Review Article

Absolute and Relative Power of LF Waves of Cardiointervalogram in Athletes (Literature Review)

DA Kataev^1,2, VI Tsirkin^3*, VV Kishkina^1,4, AN Trukhin¹ and SI Trukhina¹

¹FGBOU Vyatka State University, Kirov, Russia

²ROO Ski Racing Federation of the Republic of Tatarstan, Kazan

³FGBOU Kazan State Medical University, Kazan, Russia

⁴GAUZ OTKZ “Chelyabinsk City Clinical Hospital”, Chelyabinsk, Russia

Submission: May 15, 2023; Published: June 19, 2023

*Corresponding author: VI Tsirkin, FGBOU Kazan State Medical University, Kazan, Russia Email: esbartsirkin@list.ru

How to cite this article: DA Kataev, VI Tsirkin, VV Kishkina, AN Trukhin, SI Trukhina. Absolute and Relative Power of LF Waves of Cardiointervalogram in Athletes (Literature Review). Anatomy Physiol Biochem Int J: 2023; 6(4): 555695. DOI: 10.19080/APBIJ.2023.06.555695.

Abstract

The review systematizes information about the average values or medians of such indicators of heart rate variability (HRV) as the total power of the spectrum (TP), absolute (AMLF) and relative (LF%) power of low-frequency waves (LF-waves) of the cardiointervalogram recorded in the prone position. It has been established that the values of TP and AMLF increase with high motor activity, with an increase in the fitness of the athlete (experience in sports), with aerobic training, i.e., they depend on the sports specialization, as well as on the volume and intensity of training loads, which was revealed in relation to the values of TP. For cross-country skiers, the variability of the values of TP and AMLF during each of the three periods of the annual cycle is characteristic, but in general, there is an increase in their preparatory period, the preservation of high values in the competitive period and a decrease in the transition period. The idea is confirmed that TP reflects the influence of the parasympathetic department, and AMLF - the sympathetic department of the autonomic nervous system. Information regarding the value of LF% of athletes are few in number. Judging by the results of the study of the elite athlete K.D. (the first author of the article) and 7 members of the national team of the Republic of Tatarstan in cross-country skiing, this indicator increases significantly in the competitive period and decreases in the transition period. This is regarded as a reflection of the formation of emotional stress in the competitive period and, probably, the formation of the anti-apoptic myocardial system in the preparatory period.

Keywords: Athletes; Heart rate variability; Total spectrum power; Absolute and relative power of LF-waves; Training cycle periods; Pre-Start Emotional State

Abbreviations: HRV: Heart Rate Variability; ECG: Electrocardiogram; ANS: Autonomic Nervous System

Introduction

Analysis of heart rate variability (HRV) is mainly used to evaluate patients with various diseases, including cardiovascular diseases [1]. The use of HRV analysis in athletes, including endurance athletes, is only gaining popularity [2]. HRV analysis, i.e. the variability of the duration of intervals R-R of the electrocardiogram (ECG) provides an assessment of the autonomic control of cardiac activity, including the effect on the activity of the heart of the sympathetic (SO) and parasympathetic (PO) divisions of the autonomic nervous system (ANS), as well as a number of biologically active substances [3]. A working group of the European Society of Cardiology and the North American Society for Stimulation and Electrophysiology ( EKO and NASPLE ) recommended the use of a range of temporal and spectral measures [4]. Among the latter is the power of the three main types of the CIG spectrum: 1) high-frequency (or HF-waves), for which the limits of the ranges are 0.15-0.40 Hz; 2) low-frequency (or LF-waves) with boundaries of 0.04-0.15 Hz; 3) very low-frequency (or VLF-waves) with boundaries of 0.003-0.04 Hz. In this case, the sum of all powers of the spectrum in the range from 0.003 to 0.5 Hz, i.e. HF + LF + VLF , is defined as the power of the total oscillation spectrum, or TP [4].

Earlier in our reviews, attention was paid to such indicators of the HRV of athletes, including cross-country skiers as TP, the absolute and relative power of VLF waves [5], and the absolute and relative power of HF waves [6], and their dependence on a number of factors. At the same time, it was found that the values of TP, the absolute power of VLF and HF-waves, and especially the relative power of VLF-waves, i.e. VLF% reflect the degree of influence of ANS software on the heart, and VLF% probably reflects the intensity of synthesis of non-neuronal acetylcholine by cardiomyocytes, and the values of the relative power of HFwaves, i.e. HF% reflect the formation of a state of anxiety. Also, in general, for the annual cycle, a direct dependence of the median TP on the volume of training loads, V km (the higher the volume, the higher the median TP), and a direct dependence of the median of the absolute power of VLF waves on the volume (V km) and intensity (N hrs) loads [7].

