Functional state assessment on the dynamics of interparametric concatenations during exercise tests

Poderys, Jonas; Venskaitytė, Eurelija; Poderienė, Kristina; Buliuolis, Alfonsas; Vainoras, Alfonsas

doi:10.3390/medicina46060060

Cited by 5 publications

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“…The initial data were normalized according to the minimum and maximum values of physiological indices. Subsequently, the variables were described using mathematical links [20].…”

Section: Near-infrared Spectroscopy Was Used For the Non-invasive Monmentioning

confidence: 99%

“…The dynamics of the parameters and functional changes of the body system or its separate parts are largely dependent on these interactions. The matrix analysis, which allows the analysis of the dynamics of interparametric interactions, is applied when two or three synchronized parameters are co-integrated into the matrix, using only three data points from each [20]. In this study, co-integration of the following central and peripheral parameters were analysed: HR, the JT interval, JT/ RR, ST-segment depression, StO 2 , systolic ABP, diastolic ABP and ABF.…”

Section: Introductionmentioning

confidence: 99%

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Cardiovascular Response to Breath-Holding Explained by Changes of the Indices and their Dynamic Interactions

Grūnovas¹,

Trinkūnas²,

Buliuolis³

et al. 2015

Biol syst Open Access

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Background: Endogenous triggers such as voluntary breath-holding induce various cardiovascular responses typically including modification of blood CO 2. During dynamic exercise these responses may have a negative impact on performance or may associate with cardiovascular risk subjects. Therefore, we hypothesized that voluntary breathing tests induce changes in cardiovascular (CV) oxygenation that lead to cardiovascular-functional changes, measured by a complex of integrated cardiovascular parameters and their interactions. So the aim of the study was to determine the impact of the voluntary breath-holding on changes and interplay of cardiac and peripheral parameters. Method: 18 girls (average age: 23.4 ± 1.3 years) performed 2 voluntary breath-holdings to failure, with a 5 min rest. Cardiac functional parameters were recorded using the electrocardiogram (ECG) analysis system "Kaunas-load". The blood flow in the calf was determined by venous occlusion plethysmography. Near-infrared spectroscopy (NIRS) was used for non-invasive monitoring of oxygen saturation in tissues (StO 2). Results: Throughout the first breath-holding, heart rate (HR) increased from 89.5 ± 3.9 bpm to 107.6 ± 4.2 bpm (P<0.05). The ECG JT interval decreased at the onset of breath-holdings, the intervals ratio (JT/RR) increased (P<0.05), and the ST-segment depression was not altered significantly. Arterial blood flow (ABF) was reduced from 3.5 ± 0.47 mL/100 mL/min to 1.64 ± 0.38 mL/100 mL/min (P<0.05) at the end of the first breath-holding. The StO 2 of the calf muscles decreased during both breath-holdings. Within 60 s of recovery time, StO 2 exceeded baseline 9.5% (P<0.05). Conclusion: Breath-holding impact changes in the systemic (central) circulation and caused significant peripheral changes, i.e., decrease in arterial blood flow and oxygen saturation. The most essential alteration occurred between the HR and arterial blood pressure (ABP) parameters. The strongest interaction observed between HR and ABP, and in calf muscles-between ABF and StO 2 .

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“…The initial data were normalized according to the minimum and maximum values of physiological indices. Subsequently, the variables were described using mathematical links [20].…”

Section: Near-infrared Spectroscopy Was Used For the Non-invasive Monmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cardiovascular Response to Breath-Holding Explained by Changes of the Indices and their Dynamic Interactions

Grūnovas¹,

Trinkūnas²,

Buliuolis³

et al. 2015

Biol syst Open Access

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“…This test is very popular and usually used to assess muscle functional performance of athletes. Special research on the functional changes of the cardiovascular system was carried out using Bosco test and the modified version of it of this test, i. e. the 30-s vertical jump test (Poderys et al, 2005(Poderys et al, , 2010. 60 s vertical jump test is a very difficult test, so this testing procedure is recommended only for well prepared athletes to determine the changes in their anaerobic capacity.…”

