Download Results and conclusions: The perceived exertion differed

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LITHUANIAN UNIVERSITY OF HEALTH SCIENCES
MEDICAL ACADEMY
FACULTY OF NURSING
INSTITUTE OF SPORTS
AGNĖ SLAPŠINSKAITĖ
CONCATENATION BETWEEN CARDIOVASCULAR
SYSTEM’S FUNCTIONAL PARAMETERS AND PERCEIVED
EXERTION IN HEALTHY YOUNG MEN DURING REST,
PHYSICAL TASK AND RECOVERY
Master study program “Health rehabilitation through physical exercise” final work
Research supervisor
PhD Ernesta Sendžikaitė
KAUNAS, 2014
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LITHUANIAN UNIVERSITY OF HEALTH SCIENCES
MEDICAL ACADEMY
FACULTY OF NURSING
INSTITUTE OF SPORTS
APPROVED:
Dean of Nursing faculty
Prof. Jūratė Macijauskienė
________________
___December 2013
CONCATENATION BETWEEN CARDIOVASCULAR
SYSTEM’S FUNCTIONAL PARAMETERS AND PERCEIVED
EXERTION IN HEALTHY YOUNG MEN DURING REST,
PHYSICAL TASK AND RECOVERY
Master study program “Health rehabilitation through physical exercise” final work
Consultant
Prof. Natalia Balague
________________
9th December 2013
Research supervisor
PhD Ernesta Sendžikaitė
________________
9th December 2013
Reviewer
Work accomplished by
____________________
Master student
____________________
Agnė Slapšinskaitė_____
_____, _______, 2013
9th December 2013
KAUNAS, 2014
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CONTENTS
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SUMMARY .............................................................................................................................................
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SANTRAUKA ......................................................................................................................................... 5
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GLOSSARY .............................................................................................................................................
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INTRODUCTION ....................................................................................................................................
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AIM OF THE RESEARCH .....................................................................................................................
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OBJECTIVES OF THE RESEARCH......................................................................................................
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1. LITERATURE REVIEW ...............................................................................................................
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1.1
Complex systems......................................................................................................................10
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1.2
Cardiac adaptation in various physical activities.....................................................................12
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1.3
Electrocardiogram indices and concatenations.........................................................................15
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1.4
Perceived exertion....................................................................................................................17
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2. MATERIAL AND METHODS .....................................................................................................
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2.1
Participants...............................................................................................................................20
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2.2
Rating of perceived exertion scale...........................................................................................21
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2.3
Electrocardiography.................................................................................................................21
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Procedure of the testing............................................................................................................22
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2.5
Mathematical method...............................................................................................................23
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2.6
Statistical analysis.....................................................................................................................26
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3. RESULTS.......................................................................................................................................
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3.1
Perceived exertion....................................................................................................................27
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3.2
Cardiovascular system’s parameters........................................................................................31
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3.3
Cardiovascular system’s functional parameters.......................................................................33
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3.4
Correlations..............................................................................................................................39
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Mathematical method for the investigation of fluctuations
stability........................................45
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3.5.1
Endurance group .....................................................................................................................
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3.5.2
Endurance celerity group ........................................................................................................
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DISCUSSION...........................................................................................................................51
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CONCLUSIONS......................................................................................................................54
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PRACTICAL RECOMMENDATIONS:.................................................................................55
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LITERATURE LIST...............................................................................................................................56
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APPENDIX ............................................................................................................................................
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SUMMARY
a
š
t
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Slapšinskaitė A. Concatenation between cardiovascular
system’s functional parameters and
l
perceived exertion in healthy young men during rest, physical
task and recovery/ final work of master
i
a
studies research supervisor PhD E. Sendžikaitė, consultant prof.
Natalia Balague. Lithuanian University
u
of Health Sciences; Faculty of Nursing, Institute of Sports –d Kaunas, 2013, p. 62.
Evaluation of inner/intra-systemic concatenationsais becoming more popular. The application
r
of complex systems theories for differently trained people
may boost the knowledge about the
a
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intersystem concatenations and may provide more information
about functional state and perceived
exertion.
p
r
The aim of the research: To evaluate the connection
between cardiovascular system’s
i
functional parameters and perceived exertion during rest, physical
task and recovery.
k
l
Objectives of the study: 1. To assess perceived exertion
during bicycle ergometer test. 2. To
a
determine cardiovascular system‘s functional parameters ofu differently trained subjects during different
s out the correlations between perceived
test performance stages: rest, load and recovery. 3. To find
y
exertion and functional parameters of cardiovascular system
s during rest, load and recovery.
n
Contingent and methods: We had 57 young volunteers
aged (22.75 ± 0,4 year) participating
u
in this study. We divided participants according the trained
o feature in 4 groups: endurance (n=12,
t
23±0.35 year), endurance-celerity group (n=16, 20.5±0.55 year),
strength group (n=10, 24.3±0.53 year)
o
and non-active group (n=19, 23.21±2.22 year).
,
a load until volitional exhaustion. CelerityMethods: Endurance group went through constant
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endurance, strength and non-active groups went through incremental
loading where the increase was
m
a
made every minute by 50 W. The perceived exertion was measured
every 15 seconds with Borg (RPE6n
20) scale. Ongoing registration of ECG was proceeded with
ECG registration and analysis system
s
i
“Kaunas-load”. The data analysis were made with “Kaunas-load”,
special mathematical methods and
ū
statistical analysis.
l
y differed more at lighter loading while
Results and conclusions: The perceived exertion
s
gathering information about exertion every 15 seconds. During
g the maximal load of 250W the perceived
exertion was rated as very hard. The evaluation of functionalr indices of cardiovascular system registered
į
during incremental load and recovery revealed the dynamic
ž of regulatory system and myocardium
metabolism that demonstrated increase during the loadingt and decrease in recovery. The statistically
į
confident relationship was found between rating of perceived
exertion values and cardiovascular
B
a
system’s functional parameters.
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SANTRAUKA
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Slapšinskaitė A., Jauno amžiaus vyrų širdies ir kraujagyslių sistemos funkcinių rodiklių bei
i
a ir atsigavimo metu, magistro baigiamasis
suvokiamų pastangų ryšio įvertinimas ramybės, fizinio krūvio
u
darbas – mokslinė vadovė dr. E. Sendžikaitė; konsultantė prof. Natalija Balague, Lietuvos sveikatos mokslų
d
universitetas, Medicinos akademija, Slaugos fakultetas, Sporto
a Institutas – Kaunas, 2013, p. 62.
r
Populiarėjant sistemų sąveikų analizei, daugiau dėmesio
skiriama sąsajų tarp skirtingų sistemų
a
nustatymui bei vertinimui. Kompleksinių sistemų teorijos pritaikymas
skirtingai adaptuotiems asmenims gali
u
suteikti daugiau informacijos apie jų funkcinį pajėgumą bei suvokiamas
pastangas.
p
Darbo tikslas: Įvertinti suvokiamų pastangų ir širdies
r ir kraujagyslių sistemos funkcinių rodiklių
i
ramybės, fizinio krūvio ir atsigavimo metu.
k
Tyrimo uždaviniai: 1. Nustatyti suvokiamas pastangas atliekant veloergonometrinį testą. 2.
l
Nustatyti širdies kraujagyslių sistemos funkcinių rodiklių kitimą
a ramybės, fizinio krūvio ir atsigavimo metu
u
tarp skirtingo kryptingumo fiziškai adaptuotų vyrų. 3. Nustatyti
koreliacijas tarp suvokiamų pastangų ir
s
širdies kraujagyslių sistemos funkcinių rodiklių ramybės, fizinio
y krūvio ir atsigavimo metu.
s
Tiriamųjų kontingentas: Tiriamųjų kontingentą sudarė
57 jauno amžiaus vyrai, kurių amžiaus
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vidurkis tyrimo pradžioje buvo 22,75 ± 0,41 metai. Tiriamieji
buvo suskirstyti į keturias grupes pagal
u
treniruojamą fizinę ypatybę: ištvermės grupė (n=12, amžius o23±0,35metai), greičio-ištvermės grupė (n=16,
t
amžius 20,5±0,55 metai), jėgos grupė (n=10, amžius 24,3±0,53 metai) ir nesportuojančiųjų grupė (n=19,
o
,
amžius 23,21±2,22 metai).
a
Tyrime taikyti metodai: Ištvermės grupė atliko ištvermės mėginį, važiuodami pastoviu krūviu (W)
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iki išsekimo, greičio-ištvermės, nesportuojančiųjų bei jėgos
m grupės atliko pakopomis didėjančio fizinio
a
krūvio mėginį, kur apkrova buvo keičiama kas minutę, pridedant
po 50W. Atliekant testą suvokiamos
n
pastangos buvo sekamos kas 15s iki testo pabaigos, naudotas Borgo suvokiamų pastangų (RPE6-20) skalė.
Veloergometrinio testo metu registruota EKG naudojantis i„Kaunas-krūvis“ programa. Duomenų analizė
ū
atlikta naudojant analizės sistemą „Kaunas-krūvis“, specialius
matematinius metodus ir matematinę
l
y
statistiką.
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Rezultatai ir išvados: Suvokiamų pastangų dydis statistiškai reikšmingai kito esant nedideliam
g
fiziniam krūviui. Maksimalaus fizinio krūvio metu (250 W)r stebėtos labai didelės suvokiamos pastangos.
į
Fizinio krūvio metu buvo stebimas širdies funkcinių parametrų
atspindinčių reguliacinę sistemą ir širdies
ž
metabolizmą intensyvėjimas, priešinga dinamika stebėta atsigavimo
laikotarpiu. Nustatytas statistiškai
t
į bei širdies ir kraujagyslių sistemos funkcinių
patikimas vidutinio stiprumo ryšys tarp suvokiamų pastangų
B
parametrų.
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ABBREVIATION
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Bpm – beats per minute
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EceG - endurance-celerity group
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EG - endurance group
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D – Diastolic blood pressure
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IPAQ - the International Physical Activity Questionnaires
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HRV – heart rate variability
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HF - high-frequency
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LF - low-frequency
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NAG - non-active group
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Rec. – Recovery
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RPE - the Borg Rating of Perceived Exertion
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Rpm - revolutions per minute
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RR/JT - a functional relationship between supplying and regulatory systems reflecting the
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relation between JT and RR intervals
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RR/QRS - a concatenation between QRS segment and RR interval is used to study regulatory
t
changes of the inner heart
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,
S - Systolic blood pressure
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SG - strength group
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m
From 50W_1 to 250W_1 – the loading (W) and the first 15s. of perceived exhaustion
a
From 50W_2 to 250W_2 – the loading (W) and the
n second 15s. of perceived exhaustion
s
From 50W_3 to 250W_3 – the loading (W) and the third 15 s. of perceived exhaustion
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From 50W_4 to 250W_4 – the loading (W) and the
ū fourth 15 s. of perceived exhaustion
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R1- the first recovery minute
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R2- the second recovery minute
s
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R3-the third recovery minute
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R4- the fourth recovery minute
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R5- the fifth recovery minute
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GLOSSARY
Athlete's heart - A heart typical of trained athletes, and characterized by increased left
ventricular diastolic volume and increased thickness of the left ventricular wall.
