Human Movement 17 (2) 2016 - Akademia Wychowania Fizycznego

Transcription

University School of Physical Education in Wrocław
University School of Physical Education in Kraków
vol. 17, number 2 (June), 2016
University School of Physical Education in Wrocław (Akademia Wychowania Fizycznego we Wrocławiu)
University School of Physical Education in Kraków (Akademia Wychowania Fizycznego im. Bronisława Czecha w Krakowie)
Human Movement
quarterly
vol. 17, number 2 (June), 2016, pp. 61– 136
Editor-in-Chief Alicja Rutkowska-Kucharska
University School of Physical Education, Wrocław, Poland
Associate EditorEdward Mleczko
University School of Physical Education, Kraków, Poland
Editorial Board
Physical activity, fitness and health
Wiesław Osiński
University School of Physical Education, Poznań, Poland
Applied sport sciences
Zbigniew Trzaskoma Józef Piłsudski University of Physical Education, Warszawa, Poland
Biomechanics and motor control
Tadeusz Bober
Kornelia Kulig
University of Southern California, Los Angeles, USA
Physiological aspects of sports
Andrzej Suchanowski
Józef Rusiecki Olsztyn University College, Olsztyn, Poland
Psychological diagnostics of sport and exercise
Andrzej Szmajke
Opole University, Opole, Poland
Advisory Board
Wojtek J. Chodzko-Zajko
Gudrun Doll-Tepper Józef Drabik
Kenneth Hardman
Andrew Hills
Zofia Ignasiak
Slobodan Jaric
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
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
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, Germany
Cleveland Clinic Foundation, Cleveland, Ohio, USA
Pennsylvania State University, State College, Pennsylvania, USA
University School of Physical Education, Kraków, Poland
Translation: Agnieszka Piasecka
Design: Agnieszka Nyklasz
Copy editor: Beata Irzykowska
Statistical editor: Małgorzata Kołodziej
Indexed in: SPORTDiscus, Index Copernicus, Altis, Sponet, Scopus, CAB Abstracts, Global Health
14 pkt wg rankingu Ministerstwa Nauki i Szkolnictwa Wyższego
© Copyright 2016 by Wydawnictwo AWF we Wrocławiu
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This is to certify the conformity with PN-EN-ISO 9001:2009
Circulation: 100
HUMAN MOVEMENT
2016, vol. 17 (2)
contents
review article
Jan Gajewski, Joanna Mazur-Różycka
The H-reflex as an important indicator in kinesiology....................................................................................... 64
ph y sic a l ac t i v i t y, f i t n e s s a n d h e a lt h
Janusz Maciaszek, Damian Skrypnik, Marzena Ratajczak, Rafał Stemplewski, Wiesław Osiński,
Paweł Bogdański, Edyta Mądry, Jarosław Walkowiak, Joanna Karolkiewicz
Two aerobic exercise programs in management of back pain among middle-aged obese women:
a randomized controlled study..............................................................................................................................72
Maria Teresa da Silva Pinto Marques-Dahi, Flavio Henrique Bastos, Ulysses Okada de Araujo,
Cinthya Walter, Andrea Michele Freudenheim
Verbal instructions on learning the Front-Crawl: emphasizing a single component or the interaction
between components?............................................................................................................................................ 80
Aleksandra Stachoń, Jadwiga Pietraszewska, Anna Burdukiewicz, Justyna Andrzejewska
The differences in fat accumulation and distribution in female students according to their level
of activity................................................................................................................................................................87
Erika Zemková
Differential contribution of reaction time and movement velocity to the agility performance
reflects sport-specific demands............................................................................................................................. 94
Alan Queiroz Rodrigues, Fábio Mendonça Martins, Alexandre Carvalho Barbosa,
Pedro Scheidt Figueiredo, Márcia Oliveira Lima, Edgar Ramos Vieira
Effects of an eleven-week Pilates exercise program on progressive-speed walking capacity
in sedentary young women: a pilot study............................................................................................................102
Mohsen Shafizadeh
Movement coordination during sit-to-stand in low back pain people..............................................................107
biomechanics and motor control
Jonathan Sinclair, Stephanie Dillon
The influence of energy boost and springblade footwear on the kinetics and kinematics of running..........112
Michał Staniszewski, Przemysław Zybko, Ida Wiszomirska
Evaluation of laterality in the snowboard basic position...................................................................................119
Psychological diagnostics of sport an d exercise
Marcelo Odilon Cabral de Andrade, Guilherme Figueiredo Machado, Israel Teoldo
Relationship between impulsiveness and tactical performance of U-15 youth soccer players....................... 126
Publishing guidelines – Regulamin publikowania prac........................................................................................ 131
63
HUMAN MOVEMENT
2016, vol. 17 (2), 64–71
The H-reflex as an important indicator in kinesiology
doi: 10.1515/humo-2016-0018
Jan Gajewski1 *, Joanna Mazur-Różycka2
1
Faculty of Physical Education, Józef Piłsudski University of Physical Education, Warsaw, Poland
Department of Biomechanics, Institute of Sport – National Research Institute, Warsaw, Poland
2
Abstract
Hoffmann’s reflex (the H-reflex) is an electrically-induced reflex that is analogous to the mechanically-induced stretch reflex
controlled by the spinal cord. The most common measurements performed concerns the electrical response of the soleus muscle
to electrical stimulation in the tibial nerve. The response is induced by a discharge occurring in a motoneuron. The latency time
of the H-reflex for the soleus muscle amounts to 30–40 ms. An increase in the strength of the stimulus induces a direct response
from the muscle, i.e. the M-wave. The latency time of the M-wave amounts to 4–5 ms. A sport training may affect the parameters of the H-reflex. The Hmax/Mmax ratio is the highest among persons engaged in endurance sports and the lowest among
those practising speed and strength sports. A decreased amplitude of the H-reflex characterises the level of the central fatigue,
while a decrease in the M-wave amplitude is attributed to the peripheral fatigue. Usually, a decrease in Hmax/Mmax ratio is
observed post-exercise. Different times of recovery were reported in the literature. No clear quantitative laws have yet been established that govern the course of the reflex as a result of fatigue. The H-reflex still remains within the scope of the interests of
kinesiology as a valuable source of information about the reflex functions in the human motor system.
Key words: H-reflex, M-wave, central fatigue, peripheral fatigue, electrical stimulation
Introduction
According to Górski [1], a reflex is a ‘relatively stereotypical response to an involuntary, specific sensory
stimulus that takes places through the central nervous
system’. The stimulus travels from the receptor to the
effector along the reflex arc, which comprises a receptor,
an afferent pathway, a reflex centre, a efferent pathway,
and an effector. Based on the number of interneurons
that are involved in transmitting the stimulus between
a sensory neuron and a motor neuron, reflexes can be
divided into ‘polysynaptic’ and ‘monosynaptic’ types.
Polysynaptic reflexes involve at least one interneuron
(e.g., the flexor reflex). In turn, monosynaptic reflexes
do not involve an interneuron, which means that the
stimulus travels through only one synapse on its way from
the receptor to the effector. Examples of monosynaptic
reflexes are the ‘stretch reflex’ and ‘Hoffman’s reflex’.
Hoffman’s reflex
Hoffman’s reflex (the H-reflex) was described by
Paul Hoffmann in 1910 as an electrically-induced reflex that is analogous to the mechanically-induced
stretch reflex controlled by the spinal cord. The assessment of the H-reflex is an objective method for characterising the stretch reflex. The main difference between
* Corresponding author.
64
the H-reflex and the stretch reflex is that the former
omits the muscle spindles, which makes the reflex a good
tool for assessing the modulation of the activity of a monosynaptic reflex in the spinal cord. However, an interpretation of the H-reflex should take into account the
presynaptic inhibition (PSI) [2], as this may significantly affect the response of the H-reflex (resulting in
a lowered amplitude), especially in adults. Researchers
have also observed that the PSI may reduce the amplitude of the H-reflex in the soleus muscle when the
patient sits up or stands from a lying position [3, 4].
The most common type of measurement performed
concerns the response of the soleus muscle to stimulation in the tibial nerve [4–6]. This measurement was
used to describe the course of the H-reflex modified
after Aagaard et al. [7] (Figure 1).
When the tibial nerve is stimulated transdermally
with a short, low-intensity electrical impulse, the type
Ia sensory afferent fibres are the first to be excited due
to their large diameter (2). This stems from the fact that
the higher the diameter of a nerve fibre is, the lower its
excitability threshold will be in response to an electrical
current. The excitation and the electrical response of
a muscle are induced by a discharge occurring in a motoneuron, as long as a sufficient number of afferent fibres
are activated. The latency time of the H-reflex for the
soleus muscle amounts to 30–40 ms. The amplitude of
the H-reflex increases with an increase in the current
because a growing number of the muscle fibres and
motoneurons are activated and absorbed into a motor
response. An increase in the strength of the stimulus
HUMAN MOVEMENT
J. Gajewski, J. Mazur-Różycka, The H-reflex as an important indicator in kinesiology
Figure 1. Course of Hoffman’s reflex modified
after Aagaard et al. [7]; 1 – the M-wave,
1* – the antidromic wave, 2 – action potential elicited
in the sensory Ia afferents, 3 – the H-reflex
Figure 2. Recruitment curve of the H-reflex
and the M-wave for the soleus muscle during stimulation
of the tibial nerve
induces a response from the motor (efferent) fibres of the
nerve, which in turn induces a response from the muscle,
i.e. the M-wave (1). The latency time of the M-wave
amounts to 4–5 ms. A further increase in the current will
lead to an increase in the amplitude of the M-wave up
to the maximal value; whereas the H-reflex decreases
until it disappears completely. The main cause for this
phenomenon is the antidromic wave (1*), which is formed
through a stimulation performed with a high current
that travels along the nerve fibres toward the spinal cord
and causes a collision with the stimulus that induces the
H-reflex. As a result, the H-reflex disappears. Based on
these phenomena, the recruitment curve of the H-reflex
and the M-wave can be drawn, as is presented in Figure 2.
A further increase in the intensity of the stimulus may
also induce what is referred to as the F-wave, at a latency
that is similar to that of the H-reflex. The F-wave occurs as a secondary response to the volley of antidromic
action potential in the activated motor nerves.
The recruitment curve is most frequently used to
analyse the maximal amplitude of the M-wave (Mmax),
Figure 3. Example of an EMG reading of the M-wave and
the H-reflex; A – artefact of the stimulus, B – the point
at which the M-wave crosses the baseline, C – the point
at which the amplitude of the H-reflex crosses the baseline
which represents the activation of the motor pool, i.e.,
a maximal stimulation of the muscle. The M-wave can
also be used to determine the level of the patient’s peripheral fatigue [8]. Another parameter that can be useful for the analysis is the maximal amplitude of the
H-reflex (Hmax), which may help to assess the number of motoneurons that have been activated by means
of the reflex under a set of given conditions. The amplitude of the H-reflex characterises the level of the patient’s
central fatigue [9]. In turn, the ratio of these two parameters (Hmax/Mmax) indicates the proportion between
the motoneurons that can be activated by means of the
reflex and the total number of neurons in the motor pool.
In addition to the aforementioned parameters which
can be determined based on the recruitment curve, other
frequently analysed parameters include the latency
time [10]. In the literature, the latency time is usually
measured from the artefact of the stimulus to the deflection from the baseline [11]. However, the point at which
the deflection from the baseline occurs is usually difficult to establish. As one example, Mazur-Różycka et al.
[12] measured the latency time first from the artefact
of the stimulus (A) to the point at which the M-wave
crosses the baseline (B), and then from the H-reflex (C)
to the baseline (the blue line at 0, see Figure 3).
Measurement of the H-reflex
The activity of the soleus muscle is determined using
an EMG, usually with two bipolar surface electrodes
located about 2 cm from each other, as SENIAM (Surface ElectroMyoGraphy for the Non-Invasive Assessment of Muscles) recommends.
Before the electrodes can be placed on the muscle, the
skin should be shaved, exfoliated, and washed with
a disinfectant. Any impedance between the electrodes
should not exceed 5 k . The grounding electrode is usu65
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Figure 4. Placement of the electrodes
(stimulating and reference) [12]
ally placed on the head of the fibula. To elicit the H-reflex and the M-wave, the tibial nerve is then stimulated in the popliteal fossa. Usually, a 1-ms square-wave
stimulus pulse is delivered [12, 13]. In order to obtain
the recruitment curves for the H-reflex and the M-wave,
the current is systematically increased by 1.2–2.5 mA
every 5–8 s until the H-reflex disappears, after which
time it is increased by 5–10 mA until the Mmax is reached.
Some researchers [14] prefer to increase the impulse
by 0.05 or 0.10 MT (the threshold intensity for inducing an M-wave). However, this requires the threshold
to be determined first.
During the stimulation, the cathode (1.5 × 1.5 cm)
is placed at the stimulation site in the popliteal fossa
(Figure 4), and the anode (5.0 × 8.0 cm) is placed over
the patella.
Measurement position
One factor that should be taken into special consideration is the patient’s position during the measurement,
which may significantly affect the parameters of the
H-reflex [15]. Therefore, the angles at the hips, knees
and ankle joints, as well as the position of the head, and
the audio and visual stimuli must be standardised for
this measurement. The H-reflex is usually measured
with the patient in a sitting or lying position [16–18].
However, some researchers have also used a standing
position [19]. Kimura [20] states that a lying position
(0° flexion at the hip joint, 30° flexion at the knee joint,
and 30° plantar flexion at the ankle joint) will help to
keep the muscles of the lower leg relaxed. Research has
also shown that a plantar flexion of the ankle joint will
increase the excitation of the motor pool for the soleus
muscle; whereas a dorsiflexed ankle joint will impede
66
the motor pool [21]. The amplitude of the H-reflex can
also be affected by the tonic neck reflexes [22], which
is why the head should be supported during the measurement.
Despite this, the literature remains inconclusive on
what measurement position is the most suitable for the
assessment of the H-reflex. Measurements of the excitability of the soleus muscle are variously taken in a prone
[18, 23], supine [24], sitting [25], or a standing position
[26], as well as in a standing position while wearing
unstable footwear [27]. This study involved measurements in two positions (a standing position with an equal
load on both legs and a prone position). However, previous studies found that when a standing position was
used instead of a lying one, the amplitude of the H-reflex either decreased [3, 28] or remained the same [29].
A higher amplitude of the H-index (Hmax) was observed
in a lying position than in a standing one, which is consistent with studies conducted by other authors [30].
Kim et al. [19] observed a lower ratio of the Hmax and
Mmax (Hmax/Mmax) in a standing position (under
a load on both one leg and on two legs) than in a lying
position. This effect was also confirmed in the first stage
of this study. Furthermore, research has observed changes
in the amplitude of the M-wave which so far have not
been clearly confirmed under resting conditions [19, 31].
The results of this study confirm those obtained in
a study by Takahara et al. [31], in which all of the measured parameters (Hmax, Mmax, and Hmax/Mmax) were
significantly lower in a standing position than in a prone
one. However, it has been speculated that the above
measurement could not have been conducted until the
Mmax was reached, which may have been affected by
a high current in the final stage of the research that
caused the patients’ discomfort.
The excitability of the motoneurons may change under
different conditions, depending on spinal and supraspinal factors. The effect of these factors is considered as
one of the main causes for the decrease in the H-reflex
amplitude in the soleus muscle when the patient is standing or walking, as this decrease takes place through a presynaptic inhibition [32]. Other sources of H-reflex inhibition can include changes to the entry points of the
peripheral muscle receptors of the feet or the receptors
of the ankle joint [33, 34]. The literature also indicates
that swaying has an effect on the amplitude of the Hreflex when measurements are conducted in a standing
position. The differences in such amplitudes reached as
much as 14% [35]. Furthermore, studies have indicated
that a decrease in the Hmax/Mmax ratio in the soleus
muscle (by about 12%) occurs when the patient is bent
forward, compared to a backward bend in a standing
position [36]. The research results provided in the literature clearly indicate that the measurement position has
a considerable effect on the parameters of the H-reflex, and prove that a standing position is not suitable
for the measurements. This is especially true for those
HUMAN MOVEMENT
measurements conducted after exercise, when the patient may find it difficult to maintain a stable (unchanging) position during the subsequent measurements
due to fatigue.
It seems that the most comfortable position for the
patient during the measurement of the H-reflex in the
soleus muscle is the prone position, with the head resting
against a special depression in the couch, and the upper
limbs aligned symmetrically, parallel to the trunk.
Applications of the Hoffman’s
reflex measurement
The M-wave can be induced in any muscle in which
a nerve is available for stimulation. However, inducing
the H-reflex in many muscles is either impossible or is
very difficult. The most commonly assessed muscles
of the lower limbs that display the H-reflex are the soleus
muscle [37–40] and the gastrocnemius muscle [5, 41, 42].
While the H-reflex is very difficult to observe in the
upper limbs, some researchers have shown that it can
be induced for the flexor carpi radialis muscle [43–47]
and the extensor carpi radialis muscle [43, 46].
Many factors can affect the parameters of the H-reflex. Among them are the patient’s gender [48], age [49,
50], measurement position [25, 51], muscle tension [52],
body temperature [53] and the anthropometric parameters [11, 54]. The highest changes in the H-reflex parameters were observed in patients over the age of 40 years.
Due to differences in their body build, measurements
conducted in women require a higher current to induce
the H-reflex, which may cause discomfort to the patients.
Furthermore, the latency time of the H-reflex is longer in
men than it is in women, primarily due to the length of
the lower limbs. Additionally, Murata et al. [23] suggest
that among women, the Hmax/Mmax ratio can be more
variable, for instance due to the menstrual cycle. A significant positive correlation between the latency time
and the lower limb length has also been confirmed [11].
The H-reflex is frequently measured to assess the
monosynaptic reflex in, among others, stroke patients
[47] and healthy persons who do not engage in sport
[39, 55]. Researchers have also observed that sport training may affect the parameters of the H-reflex; hence,
a growing number of studies in the literature are being
conducted among athletes engaged in endurance sports
[37], as well as in speed and strength sports [5, 41]. For
example, Casabona et al. [41] compared the H-reflex
in the soleus and gastrocnemius muscles between persons engaged in speed and strength sports (sprinting and
volleyball) and persons not engaged in a sport. They
observed that the mean Hmax/Mmax ratio was lower
among the former group than among the latter one,
which was caused by a lower amplitude of the H-reflex.
Maffiuletti et al. [37] compared persons who were engaged in speed and strength sports with persons engaged
in endurance sports, and with persons not engaged in
a sport. They observed that the Hmax/Mmax ratio was
the highest among the persons engaged in endurance
sports and was the lowest among the persons engaged
in speed and strength sports.
Other studies have investigated changes in the amplitude of the H-reflex during various types of contractions of the soleus muscle [56, 57]. In one study, Duclay and Martin [58] obtained the lowest value of the
Hmax/Mmax ratio during the eccentric contraction;
whereas the isometric and concentric contractions
showed a similar ratio. The effect of different types of
training on the amplitude of the H-reflex has also been
researched. Aagaard et al. [7] analysed the amplitude
of the H-reflex following a 14-week programme of endurance training. They observed a 20% increase in the
amplitude of the H-reflex following the training. Furthermore, Vila-Chã et al. [59] compared the effect of
a 3-week strength training and endurance training programme. They found that the amplitude of the H-reflex
for the soleus muscle increased significantly among those
persons who participated in strength training. However, no such increase was observed among the persons who participated in endurance training. Further
attempts have been made to analyse the parameters of
the H-reflex during a drop jump from a particular height
among persons of different ages [40, 60, 61]. Other studies have analysed the changes in the amplitude of the
H‑reflex during different times of day. No daily changes
were observed for the responses of the flexor carpi radialis muscle [44–46]. On the other hand, the soleus
muscle showed a significant increase in the amplitude
of the H-reflex after 12 hours [45]. This indicates that
future research on the H-reflex in the soleus muscle
should be conducted under the same conditions and
at the same time of day for all of the participants.
As was mentioned above, the M-wave can be used to
determine the level of the patient’s peripheral fatigue
[8], which is defined as a post-exercise reduction in
the ability of muscles to create power or strength [62,
63]. Peripheral fatigue builds up gradually during exercise [64], and depends on the duration and intensity
of the exercise [65]. Moreover, during short, intense
exercise, the amplitude of the M-wave decreases. This
effect may be caused by a decrease in the sodium and
potassium ion gradients between the membranes due
to the activation of the sodium-potassium pump. During
long-term exercise, changes in the course of the M-wave
are minute, which suggests that the excitability of the
sarcolemma has an insignificant role in limiting the
efficiency of such activity. Froyd et al. [66] showed that
during exercises of different intensities, the neuromuscular functions decreased during the first 40% of the
duration of the exercise, and then gradually returned
between 1–2 minutes after the exercise. Froyd et al. confirmed the results obtained by other authors [67, 68] and
suggested that measurements of the muscle functions
should be conducted directly after exercise. However,
67
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some other studies have recommended conducting measurements during breaks of 3–10 minutes between periods of exercise [69] or between 2–4 minutes after the
exercise [70].
The analysis of the literature showed that the maximal amplitude of the H-reflex during rest amounts to
about 50–60% of the maximal amplitude of the M-wave
for the soleus muscle [16, 52], and to 25% for the gastrocnemius muscle [51]. Maffiuletti et al. [37] conducted
a study on the excitability of the soleus muscle among
persons who engaged in endurance sports, persons who
engaged in speed and strength sports, and persons who
did not engage in a sport. They showed that the amplitude of the H-reflex was the highest among those persons
who engaged in endurance sports (4.15 ± 2.99 mV) and
was the lowest among those who engaged in speed and
strength sports (2.37 ± 0.98 mV). Also, the mean amplitude of the M-wave was the highest among the persons who engaged in speed and strength sports (6.86
± 3.57 mV) and the lowest among those who engaged in
endurance sports (6.24 ± 4.45 mV). No significant differences were found between the values of the analysed
parameters within the individual groups of persons. However, a statistically significant difference in the Hmax/
Mmax ratio occurred between the persons who engaged
in endurance sports and those who engaged in speed
and strength sports. The amplitude of the H-reflex among
the persons who did not engage in a sport amounted
to 5.70 ± 2.50 mV [71]. The Hmax/Mmax for this
group amounted to about 45% [71] or 51% [72], although
some studies reported a value of about 70% [30]. Some
authors also reported that the H/M ratio decreased
after exercise [73, 74]. However, in most studies, either
the MVC test [74–76] or the jump up on an inclined
surface test [40] were used as the exercise that the participants were asked to perform.
The parameters of the H-reflex change significantly
as a result of physical exercise. Usually, no changes in the
amplitude of the M-wave following exercise are observed
compared to the measurements taken during rest [8,
77]. However, some studies report that long periods of
exercise with a low strength of contraction can affect
the amplitude of the M-wave [78, 79]. In these cases, the
amplitude decreased directly after the exercise and
then quickly returned to its resting values 10 minutes
afterwards [80].
The literature also widely discusses changes in the
amplitude of the H-reflex after various types of exercise
[81]. For instance, a 30-minute walk on a treadmill was
shown to cause a short-term decrease in the amplitude
of the H-reflex among healthy adults [82]. Phadke et
al. [83] also observed the effect of a 20-minute walk
on the decrease in the amplitude of the H-reflex due
to the presynaptic inhibition. Studies also show that
the amplitude of the H-reflex may return to its resting
values as quickly as a minute following exercises on
a cycloergometer [57].
68
Conclusions
It is surprising that, despite the 100-year history of
research that has been performed on the H-reflex, no
clear quantitative laws have yet been established that
govern the course of the reflex as a result of fatigue. The
studies have often provided contradictory results. The
reason for this can be assumed to stem from the presynaptic inhibition, which depends to a great extent on
the different research conditions. There are also many
unexpected external factors that can affect the H-reflex, such as the phase of the menstrual cycle in women,
the position of the patient’s head during the measurement, or other movements performed by the patients.
Perhaps the affinity between the H-reflex and the stretch
reflex is only qualitative, since in the case of the former
(in contrast to the latter) an electrical stimulus activates the afferent as well as the efferent pathways. Antidromic waves appear and cause echoes. Therefore, the
observed response of the motoneuron is not as simple
or as obvious as in the stretch reflex. All these facts indicate that this phenomenon requires further research.
Nonetheless, the H-reflex still remains within the scope
of the interests of kinesiology as a valuable source of
information about the reflex functions in the human
motor system. The parameters of the H-reflex constitute potential indicators that could help researchers to
differentiate between the overlapping effects of central and peripheral fatigue.
