://www.humankinetics.com/ejss/bissues.
cfm/, doi: 10.1080/17461391.2002.10142575.
Donsmark M., Langfort J., Ploug T., Holm C., Enevoldsen L.H., Stallknech B. et al., Hormone-sensitive lipase
(HSL) expression and regulation by epinephrine and exercise in skeletal muscle. Eur J Sport Sci, 2 (6), 2002. Available
from: URL: http://www.humankinetics.com/ejss/bissues.
cfm/, doi: 10.1080/17461391.2002.10142575.
8. The main text of any other articles submitted for consideration should maintain a logical continuity and that the
titles assigned to any sections must reflect the issues discussed within.
8. Tekst główny w pracach innego typu powinien zachować
logiczną ciągłość, a tytuły poszczególnych części muszą
odzwierciedlać omawiane w nich zagadnienia.
9. Footnotes/Endnotes (explanatory or supplementary to the
text). Footnotes should be numbered consecutively throughout the work and placed at the end of the main text.
9.Przypisy (objaśniające lub uzupełniające tekst) powinny
być numerowane z zachowaniem ciągłości w całej pracy
i umieszczone na końcu tekstu głównego.
10. Tables, figures and photographs
– Must be numbered consecutively in the order in which
they appear in the text and provide captions
–Should be placed within the text
– Additionally, figures or photographs must be attached
as separate files in .jpg or .pdf format (minimum resolution of 300 dpi)
– May not include the same information/data in tables
and also figures
– Illustrative materials should be prepared in black and
white or in shades of gray (Human Movement is published in such a fashion and cannot accept color)
–Symbols such as arrows, stars, or abbreviations used in
tables or figures should be clearly defined using a legend.
10. Tabele, ryciny i fotografie
– należy opatrzyć numerami i podpisami;
– należy umieścić w tekście artykułu;
–dodatkowo ryciny i fotografie trzeba dołączyć w postaci osobnych plików zapisanych w formacie *.jpg lub
*.pdf (gęstość co najmniej 300 dpi);
–nie można powtarzać tych samych wyników w tabelach i na rycinach;
–materiał ilustracyjny powinien zostać przygotowany
w wersji czarno-białej lub w odcieniach szarości (w taki
sposób jest drukowane czasopismo Human Movement);
– symbole, np. strzałki, gwiazdki, lub skróty użyte w tabelach czy na rycinach należy dokładnie objaśnić w legendzie.
Manuscripts not prepared as per the requirements set forth
in “Publishing Guidelines” will be returned to the author for
correction. The Editorial Office reserves the right to make
any language corrections or remove abbreviations found in
the manuscript. Once the Editorial Office accepts an article
for publication, a proof will be sent to the author for approval.
It is the author’s responsibility to accept any changes or submit any corrections within one week of receiving the proof.
Praca przygotowana niezgodnie z wymogami „Regulaminu publikowania prac” zostanie odesłana autorowi do
poprawy. Redakcja zastrzega sobie prawo usuwania usterek
językowych oraz dokonywania skrótów. Artykuł po opracowaniu redakcyjnym zostanie przekazany autorowi do akceptacji. Obowiązkiem autora jest przesłanie ewentualnych
uwag i poprawek w ciągu jednego tygodnia.
Prior to printing, the author will receive their article in
.pdf format. It is the author’s responsibility to immediately
inform the Editorial Office if they accept the article for
publication. At such a point in time, only minor corrections
can be accepted from the author.
Przed drukiem autor otrzyma swój artykuł do akceptacji w formie pliku pdf. Obowiązkiem autora jest niezwłoczne przesłanie do Redakcji Human Movement informacji
o akceptacji artykułu do druku. Na tym etapie będą przyjmowane tylko drobne poprawki autorskie.
193
HUMAN MOVEMENT
The Journal is subject to copyright as per the Berne
Convention and the International Copyright Convention,
except where not applicable pursuant to a country’s domestic law.
Publikacje podlegają prawu autorskiemu wynikającemu
z Konwencji Berneńskiej i z Międzynarodowej Konwencji
Praw Autorskich, poza wyjątkami dopuszczanymi przez prawo krajowe.
The Editorial Office accepts advertising in Human Movement, which may be located on the second or third page of
the cover or as additional separate pages. Ad rates are negotiated separately.
Redakcja przyjmuje zamówienia na reklamy, które mogą
być umieszczane na 2. i 3. stronie okładki lub na dodatkowych kartach sąsiadujących z okładką. Ceny reklam będą
negocjowane indywidualnie.
Authors should contact the Editorial Office of Human
Movement only by email.
Autorzy powinni się kontaktować z Redakcją Human Move
ment wyłącznie za pośrednictwem poczty elektronicznej.
THE RULES OF SUBSCRIBING THE HUMAN MOVEMENT JOURNAL
ZASADY PRENUMERATY CZASOPISMA HUMAN MOVEMENT
The price of annual subscription (four issues) for individual subscribers is PLN 54 and PLN 110 for institutions.
All subscriptions are payable in advance. Subscribers are
requested to send payment with their order whenever
possible. The orders should be sent to the Editorial Office:
e-mail: hum_mov@awf.wroc.pl or
Human Movement Editorial Office
University School of Physical Education
Cena rocznej prenumeraty (cztery numery) dla odbiorców indyw idualnych w kraju wynosi 54 zł brutto, dla
instytucji 110 zł brutto. Zamówienie wraz z potwierdzeniem dokonania wpłaty należy przesłać na adres mailowy:
hum_mov@awf.wroc.pl lub
The issues of the journal are sent by post after receiving
the appropriate transfer to the account:
Numery czasopisma wysyłamy pocztą po otrzymaniu od
pow iedniej wpłaty na konto:
BPH PBK S.A. O/Wrocław
42 1060 0076 0000 3210 0014 7743
al. Paderewskiego 35, 51-612 Wrocław, Poland
with the note: Human Movement subscription.
BPH PBK S.A. O/Wrocław
42 1060 0076 0000 3210 0014 7743
al. Paderewskiego 35, 51-612 Wrocław
z dopiskiem: Prenumerata Human Movement.
We ask the subscribers to give correct and clearly written addresses to which the journal is to be sent.
Prosimy zamawiających o bardzo wyraźne podawanie adresów, pod które należy wysyłać zamawiane egzemplarze
czasopisma.
Pojedyncze egzemplarze można zamówić w ten sam sposób, wpłacając 16 zł brutto (odbiorca indywidualny) i 30 zł
brutto (instytucja) na podane konto.
Single copies can be ordered in the same way, by transferring PLN 16 (individual subscribers) and PLN 30 (institutions) to the above mentioned account.
194
Redakcja czasopisma Human Movement
51-612 Wrocław

Human Movement 13(2) 2012 - Akademia Wychowania Fizycznego

Transcription

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