Complex systems - Acknowledged features of a complex system are the following: the system
is composed of a large number of elements; the elements are often of different types and have an essential
internal structure; the elements are related by nonlinear interactions, often of several different types; the
system experiences inputs at several scales.
Cardiovascular systems’ functional parameters - RR interval (the period between two beats
of the heart), JT interval (associated with intensity of metabolic reactions) and QRS complex (brings
specific information about intrinsic regulation of the heart) are called functional parameters because they
provide additional information about human state.
Heart rate variability - is the physiological phenomenon of variation in the time interval
between heartbeats. It is measured by the variation in the beat-to-beat interval.
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INTRODUCTION
Many people participate in various sporting activities such as: long and short running, fast
walking, different contact games or weight lifting. This short list could be prolonged but encompasses
the primary sporting categories. According to the physical feature that is trained we can divide the
sporting activity into different groups. In literature usually these groups are separated into - endurance,
endurance-celerity, celerity or strength categories. However, it is important to understand that each
physical activity associates with hemodynamic changes and alters the loading conditions of the heart
[37]. Indeed, certain sports require different mental and physical skills and body build than others (just
compare basketball and gymnastics), which in turn provoke different changes in the heart caused by
exercise stimulation [77].
To date the importance to evaluate the relationship of the systems started to boost. The
importance of the analysis of the concatenations of ECG indices is based on revelation of additional
information about the person and the changes occurring in different states - rest, activity or recovery.
The ECG concatenations also exhibit a connection of the cardiovascular system with other systems in
an organism [10]. In this case, we can catch a broadened picture of processes and interactions that exist
in the human body at the indicated time with specific distraction or changes.
For evaluation of changes that appears during the physical actives both subjective and objective
methods might be used. Heart-rate assessment is a commonly used method for monitoring exercise
intensity [31, 69, 70]. The ratings of perceived exertion (RPE) provide a model for subjective estimation
of exercise intensity. Further, the RPE is effective for prescribing and regulating exercise intensity [49,
83].
Specialists of health rehabilitation through physical exercise have to manage to dose physical
activity in a most healthy way while combining subjective and objective methods. Now, smart tools are
being created with the integration of RPE scale [45]. In this case, the better understanding of the
perception during the training is valuable. Finally, it is important to evaluate the connection between
cardiovascular system’s functional parameters and perceived exertion during physical task.
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AIM OF THE RESEARCH
To evaluate the connection between cardiovascular system’s functional parameters and
perceived exertion during rest, physical task and recovery.
OBJECTIVES OF THE RESEARCH
1.
To assess perceived exertion during bicycle ergometer test.
2.
To determine cardiovascular system‘s functional parameters of differently trained subjects
during different test performance stages: rest, load and recovery.
3.
To find out the correlations between perceived exertion and functional parameters of
cardiovascular system during rest, load and recovery.
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1. LITERATURE REVIEW
1.1 Complex systems
Complex systems is a relatively new and broadly interdisciplinary field that deals with systems
composed of many interacting units, often called “agents”. We should put some classic examples of
complex systems as ecosystems, the economy and financial markets, the brain, the immune system,
granular materials, road traffic, insect colonies, schooling behavior in birds, the Internet, and even entire
human societies [62]. The main characteristics of complex systems are that multiple variables, or
dimensions, which are interconnected and interdependent, characterize it. The degree of connectivity
between these elements, dimensions and levels has a profound influence on how change happens within
the broader system [63].
Emphasis is going to be put on complex systems such as people or a person’s heart. Complex
systems such as people and hearts have multiple emergent levels. It is worth noting that each level
generates phenomena that are more than the sum of the parts and is not reducible to the parts [62].
Overall it is agreed that more may be learned about complex systems by trying to understand the
important patterns of interaction and association across different elements and dimensions of such
systems [89]. It appears that the stepping stone for the understanding the complex systems is to start to
understand the interconnections and associations between interconnecting elements, parts, and systems.
The last year studies have shown a great importance of complexity in body functioning [9]. As
well as in sports where the importance of wholeness occurred - the most important thing is not to be
focused on a certain aspect, but always bearing in mind the influence of the whole organism [85]. More
scientists discover the nonlinear complex systems approach and the linear approach becomes less
auspicious. The biggest difference between nonlinear and linear approaches are that the linear ones state
that living organisms may be successfully understood and modeled as technical devices decomposed on
control with explicit feed-forward and feedback loops. Linear systems are proportional in a sense that
the output of the system is always proportional to the input [51].Conversely, in complex systems the
changes are not proportional - small changes in any one of the elements can result in large changes
overall [51, 63].
A simple question may begin to appear: how does one study complex systems, which as their
name makes clear are complex and complicated for studying and understanding? In the past decade,
some models that help to evaluate the complexity of the systems have appeared. One of the most
successful attempts to understand the human organism as a complex system is to observe an integral
evaluation model (Vainoras, 1996) of the human functional state. This model integrates the main
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functional systems of the human body which were distinguished by Vesalius. The theory states that the
human organism can be subdivided into the following components– skeletal and muscle system (also
known as the performing system), cardiovascular system (supplying system), central nervous system
(regulatory system). The fourth system – known as the breathing system was integrated with the
cardiovascular system forming the supplying system (Figure 1). Together these systems are called
holistic systems [20]. It has been accepted that the adaptation of an organism arises due to integration of
aforementioned system reactions.
Fig.1 An integral evaluation model (Vainoras, 1996)
Due to the human body’s organization as a holistic system, it is not sufficient to solely evaluate
one of the chosen systems for the accurate assessment of functional states in active and non-active
persons. It is highly recommended to evaluate all the systems, their dynamics and concatenation [18].
It is known that the integral evaluation model of the human functional state allows for the
assessment of functional between the three systems cardiovascular system, central nervous system and
muscles. The cardiovascular system is one of the holistic systems of the human body presents the
reactions of cardiovascular system to constant load test and all-out test reveal the peculiarities of body
functioning. Thus, all three systems react together through adaptation processes in an organism, and the
general reaction of the body is always the combination of responses from these systems [20].
For evaluation of the different systems – performing, regulatory, supplying different
measurement values were selected. The performing system was measured by the reached power (N) or
(S-D) – pulse pressure (difference between systolic (S) and diastolic (D) arterial blood pressure). The
regulatory system was evaluated by examining the RR interval (the period between two beats of the
heart), as well as systolic arterial blood pressure. Changes in supplying system were measured by the
changes in the JT interval. The changes in those indices (marked by ∆) define the adaptation of the
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organism while performing an assigned physical task [11]. This model was used in various research
projects that included healthy non-active people [11], athletes of different sporting backgrounds [5, 21],
and patients with ischemic heart disease where the aims of the study were to evaluate the structural and
functional features that are determined by the physical load. One of the most important advantages of
this integral evaluation model is that is highly informative for evaluations of inner/intra-systemic
concatenations [1, 2].
With regard to complex system theory, it attempts to analyze the features, dynamics, complexity
and changes of adaptation of the main organism’s systems, as well as try to formulate incorporated
conclusions sustaining gained results.
Since, the systems of human body can be explored in different fractal levels (e.g. molecules,
cells, tissues, organs, systems) it is difficult to deny the requirement to analyze the complexity, dynamics
and concatenations between systems in different fractal levels.
1.2 Cardiac adaptation in various physical activities
In this part we will explore the reasons why cardiac adaptation may differ from human to
human. First, cardiac adaptation subjects to activity type and engagement. A significant role in
developing and improving the velocity of adaptation of cardiovascular system at onset of exercise is
played by the exercise type or type of adaptation and the phenotype of the participant [9, 37, 82]. It is
well known that non-trained persons cardiac reactions strongly differ from active ones and the active
group differs in itself because of different training types. Even persons who train the same physical
feature tend to differ because of personal morphological differences. In recent years, research has
focused on cardiac morphologic and functional changes caused by professional, long-term physical
training [79]. Intensity of exercising is the third reason that makes the cardiac adaptation vary. Finally,
the importance of the loading must not be overlooked due to the fact that heart adaptation for high loads
is one of the most important conditions which influence adaptation of the organism to the surrounding
environment [1, 2].
The enlarged in literature called as “athlete's heart” represents the most striking evidence of
functional and structural adaptation of the heart to long-term, frequent physical training. Early studies
reviewed that 3 hours of exercise per week are necessary to increase myocardium mass [39]. The
myocardium mass and wall thickness with or without changes in chamber size depend on exercise
amount. For athletes involved in sports with mainly static or isometric exercise, myocardium mass is
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thought to develop as a consequence of pressure load [37]. Practicing a strength discipline predisposes
athletes to an occurrence of concentric remodeling – the muscle overgrows inside the heart [53, 54],
whereas in endurance athletes, combined thickening of the ventricular wall and cavity dilation can be
observed (eccentric hypertrophy) [54]. Recently it was confirmed that dynamic sports lead to an
eccentric remodeling – where ventricular cavity enlargement is accompanied by a proportional increase
in wall thickness [53]. Dynamic, aerobic exercise training has functional and morphologic
cardiovascular effects, which include, among others, resting bradycardia, blood pressure reduction,
increased maximal oxygen uptake, as well as ventricular dilation and hypertrophy [37]. Half of athletes
that participate in endurance or strength training have a defined hypertrophy of the left ventricle and
consequently an increased mass of the myocardium [12]. Exercising with a hypertrophied heart doubles
or triples the mass of blood distributed through the body with a single beat in half the time compared to
non-athletes.
Further interest is in the difference of dynamic sports e.g. the sprint and endurance categories may cause different functional changes in the heart reaction. The main differences in the content of
training between the sprint and endurance cohorts consist in prevailing the interval methods of training
in sprinting and sustained exercise in endurance events [67]. Sudden changes in intensity of workloads
during a fight are a typical characteristic of combative group sports. Thus, these differences could
possibly explain the variances in values of the velocity of adaptation between the endurance and sprint
or dualist cohorts that was found out in study [9]. The changes of cardiac adaptation differ from
individual to individual because of variations in the type of exercises, duration and intensity of training
and differences of the surrounding environment.