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Paper received by the Editor: May 15, 2016
Paper accepted for publication: June 17, 2016
Correspondence address
Jan Gajewski
Akademia Wychowani Fizycznego
Józefa Piłsudskiego
ul. Marymoncka 34
00-968 Warszawa, Poland
e-mail: [email protected]
71
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2016, vol. 17 (2), 72–79
Two aerobic exercise programs in management
of back pain among middle-aged obese women:
a randomized controlled study
doi: 10.1515/humo-2016-0016
Janusz Maciaszek1, Damian Skrypnik2 *, Marzena Ratajczak3, Rafał Stemplewski1,
Wiesław Osiński1, Paweł Bogdański4, Edyta Mądry5, Jarosław Walkowiak6,
Joanna Karolkiewicz3
1
Department of Physical Activity Science and Health Promotion, E. Piasecki University School of Physical Education in Poznan,
Poznan, Poland
2
Department of Internal Medicine, Metabolic Disorders and Hypertension, Poznan University of Medical Sciences, Poznan, Poland
3
Department of Biochemistry, Physiology and Hygiene; E. Piasecki University School of Physical Education in Poznan, Poznan, Poland
4
Department of Education and Obesity Treatment and Metabolic Disorders, Poznan University of Medical Sciences, Poznan, Poland
5
Department of Physiology, Poznan University of Medical Sciences, Poznan, Poland
6
Department of Pediatric Gastroenterology and Metabolic Diseases, Poznan University of Medical Sciences, Poznan, Poland
Abstract
Purpose. Back pain is a frequent symptom in the obese. The purpose of the study was to compare the effects of two training programs on the reduction of back pain among obese women. Methods. The study included 30 obese women who reported back
pain within 3 months preceding enrollment. The subjects were randomly allocated to endurance or endurance/resistance
physical exercise. The intensity of the exercise was adjusted to 50–80% of HRmax. Back pain intensity, muscle strength of knee
flexors and extensors, body balance and body composition were measured before and after training. Results. Both the endurance
and endurance/resistance training exerted positive effect on back pain (p < 0.05). Similarly, in both training groups the significant
increase in the strength of knee flexors and marked reduction of body fatness (p < 0.001 for all) was documented. However,
the interventions’ efficacies in selected groups did not differ. Conclusions. Both endurance and combined endurance/resistance
training exert positive effect in decreasing back pain and improving functional fitness of obese women. Therefore, both forms
of training may be recommended for individuals dealing with the abovementioned problems.
Key words: aerobic exercise, back pain, obesity
Introduction
Most back pain-related problems are non-specific
[1, 2], and a small percentage (5 to 10%) of people suffering from back pain develops chronic pain. Heavy
physical strain, repetitive lifting, age, gender, smoking,
static positioning, previous history of pain, physical
fitness, and genetics have also been well-documented
risk factors for back pain [3]. Hue et al. has also reported
on different spinal pathology-associated problems in
obese persons [4].
The relation between obesity and functional impairment of the spine, secondary to weakness and stiffness
of muscles, leading to back pain while performing such
daily activities as standing up, walking, getting up,
and running was already observed [5–8]. A significant
positive association between Body Mass Index (BMI)
and risk of low back pain was found in analyses adjusted
for age, education, work status, physical activity at work
and in leisure time, smoking, blood pressure, and serum
lipid levels [9]. Also Payne et al. [10] indicated that physical fitness (muscle strength and endurance) and participating in physical activity were significantly higher among
72
people with no history of low back pain than among
those who suffered from this condition.
The multitude of potential causes of back pain inspires research on various forms of its prevention and
treatment. Regular physical exercise and good mental
health both have a protective effect. Physical exercise
helps to keep the musculoskeletal system in shape and
maintain psychological well-being [11]. Physical exercises may, in some aspects, be particularly important for
women. Groessl et al. observed that exercising women
had larger decreases in depression and pain and larger
increases in energy than men [12].
The issues related to the rehabilitation of the spine
were also studied by Demirel et al. [13], Sorensen et al.
[14], and Lewandowski et al. [15], who suggested that
physical exercise accompanied by other therapeutic
methods, e.g. education or physiotherapy, can markedly
reduce the demand for pharmacotherapy. As noted by
Oldervoll et al. [16], increased body awareness is one
of the consequences of programs designed to promote
physical fitness, which may be of therapeutic importance
in the treatment of every type of pain [17]. On the other
hand, some authors have failed to find any effect of
physical exercise on musculoskeletal pain [18–20]. Still,
it is difficult to determine which specific components
are the most important for back pain reduction.
HUMAN MOVEMENT
J. Maciaszek et al., Exercise in back pain among obese women
Measurements
Grønningsæter et al. [18] did not find any correlation
between pain reduction and increased aerobic fitness.
This suggests that aerobic fitness has no important role
in musculoskeletal pain management. On the other hand,
Larivière et al. [21] claim that poor aerobic fitness is an
important factor in the development of musculoskeletal
pain. To the best of our knowledge, there are no studies
comparing two different training programs aimed at
reducing back pain in obese women.
The purpose of the present study was to compare the
effects of two aerobic training programs on reduction
in back pain among obese women. One exercise program
was aimed exclusively at improving cardiovascular fitness (aerobic fitness), whereas the other was designed to
improve both muscle strength and fitness. Thanks to the
anticipated stronger effect of resistance exercise on muscle
tone, it was possible to assume that this training program
will be more effective in improving the functional ability
of obese women than endurance exercise. Furthermore,
strengthening the muscles should help reduce back pain.
Measurements of muscle strength of the knee flexors
and extensors were taken on a UPR-02 A/S chair with
Moment II by Sumer software (Sumer, Opole, Poland)
[22, 23]. The study was performed in a sitting position,
with stabilization of the thighs and trunk. Isometric
knee extension torque was measured in both settings,
with a knee joint angle of 90° (0° full extension) and
a hip angle of 85° (0° supine position).
Balance was measured by having the subject stand
on one leg with eyes open (balance with feet fixed). The
subject stood on one leg for as long as she could, or until
she was told to stop. Timing started when the foot was
lifted, and stopped when the foot was placed on the
ground, a hand touched a chair/table, the foot moved,
or 30 seconds have passed.
Fat mass (%) was determined by impedance analysis
using a Bodystat analyzer (1500 MDD; Bodystat, Isle of
Man, UK).
Material and methods
Procedure
The 3-month intervention consisted of a physical exercise program including 3 sessions of training per week
(on Mondays, Wednesdays, and Fridays). A total of 36
training sessions were carried out in each group. Training was performed under the supervision of a qualified
fitness instructor and at least one investigator in a professional training room. Subjects were divided randomly
into two groups (E – endurance and M – mixed) based
on the type of training. Group E underwent endurance
training on cycle ergometers (Schwinn Evolution,
Schwinn Bicycle Company®, Boulder, Colorado, USA).
Training sessions consisted of a 5-minute warm-up
(stretching exercises) of low intensity (50–60% of maximum heart rate); 45-minute training, at an intensity
between 50–80% of maximum heart rate; 5-minute
cycling without load, and 5-minute closing stretching
and breathing exercises of low intensity. Group M underwent endurance–resistance training which consisted of
Informed written consent was obtained from all subjects, and the study was approved by the Ethics Committee of Poznan University of Medical Sciences, registered as case no. 1077/12 with supplement (no. 753/13).
The privacy rights of human subjects were observed. The
study was carried out in accordance with the revised Declaration of Helsinki of 1975 and the Uniform Requirements for Manuscripts Submitted to Biomedical Journals. The study included 30 subjects aged 37 to 62 years
(mean 49.8). We identified women who had reported
obesity (BMI above 30 kg/m2) and had incidence of back
pain during the last 3 months prior to the beginning of
study (Table 1). The participants were randomly assigned
to Group E – with endurance intervention (N = 15) or
Group M – mixed, endurance with resistance exercises
(N = 15).
Table 1. Baseline characteristics of the study subjects
Parameters
Group
M
Min.
Max.
SD
F
p
Height (cm)
E
M
160.8
162.8
161
164
152
151
170
173
5.3
6.3
2.43
0.130
Weight (kg)
E
M
94.3
93.2
90
93
74
72
155
117
20.2
13.9
0.01
0.904
BMI (kg/m 2)
E
M
36.2
35.3
34
34.7
31.4
30.1
52.1
42.7
5.4
3.8
0.24
0.626
Fat (%)
E
M
47.8
46.5
45.8
46.2
40.5
37.5
62.9
55.8
5.4
5.11
0.85
0.362
BMI – body mass index
73
HUMAN MOVEMENT
a 5-minute warm-up (stretching exercises) of low intensity (50–60% of maximum heart rate), a resistance component, an endurance component, cycling without
load, and closing exercises. The resistance component
involved 20 minutes of resistance exercise with a neck
barbell and stability ball. To allow muscle power to
regenerate, the resistance component was variable and
repeated regularly every week. On Mondays, upper limb
exercises were performed with a neck barbell; Wednesday sessions involved spine-stabilizing exercises, deep
muscle strenghening exercises, and balance-adjusting
exercises with a stability ball; on Fridays, lower limb exercises with a neck barbell were carried out. The exercises
were repeated in series. The number of repetitions of each
exercise in the series was dependent on the subjects’
muscle strength and was equal to the number of repetitions performed correctly. The number of repetitions
was systematically increased with increasing subjects’
muscle strength. Between the series of resistance exercises, 10–15 second regeneration pauses were taken, during
which subjects performed isometric exercises. Directly
after the resistance exercises, subjects underwent 25 minutes of endurance exercise on cycle ergometers (Schwinn
Evolution, Schwinn Bicycle Company®, Boulder, Colorado, USA) of intensity between 50–80% of maximum
heart rate, 5 minutes of cycling without load, and 5 minutes of closing stretching and breathing exercises of low
intensity. Heart rate during training was monitored with
a Suunto Fitness Solution® device. Both training programs
were comparable in exercise volume, and varied only in
the nature of the effort.
Statistical analysis
Statistical analyses were performed using Statistica 10
software (StatSoft Inc, Krakow, Poland) and statistical
significance was defined as p < 0.05. ANOVA was used to
determine the significance of differences between groups
E and M, and for the analysis of variation between pretest and post-test results. The analysis with two levels
of the first factor (within-subject factor: “training” –
pre-test, post-test) and two levels of the second factor
(between-subject factor: “group” – endurance or endurance-resistance training) was performed. For interaction
effects, the Eta2 ( 2) effect size was calculated. Eta2 can be
defined as the proportion of variance associated with
or accounted for by each of the main effects, interactions,
and error in an ANOVA study. It was also used to calculate the power of significant effects. Pearson chi-square
statistics were used to identify the potential differences
between the kinds of training and changes in back pain.
Results
Changes in the well-being of the studied women represented important response to training. Both types of
training, endurance and endurance/resistance, exerted
beneficial effect on back pain (p < 0.05). Similarly, in both
training groups the significant increase in the strength
of knee flexors and marked reduction in body fatness
(p < 0.001 for all) was documented. However, the interventions’ efficacies in selected groups did not differ. As
many as 70% of women participating in the training reported a lack of pain during the three months of the program, and 30% still complained of back pain (Table 2).
Neither of the two types of training caused significant changes in the strength of knee joint extensors
(Figure 1 and 2). Furthermore, we did not observe significant intergroup differences with regard to this variable.
In contrast, the significant (p < 0.01) increase in the
strength of knee flexors (Figures 3 and 4) was documented in both training groups (F = 23.30, p = 0.001, 2 = 0.45
for the left, and F = 18.48, p = 0.001, 2 = 0.39 for the
right leg). Therefore, the effect of the training proved to
be strong but limited to muscles which are usually considered as weaker, i.e. the knee flexors rather than the
extensors.
Participants from groups E and M did not differ significantly in terms of training-induced changes.
No significant results of the training were documented
in body balance measured during the single stance test.
The time of standing on both left (Figure 5) and right
(Figure 6) leg did not change significantly in response
to either training regimen. Moreover, we did not observe significant differences in body balance response
to endurance and endurance/resistance training.
The fact that participation in both training programs
was reflected by marked reduction in body fatness (F =
25.90, p = 0.001, 2 = 0.48) and BMI (F = 33.12, p =
0.001, 2 = 0.55) should be considered positive, as baseline analysis of body composition revealed high values of this parameter in the studied women. However,
the results of the training in groups E and M proved
equally positive (Figure 7 and 8).
Table 2. Changes in back pain after different kinds of training
Group
Group E (N = 15)
Group M (N = 15)
Total
p
74
Pre
Post
Pain
Lack of pain
Pain
Lack of pain
Chi2
p
100% (N = 15)
100% (N = 15)
100% (N = 30)
0% (N = 0)
0% (N = 0)
0% (N = 0)
66.7% (N = 10)
73.3% (N = 11)
70.0% (N = 21)
33.3% (N = 5)
26.7% (N = 4)
30.0% (N = 9)
19.1
22.03
p < 0.05
p < 0.05
p > 0.05
p > 0.05
p > 0.05
p > 0.05
HUMAN MOVEMENT
Main ef f ect: F=3.88, p=0.058, 2=0.12
Interaction ef f ect: F=0.45, p=0.503, 2=0.02
120
130
115
125
110
120
105
115
110
95
Torgue (Nm)
Torgue (Nm)
100
90
85
80
75
100
95
85
65
80
60
Pre
Main effect: F=23.30, p=0.001,
75
Group "E"
Group "M"
Post
Figure 1. Changes in the muscle strength of the left knee
extensors among women from “E” and “M” groups
70
Post
Group "E"
Group "M"
Main ef f ect: F=18.48, p=0.001, 2=0.39
=0.45
2
=0.01
52
48
50
46
48
44
46
42
44
Torgue (Nm)
50
40
38
36
34
42
40
38
36
34
32
32
30
30
28
26
Pre
Figure 2. Changes in the muscle strength of the right knee
extensors among women from “E” and “M” groups
2
Interaction effect: F=0.01, p=0.917,
Torgue (Nm)
105
90
70
55
Main effect: F=0.79, p=0.379, 2=0.02
Interaction efect: F=0.04, p=0.837, 2=0.01
28
Pre
Post
26
Group "E"
Group "M"
Pre
Post
Group "E"
Group "M"
Figure 3. Changes in the muscle strength of the left knee
flexors among women from “E” and “M” groups
Figure 4. Changes in the muscle strength of the right knee
flexors among women from “E” and “M” groups
Main effect: F=0.02, p=0.889, 2=0.01
Interaction effect: F=0.22 p=0.636, 2=0.01
Main effect: F=0.36, p=0.548, 2=0.01
Interaction effect: F=0.53, p=0.468, 2=0.02
33
32
32
31
31
30
29
29
28
28
Balance (s)
Balance (s)
30
27
26
25
24
27
26
25
23
24
22
23
21
20
22
Pre
Post
Group "E"
Group "M"
Figure 5. Changes in the body balance on left leg
among women from “E” and “M” groups
21
Pre
Post
Group "E"
Group "M"
Figure 6. Changes in the body balance on right leg
among women from “E” and “M” groups
75
HUMAN MOVEMENT
52
Main effect: F=33.12, p=0.001, 2=0.55
Interaction effect: F=0.43, p=0.513, 2=0.01
Main ef f ect: F=25.90, p=0.001, 2=0.48
40
51
39
49
38
48
37
BMI (kg/m2)
Fat (%)
50
47
46
45
44
36
35
34
43
33
42
32
41
40
31
Pre
Post
Pre
Group "E"
Group "M"
Post
Group "E"
Group "M"
Figure 7. Changes in the body fatness among women
from “E” and “M” groups
Figure 8. Changes in BMI among women
from “E” and “M” groups
Additional information was obtained by the use of
multiple regression analysis. Because of the multifactorial
determinants of back pain, attempts to introduce all
measured variables to the regression equation were being
undertaken. The most optimal model built to measure
changes in back pain includes 9 variables, but only
changes in these variables “the body balance on left leg
before training”, “pain on the left side before training”,
“BMI before training”, “changing in strength of the right
knee flexor after training” are, though rather small, statistically significant (Table 3). Correlation of changes in
the dependent variable (pain) with the observed independent variables is small, on the verge of statistical significance (R = 0.842, F = 2.98, R 2 = 0.709, p = 0.045).
pain in women from groups E and M, marked improvement documented in most participants from both groups
should be considered as a highly positive finding. We did
not confirm the hypothesis that endurance training combined with resistance exercise exerts more favorable effects on functional fitness and reduction in back pain
than purely endurance training. Nevertheless, women
from groups E and M obtained higher well-being scores,
better fitness and lower body fatness. It should be regarded
as a positive determinant of their health [24]. As shown
by Brown et al. [25], each reduction of body weight is
associated with lower risk of headaches, back pain, difficulty in sleeping, as well as with higher scores in physical functioning and general health. On the other hand,
high values of body mass index and prevalence of emotional problems may predispose women to both acute
and chronic low back pain [26], and participation in
physical exercise exerts positive effect in terms of stress
reduction. The role of physical activity in mitigating
back pain risk is shown to be considerable in the overweight and obese populations [27, 28].
Discussion
Our findings raise hopes for better functioning of
obese women with concomitant back pain. Although
we expected significant differences between the effects
of both training regimens on subjectively assessed back
Table 3. Most appropriate multiple regression model for the disappearance of back pain ( ) among all women trainees.
Takes into account all the variables studied
Source
Constants
Body balance on left leg before training
Changing after training of the right knee extensor strength
Left knee extensor strength before training
Pain on the left side before training
BMI before training
Fat% before training
Changing after training of the right knee flexor strength
Right knee extensor strength before training
Changing after training of the left knee flexor strength before training
R = 0.842, F = 2.98, R 2 = 0.709 p = 0.045
BMI – body mass index, SEB – standard error of coefficient, B – Beta coefficient
76
B
SEB
p
0.85
0.02
0.01
0.01
0.75
0.16
0.09
0.03
0.01
0.01
1.21
0.01
0.01
0.01
0.28
0.06
0.05
0.01
0.01
0.01
0.498
0.011
0.134
0.409
0.023
0.019
0.107
0.021
0.080
0.153
HUMAN MOVEMENT
Therefore, the fact that 33.3% of overweight women
from group E and 26.7% from group M declared the back
pain had disappeared should be considered a positive
effect of both types of training. However, the participation in the exercise programs might not have been the
only factor that induced such positive changes among
obese women. The increase of muscle strength in exercising women constitutes potential biological rationale
for such interpretation. The changes were especially pronounced in the case of relatively weaker muscle groups,
i.e. the knee flexors (F = 14.34, p = 0.001, 2 = 0.26 for
the left, and F = 11.78, p = 0.001 for the right leg). This is
a very positive effect of the training since the imbalance
between flexor and extensor strength can lead to improper muscle tonus, incorrect positioning of the lower
limbs, and consequently pelvis and spine [29, 30]. Therefore, increased strength of these weaker muscles should
be considered positive as it can be reflected by lower
intensity of back pain. Dystonia muscle strength and
concomitant disorders of balance can additionally predispose to decreased levels of everyday physical activity
in obese individuals and be associated with back pain.
Although we did not observe direct improvement of
static balance, this aspect of functional fitness should
be included in future experiments.
Taking into account the improvement of the frame
of mind among obese women with back pain, we assumed
that the proposed programs of training should enhance
a reduction in body weight. The findings of Shiri et al.
indicate that both obesity and a low level of physical activity are independent low back pain risk factors [31].
They suggest that a moderate level of physical activity is
recommended for the prevention of low back pain, especially in obese individuals. Therefore, reduction in body
fat was included among the endpoints of the present
study. Although some researchers put into question the
association between overweight, weight reduction, and
back pain reduction. Garzillo and Garzillo [32] revealed
a possible association between obesity and low back pain
only in the upper quintile of obesity, and Brooks et al. [33]
confirmed no evidence of a temporal relationship between changes in weight and low back pain. Nevertheless, our findings suggest that reduction in body weight
is associated with decreased prevalence of back pain. Also
Heuch et al. [9] observed a significant positive association between BMI and low back pain risk. Weight reduction is associated with decrease in low back pain [24].
The loss of every kilogram of body weight translates into
lower load on the lower limbs and spine. This in turn
facilitates maintaining correct posture during various
activities of daily living.
Conclusions
It is to conclude that both endurance training and
combined endurance/resistance training exert a positive
effect leading to decreased back pain and improved
functional fitness of obese women. Therefore, both forms
of training may be recommended for individuals affected by the abovementioned problems.
Authors contribution
JM and JK conceived the study, made substantial contribution
to its design, performed the statistical analysis and drafted the
manuscript. DS and MR participated in the design of the
study and helped to draft the manuscript and carried out
the data acquisition. JW, WO and RS critically revised the
manuscript for important intellectual content. PB and EM
coordinated the study and critically revised the manuscript
for important intellectual content. All authors have read
and approved the final manuscript.
Statement of Interests
The authors report no personal or funding interests. All authors declare no actual or potential conflict of interest including any financial, personal or other relationships with
other people or organizations within three years of beginning
the submitted work that could inappropriately influence, or
be perceived to influence, their work.
Acknowledgements – funding sources
The study was funded by Poznan University of Medical Sciences and by the University School of Physical Education in
Poznan. No commercial funder provided financial support
for the conduct of the research and/or preparation of the article and played no role of the sponsor in study design; in the
collection, analysis and interpretation of data; in the writing
of the report; and in the decision to submit the article for
publication.
Disclosures
The authors received no financial support for the research or
authorship of this article. No commercial party having a direct financial interest in the results of the research supporting this manuscript has or will confer a benefit on the authors
or on any organization with which the authors are associated.
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Paper received by the Editor: March 10, 2016
Damian Skrypnik
Department of Internal Medicine,
Metabolic Disorders and Hypertension
Poznan University of Medical Sciences
ul. Szamarzewskiego 84
60-569 Poznan, Poland
79
HUMAN MOVEMENT
2016, vol. 17 (2), 80– 86
Verbal instructions on learning the Front-Crawl:
emphasizing a single component or the interaction
between components?
doi: 10.1515/humo-2016-0017
Maria Teresa da Silva Pinto Marques-Dahi1 *, Flavio Henrique Bastos1,
Ulysses Okada de Araujo1, Cinthya Walter2, Andrea Michele Freudenheim1
1
2
School of Physical Education and Sport, University of São Paulo, São Paulo, Brazil
Federal University of Maranhão, São Luis, Brazil
Abstract
Purpose. In Front-Crawl swimming stroke, the interaction between two of its components, i.e. arm stroke and breathing, affects
the performance of the motor skill as a whole and therefore can be considered a critical aspect of the skill. The purpose of our
study was to investigate if a verbal instruction emphasizing this interaction could lead to learning gains when provided along
with video demonstrations. Methods. Participants (children) were randomly assigned to three experimental groups according
to the type of verbal instruction provided. Component and Interaction groups received their specific instructions along with
video demonstrations of a model execution of the Front-Crawl. The Control group watched the same video, but received no
further instruction concerning the movement pattern. In the Acquisition phase (AQ) all groups performed 160 trials (organized
in 4 sessions) of the task that consisted in swimming 8 meters the Front-Crawl at a comfortable velocity. To assess learning gains,
a retention test (RT) and a transfer test (TR) were carried out one week after the end of the AQ. Results. Regarding RT and TR,
the one-way ANOVA on the movement pattern score showed a significant difference between groups, with post-hoc tests revealing that the Interaction group achieved higher score than the Control group. Conclusions. The results reveal that enhancing
aspects of a video demonstration with verbal instruction improves learning gains of the Front-Crawl in children. Additionally,
the results suggest that providing verbal instructions about the interaction between stroke and breathing might promote learning
gains, compared to providing instructions about the stroke component individually.
Key words: motor learning, swimming, observational learning, verbal cues, ecological validity
Introduction
Efficient performance in Front-Crawl swimming has
been associated with the pattern of interaction between
the action of the arms in skilled swimmers [1]. The importance given to this pattern of interaction is due to
the fact that it modifies the ability to produce propulsion
and, therefore, a swimmer’s efficient forward movement [2]. Although the arm stroke is the most investigated component of the Front-Crawl, since it produces
about 90% of the swimming propulsion [3, 4], when
considering the learning process it is essential to take
other components into account [5]. A beginner, still
refining their movement pattern, performs relatively
inefficient body movements that generate more hydrodynamic resistance compared to an experienced
swimmer [6].
In addition to emphasizing aspects that have the potential to produce hydrodynamic resistance, considering that the other components, besides the arm stroke,
can also emphasize effects that one component has on
another one, i.e. interdependence between them. For
example, breathing can affect arm stroke efficiency [7–9].
In less skilled swimmers, breathing affects the relation-
80
ship between the arms increasing the discontinuity of
the forward movement [8, 9]. Furthermore, breathing
can modify the timing [7], as well as the symmetry
between arm strokes [8] in less proficient swimmers.
