LITHUANIAN UNIVERSITY OF HEALTH SCIENCES MEDICAL ACADEMY FACULTY OF NURSING INSTITUTE OF SPORTS AGNĖ SLAPŠINSKAITĖ

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1 1 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

2 2 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 9 th December 2013 Research supervisor PhD Ernesta Sendžikaitė 9 th December 2013 Reviewer,, 2013 Work accomplished by Master student Agnė Slapšinskaitė 9 th December 2013 KAUNAS, 2014

3 3 CONTENTS SUMMARY... 4 SANTRAUKA... 5 GLOSSARY... 7 INTRODUCTION... 8 AIM OF THE RESEARCH... 9 OBJECTIVES OF THE RESEARCH LITERATURE REVIEW Complex systems Cardiac adaptation in various physical activities Electrocardiogram indices and concatenations Perceived exertion MATERIAL AND METHODS Participants Rating of perceived exertion scale Electrocardiography Procedure of the testing Mathematical method Statistical analysis RESULTS Perceived exertion Cardiovascular system s parameters Cardiovascular system s functional parameters Correlations Mathematical method for the investigation of fluctuations stability Endurance group Endurance celerity group DISCUSSION CONCLUSIONS PRACTICAL RECOMMENDATIONS:...55 LITERATURE LIST...56 APPENDIX... 63

4 4 SUMMARY Slapšinskaitė A. Concatenation between cardiovascular system s functional parameters and perceived exertion in healthy young men during rest, physical task and recovery/ final work of master studies research supervisor PhD E. Sendžikaitė, consultant prof. Natalia Balague. Lithuanian University of Health Sciences; Faculty of Nursing, Institute of Sports Kaunas, 2013, p. 62. Evaluation of inner/intra-systemic concatenations is becoming more popular. The application of complex systems theories for differently trained people may boost the knowledge about the intersystem concatenations and may provide more information about functional state and perceived exertion. The 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 study: 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. Contingent and methods: We had 57 young volunteers aged (22.75 ± 0,4 year) participating in this study. We divided participants according the trained feature in 4 groups: endurance (n=12, 23±0.35 year), endurance-celerity group (n=16, 20.5±0.55 year), strength group (n=10, 24.3±0.53 year) and non-active group (n=19, 23.21±2.22 year). Methods: Endurance group went through constant load until volitional exhaustion. Celerityendurance, strength and non-active groups went through incremental loading where the increase was made every minute by 50 W. The perceived exertion was measured every 15 seconds with Borg (RPE6-20) scale. Ongoing registration of ECG was proceeded with ECG registration and analysis system Kaunas-load. The data analysis were made with Kaunas-load, special mathematical methods and statistical analysis. Results and conclusions: The perceived exertion differed more at lighter loading while gathering information about exertion every 15 seconds. During the maximal load of 250W the perceived exertion was rated as very hard. 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 statistically confident relationship was found between rating of perceived exertion values and cardiovascular system s functional parameters.

5 5 SANTRAUKA Slapšinskaitė A., Jauno amžiaus vyrų širdies ir kraujagyslių sistemos funkcinių rodiklių bei suvokiamų pastangų ryšio įvertinimas ramybės, fizinio krūvio ir atsigavimo metu, magistro baigiamasis darbas mokslinė vadovė dr. E. Sendžikaitė; konsultantė prof. Natalija Balague, Lietuvos sveikatos mokslų universitetas, Medicinos akademija, Slaugos fakultetas, Sporto Institutas Kaunas, 2013, p. 62. Populiarėjant sistemų sąveikų analizei, daugiau dėmesio skiriama sąsajų tarp skirtingų sistemų nustatymui bei vertinimui. Kompleksinių sistemų teorijos pritaikymas skirtingai adaptuotiems asmenims gali suteikti daugiau informacijos apie jų funkcinį pajėgumą bei suvokiamas pastangas. Darbo tikslas: Įvertinti suvokiamų pastangų ir širdies ir kraujagyslių sistemos funkcinių rodiklių ramybės, fizinio krūvio ir atsigavimo metu. Tyrimo uždaviniai: 1. Nustatyti suvokiamas pastangas atliekant veloergonometrinį testą. 2. Nustatyti širdies kraujagyslių sistemos funkcinių rodiklių kitimą ramybės, fizinio krūvio ir atsigavimo metu tarp skirtingo kryptingumo fiziškai adaptuotų vyrų. 3. Nustatyti koreliacijas tarp suvokiamų pastangų ir širdies kraujagyslių sistemos funkcinių rodiklių ramybės, fizinio krūvio ir atsigavimo metu. Tiriamųjų kontingentas: Tiriamųjų kontingentą sudarė 57 jauno amžiaus vyrai, kurių amžiaus vidurkis tyrimo pradžioje buvo 22,75 ± 0,41 metai. Tiriamieji buvo suskirstyti į keturias grupes pagal treniruojamą fizinę ypatybę: ištvermės grupė (n=12, amžius 23±0,35metai), greičio-ištvermės grupė (n=16, amžius 20,5±0,55 metai), jėgos grupė (n=10, amžius 24,3±0,53 metai) ir nesportuojančiųjų grupė (n=19, amžius 23,21±2,22 metai). Tyrime taikyti metodai: Ištvermės grupė atliko ištvermės mėginį, važiuodami pastoviu krūviu (W) iki išsekimo, greičio-ištvermės, nesportuojančiųjų bei jėgos grupės atliko pakopomis didėjančio fizinio krūvio mėginį, kur apkrova buvo keičiama kas minutę, pridedant po 50W. Atliekant testą suvokiamos pastangos buvo sekamos kas 15s iki testo pabaigos, naudota Borgo suvokiamų pastangų (RPE6-20) skalė. Veloergometrinio testo metu registruota EKG naudojantis Kaunas-krūvis programa. Duomenų analizė atlikta naudojant analizės sistemą Kaunas-krūvis, specialius matematinius metodus ir matematinę statistiką. Rezultatai ir išvados: Suvokiamų pastangų dydis statistiškai reikšmingai kito esant nedideliam fiziniam krūviui. Maksimalaus fizinio krūvio metu (250 W) stebėtos labai didelės suvokiamos pastangos. Fizinio krūvio metu buvo stebimas širdies funkcinių parametrų atspindinčių reguliacinę sistemą ir širdies metabolizmą intensyvėjimas, priešinga dinamika stebėta atsigavimo laikotarpiu. Nustatytas statistiškai patikimas vidutinio stiprumo ryšys tarp suvokiamų pastangų bei širdies ir kraujagyslių sistemos funkcinių parametrų.

