Heart rate variation profiling by stress tests in precompetitive, competitive and rehabilitation periods

Dr.Med., Professor A.L. Pokhachevsky1,2
K.G.K. Abdullayeva1
PhD M.V. Akulina2
PhD, Associate Professor Yu.M. Reksha3
F.R. Gadzhimuradov4
1I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow
2Ryazan State Medical University, Ryazan
3Ryazan Branch of the Kikot Moscow University of the Ministry of Internal Affairs of Russia, Ryazan
4The Academy of the FPS of Russia, Ryazan4

Keywords: cardiac rhythmogram, mathematical model, physical workload tolerance forecast, physical work capacity.

Introduction. Physical workload tolerance and the possibility of its forecast in load-free conditions or under minimal physical loads remains an urgent subject of study both in the field of physical culture and sports and cardiac rehabilitation. The theory and practice of sports training, as well as sports physiology, are bound to this issue by the possibility of operational control of physical workload tolerance, its assimilation and formation of a training effect [4, 7, 5].

Objective of the study was to analyze time series of cardiac rhythmogram of the precompetitive, competitive, training and rehabilitation periods of stress tests by mathematical modeling tools.

Methods and structure of the study. Sampled for the study were the 18-23 year-old apparently healthy male athletes (n=35) of mass categories who did not have any systemic functional disorders in past medical history (one calendar year ahead of the examination); the subjects trained according to the curriculum of their educational institution or independently - irregularly, not more than 1-3 times a week for 45-90 min. The cycle ergometer stress test was performed of an individual record. The capacity W1 (Watt) of the first stage lasting 3 min was calculated based on the due basal metabolic rate in kilocalories according to the formula: W1(W)=DBM×0.1. Further on, the load incrementally increased every minute by 30 W by an individual maximal value, the pedaling speed reduced to 30 rpm, indicating the load end and the beginning of the recovery period lasting 7 min [2, 3, 8, 6, 1].

The load tests were performed before noon from 8 a.m. to 12 p.m. using a Lode Corival bicycle ergometer (with the load range of 7-1000 W). During the entire testing, the ECG system PolySpectr-12 (Neurosoft) recorded a digitized electrocardiogram, from which an array of R-R intervals was distinguished — a cardiac rhythmgram. The time series of the cardiac rhythmogram in the training and rehabilitation periods were analyzed as linear mathematical models: Y=aX+b, where X - the interval serial number in the time series of the cardiac rhythmogram, Y - the interval duration, “a” - the “slope” (S) model, which characterizes the variation range of the time series, and “b” - the “y-intercept” (I) model, which determines its constant component. The models were optimized by the least-square method. The mathematical modeling was applied to the time series of the cardiac rhythmogram in the precompetitive period – a 30 sec interval before exercise; competitive period – a 30 sec interval from the end of the precompetitive period with the workload of 50 W; early training and rehabilitation periods: separately during the 1st, 2nd, 3rd min; in pairs: 1st-2nd min; 2nd-3rd min; the entire early adaptation period: 1st-3rd min. When studying physical workload tolerance, we determined dW: the difference between the maximum (Wmx) and the first stage (W1) values: W=Wmx-W1. The research results were processed using a statistics package Statistica 10.0. In respect that the value distribution differed from the standard, the data were presented in the form of percentile (Pc) series (25-Me-75), the correlation analysis (Spearman) was applied.

Results and discussion. The peculiarity of this study was the analysis of the cardiac rhythmogram segments not analyzed previously. The first one - precompetitive - a 30 sec cardiac rhythmogram segment measured at rest prior to ergometry, the second - competitive - the first 30 sec of bicycle ergometry with the minimum initial workload of 50 W.

The study of correlations between the first- and third-minute cardiac rhythmogram in the early training period and that in the precompetitive period showed that the more pronounced “I” marker of the precompetitive period corresponded to the same cardiac rhythmogram markers in the training period. In this case, the correlation coefficient increased from the 1st to the 3rd min of loading. No dynamics was observed on the paired segments, but the correlation was moderate and corresponded to that determined during the 2nd min of loading.