Thus, considering the importance of the stroke to produce propulsion, the interdependence between arm
strokes and breathing is a determinant factor of the
swimming performance. In this sense, one could argue
that the learning of the Front-Crawl could be enhanced
by the use of instructions highlighting the interaction
between these two components. Moreover, if the aim
of an instruction is to convey information about ‘what
to do’and ‘how to do it’ [10], critical elements should be
included to guide the learner towards an optimal movement pattern. In the case of the Front-Crawl, although
breathing and arm strokes can be clearly identified as
important components because of their contribution
to performance (as shown above), it is not clear how
they should be addressed during the learning phase is
taking place. In other words, although their importance and interdependence is hard to question when it
comes to performance, it is not clear whether the interaction between them is a critical element worth
highlighting when the motor skill ‘Front-Crawl’ is being learned.
Studies investigating the effect of verbal instructions
on the learning of complex motor skills are scarce. Regarding the effect an instruction emphasizing interac-
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M.T. da Silva Pinto Marques-Dahi et al., Verbal instructions on the front-crawl
tion can have in the learning of a motor skill, Masser
[11] conducted an experiment to investigate the effects
of two verbal instructions (cues) on the learning of the
forward roll. Two groups received either of the two cues:
‘forehead on knees’ or ‘keep yourself in a tight ball’.
Both groups practiced the motor skill for three weeks
and were tested two months after the end of an acquisition period (retention test). The group receiving the cue
considering the interaction between two body parts
(‘forehead on knees’) showed superior performance in
the retention test. In a study carried out by Wulf and
Weigelt [12], participants learned how to perform oscillatory movements on a ski simulator. Specifically,
the task involved moving the platform of the ski simulator rhythmically as far as possible left and right. The
results indicated that the group receiving verbal instruction about the mechanical principles of the skill (the
moment one should apply force on the platform to maximize performance) showed lower movement amplitude (worse performance) than the group without this
instruction. The authors concluded that providing verbal
instruction about mechanical principles of the skill considered difficult to verbalize can be detrimental to learning. In this sense, in order to be effective, a verbal instruction should not only highlight the critical aspect
of the skill being learned, but also be structured in a way
that is meaningful to the learners.
The aim of the present study was to investigate whether a verbal instruction emphasizing the critical aspect
of the Front-Crawl – i.e. the interaction between arm
stroke and breathing – could lead to learning gains
when provided along with video demonstrations. Specifically, the verbal instruction emphasizing the component ‘stroke’ was given to the Component group
and the verbal instruction emphasizing the interaction between the components ‘stroke’ and ‘breathing’
(i.e. the moment during the stroke cycle in which the
two components can be meaningfully linked together)
was given to the Interaction group. Both groups received
these instructions in addition to watching a video demonstration of the model task execution. Control group
watched the same video, but received no further instruction concerning the movement pattern that characterizes the Front-Crawl.
Considering the interdependence between the components ‘stroke’ and ‘breathing’, we expected to observe
better learning (retention and transfer) of the movement pattern for the Interaction group. Furthermore, we
expected that both groups receiving verbal instruction
(Interaction and Component), in addition to the video
demonstration, would show better learning (retention
and transfer) of the movement pattern than the group receiving only the video demonstration (Control group).
Participants
An invitation to participate in the research was
published in a local newspaper of Atibaia city – State
of São Paulo – and in leaflets distributed to private
and public schools. We employed the following inclusion
criteria: chronological age between 12 and 13 years, no
prior experience with the task, and ability to perform
basic aquatic skills: buoyancy, submersion and blow
bubbles with the whole face in the water – respiratory
control [13]. Reporting fear of water was an exclusion
criterion. Out of the group of 90 children attending
the initial meeting, 53 agreed to participate, but only
21 took part in the study submitting consent forms
signed by their parents. There was only one dropout
during the study, totaling 20 participants (8 boys and
12 girls, mean age = 12 years old, SD = 0.63) – Figure 1.
The study was approved by the local Ethics Committee of the School of Physical Education and Sport –
University of São Paulo.
Figure 1. Flow diagram of participants’ recruitment
Task and procedures
Participants were randomly assigned to three experimental groups, according to the verbal instruction provided. The Component group received instruction emphasizing only the ‘arm stroke’ component: ‘push the
water back with your hand’. This verbal instruction was
the same employed by Freudenheim et al. [14].
The verbal instruction provided to the Interaction
group based on the one received by the Component group,
but emphasized the interaction between ‘stroke’ and
‘breathing’. Specifically, the moment in the stroke cycle
81
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in which breathing should take place was added to the
previous instruction: ‘push the water back with your
hand and, at the end of the arm stroke, turn your face to
breathe’. Participants allocated in the Control group did
not receive any verbal instruction concerning the movement pattern. It is important to clarify that the instructions mentioned above were provided in Portuguese using
ordinary expressions.
All groups, including the Control group, watched
a video that showed a model execution of the Front-Crawl
Stroke. This procedure was adopted considering the evidence supporting that verbal instruction combined with
demonstration leads to more learning gains than when
provided alone [15–18].
The experiment consisted of four phases: Entry Test
(ET), Acquisition phase (AQ), Retention Test (RT) and
Transfer Test (TR). Each AQ and test trials consisted in
leaving an underwater platform, swimming 8 meters
using the Front-Crawl at a comfortable velocity, and
finishing the trial touching the edge of the pool. Participants performed all AQ and test trials individually,
and were recorded performing the task with a camera
Sony Cyber-Shot DSC-H9 (640 × 480 @30Hz) positioned
laterally to the direction of the forward movement.
Before starting the ET, all participants were familiarized with the research environment by swimming freely from the starting platform to the edge of the pool
three times. The ET consisted of five trials in which the
participants were told to swim using the Front-Crawl
– ‘as they knew it’ – without watching the video demonstration. This procedure aimed to ensure that all participants were in a similar condition before starting
the experiment. The AQ consisted of four practice sessions (AQ1, AQ2, AQ3, and AQ4), ranging from two
to three times a week, according to the availability of
each participant. Each session comprised eight sets of
five trials, with twenty seconds of interval between
the trials and two minutes between the sets. In the AQ
all groups performed a total of 160 practice trials.
Before starting each practice session, participants
completed three trials of familiarization as described
above, and then the video demonstration was shown
three times. The verbal instructions for the Component and Interaction groups were provided at the beginning of each AQ session, between the video presentations and in the interval before each set of trials.
At the end of each AQ session the Borg Scale of Perceived Exertion was applied to verify the fatigue level
of the participants. Participants in the Interaction and
Component groups were also asked to complete an attention questionnaire at the end of each session in which
they answered ‘yes’ or ‘no’ to the question whether
they had paid attention to the instructions provided.
After the last practice session (AQ4), participants were
instructed not to practice the task for a week. The RT,
performed after this one-week interval, consisted of
10 trials with the same procedures as in the AQ but without any verbal instruction concerning the movement
82
pattern or video demonstration. Before the test, participants were asked to recall the verbal instructions and
video demonstrations provided in the AQ to perform
the Front-Crawl. The TR began fifteen minutes after the
end of the RT and followed the RT procedures, with no
verbal instruction or demonstration provided. However,
in the TR all participants were asked to swim as fast as
possible in each trial, performing two sets of five trials
and resting for one minute between the trials and five
minutes between the sets. After the TR, participants’
height and weight were measured.
Measures
The score regarding the movement pattern (Score)
and the time needed to complete the task were the dependent measures of interest. The Score was obtained
from a Front-Crawl Stroke checklist [19]. The referred
checklist includes an additional item that allows the
evaluation of the head position, and removes items related to water entry, buoyancy and movement combinations from the originally proposed checklist. These
changes aimed a better evaluation of the Front-Crawl
stroke by minimizing items related to water adaptation.
Arm propulsion and arm recovery were also merged
in one new item that evaluates arm actions. Therefore,
the checklist used in this study appraises the actions
of five components of the Front Crawl: body position,
head position, breathing, arm actions and leg actions.
The recordings of the third and fourth stroke cycle
of each trial were analyzed according to the checklist.
Each Front-Crawl component was assessed and rated on
a scale rating ranging from 1 to 5, corresponding to the
least efficient movement pattern and the most efficient
movement pattern, respectively. The percentage of occurrence of each rating, in each block, was multiplied by
its corresponding relative ratio, from one to five, resulting in 5 values, one for each component. The Score
of each participant, varying from a minimum of 100
to a maximum of 500, was produced by the sum of
these 5 values, per block of trials.
All recorded trials were analyzed by one swimming
coach expert using the above mentioned checklist. To
evaluate intra-observer reliability, the expert reassessed
all the ET trials one month after the first assessment. Reliability was measured with the Inter-Observer Agreement procedure – IOA – resulting in an agreement of 0.90.
The time needed to complete the task was registered
by the experimenter with a digital chronometer, beginning when the participant left the starting platform
and finishing when they reached the edge of the pool.
Data analysis
Homogeneity of variance (Levene’s test) and sphericity (Mauchly’s test) were verified before performing all
analyses. One-way ANOVAs with repeated measures
were performed for both dependent measures, for each
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group and block (sessions) of the AQ (AQ1-AQ4) to verify
performance improvements in practice. One-way
ANOVAs were also performed for the anthropometric
measures and for both dependent measures to compare groups in each test (ET, RT and TR). Sequential
t-tests with False Discovery Rate correction [20] were
employed as post hoc tests. Significance level was set
at = 0.05.
The Borg Scale of Perceived Exertion data did not
meet the assumptions for parametric analysis and the
Kruskal-Wallis tests were carried out to verify the differences in perceived exertion. Data were organized,
analyzed and plotted using R, a language and environment for statistical computing [21].
Results
Complementary measures
With respect to the attention questionnaire, all participants of the Component and Interaction groups reported paying attention to the verbal instructions provided.
No differences between groups were detected in perceived exertion (Borg Scale of Perceived Exertion) in
any experimental phases, indicating comparable fatigue
for all groups. Additionally, no differences between
groups were found regarding height or weight, indicating that those anthropometric measures were similar
for all groups.
Figure 2. Movement pattern Score in all experimental
phases – ET: entry test; AQ1-AQ4: first to fourth
acquisition blocks; RT: retention test; TR: transfer test.
# significant differences between blocks;
* significant differences between groups
Movement Pattern
No differences were found between groups in the ET,
F(2,17) = 0.06, p > 0.05, ² < 0.01, indicating equivalent movement pattern at the beginning of the experiment for all groups (Figure 2).
With respect to the AQ, no differences were detected between blocks of trials in the Component or
the Control group, F(4, 24) = 1.98, p > 0.05, ² = 0.18 and
F(4, 20) = 2.4, p > 0.05, ² = 0.20, respectively. Conversely, a difference between blocks was detected in the Interaction group, F(4, 24) = 9.12, p < 0.05, ² = 0.39. The
post hoc test revealed a lower Score in the first block
compared to all the remaining blocks, indicating that
participants in the Interaction group enhanced their performance in the AQ. As shown in Figure 3, throughout the AQ, the Interaction group ceased receiving ‘1’
(the lowest rating in the movement pattern checklist),
reduced the percentage of ‘2’ and ‘3’ and increased the
percentage of ‘4’ and ‘5’, suggesting a distinctive improvement in the movement pattern for this group
compared to both Control and Component groups.
With regard to the RT, one-way ANOVA on the Score
found a significant difference between groups, F(2, 17)
= 3.72, p < 0.05, ² = 0.30, with post hoc tests revealing that the Interaction group achieved higher Score
than the Control group. As can be seen in Figure 3, the
Figure 3. Percentage of ratings assigned to participants
(ranging from 1 to 5) in all experimental phases –
ET: entry test; AQ1-AQ4: first to fourth acquisition blocks;
RT: retention test; TR: transfer test
Interaction group showed a lower percentage of low and
intermediate ratings (‘1’, ‘2’ and ‘3’) and a greater percentage of higher ratings (‘4’ and ‘5’) compared to the
other groups. Similar results were found for the TR.
Specifically, a difference between groups was found,
F(2, 17) = 4.49, p < 0.05, ² = 0.34, and post hoc tests
indicated that the Interaction group achieved higher
Score than the Control group. Despite the lack of statistical significance between the Score in the RT and
the TR (Figure 2), a qualitative comparison between the
Interaction and the Component groups reveals that the
Component group showed a greater percentage of lower
ratings compared to the Interaction group (Figure 3),
which suggests a better movement pattern of the participants in the Interaction group.
83
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Figure 4. Mean time needed to complete the task
in all experimental phases – ET: entry test; AQ1-AQ4:
first to fourth acquisition blocks; RT: retention test;
TR: transfer test
Time to complete the task
With respect to the ET, one-way ANOVA found no
differences between groups in the time needed to complete the task, F(2, 17) = 0.11, p > 0.05, ² = 0.01, indicating that all participants began the experiment swimming with similar efficiency. With regard to the AQ,
repeated measures ANOVA found no differences in the
time needed to complete the task for the Component
and Interaction groups, F(4, 24) = 2.13, p > 0.05, ² =
0.06 and F(4, 24) = 1.04, p > 0.05, ² = 0.01, respectively. A difference between blocks was found for the
Control group, F(4, 20) = 5.29, p < 0.05, ² = 0.13, but
the post hoc test was unable to locate the differences.
Although the Interaction group showed better performance compared to both Control and Component groups
in the RT and TR tests (Figure 4), one-way ANOVA found
no differences between groups regarding the time needed
to complete the task.
Discussion
The present study aimed to investigate whether verbal instructions focusing on different elements of the
Front-Crawl would affect the learning of the motor skill.
The study was based upon two premises: (1) a verbal
instruction provided along with demonstrations of the
motor skill being learned is more efficient if constituted
by critical elements of this motor skill; (2) the interdependence between breathing and stroke can be considered a critical element of the Front-Crawl, given the
effect the former has on the latter [7–9]. Thus, we expected to observe better learning of the movement pattern
(retention and transfer) in the group receiving instruction about the interaction between those two components
84
(stroke and breathing) compared to receiving the instruction focusing on the stroke component alone. Furthermore, we expected that receiving verbal instruction,
in addition to the video demonstration, would lead to
better learning compared to receiving the video demonstration only (Control group).
Anthropometric measurements and the Borg Scale
of Perceived Exertion indicate that the possible effects of
the independent variable cannot be attributed to sample
heterogeneity or differences in the effort required by
each specific condition.
With regard to the AQ, results indicate that providing verbal instructions focusing on different elements
of the Front-Crawl did not affect the acquisition process
since there was no difference between groups during
this phase. However, participants of the Interaction group
improved their movement pattern between the first and
last session of the AQ, which was not observed in the
other groups. This improvement underscores the increasing number of higher ratings obtained by the Interaction group, while the other groups, despite performing the same number of trials, obtained lower ratings
during the AQ.
With regard to the RT and TR tests, the results indicate that providing verbal instruction focusing on the
interaction between arm stroke and breathing brings
learning gains compared to the presentation of the video
demonstration only. Most studies investigating the relationship between demonstration and motor performance adopted Bandura’s Social Learning Theory [22],
which suggests that learners form a cognitive representation of a motor skill through observing a model, and
that this representation subsequently guide their motor
performance. However, our results indicate that the demonstration itself does not suffice to form this cognitive
representation of the Front-Crawl. Specifically, the
group receiving only the video demonstration showed
no improvement in performance during the AQ phase,
maintaining the same level of performance during the
RT and TR tests. An explanation for this result is that
participants failed to extract the relevant information
from the model to benefit in practice. Our findings corroborate previous studies investigating the effects of providing verbal instructions and demonstrations which
showed that demonstration combined with verbal instruction leads to better learning than when provided
separately [15–18].
The combined use of instruction and demonstration as a way of guiding learners to an optimal motor
performance was shown to benefit the learner only when
part of what is being learned is already in the learner’s
repertoire [10]. Our results do not corroborate with this
statement, since all participants of the present study
had no previous experience with the experimental task
(Front-Crawl) and those who received demonstration
associated with verbal instruction showed learning gains.
One explanation for this incongruence is that the tasks
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used in previous studies [12, 23, 24] had a lower degree
of complexity compared to the Front-Crawl. In this sense,
it is reasonable to suppose that as task complexity increases, also the need of information to guide the learner
to key aspects of the skill increases.
The verbal instruction provided to the Interaction
group was longer than the one provided to the Component group. In this sense, one could argue that this
could overload the learner’s attentional resources, especially in the initial phase of learning [12]. However,
our results do not give support to this interpretation,
since the participants in the Interaction group not only
did not show any impairment at the beginning of the
AQ compared to the other groups, but improved their
movement pattern during the AQ phase, which was
not observed for the other groups.
With respect to our prediction that the effectiveness
of the instruction would depend on whether critical
aspects of the motor skill being learned are included
[11, 25], the lack of statistical difference between the
Component and Interaction groups fails to strongly
support this hypothesis. Nevertheless, a descriptive analysis of the ratings obtained by those groups – during
the AQ and both tests – suggests that the group receiving
instruction about the interaction between stroke and
breathing showed qualitatively superior movement
pattern compared to the one receiving instruction about
the stroke component only. Additionally, inferential
analysis indicated that the groups completed the task
within similar time, both during the AQ and in the RT
and TR tests. Nevertheless, descriptive results indicate
that the Interaction group needed less time to complete
the task in the TR and RT tests, which suggests that the
qualitatively better movement pattern was also the more
efficient in the displacement of the swimmer. Considered
together, these results do not rule out the hypothesis
that instructions including critical aspects of the motor
skills benefits learning, especially those with interdependence between the components, as is the case of the
Front-Crawl. This issue, in this sense, remains open and
should be tackled in future studies.
Conclusions
The results of this study clearly indicate that enhancing aspects of a video demonstration with a verbal instruction improves learning of the Front-Crawl in children, compared to providing video demonstration only.
Additionally, there were indications that providing
verbal instructions about the interaction between the
components of stroke and breathing might promote
better learning gains compared to the instructions about
the stroke component alone.
Acknowledgements
This work was supported by CAPES Foundation, Ministry
of Education of Brazil.
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Paper received by the Editor: February 18, 2016
Maria Teresa da Silva Pinto Marques-Dahi
Laboratório de Comportamento Motor
Escola de Educação Física e Esporte
Universidade de São Paulo
Av. Prof. Mello Moraes, 65, Cidade Universitária
São Paulo, Brazil
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2016, vol. 17 (2), 87 – 93
The differences in fat accumulation and distribution
in female students according to their level of activity
doi: 10.1515/humo-2016-0009
Aleksandra Stachoń*, Jadwiga Pietraszewska, Anna Burdukiewicz,
Justyna Andrzejewska
Abstract
Purpose. The appropriate percentage of body fat is essential for women’s health and biological condition. Both accumulation
of fat and distribution pattern of adipose tissue are connected with health risk, which justifies the investigation and permanent
monitoring of their diversity in different sub-populations. The aim of the study was to evaluate the percentage of body fat and its
distribution in female students representing different physical activity levels. Methods. Fat proportion was estimated with use
of classic anthropometric method and bioelectrical impedance analysis (BIA). The distribution of subcutaneous fat was calculated including waist and hip circumferences, and extremities and trunk skinfolds. The participants’ level of physical activity
was determined according to the IPAQ questionnaire. Results. Analysis showed that female students with medium level of
physical activity had 26.5 ± 5.1% of total body fat estimated by BIA, whereas in the most active females almost 3% lower total
body fat values were common. The bioelectrical impedance analysis indicated about 8% higher body fat content than classic
anthropometry. Examined skinfolds revealed a tendency to decrease with increasing physical activity. The distribution pattern
of subcutaneous fat varied according to level of activity. Conclusions. The study showed that estimation of fat content in young
women differed depending on the applied method and the level of physical activity. We emphasize the need to select adequate
reference data for measurement methods and consider the level of physical activity during fat percentage assessment. Another
conclusion is that the high level of physical activity is connected with masculinization of subcutaneous fat pattern, both in
extremities/trunk fat proportion and waist/hip proportion.
Key words: adiposity, BMI, WHR, skinfolds, body composition, female, physical activity
Introduction
Adiposity is conditioned by polygenic inheritance that
causes high susceptibility to external factors among which
diet and physical activity seem to be crucial. Maintaining an appropriate amount of body fat as well as distribution of fat tissue is important for one’s good health
and fitness [1–4]. During medical examination excessive
fatness is diagnosed mainly on the base of body mass
index (BMI), whereas a previous study showed that diagnostic values of the index varies in different subpopulations [5]. It is worth to noticed that frequencies of adiposity assessment connected with medical examination
is not adapted to the dynamics of real changes in body
composition. Moreover, the development of modern imaging techniques, allowing evaluation of visceral fat, diverted scientist’s attention from subcutaneous fatness.
However, some studies report the connection between
the pattern of subcutaneous adiposity and health risk,
which justifies the investigation of factors related to distribution of subcutaneous fat [1, 6]. The connection between adipose tissue, biological condition and physical
activity is multifarious and based on many various mechanisms [7].
The study of Kruschitz et al. [8] proved there are differences in subcutaneous fat patterns between young
athletes and non-athletes. The abovementioned findings suggest that compared to BMI levels, subcutaneous
fat patterns are a more accurate way of discriminating
between young athletes and non-athletes. In women,
the upper back and arms compartment were the best sites
to discriminate between athletes and non-athletes [8].
Subcutaneous fat topography was also proven to be
associated with decreased aerobic capacity [9].
The purpose of the study was to prove that higher
level of physical activity is connected not only with decreased total body fat and subcutaneous fat, but also
with a different subcutaneous fat distribution expressed
by the extremity to trunk skinfold ratio. We assumed
that highly active women are characterized by stronger
reduction in subcutaneous fat in the extremities compared to the trunk, which can be explained by greater
activity of the extremities than the trunk during sport
exercises as well as by the fact that trunk fatness constitutes fat storage that secures fertility in women. The
second aim of the study was to check the diagnostic value
of widely used indexes BMI and WHR in a group of
women with different level of physical activity.
87
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A. Stachoń, J. Pietraszewska, A. Burdukiewicz, J. Andrzejewska, Physical activity vs. fat distribution in women
Participants
The survey contains results from cross-sectional
anthropological measurements as well as a questionnaire
study conducted among first-year female students
(N = 255) of the University School of Physical Education
in Wrocław, Poland (Faculty of Physical Education N =
176; Faculty of Sport N = 45), and the Medical University
in Wrocław (Faculty of Health Sciences – studies in
nursing N = 34). All the students who gave their informed
consent were included in the study. The questionnaire
study enabled the authors to excluded females which
were on slimming or other special diets. Mean age of the
examined women was 20.0 ± 1.0 years. All students
underwent medical examination before they participated
in the study and were recognized as healthy. The study
was approved by the appropriate Ethics Committee.
Females were informed of the benefits and risks of the
investigation and were given an explanation of their
own data.
Measurements and procedure
The anthropometric measurements were performed
using the Martin-Saller method [10]. Body height (B–v)
was measured with an anthropometer and circumferences with an anthropometric tape (to the nearest 0.1 cm;
Siber Hegner Machinery Ltd., UK), skinfold thickness
with a Tanner/Whitehouse skinfold caliper (Holtain, UK,
with 0.2 mm graduation). Body mass was measured with
an electronic weighing scale (to the nearest 0.1 kg).
Body fat estimation was performed with a BIA-101 Anniversary Sport Edition analyzer (Akern, Italy) in the
standard conditions recommended by the producer.
Tissue analysis was performed using the Bodygram 1.3.1.
software package with the Akern analyzer.
The results of anthropometric measurements enabled the authors to assess body composition using the
classic anthropometric method. Fat mass (FM) and fat
free mass (FFM) proportions were estimated on the basis
of Slaughter–Lohman–Boileau regression equation for
body fat in females as follows [11]:
The Slaughter–Lohman–Boileau et al.
equation-calculation of body fat in females
Fat (%) = 1.33* (triceps skinfold + subscapular skinfold) −
0.013* (triceps skinfold + subscapular skinfold)2 − 2.5
The massiveness of body build and type of fat distribution around the waist and hip were estimated, according to WHO recommendations, on the basis of Body
Mass Index (BMI) and Waist-Hip Ratio (WHR) [12, 13].
The distribution of subcutaneous fat between the extremities and trunk was also estimated on the base of subcutaneous fat distribution index. Values below 100 mean
predominance of trunk fatness, and the smaller the val88
ues of this index, the larger the difference between subcutaneous adiposity of trunk and extremities.
Subcutaneous fat
=
distribution index
(triceps skinfold +
calf skinfold)
(subscapular skinfold +
suprailiac skinfold)
· 100
The participants’ level of physical activity was determined using a shorter form of the International Physical
Activity Questionnaire (IPAQ, Polish version) [14]. According to the protocol, participants were previously
divided into groups with different level of physical activity (minimally active, HEPA active = health enhancing
physical activity) [14, 15]. With regard to the high percentage of women involved in competitive sport and
a wide range of MET-min/week that characterized the
examined group of female students, we decided to divide
HEPA active women into: medium active group (1500–
3000 MET-min/week; N =76) and highly active group
(more than 3000 MET-min/week; N = 90). Minimally
active group consisted of 49 females.