The variation in the timing between beats of the cardiac cycle, known as heart rate variability
(HRV) is one of the most frequently, extensively and easily analyzed functional parameters of
cardiovascular system. An average resting heart rate in an adult is about 70 beats per minute (bpm). The
normal range is highly variable. Trained athletes may have resting heart rates of 50 bpm or less, while
someone who is excited or anxious may have a rate of 120 bpm or higher. The main hemodynamic
features that increase during physical load are not only the heart rate but also stroke volume [39]. Due
to the variability of the first parameter focus should be directed on the second component of cardiac
output – stroke volume. Stroke volume is the volume of the blood pumped by ventricles per contraction,
and is directly related to the force generated by the cardiac muscle during contraction. As contraction
force increases a subsequent increase in stroke volume appears as well. Hemodynamic changes are
determined by the increase of heart rate and arterial blood pressure. These changes demonstrate the
ability of physical loads to modify the cardiac autonomic outflow and by bar reflexes [20].
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Myocardial contractility is controlled by the nervous and endocrine systems. The autonomic
nervous system regulates homeostatic function of the body including cardiovascular function during
exercise and recovery, executing a rapid shift in autonomic output during the transition from one state
to the other [72]. The regulatory system includes vagal cholinergic and sympathetic noradrenergic nerves
that supply the heart and sympathetic noradrenergic nerves that enmesh arterioles, which are a major
determinant of total peripheral resistance to blood flow in the body and therefore have influence as the
blood pressure [44]. Parasympathetic vagal nerve impulses cause the heart rate to slow, and sympathetic
impulses cause the heart rate to increase. To date, heart rate variability was widely used as a noninvasive
method to estimate function of autonomic nervous system during rest, exercise, and recovery [72]. It is
well known that the lowered variability of heart rate is associated with heart pathologies because in a
healthy state, heart rate intervals fluctuate [87] with deterministic chaos (i.e., patterns of fluctuations
recur in larger fluctuations over time and are sensitive to the initial state) [69, 79].
Many different nonlinear analysis methods have been applied for the evaluation of
cardiovascular variability [91]. Standard methods of HRV estimation are based on the measurement of
intervals between heart beats using peaks of R waves in the electrocardiogram (ECG) as markers
[59].The extraction and evaluation of physiologically relevant information from HRV data is supported
by both the time and frequency domain methods [24, 44]. Measures in the time domain include the
standard deviation of heart rate and the standard deviation of heart rate normalized for absolute heart
rate [44]. The conventional frequency domain measure is the power spectrum of HRV. Consistently
identified features of this spectrum are a low-frequency (LF) component centered around 0.1 Hz
(frequency band between 0.04 and 0.15 Hz) and a high-frequency (HF) component which usually
appears in the frequency band between 0.15 Hz and 0.5 Hz [57]. Over recent years’ time–frequency
analysis has emerged as the most favored approach to improve the analysis and interpretation of the
changing spectral composition of non-stationary HRV [25, 26, 44]. However, the time–frequency
analysis of HRV signals represents a major methodological challenge, because conventional techniques
of digital spectral analysis, such as the fast Fourier transform (FFT) [32], are not suitable for short-term
spectral decompositions. Unfortunately, there is insufficient detailed physiological knowledge to
describe HRV in terms of adequate mathematical models pertinent for both the time and frequency
domains [57].
A number of recent studies reported influential variables to the heart rate and blood pressure
as: age, gender, heart disease, neurological disease and exercise [26, 34, 36] . As it was discussed in the
previous chapter, during rest, heart rate depends on person’s activity status. For the athletes the heart
rate is lower compared to non-active [32]. By increasing the intensity of exercise, the heart rate will rise
15
till it reaches its maximal output. As a result, the intensified physical load makes the heart rate become
more rapid due to an increase in the activity of sympathetic nervous system.
Although an increase in heart rate is proportional to the increase of the load, there is some
contradictory evidence showing that the heart rate, increases differently according to the training.
Kajeniene (2010) demonstrated that heart rate of healthy athletes at rest was slower, and during load, it
increased more slowly than that of non-athletes [4]. In this case, a decreased reaction of the heart rate to
the same physical load shows improved function and contraction of myocardium [32, 79] as well as
body adaptation to physical load [4].
There are contrary research positions about the vagal and sympathetic influence on the recovery
period. One group of scientists make the assertion that heart rate during recovery is mainly modulated
by fluctuations in vagal nerve activity [55]; while others generally agree that an increase in vagal activity
plays a major role in decreasing heart rate solely during the first minute of recovery, with further
decreases in heart rate mediated by both sympathetic and vagal systems [72]. The recovery period is
influenced by the intensity of exercise. For instance, a significant delay in post-exercise heart rate
recovery has been observed following moderate to high intensity exercise when compared with exercise
at a low to moderate intensity [31, 52, 55, 56, 81]. A significant decrease in heart rate was found during
the first five recovery minutes [71]. Until now analysis of heart rate variability is used in science [14,
76].
1.3 Electrocardiogram indices and concatenations
The electrical activity of the heart is recorded as an electrocardiogram (ECG). In clinical
practice, electrocardiography is a widely used non-invasive, inexpensive, and useful procedure. It is well
known that any electrocardiographic waveform reflects a potential change in the electrical field on the
body surface, generated by a corresponding change in membrane potentials within the heart [32]. ECG
provides an enormous amount of information. The normal ECG is composed of several different
waveforms that represent electrical events during each cardiac cycle in various parts of the heart. ECG
waves are labeled alphabetically starting with the P wave, which corresponds to depolarization of the
atria. The next trio of waves is QRS complex, represents the progressive wave of ventricular
depolarization. The final wave, the T wave, represents the repolarization of ventricles. The J point is the
junction between the end of the QRS and the beginning of the ST segment [73]. Below we observe the
16
functional parameters of an ECG and provide a picture of different complexities associated with each of
the functional parameters [49] (Figure 2).
RR interval – represents the time interval between two beats of the heart. The interval of two
beats depends on person’s physical activity level, and its adaptation towards specific tasks.
QRS complex – represents the progressive wave of ventricular depolarization. It is also one of
the functional parameters of the ECG. It brings specific information about intrinsic regulation of the
heart.
JT interval is not dependent on the ventricular depolarization pattern and can be used as an
accurate means of following the duration of ventricular repolarization. In ECG the shortening of JT
interval is associated with intensity of metabolic reactions [15].
ST segment occurs after ventricular depolarization has ended and before repolarization has
begun. It is a time of electrocardiographic silence. The ST segment is usually isoelectric (zero potential
as identified by the T-P segment) and has a slight upward concavity. However, it may have other
configurations depending on associated disease states (e.g., ischemia, acute myocardial infarction, or
pericarditis). In these situations, the ST segment may be flattened, depressed (below the isoelectric line).
Depression is related to the origin of ischemic phenomenon in the myocardium [3]. When a person is
participating in sporting activities and the blood flow in heart vessels stops being sufficient, the
occurrence of misbalanced changes in metabolic begins and appears in ECG [13]. The evaluation of
ischemic phenomenon during the physical load has its significance and shows the functional capacity of
the heart. ST segment depression positively could be influenced by improving myocardial nutrition as
well as increasing effectiveness of oxygen delivery to the supply system [15].
Fig.2 ECG complexity according time
ECG complexity differs from one ECG index to another. The most important thing that creates
the biggest influence to the complexity of ECG is the duration of the indexes. As it is seen from the
17
figure 2 the highest complexity is gained at QRS complex, where the time is the shortest; while, the
lowest complexity belongs to RR interval. Time and complexity are oppositely dependent.
In general, the importance of the analysis of the concatenations of ECG indices is based on
revelation of additional information about the person and the changes occurring in different states - rest,
activity or recovery. The ECG concatenations also exhibit a connection of the cardiovascular system
with other systems in an organism [7]. In this case, we can catch a broadened picture of processes and
interactions that exist in the human body at the indicated time with specific distraction or changes. For
instance, in some studies that investigated the concatenation of ECG parameters by applying an algebraic
method of data co-integration attained surprisingly interesting results [16, 20]. It was defined that
different ECG indices concatenations provide a unique meaning in these relations:
1) RR/JT concatenation. Scientists analyzing the complexity of organism functions determined
a functional relationship between supplying and regulatory systems reflecting the relation between JT
and RR intervals [9]. Therefore, some proposals for the evaluation of velocity of adaptation were set
down. It is recommended to observe JT interval variations in comparison with RR variations [49]. What
is even more interesting is the fact that individual peculiarities and differences between cohorts in
velocity of cardiovascular system adaptation at the onset of exercise can be evaluated making use of the
difference between the relative changes of RR/JT intervals of the ECG. Hence, the changes in the RR/JT
ratio were dependent on the performance abilities (training experience) and functional state [9]. In
regards to RR/JT concatenation, highly-individual knowledge about the participant presentable
functional state can be gathered just from observation of the interactions between the systems that retain
prehistoric training patterns.
2) ST/JT concatenation. A concatenation between ST segment and JT interval was analyzed in
order to study the inner heart functional changes. The changes occurring with the beginning of the
recovery can indicate a certain changes of the heart function, which allows the returning to the general
state [5].
3) RR/QRS concatenation - a concatenation between QRS segment and RR interval was
analyzed in order to study regulatory changes of the inner heart.
1.4 Perceived exertion
Perceived exertion can be defined in many ways, but the most frequently used definition is: a
configuration of sensations: strain, aches, and fatigue involving the muscles and the cardiovascular and
18
pulmonary systems during exercise [42]. Hence, it can be understood as a great strain on the
musculoskeletal, cardiovascular, and pulmonary systems [60]. Rating of perceived exertion measures
one’s individual judgment of the personal working capacity. It has been considered a pivotal feature of
exercise that is involved in the regulation of pacing [90].
It is important to understand that the interaction of various factors, influences the rating of the
perceived exertion value and some of these factors may intuitively be given a subjective weight during
questioning. For example, it has been demonstrated that subjects perceived an equivalent exercise
intensity to be lower when they believed that the exercise would last longer; that is, lower RPE scores
were reported [27, 28] . These results were believed to have been caused by psycho-physiological
mechanisms such as run economy [28] and attention focus [27, 71]. Even though, exertion results from
the complex integration of different inputs to the central nervous system [48]. These inputs include
afferent feedback from the peripheral organs most active during aerobic exercise (i.e., skeletal muscles,
heart, and lungs) with or without additional inputs from the CNS itself, such as knowledge of the exercise
task endpoint [50, 57]. The individual’s expectation concerning the amount of work to be performed
during exercise has previously been recognized as an important psychological factor in determining how
individuals perceive exertion [47, 65]. Conversely, when it was though [57], the newest papers present
that rating of perceived exertion are independent of gender [68], age [78] and fitness status [40].
Therefore, RPE was invented to describe the intensity of exercise. Unpredictably, some studies
found that RPE was able to foresee the maximal work capacity with less error compared to heart rate.