This review reflects the literature and our own data on the absolute and relative power of slow (LF) waves.

It was previously believed that slow or LF waves (0.04- 0.15 Hz) mainly reflect the activity of baroreceptors at rest [8], i.e. implementation of the baroreceptor reflex [2,9]. Then it was postulated that LF waves reflect the state of the SO of the ANS [4,10,11], or the state of both parts of the ANS, i.e. SO and software. However, the interpretation of LF-waves as an indicator of sympathetic control of cardiac activity is questioned [12]. A number of authors associate the power of LF-waves with changes in blood pressure [13], or with the formation of psychological stress, since its presence increases the power of LF waves [14,15]. We proceeded from the idea that the power of LF waves mainly reflects the influence of SO ANS on the activity of the heart.

An analysis of the literature shows that information about the dynamics of the absolute power of LF-waves (AMLF) and relative (i.e. expressed as a percentage of TP) power of LF-waves (LF%) in athletes, including depending on the volume and intensity training loads are few. In connection with the need to optimize the training process, the purpose of this review is to systematize the data of the literature and our own research on the values of the absolute and relative power of LF waves, characteristic of athletes, including those training for endurance. We add that in many works cited below, as a rule, the values of LF% were absent, and therefore we calculated them on the condition that the values of TP and AMLF were indicated in the cited work and marked with the symbol*.

LF and LF% values depending on the level of motor activity (athletes, non-athletes)

Examining 65 teenage hockey players (mean age-14.7 years; “experience” of training-8.7 years) and 30 healthy adolescents not involved in sports, G.N. Shangareeva [16] found that the values of AMLF and LF% in hockey players were higher (1877 ms² and 35.1%, respectively) than in non-athletes (1164 ms² and 29.3%, respectively). In a study of 22 volleyball players (18-21 years old), members of the men’s youth team of KhMAO-Yugra, and 22 healthy non-athletes (students of a medical university), it was found [17] that AMLF values in volleyball players were statistically significantly higher than in students (respectively, 1193 ms₂ versus 657 ms₂) , while the value of LF%, on the contrary, is lower (27% versus 35.3%). In the study of athletes and healthy people comparable in age, [18] came to the conclusion that in athletes, AMLF was statistically significantly higher than in non-athletes.

Thus, literature data indicate that AMLF values are higher in those involved in sports than in non-athletes [16-18]; indirectly, this indicates a more pronounced effect of SO ANS on the activity of the heart (at rest) compared to non-athletes. Information about the value of LF% is ambiguous - according to some data, it is higher in athletes [16], and according to others, on the contrary, it is lower [17].

The values of AMLF and LF% depending on the “experience” (training) of the athlete and his sportsmanship

V.M. Mikhailov [19] studied two groups of football players - under the age of 21 and over 32 years old. He found that the AMLF was 2345 ms² and 1235 ms², respectively. This allows us to state that with an increase in the experience of sports training, i.e. AMLF may decrease with age. However, other authors [20], who studied swimmers from 8 to 14 years old, believe that with the training and age of the athlete, the value of AMLF increases , i.e. the influence of SO ANS on the activity of the heart at rest increases (information on the values of LF% is not given in this work). During the study of 16 skiers (18-25 years old) of different levels of sportsmanship (1st category, CMS and MS) during the training camp, it was found [21] that the median AMLF was 1544 ms², 1089 ms² and 3772 ms², respectively, and the median LF% 27.3%*, 14.4%*, 33.5%*, respectively. These data indicate that in athletes, including cross-country skiers, the AMLF value recorded under clinostasis conditions is higher than in non-athletes, while with an increase in sportsmanship it can change, including increase. This suggests that athletes in conditions of clinostasis have a higher effect of SO ANS on the activity of the heart than non-athletes. Regarding the LF% indicator, information is scarce and ambiguous.

LF and LF% values for overtraining

According to T N Solomka [22], 1-3 weeks before the decrease in sports results, the relative power of LF waves increases, i.e. LF%, and the relative power of fast (HF) waves decreases, which is estimated as excessive activation of SO ANS. E A Gavrilova [23] believes that a decrease in AMLF and LF% against the background of an increase in the absolute power of HF waves indicates an increase in the functional state of an athlete (this allows predicting an increase in the level of training of an athlete), while an increase in AMLF and LF% indicates a decrease in the functional state athlete and his overtraining. The literature also reports that a decrease in AMLF may indicate an athlete’s fatigue [24-26], and his overtraining [27]. Thus, literature data on this issue are ambiguous.