Means and standard deviations (SD) of the measured
characteristics in the three groups of women were calculated. The compatibility of distribution of the analyzed
data with normal distributions was checked with the
Shapiro–Wilk test. Levene’s test was used to check the
homogeneity of variances. In order to investigate the
differences between groups of women with various
levels of physical activity, parametric statistical tests
(ANOVA, post-hoc Scheffe test) and nonparametric
tests (Kruskal–Wallis test, nonparametric test for multiple comparisons) were used. Statistical significance was
set at p < 0.05. Statistical analyses were carried out with
the StatSoft® Statistica 12 software. The obtained results were presented in charts using Microsoft ® Office
Excel 2003.
Results
The analyzed groups of women characterized by
various levels of physical activity did not differ in age
(ANOVA; F = 1.57; p = 0.2096), body height (ANOVA;
F = 1.18; p = 0.3102), BMI and WHR (Table 1). The women’s average body height (B-v) amounted to 168.1 ± 6.5 cm,
the mean BMI was 21.5 ± 2.3 kg/m2. The majority of women (87%) had normal body weight according to BMI
categories (WHO). The mean WHR was 0.735 ± 0.058,
which is a correct value for young women.
Although the groups of women did not vary significantly in body mass (ANOVA; F = 1.11; p = 0.3312),
the average body mass tends to increase as the level of
activity increases: 59.3 kg in minimally active, 59.7 kg
in medium active and 61.2 kg in highly active.
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Table 1. Body Mass Index and Waist-Hip Ratio in groups of women with various levels of physical activity
Index
Mean ± SD
ANOVA
minimally active
medium active
highly active
F
p
21.6 ± 2.5
0.73 ± 0.05
21.7 ± 2.2
0.73 ± 0.04
21.6 ± 2.5
0.74 ± 0.07
0.05
1.19
0.9551
0.3062
BMI [kg/m2]
WHR
Table 2. The thickness of skinfolds and body composition in groups of women with various levels of physical activity
Mean ± SD
Skinfolds (mm)
minimally
active
medium
active
highly active
F*/H**
p
Subscapular
Triceps
Abdomen
Suprailiac
Calf
11.9 ± 3.7
11.6 ± 3.5
15.3 ± 4.4
12.0 ± 4.6
10.0 ± 3.5
10.8 ± 3.0
9.9 ± 3.9
13.5 ± 4.8
11.9 ± 4.9
8.4 ± 3.0
9.3 ± 2.4
8.2 ± 2.9
11.4 ± 4.1
10.2 ± 3.5
7.1 ± 2.6
6.20*
26.25**
4.39*
8.61**
14.88*
0.0024
0.0000
0.0135
0.0135
0.0000
80.9 ± 4.4
19.1 ± 4.4
81.4 ± 4.9
18.6 ± 4.9
83.5 ± 3.9
16.5 ± 3.9
7.26*
7.26*
0.0009
0.0009
72.6 ± 5.3
27.4 ± 5.3
73.3 ± 5.0
26.7 ± 5.0
75.3 ± 5.2
24.7 ± 5.2
5.61*
5.61*
0.0042
0.0042
Body composition (skinfolds thickness)
FFM (%)
FM (%)
Body composition (BIA)
FFM (%)
FM (%)
* ANOVA, ** Kruskal–Wallis test
Women declaring the highest physical activity level
were characterized by the lowest subcutaneous fatness.
They had the slimmest subscapular skinfold, triceps skinfold, suprailiac and abdomen skinfolds and calf skinfold
(Table 2). All the examined skinfolds revealed a tendency
to decrease as physical activity increases (Table 2). The
differences in subcutaneous fatness between women
declaring a medium physical activity and women declaring the highest activity level amounted to 2–4 mm.
Detailed post-hoc test results are presented in Table 3.
Subcutaneous fat distribution index (SFDI) revealed a wide range of variability of fat distribution
between the trunk and the extremities (Figure 1; min
= 36.2; max = 195.2). In the majority of examined female students, the subcutaneous fat tissue of the extremities was lower than the subcutaneous fat tissue
of the trunk, which is showed by SFDI values < 100
(Figure 1). In minimally active, medium active and
highly active group this index differs significantly
(Figure 2; Kruskal–Wallis test H =17.825; p =0.0001).
The differences were most visible in minimally active
women, while the highly active women displayed similar fat pattern as medium active group (Figure 2).
HEPA active groups (medium and high level of activity) had on the average about 20% slimmer skinfolds
in the extremities than in their trunk, whereas minimally active women mostly had similar skinfolds in
the trunk and the extremities (SFDI = 95.7).
The average body fat content according to BIA in
all examined women amounted to 26.1% ± 5.2%.
Body fat (FM), estimated both on the basis of regression equations and BIA, also decreased as the level of
physical activity increased (Table 2). Women who
were the least active had 2.6% more fat mass than the
vigorously active women when using classic anthropometry, and 2.7% more when using BIA (Table 2).
Women characterized by the highest level of physical
activity were most muscular, i.e. they had more fat
free mass than the others. Detailed post-hoc test results are presented in Table 3. Individual values of fat
content estimated with BIA were higher than values
estimated with classic anthropometry. The differences
between these two methods were similar in all groups
of women (8.3% in the group of the most active women, 8.1% in the less active, and 8.2% in the least active
women).
Discussion
The multiplicity of body fat estimation methods
with their varied reference values often causes confusion. Although there is a proven moderate concordance between results obtained with different methods,
concrete results obtained with the use of these methods vary [16–18]. Frisch claims, without referring to
any particular measurement method, that more than
89
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Table 3. Detailed results of the post-hoc test of significance of differences between groups of women.
The numbers in the table are values of probability (p)
Physical activity
Variables
Subscapular skinfold* (mm)
Triceps skinfold** (mm)
Abdomen skinfold* (mm)
Suprailiac skinfold** (mm)
Calf skinfold* (mm)
FFM (skinfolds)* (%)
FM (skinfolds)* (%)
FFM (BIA)* (%)
FM (BIA)* (%)
minimally
minimally
medium
highly
0.9964
0.1386
minimally
medium
highly
0.0000
0.0186
minimally
medium
highly
0.8889
0.0376
minimally
medium
highly
0.0000
0.0133
minimally
medium
highly
0.2225
0.0006
minimally
medium
highly
0.7854
0.0042
minimally
medium
highly
0.7854
0.0042
medium
highly
0.9964
0.1386
0.0448
0.0448
0.0000
0.0723
0.8889
0.0042
0.0120
0.0120
0.7854
0.9038
0.0413
0.0006
0.0006
0.0006
0.7854
minimally
medium
highly
0.0133
0.0263
0.0263
0.2225
0.9038
0.0413
0.0376
0.0624
0.0624
0.0000
minimally
medium
highly
0.0186
0.0723
0.0042
0.0120
0.0120
0.9038
0.0413
0.0993
0.0993
0.9038
0.0413
0.0993
0.0993
* Scheffe’s test, ** Wilcoxon rank sum tests for multiple comparisons
26–28% of body fat may negatively influence the reproductive ability of young women [19]. Jeukendrup and
Gleeson classified > 30% body fat content in women
as overweight [20].
In the present study, according to bioelectrical impedance analysis, young women had an average of 26.1%
± 5.2% body fat. The fat content in women varied significantly depending on the level of their physical activity. Less active women had little more than 27% body
fat, whereas the most active women had almost 3% less
fat. Applying BIA in their study, Lukaski et al. [21] estimated body fat in American women at 25.1%, whereas
it amounted to 26.05% in young Polish women examined by Major-Gołuch et al. [22]. The body fat content
estimation in Polish women obtained with the use of DXA
method was slightly higher (32.05%) [22]. In a South
African study, healthy active women were shown to
90
have 29.5% of body fat with the use of the DXA method,
but 28.4% with the use of the near infrared reactance
method [16], whereas resistance-trained women from
the same study had 10% less body fat and 5.5% less
body fat, respectively [16]. Scientists from the Czech
Republic showed that elite female players in five different sports games were characterized by 20–21% of fat
mass in BIA 2000 M estimation [23], whereas in the
study of Buśko and Lipińska female volleyball players
had about 29% body fat according to BIA with use of
Tanita TBF-300 [24]. According to Lugito et al., female and
male students with low-moderate activity had a higher
risk of obesity compared to students with high activity
[25]. Friedl et al. revealed a 2% decrease in body fat content after a few weeks of combat training in women [26],
whereas Gutin et al. proved that the lower adipose tissue
level was mainly connected with the frequency of vigor-
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50
Number of females
45
40
35
30
25
20
15
10
5
<
45
.0
45
.1
-5
5.
0
55
.1
-6
5.
0
65
.1
-7
5.
0
75
.1
-8
5.
0
85
.1
-9
5.
0
95
.1
-1
05
10
.0
5.
111
5.
11
0
5.
112
5.
12
0
5.
113
5
.0
13
5.
114
5
.0
14
5.
115
5.
15
0
5.
116
5.
0
>
16
5.
1
0
Subcutaneous fat distribution index
Figure. 1. The distribution of subcutaneous fat distribution
index between the extremities and the trunk
in the examined female students
100,0
90,0
95,7
Subcutaneous fat
distribution index
80,0
78,5
70,0
77,3
60,0
50,0
40,0
30,0
20,0
10,0
0,0
Minimally active
Medium active
Highly active
Level of physical activity
Figure 2. Subcutaneous fat distribution index in groups
of women with various levels of physical activity
(it reflects the relation between the extremities
and trunk subcutaneous adiposity)
ous activity, but not with the intensity of moderate activity,
which was also revealed in our study [27]. A similar suggestion was made by Ness et al., who claimed that physical activity of higher intensity may be more important for
obesity prevention than the total amount of activity [28].
Fat content in women estimated in the present study
on the basis of skinfolds thickness was lower by about
8% compared with the BIA results. The women under
study who were the most physically active featured the
thinnest skinfolds. The differences in skinfold thickness
between the most and the least active women amounted
to 2.0–4.0 mm and were the most visible in triceps,
abdomen and calf skinfolds. According to Kruschitz
et al., in women, the upper back (subscapular) and arms
(triceps) skinfolds provided the strongest discrimination between athletes and non-athletes [8]. In another
study the thickness of subscapular skinfold as well as
total body fat also correlated negatively with the level
of physical activity connected with sport or work [16, 29].
According to Washburn et al., students who declared
higher physical activity than their counterparts had simultaneously significantly lower values of triceps skinfold thickness and resting heart rates [30]. However,
on the basis of our previous study we concluded that
athletes doing some sports were characterized by a higher
content of body fat, as compared with active but not
trained women [31].
Despite the differences in body fat content between
more active and less active female students, they did not
differ significantly in the aspects of body massiveness
(BMI) and shape (WHR). This proved that the mentioned indices are not appropriate tools to estimate fat
content in this group of women. It was probably caused
by some diversification of the aforementioned features
in the studied group. The vast majority of subjects (90%)
had normal weight according to WHO [12]. Fewer than
3% of women had WHR values outside the range of
0.7–0.8, which means that they had correct body fat distribution between the waist and hips for their sex and
age [13]. The use of body weight and BMI as a diagnostic
tool for overweight and obesity has been previously
criticized, particularly in athletic populations [32]. Unlike
in the female students in the present study, the increasing BMI values together with the decreasing level of physical activity were found in middle-aged adults [28, 33].
Choi et al. indicated a protective role of increased physical activity in women whose body weight increases
during the midlife years [34].
The evaluation of fat distribution on the basis of the
subcutaneous fat distribution index revealed that the
subcutaneous fatness pattern in the trunk and the extremities strongly depends on the level of physical activity. We noticed that a high level of physical activity
mainly reduces fat accumulation in the extremities, because trunk fatness constitutes fat storage that secures
fertility in women. However, with regard to WHR, it is
worth mentioning that there is a slight tendency toward
increased android fatness in the most active women,
which means that the very high level of physical activity
promote the reduction in fat accumulation in the place
specific for women, i.e. the hips. Trichopoulou et al. indicated earlier that WHR values strongly depended on the
level of physical activity in males, but not in females, which
was probably caused by the hormonal influence [35].
The subcutaneous fat distribution index revealed
a wide range of variability of fat distribution between
the trunk and the extremities. In the majority of examined
female students the subcutaneous fat tissue of the extremities was lower than the subcutaneous fat tissue
of the trunk. Values of SFDI less than 100 mean predominance of trunk fatness, and the smaller the values of this
index, the larger the difference between subcutaneous
adiposity of the trunk and the extremities. The mentioned tendency can be interpreted as masculinization
of subcutaneous fat pattern [36]. In previous studies
females were characterized by thicker extremities skinfolds comparing to males, whereas the thickness of trunk
skinfolds were similar in both sexes [37].
In this study the pattern of subcutaneous fat distribution among trunk and extremities depends on the level
of activity. The differences between trunk and extremities skinfolds were more visible in HEPA women (me91
HUMAN MOVEMENT
dium and highly active). Their subcutaneous fat distribution index (about 77–78) was lower than in minimally
active group (about 95), which made their fatness pattern
masculinized [38]. As it was proven in our previous
study, the subcutaneous fat distribution index is about
50–60 in male athletes [38].
Conclusions
The results of the present study showed that estimation of fat content differed depending on the applied
method and the level of physical activity. Therefore we
emphasize that during body fat content analysis one
should pay attention to using appropriate reference values adequate to the applied method and measurement
device. The fat content estimated by bioelectrical impedance analysis is about 8% higher than the body fat
content estimated on the basis of anthropometric measurements of skinfolds. On the basis of BIA method we
showed that female students from the University School
of Physical Education and Medical University were
characterized by average amount of 26.1% ± 5.2% body
fat, whereas in the most active females about 3% lower
values of total body fat, comparing to minimally active
group, were common. We also showed that the commonly
used in medical practice indexes estimating fatness
(BMI, WHR) are not appropriate tools for evaluation
of fat tissue in this group of women. We also concluded
that high level of physical activity is connected with
masculinization of the subcutaneous fat pattern, both
in extremities/trunk fat ratio and waist/hip ratio. Reduction in fat accumulation in the extremities is more visible
because trunk fatness constitutes fat storage securing
women’s fertility.
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Paper received by the Editor: October 9, 2015
Paper accepted for publication: May 13, 2016
Aleksandra Stachoń
Zakład Antropologii Fizycznej
Akademia Wychowania Fizycznego
al. I.J. Paderewskiego 35
51-612 Wrocław, Poland
93
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2016, vol. 17 (2), 94– 101
DIFFERENTIAL CONTRIBUTION OF REACTION TIME
AND MOVEMENT VELOCITY TO THE AGILITY PERFORMANCE
REFLECTS SPORT-SPECIFIC DEMANDS
doi: 10.1515/humo-2016-0013
ERIKA ZEMKOVÁ
Department of Sports Kinanthropology, Faculty of Physical Education and Sports,
Comenius University in Bratislava, Slovakia
Abstract
Purpose. This study estimates the contribution of reaction time and movement velocity to the reactive agility time while covering
varied distances. Methods. A total of 95 athletes of karate, hockeyball and soccer participated in a simple reaction, two choice
reaction, step initiation and reactive agility test. Results. Agility time was significantly better in karate-kumite than karate-kata
practitioners when covering a distance of 0.8 m (8.2%, p = 0.045), better in hockeyball players than goalies when covering a dis
tance of 1.6 m (10.9%, p = 0.028) and better in soccer players than goalies when covering a distance of 3.2 m (14.2%, p = 0.009).
Movement velocity to agility time contributed to a lesser degree in the case of karate-kata competitors, hockeyball and soccer
players (33.5%, 28.3%, and 19.9% respectively) than the karate-kumite competitors, hockeyball and soccer goalies (44.2%, 42.7%,
and 39.4% respectively). Furthermore, both simple and two choice reaction times were highly related to the agility time when
covering distances of 0.8 m, 1.6 m, and 3.2 m (r in range from 0.72 to 0.88). Movement velocity also significantly correlated
with the agility time in the test with a distance of 0.8 m (r = 0.76) but not with longer movement distances of 1.6 (r = 0.61) and
3.2 m (r = 0.52). Conclusions. Reaction time and movement velocity differentially contribute to the agility time in athletes of
varied specializations. This reflects their specific demands on agility skills, and therefore should be addressed in agility testing
in order to identify an athlete’s weakness.
Key words: reactive agility, reaction, step initiation, testing
Introduction
For many years, agility was classified as the ability to
execute rapid movements and the capacity to stop and
restart quickly. As a result, the majority of agility research
was devoted to pre-planned and change-of-direction
speed tests. Eventually, agility tests that combined change
of direction speed and cognitive measures were developed. These reactive agility tests also included anticipation and decision-making components in response to
the movements of a tester. Sheppard et al. [1] discovered
that the reactive agility test differentiated between players
of varied performance levels in Australian football, but
traditional closed skill sprint and sprint with direction
change tests did not. As a result, agility has been redefined as a rapid whole-body movement, with change of
velocity or direction in response to a stimulus [2]. This
definition implies three information-processing stages:
stimulus perception, response selection, and movement
execution.
The first two components of agility performance can
be indirectly estimated by measuring the simple and
multi-choice reaction time. Reaction time figures prominently in the open skills required in many sports (e.g.,
boxing, ice-hockey). Take baseball, for example. The
entire trajectory of a pitch in baseball might measure
only 400 ms and the bat swing may take 120 ms to
execute. Thus, if a batter takes an extra 100 ms to detect
the speed and trajectory of the pitch, this could severely
94
limit the chance of successful contact [3]. Equally, processing delays in a sprinter or goalie may significantly
impact their performance. An individual who has the
ability to minimize such delays has an advantage in events
such as the 100-m dash or when catching a ball.
Decision time strongly influences total reactive agility
time, therefore it is important to address perceptual skills
in agility testing and training. Young and Willey [4] discovered that of the three components that make up the
total time, decision time had the highest correlation
(r = 0.77, p = 0.00) with the total time. This correlation
with total time was greater than for the response movement time (r = 0.59) or tester time (r = 0.37), indicating
that decision time is the most influential of the test components for explaining the variability in total time. The
decision time component within the reactive test condition also revealed that highly-skilled players made significantly faster decisions than the lesser skilled players
[5]. Also, as the results of Gabbett and Benton [6] demonstrate, decision and movement times in the reactive agility
test were quicker in more highly skilled players, without
compromising response accuracy. Similarly, Serpell et al.
[7] revealed a significant difference in mean time in the
sport-specific reactive agility test (RAT) between the
elite group and the subelite group of the rugby league.
Performance differences on the RAT were attributed
to differences in perceptual skills and/or reaction ability.
The review of Paul et al. [8] showed that decision-making and perceptual factors are often propositioned as
HUMAN MOVEMENT
E. Zemková, Agility: reaction plus movement time
discriminant factors; however, the underlying mechanisms are relatively unknown.
The third component of agility performance is movement execution. To evaluate the speed of step initiation,
one can perform simple or two-choice stepping reaction
test to visual stimuli. The test begins with the subject
standing on two mats placed in front of a light signal.
When the light is activated, the subject performs two
steps towards mats placed 60–70 cm apart and marked
with tape on the floor. The time from foot-off (onset of
unloading) and foot contact time (from foot-off to footcontact) is recorded. However, due to problematic issues
in reproducing the task, plus a lack of acceptable precision when compared to accurate laboratory tests, the
practicality of using the visually-triggered step initiation
test is limited, especially when quantifying slight changes
in performance of individuals and small groups [9].
Therefore, it is preferable to utilise the diagnostic system
based on a precise analogue velocity sensor with a sampling rate of 100 Hz to measure the velocity of step initiation. This measurement is more reliable and better
equipped to distinguish between individuals of different
ages and levels of physical fitness [10]. Since there is
a significant relationship (r = 0.837) between the foot
contact time (from foot-off to foot-contact) and the maximal velocity of the step, such measurement may be a viable alternative to the previous test.
Performance in the change of direction test more
strongly correlates with acceleration speed than with
maximum running speed [11–13]. Therefore, stronger
correlation may be assumed between maximal step velocity and agility time over a shorter than longer distance. Recently, Sayers [14] reported that measuring
change of direction speed over a distance of 1 m provides
an effective compromise between test reliability and the
need to discriminate change of direction ability from
high-speed linear running ability. The proportionally
weaker correlations between high-speed linear running
ability and change of direction speed measures for distance 1 m suggest that the distance over which change
of direction speed is measured currently ( 5 m) may
be too long.
Our experience also showed that athletes prefer distances shorter than 5 m, for instance soccer players 3.2 m,
badminton, basketball and hockeyball players 1.6 m, and
karate and tae-kwon-do practitioners 0.8 m, when testing agility skills [15]. It is likely that acceleration and
deceleration phases determine the agility performance
more over shorter than longer distances. However, the
reactive agility test only provides information on agility
time (AT) which includes both reaction time (RT) and
movement time (MT). From a practical perspective, estimating the contribution of these two components to the
agility performance may provide additional relevant data
for groups of athletes with diverse demands on decisionmaking and movement velocity. Such information on
both reaction and movement times may be useful when
designing training programs specifically focused on the
improvement of weak components in agility performance. To address this issue, we estimated the contribution of simple reaction time, two-choice reaction time
and step initiation velocity to the reactive agility time,
while covering different distances in athletes of varied
specializations.
Participants
Six groups of athletes volunteered to participate in
the study (Table 1). They were required to be active in
selected sports. They each had over 10 years’ experience
in particular sports with at least 7 years’ experience in
a competition. Those who met the inclusion criteria
were allocated to the study. Approximately 77% of the
athletes enrolled in the selected sports clubs participated.
They were asked to avoid any strenuous exercise for the
duration of the study. All participants were informed
of the procedures and main purpose of the study. The
procedures presented were in accordance with the ethical standards on human experimentation as stated in
the Helsinki Declaration.
Experimental procedure
Prior to the study, participants attended a familiarization session during which the testing conditions were
explained and trial sets carried out. Participants were
encouraged to practice the measurement procedure beforehand in order to eliminate unfamiliarity with the
exercise. Then they performed four tests in no particular order as follows:
In the Reaction Tests, participants were required to
respond to either one visual stimulus (simple reaction
Table 1. Characteristics of groups of athletes (Mean ± SD)
Groups of athletes
Karate-kata practitioners
Karate-kumite practitioners
Hockeyball goalies
Hockeyball players
Soccer goalies
Soccer players
n (1)
Age (years)
Height (cm)
Weight (kg)
14
22
6
17
9
27
20.3 ± 2.2
21.5 ± 3.6
23.8 ± 3.5
22.8 ± 3.1
24.1 ± 4.7
21.9 ± 2.9
171.8 ± 4.6
174.5 ± 7.3
177.7 ± 5.7
178.6 ± 6.4
178.1 ± 6.3
174.6 ± 5.1
64.5 ± 7.9
70.3 ± 6.5
74.9 ± 6.7
72.6 ± 5.7
76.0 ± 7.7
69.5 ± 6.3
95
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Table 2. Protocols of the Reactive Agility Test
Distance covered (m)
Number of stimuli (1)
Number of best responses
used for analysis (1)
0.8
40
32
1.6
3.2
20
10
16
8
time) or two visual stimuli in the form of a circle, square,
triangle or cross (2-choice reaction time) positioned on
mats on the floor. The mats had to be touched in accordance with the stimulus on the screen. Participants
were instructed to keep their legs as close as possible
to the mats in order to eliminate the influence of their
leg movements on the outcome. They performed three
trials of 40 responses in each test. Data from the best
trial of simple RT and 2-choice RT were selected for
analysis.
Reaction times were measured using a diagnostic system FiTRO Reaction Check (FiTRONiC, Slovakia) that
consists of two mats connected by means of an interface
to a computer. A special software measures the time the
subject requires to accomplish leg contact with the mat
corresponding to the stimulus on the screen. Software enables storage, analysis and extensive reporting of the data.
In the Step Initiation Test, participants performed
voluntary steps in their own time. A device (decribed
below) was anchored to the wall and tethered by a nylon
rope to the ankle of the subject. The participant was instructed to perform, as quickly as possible, the steps
while pulling the nylon tether of the device. Data from
the best of 3 trials were utilised for analysis.
Movement velocity of the step was measured using the
computer based system FiTRO Dyne Premium (FiTRONiC,
Slovakia) that consists of a sensor unit based on a precise encoder mechanically coupled with a reel. While
pulling the tether (connected to the ankle of the subject) the reel rotates and measures velocity. Signals from
a sensor unit are conveyed to the computer by means
of a USB cable. Comprehensive software allows the collection, calculation and on-line display of the basic biomechanical parameters involved in exercise. In this
study, only maximal step velocity was utilised for analysis. Previous studies showed that intraclass correlation
coefficient (ICC) was high for maximal step velocity
and moderate for mean velocity in the acceleration phase
as well as for maximal and mean acceleration [10]. In the
same way, the standard error of measurement (SEM)
was low for maximal step velocity and greater for the
remaining parameters. Taking these findings into account, maximal velocity of the step initiation, as the
most reliable parameter, was used for analysis in the
present study.