The total work performed was estimated to be within an average of 1% of maximal work capacity when
using RPE. Heart rate overestimated the maximal work capacity by ∼15% in the same study [40]. It is
possible to compare the actual and perceived exertion of the subjects during the exercise test using the
heart rate to rating of perceived exertion ratio (HR:RPE). Moreover, subjective nature takes the duration
of exercise into account, during exercise and recovery [65]. The rating of perceived exertion primarily
depends on beliefs, knowledge, experience, and expectations of the participants (exertion of periphery
itself is being integrated in CNS in a complex way).
Rating perceived exertion values can be compared with physiological measurements such as
heart rate, blood lactate [86]. Heart rate may be only one of several sensory cues that mediate perceived
exertion. For instance, rating of perceived exertion is closely related both to metabolic (lactate
concentrations) and cardiac (heart rate) intensity parameters. It is estimated that in healthy subjects,
strong relationships exist between rating of perceived exertion and heart rate during physical activity (1
RPE point is approximately 10 bpm) [78]. In addition, the relationship between heart rate variability and
rating perceived exertion supports assumption that the RPE rating is a “basic” subjective variable giving
decent knowledge about the exercise-induced tolerance levels [65, 84]. It is challenging to define
19
coordination dynamics not only at the level of coordinating neural systems, muscles, and actions but
also at the level of interactions between psychological and physiological processes in performance
during exercise [29]. In conclusion it should be noted that reactions to the load differ from subject to
subject due to the organism’s ability to adapt to various physical requirements at different health statuses.
In this case, the application of complex systems theories for people with different types of adaptation
may boost the knowledge about intersystem concatenations by providing additional information for
future training. Additionally, the analysis of concatenation between subjective and objective data will
hopefully provide useful information for real life applications.
20
2. MATERIAL AND METHODS
2.1 Participants
Healthy participants aged from 18 to 28 volunteered for this single procedure study after
bioethical comity gave as permission (number BEC-SRFP(M)-87) for the investigations (appendix Nr
1). The participants were selected of relatively similar age group as the mechanism of fatigue varies with
age. The total number of participants was 57 (45 Lithuanian participants and 12 Spanish). The
characteristic of the groups are provided in the table nr 1. Only males were chosen as the ECG varies
with gender. Four groups were formed according their respective physical activity type (endurance
group (EG), endurance-celerity group (ECeG), strength group (SG) and non-active group (NAG).
Groups except of endurance group were made of Lithuanian participants. For participation in endurance
group we involved Spanish participants. Each participant in the group was required to have been trained
the same physical feature for at least 2 years. The exception was made only for the physically non-active
man group where participants with 1 year of physical inactivity and without previous history of high
level physical activity were included. The participants prior the testing answered the Physical Activity
Readiness Questionnaire (PAR-Q) and none of contraindications have been observed. Limitations of
gender, age and term of physical activity/non-activity were put in order to acquire relatively homogenous
groups. Comparing the time (days and minutes) spent in vigorous physical activity the non-active group
showed statistically significant differences comparing with strength and endurance-celerity groups
(p<0.05). Groups haven’t differed in days spent walking, although the time spent walking was
statistically lowest in the non-active group. The group of non-active persons spend more time in sitting
position.
Table 1. Characteristic of the subjects
BMI (kg)
(average±SEM)
Age
(average±SEM)
ECeG
(n=16)
I
EG
(n=12)
II
SG
(n=10)
III
NAG
(n=19)
IV
21,87±0,38
22,17±0,28
27,54±0,84
23,21±0,47
20,5±0,55
23±0,35
24,3±0,53
23,21±2,22
I:III;II:III; III:IV,
p<0.05
I:II; I:III; I:IV;
p<0.05
21
2.2 Rating of perceived exertion scale
The Borg 6–20 RPE Scale was designed to assess sensations of exertion in relation to
physiological markers that rise with increments in exercise intensity. For determination of the task
objective (e.g. heart rate) but also subjective markers (e.g. Borg’s Scale) were used [30].
The 15-point RPE6-20 scale is illustrated below examples of the rating equivalents where: 6
points equated to sitting down and doing nothing, 9 would be walking gently, 13 a steady exercising
pace and 19/20 the hardest exercise you have ever done.
Fig. 3 Borg 6–20 RPE Scale
2.3 Electrocardiography
A computerized electrocardiogram (ECG) analysis system “Kaunas-load” that was developed
at the Institute of Cardiology of Lithuanian Health Science University (LUHS) was applied in this study
[19]. The ECG analysis system was used throughout experiments for the monitoring of reactions
occurring in the cardiovascular system while simultaneously recording 12-lead standard derivations.
Intervals of RR, JT, QRS and concatenations of RR/JT, RR/QRS were measured during entire test using
specialized computer software “Kaunas-Load”.
22
2.4 Procedure of the testing
Participants received the explanation of the test setup: all of the participants were familiarized
with Borg‘s RPE 6-20 scale prior the test. Subjects had to self-monitor rating of perceived exertion
according to RPE6-20 scale while performing a bicycle ergometer task. To ensure accurate perception
of RPE and maximal focus on the task, the participants were asked “report” every 15 seconds.
The testing procedure for all groups involved cycling protocol with ongoing ECG registration,
as well as indirect measurements of arterial blood pressure utilizing Korotkov method during rest and in
every minute of cycling and recovery.
The bicycle ergometer task differed between endurance-celerity group, strength group, nonactive group and endurance group. The bicycle ergometer test consisted of an incremental increase
during the provocative workload (graded stress) test (figure 4). It was divided into three parts for
endurance-celerity group, strength group and non-active group groups: 1) rest of 1 minute, 2)
incremental cycling took 5 minutes, where the capacity was increased 50 watts every 60 seconds (70
rpm) unless distressing cardiovascular symptoms appeared 3) the recovery period consisted of 5
minutes. General length of the test for endurance-celerity group, strength group and non-active group
groups was 11 minutes.
Fig. 4 provocative work load protocol applied for subjects of ECeG, NAG, SG groups
23
The constant-load bicycle ergometer test (figure 5) for endurance group was divided into 5
parts: 1) rest – 1 minute interval before the test 2) constant-power cycling (70 rpm) at individually
chosen intensity until reaching value of 15 in RPE 3) middle minute between RPE=15 and volitional
exhaustion 4) cycling and reporting the RPE (every 15 sec.) until reaching the volitional exhaustion 5)
5 minutes of recovery after the volitional exhaustion. Length of the test was dependent upon reach of
voluntary exhaustion.
Fig. 5 The constant-load bicycle ergometry test
2.5 Mathematical method
A special algebraic algorithm based on the rank of a sequence, for the analysis of ECG signals
was proposed. It was applied for analysis of physiological processes during the bicycle ergometer test
[6]. The concept of ranking a sequence and its application for the investigation of fluctuations stability
and the characteristic Hankel determinant for the sequence
is defined by Navickas Z. et al.
2006:
Let
is the time series of ECG parameter of length N consisted of several
segments of algebraic progressions (Eq. (7)). There was proposed the segmentation method for algebraic
progressions. Method was applied for sequences without noise. Unfortunately, time series of ECG
24
parameters are noisy thus the proposed method could not be applied directly. The main problem is that
the rank of the noisy sequence does not exist.
Let time series of ECG parameters Y is segmented manually into k non-overlapping contiguous
segments (Figure 6):
where
,
is the start and
- the end position of
segment
Figure 6. ECG parameter segmentation into k segments
The main task of the method is to find an algebraic progression
of segment
with the condition:
(10)
The detailed method for algebraic progression
construction is given in[6].
25
Now, let
the segment
algebraic progression
,
,
,
. Then accordingly parameter
is an algebraic progression of
could be distinguished components of
:
Every distinguished component has different nature of dynamic: (a) inhibitory, (b) stationary,
and (c) stimulant:
(a)
(b)
(c)
Parameters
with different nature of dynamic are placed on the unit circle (Fig.7).
(a)
(c)
Figure 7. Parameters values
(b)
(d)
of algebraic progression: (a) inhibitory, (b) stationary, (c)
stimulant, (d) all;
26
2.6 Statistical analysis
Data were analyzed using mathematical statistical methods. All the parameters are provided by
their means (𝑥̅ ) and the standard error of the means (SEM). Hypothesis about differences between the
distributions were calculated by non-parametric Wilkinson test for related data. For statistical
calculations we used SAS and excel 2007 software. Significance was set at a level of p<0.05. For
estimation of dependence between two random variable and two sets of data was calculated by
Spearman's rank correlation coefficient. Correlation coefficient was interpreted:
If 0 <r< 0.3 weak correlation
If 0.3<r<0.7 moderate correlation
If 0.7<r<1 strong correlation.
The correlation was considered statistically significant when p<0.05.
27
3. RESULTS
3.1 Perceived exertion
RPE values were received in endurance-celerity group, non-active group, strength group groups
(n=45) while every 15 seconds participants evaluated their configuration of sensations: strain, aches, and
fatigue that appeared in the muscles and the cardiovascular and pulmonary systems during the load
(figure 8). The total number of requests for scoring RPE during the task was 44. Significant difference
of the perceived values were found only between 9 close values (p<0.05) (figure 8). The minimal values
of RPE=6 were found during the rest 1st-4th requests and evaluation of RPE value. The maximal value
of exertion was reached at 250W in the last request of loading (RPE=18.13±0.55).
Figure 8. Dynamic of perceived exhaustion collected every 15 sec. (* - p<0.05; difference
between distributions)
The 15 seconds of observation provided a possibility to look for the dependency of the
perception and loading (figure 9). There were fewer significantly different values at higher intensity
28
starting from 150W second request of RPE values till third request of 250W (p>0.05). More differences
of RPE values were observed in lighter loading while monitoring and evaluating values every 15 seconds
The perception differed at these intervals 50W 3rd and 4th requests also in 100W 4th request of RPE and
150W first request (p<0.05). From the figure 9 you can see that the first difference of rating of perceived
exertion appeared when the load was induced (4th request of the first recovery minute and 50W first
evaluation of the perceived exertion) (p<0.05). RPE values at the load started to differ - RPE value at
the 50 third and fourth requests of RPE scores (p<0.05).The values of RPE also differed at 3rd and 4th
request of RPE values at intensity of 100W and the periods of 100W 4th request and 150W first request
(p<0.05). When the loading was finished we found a difference of the perceived exertion at the last 15
seconds of loading of 250 W and the beginning of first 15 seconds (first request) of the rest (p<0.05).
The last difference appeared at the first recovery minutes ‘second and third requests (p<0.05).