LF and LF% values depending on the type of sports specialization

In a study of 29 adolescents aged 11-14 years old involved in cross-country skiing and 40 of their peers involved in sports tourism, it was found [28] that skiers had higher AMLF values than tourists (the authors do not give specific values of LF% of the indicator). In a study of 18-19-year-old swimmers, hockey players, and weightlifters, it was found [29] that AMLF values were the highest in hockey players (2308 ms²), lower in swimmers (1380 ms²), and even lower in weightlifters (810 ms²); but LF% in swimmers were higher - 30.3% than in hockey players (26.6%) and swimmers (28.2%). These data indicate that the values of AMLF and LF% depend on sports specialization. In a study of 18-23-years-old athletes (66 skiers, 20 swimmers, 33 Greco- Roman wrestlers), it was found [30] that the AMLF values were maximum in skiers - 2137 ms², significantly less in wrestlers - 1330 ms², and even less in swimmers - 1102 ms²; and the values of LF% were maximum in swimmers - 33.3%, less in wrestlers - 28.5%, and even less in skiers - 22%. A postgraduate student of our laboratory, a doctor of functional diagnostics (V.V. Kishkina 2017, unpublished data), conducting daily ECG monitoring of 40 adolescents aged 15-16 years, found that in 20 of them involved in swimming or athletics, the average daily median AMLF was 4040 ms², and for 20 adolescents involved in football or basketball-3900 ms², i.e. it was not possible to reveal the influence of sports specialization in adolescents in the conditions of their examination in a hospital. In a study of 1005 men aged 16 to 40 years, of which 305 were engaged in cyclic sports, 200 in complex coordination sports, 150 in team sports and 350 in martial arts, it was found [31] that the values of LF% depend little on sports specialization (they were 29.1%, 30.8%, 27.6% and 30.2%, respectively, and the differences between them were statistically insignificant (p>0.05) Commenting on these data, E. A. Rul & O. N. Kudrya [32] believe that AMLF and LF% values do not depend on sports specialization, but are determined by the type of regulation of cardiac activity, which is discussed in more detail below.

Since the opinions of the authors differ, we considered it necessary to summarize the literature data on the values of AMLF and LF% of athletes, taking into account their sports specialization.

Ski Racers

In a study of 18-25-year-old elite ski racers (masters of sports, MS) it was revealed [21] that even at one training camp, the median AMLF for these skiers varied from 3334 to 3772 ms², and the median LF% varied from 26% to 33.5%. In a study of 34 crosscountry skiers aged 17-22 years, it was noted [32] that, due to the presence of different types of regulation of the activity of the heart, they are characterized by a high variation in AMLF values - from 334 to 12231 ms² (LF% values are not given). It was shown [33] that in 20-22-year-old skiers, the TP values at one training camp (TC) varied from 3320 to 3399 ms², the AMLF values varied from 1196 to 1222 ms², and the LF% values varied from 35.9% to 36.7%. According to our data [7], which are described in more detail below, the elite skier-racer master of sports K.D. during the annual season, the medians TP, AMLF and LF% varied from 5754 to 11099 ms², from 1107 to 2912 ms², and from 16.2 to 25.8%, respectively. R. Hedelin et al. [34], who studied cross-country skiers aged 16-19, found that the values of AMLF and LF% change throughout the entire ski season. In particular, after the preparatory and competitive periods, i.e. during the transitional period, the AMLF values decreased, and the LF% values increased only in the competitive period, which is regarded as a reflection of the growth of SO activity, especially in the competitive period.

So, ski racers have high values of TP and AMLF, as well as a high variability of AMLF and LF% throughout the entire annual training cycle [5-7,21,32-34], which probably reflects undulating activity SO ANS depending on the volume and intensity of the training load.

Biathletes

In a study of 25 biathletes (CCM, MS at the age of 21.5 years) in July (the beginning of the preparatory period), in November (the end of the preparatory period) and in March (the end of the competitive period), it was found [35] that the values of TP, AMLF and LF% varied respectively from 5228 ms² to 7059 ms², from 1343 ms² to 1614 ms², and from 22.8%* to 25.6%*. In 46 biathletes (CMS, MS) aged 18-25 years, the values of TP, AMLF and LF% depended on the type of regulation of cardiac activity [36] in 21 athletes with a central type of regulation, the values of TP, AMLF and LF% amounted to 2681, respectively ms², 1830 ms² and 68.2%*, and 25 in athletes with an autonomous type of regulation- 5735 ms², 1290 ms² and 22.4%*, respectively.