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confidence: 99%

Dynamics of Muscular Performance Indices during the 30‑s Vertical Jump Test in Endurance and Sprint Cohorts

Trinkūnas¹,

Buliuolis²,

Sadzevičienė³

et al. 2018

BJSHS

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Research background and hypothesis. According the methodological requirement testing procedures do not modify the main training objectives. It is well known that specificity of performance assessment tests is important if we want to compare performance of different groups tested.Research aim was to find out the peculiarities of muscular performance indices in sprint and endurance cohorts while performing a 30-s vertical jump test.Research methods. The participants of the study were two groups of athletes, i. e. sprint and endurance runners. The participants of the study performed a 30-s vertical jumps test with maximal efforts. The sum of height of all jumps was calculated by a special computerized program and the values in height of jumps, contact time and relative power at onset of workload and at each 5 seconds of the test were analyzed.Research results. The difference between the cohorts in jumps height was statistically significant (p < 0.05) during the whole series of jumps. Athletes’ adaptation to speed-power type of exercising produced higher anaerobic muscle performance indices while performing 30-s duration jumps test. Endurance runners usually demonstrate lower results in jumping than sprint- athletes and they compensate reduced muscular power with prolonged duration of take-off.Discussion and conclusions. Despite some methodological limitations the results of this study show that the assessment of muscular performance indices allows assessing the peculiarities of dynamics of relative muscle power and fatigue.

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“…Complex systems are well represented in their universal features by biological, evolutionary systems and that means that we can observe classes of behavior and transitions between those types of behavior that do not depend on the details of the system that is studied (Mayer, 2001). The methods applying to human data analysis are very important because many crucial variables are not directly measurable or even identifiable (Torrents, Balagué, 2006;Latash et al, 2010;Poderys et al, 2010). All the above-named and other algorithms of complexity measuring are used to compute the regularity of time series and classify the time series as being of one type or another on this basis.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Measuring the Complexity of a Physiological Time Series: a Review

Pukėnas¹,

Poderys²,

Gulbinas³

2018

BJSHS

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Research background and hypothesis. Complex Systems Theory indeed is a solid basis for a scientific approach in the analysis of living, learning, and evolving systems. A number of different entropy estimators have been applied to physiological time series attempting to quantify its complexity. Research aim. The aim of the paper is to review most popular complexity estimators (entropies) applied in biological, medical, sport and exercise sciences and their performances.Research results. Various measures of complexity were developed by scientists to compare time series and distinguish regular (e. g. periodic), chaotic, and random behavior. In this paper a brief review of most popular complexity estimators – Sample Entropy, Control Entropy, Spectral Entropy, Wavelet Entropy, Singular-Value Decomposition Entropy, Permutation Entropy, Base-Scale Entropy, Entropy based on Lempel-Ziv algorithm – and their performances is presented. In biological applications they are used to distinguish peculiarities in behavior of biological systems or may serve as non-invasive, objective means of determining physiological changes under steady or non-steady state conditions.Discussion and conclusions. The choice of a particular entropy estimator is determined by the goal type, the capability of estimators in characterizing the constraints on a physiological time series, its robustness to noise considering the above-mentioned advantages and disadvantages of particular algorithms. It is difficult to apply analytical solutions in the analysis of behavior of living, learning, and evolving systems and new approaches and solutions remain on the agenda.

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Functional state assessment on the dynamics of interparametric concatenations during exercise tests

Abstract: Abstract. The aim of this study was to complement an analytical approach by new methodology of data sequences analysis of muscular and cardiovascular indices

Cited by 5 publications

References 0 publications

Cardiovascular Response to Breath-Holding Explained by Changes of the Indices and their Dynamic Interactions

Cardiovascular Response to Breath-Holding Explained by Changes of the Indices and their Dynamic Interactions

Dynamics of Muscular Performance Indices during the 30‑s Vertical Jump Test in Endurance and Sprint Cohorts

Measuring the Complexity of a Physiological Time Series: a Review

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