Figure 9. Significantly different RPE6-20 scale scores while comparing closest 15 s values
(* - p<0.05; difference between distributions)
29
As only few differences were found we decided to conjugate the values of two 15 seconds
periods. In this case we got twice fewer periods of RPE. The value of RPE in the last minute of recovery
and the first 30 seconds of the first minute of 50W loading differed (p<0.05). We also found the
differences in all intervals of incremental loading starting from 50W first request and ending 250W
second request (p<0.05). The significantly different values of the RPE were got only from the last
loading request till first 30 recovery seconds (p<0.05) and also the values at the recovery differed at this
interval - last 30 seconds of the first recovery minute and the first 30 seconds of the second recovery
minute (p<0.05).
Figure 10. Dynamic of RPE6-20 scale scores collected every 30 sec. during incremental
loading (* - p<0.05; difference between distributions)
To demonstrate the dynamics of perceived exertion in different groups we calculated the
averages of RPE scores respectively in all groups. The changes of RPE values variation within the group
is presented in the figure 11. Subjects from strength group had the highest augmentation of RPE values
during the load: 50W 3rd and 4th requests-100W first request; 100W 4th request and 150W first request,
150W 4th request and 200W first request (p<0.05). In non-active group a period of loading different
values of RPE were noticed in 100W 3rd and 4th request of RPE values. The last 15 seconds of the loading
30
and the first 15 seconds of recovery showed significant decrease of the RPE values in all subjects
(p<0.05).
Figure 11. Dynamic of RPE6-20 scale scores collected every 15 sec. in ECeG, NAG, SG
groups, during incremental loading (* - p<0.05; difference between distributions)
We wanted to get extra information about correlation with subject’s physical activity amount
and the perceived exertion during the loading. The correlation between the IPAQ and RPE showed that
only two questions have not correlated with RPE (1) days with moderate vigorous physical activities (2)
time in minutes of moderate vigorous activity during the day. Vigorous physical load had positive
correlation with 50W in the first request (r=0.36) and in 50W in the 4th request (r=0.309), while the time
period of vigorous intensity load correlated only with 50W 4th request (r=0.306) (p<0.05). The days in
which the person was walking had negative correlation with RPE value at 150W 4th request of RPE
value (r=-0.40) (p<0.05) as well as a time spend walking had a negative correlation at rest first minute
first request of RPE value (r=-0.32). The period spent sitting had a positive correlation with RPE values
at 50W, 100W, and 150W (0.3<r<0.7) (p<0.5).
31
Table 2. IPAQ and RPE values of correlation coefficient r
RPE
IPQ
Vigorous
physical
activity (days)
Vigorous physical
activity (min)
Walking
(days)
Walking
(min)
Sitting
(min)
50W
1st request
0.37*
0.23
0.04
-0.12
0.11
50W
3rd request
0.23
0.12
0.05
-0.04
0.38*
50W
4th request
0.30*
0.31*
0.12
0.19
0.21
100W
1st request
0.15
0.18
-0.09
0.05
0.33*
100W
3rd request
0.10
0.14
-0.01
0.13
0.31*
100W
4th request
0.02
0.06
0.02
0.05
0.42*
150W
1st request
0.13
0.20
-0.15
0.12
0.12
150W
4th request
-0.03
0.14
-0.4*
-0.04
0.38*
Rec.
1stmin.
1strequest
0.22
0.17
-0.15
0.32*
0.16*
(*-p<0.05)
3.2 Cardiovascular system’s parameters
Systolic and diastolic blood pressure was measured in all groups during the test. The rising
alteration of systolic blood pressure was observed from the onset of the physical task till the end of the
increase of the 250W load (p<0.05) (figure 12). At the recovery period the decrease of systolic pressure
was observed (p<0.05). The highest value of systolic blood pressure in the maximal load 250W showed
non-active group (189±8) while minimal values were reached by endurance-celerity group (180±6). In
the first recovery minute subjects from non-active group, endurance-celerity group showed a decreasing
dynamics while the value of systolic blood pressure stayed at similar level in the strength group (183±3).
Diastolic values were at the same level around 76±3 mmHg in all groups in the beginning of
the protocol. The same level was kept in the end of resting. The dynamics of diastolic blood pressure
during the test was not homogenous as in the systolic blood pressure. Participants from the sprint cohort
32
endurance-celerity group demonstrated increased diastolic values with the increase of the load. Opposite
tendency was observed in strength group where diastolic blood pressure moved down until reaching
200W. Afterwards some fluctuations were observed. The decrease of diastolic blood pressure from
150W to 200W was statistically significant (p<0.05). The highest change of the values of diastolic blood
pressure appeared in the highest loading minute until first recovery minute (p<0.05).
Figure 12. Values of systolic and diastolic blood pressure in ECeG, NAG, SG group during
incremental task (* - p<0.05; difference between distributions)
All participants irrespectively to the attending group started with an increased heart beat (figure
13). In endurance-celerity group the beginning value was 94±3 heart beats per minute (bpm) while in
non-active group 92±3 bpm and 87±35bpm in the strength group. The values of HR in every interval
with the load increased significantly for all the subjects (p<0.05). The maximal values of HR of the
groups were reached in the loading of 250W (endurance-celerity group presented 169±3bpm, non-active
group HR=175±2bpm and strength group=140±15 bpm). The HR values stopped increasing when the
33
recovery started. The significant lowering of the HR in all groups was observed till 2nd recovery minute
(p<0.05).
Figure 13. Values of heart rate in ECeG, NAG, SG groups during incremental task
(* - p<0.05; difference between distributions)
3.3 Cardiovascular system’s functional parameters
JT interval showed a decreasing tendency in dynamics during the load and significant
increasing dynamics at the recovery period (p<0.05) (figure 14). The lowest value of JT interval in each
group was reached at different intensity for non-active group JT=139±5 (s.) showed its minimum at
200W, in 250W endurance-celerity group reached 155±3 (s.) that was their minimal value. Strength
group had the shortest value (JT=164.8±4s.) at the 1st minute of recovery.
34
Figure 14. JT interval changes in ECeG, NAG and SG groups during incremental task
(* - p<0.05; difference between distributions)
QRS complex showed fluctuating dynamics in all groups in the load and the recovery periods
(figure 15). The biggest amount of significant changes of the QRS complex was observed in non-active
group in these periods (150W-200W, 200W-250W) (p<0.05). The strength group showed significant
decrease in (QRS) interval at 250W and the first recovery minute (p<0.05) while endurance-celerity
group showed an increase (p<0.05) and non-active group a tendency of increase (0,1<p<0.05).
35
Figure 15. QRS complex changes in ECeG, NAG, SG groups during incremental task
(* - p<0.05; difference between distributions)
The subjects from three different groups: endurance-celerity, non-active and strength attended
the same protocol with ongoing ECG registration. Analysis system “Kaunas-load” helped to calculate
the concatenations of ECG functional parameters – RR/JT and RR/QRS. Firstly, the averaged data of 45
participants presented in figure 10 showed no significant differences at the beginning of the test (1
minute rest interval and 50W). The differences of RR/QRS concatenation were discovered with the
increase of the load. The first significantly different augmentation of the discriminant value was found
in 50W and 100W (p<0.05). The discriminant of RR/QRS was increasing in 100W-150W (p<0.05). The
peak of augment was reached at maximal loading from 200W till 250W (p<0.05). During the data
exploration, RR/QRS discriminant decrease in the first recovery minute wasn’t noticed to be statistically
different. The rapid change with decreasing trajectory of RR/QRS concatenations’ discriminant value
was determined in comparison of first and second recovery minutes (p<0.05). The values of RR/QRS
discriminant were fluctuating in the following recovery 2nd-5th minutes, but no significant difference was
found.
36
Figure 16. Dynamic of discriminant of RR/QRS of all subjects during the incremental task
(* - p<0.05; difference between distributions)
We observed not only the dynamic of RR/QRS but also dynamic of RR/JT. With the onset of
the load the discriminant of the concatenation of RR/JT has increased (figure 17) (p<0.05). However,
the increased dynamic was characteristic only for the inducement of the load. The following changes
that we observed had an opposite dynamics. The discriminant values of RR/JT showed reduction of the
discriminant value towards the increase of the load (50-100W and 150-200W) (p<0.05) and for the
intervals (100W-150W and 200W-250W) we get the tendency of decrease of discriminant of RR/JT
concatenation (0.06<p>0.05). In the first minute of recovery the dynamics remained similar to the
loading and kept decreasing pattern (p<0.05). In the recovery period the values of RR/JT discriminant
appeared to be growing statistically significantly in the period (R3-R4) (p<0.05). Intervals (R1-R2, R2R3, R4-R5) demonstrated the tendency of increased dynamics (0.1<p>0.05).
37
Figure 17. Dynamic of discriminant of concatenation RR/JT of all subjects during the incremental
task (* - p<0.05; difference between distributions)
The differences of RR/JT discriminant within the groups are provided in the figure 18. Most of
differences of the changes in discriminant value were found in the endurance-celerity group during these
intervals (150W-200W, 200W-250W, R1-R2, R2-R3) (p<0.05). In strength group and non-active group
at the interval 100W-150W the changes of discriminant were reversed (the change in non-active group
was from 0,11±0,03 to 0,088±0,02 while in strength group from 0,15±0,06 increased to 0,17±0,05)
(p<0.05).
38
Figure 18. Dynamic of discriminant of concatenation RR/JT in ECeG, NAG, SG groups
during the incremental task (* - p<0.05; difference between distributions)
The discriminant values of RR/QRS started to differ in endurance-celerity group from 50W and
100W (p<0.05). The significant difference was also found in 100W and 150W in endurance-celerity
group (p<0.05). The last difference in this group was found in recovery 1st and 2nd minutes’ values of
discriminant. In strength group values of RR/QRS discriminant did not significantly differ during the
test. Most differences occurred in the NAG values of RR/QRS discriminant, where value (0.1±0.03) in
50W differed from (0.11±0.025) in 100W and (0.14±0.03) in 150W (p<0.05). Also, RR/QRS value in
150W was not equal to discriminant value in 200W (0.13±0.02). The last significant difference was
found between recovery 1st discriminant value (0.43±0.1) and 2nd minute discriminant value (0.38±0.08)
(p<0. 05)
39
Figure 19. Dynamic of discriminant of concatenation RR/QRS in ECeG, NAG, SG groups
during the incremental task (* - p<0.05; difference between distributions)
3.4 Correlations
We assessed RPE correlation with these parameters: HR (table 3), diastolic (table 4) and
systolic blood pressure (table 5), ECG functional parameters (RR, JT, QRS) (table 6), concatenations of
ECG functional parameters RR/QRS and RR/JT (table 7) and RPE (table 8).
Only the recovery period 1st-4th min. of HR showed correlations with RPE values. The main
correlation showed weak positive dependence in 100W; 150W in the first and second requests; 1st
recovery minute first and third requests (0<r<0.3) (p<0.05). Moderate correlation was found (0.3<r<0.7)
with 50W 3rd request of RPE value, 150W 3rd and 4th requests, 150W_4 and the following requests at
the recovery – 1st, 2nd and 3rd (p<0.05).