Swimmers

In 8-9-year-old swimmers, the values of TP, AMLF, and LF% were 3226 ms², 1437 ms², and 44.5%*, respectively, and in 14-15-year-old swimmers, they were 4536 ms², 1503 ms², and 33.1%*, respectively [20]. This means that with an increase in the swimming experience, the value of TP increases and the value of LF% decreases. According to [29], in 18-19-year-old swimmers, the values of TP, AMLF, and LF% were 4546 ms², 1380 ms², and 30.3%*, respectively. It was established [30] that in 18-23-yearold swimmers, the values of TP, AMLF, and LF% were 3310 ms², 1102 ms², and 33.3%*, respectively. At the same time, F.B. Litvin et al. [37], studying 50 swimmers (15.5 years old), found that the values of TP, AMLF and LF% depend not on sportsmanship, but on the type of regulation of cardiac activity in sympathicotonics, the values of these indicators were respectively 1173 ms², 404 ms² and 34.4%*, in normotonics, respectively, 4234 ms², 1319 ms², and 31.1%*, and in vagotonics, 10370 ms², 4215 ms², and 40.6%*.

Team Sports (Hockey players, Volleyball players, Handball players, Football players, Basketball players)

In 22 male volleyball players (18-21 years old), the values of TP, AMLF and LF% were 4405 ms², 1193 ms² and 27%*, respectively [17]; in 14-15-year-old hockey players, the values of TP, AMLF and LF% were 5347 ms², 1877 ms² and 35.1%* [16]; in 18-year-old hockey players they were 8649 ms², 2308 ms², and 26.6%*, respectively [29]. According to N.V. Ivanova [31], in 150 men (16-40 years old) involved in team sports, the LF% values were 27.6%. HE. Kudrya [38], examining 22 male handball players (1 adult, CMS, MS), aged 20.3 years, experience 8-14 years) 6 times during the annual cycle: at the beginning and at the end of the preparatory period; at the beginning and at the end of the 1st round of games, as well as at the beginning and at the end of the 2nd round of games, found that the values of TP, AMLF and LF% varied from 2781 ms² to 8015 ms², respectively; from 904 ms² to 2271 ms² and from 28.3%* to 32.5%*.

Martial arts (Greco-Roman Wrestlers, Taekwondo, Jiu- Jitsu)

In 18-23-year-old Greco-Roman wrestlers (n=33), the values of TP, AMLF and LF% were 4668 ms², 1330 ms² and 28.5%*, respectively [30]. In a study of 14 highly qualified taekwondo athletes (23.5 years old), members of the Russian national team, of which 7 are women, it was found [39] that in men the values of TP, AMLF and LF% varied, respectively, from 5506 to 20918 ms², from 1989 to 6928 ms² and from 33.1%* to 36.1%*, while in women these figures varied from 3035 to 16738 ms², respectively; from 1075 to 5229 ms² and from 31.2%* to 35.4%*. Thus, in both men and women, TP and AMLF values show high variability, while LF% values are relatively stable. In 25-year-old male athletes ( n=18), engaged in Brazilian jiu-jitsu, the values of TP, AMLF and LF% were 3283 ms², 1215 ms² and 37%*, respectively [40]. It was established [31] that in 350 men (16 to 40 years old) engaged in martial arts, the values of LF% were 30.2%.

Other Sports

In 23 men (aged 17-28 years) involved in bullet shooting, the values of TP, AMLF and LF% were 4475 ms², 1634 ms² and 34.3%, respectively [41], and in 18-year-old weightlifters, respectively, 2871 ms², 810 ms² and 28.2%* [29].

Section Summary

So, the analysis of the literature shows that the values of TP, AMLF in athletes of different specialties have a wide range and at the same time depend on sports specialization. The highest values were noted among taekwondo fighters [39], as well as among cross-country skiers [21]. At the same time, the variability of these indicators during the annual training cycle was noted, which is discussed in more detail below. In other sports, there is also a fairly wide range of TP and AMLF values [36-38,41]. It is most likely that the variability of the values of TP and AMLF depends on the degree of training and the stage of preparation. Regarding the values of LF%, the literature data are ambiguous.

The values of TP, AMLF, LF% depending on the volume and intensity of training loads and on the period of the annual cycle

D. Plews et al. [42] indicate that in elite athletes training for endurance, SO activity correlates with high load intensity. The same conclusion is reached by V. Manzi et al. [10], pointing out that the AMLF values not only increase in response to a high load intensity, but also correlate with the athletic performance of amateur athletes in marathon running (the LF% indicator is not mentioned). It has been shown [2] that an increase in the total volume of load in elite endurance athletes reduces HRV, but the amount of training volume required to reduce HRV may depend on the population. For example, HRV was not affected by 60- and 120-minute loads, provided they were performed at low intensity (50 and 63% MIC max ). On the other hand, in a population of moderately active non-athletes, a 90-minute run (60% of VO2max ) reduced HRV, whereas 30- and 60-minute runs at the same intensity did not reduce HRV.