In the Agility Test, participants were required to touch
as fast as possible with either the left or the right foot
96
Groups of athletes
karate-kata and -kumite
practitioners
hockeyball players and goalies
soccer players and goalies
Table 3. Formula for calculation of the Agility Index
Movement distance (m)
Agility Index calculation
0.4
0.8
1.6
3.2
1 - (RT / AT)
1 - [RT / (AT/1.3)]
1 - [RT / (AT/1.9)]
1 - [RT / (AT/3.1)]
one of two mats located outside the two corners of a predefined square (Table 2). The mats had to be touched in
accordance with the location of a stimulus in one of the
corners of a screen. The test consisted of a pre-defined
number of visual stimuli with random generation of their
location on the screen, and the time of generation from
500 to 2500 ms, depending on the movement distance.
The result was a sum of previously determined best agility
times. In addition, the Agility Index (AI) was calculated
as shown in Table 3. The Agility Index quantifies the
relative contribution of movement time to the agility
performance. The rest is attributed to change of direction running speed and anaerobic/aerobic capacity,
when covering longer distances.
Agility time was measured by means of the computer based system FiTRO Agility Check (FiTRONiC,
Slovakia). The system consists of contact switch mats
connected by means of an interface to the computer.
A special software measures the time required by the
subject to establish foot contact with the mats, corresponding with the position of stimulus located in one of
the four corners of the screen. Software enables storage
of the data, their analyses and extensive reporting. The
reliability of the test procedure was previously verified, and the testing protocol had been standardized
by the examination of 196 participants [16]. The analysis of repeated measurements showed a measurement
error of 7.1%, which is within range comparable to
common motor tests.
The testing procedure and time of day were identical
for all subjects. All tests were carried out mid-morning.
The same experienced researchers conducted the measurements during testing sessions.
Data analysis
Data analysis was performed using the statistical
program SPSS for Windows version 18.0 (SPSS, Inc., Chi-
HUMAN MOVEMENT
cago, IL, USA). The calculation of the sample size was
conducted with = 0.05 (5% change of type I error) and
1 – = 0.80 (power 80%) and using the results from
our preliminary studies that identified variations in
agility time between athletes of different sports. This
provides a sample size of 16 for this study. However,
the sample size in three groups was below this limit,
as the inclusion criteria required participants to be active in a particular sport. Therefore, the statistical power
for a group of size n ranged from 0.74 to 0.80. To compensate for the lower power and to estimate the magnitude of the differences across these groups, the effect
size (ES) was calculated. Where statistical significance
was not established, retrospective power calculations
were performed to identify the power associated with
the comparisons in question. Power calculations were
performed on variables which showed a moderate effect size difference between these groups. The power for
each level of comparison was calculated for a given difference between two mean values and the group sample
sizes, and the standard deviation for each group mean.
Sample sizes were calculated for each group, to provide
the estimated sizes required to show a significant difference between groups in variables of simple RT, twochoice RT, maximal step velocity, and agility time.
One way analysis of variance (ANOVA) was used to
estimate significant between-group differences in simple
RT, two-choice RT, maximal step velocity, and agility
time. The level for statistical significance was set at p <
0.05. Where the results of ANOVA indicated significant
F-ratios between groups, the Scheffé test was applied
post hoc to determine in which groups the differences
occured. Sex data, determined to be normally distributed,
were analyzed in previous studies using the independent samples t-test and showed no significant differences
in agility time between men and women. Nevertheless,
only male athletes were selected for the present study.
This was because longer movement distances were used
in this study in comparison with the original version of
the agility test, which could lead to different physiological responses and consequently affect the outcome in
female and male athletes. Additionally, correlations between agility time and variables of simple RT, two-choice
RT, and maximal step velocity were assessed by calculating Pearson’s product moment (r). Data on reaction
times, movement velocity, and agility time for all examined groups are presented as the mean ± the standard
deviation (SD).
Results
Results in karate-kata and karate-kumite practitioners are shown in Figures 1 a–d. Simple RT was significantly better in karate-kumite practitioners than
in karate-kata ones (19.6%, p = 0.006; ES > 1.0). Two-choice RT was also significantly better in karate-kumite practitioners than in karate-kata ones (23.0%, p =
Kumite karateists
**
Kata karateists
a)
0
50
100
150
200
250
300
350
Simple reaction time (ms)
Kumite karateists
**
Kata karateists
b)
0
100
200
300
400
500
Tw o-choice reaction time (ms)
Kumite karateists
Kata karateists
c)
0
50
100
150
200
250
Maximal step velocity (cm/s)
Kumite karateists
*
Kata karateists
d)
0
200
400
600
800
1000
Agility time (ms)
Figure 1. Simple reaction time (a), two-choice reaction
time (b), maximal step velocity (c), and reactive agility
time in karate-kumite and -kata practitioners (d)
(*p < 0.05, **p < 0.01)
0.003; ES > 1.0). Likewise, the agility time with a movement distance of 0.8 m between the subject and the
mats was significantly better in karate-kumite practitioners than in karate-kata ones (8.2%, p = 0.045; ES
= 0.85). However, maximal step velocity did not differ
significantly between these groups (3.3%, p = 0.233;
ES < 0.2).
Results in hockeyball goalies and players are shown
in Figures 2 a–d. Goalies achieved significantly better
values than players in both simple RT (7.9%, p = 0.048;
ES = 0.75) and two-choice RT (10.3%, p = 0.031; ES =
0.93). On the other hand, agility time with a movement
distance of 1.6 m between the subject and the mats was
significantly better in players than in goalies (10.9%,
p = 0.028; ES = 0.99). Also, maximal step velocity was
significantly higher in players than in goalies (13.8%,
p = 0.017; ES > 1.0).
Results in soccer goalies and players are shown in
Figures 3 a–d. Goalies surpassed players in both simple
RT (10.3%, p = 0.034; ES = 0.85) and two-choice RT
(12.0%, p = 0.024; ES = 0.96). However, agility time
with a movement distance of 3.2 m between the subject
and the mats was significantly better in players than
in goalies (14.2%, p = 0.009; ES > 1.0). The maximal
step velocity was also significantly higher in players
than in goalies (16.8%, p = 0.007; ES > 1.0).
Further analysis showed that simple RT, two-choice
RT, and maximal step velocity were highly related to
97
HUMAN MOVEMENT
Hockeyball goalies
Soccer goalies
*
Hockeyball players
a)
0
50
100
150
200
250
300
*
Soccer players
a)
350
0
50
100
150
Hockeyball goalies
*
0
350
100
200
300
400
*
Soccer players
500
b)
0
100
200
300
400
500
Hockeyball goalies
Soccer goalies
*
Hockeyball players
0
50
100
150
200
**
Soccer players
250
c)
0
50
100
150
200
250
*
Hockeyball goalies
**
Soccer goalies
Hockeyball players
Soccer players
0
d)
300
Soccer goalies
Hockeyball players
c)
250
b)
200
200
400
600
800
1000
1200
1400
1600
0
d)
400
800
1200
1600
2000
2400
Agility time (ms)
Agility time (ms)
time in soccer goalies and players (d)
(*p < 0.05, **p < 0.01)
agility time in the test with a shorter movement distance of 0.8 m (r = 0.82, p < 0.01; r = 0.88, p < 0.01; r =
0.76, p < 0.05). While simple RT and two-choice RT also
strongly correlated with agility time in the test with
longer movement distances of 1.6 m (r = 0.76, p < 0.05;
r = 0.72, p < 0.05) and 3.2 m (r = 0.79, p < 0.05; r = 0.75,
p < 0.05), there still remains considerable variation in
the factors that contribute to performance over each
movement distance. There were no significant relations
of maximal step velocity to agility time in the tests with
movement distances of 1.6 m (r = 0.61) and 3.2 m (r =
0.52). Therefore, other factors very likely contributed
to performance, namely change of direction running velocity and anaerobic/aerobic capacity, when responding
to 20 stimuli over a distance of 1.6 m and 10 stimuli
over a distance of 3.2 m.
These findings may be corroborated by a higher
Agility Index found in karate-kumite than karate-kata
practitioners (on average 0.442 and 0.335, respectively)
(Figure 4a). Similarly, a higher Agility Index was identified in hockeyball and soccer goalies (on average 0.427
and 0.394, respectively) than in hockeyball and soccer
players (on average 0.283 and 0.199, respectively)
(Figure 4b).
0,6
0,5
0,5
0,4
0,4
0,3
0,3
0,2
0,2
0,1
0,1
0,0
2
4
6
8 10 12 14 16 18 20 22
Subjects (1)
Karate-kumite
Karate-kata
0,0
b)
Agility Index (1)
0,6
Agility Index (1)
Agility Index (1)
a)
0,6
0,6
0,5
0,5
0,4
0,4
0,3
0,3
0,2
0,2
0,1
0,1
0
Mean
3
8
13
18
23
28
33
38
43
Subjects (1)
Players
Goalies
Figure 4. The Agility Index in karate-kumite and karate-kata practitioners (a)
and players and goalies of hockeyball and soccer (b)
98
0
Mean
Agility Index (1)
time in hockeyball goalies and players (d)
(*p < 0.05)
HUMAN MOVEMENT
Discussion
Although covering the same total distance, the protocols used in the present study involved more multiple
direction changes over a short distance (40 × 0.8 m)
compared to those designed for longer distances (20 ×
1.6 m and 10 × 3.2 m). Results confirmed our assumption that the strength of the associations between maximal step velocity and agility time decrease when the
later is measured over longer distances. Relative speed
of the first acceleration step appears to be an important
component in determining change of direction ability
over short distances [14]. This is consistent with previous studies that reported acceleration as an important
aspect in agility and change of direction movement
[2, 17–19]. However, the ability to decelerate CoM rapidly
is also a key component of change of direction ability [14].
When undertaking a 180° change of direction test, deceleration movement times are extremely variable within
and between individuals, particularly when compared
with acceleration movement times [14]. Accordingly,
the change of direction tests evaluate an athlete’s ability
to rapidly decelerate and reaccelerate in the new direction. When longer distances are covered, the total running time contains both change of direction ability and
straight-line sprinting. In addition to this, agility tests
include also perceptual and decision making components. Thus, breaking down the agility performance into
two distinct phases is warranted. Recently, Sekulic et al.
[20] reported that calculating stop’n’go change of direction speed to stop’n’go reactive-agility test ratio may
allow strength and conditioning coaches to indirectly
determine the perceptual and reaction capacities of their
athletes. In the present study, higher Agility Indexes indicate the use of both sensory and motor components in
athlete’s performance, whereas their lower values signify the predominant contribution of motor abilities to
agility performance. On the basis of our research, players
may require varied agility skill training utilising motor
abilities rather than sensory functions, as the longer distances appear more reliant on change of direction running velocity and anaerobic/aerobic capabilities. On the
other hand, our results also identified the importance
of cognitive factors in reactive agility performance for
goalies and karate-kumite practitioners and suggest that
specific methods may be required for training and testing of reactive agility and change of direction speed.
Taking into account the competition area in karate,
the distance of 0.8 m between contact mats was used
for the agility test, which identified better simple and
two-choice reaction times in karate-kumite than the
karate-kata practitioners. This very likely contributed
to enhanced agility performance, as there were no significant differences in movement velocity between these
groups. The contribution of movement time to the agility
time was 33.5% in karate-kata practitioners and 44.2%
in karate-kumite ones.
These results are in accordance with previous findings, in which individual differences in each component of the Agility Test were estimated [21]. In subject 1,
multi-choice reaction time accounted for 64% of the
agility time. However, in subject 2, multi-choice reaction time accounted for only 43% of the agility time.
Interestingly, subject 1, with both longer simple and multichoice reaction times, was able to achieve better agility
times than subject 2. This was probably due to the fact
that the participant initiated his/her foot responses
more rapidly than his/her counterparts. This individual was in fact an elite karate-kata competitor, who does
not respond to any stimuli but must be able to cover
a short distance very quickly. Thus, it may be assumed
that this result reflects a sport-specific adaptation. In
contrast, despite better simple and multi-choice reaction times in subject 2, he/she was not able to transfer
this capability into improved agility performance. These
findings indicate that individual differences in agility
time (RT + MT) are greater (about 26%) than in simple
and multi-choice reaction times (18% and 9%, respectively).
In the present study, faster simple and two-choice reactions were found in hockeyball goalies than players
when covering a distance of 1.6 m and in soccer goalies
than players when covering a longer distance of 3.2 m.
In contrast, higher maximal step velocity was recorded
in hockeyball players than goalies. These differences
were even more pronounced between soccer players and
goalies. Higher movement time most likely contributed
to better agility time in both hockeyball and soccer
players than goalies. The contribution of movement
time to the agility time was lower in players of hockeyball (28.3%) and soccer (19.9%) than goalies of hockeyball (42.7%) and soccer (39.4%).
Similarly, the changes in each component of agility
performance following exercise or long-term systematic training can be evaluated. One study evaluated the
effect of soccer match induced fatigue on neuromuscular performance [22]. After the first 45 min of the
game, only dynamic balance with eyes closed was impaired, and ground contact time increased. A further
increase was observed after the second period of the
game. Along with dynamic balance with eyes open,
agility performance (in the test with shorter – 0.8 m
distance between mats) was also affected. On the other
hand, there were no significant pre-post match changes
in static balance, agility time (in the test with longer –
1.5 m distance between mats), speed of the step initiation and the soccer kick, height of the squat and countermovement jump. These findings indicate that fatigue
impairs cognitive functions more than motor functions in highly skilled soccer players.
Another study compared the effect of 6 weeks of training consisting of reaction tasks similar to game situations on wobble boards (experimental group 1) and on
a stable surface (experimental group 2) on neuromus99
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cular performance in basketball players [23]. Following
the training, there were no significant changes in simple
and multi-choice reaction times when fingers were close
to the buttons. However, a significantly shorter agility
time was identified when subjects were required to move
to the contact mats. Maximal velocity of step initiation
also significantly improved after the training. This faster movement execution most probably contributed to
the enhancement of agility performance. This assumption was corroborated by a significant correlation (r =
0.78) between the reduction in agility time and an increase in maximal velocity of step initiation after the
training. Also of interest was the additional finding that
the improvement in agility performance in older players
(21 years on average) was greater than in their younger,
less experienced counterparts (15 years on average).
These results may be attributed to more rapid feedback
control of movement execution, i.e. as the experience
level increased with practice, the movement time decreased.
These findings indicate that assessment of simple and
multi-choice reaction times, as well as movement time
or movement velocity may provide additional information on individual differences in sensory and motor
contribution to the agility performance. This would
enhance differentiation of athletes with varied demands
on agility skills, plus improve the evaluation of the efficiency of agility training.
Conclusions
The present study highlighted differential contributions of reaction time and step velocity to the agility
time, depending upon a movement distance. It appears
that cognitive and motor skills are better in karate-kumite than karate-kata practitioners, when only stepping reactions are required. In the case of longer distances,
better agility time was found in players than in goalies of
soccer and hockeyball. While the motor component
of agility performance seems to be predominant in
players in terms of faster movement execution, in goalies
it is the sensory component allowing faster decision
making. Hence, the agility test complemented with measurements of simple and multi-choice reaction times,
and movement time or movement velocity provides
additional information on agility performance in athletes with diverse demands on their agility skills. This
differential contribution of reaction time and movement velocity to the agility performance should be addressed in agility testing in order to identify an athlete’s
weakness.
Acknowledgments
This work was supported by the Scientific Grant Agency of the
Ministry of Education, Science, Research and Sport of the
Slovak Republic and the Slovak Academy of Sciences (No.
1/0373/14).
100
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Erika Zemková
Department of Sports Kinanthropology
Faculty of Physical Education and Sports
Comenius University in Bratislava
Nábr. arm. gen. L. Svobodu 9
814 69 Bratislava, Slovakia
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2016, vol. 17 (2), 102– 106
EFFECTS OF AN ELEVEN-WEEK PILATES EXERCISE PROGRAM
ON PROGRESSIVE-SPEED WALKING CAPACITY IN SEDENTARY
YOUNG WOMEN: A PILOT STUDY
doi: 10.1515/humo-2016-0011
Alan Queiroz Rodrigues1, Fábio Mendonça Martins1,
Alexandre Carvalho Barbosa2 *, Pedro Scheidt Figueiredo1,
Márcia Oliveira Lima1, Edgar Ramos Vieira3
1
Department of Physical Therapy, Federal University of Jequitinhonha and Mucuri Valleys, Diamantina, Brazil
Department of Physical Therapy, Federal University of Juiz de Fora, Governador Valadares, Brazil
3
Department of Physical Therapy, Florida International University, Miami, USA
2
Abstract
Purpose. To assess the effects of an 11-week Pilates exercise program on the functional capacity of young sedentary women.
Methods. Ten subjects underwent the shuttle walking test. A portable metabolic system was used during the shuttle walking
test to measure the maximum heart rate and VO2 max. The heart rate recovery and the predicted maximal heart rate were also
assessed. Results. The findings showed increased walking distance, maximum heart rate and heart rate recovery after completing
the protocol. The peak of VO2 was not significantly different but showed a tendency to increase, being significantly correlated
with the covered distance. Conclusions. The current results suggest that Pilates exercises significantly improve walking functional capacity.
Key words: heart rate, functional capacity, peak of VO2 , recovery
Introduction
Pilates exercises are performed on a mat or using
Pilates equipment to assist the subject to practice the
exercises properly [1]. Pilates exercises include controlled breathing, concentration, and precision of the
movement, tightening the core muscles including the
abdominals, the lumbar multifidus and the pelvic floor
muscles. The core muscles are responsible for static and
dynamic stabilization, and are associated with breath
control [2, 3]. The core muscles support the diaphragmatic
function by activating the abdominals, and helping to
increase lung volume and capacity [4].
A recent review found contradictory or inconclusive
results on the effects of Pilates exercise on pain, quality
of life and lower extremity endurance in women [3]. On
the other hand, another review found strong evidence
to support the use of Pilates exercises to improve flexibility and dynamic balance [5]. Recent studies found increased muscular endurance among subjects who started
to practice Pilates exercises compared to inactive subjects and with subjects who maintained their normal
activity routine [6, 7]. Also, some improvements in
lower limb strength and muscle endurance were found
in older adults and patients with fibromyalgia [8, 9].
Nevertheless, the effectiveness of Pilates exercises on
increasing progressive-speed walking capacity needs to
be further evaluated. Cardiopulmonary exercise (e.g. fast
102
walking) capacity can be used for exercise prescription
[10]. The shuttle walking test can be used to evaluate the
functional walking capacity of healthy and unhealthy
subjects [11, 12]. This low-cost/easily administered test
imposes a cardiopulmonary challenge; the information
on change in walking speed is communicated to the participants using an audio signal for progressive effort to
assess their functional walking capacity [10–13].
Pilates exercises are often used by health professionals (e.g. physical therapists) to treat patients, but further
studies are needed to evaluate the benefits claimed by
Pilates himself, including its potential effects on functional walking capacity [14]. Therefore, the aim of the
study was to assess the effects of an 11-week Pilates
exercise program on the functional capacity of young
sedentary women. The hypothesis was that the Pilates
exercises would increase the functional capacity of young
sedentary women.
Participants
A sample of ten healthy but sedentary young women
participated in this study (Table 1). Subjects were recruited through public call at the city of Diamantina,
Minas Gerais, Brazil. Inclusion criteria were: age between
18 and 21 years of age, weight between 50 and 70 kg,
International Physical Activity Questionnaire – IPAQ [15]
score classification as inactive or minimally active (sedentary lifestyle). The subjects were assessed by a trained
physical therapist, and the exclusion criteria were: leg
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A.Q. Rodrigues et al., Pilates exercise vs. women’s walking capacity
Table 1. Participants’ characteristics
Characteristics
Age (years)
Height (cm)
Weight (kg)
Body mass index (kg/m2)
Resting Heart Rate (bpm)
Mean ± SD
23 ± 2
163 ± 6
62 ± 13
23 ± 4
85 ± 13
length discrepancy, pregnancy, ankylosing spondylitis,
shoulder impingement, paresthesia, and absence of tendon reflexes. The local ethics committee for human
research approved the study protocol, and all subjects
signed an informed consent form prior to participation
(Protocol 531.995/CEP-UFVJM).
Figure 1. Pilates method exercises: (1) initial position, (2) final position; (A) the cat, (B) the bridge, (C) the hundred,
(D) Monkey, (E) Standing on Floor, (F) Arms up and down
Figure 2. Pilates method exercises: (1) initial position, (2) final position; (A) Climb a Tree, (B) Leg Extension,
(D) Step Down, (E) Swan Front
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Procedures
The following sequence of Pilates exercises was performed twice a week, in non-consecutive days (~ 45 minutes/session), during the 11-week protocol (22 sessions):
Mat Pilates: “the cat”, “the bridge” and “the hundred”.
Exercises on the Cadillac (Pilates exercise equipment):
“Monkey” and “Standing on Floor”; on the Reformer:
“Arms up and down” (Figure 1); on the Chair: “Step
Down” and “Swan Front”; on the Barrel: “Climb a Tree”
and “Leg Extension” (Figure 2). All the exercises were
performed using the basic Pilates breathing technique
that consists of (1) deep exhalation through the mouth
– with the lips slightly pursed, (2) inhalation through
the nose before the movement and (3) deep exhalation
through the mouth – with the lips slightly pursed –
during the movement [1].
Outcome measurements
The shuttle walking test was performed in a 10 m
course marked by two cones placed 0.5 m from each
end point. Participants went from walking to running
around the 10 m course according to the speed communicated by an audio signal. The initial walking cadence was 0.5 m/s increased by 0.17 m/s each minute.
The cadence increment was always indicated by a triple audio signal. All tests were administered by the
same trained physical therapist and all explanations
were standardized.
The shuttle walking test was interrupted when participants became unable to maintain the required speed
due to dyspnea or fatigue, or if the subject failed to complete the course within the specified time twice. The
therapists approached the subject with a chair and recorded
the total distance walked. The number of turns was
counted, and a digital chronometer AK71® (AKSO®,
St. Leopoldo, RS, Brazil) was used to measure the time.
The reduction in heart rate (heart rate recovery) during
a 1-minute long rest immediately after completion of
the test was recorded [16], and the predicted maximal
heart rate was calculated as 220 minus age (in years).
A portable metabolic system (VO2000®, MedGraphics®,
St. Paul, MN, USA) including a metabolic unit, a battery
pack, a harness, a heart rate monitor, a face mask, and
a breathing valve was used during the shuttle walking
test to measure the peak heart rate and peak of VO2 [17].
The reference values for the shuttle walking test in
women is largely explained by gender, age and BMI
using the following equation [10]: SWTpred = 1449.7 –
(11.7 × age) + (241.9 × gender) – (5.7 × BMI); where
male gender = 1 and female gender = 0. Based on the
sample demographics, the SWTpred was 1049 m.
BioEstat 5.3 software (Belém, PA, Brazil) was used for
statistical analyses. Descriptive statistics were calcu104
Figure 3. Shuttle walking test distance. A1 – 1st assessment;
A2 – 2nd assessment; * p < 0.05
lated for the participants’ characteristics. The Shapiro
Wilk test was performed to evaluate the Gaussian distribution of the data. Paired t-tests were used to compare
differences between assessments (before vs. after). Correlations were calculated using Pearson’s correlation
coefficient. All were conducted with a significance level
set at = 0.05.
Results
Figure 3 shows the increased walking distance after
completing the Pilates exercise program (mean difference 95% CI = 29 to 167 m, p = 0.005). The peak heart
rate also increased after completing the Pilates exercise
program (180 ± 17 bpm before vs. 194 ± 10 bpm after,
mean difference 95% CI = 5 to 22, p = 0.003), and the
heart rate recovery increased from 27 ± 12 bmp before
to 42 ± 12 bpm after (mean difference 95% CI = 0.7 to
29, p = 0.021).
Peak VO2 tended to increase, but the differences were
not statistically significant (30 ± 4 mL/kg/min before
vs. 32 ± 4 mL/kg/min after). Peak VO2 was also significantly correlated with covered distance (r = 0.63, p =
0.048, 95% CI = 0.01 to 0.90).
Discussion
There were no adverse effects and all subjects stated
no difficulties and a very satisfying experience in completing the Pilates exercise program. There was an improvement in heart rate recovery after the Pilates exercises
protocol. Faster heart rate recovery was also found in
aerobically trained subjects [18, 19]. Type of exercise is
an important determinant of the post-exercise autonomic
recovery and also of the effects of interaction between
sympathetic withdrawal and parasympathetic activation [20]. On the other hand, a recent study found no
difference between cardiac autonomic recovery after continuous and intermittent maximal exercise [21].