40
Table 3. Heart rate and RPE values of correlation coefficient r
...HR
RPE
Rec.
1st
minut
e
Rec.
2nd
minut
e
Rec.
3rd
minut
e
Rec.
4th
minut
e
100W
1st
request
150W
4th
request
Rec.
1st min.
1st
request
Rec.
1st min.
2nd
request
0.19
0.27
0.24
0.28
0.36*
0.26*
0.33*
0.26*
0.32*
0.22
0.30
0.21
0.30
0.16
0.26
0.15
0.05
0.15
0.07
0.30
0.08
50W
3rd
request
50W
4th
request
100W
3rd
request
100W
4th
request
0.36*
0.17
0.29
0.21
0.31*
0.35*
0.14
0.33*
0.27*
0.29
0.13
0.28
0.05
0.40
0.06
150W
1st
request
Rec.
1st min.
3rd
request
0
0.29*
0
0.35*
0
0.32*
0
0.03*
(*-p<0.05).
The majority of the correlations between diastolic blood pressure and RPE were presented in
recovery periods. RPE values at the 1st minute of recovery 1-3 requests showed correlation with diastolic
blood pressure values in these intervals - rest, 50W, 100W, 150W, 200W. The founded positive
correlation was moderate (0.3<r<0.7),(p<0.05). 5th minute of recovery value of diastolic blood pressure
showed negative link between RPE value at the first request of 50W (r=-0.4) and first request of RPE in
100W (r=-0.41) (*-p<0.05).
41
Table 4. Diastolic blood pressure and RPE values of correlation coefficient r
RPE
Diastolic
blood
pressure in
1st rest
Diastolic
minute
50W
0.19
Diastolic
blood
pressure
in 50W
Diastolic
blood
pressure in
100W
Diastolic
blood
pressure in
150W
Diastolic
blood
pressure in
200W
Diastolic blood
pressure in 5th
minute of rec.
0.21
0.18
0.008
0.08
-0.4*
1st request
50W
3rd request
0.15
0.16
0.2
0.1
0.26*
-0.34
100W
1st request
0.22
0.23
0.06
0.13
0.16
-0.41*
Rec.
1st min.
1strequest
Rec.
1st min.
2nd request
Recovery
1st min.
3rd request
0.37*
0.43*
0.5*
0.39*
0.45*
-0.16
0.31*
0.33*
0.39*
0.26
0.3
-0.1
0.31*
0.3
0.25
0.2
0.2
0.06
(*-p<0.05)
Recovery values of RPE had moderate positive correlation with systolic blood pressure in rest,
50 W and 100W values (<0.3<r<0.7). Values of systolic blood pressure in 3rd and 4th recovery minute
correlated with 50W first and fourth requests of RPE values and 100W first, third and fourth requests
(0.3<r<0.7). (p<0.05).
42
Table 5.Systolic blood pressure and RPE values of correlation coefficient r
Systolic
blood in
1st rest
minute
Systoli
c blood
in 50
W
50W
1st request
0.17
0.3*
50W
3rd request
0.17
50W
4th request
Systolic
blood in
rec. 3rd
minute
Systolic
blood in
rec. 4th
minute
Systolic
blood in
200W
Systolic
blood in
250W
0.19
0.27
0,21
0,36*
0,31
0.27
0.17
0.3*
0.20
0.22
0.20
0.21
0.25
0.24
0.5*
0.40
0.36*
0.36*
100W
1st request
0.25
0.28
0.23
0.38
0.26
0.32*
0.36
100W
3rd request
0.29
0.26
0.20
0.28
0.09
0.34*
0.4*
100W
4th request
0.21
0.24
0.14
0.29
0.15
0.28
0.32*
150W
1st request
0.23
0.22
0.17
0.3*
0.10
0.21
0.24
150W
4th request
-0.02
0.03
-0.10
-0.05
0.08
-0.23
-0.05
0.21
0.37*
0.4*
0.14
0.03
0.14
0.17
0.22
0.37*
0.37*
0.13
0.07
0.13
0.19
0.20
0.3*
0.24
0.09
0.10
0.14
0.22
0.3*
0.3*
0.32*
0.10
0.13
0.14
0.23
Recovery
1st min.
Systolic
blood in
100W
1strequest
Recovery
1st min.
nd
2 request
Recovery
1st min.
rd
3 request
Recovery
1st min.
4th request
(*-p<0.05).
ECG functional parameters: RR, JT, QRS showed different quantity of correlations with RPE.
The biggest amount of correlation was gotten in RR 1-4th minute’s recovery intervals. We observed
negative moderate correlation with 50W and 100W 1st-3rd requests of RPE values and RR intervals
(-0.7<r<-0.3) (p<0.05). The negative correlations of JT interval and RPE values were found between
resting JT value at the first minute of the rest and 3rd request of RPE value at 50W (r=-0.32) and 4th
request at 100W (-0.3)(p<0.05). No significant correlation between QRS complex and RPE values was
found.
43
Table 6. ECG parameters (RR, JT and QRS) and RPE values of correlation coefficient r
250W
1st rec.
2nd rec.
3rd
4th rec.
JT 1st rec.
RR
minute
minute
recovery. minute
minute
minute
RPE
RPE
50W
1st request
-0.11
-0.21
-0.20
0.02
0.02
50W_1
-0.15
50W
3rd request
-0.35
-0.39*
-0.47*
-0.26*
-0.22
50W_3
-0.32*
50W
4th request
-0.17
-0.21
-0.27
-0.08
-0.06
50W_4
-0.20
100W
1st request
-0.18
-0.33*
-0.33*
-0.17
-0.18
100W_1
-0.17
100W
3rd request
-0.19
-0.26
-0.30
-0.14
-0.14
100W_3
-0.20
100W
4th request
-0.32*
-0.35*
-0.41*
-0.24
-0.23
100W_4
-0.30*
(*-p<0.05)
The correlation of RPE values at different intensities was evaluated (table 7).First and third
request of RPE at 50W and first in 150 showed only moderate correlation (0.3<r<0.7).While in 50W 4th
request, 1st request, 3rd and 4th at 100W high correlation was observed (r>0.7). Recovery values of RPE
highly correlated with recovery values of RPE (r>0.7).
44
Table 7. RPE and RPE values of correlation coefficient r during the load
RPE
50W
1st
request
50W
3rd
request
50W
4th
request
100W
1st
request
100W
3rd
request
100W
4th
request
150W
4th
Rec.
1stmin.
1strequ
est
Rec.
1stmin.
2nd
request
Rec.
1stmin.
3rd
request
0.66*
0.54*
0.54*
0.35*
0.37*
0.38*
0.13
0.14
0.12
0.26
0.67*
0.61*
0.54*
0.64*
0.55*
0.44*
0.28
0.24
0.12
0.76*
0.65*
0.64*
0.65*
0.46*
0.18
0.20
0.10
0.86*
0.80*
0.90*
0.67*
0.28
0.28
0.35*
0.92*
0.90*
0.58*
0.06
0.06
0.28
0.87*
0.59*
0.31*
0.28
0.06
0.68*
0.34*
0.29
0.36*
0.92*
0.83*
request
RPE
50W
1st
request
50W
3rd
request
50W
4th
request
100W
1st request
100W
3rd
request
100W
4th
request
150W
1st
request
Recovery
1stmin.
1strequest
Recovery
1stmin.
2nd
request
0.89*
We searched for correlation between RR/QRS, RR/JT concatenations and RPE values. RPE
values that were perceived at 100W 3rd and 4th requests and the 1st request of RPE values at 150W had
coupling with RR/QRS concatenation at the 2nd recovery minute. This correlation was positive and
moderately strong (0.3<r<0.7). RR/JT concatenation also had correlation with RPE values: recovery
values of RR/JT were negatively correlating with 3rd requests value at 100W (r=-0.3) and 4th request at
100W(r=-0.3).
45
Table 8. Concatenations RR/QRS, RR/JT and RPE values of correlation coefficient r
RPE
RR/JT
Rec.2nd
minute
RR/JT
Rec.3rd
minute
RR/JT
Rec.4th
minute
RR/JT
Rec.
5th
minute
50W
3rd request
-0.21
-0.05
-0.07
-0.09
0.31*
100W
3rd request
-0.22*
-0.24*
-0.32*
-0.30*
0.19
0.36*
100W
4th request
-0.31*
-0.21*
-0.30*
-0.29*
0.14
0.31*
RR/Q
RS250
W
RR/QRS
Rec.1st
minute
RR/DQRS
Rec.
2nd minute
-0.15
0.25
0.30*
0.08
0.07
-0.04
0.11
RPE
RR/JT
RR/QRS
50W
3rd
request
100W
3rd
request
100W
4th
request
150W
1st
request
(*-p<0.05)
3.5 Mathematical method for the investigation of fluctuations stability
3.5.1 Endurance group
EG constant-cycling power= 175 ± 30W and time until volitional exhaustion was t=23 ±
2.25min. During rest, load and recovery the following time series of ECG parameters were evaluated:
RR interval, JT interval, QRS complex.
The length of the time series of each ECG parameter was individual. Time series were
segmented into 9 segments: 1 -the rest minute, 2 - 4 minutes represents the load (2 is the moment in time
when the subjects first reported RPE = 15), 3 – the minute between RPE = 15 and the end of the task, 4
- the last minute before the end of the load), 5 - 9 represents five minutes of recovery (Fig. 20-22 x-axes).
Algebraic progressions were then constructed (with parameter  1  0.01) for each segment and
accordingly to parameter  2  0.01 their components with different nature of fluctuation were
distinguished. The same procedure was repeated for all times series of ECG parameters of all
participants. Then for every ECG parameter segment the total count of components and count of
components with different nature of fluctuation were calculated. Dividing counts of components of each
process of each segment by the total number of components of the current segment the normalized values
of different processes (inhibitory (value – α), stationary (value - β ) and stimulant (value – γ) (Fig. 20-
46
22 y-axes) were calculated. The procedure to find normalized values of inhibitory, stationary, and
stimulant processes was separately performed for RR interval (Fig.20), JT interval, (Fig. 21) and QRS
complex (Fig. 22).
During the evaluation of fluctuations, greater attention was focused to the last 5 minutes of
recovery in the dynamics of inhibitory, stationary and stimulatory processes for RR, JT and QRS
complex. The dynamics of stationary and stimulatory processes show moderately similar dynamics in
recovery. At the end of the recovery stage there is a visible increase in stationary and decrease of
stimulatory processes in RR, JT and QRS complex. The inhibitory processes show differing dynamics
in RR, JT, and QRS complex. While the JT interval remains relatively stable, QRS and RR intervals
display unique characteristics. In the QRS complex, the inhibitory processes start to decrease in the 3rd
recovery minute. These actions are antagonistic to what is seen in the RR interval where the inhibitory
processes increase. This illustrates that recovery processes are not synchronous in different levels of the
organism.