In the annual training cycle, in particular, it is customary for cross-country skiers to single out preparatory, competitive, and transitional periods [43]. Some authors note that the highest TP values in athletes are typical for the preparatory period, while in the competitive period the TP values may decrease, and the AMLF values increase [23,44]. The same conclusion is reached by Hedelin et al. [34], who studied cross-country skiers aged 16- 19; the authors found that in the transitional period the value of AMLF is lower than in the preparatory and competitive periods; this, in their opinion, indicates a decrease in the activity of SO ANS in the transition period. In a study of 25 biathletes (CMS, MS, 21.5 years old) in July (the beginning of the preparatory period), in November (the end of the preparatory period and the beginning of the competitive period) and in March (the end of the competitive period), it was found [35] that the TP values were respectively 5868 ms², 5228 ms² and 7059 ms²; the AMLF values are 1576 ms², 1344 ms², and 1614 ms², respectively, and the LF% values are 26.8%*, 25.7%*, and 22.8%*, respectively. It can be concluded that AMLF is relatively stable, while LF% decreases in the end of the competitive period. Studying 22 male handball players (1 adult, CMS, MS; average age -20.3 years, sports experience 8-14 years) with 6-fold registration of the CIG, including at the beginning and at the end of the preparatory period, at the beginning and in at the end of the 1^st round of games, as well as at the beginning and end of the 2^nd round of games, O.N. Kudrya [38] found that the TP values were 2781 ms², 4664 ms², 5275 ms², 8015 ms², 4431 ms², and 3926 ms², respectively; AMLF values are 1099 ms² , 1445 ms², 1579 ms², 2271 ms², 1307 ms², and 904 ms², and the values of LF% are respectively 39.5%*, 30.9%*, 29.9%*, 28.3%*, 29.4%* and 23%*. The author concludes that in the preparatory period and in the first half of the competitive period there is a significant increase in the values of TP and AM LF, and the value of LF% gradually decreases from the beginning of the preparatory period to the end of the competitive period.

We have found that the elite skier-racer athlete K.D. (the first author of the review), which is reported in more detail in our article [5], the values of TP, AMLF and LF% vary throughout the annual cycle. These data were obtained during the registration of CIG, carried out in the morning of the next day (i.e., after training or competition), before meals in the supine position using VNS-Micro by the Neurosoft company (Ivanovo) using the Poly- Spectrum” of the same company. At the same time, the CIG parameters were compared with the volume and intensity of the training loads of the athlete K.D. It was established (Figure 1) that the athlete K.D. median TP changed throughout the sports season (from 5754 to 11099 ms²), including increased in the preparatory period (up to 9473 ms²), remained high in the competition period (8047 ms²) and statistically significantly (p<0.05) decreased in the transition period (6961 ms²). At the same time, in the preparatory and transitional periods, the median TP fluctuated from month to month, while in the competitive period it was relatively stable (Figure 2). Calculation of the Spearman coefficient showed that the values of TP are directly dependent on the volume of the training load, expressed by the length of the running route for 1 training day (V km), as well as on the intensity of the training loads (N hrs), judging by the value of the “working” pulse (beats per minute) during exercise.

We found (Figure 3) that the median AMLF in athlete K.D. also changed during the annual season (from 1107 to 2912 ms²), including increased in the preparatory period (1962 ms²), remained high in the competition period (2032 ms²), and decreased in the transition period (1480 ms²) (Figure 1). At the same time, in all three periods, there were statistically significant differences between the individual months of the period. It was not possible to reveal the dependence of AMLF on the volume (V km, V min) and intensity (N hrs) of the load, including in general throughout the entire annual season, it was not possible.

Median LF% in athlete K.D. also changed during the sports season (from 16.2% to 25.8%) (Figure 4). In the preparatory period, it was 22.5%, statistically significant (p<0.05) increased during the competition period (24.5%) and tended to decrease during the transition period (21.7%) (Figure 1). It was not possible to reveal the dependence of the median LF% on the volume (V km, V min) and intensity (N hrs) of training loads, including, in general, for the annual cycle, it was not possible.

We have also shown (Figure 5) that for 8 members of the national team of Tatarstan in cross-country skiing (6 MS and 2 MSMK) during the preparatory and competitive periods, the median TP and LF% have similar dynamics, as in the athlete K.D. , and namely, in the preparatory period the median TP were significantly (p<0.05) higher than in the competitive period (respectively 9923 ms² versus 7864 ms²), the median LF% in the preparatory period was lower than in the competitive period (respectively 19.9% versus 22.7 %). We explain the growth of LF% in the competitive period by the fact that this period, as is known [45], is characterized by the formation of a feeling of anxiety, which reflects the increased influence of the SO ANS on the work of the heart at rest. With regard to the AMLF dynamics , there are differences between the athletes of the Tatarstan team in athletes of the Tatarstan team the AMLF values (2057 ms²) in the preparatory period were higher than in the competition (1728 ms²), but these differences were statistically insignificant (p>0.05).