Guimarães et al. [22] assessed heart failure patients
randomly assigned to either a 16-week Pilates exercise
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program or conventional cardiac rehabilitation and, consistently with the trends observed in this study, they
found increased ventilation, peak VO2 and O2 saturation
only for the Pilates exercise group. Jürgensen et al. [23]
reported that healthy subjects reached 78% of their maximal predicted heart rate when performing the shuttle
walking test, while Probst et al. [10] reported that subjects reached 99% of their maximal predicted heart rate.
Similarly to the latter, in the current study, subjects reached
98% of their maximal predicted heart rate. Additionally,
heart rate recovery was found to be faster in groups with
progressive training load, independently of the VO2max
level [24].
Pilates exercises characteristics might be beneficial to
pulmonary function and functional capacity [4]. The increased recruitment of the abdominal, gluteal and lumbar
muscles and breathing technique results in increased
blood flow and O2 consumption. Aerobic training atintense levels (as opposed to moderate) also increases
heart rate recovery [25]. However, the Pilates exercises
prescribed during the 11-week protocol were not an
intense routine, as classified by the participants. The
findings showed changes in heart rate recovery due to
a moderate level of Pilates exercise. Another important
finding is that the subjects progressively needed less time
to complete the exercise sequence. Until the 6th week, the
average time to complete the sequence was 45 minutes;
afterwards the average time was 35 minutes.
Some limitations of this study can be addressed. The
sample size of the current study was small, so larger studies are encouraged including the evaluations of different
populations and people with different demographics
because we only assessed young sedentary women. Also,
more parameters need to be assessed including gait characteristics and muscle activation. Furthermore, a control group would strengthen the design of future studies.
However, the significantly longer distance covered after
the protocol reinforces the hypothesis of improvement
in the functional walking capacity. Subjects were closer
to reach the predicted shuttle walking test distance
(SWTpred = 1049 m) after (actual distance = 81% of SWTpred)
than before completing the program (actual distance =
76% of SWTpred). A study compared breast cancer subjects who completed an 8-week Pilates exercise protocol
along with home exercises with subjects that just did
the home exercises and also found that the group that
also did Pilates exercises had significantly larger improvements, among others, in walking distance [26].
Conclusions
Pilates exercises may significantly improve walking
functional capacity including walking distance/speed,
peak heart rate and heart rate recovery (cardiopulmonary function).
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Paper received by the Editor: January 4, 2016
Alexandre Barbosa
Campus JK - Diamantina/MG
Rodovia MGT 367 - Km 583, nº 5000
Alto da Jacuba CEP 39100-000, Brazil
HUMAN MOVEMENT
2016, vol. 17 (2), 107– 111
Movement coordination during Sit-to-Stand
in low back pain people
doi: 10.1515/humo-2016-0012
Mohsen Shafizadeh
Sheffield Hallam University, Sheffield, United Kingdom
Abstract
Purpose. The purpose of this study was to compare the inter-joint coordination during sit-to-stand (STD) and stand-to-sit (SIT)
execution between healthy people and people with low back pain. Methods. Fifteen healthy adults (age = 45.14 ± 5.18 years)
and fifteen age-matched (age = 46.17 ± 8.26 years) people with chronic low back pain were selected voluntarily. They performed
three repetitions of STD and SIT movement patterns in their preferred pace. Motion analysis system was used for measuring
3-dimensional (3D) angular displacement of hip, knee and ankle joints during execution of movement patterns. Decomposition
indices were analysed and were compared between two groups through Hotelling T2 Multivariate Analysis of Variance (MANOVA)
and follow-up Analysis of Variance (ANOVA). Results. The results showed that there is a significant difference (T 2 = 18.32,
F14, 5 = 8.33, p < 0.05) between the groups on decomposition indices. The ANOVA follow-up results showed that there are significant
differences between two groups on decomposition indices of the whole pattern of STD (F1, 18 = 7.96, p < 0.05), whole pattern of
SIT (F1, 18 = 5.37, p < 0.05), the first-half phase of STD (F1, 18 = 7.26, p < 0.05) and the first-half phase of SIT (F1, 18 = 6.33, p < 0.05).
Conclusions. People with low back pain have dis-coordination in the function of different body parts, and results in pausing
of one segment while the other segment moves independently. This knowledge may help in the development of rehabilitation
strategies for movement in this population.
Key words: decomposition index, inter-joint coordination, low back pain
Introduction
Low back pain [LBP] is common in many developed
countries [1–4]. According to a national survey in the UK
[1] it is reported that 40% of adults have experienced
back pain lasting more than one day in the previous
12 months. In addition, it is reported that 15% of people with back pain said they were in pain throughout
the year. The European Union Commission study [2]
in 2007 reported that 67 million people of the European
countries had experienced pain in their lower or upper
back in the previous week. Strine and Hootman [3], based
on the 2002 National Health Interview Survey in the USA
comprising adults over 18 years, reported that 34 million
people suffered from low back pain. Fernández-de-lasPeñas et al. [4] in a recent report on Spanish population
reported that 1-year prevalence of low back pain in adults
over 16 years was approximately 20%.
Low back pain has physical, psychological, social and
economic consequences for the individual. It is believed
that adults with low back pain exhibit more psychological distress, engage in more risky health behaviours than
adults without back pain [3] and are more likely to experience depression and other physical complaints such
as arthritis and osteoporosis [4, 5].
Some surveys reported that in the UK 12.5% of all
sick days were found to be related to low back disorders.
In Sweden it is estimated that 13.5% of sick days were
the result of lower back problems [6]. The economic cost
of back pain on society in the Netherlands has been
estimated to be 1.7% of the gross national product [7].
In another survey in the UK it is reported that the direct
health care cost of back pain in 1998 was 1632 million,
of which approximately 35% relates to services provided in the private sector [8].
Physical and behavioural consequences of low back
pain are interrelated so that behavioural changes often
are accompanied with physical limitations in painful
regions. In a severe level of back pain, it can result in
movement disability that ultimately may lead to sufferers avoiding their daily activities or occupations in
the short or long term [9]. Since mechanical stressors
in the workplace are the most important cause of low
back incidence in the developed countries and its manifestations are physical complaints in different forms such
as back ache, back pain, muscle soreness, muscle stiffness
and limited joint range of motion due to pain [10].
Keefe and Block [11] labelled the pain behaviours
in low back persons into 4 categories including guarding, bracing, rubbing and grimacing, which were later
expanded by McDaniel et al. [12] into 8 categories including guarding, bracing, grimacing, sighing, rigidity, selfstimulation, passive and active rubbing.
Guarding is one of the observable features of pain
behaviours that has attracted the attention of scien107
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M. Shafizadeh, Movement coordination during Sit-to-Stand in low back pain people
tists investigating low back pain. Keefe and Block [11]
defined guarding as abnormal stiff, interrupted, or rigid
movement while moving from one position to another.
This behaviour is observable in movements such as
sitting, standing, reclining, walking or other movement
patterns that require shifting from one position to another. McDaniel et al. [12] later revised the original
characteristics that were defined by Keefe and Block.
They assumed that the guarding cannot occur during
a stationary position such as sitting, standing and reclining. They included other features in their definition
for guarding which were hesitation in the execution
of movement that was different from movement undertaken at a slow velocity. Guarding that is considered to
be an adaptive mechanism in response to acute pain in
people with low back pain [13] is accompanied with increased muscle activity during flexion-extension tasks
and walking [14–17] and restricted optimal trunk movement [18, 19]. These two guarding features that are known
as muscle stiffness and joint rigidity are responsible for
stabilising the spine via changes in the reflex control of
trunk muscles [20].
Coordination between different body parts or muscle
groups is necessary in order to control the multi-joint
movement in a fluent manner. This synergy [21] might
be deteriorated by factors such as pain, muscle stiffness,
decreased joint range of motion [22, 23] and neurological problems [24] which may eventually result in the
lack of coordination between different body parts. Silfies
et al. [22] demonstrated that in a standing reach task
lumbar-pelvic coordination was more separated in time
and more variable in people with chronic low back pain
compared to healthy participants. This lack of coordination was attributed to freezing the motion of the
lumbar spine in the subjects with low back pain [21,
22, 25] in contrast to healthy people who simultaneously moved their lumbar spine and pelvis in the same
direction during trunk bending [26].
Previous studies [23, 25] have shown that inter-joint
coordination is altered in the lumbar spine and hips
during sit-to-stand (STD) and stand-to-sit (SIT) in people
with LBP. The method used to compute joint coordination in these studies was the relative phase, quantified
by subtracting the phase angle (inverse tangent of angular
velocity relative/angular displacement) of one joint from
the other [29]. Positive or negative values of relative phase
represent the earlier onset, or delay of movement, in one
joint relative to other joint. For example, if relative phase
between hip to lumbar spine is negative, the hip movement is delayed until after onset of the lumbar spine
movement. Relative phase is an indicator of positional
changes in coordination of two joints rather than a time
parameter of joint coordination. An alternative method
for representing joint coordination is the decomposition
index. This is defined as an index of dis-coordination
between two segments in terms of smooth or hesitant
movement on the basis of timing [24]. It shows whilst
one segment is moving another segment is stopping.
108
This index is applicable for studying the pain behaviours
such as hesitation in guarding behaviour.
There are no previous studies which have investigated
joint motion based on the decomposition index in a population with low back pain, thus the aim of this study
was to compare movement coordination between the
lumbar spine and hip joints using this method in participants with and without low back pain.
Participants
Fifteen adult (age = 46.17 ± 8.28 years) subjects (male
= 7, female = 8) with chronic low back pain and 15 agematched (age = 45.14 ± 5.18) asymptomatic healthy
people (male = 7, female = 8) were selected voluntarily.
All subjects completed an informed consent form, the
Recent Physical Activity Questionnaire (RPAQ) and
the Visual Analog Scale (VAS) prior to participation in
this study. They suffered from chronic pains in low
back area and were inactive in the past year according
to their responses in questionnaires. The Research Committee of the University approved all stages of study.
Instrument
An 8-camera motion analysis system (Simi motion,
co) was used to calculate angular displacement during
STD and SIT according to a standard protocol. For the
purpose of this study only lumbar spine and thigh
markers were analysed for calculating movement coordination. Markers were placed on the body on the
second sacral vertebra (S2), right and left Anterior Superior Iliac Spine (ASIS). Right and left thigh wands and
markers were placed nearly 15 cm above the patella.
Procedure
Information about the execution of movement patterns was presented verbally. Participants performed
three repetitions of STD and SIT according to their preferred speed without using their hands. They stood in
front of adjustable chair (30–40 cm height) with neither
armrest nor backrest. The height of the chair was adjusted so that the knee angle in the sitting position was
90º regardless of the person’s height. The movement was
started from a sitting position then was progressed to
a standing position to complete one repetition of STD
movement. After a few seconds (2–3 seconds) the movement was continued from a standing position and finished in a sitting position to complete one repetition
of SIT movement. This sequence was repeated 3 times
in a row. Figure 1 depicts the whole sequence and two
phases of STD and SIT that are segmented according
to the muscle power and type of muscle contraction in
the lumbar spine. In the first phase of both STD and
HUMAN MOVEMENT
a and b represent seat-off phase or the first-half of STD; c and d represent
stand-up phase or the second-half of STD; d and e represent sit-down
phase or the first-half of SIT; f and g represent seat-on or the second-half
of SIT
Figure 1. Different phases of sit-to-stand (STD)
and stand-to-sit (SIT) movement patterns
SIT, the eccentric contraction and negative power are
produced, whereas in the second phase of STD and SIT
the concentric contraction and positive power are produced [25].
Data analysis
Inter-joint coordination
Angular velocities of hip and lumbar spine joints were
computed through dividing of angular displacement (degree) of flexion-extension (frontal) axis to time (second).
The instantaneous velocity was computed for each frame
number in order to acquire the detailed changes in movement sequence. Decomposition index values as indicators of inter-joint coordination were the percentage
of STD and SIT time during which movement was decomposed. A joint was considered to pause when its angular
velocity dropped below 5º/s [24]. Average decomposition
index values (%) were calculated for the lumbar-hip joint
pair in each phase of STD, SIT and whole STD and SIT
when one joint was moving while the other joint paused.
Descriptive statistics include mean and standard deviation. Hotelling’s T2 MANOVA test was used to compare movement coordination between healthy and patient
groups. If the results were significant, follow-up ANOVA
tests were used to find the between-group differences in
decomposition indices of STD and SIT and their phases.
Confidence interval value was set at 95% and two-sided.
Results
Figure 2 demonstrates the mean decomposition index
changes in different phases of STD and SIT. According to
the results, decomposition index changed differently between two groups so that for low back pain persons’ decomposition indices of the first-half phase were higher
Sit-to-Stand Phases
Stand-to-Sit
Figure 2. Decomposition index of control group and low
back pain group in different phases of STD and SIT
than the second-half phase in STD and SIT, but for
healthy group the second-half phase had higher score
than the first-half phase for both STD and SIT.
The Hotelling T2 test result showed that there is a significant difference (T2 = 18.32, F14, 5 = 8.33, p < 0.05)
in decomposition indices between the low back pain
group and the healthy one. ANOVA follow-up results
showed that there are significant differences between
the two groups for decomposition indices of whole pattern of STD (F1, 28 = 7.96, p < 0.05), whole pattern of SIT
(F1, 28 = 5.37, p < 0.05), the first-half phase of STD (F1, 28 =
7.26, p < .05) and the first-half phase of SIT (F1, 28 = 6.33,
p < 0.05). Low back pain people had significantly higher
decomposition indices relative to healthy group in whole
STD (21.16 vs. 15.35), whole SIT (22.18 vs. 18.95), the firsthalf of STD (21.35 vs. 16.04), and the first-half of SIT
(23.04 vs. 13.18).
Discussion
The aim of this study was to examine the effects of
chronic low back pain on movement coordination in
the lumbar spine and hip joints during two functional
movement abilities including STD and SIT. Our findings showed that there were significant differences
between low back pain people and healthy ones in
decomposition indices of STD, SIT and the first-half
phases of STD and SIT. These findings are indicative
of the lack of synergy between movements of two joints
that move independently due to lack of coordination.
On the other hand, while the hip joint flexed lumbar
joint paused and vice versa. These findings also support
the findings of previous studies about the incidence of
hesitation due to pain in low back pain people [11, 12].
Silfies et al. [22] showed that lumbar-pelvic coordination was more separated in time and more variable
in people with chronic low back pain. Shum et al. [23]
have demonstrated that low back pain people showed
109
HUMAN MOVEMENT
different lumbar-hip coordination relative to healthy
people. In fact, the contribution of the lumbar spine in
STD and SIT movements was reduced due to immobility
in these joints induced to protect the spine against pain.
Shum et al. [25] in another study have revealed that muscle
moment reduction in the lumbar spine in the sagittal
plane is the reason why STD and SIT strategies change in
low back pain people. They minimise the trunk motion
and thereby reduce the muscle moment on the joint that
in turn changes inter-joint coordination. Another study
[30] showed a decreased power flow from the pelvis to
the lower limbs in low back pain people during STD. The
present findings also showed dis-coordination of joints
due to pausing of one joint whilst the other joint is moving.
The method of current study was different from previous studies [22, 25] that measured inter-joint coordination through relative phase as an indicator of phase
difference between paired-joints such as hip and lumbar
spine joints. Relative phase is an indicator of positional
changes in coordination (leading or lagging joint into degree) rather than time parameter (pausing one joint for
a millisecond). In fact, guarding behaviour as a form
of muscle stiffness or joint freezing [14–20] that is observable in low back pain people resulted in limitation
in trunk or thigh movements and it led to inter-joint
dis-coordination.
The additional data analysis of decomposition index
of the lumbar and hip joints showed different contribution of them in inducing dis-coordination in healthy
and low back pain groups. In healthy group the pausing
percentage in the lumbar and hip joints in entire movements were 77% and 22%, respectively (lumbar to hip
ratio: 3.5), whereas in low back pain group the pausing
percentage for lumbar and hip joints were 60% and 42%,
respectively (lumbar to hip ratio:1.42). Thus, the hip joint
slightly (25% less than in healthy people) contributed to
body weight transfer in low back pain people. These findings are important as they show to what extent a hesitant movement is shared between two different body
parts so that STD and SIT could be executed.
In addition, as Figure 2 shows that the decomposition
index for low back pain people in different phases of
STD and SIT are different – in the first-half of STD and
SIT they demonstrated more pausing than in the secondhalf. This pattern was different in healthy people who
showed more pausing in the second-half of STD and SIT.
Shum et al. [25] revealed that muscle powers are different in different phases of STD and SIT, namely in the
first-half phase the muscle work is negative because the
type of muscular contraction is eccentric. It seems that
keeping the trunk upright during seat-off phase to peak
lumbar spine flexion (a, b and c in Figure 1) due to painful condition deteriorates inter-joint coordination by
reducing the fluent motion and converting it into a hesitant movement. Again during SIT movement, the type of
muscle contraction in first-half phase is eccentric that
will interrupt the joints’ synergy which caused more
pausing during movement execution.
110
Reduction in the angular velocity of both lumbar and
hip joints during STD and SIT have been demonstrated
in previous studies [23, 31] and were explained as a preventive mechanism against pain that is caused by muscle
contraction and high levels of acceleration. Difficulty
in transferring the muscle force from the pelvis to the
lower limbs causes an interruption in the execution of
closed kinetic chain that in turn is responsible for transferring the force from the upper to lower body parts [30].
These findings suggest that reducing angular velocity in
the lumbar spine is helpful to reduce the angular moment
between two joints and subsequently prevents the risk
of losing balance. But reducing it beyond the normal
values relative to hip movement is a preventive mechanism that is observable in low back pain people that could
change the mechanics of movement into hesitant behaviours. Thus, in rehabilitation programmes of low back
pain, emphasising on a constant and fluent motion and
prevention from hesitant movement reduce the pressure on the lumbar spine through efficient utilisation of
the hip in coordination with the lumbar spine by means
of a closed kinetic chain.
Future studies should investigate the possible mechanisms of hesitation behaviours through electromyography [EMG] study to confirm the biomechanical
findings that have been revealed in the present study.
In conclusion, low back pain causes dis-coordination
in the function of different body parts and results in
pausing in one segment while the other segment moves
independently. Therapeutic exercises that emphasise
coordinative movement of the pelvis and the hip joints
could reduce dis-coordination due to freezing in movement segments.
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Paper received by the Editor: July 23, 2015
Mohsen Shafizadeh
Academy of Sport and Physical Activity
Faculty of Health and Wellbeing
Sheffield Hallam University
Sheffield, UK, S10 2BP
111
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2016, vol. 17 (2), 112– 118
The influence of energy boost and springblade footwear
on the kinetics and kinematics of running
doi: 10.1515/humo-2016-0010
Jonathan Sinclair1 *, Stephanie Dillon2
1
Centre for Applied Sport and Exercise Sciences, School of Sport and Wellbeing, College of Health and Wellbeing,
University of Central Lancashire, Preston, Lancashire, United Kingdom
2
International Institute of Nutritional Sciences and Applied Food Safety Studies, School of Sport and Wellbeing,
College of Health and Wellbeing, University of Central Lancashire, Preston, Lancashire, United Kingdom
Abstract
Purpose. The aim of the current study was to comparatively examine the effects of energy return, spring and conventional footwear on the kinetics and kinematics of running. Methods. Twelve male runners ran over an embedded force platform at 4.0 m · s–1
in the three footwear conditions. Lower limb kinematics were collected using an 8 camera motion capture system and tibial accelerations were obtained using an accelerometer. Differences in kinetic and kinematic parameters between footwear were examined
using one-way repeated measures ANOVA. Results. The results showed that there were no significant differences in kinetic
parameters between footwear. However, it was shown that that spring footwear were associated with significantly greater angles
of peak eversion (–12.49°) and tibial internal rotation (13.09°) in comparison to the conventional footwear (eversion = –10.52°
& tibial internal rotation = 10.84°). Conclusions. Therefore, the findings from the current investigation indicate that spring
footwear may place runners at increased risk from chronic injury related to excessive ankle eversion/tibial internal rotation.
Key words: footwear, biomechanics, running
Introduction
Recreational distance running is known to mediate
a number of physiological benefits [1]. However, despite
this, runners are also known to be highly susceptible to
chronic injuries [2], with an incidence rate of around 70%
over the course of one year [3]. A number of intrinsic
and extrinsic risk factors have been associated with the
aetiology of running injuries, such as mileage, previous
injury, number of years of training, training characteristics, running mechanics, surface and footwear [4]. The
most common chronic injuries associated with distance
running are iliotibial band syndrome, tibial stress fractures, patellofemoral pain, Achilles tendinitis, and plantar fasciitis [4].
A large range of preventative/treatment modalities
have therefore been explored, such as modifications to
training schedules [5–7], stretching regimens [8–10],
warm ups/cool downs [11], external supports [12], orthoses [13, 14] and footwear [15] in an attempt to mitigate the high risk of injury in runners. A key preventative
modality is to select running shoes with appropriate
mechanical midsole characteristics, which can influence the biomechanical mechanisms linked to the aetiology of injury. The properties of running shoe midsoles have therefore been proposed as a mechanism by
which chronic injuries can be managed [16].
112
Energy return has become a contemporary topic in
footwear biomechanics literature [17–19]. The first energy
return footwear utilized a thermoplastic polyurethane
midsole which is designed to reduce energy loss in
comparison to traditional ethylene-vinyl acetate footwear midsoles. There has been only one study which
has examined biomechanics of running in these footwear. Sinclair et al. [17] investigated the effects of conventional and energy return footwear on the kinetics
and kinematics of running. Their results showed that
the conventional running footwear with an ethylenevinyl acetate footwear midsole were associated with
significantly reduced tibial accelerations and peak eversion angles in comparison to energy return. In addition,
a further footwear design has also been introduced, which
also claims to increase energy return via 16 curved
springs designed to store and release energy. Currently,
there is no published information regarding the biomechanics of the spring footwear. However, given the
high incidence of chronic pathologies in runners and
the popularity of these new footwear models, research
of this nature would be of both practical and clinical
significance.
Therefore, the aim of the current study was to comparatively examine the effects of energy return, spring
and conventional footwear on the kinetics and kinematics of running. Given the high rate of chronic pathologies in runners, a study of this nature may give key
information to runners and clinicians with regards to
the selection of footwear for the reduction of injury.
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J. Sinclair, S. Dillon, Effects of energy and spring footwear
Participants
Twelve male participants volunteered to take part in
the current investigation. The mean ± SD characteristics
of the participants were; age 23.59 ± 2.00 years, height
177.05 ± 4.58 cm and body mass 77.54 ± 5.47 kg. All
were free from musculoskeletal pathology at the time
of data collection and provided written informed consent. The procedure utilized for this investigation was
approved by the University ethical committee in accordance with the principles outlined in the Declaration of
Helsinki.
Procedure
The runners completed five successful trials in which
they ran through a 22 m walkway at an average velocity
of 4.0 m · s–1 in each of the three running shoe conditions.
The participants struck a piezoelectric force platform
(Kistler Instruments) embedded into the middle of the
laboratory with their right foot [20]. The force platform
data was collected with a frequency of 1000 Hz. Running
velocity was controlled using timing gates (SmartSpeed
Ltd UK) and a maximum deviation of 5% from the predetermined velocity was allowed. Three-dimensional
(3D) kinematic information from the stance phase of the
running cycle were obtained using an eight-camera
motion capture system (Qualisys Medical AB, Goteburg,
Sweden) with a capture frequency of 250 Hz. To prevent
any sequence effects, the order in which participants
performed in each footwear condition was counterbalanced. Each footwear condition had four participants who received it first, second and last. The stance
phase was delineated as the duration over which > 20 N
of vertical force was applied to the force platform.
To quantify lower extremity joint kinematics in all
three planes of rotation, the calibrated anatomical systems technique was utilized [21]. Retroreflective markers
(19 mm) were positioned unilaterally allowing the right
foot, shank and thigh to be defined. The foot was defined via the 1st and 5th metatarsal heads, medial and
lateral malleoli and tracked using the calcaneus, 1st metatarsal and 5th metatarsal heads. The shank was defined
via the medial and lateral malleoli and medial and lateral
femoral epicondyles and tracked using a cluster positioned onto the shank. The thigh was defined via the
medial and lateral femoral epicondyles and the hip joint
centre and tracked using a cluster positioned onto the
thigh. To define the pelvis additional markers were positioned onto the anterior (ASIS) and posterior (PSIS)
superior iliac spines, and this segment was tracked using
the same markers. The hip joint centre was determined
using a regression equation that uses the positions of the
ASIS markers [22]. The centers of the ankle and knee
joints were delineated as the mid-point between the
malleoli and femoral epicondyle markers [23, 24]. Static
calibration trials were obtained allowing for the anatomical markers to be referenced in relation to the tracking
markers/ clusters. The Z (transverse) axis was oriented
vertically from the distal segment end to the proximal
segment end. The Y (coronal) axis was oriented in the
segment from posterior to anterior. Finally, the X (sagittal) axis orientation was determined using the right hand
rule and was oriented from medial to lateral.