The physiological interpretation of this indicator is determined by the correspondence of the longer R-R interval in the precompetitive period to the longer R-R interval in the training period. In other words, the lower heart rate (HR) in the precompetitive period will correspond to the lower chronotropic response rate under loading. In this case, the maximum forecast level is reached during the 3rd min of the training period. The principal correlations of the “S” model cannot be determined unambiguously, since at this stage the marker is hardly pronounced, which is not surprising, as the interval variability rate cannot be significantly expressed at rest.

The study of the correlation between the cardiac rhythmogram in the precompetitive period and the load segment (1st-3rd min) in terms of the marker of the “I” mathematical model reveals the same dynamics of correlations in terms of all indicators, although with a lower intensity. Despite the fact that the marker of the “S” model is significantly pronounced in this segment, it is also impossible to interpret its relations with the load indicators unambiguously. The latter is associated with a small number of statistically significant relationships. However, their presence suggests that the slower decrease in the R-R interval in the precompetitive period contributes to the faster increase in HR, at least during the 3rd min of loading.

The pronounced positive correlation between the precompetitive period and mathematical modeling of the cardiac rhythmogram in the rehabilitation period is manifested in the correspondence of the longer duration of the R-R intervals in the precompetitive period to the same during the rehabilitation period. At the same time, the maximum connection increases from the 1st to the 3rd min of this period. This regularity is also proved by the “S” model value. In other words, the decreased HR in the precompetitive period manifests itself as the decreased HR during the rehabilitation period and, accordingly, higher speed of this process.

The same patterns characterize the warming-up period. In this case, the lower HR during the competitive period contributes, accordingly, to the lower HR during rehabilitation but at the higher recovery rate. In addition, the lower variability in the competitive period determines the lower HR and the higher recovery rate.

Since under the influence of physical loads, the duration of R-R interval decreases, the marker of the “S” model has a negative value, respectively, during rehabilitation - positive. At the same time, an increase in the negative value during loading indicates its mathematical decrease, while an increase in the “S” value during rehabilitation also testifies to the mathematical increase in this indicator. In other words, in practice, we observe the correspondence of the faster increase of HR under loading to the higher recovery rate. Thus, this phenomenon deserves further clarification. It is based on the HR variability rate. Moreover, if the maximal HR is characterized by genetic and age restrictions, then HR in healthy people at rest and at the first stage is determined by the chronotropic reserve formed as a result of endurance training. In people with well-developed endurance, the resting HR will be provided by the decreased HR, while the workload will be provided by the large variation range [4, 7, 6]. The correspondence of the long R-R interval (the value of the “I” model) in the training period to that during the rehabilitation period indicates a synergy of the load and recovery regulation, due to more pronounced chronotropic inhibition.

Conclusion. The precompetitive cardiac rhythmogram variations precede the physical workload and the competitive cardiac rhythmogram varies with the minimal physical workload versus the first- and third-minute cardiac rhythmogram dependent on the sub-maximal physical workload, each of the test options may be applied for the customizable physical workload forecast tests in the cardiologic, rehabilitation and training practices.

References

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Corresponding author: sport_med@list.ru

Abstract

Early heart rate variations in the precompetitive, competitive and training periods are rather critical for the forecasts of the athletes’ physical work capacity and rehabilitation resource. Spearman correlation analysis and practical mathematical modeling of the precompetitive, competitive and training periods supported by cardiac rhythmograms made it possible to find efficient mathematical modeling markers for every period versus the rehabilitation period and find meaningful correlations of the cardiac rhythmograms mathematical modeling for precompetitive, competitive versus training periods. Fatigue tolerance under workloads may also be rated by the first-minutes cardiac rhythmograms and by the precompetitive and competitive chronotropic mobilization rates. Whilst the precompetitive cardiac rhythmogram variations precede the physical workload and the competitive cardiac rhythmogram varies with the minimal physical workload versus the training cardiac rhythmograms dependant on the sub-maximal physical workload, each of the test options may be applied for the customizable physical workload forecast tests in the cardiologic, rehabilitation and training practices. The heart rate variability forecasts after the sub-maximal physical workload may be made based not only on the first-minutes cardiac rhythmograms but also on the precompetitive and competitive chronotropic mobilization rates. We found most beneficial for the forecasts the precompetitive, competitive and training mathematical models of the first- and third-minute cardiac rhythmograms.