6 6 ABBREVIATION Bpm beats per minute EceG - endurance-celerity group EG - endurance group D Diastolic blood pressure IPAQ - the International Physical Activity Questionnaires HRV heart rate variability HF - high-frequency LF - low-frequency NAG - non-active group Rec. Recovery RPE - the Borg Rating of Perceived Exertion Rpm - revolutions per minute RR/JT - a functional relationship between supplying and regulatory systems reflecting the relation between JT and RR intervals RR/QRS - a concatenation between QRS segment and RR interval is used to study regulatory changes of the inner heart S - Systolic blood pressure SG - strength group From 50W_1 to 250W_1 the loading (W) and the first 15s. of perceived exhaustion From 50W_2 to 250W_2 the loading (W) and the second 15s. of perceived exhaustion From 50W_3 to 250W_3 the loading (W) and the third 15 s. of perceived exhaustion From 50W_4 to 250W_4 the loading (W) and the fourth 15 s. of perceived exhaustion R1- the first recovery minute R2- the second recovery minute R3-the third recovery minute R4- the fourth recovery minute R5- the fifth recovery minute

7 7 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.

8 8 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.

9 9 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.

10 Complex systems 1. LITERATURE REVIEW 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

11 11 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

12 12 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

13 13 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].

14 14 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 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 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 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 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 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 Participants 2. MATERIAL AND METHODS 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 ECeG (n=16) EG (n=12) SG (n=10) NAG (n=19) I II III IV BMI (kg) (average±sem) 21,87±0,38 22,17±0,28 27,54±0,84 23,21±0,47 Age (average±sem) 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 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 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 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 2006: is defined by Navickas Z. et al. 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 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): segment where, is the start and - the end position of 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 25 Now, let,,, is an algebraic progression of the segment. Then accordingly parameter could be distinguished components of algebraic progression : and (c) stimulant: Every distinguished component has different nature of dynamic: (a) inhibitory, (b) stationary, (a) (b) (c) Parameters with different nature of dynamic are placed on the unit circle (Fig.7). (a) (b) (c) Figure 7. Parameters values (d) of algebraic progression: (a) inhibitory, (b) stationary, (c) stimulant, (d) all;

26 Statistical analysis Data were analyzed using mathematical statistical methods. All the parameters are provided by their means (x ) 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 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 1 st -4 th 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 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 3 rd and 4 th requests also in 100W 4 th 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 (4 th 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 3 rd and 4 th request of RPE values at intensity of 100W and the periods of 100W 4 th 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 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 3 rd and 4 th requests-100w first request; 100W 4 th request and 150W first request, 150W 4 th request and 200W first request (p<0.05). In non-active group a period of loading different values of RPE were noticed in 100W 3 rd and 4 th request of RPE values. The last 15 seconds of the loading

30 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 4 th request (r=0.309), while the time period of vigorous intensity load correlated only with 50W 4 th request (r=0.306) (p<0.05). The days in which the person was walking had negative correlation with RPE value at 150W 4 th 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 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 1 st request 50W 3 rd request 50W 4 th request 100W 1 st request 100W 3 rd request 100W 4 th request 150W 1 st request 150W 4 th request Rec. 1 st min. 1 st request (*-p<0.05) 0.37* * 0.30* 0.31* * * * * * * 0.16* 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 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 33 recovery started. The significant lowering of the HR in all groups was observed till 2 nd 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 1 st minute of recovery.

34 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 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 2 nd -5 th minutes, but no significant difference was found.

36 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 W) (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, R2- R3, R4-R5) demonstrated the tendency of increased dynamics (0.1<p>0.05).

37 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).