To quantify tibial accelerations an accelerometer
(Biometrics ACL 300, Gwent UK), sampling at 1000 Hz
was utilized. The accelerometer was attached onto a piece
of lightweight carbon-fibre material using the protocol
outlined by Sinclair et al. [25], and strapped securely
to the distal anterio-medial aspect of the tibia in alignment with its longitudinal axis 0.08 m above the medial malleolus [26].
Footwear
The footwear used during this study consisted of
conventional footwear (New Balance 1260 v2), energy
return (Adidas energy boost) and spring (Adidas springblade drive 2) footwear, (shoe size 8–10 in UK men’s
sizes).
Processing
Trials were processed in Qualisys Track Manager in
order to identify anatomical and tracking markers, then
exported as C3D files. Kinematic parameters were quantified using Visual 3-D (C-Motion Inc, Gaithersburg, USA)
after marker data was smoothed using a low-pass Butterworth 4th order zero-lag filter at a cut off frequency
of 12 Hz. Kinematics of the hip, knee, ankle and tibial
segment were quantified using an XYZ cardan sequence
of rotations (where X is flexion-extension; Y is ab-adduction and Z is internal-external rotation). 3D kinematic measures from the hip, knee, ankle and tibia which
were extracted for statistical analysis were 1) angle at footstrike, 2) angle at toe-off, 3) peak angle during stance
and 4) relative range of motion (ROM) from footstrike
to peak angle.
From the force platform vertical force parameters of
impact peak, time to impact peak, average loading rate
and instantaneous loading rate were calculated. The impact peak was taken as the vertical ground reaction force
peak that occurred early in the stance phase. The average loading rate was calculated by dividing the impact
peak by the duration over which the impact peak occurred whereas instantaneous loading rate was calculated as the maximum increase between adjacent data
points [16]. The acceleration signal was filtered with
a 60Hz low-pass Butterworth 4th order zero-lag filter.
Peak tibial acceleration was defined as the highest positive acceleration peak measured during the stance phase.
Tibial acceleration slope was quantified by dividing the
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HUMAN MOVEMENT
comparisons were conducted on all significant main effects. The data was screened for normality using a Shapiro–Wilk which confirmed that the normality assumption was met. All statistical actions were conducted using
SPSS v22.0 (SPSS Inc., Chicago, USA).
peak tibial acceleration magnitude by the duration over
which the acceleration occurred, whereas tibial acceleration instantaneous loading rate was calculated as the
maximum increase in tibial acceleration between adjacent data points [16].
All of the aforementioned kinetic and kinematic
parameters were extracted from each of the five trials
and the data was then averaged within subjects for
statistical analysis. Hip, knee and ankle joint kinematic
curves were time normalized to stance and were ensemble averaged across subjects for graphical purposes.
Results
Kinetics
Table 1 presents the kinetic parameters obtained as
a function of the different experimental footwear.
The results show that there were no differences (p >
0.05) between footwear were found for any of the kinetic
parameters.
Means and standard deviations were calculated for
each outcome measure for all footwear conditions. Differences in kinetic and kinematic parameters between
footwear were examined using one-way repeated measures ANOVAs, with significance accepted at the p < 0.05
level. Effect sizes were calculated using partial eta2 (p 2),
with p 2 = 0.2 considered small, p 2 = 0.5 considered medium and p 2 = 0.8 considered large. Post-hoc pairwise
Kinematics
Tables 2–5 and figures 1–2 presents the 3D kinematic parameters obtained as a function of the different experimental footwear.
Table 1. Kinetic and tibial acceleration parameters as a function of footwear
Conventional
Impact peak (N/kg)
Time to impact peak (s)
Load rate (N/kg/s)
Instantaneous load rate (N/kg/s)
Peak tibial acceleration (g)
Time to peak tibial acceleration (s)
Tibial acceleration slope (g/s)
Tibial acceleration instantaneous slope (g/s)
Mean
SD
17.58
0.03
678.23
1102.82
6.60
0.05
242.28
709.67
6.10
0.01
74.04
196.37
2.47
0.03
145.69
353.88
Energy return
Mean
17.86
0.03
653.63
1287.51
7.03
0.04
225.71
659.42
SD
6.28
0.01
91.27
373.67
2.79
0.03
112.99
290.16
Spring
Mean
18.32
0.03
669.36
1438.55
7.53
0.05
241.17
720.26
SD
6.16
0.01
133.84
435.65
2.75
0.03
154.88
298.88
Table 2. Hip joint kinematic parameters as a function of footwear
Conventional
Sagittal plane (+ = flexion & – = extension)
Angle at footstrike
Angle at toe-off
Peak angle
ROM
Coronal plane (+ = adduction & – = abduction)
Angle at footstrike
Angle at toe-off
Peak angle
ROM
Transverse plane (+ = internal & – = external)
Angle at footstrike
Angle at toe-off
Peak angle
ROM
114
Energy return
Spring
Mean
SD
Mean
SD
Mean
SD
23.79
–17.40
24.95
1.16
7.67
9.25
6.80
2.26
27.02
–15.24
27.41
0.39
10.07
11.11
9.94
0.77
24.03
–17.21
24.78
0.76
6.43
8.95
6.56
0.76
0.78
–3.88
7.31
6.53
3.62
5.25
4.36
2.46
1.00
–4.08
7.14
6.14
2.39
4.15
3.42
1.68
1.65
–4.01
7.61
5.96
4.56
4.68
3.33
3.17
–11.40
–9.89
–18.16
6.76
6.55
9.78
6.77
5.86
–12.52
–8.80
–17.88
5.36
7.90
9.87
7.00
4.33
–12.76
–8.58
–17.63
4.86
8.29
10.73
7.20
3.59
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Table 3. Knee joint kinematic parameters as a function of footwear
Conventional
Energy return
Spring
Mean
SD
Mean
SD
Mean
SD
Sagittal plane (+ = flexion & – = extension)
Angle at footstrike
Angle at toe-off
Peak angle
ROM
9.81
14.05
37.98
28.17
8.76
8.02
5.27
8.24
10.04
12.91
35.56
25.52
9.57
4.69
5.05
6.18
9.57
12.93
36.22
26.65
7.43
4.06
2.98
6.80
Coronal plane (+ = adduction & – = abduction)
Angle at footstrike
Angle at toe-off
Peak angle
ROM
–0.61
–3.26
–7.77
7.16
7.16
4.08
7.32
3.68
–0.25
–2.52
–6.87
6.62
6.60
4.12
7.43
2.82
–0.68
–3.02
–7.03
6.35
7.14
4.17
7.15
3.28
Angle at footstrike
Angle at toe-off
Peak angle
ROM
–2.13
–1.72
11.05
13.17
6.37
6.05
5.76
6.24
–0.86
–1.74
11.36
12.23
6.52
4.78
6.28
4.81
–0.58
–1.22
10.76
11.34
7.24
6.18
6.25
6.42
Table 4. Ankle joint kinematic parameters as a function of footwear
Conventional
Energy return
Spring
Mean
SD
Mean
SD
Mean
SD
Sagittal plane (+ = dorsiflexion & – = plantarflexion)
Angle at footstrike
Angle at toe-off
Peak angle
ROM
4.75
–23.70
19.29
14.53
12.26
5.42
5.39
7.91
3.48
–20.91
18.56
15.08
10.87
6.60
4.32
7.63
4.03
–19.79
21.55
17.52
12.13
6.59
5.19
7.55
Coronal plane (+ = inversion & – = eversion)
Angle at footstrike
Angle at toe-off
Peak angle
ROM
0.75
3.75
–10.52
11.27
4.99
4.76
7.01
3.80
0.29
1.63
–11.17
11.46
5.64
4.78
6.58
3.92
–1.08
0.84
–12.49
11.41
4.93
5.28
6.92
3.92
Transverse plane (+ = external & – = internal)
Angle at footstrike
Angle at toe-off
Peak angle
ROM
–10.83
–5.60
–0.95
9.88
5.14
6.83
3.97
2.54
–10.72
–6.41
–0.38
10.34
5.35
7.24
4.50
1.78
–12.64
–8.57
–1.85
10.79
5.62
6.08
4.31
2.91
Table 5. Tibial internal rotation parameters as a function of footwear
Conventional
Angle at footstrike
Angle at toe-off
Peak angle
ROM
Energy return
Spring
Mean
SD
Mean
SD
Mean
SD
3.29
1.87
10.84
7.55
7.18
7.86
7.16
2.98
3.82
3.50
11.27
7.44
7.12
7.90
7.19
3.49
5.84
5.56
13.09
7.24
6.74
7.57
6.85
3.41
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HUMAN MOVEMENT
Figure 1. Lower extremity kinematic parameters in the a sagittal, b. coronal and c. transverse planes (grey = conventional,
black = spring, dash = energy return), (FL = flexion, DF = dorsiflexion, AD = adduction, IN = inversion, INT = internal,
EXT = external)
Hip
No differences (p > 0.05) in hip joint kinematics were
found between footwear.
Knee
No differences (p > 0.05) in knee joint kinematics were
found between footwear.
Ankle
In the coronal plane a main effect (p < 0.05, p 2 = 0.45)
was shown for the angle at footstrike. Post-hoc analysis
revealed that the eversion was significantly greater in the
116
spring footwear compared to the conventional and energy return conditions. A main effect (p < 0.05, p 2 = 0.71)
was also shown for the angle at toe-off. Post-hoc analysis
revealed that the inversion was significantly reduced in
the spring footwear compared to the conventional condition. Finally a main effect (p < 0.05, p 2 = 0.41) was found
for the angle of peak eversion. Post-hoc analysis revealed
that the angle of eversion was significantly greater in the
spring footwear compared to the conventional condition.
Tibia
A main effect (p < 0.05, p 2 = 0.36) was shown for
the angle at footstrike. Post-hoc analysis revealed that the
internal rotation was significantly greater in the spring
HUMAN MOVEMENT
Figure 2. Tibial internal rotation kinematics
(grey = conventional, black = spring, dash = energy
return), (INT = internal)
footwear compared to the conventional and energy return conditions. A main effect (p < 0.05, p 2 = 0.54) was
also shown for the angle at toe-off. Post-hoc analysis
revealed that internal rotation was significantly greater
in the spring footwear compared to the conventional
condition. Finally a main effect (p < 0.05, p 2 = 0.45) was
found for the angle of peak internal rotation. Post-hoc
analysis revealed that the angle of peak internal rotation was significantly greater in the spring footwear
compared to the conventional condition.
Discussion
The aim of this work was to determine the influence
of energy return, spring and conventional running
footwear on the kinetics and kinematics of running. To
the authors knowledge, this study represents the first
to comparatively examine the biomechanical effects
of running in energy return and spring footwear.
The first key observation from the current investigation is that no significant differences in impact kinetics
were shown between any of the experimental footwear.
This finding is interesting in light of the vastly different
midsole characteristics between the three footwear conditions and shows that different materials can have the
same impact attenuating properties. This finding disagrees with the findings of Sinclair et al. [5] who demonstrated that conventional running shoes were associated with reduced tibial accelerations compared to
energy return footwear. Therefore, importantly based on
the findings from this study, it appears that the experimental footwear used in this research does not appear to
affect runner’s susceptibility to impact related chronic
disorders.
That the peak angle of eversion and tibial internal
rotation were significantly greater in spring footwear
compared to conventional running shoes is also an important observation. It is proposed that this finding re-
lates to the lack of medial support in the spring footwear
in relation to the conventional running shoes, meaning
that their mechanical characteristics are unable to physically restrain the coronal plane motion of the ankle and
associated inward rotation of the tibia. This observation
may have clinical significance as increased eversion and
tibial internal rotation parameters have been linked to
the aetiology of chronic injuries [27]. Therefore the
current investigation indicates that spring footwear
may place runners at increased risk from chronic injuries in comparison to conventional running shoes.
A potential drawback of this study is that it investigated only the kinetics and 3D kinematics of running.
This procedure represents a useful practice when investigating the effects of different footwear on running
biomechanics. However the mechanical characteristics
of the energy return and spring footwear are designed
specifically to increase energy return from the midsole
and reduce the metabolic requirements of running.
Therefore in addition to the current work, future research should seek to determine whether these footwear can influence the metabolic cost of running.
Conclusions
In conclusion, the present investigation adds to the
current knowledge by providing a comprehensive evaluation of both kinetic and kinematic parameters when
running in energy return, conventional and spring
footwear. Firstly, the current study demonstrated that
despite the infinitely different midsole characteristics
of the three footwear conditions and the manufacturers
claims, there were no differences in impact attenuation.
Secondly, the current study showed that the peak angles
of eversion and tibial internal rotation were significantly
larger in spring in comparison to conventional footwear.
Therefore the findings from the current investigation
indicate that spring footwear may place runners at increased risk from chronic injury related to excessive
ankle eversion/ tibial internal rotation.
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Jonathan Sinclair
Centre for Applied Sport Exercise
and Nutritional Sciences
School of Sport and Wellbeing
College of Health and Wellbeing
University of Central Lancashire
Preston, Lancashire
UK, PR1 2HE
HUMAN MOVEMENT
2016, vol. 17 (2), 119– 125
Evaluation of laterality in the snowboard basic position
doi: 10.1515/humo-2016-0015
Michał Staniszewski 1 *, Przemysław Zybko1, Ida Wiszomirska2
1
2
Faculty of Physical Education, Józef Piłsudski University of Physical Education, Warsaw, Poland
Faculty of Rehabilitation, Józef Piłsudski University of Physical Education, Warsaw, Poland
Abstract
Purpose. Snowboarding requires a lateral positioning of the body. Moreover, a person must continuously control their balance
and use this in order to manoeuvre on the slope applying properly pressure on the lower limb closest to the nose of the board
(the leading leg). The present study is an attempt to determine the interdependencies between side preference while snowboarding
and laterality when performing other tasks. The dynamic stability in the neutral standing position, as well as in the lateral positions (left or right) was also evaluated. Methods. The survey participants (100 active snowboarders) answered a set of questions
concerning laterality while carrying out basic everyday tasks and while doing sports. The respondents were divided into two
groups based on their preferred leading side in snowboarding. Additionally, in the case of 34 people, muscle torques values of
the lower limbs were measured under static conditions and the postural stability was evaluated using AccuSway AMTI platform
and Biodex Balance System platform. Results. Over 90% of the participants declared right-handedness and right-footedness.
However, with regard to snowboarding, only 66% indicated their right leg as leading. No significant dependence was found
between the directional stance on the board and the leading hand, dominant leg, or leading eye. The stability measurements
revealed statistically significant differences between the neutral stance and the lateral positioning. Conclusions. Based on the
study results, it may be assumed that the declared directional stance on the snowboard is not contingent on the person’s basic
laterality, and that the lateral stance on the board significantly affects the posture control.
Key words: biomechanics, posture control, muscle torques, leading lower limb
Introduction
The combination of three fundamental elements –
board’s design, the snowboarder’s stance and riding
technique – determines a snowboarder’s performance.
However, all of them require an asymmetrical lateral
positioning of the body with regard to the slide’s direction, and turning is then controlled by leaning outward
on the board’s edges in the direction of the desired turn.
The most difficult element when learning to snowboard is how to control posture. The sideways stance
(in snowboarding known as the basic position) on the
board is not a natural position for human locomotion,
and because the lower limbs are bound, their role in
posture control is limited. The direction impulse which
results in taking a turn starts when the snowboarder
puts their weight on one edge of the board to make it
change direction. Therefore, in order to do it, the snowboarder must unbalance themselves in a controlled
way and lean into the direction of the intended turn.
Stability is maintained during the turn due to the occurrence of an inertial force – which is characterised
by the same velocity and magnitude, but is in the opposite direction to the centripetal force. The value of
the inertial force, commonly referred to as the centrifugal force, cancels out the forces which might overturn
the snowboarder, and is directly proportional to the
square of the rider’s velocity and inversely proportional
to the curve’s radius. In order to stay on the curved path,
the snowboarder must lean at a proper angle into the
turn maintaining the body’s centre of gravity in the
centre of the curve – for a greater velocity or a smaller
turn radius, the lean angle must be greater [1, 2].
Snowboarding is positively linked with constant balancing. Standing on the board is conditioned by the
rider’s ability to keep posture control, whilst a turn is
executed due to conscious unbalancing. The snowboarder’s stability is affected by the size of the base of
support; the position of the centre of pressure (COP);
the person’s body mass; and the direction of the gravitational force [3, 4]. Furthermore, posture control during the slide is affected by the velocity and momentum
of the counteracting forces. It is worth noting that the
balance control necessary to make a turn requires the
rider to lean in the sagittal (anterior/posterior) plane
while their head must face the slide’s direction, which
occurs in the frontal (medial/lateral) plane.
In winter sports such as snowboarding and skiing,
posture control is an element that can be assessed in
relation to many different aspects [5]. For instance, Platzer
et al. [6] evaluated the one-legged stance among members of the Austria national snowboarding team using
119
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M. Staniszewski, P. Zybko, I. Wiszomirska, Evaluation of laterality in the snowboard basic position
the Biodex Balance System platform. The authors monitored changes in their posture control during successive stages of training and related the gathered data to
the specificity of the given competition, combined with
the number of points accumulated in the World Cup.
In another study, Noé and Paillard [7] evaluated the
posture control of amateur skiers and members of the
national skiing team. The subjects were evaluated
both while wearing skiing boots and without them.
Their results showed that in the measurements without boots the amateurs had better posture control than
the professionals. In turn, the national team members
had far better results when tested in skiing boots,
which proves the long-lasting effects of wearing them
on their balancing capabilities. Still, this sort of compensation phenomenon occurred only under conditions characteristic of the practiced sport.
In the proper snowboarding technique, the slide trajectory is determined by putting more pressure on the
lower limb positioned closer to the nose of the board
– the so-called ‘leading leg’ [8]. For beginners, determining the leading limb is essential to an efficient
learning process; whereas the reversed slide (with the
opposite limb in front) is taught at a later stage of
training due to its difficulty. The ability to ride freely
with either leg in front requires many days of practice,
and attests to a certain level of the snowboarder’s proficiency.
The asymmetry of snowboarding is significantly
displayed when freestyle techniques are performed.
McAlpine et al. [9] point out that when landing after
the flight phase, the pressure on the rear leg is significantly greater than on the limb in the front. After taking into consideration some other additional factors,
the authors validate the assumption that, from the perspective of sliding technique and biomechanics, the
lower limbs should be assessed separately when determining laterality.
Laterality occurs naturally in the course of human
development and applies to several different functions
of the human body. The inclination for the organs located on either the left or right side to perform given
tasks is comprehensively described in literature. However, basic analyses of functional laterality tend to focus
on determining the dominant upper and lower limb
when performing basic everyday life and motor activities, such as kicking a ball or writing. The differentiation between dominant side and non-dominant one
may also apply to other functions, such as hearing,
seeing or brain functions, and generally plays a role in
the choice of the best limb to carry out a given task or
when rotating the body [10]. Additionally, some researchers have pointed out the differences between
each side with regard to: the ranges of motion in specific joints [11, 12], the discrepancies in the anatomy
and biomechanical functions of body parts based on
the preferred side [13–16] or the differentiation in lat120
eralisation factors stemming from the gender of the
study participants [17].
Since at present there are no papers which analyse
the relationship between the laterality associated with
the choice of the leading lower limb in snowboarding
and the person’s functional laterality, this paper will
attempt to establish whether there is such a connection,
and determine the influence of the snowboarder’s laterality on their body stability.
The study population included 100 participants who
were randomly chosen from a snowboarding course
consisting of 46 females (21 ± 4 years old) and 54 males
(22 ± 3 years old). The participants were asked questions in the form of a survey concerning their laterality while performing basic everyday chores and sports
activities. The dominant upper limb was determined
based on the respondents’ statements concerning onehanded throwing. Whereas the laterality of the lower
limb was not so easy to determine. We needed to take
into account many activities involving the use of the
lower limbs in human life. Based on this, the laterality
in four tasks was determined: ball kicking, one-legged
bouncing off the ground after a running start, onelegged stance and the declaration of their leading leg
during snowboarding ride (the leg at the front of the
snowboard). Additionally, the respondents were asked
about their preferred side while looking through one
eye and their favoured rotation direction when jumping with both feet with a 360 degree spin. Next, the
respondents were divided into two groups based on
their leading leg in snowboard stance. Moreover, 34
people participating in a snowboarding instructor’s
course (16 females, aged 22 ± 1 year old, mean body
mass: 59 ± 7 kg and mean height: 170 ± 7 cm; and 18
males aged 22 ± 3 years old, body mass: 80 ± 7 kg and
mean height: 182 ± 6 cm) underwent an assessment of
their body stability and an evaluation of the muscle
torque in their lower limbs. All of the respondents
were informed about the research protocol and were
acquainted with the requirements and conditions of
the experiment. Also, each participant personally signed
an informed consent form prior to participating in the
research study, and was notified of their right to leave
the experiment without any consequences. The study
was approved by the Scientific Research Ethics Committee.
To evaluate the laterality of strength between front
and rear leg on the snowboard, measurements of the
muscle torques of the flexors and extensors in both
knee joints and plantar flexors in both ankle joints were
taken under isometric conditions. The participants were
examined on a Biodex System 3 Pro machine (USA) in
a sitting position with a stabilized torso and their knees
bent at 90°.
HUMAN MOVEMENT
Figure 1. Testing positions during the postural stability assessment under dynamic conditions performed
on a Biodex Balance System platform: a) straight, b) left side to the screen, c) right side to the screen
To evaluate the laterality of posture control between
front and rear leg on the snowboard, measurements of
the static stability were tested on the AccuSway AMTI
(USA) stabilographic platform with a stable floor. Testing protocols were performed with the subjects’ eyes
open and closed in one-legged stances. The path length
of the COP was used for comparison of the stability
between legs.
Dynamic stability measurements were performed using the Biodex Balance System (USA) platform. Three
testing protocols were carried out at the 8th level of
instability (scale of platform settings), with each protocol
consisted of 3 trials – each lasting 20 seconds with
a 10-second interval between the trials. A Postural
Stability Test (PST) was performed with biofeedback,
requiring the participant to stand straight in front of
the screen and to stand sideways with their head facing the screen (mimicking the snowboarding stance,
Figure 1). For the analysis of the results, the Overall
Stability Index (OSI) was used.
The registered data was statistically analysed using
STATISTICA (a data analysis software system), Version 10 by StatSoft, Inc. (2011). In order to establish
the conformity of the analysed dispersion matrix to a normal distribution, the Shapiro–Wilk test was utilised.
The statistical significance of the dependencies between
all of the lateralisation parameters was then calculated
using the Spearman correlation coefficient. As to postural stability on the unstable Biodex platform, the significance of the determined differences after logarithmisation was checked using the analysis of variance
(ANOVA) method. When statistically significant differences were reported, a post-hoc test was performed
(Fisher’s LSD test of the lowest significant differences).
The evaluation of the differences in the static postural
stability parameters (AMTI platform) and the torque
values between the leading and the non-leading limb was
performed using the non-parametric Mann-Whitney
U test. For all the tests, the significance level was set at
p < 0.05.
Results
Among the surveyed snowboarders, the overwhelming majority (over 90%) declared themselves to be righthanded when throwing and right-footed when kicking
a ball. These two parameters determine whether the
person is considered right handed or left handed and
right footed or left footed, respectively. Thus, these
parameters decidedly correlate with each other (Table 1).
Moreover analysis showed that 72% of the respondents bounced off the ground from their left lower limb
and 28% from their right one when jumping forward on
one leg combined with a running start. The data re-
Table 1. Laterality declared by the participants of the survey while performing certain everyday life and sports activities
Dominant
side
Left
Right
One-handed
throwing a
Kicking
a ball a, b
Bouncing
off the ground b, c
One-legged
stance
Seeing with
one eye
360° rotation
jump c
Leading leg
on a board
8%
92%
11%
89%
72%
28%
46%
54%
42%
58%
59%
41%
34%
66%
a
statistically significant interdependency between one-handed throwing and kicking a ball; (p < 0.001, r = 0.60)
statistically significant reverse interdependency between kicking a ball and bouncing off the ground; (p < 0.001, r = – 0.35)
c
statistically significant interdependency between bouncing off the ground and 360 rotation jump; (p = 0.003, r = 0.29)
b
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garding ball kicking and one-legged bouncing off the
ground are the only ones to show a negative correlation.