Fig. 20.Inhibitory (a), stationary (b) and stimulatory (c) processes and their fluctuations in
RR interval dynamic during volitional exhaustion ergometer test. 1 is the rest minute, 2 is the moment
in length of one minute when the subject reported RPE = 15 for the first time, 3 – middle minute
between RPE= 15 and the end of the task, 4 the last minute before the end of the load, 5 - 9 represents
five minutes of recovery
47
Fig. 21. Inhibitory (a), stationary (b) and stimulatory (c) processes and their fluctuations in
JT interval dynamic during volitional exhaustion ergometer test. 1 is the rest minute, 2 is the moment
in length of one minute when the subject reported RPE = 15 for the first time, 3 – middle minute
between RPE = 15 and the end of the task, 4 the last minute before the end of the load, 5 - 9 represents
five minutes of recovery
Fig. 22. Inhibitory (a), stationary (b) and stimulatory (c) processes and their fluctuations in
QRS complex dynamic during volitional exhaustion ergometry test. 1 is the rest minute, 2 is the
moment in length of one minute when the subject reported RPE = 15 for the first time, 3 – middle
minute between RPE= 15 and the end of the task, 4 the last minute before the end of the load, 5 - 9
represents five minutes of recovery
48
3.5.2 Endurance celerity group
Ten healthy participants (20.1±2.23) volunteered for this study, which performed a bicycle
ergometer test and the ECG was recorded continuously during all test, 12 leads synchronously. Only
males with experience in sprint (practicing more than 2 years) were chosen
The test was performed in eleven minutes where 1-2 minutes represent rest interval, 2-7 the
load minutes and 7-11 are the interval of recovery of the test (figure 23-25 x-axes). In every minute of
the test the normalized values of those different processes (inhibitory (value -  ), stationary (value -  )
and stimulant (value -  )) were calculated (y-axes) dividing Hankel algebraic equation roots of each
process to the total number of Hankel algebraic equation roots which were placed on the circles. The
procedure to find normalized values of inhibitory, stationary and stimulant process was separately
performed for RR interval (Fig. 23), JT interval (Fig. 24) and QRS complex (Fig. 25).
From the data it can be seen that at the beginning of the physical load starts the inhibitory
process begin to lead and start to be highly visible at the last minute of the load in this case it is an
interval from minute 5 till 6 , Fig. 23(a), Fig. 24(a) and Fig. 25(a). As expected, the influence of the
stimulant process decreases (Fig. 23(c), Fig. 24(c) and Fig. 25(c)) especially in the last minute of the
load. This phenomenon can be explained physiologically - working muscles tries to satisfy the expanded
energetic demand during the physical load while other organs of the human body has to adapt to lowered
supply.
Fig. 23. Inhibitory (a), stationary (b) and stimulant (c) processes and their fluctuations in
RR interval dynamic during bicycle ergometer test. The test was performed in eleven minutes where
1 minute represent rest interval, 2-7 the load minutes and 7-11 are the interval of recovery of the test
(x-axes)
49
Fig. 24. Inhibitory (a), stationary (b) and stimulant (c) processes and their fluctuations in JT
interval dynamic during bicycle ergometer test. The test was performed in eleven minutes where 1
minute represent rest interval, 2-7 the load minutes and 7-11 are the interval of recovery of the test
(x-axes)
Fig. 25. Inhibitory (a), stationary (b) and stimulant (c) processes and their fluctuations in
QRS complex dynamic during bicycle ergometer test. The test was performed in eleven minutes where
1 minute represent rest interval, 2-7 the load minutes and 7-11 are the interval of recovery of the test
(x-axes)
Another interesting finding related to complexity of ECG intervals RR, JT and QRS is the delay
in recovery processes. In the first minute of recovery RR interval shows overturned situation of
influencing process - stimulant processes rapidly increases (Fig. 23(c) from 6-th to 7-th minute) while
inhibitory process reacts (Fig. 23(a) from 6-th to 7-th minute) to opposite direction. For JT and QRS
complex it is clear expressed delay of stimulant processes in recovery interval (Fig. 24(c), Fig. 25(c) 7-
50
th to 8-th minute). It can illustrate that recovery processes are not synchronous in different organism
systems.
Stationary processes (Fig. 23(b), Fig. 24(b), Fig. 25(b)) vary insignificantly during the load and
the first four minutes of recovery. It suddenly goes up and reaches its highest value at the last minute of
recovery becoming the predominant process influencing the RR, JT and QRS complexes dynamic. This
fact may confirm that the recovery processes are over.
51
4. DISCUSSION
The current study has examined healthy young man’s rating of perceived exertion, functional
parameters of ECG during load and the correlation between these parameters. Three groups went through
incremental load and constant load was proceeded in one group. We hypothesized that perceived
exertion and functional parameters of cardiovascular system was linked. Even though our results are
consistent with this hypothesis, although study demonstrated that connections homogeneity differs from
one functional parameter to another.
First of all, we would like to discuss results that were gotten for evaluation of the changes of
rating of perceived exertion. It was unexpected that while observing rating of perceived exertion values
every 15 seconds at incremental load test (additional 50W/min.) only 9 out of 44 values differed. In
comparison 30 seconds evaluation brought on 13 of 22 differences (the differences existed during the
load independently from the workload). While rating of perceived exertion was evaluated every 15sec,
more differences were found in a light loading. Scientists have noted that rating of perceived exertion is
not sensitive to peripherally generated afferent signals at low exercise intensities, when there is little
threat to the overall body homeostasis. Cerebral metabolism rather than peripheral metabolism could
have played a pivotal role for the rating of perceived exertion generation during moderate exercise bouts
[41]. Similarly, St. Clair Gibson noted that cerebral activity, and cognitive processes, such as emotion,
memory, and motivation are the main factors that influence rating of perceived exertion at low intensity
[81].
A possible explanation could drive from the incorporations in subconscious brain. It is well
know that rating of perceived exertion is a combined result of cerebral activity and afferent signals
representing peripheral physiological changes [35, 81]. The question arrives: “Does our brains are
capable to make correct calculations of exertion in a short time while perceiving harder load?” Previous
work has shown that except for light intensity, metabolic compounds that can impudence central nervous
system metabolism, such as blood glucose, dopamine, and noradrenalin concentrations during high load
[66, 59].
A second explanation could lean on the fatigue mechanisms [66, 74]. Recently studies have
demonstrated that the response of neurons in low-level areas can be influenced by contextual information
and by feedback from higher-level areas [60]. The capacity to incorporate the effects of exhausted
cardiopulmonary and metabolic variables, loading and origination of fatigue could negatively affected
the perception itself.
52
Previous studies had pointed out responses towards exercise maximum intensity level:
sympathetic discharge became maximal and parasympathetic stimulation was inhibited, resulting in
vasoconstriction in most circulatory body systems, except in exercising muscle and in the cerebral and
coronary circulations. Resulting in total calculated peripheral resistance decrease, while systolic blood
pressure and pulse pressure increased. These reactions were observed in this experiment. Diastolic blood
pressure remained unchanged in non-active subjects group, decreased in strength group because of
vasodilatation of the vascular bed [42]. However, different reaction was observed in endurance-celerity
group. A possible explanation may be that a rise in diastolic blood pressure during exercise of >10 mm
Hg above the resting value is considered predict an increased likelihood of coronary artery disease [43,
44].
It is well known that physical load evokes systemic reaction of organism functions combining
cardiovascular system, central nervous system and muscles [65].We evaluated concatenations between
ECG functional parameters RR/JT by observing information about relation of regulation and supplying
systems. Difference between differently trained groups of cardiovascular system mobilization was
gathered also in our study [20]. The effect of physical load can also be demonstrated through the
sequence of ECG indices recovery. The first index that recovers after physical load is concatenation
between RR/JT intervals that shows relation between regulatory and supplying systems. The
concatenation of JT/RR is useful for outlining at what extent of cardio vascular function was mobilized
[56]. Subjects from non-active group reached the lowest value of discriminant of RR/JT in 200 W, young
male in endurance-celerity group at 250W and finally for males in strength group it was the first recovery
minute. That RR/JT parameter lowering dependence on adaptation while doing long physical activity
was provided in trials [5, 21, 22]. One more interesting finding was observed that values of JT/RR
equaled in groups at the second minute, but afterwards the values became different in the groups. This
data of RR/JT parameters repeated the results for basketball man, football man and non-active man [5].
All the observation evaluated rating of perceived exertion correlation with different parameters.
It was demonstrated that rating of perceived exertion values during incremental load correlated only with
HR recovery values. These results correspond the fact that heart rate is a better estimate for intensity
control in high-intensity exercises for e.g. the use of HR monitors increased exercise intensity [23].
However, a lot of papers emphasize a high or moderate positive correlation between a subjective marker
of internal load (sensation of perceived exertion) and parameters of external load (maximal HR) in
training in open field were observed [32]. The measured correlation between rating of perceived exertion
and heart rate was moderate or high [58, 76]. These findings suggest that the session of exertion can be
a useful tool to monitor internal training load during interval training. Another explanation of these
results could be backed-up towards theory of attention focus. Every 15 seconds we induced internal
53
attention focus. Different results could be gathered when attention is focused externally [68]. The
external focus resulted in decreases in perceptions of exertion, as participants were attending less to
physical/bodily sensations [65] and also associative strategy could have made a difference [81, 84]. Also,
induced loading every minute by 50 W could have influenced the correlation between heart rate and
perceived exertion. When an external stimulus is presented, sensory information is rapidly transmitted
through ascending connections from lower level sensory areas to higher-level areas analyzing
increasingly complex and “global” aspects of the stimulus. This first stage of information processing is
often referred to as the “feed-forward sweep” of information processing, which is thought to be
completed within 100 milliseconds after stimulus onset [77].
In this study we searched for correlation between subjective and objective methods while young
subjects performed the physical task. We found out that rating of perceived exertion moderately
correlated with ECG functional parameters RR, JT. However, the correlation was found only with
recovery values. None of scientific papers with the same evaluation sequence was found. We speculate
that the reasons for not finding any correlations during physical load could be similar to the ones
described in rating of perceived exertion and heart rate section.
As we expected special mathematical methods was useful for extracting dynamics of
physiological data at different protocols – incremental, constant load. In literature exist some evidence
that additional information about the recovery processes could be extracted through the algebraic
analysis. Algebraic analysis revealed intervals with stable and/or unstable physiological features in
different ECG functional parameters [6]. In endurance group inhibitory and stationary processes were at
the same level, while in less exhausting tasks (incremental loading test) endurance-celerity group
showed the predominate processes of stationary processes where a significant decrease in inhibitory
processes was found during the first five recovery minutes. Results indicate that the changes in
physiological conditions can be very rapid and highly depended on the task and the trainability.