According to other authors, the values of TP, AMLF and LF% in skiers change throughout the annual training cycle and even during one training camp (TC). Thus, in a study of 18-25-years-old elite cross-country skiers (MS), it was found [21] that the median TP at the beginning of the TC was 11258 ms², and at the end it was 12779 ms², AMLF was 3772 ms² and 3334 ms², respectively , and LF%- 33.5%* and 26%*. In a study of 20 cross-country skiers (20–22 years old), it was found [33] that the TP values before and after the completion of the TC were 3399 ms² and 3320 ms², respectively, AMLF were 1196 ms² and 1222 ms², and LF% were 35.1%* and 36.8%*, i.e. did not change significantly. According to our data, the athlete K.D. even during one month of the next period, there are statistically significant wavy changes in the medians of TP, AMLF and LF% (Figure 2, 3, 4), which we explain by a change in the amount of training or competitive load.

Recommendation

According to a number of authors whose works have already been cited above, the values of TP, AMLF and LF% significantly depend on the type of regulation of cardiac activity [32,36,37,46,47]. In particular, based on the values of the stress index (SI) and the absolute power of VLF waves (AM VLF ), it was proposed to distinguish four types (I, II, III, and IV) of the regulation of cardiac activity, which is determined by the severity of central and autonomic regulation [32,47]. I and II types of regulation are athletes, respectively, with moderate (SI>100; AM VLF>240 ms²) the predominance of central regulation, and types III and IV, respectively, with moderate (SI-30-100; AMVL>240 ms²), or with pronounced (SI<30; LVLF>500 ms²) predominance of autonomous regulation. In fact, the central regulation, according to N.I. Shlyk [32,47] is the dominance of the effect of SO ANS on the heart under conditions of clinostasis, and autonomous regulation is the dominance of the effect of PO ANS under conditions of clinostasis. Thus, in a study of 46 biathletes (CMS, MS, 18–25 years old), it was found [36] that in 21 athletes with a central type of regulation, the values of TP, AMLF and LF% were respectively 2681 ms², 1830 ms², and 68.2%*, and in the autonomous type of regulation (n=25)-5735 ms², 1290 ms² and 22.4%*, respectively.

Another option for classifying athletes is to divide them into sympathicotonic, vagotonic and normotonic [37,46]. The values of such HRV indicators as RRNN and SI [46], or the value of TP [37] were taken as the division criterion. Thus, in a study of 50 swimmers (age 15.5 years), it was found [37] that in sympathicotonic patients, the values of TP, AMLF and LF% were 1173 ms², 404 ms² and 34.4%*, respectively, in normotonics, respectively, 4234 ms², 1319 ms² and 31.1%*, while in vagotonics it was 10370 ms², 4215 ms² and 40.6%*, respectively. In a study of young cross-country skiers aged 10 and 11 years ( n=32), it was shown [46] that the values of TP, AMLF and LF% in sympathicotonic patients (n=6) were 1798 ms², 608 ms² and 33.8%, respectively, in normotonic (n=14) - 5621 ms², 1503 ms² and 26.7%*, respectively, and in vagotonics ( n =12) - 19028 ms², 4313 ms² and 22.6%*, respectively. We believe that the question of the dependence of TP, AMLF and LF% on the type of regulation of cardiac activity, raised in a number of works [32,36,37,46,47], requires additional evidence, including in experiments with animals.

Conclusion

It is believed that the AMLF values reflect the state of the SO of the ANS [4,10,11], or the state of both parts of the ANS, i.e. SO and PO, or realization of the baroreceptor reflex [2,9]. However, some authors question the interpretation of LF waves as an indicator of sympathetic control of the activity of the heart [12]. Some authors associate the index of LF waves with changes in blood pressure [13], or with the formation of psychological stress, which increases the power of LF waves [14,15].

A review of the literature allows us to state that with high motor activity, i.e. when practicing many sports, the degree of influence of SO ANS on the activity of the heart increases, which is reflected in an increase in the average values or median of TP [16,17] and AMLF [18]. However, in relation to LF%, the information is ambiguous - according to some data, it is higher in athletes than in non-athletes [16], and according to others, on the contrary, it is lower [17].