This observation indicates a proportionally inverse dependence, i.e. where people kick a ball with their right
foot but bounce off the ground using their left limb,
and vice versa. The statements concerning bouncing off
the ground and aerial rotation correlated. However, no
compelling relationship between the direction of the
snowboarding stance and the other discussed lateralization parameters in everyday life was established. In
the study group, 68% subjects said that they ride on
snowboard with the right foot in front (in snowboarding
known as the ‘goofy’ stance) and 32% with the left foot
in front (known as the ‘regular’ stance).
As was to be expected, the majority of respondents
were right-handed and right-footed; hence, analysis of
the interdependency between the right/left lower limb
positioned at the front of the snowboard and the lateralisation factors would have been unjustified (i.e. if
we were to discuss the results in this way, the significant correlation would only exist in the case of people
snowboarding with the right side to the front). Therefore, the interdependencies were not analysed in terms
of the left/right axis, but instead with regard to the dominant side of the front/rear lower limb on the board
plane while performing a given motion (Table 2). Assuming this approach to be the right one, no significant interdependencies were found between the dominant side while snowboarding and the lateralization
parameters for the everyday human activities.
Moreover, static muscle torques were measured for
the knee flexors and extensors and the ankle plantar
flexors. Data for the left and right lower limbs was collected; however, in the course of a further comparative
analysis the data was calculated only for the anterior/
posterior position on the board (Figure 2). A closely corresponding set of values was calculated for each function, and these were not differentiated with regard to
the muscle torque of the lower limb depending on the
direction of the stance on the board.
In evaluating the measurement results of the one leg
stance on the AMTI platform, the same division was
utilized for the front/rear leg. Table 3 shows the values
for the centre of pressure path length. Considerably higher
(p < 0.001) values of the parameters were reported when
the test was performed with the subjects’ eyes closed; but
Figure 2. Mean ± SD values of the muscle torques
under static conditions for the knee flexors and extensors,
and ankle plantar flexors of the front and rear lower limbs
on the board
*** Values significantly higher (p < 0.001) than in the neutral position
Figure 3. Mean ± SD value of the overall stability index
(OSI) in the neutral standing position heading front
and with the left and right side to the front
Table 2. Interdependency between the leading leg on the board (the one at the front) and the declared laterality
while performing other tasks
Leg on a board
Front
Rear
One-handed
throwing
Kicking
a ball
Bouncing off the
ground
One-legged
stance
Looking with one
eye open
360° rotation
jump
55%
45%
55%
45%
51%
49%
60%
40%
49%
51%
57%
43%
Table 3. Mean ± SD values of the COP path length during the postural stability test on one leg on the AMTI platform
in regard to the lower limb’s position on the board – at the front and at the back
Protocol
Eyes open
Eyes closed
Path length (cm)
Path length (cm)
Front leg
Rear leg
37.52 ± 11.95
101.48*** ± 35.17
36.01 ± 10.22
105.18*** ± 44.80
*** values significantly higher (p < 0.001) than with eyes open
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no statistically significant differences in the postural stability parameters were found between the front and rear
leading leg on the board.
The dynamic postural stability was evaluated using the Biodex Balance System platform. The subjects
were given the task of keeping their balance on the
dynamic platform with their eyes open while visually
controlling the COP deviation on the biofeedback screen.
The first testing protocol was performed in a neutral
position – with the subject standing in front of the screen.
The subsequent two protocols required the subjects to
stand with either their right or left side to the front, while
their face was directed at the screen, i.e. in a head position similar to the snowboarding stance. The overall
stability index (OSI) values for all the trials are presented
in Figure 3. It should be noted that the postural stability
values obtained for lateral/directional stance are higher
(and are statistically significant at p < 0.001) than the
values recorded in the neutral position.
Discussion
Eighty seven out of 100 participants were characterised by right-sidedness, i.e. their right hand was dominant while performing manipulative motor activities
and their right leg was dominant as the swinging limb
when kicking a ball. 6% of the respondents were decidedly left-sided; while 5% showed signs of Type I crosslateralisation (right handed/left footed). In the case of
two subjects, Type II cross-lateralisation was established (left handed/right footed). The occurrence of
these four basic laterality models within the handfoot system established during this study corresponds
to the data gathered for the overall European population by other authors [14, 18].
Based on the visuospatial study of laterality in volleyball players and rowers conducted by Giglia et al. [19],
the researchers proved that differences between the right
and left side are only present in the case of the top-notch
professionals. However, the lateralization results gathered
from ski carving turns by Vaverka and Vodickova [20]
showed that when the preferred lower limb is also the
outer leg, it affects efficacy of a turn. Their study of laterality during symmetrical ski turns showed that the
functional preference of the lower limb can affect the
execution of a turn, even in the case of professional skiers.
Furthermore, Jandová and Charousek [21] performed
an evaluation of ski runners and demonstrated that even
for motions such as a two-step glide (V2 Alternate),
which is symmetrical, the kick off will be performed
quicker and in a shorter time by the dominant leg. The
practical meaning of this is that when training, the ski
runners should focus on improving their explosive
power, especially in the case of the non-dominant leg.
Danielsson [11] analysed the lateralisation parameters
among proficient snowboarders and compared the data
with the results of people who did not train snowboard-
ing. He did not find any differences with regard to the
ranges of motions within the main joints or the right
and the left side. The only meaningful interdependencies were noted when he analysed the strength of the
lower limbs and the hip circumference. The author highlighted the fact that the snowboarders he evaluated
presented a higher muscle strength value in the leg placed
toward the rear end of the board. This may be because
the back leg functions as a support and is bounced off
while performing most of the elements of snowboarding
freestyle techniques.
Our study indicates the lack of a significant relationship between the selected parameters in regard to the
declared laterality and the directionality of the stance
while snowboarding. The muscle torque values under
static conditions and the postural stability parameters
also did not differentiate the limbs based on their position on the board. It is worth mentioning that the stability
evaluation was performed on the participants who were
experienced snowboarders taking part in an snowboard
instructor course. Therefore, it may be assumed that the
lack of a consequential difference in the stability results between the front limb and the rear limb results
is a consequence of the riders’ skills and their frequent
shifting in stance direction while performing this sport.
Executing freestyle elements of snowboarding and freeriding techniques often results in a forced bi-lateralisation, which could have affected the symmetry distribution of the stability factors. To verify this hypothesis,
further studies will need to evaluate people who do not
ride on regular basis and participants of a snowboarding
course for beginners utilizing the same protocols as in
the present study.
As a rule, in order to maintain the proper posture and
posture control, the entire motor system is involved in
the execution of a given task, which means that various
groups of muscles undergo a concomitant strain [22, 23].
Snowboarding requires constant activity of these muscles
under conditions of a relative dynamic stability and
the learning process encompasses sets of exercises and
activities aimed at increasing/improving the balance
kinesiology while riding a snowboard. Physical exercises
done under conditions where balance is disrupted result not only in the enhancement of postural stability, but
also affect the muscles and build up strength. Myer et
al. [24] indicated that a training protocol including
balancing exercises is just as effective as plyometric training in terms of strengthening the muscles and overall
fitness [25]. Similarly, Gorman et al. [26] and Lynn et al.
[27] conducted separate experiments, and both demonstrated the multi-dimensional efficacy of training that
included balancing exercises for the entire body and
incorporated the muscle strength of the lower limbs, and
not simply training that included only equilibrium components.
The present study has showed notable differences in
the postural stability between a neutral standing posi123
HUMAN MOVEMENT
tion and the same position but with head facing sideways (similar to the directional snowboarding stance).
Nonetheless, the available literature does not provide
sufficient accounts of studies that describe the stability
parameters in the case of snowboarders, and which evaluate the effects of snowboarding courses on changes in
the components of posture control.
Conclusions
Considering the results of the study, the stance direction on the snowboard (i.e. declaring the so-called
‘leading leg’) is yet another completely independent
functional laterality element. What is more, the lateral positioning on the board with the rider’s face simultaneously turned toward the direction of the slide poses
a considerable difficulty in keeping posture control. With
this conclusion in mind, a further study is planned
which would be specifically designed to evaluate the
stability of the body in people who are just beginning
their adventure with snowboarding. Such a study would
also assess how this sort of training affects the postural
stability parameters.
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Paper received by the Editor: February 3, 2016
Michał Staniszewski
Zakład Sportów Wodnych i Zimowych
Akademia Wychowania Fizycznego
Józef Piłsudskiego
ul. Marymoncka 34
00-968 Warszawa 45, Poland
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2016, vol. 17 (2), 126– 130
Relationship between impulsiveness and tactical
performance of U-15 youth soccer players
doi: 10.1515/humo-2016-0014
Marcelo Odilon Cabral de Andrade *, Guilherme Figueiredo Machado,
Israel Teoldo
Centre of Research and Studies in Soccer, Universidade Federal de Viçosa, Viçosa, Brazil
Abstract
Purpose. The aim of this study was to examine the relationship between impulsiveness and tactical performance of U-15 youth
soccer players. Methods. The sample comprised 100 U-15 youth soccer players. Impulsiveness and tactical performance were
assessed using the Continuous Performance Test-II (CPT-II) and the System of Tactical Assessment in Soccer (FUT-SAT), respec
tively. FUT-SAT enables evaluation of ten core tactical principles of soccer game: (i) penetration; (ii) offensive coverage; (iii)
depth mobility; (iv) width and length; (v) offensive unity; (vi) delay; (vii) defensive coverage; (viii) balance; (ix) concentration;
and (x) defensive unity. Impulsiveness values were obtained using the Omission and Commission Error analysis. Tactical performance values were obtained through the Game Tactical Performance Index (GTPI), Offensive Tactical Performance Index (OTPI)
and Defensive Tactical Performance Index (DTPI). The Kolmogorov–Smirnov test and Spearman’s Correlation one were performed
(p < 0.05) through SPSS, v. 22. Results. We observed a positive correlation between impulsiveness and GTPI (rho = 0.226; p = 0.018).
Conclusions. It is concluded that impulsiveness is related to tactical performance of U-15 youth soccer players.
Key words: soccer, impulsiveness, tactical performance
Introduction
Tactical performance in soccer and the cognitive
processes related to decision-making have been associated with the efficiency of game actions, and consequently with sport expertise [1, 2]. Tactical performance
can be defined as the result of individual and collective
actions performed in the game [3]. Thus, the knowledge about tactical domain must be examined through
the assessment of both individual performance and
players’ interactions, as well as the factors which may
affect these interactions [4, 5].
One of these factors that may influence the performance of players and have been associated with players’
behaviour is impulsiveness. Impulsiveness can be defined
as the tendency to inhibit responses to a lesser extent than
in most people before taking an action [6]. Previous
studies involving impulsiveness and motor behaviour
identified differences among people due to response inhibition in a determined task [7–10].
In this respect, investigations have examined impulsiveness in sports. In one of these investigations, Svebak and Kerr [11] examined the relationship between
impulsiveness and sport preference in an Australian
sample. They found that people engaged in explosive
sports (e.g. soccer) obtained higher scores on impulsiveness measures than people who prefer an endurance
sport. Recently, in a study involving team sports, Lage
126
et al. [12] investigated the relationship between impulsiveness and technical performance of handball players.
They found that high scores on impulsiveness (i.e. more
impulsive people) produce errors in game responses. Although some cases of impulsiveness are related to negative factors, there is no unanimity regarding its effect
in sports responses.
Therefore, studies related to physical and technical
variables present in literature advance with studies that
investigate the influences of impulsiveness in sports.
However, studies involving tactical components are scarce
and this variable demands further attention, as the quick
action responses might directly influence the tactical response and consequently team performance [12, 13].
Thus, the aim of this study was to examine the relationship between impulsiveness and tactical performance
of U-15 youth soccer players.
Sample
The sample comprised 100 U-15 youth soccer players
from soccer clubs and schools from the state of Minas
Gerais (Brazil) who performed 6,640 tactical actions.
The inclusion criteria were: (i) players from clubs and
soccer schools must participate in regular training sessions, at least three times a week; (ii) they must participate in competitions at regional level.
HUMAN MOVEMENT
M.O.C. de Andrade, G.F. Machado, I. Teoldo, Impulsiveness and tactical performance
Figure 1. Ten core tactical principles of soccer [14]
Figure 2. The Macro-Category
Observation and Macro-Category
Outcome of FUT-SAT [14]
127
HUMAN MOVEMENT
Instruments for data collection
As a means to assess the tactical performance, the
System of Tactical Assessment in Soccer (FUT-SAT) [14]
was used. This system enables the evaluation of tactical
actions according to ten core tactical principles of soccer,
as shown in Figure 1.
The FUT-SAT comprises two macro-categories (Figure 2). The Macro-Category Observation refers to aspects
that can be assessed in the instrument. The Macro-Category Outcome refers to results provided by the instrument.
Regarding the assessment of impulsiveness, the Continuous Performance Test (CPT-II) was used [15]. This
instrument is performed via computer and enables evaluation of processes related to vigilance, response inhibition, signal detection, and other aspects of performance,
among them, attentional and motor impulsiveness.
Ethical procedures
This research was approved by the Research Ethics
Committee from Universidade Federal de Viçosa, Brazil
(Of. Ref. N. 132/2012/CEPH) and meets the norms established by the National Health Council (CNS 466/2012)
and by the Declaration of Helsinki (1996). To participate
in this research, players and parents/tutors were previously contacted and provided with the information about
research procedures and thereafter they signed an informed consent. Data collection began after parents/tutors had authorized players to participate in the research.
Players were free to withdraw from the study at any
moment.
Data collection procedures
In order to assess tactical actions, the FUT-SAT field
test (GK+3 vs. 3+GK) was conducted. The structure of
the test consists of a small-sided game (playing area is
36 m long by 27 m wide) in which players are divided
into two teams, each with 3 outfield players and a goalkeeper, who wear numbered vests. Players are asked to
play for four minutes according to the rules of soccer,
except for the offside rule. Prior to the start of the test,
players are given 30 seconds to familiarize with the activity.
The field tests were filmed and recorded on a digital
camera (Sony HDR-XR100). Video footage was introduced in digital format in a laptop (POSITIVO Premium 4A015RX8T) via USB cable and converted into
AVI format through Format Factory Video Converter
software. For video processing and analysis, Soccer Analyser® software was used. The Soccer Analyser® software
enables assessment of the tactical actions performed by
participants, as it is possible to calculate the Tactical Performance Index (TPI) of each soccer player using the
following formula:
128
Tactical Performance Index (TPI) = ∑ tactical actions
(performance of principle × quality of principle
performance × place of action in the game field ×
action outcome) / number of tactical actions
In order to assess impulsiveness, the Continuous Performance Test (CPT-II) was used. The test consists in
a task in which letters show up randomly and alternately in the centre of computer screen (in this study
a POSITIVO Premium 4A015RX8T laptop computer
was used). The players sitting in front of the computer
screen in a quiet room have to press the spacebar on the
computer keyboard when letters appear, except when
the letter “X” appears. In this case, the participants
should not press the spacebar. The duration of test was
fourteen minutes. We used two main scores measured
in this test: (i) Omission Errors, which indicates the
number of times that the stimuli (non-X letters) appeared and the player did not respond to it. This variable is related to the attentional impulsiveness; and (ii)
Commission Errors, which indicates the number of
times the player responded when the letter X appeared
on the screen. This variable is related to the motor impulsiveness.
Initially, descriptive analyses were conducted (means
and standard deviations). Tactical performance data were
obtained using the Tactical Performance Indexes (TPI)
in each game phase: (i) Offensive Tactical Performance
Index (OTPI), (ii) Defensive Tactical Performance Index
(DTPI), and for both phases of play: (iii). Game Tactical
Performance Index (GTPI).
With respect to impulsiveness data, the measures used
to analyse participants’ performance were the number
of Omission Errors (when the subject omits responses)
and Commission Errors (when the subjects give inappropriate responses).
The Kolmogorov–Smirnov normality test was used
to verify data distribution. To verify the relation between
tactical and impulsiveness measures, Spearman’s Correlation Test was used. For statistical procedures, SPSS
(Statistical Package for Social Sciences) v. 22 was used.
Test-retest reliability of the observations was measured using Cohen’s Kappa test. Analyses were verified
through the reassessment of 1,260 tactical actions, i.e.
18.97% of the sample, a higher value than that indicated by literature (10%) [16]. Two observers participated in the procedure respecting a three-week interval for reanalysis, thus avoiding task familiarity issues
[17]. Values of intra-observer reliability were between
0.885 (SE = 0.009) and 0.929 (SE = 0.009), while values
of inter-observer reliability were between 0.847 (SE =
0.033) and 0.958 (SE = 0.014). These values are defined
by literature as “almost perfect” [18].
HUMAN MOVEMENT
Results
Table 1 presents the correlation between the CPT
measures (Omission Errors and Commission Errors) and
the Tactical Performance Indexes (Offensive, Defensive,
and Game). Positive correlation was observed between
CPT Commission and GTPI (rho = 0.226; p = 0.018).
For the remaining cases, no significant correlations
were found.
Table 1. Correlation between the CPT measures and
Tactical Performance Indexes
OTPI
DTPI
GTPI
Omission Errors
rho
p
0.054
0.579
–0.150
0.119
–0.012
0.905
Commission Errors
rho
p
0.165
0.086
0.030
0.753
0.226
0.018*
* significant correlation at p < 0.05
Discussion
Assessment of cognitive processes and how these processes affect the performance have received some attention in recent years. However, specific studies that
assess the influence of impulsiveness in soccer (mainly
in tactical aspects) are scarce. Accordingly, the aim of
this study was to examine the relationship between
impulsiveness and tactical performance of U-15 youth
soccer players.
Results displayed positive correlation between impulsiveness and Game Tactical Performance Index (GTPI).
Players who committed more errors by commission
(i.e. executed quicker decisions and consequently more
mistakes) have displayed higher tactical performance
than others. These data suggest that impulsive players
may present higher scores of tactical performance compared to less impulsive players.
In this respect, a pioneer investigation conducted by
Vestberg et al. [19] suggested that specific cognitive processes predict the success of soccer players. When discussing their findings, the authors suggest that what is
demanded of a “successful player” is not only quickness
of decisions during actions, but also quick inhibition
of planned decisions. In this context, this research corroborate with findings from other studies which demonstrated that more impulsive people showed more accuracy than less impulsive people when the time available
for decision-making is extremely short [6, 20].
Although game accuracy aspects might decrease as
the time set to perform actions decreases, the results
of this research suggest that high tactical performance is
positively related with quick responses. Errors by commission are related with quickness in motor responses
[15] and it seems to be a positive factor for tactical de-
mands, whereas game success depends on risk choices
even when mistakes are made. Thus, players’ behaviour
should be guided by the need to perform tactical principles within the shortest possible time, once tactical
management efficiency is closely related to response
quickness such as space creation that may influence
other game demands [21, 22].
Therefore, in learning/training process, the efficient
execution of tactical principles is related to quick game
responses. Accordingly, such responses might be influenced by impulsiveness. However, there are few studies investigating the role of impulsiveness in soccer tactical performance. Thus, we suggest further investigation
regarding impulsiveness in different samples, age groups
and expertise level to better understand its role in soccer.
Conclusions
1. We concluded that motor impulsiveness was related
to tactical performance of U-15 youth soccer players.
Players that are more impulsive presented higher tactical performance in the game.
2. The attentional impulsiveness did not show correlation to tactical performance of U-15 youth soccer
players.
3. This study furthers the knowledge about players’
impulsiveness and how it is related to the tactical domain in soccer. However, more research is necessary.
Acknowledgements
This study was funded by the State Department of Sport and
Youth of Minas Gerais through the State Act of Incentive to
Sports, by FAPEMIG, CAPES, CNPQ, FUNARBE, the Dean’s
Office for Graduate and Research Studies and the Centre of
Life and Health Sciences from the Universidade Federal de
Viçosa, Brazil.
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130
Marcelo Andrade
Centre of Research and Studies in Soccer
Universidade Federal de Viçosa, Brazil
Departamento de Educação Física
Av. P. H. Rolfs, S/N
Campus Universitário
Post Code: 36.570-900, Viçosa, Brazil
HUMAN MOVEMENT
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bądź wsparły go finansowo lub technicznie.
19. Redakcja przyjmuje zamówienia na reklamy, które
mogą być umieszczane na dodatkowych kartach sąsiadujących
z okładką. Ceny reklam będą negocjowane indywidualnie.
20. Wersją pierwotną czasopisma jest wersja papierowa.
Detailed guidelines for submitting articles
to Human Movement
Szczegółowe zasady przygotowania artykułu
do Human Movement
1. The article should be written in English.
2.Empirical research articles, together with their summary and any tables, figures or graphs, should not exceed
20 pages in length; comparative articles are limited to
30 pages. Page format is A4 (about 1800 characters with
spaces per page). Pages should be numbered.
3. Articles should be written using Microsoft Word with the
following formats:
– Font: Times New Roman, 12 point
– Line spacing: 1.5
– Text alignment: Justified
– Title: Bold typeface, centered
1. Redakcja przyjmuje prace wyłącznie w języku angielskim.
2. Tekst prac empirycznych wraz ze streszczeniem, rycinami
i tabelami nie powinien przekraczać 20, a prac przeglądowych – 30 stron znormalizowanych formatu A4 (ok. 1800
znaków ze spacjami na stronie). Strony powinny być ponumerowane.
3.Artykuł należy przygotować w edytorze tekstu Microsoft
Word według następujących zasad:
– krój pisma: Times New Roman, 12 pkt;
– interlinia: 1,5;
– tekst wyjustowany;
– tytuł zapisany pogrubionym krojem pisma, wyśrodkowany.
132
HUMAN MOVEMENT
4. The main title page should contain the following:
– The article’s title
– A shortened title of the article (up to 40 characters in
length including spaces), which will be placed in the
running head
– The name and surname of the author(s) with their affiliations written in the following way: the name of the
university, city name, country name. For example: The
University of Physical Education, Wrocław, Poland
– Address for correspondence (author’s name, address,
e-mail address and phone number)
5. The second page should contain:
– The title of the article
–An abstract of approximately 200 words divided into
the following sections: Purpose, Methods, Results, Con
clusions
– Three to six keywords to be used as MeSH descriptors
(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 angielskim;
– skrócony tytuł artykułu w języku angielskim (do 40 znaków ze spacjami), który zostanie umieszczony w żywej
paginie;
– imię i nazwisko autora (autorów) z afiliacją zapisaną
według następującego schematu:
• nazwa uczelni, nazwa miejscowości, nazwa 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 200 wyrazów) składające się z następujących części: Purpose,
Methods, Results, Conclusions;
– 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.
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.
Wyniki
Przedstawienie wyników powinno być logiczne i spójne
oraz ściśle powiązane z danymi zamieszczonymi w tabelach
i na rycinach.
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.
133
HUMAN MOVEMENT
Conclusions
In presenting any conclusions, it is important to remember
the original purpose of the research and the stated hypotheses,
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.
Wnioski
Przedstawiając wnioski, należy pamiętać o celu pracy oraz
postawionych hipotezach, a także unikać stwierdzeń 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
Należy wymienić osoby lub instytucje, które pomogły autorowi w przygotowaniu pracy, udzieliły konsultacji 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.
Odwołania do piśmiennictwa należy oznaczać 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 książkowe), 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: nazwisko 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/v10038-010- 0022-2.
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:
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.
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/v10038-010-0022-2.
Gdy autorami artykułu jest sześć lub mniej osób, należy wymienić wszystkie nazwiska, jeżeli jest ich siedem i więcej, należy
podać sześć pierwszych, a następnie 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 Sportiva, 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:
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.
134
Opis bibliograficzny rozdziału w książce powinien zawierać: nazwisko autora (autorów), inicjał imienia, tytuł rozdziału,
nazwisko autora (autorów) lub redaktora (redaktorów), 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.
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 two-distance 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 two-distance 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, year of publication, journal volume
and number, 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, 2002, 2 (6). 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.
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.
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.
8. Tekst główny w pracach innego typu powinien zachować
logiczną ciągłość, a tytuły poszczególnych części muszą
odzwierciedlać omawiane w nich zagadnienia.
9.Przypisy (objaśniające lub uzupełniające tekst)
– powinny być numerowane z zachowaniem ciągłości
w całej pracy i umieszczone na końcu tekstu głównego.
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, tak by
były czytelne i zrozumiałe niezależnie od tekstu pracy.
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.
SUBSCRIBING to THE HUMAN MOVEMENT JOURNAL
ZASADY PRENUMERATY CZASOPISMA HUMAN MOVEMENT
http://www.awf.wroc.pl/pl/article/1003/5954/Prenumerata_subscription/
135

Human Movement 17 (2) 2016 - Akademia Wychowania Fizycznego

Transcription

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