The main limitation of this study was a small and different sample of the participants in each
group. However, even with this small size we managed to find significant relationships from the data. A
lack of data or incremental protocol might be a reason of significant obstacle in finding a meaningful
relationship of cardiovascular functional parameters and rating of perceived exertion during the load.
Future research with incremental load and perceived exertion would be needed.
54
5. CONCLUSIONS
1.
The perceived exertion differed more at lighter loading while gathering information about
exertion every 15 s. During the maximal load of 250W the perceived exertion was rated in verbal
equivalent –very hard. The highest alteration of the rating of perceived value was observed in the
strength group, where rating of perceived exertion decreased by 5 scores in a period from the maximal
250W load till the first 15 seconds of recovery.
2.
The evaluation of functional indices of cardiovascular system registered during
incremental load and recovery revealed the dynamic of regulatory system and myocardium metabolism
that demonstrated increase during the loading and decrease in recovery. The peculiarities of trainability
was observed in myocardium metabolism dynamic in the 4th recovery minute where non-active group
again showed increased metabolism while strength and endurance groups showed up going recovery in
metabolism. Intrinsic regulation of the heart varied within the mark in all groups. The highest range of
changes in the heart conductive system was observed in non-active subjects.
3.
The statistically confident relationship was found between rating of perceived exertion
values and cardiovascular systems’ functional parameters, systolic, and diastolic blood pressure.
55
6. PRACTICAL RECOMMENDATIONS:
1.
In this scientific research we observed rating of perceived exertion values every 15 seconds
and found more differences in a light loading. For this reason during incremental load procedures we
recommend to evaluate rating of perceived exertion every 15 s. in a light loading (150W). In a higher
loading we recommend to evaluate rating of perceived exertion every 30 s.
2.
This research has demonstrated that young subjects from non-active group involved their
metabolism and conducive system of the heart maximally in 200W phase. Even though, all the changes
varied within the mark we advise young non-active persons to take physical activity without reaching
the line of 200 W or keeping arterial blood pressure lower when 155±4/69±3 mmHg and heart rate
should be kept around 141±3beats per minute.
3.
The outcomes of the study consider special mathematical analysis as a useful method for
estimations of recovery processes in young men and suggest applying it for evaluation of recovery
dynamic in differently trained participants or different types of testing.
4.
We should consider various physical activity frequency as associated variable with the
perceived exertion. In this we found negative correlations between days and rating of perceived exertion
value at load. Also, the amount of time spent sitting could be a possible determinant of the growth of
rating of perceived exertion. We recommend spending at least 4 days walking more when 69±7 minutes
and reduce the time spent sitting at least till 508 ±16 minutes per day.
56
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2. Bardauskienė S., The influence of starting on cardiovascular systems functional parameters in active
and non-active people. Doctoral research, 2012.
3. Berskiene, A. Lukosevicius, G. Jarusevicius, V. Jurkonis, Z. Navickas, A. Vainoras, A.
Daunoraviciene. Analysis of Dynamical Interrelations of Electrocardiogram Parameters // Electronics
and Electrical Engineering. Technologija, 2009; 7(95):95–98.
4. Kajėniene, Assessment of functional conditions of basketball and football player by applying,
Medicina; 2010; 46(6):421-8.
5. Kajėnienė A. (2008) Krepšininkių ir futbolininkių dozuoto fizinio krūvio mėginio atsigavimo
laikotarpio vertinimas naudojant integraliojo vertinimo modelį. Daktarinė disertacija. Kaunas.
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fluctuations in electrocardiography data. Vibroengineering. Journal of vibroengineering. 2013;
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APPENDIX
64
Appendix nr. 2
Attended conferences:
1. Slapšinskaitė A. Concatenations between cardiovascular system’s functional parameters and
perceived exertion in healthy young men during physical load. Conference „Move healthy
2012“december, Lithuania (Kaunas) (oral presentation).
2. Slapšinskaitė A., Sendzikaite E., Vainoras A., Karaliene D. Stability of fluctuations in
electrocardiography data in recovery after volitional exhaustion cycle ergometer test
International conference „Virtual methods in biomedicine“ 2013 m. May, Lithuania (Klaipėda)
(oral presentation);
3. Karalienė D., Navickas Z., Slapsinskaite A. A Mathematical Information Algorithm for the Analysis of
ECG Complexity. Athens institute for investigation and research 9 –th annual international
conference, 2013 m. may, Greece (Athens) (co-author).
4. Slapšinskaitė, A. 1, Balagué, N. 2, Hristovski, R. 3 Meta-stable spatio-temporal dynamic of
topologically defined areas of perceived discomfort during cycling and running until volitional
exhaustion. 18th European congress of sport science (ECSS), 2013. June, Spain (Barcelona)
(mini-oral presentation).
5. Vainoras, A., Slapsinskaite, A., Sendzikaite, E ECG parameters concatenations during bicycle
ergometry 18th European congress of sport science (ECSS), 2013. June, Spain (Barcelona) (eposter).
6. Slapšinskaitė A., Vainoras A.,Bikulčienė L. The Analysis of ECG complexity during bicycle
ergometry test. Riga technical University 54th international scientific conference, 2013 October,
Latvia (Riga) (oral presentation).
Publications:
1 Karalienė D., Navickas Z., Slapšinskaitė A., Vainoras A. Investigation of the stability of
fluctuations in electrocardiography data. Vibroengineering. Journal of vibroengineering.
2013;15(1):1392-8716
65
Appendix nr. 3
Short Last 7 Days Telephone IPAQ
-For use with Young and Middle-aged Adults (15-69 years)
READ: I am going to ask you about the time you spent being physically active in the last 7
days. Please answer each question even if you do not consider yourself to be an active person. Think
about the activities you do at work, as part of your house and yard work, to get from place to place, and
in your spare time for recreation, exercise or sport.
READ: Now, think about all the vigorous activities which take hard physical effort that you
did in the last 7 days. Vigorous activities make you breathe much harder than normal and may include
heavy lifting, digging, aerobics, or fast bicycling. Think only about those physical activities that you
did for at least 10 minutes at a time.
1.
During the last 7 days, on how many days did you do vigorous physical activities?
_____
Days per week [VDAY; Range 0-7, 8,9]
8.
Don't Know/Not Sure
9.
Refused
[Interviewer clarification: Think only about those physical activities that you do for at least 10 minutes
at a time.]
[Interviewer note: If respondent answers zero, refuses or does not know, skip to Question 3]
2.
How much time did you usually spend doing vigorous physical activities on one of those
days?
__ __
Hours per day
[VDHRS; Range: 0-16]
__ __ __ Minutes per day [VDMIN; Range: 0-960, 998, 999]
998.
Don't Know/Not Sure
999.
Refused
[Interviewer clarification: Think only about those physical activities you do for at least 10
minutes at a time.]
[Interviewer probe: An average time for one of the days on which you do vigorous activity is
being sought. If the respondent can't answer because the pattern of time spent varies widely from day to
day, ask: "How much time in total would you spend over the last 7 days doing vigorous physical
activities?”
__ __
Hours per week
[VWHRS; Range: 0-112]
__ __ __ __Minutes per week [VWMIN; Range: 0-6720, 9998, 9999]
9998.
Don't Know/Not Sure
9999.
Refused
66
READ: Now think about activities which take moderate physical effort that you did in the last
7 days. Moderate physical activities make you breathe somewhat harder than normal and may include
carrying light loads, bicycling at a regular pace, or doubles tennis. Do not include walking. Again, think
about only those physical activities that you did for at least 10 minutes at a time.
3.
During the last 7 days, on how many days did you do moderate physical activities?
____
Days per week [MDAY; Range: 0-7, 8, 9]
8.
Don't Know/Not Sure
9.
Refused
[Interviewer clarification: Think only about those physical activities that you do for at least 10
minutes at a time]
[Interviewer Note: If respondent answers zero, refuses or does not know, skip to Question 5]
4.
How much time did you usually spend doing moderate physical activities on one of those
days?
__ __
Hours per day [MDHRS; Range: 0-16]
__ __ __ Minutes per day [MDMIN; Range: 0-960, 998, 999]
998.
Don't Know/Not Sure
999.
Refused
[Interviewer clarification: Think only about those physical activities that you do for at least 10
minutes at a time.]
[Interviewer probe: An average time for one of the days on which you do moderate activity is
being sought. If the respondent can't answer because the pattern of time spent varies widely from day to
day, or includes time spent in multiple jobs, ask: “What is the total amount of time you spent over the
last 7 days doing moderate physical activities?”
__ __ __ Hours per week
[MWHRS; Range: 0-112]
__ __ __ __Minutes per week [MWMIN; Range: 0-6720, 9998, 9999]
9998.
Don't Know/Not Sure
9999.
Refused
READ: Now think about the time you spent walking in the last 7 days. This includes at work
and at home, walking to travel from place to place, and any other walking that you might do solely for
recreation, sport, exercise, or leisure.
5.
During the last 7 days, on how many days did you walk for at least 10 minutes at a time?
____Days per week [WDAY; Range: 0-7, 8, 9]
8.
Don't Know/Not Sure
9.
Refused
[Interviewer clarification: Think only about the walking that you do for at least 10 minutes at a time.]
[Interviewer Note: If respondent answers zero, refuses or does not know, skip to Question 7]
6. How much time did you usually spend walking on one of those days?
__ __
Hours per day [WDHRS; Range: 0-16]
67
__ __ __ Minutes per day [WDMIN; Range: 0-960, 998, 999]
998.
Don't Know/Not Sure
999.
Refused
[Interviewer probe: An average time for one of the days on which you walk is being sought. If
the respondent can't answer because the pattern of time spent varies widely from day to day, ask: “What
is the total amount of time you spent walking over the last 7 days?”
__ __ __ Hours per week
[WWHRS; Range: 0-112]
__ __ __ __Minutes per week [WWMIN; Range: 0-6720, 9998, 9999]
9998.
Don't Know/Not Sure
9999.
Refused
READ: Now think about the time you spent sitting on week days during the last 7 days. Include
time spent at work, at home, while doing course work, and during leisure time. This may include time
spent sitting at a desk, visiting friends, reading or sitting or lying down to watch television.
7.
During the last 7 days, how much time did you usually spend sitting on a week day?
__ __ Hours per weekday
[SDHRS; 0-16]
__ __ __ Minutes per weekday [SDMIN; Range: 0-960, 998, 999]
998 .
Don't Know/Not Sure
999.
Refused
-