With an increase in the growth of sportsmanship, the value of AMLF increases [20,21], which indicates an increase in the influence of SO ANS on the activity of the heart during CIG registration under clinostasis. It should be noted that athletes of different sports specializations have a wide range of variability of TP and AMLF [5, 21, 36-39, 41]. The values of TP, AMLF and LF% and their variability depend on the period of sports training [7,23,34,44], on the volume of the athlete’s training load [2, 5, 7, 10, 42]. In particular, it has been shown that elite athletes during the preparatory period have a wave-like change in the values of TP, AMLF, LF%, namely, in the competitive period, the values of TP can decrease, while AMLF and LF% increase [5,7,23,44], and during the transitional period, AMLF values decrease [7,34].

LF value , and especially the LF% value in the competitive period and the decrease in these values in the transitional period , established by us [7] during the registration of CIG in conditions of clinostasis, can be regarded as a consequence of the formation of emotional stress in the competitive period caused by an anxiety state in connection with the upcoming participation in competitive starts. It is important that this dynamic is also characteristic of elite skiers. Other authors [48,49], who found an increase in AMLF in an athlete on the eve of the competition, also speak about the presence of such a condition in athletes. It is important to note that, according to clinicians, anxiety in patients with Parkinson’s disease [50] or depression [51], judging by the decrease in HRV, increases the influence of SO ANS, which is reflected in the registration of CIG under clinostasis. Yes, M. Suzuki et al. [50], recording CIG during the day using POLAR chest sensors V800 and POLAR H10 in 27 patients with Parkinson’s disease revealed a general decrease in HRV. In the study by S. Koch et al. [51], carried out on 2250 patients suffering from depression (1982 - healthy people), on the basis of temporal and spectral parameters, a decrease in HRV in patients with a depressive state was also revealed.

It can be assumed that a significant increase in LF% observed during the competitive period may be a consequence of the formation in the preparatory period of the so-called antiapoptotic system of the myocardium, due to which the viability of cardiomyocytes is maintained under conditions of excessive physical exertion during sports endurance training and, at the same time, the efficiency of activation of myocardial adrenoreceptors increases. An analysis of literature data shows that this system may include endogenous antioxidants, including antioxidant enzymes such as superoxide dismutase [52-56], endogenous sensitizers of beta-adrenergic receptors, or ESBAR, including histidine, tryptophan, and tyrosine [57-60], as well as dopamine [61,62], serotonin [63], prostaglandins [64,65], nitric oxide [66- 68], non-neuronal acetylcholine [5,7,69], and other hormones and mediators that exhibit the properties of antioxidants and antiapoptotic factors, such as melatonin [53,70].

Undoubtedly, our hypothesis about the presence of an antiapoptotic myocardial system requires careful proof. But the identification of its components and formation mechanisms deserves great attention, given the high percentage of heart disease in people leading a sedentary lifestyle under stressful conditions. Particular attention should be paid to the question of the dependence of the values of TP, AMLF and LF% on the type of regulation of the cardiac activity of an athlete, including a cross-country skier [32,36,37,46,47]. We believe that this question requires additional evidence, including in experiments on animals. We do not rule out that the type of regulation of cardiac activity depends on the components of the myocardial anti-apoptotic system, the composition of which can be largely individual. At the same time, we believe that the general patterns of dependence of the values of TP, AMLF and LF% on the type of sports specialization, the level of sportsmanship, on the periods of the training macrocycle, on the volume and intensity of the training session, will be characteristic of an athlete, regardless of the type of regulation of cardiac activity.

References

1. Aparecida MC, Carlos MP, Moacir FGD, Silva ED, Takahashi ACDM, et al. (2020) Heart rate variability: are you using it properly? Standardisation checklist of procedures. Braz J Phys Ther 24(2): 91-102.
2. Christopher JL, Nicholas AF, Biltz G (2022) Practices and Applications of Heart Rate Variability Monitoring in Endurance Athletes. Int J Sports Med 44(1): 9-19.
3. Schmitt L, Regnard J, Millet GP (2015) Monitoring Fatigue Status with HRV Measures in Elite Athletes: An Avenue Beyond RMSSD? Frontiers in Physiology 6: 343.
4. Perek S, Pasteur AR (2021) [Heart rate variability: the age-old tool still remains current]. Harefuah 160(8): 533-536.
5. Kataev DA, Tsirkin V, Kishkina VV, Trukhina SI, Trukhin AN (2023) The nature of the total power of the spectrum and very low-frequency waves of the cardiointervalogram from the standpoint of the adaptation of the human body to motor activity (review). Zhurn medical biol research 11(1): 95-107.
6. Kataev DA, Tsirkin VI, Zavalin NS, Morozova MA, Trukhina SI, et al. (2023) Dynamics of TP- and HF -waves of the cardiointervalogram of a skier-racer in the preparatory, competitive, and transitional periods depending on the volume and intensity of training loads. Bulletin of sports science 1: 46-54.
7. Kataev DA, Tsirkin VI, Zavalin NS, Morozova MA, Trukhina SI, et al. (2023) Dynamics of TP, HF-, LF- and VLF -waves of the cardiointervalogram (under conditions of clinostasis) of an elite cross-country skier in the preparatory, competitive, and transitional periods depending on the volume and intensity of training loads. Human Physiology.
8. McCraty R, Shaffer F (2015) Heart rate variability: New perspectives on physiological mechanisms, Assessment of self-regulatory capacity, and Health risk. Glob Adv Health Med 4(1): 46-61.
9. Shaffer F, Ginsberg JP (2017) An Overview of Heart Rate Variability Metrics and Norms. Front Public Health 28(5): 258.
10. Manzi V, Castagna C, Padua E, Lombardo M, D'Ottavio S, et al. (2009) Dose-response relationship of autonomic nervous system responses to individualized training impulse in marathon runners. Am J Physiol Heart Circ Physiol 296(6): 1733-1740.
11. Schäfer D, Gjerdalen GF, Solberg EE, Khokhlova M, Badtieva V, et al. (2015) Sex differences in heart rate variability: a longitudinal study in international elite cross-country skiers. Eur J Appl Physiol 115(10): 2107-2114.
12. Reyes del PGA, Langewitz W, Mulder LJM, Roon AV, Duschek S (2013) The utility of low frequency heart rate variability as an index of sympathetic cardiac tone: a review with emphasis on a reanalysis of previous studies. Psychophysiology 50(5): 477-487.
13. Thayer JF, Oberg RN, Sollers JJ (1997) Thermoregulation and cardiac variability: a time-frequency analysis. Biomed Sci Instrum 34: 252-256.
14. Hulka OV (2015) [Dynamics of Spectral Indexes of Heart Variability Rate of the Students with Different Character of the Educational Loading]. Fiziol Zh 61(4): 98-104.
15. Kim HG, Cheon EJ, Bai DS, Lee YH, Koo BH (2018) Stress and Heart Rate Variability: A Meta-Analysis and Review of the Literature. Psychiatry Investig 15(3): 235-245.
16. Shangareeva GN (2014) Indicators of heart rate variability in young hockey players of the Olympic reserve. Medical Bulletin of Bashkortostan 9(1): 49-52.
17. Litovchenko OG, Maksimova AS, Chirkov AA (2021) Features of heart rate variability in young athletes-volleyball players of the Khanty-Mansiysk Autonomous Okrug - Yugra. Modern questions biomedicine 5(4): 194-204.
18. Costa O, Freitas J, Puig J, Carvalho MJ, Freitas A, et al. (1991) Spectrum analysis of the variability of heart rate in athletes. Rev Port Cardiol 10(1): 23-28.
19. Mikhailov VM (2017) Heart rate variability (a new look at the old paradigm). Ivanovo: Neurosoft LLC pp. 516.
20. Bryntseva EV, Gavrilova EA, Zagorodny GM, Churganov OA, Belodedova MD (2020) Prognosis of success in junior swimmers based on the assessment of heart rate variability. Applied sports science 2: 61-69.
21. Litvin FB, Anosov AP, Asyamolov PO, Vasileva GV, Martynov SV, et al. (2012) Cardiac rhythm and microcirculation system in skiers in the pre-competitive period of sports training. Herald Udmurt university 1: 67-74.
22. Solomka TN (2008) Peculiarities of autonomic regulation of heart rate in football players with different types of hemodynamics. In: Solomka TN, Makarova IM. Heart rate variability: theoretical aspects and practical. Application: Theses. Reports pp. 295-297.
23. Gavrilova EA (2015) Sport, stress, variability: monograph. M Sport pp. 168.
24. Gratze G, Rudnicki R, Urban W, Mayer H, Schlögl A, et al. (2005) Hemodynamic and autonomic changes induced by Ironman: prediction of competition time by blood pressure variability. J of Appl Physiol(1985) 99(5): 1728-1735.
25. Schmitt L, Regnard J, Desmarets M, Mauny F, Mourot L, et al. (2013) Fatigue shifts and scatters heart rate variability in elite endurance athletes. PLoS One 8: 71588.
26. Liao L, Li J (2022) Research on Effect of Load Stimulation Change on Heart Rate Variability of Women Volleyball Athletes. Comput Intell Neurosci.
27. Mourot L, Bouhaddi M, Perrey S, Cappelle S, Henriet MT, et al. (2004) Decrease in heart rate variability with overtraining: assessment by the Poincaré plot analysis. Clin Physiol Funct Imaging 24(1): 10-18.
28. Belova EL, Rumyantseva NV (2008) Adaptation to the conditions of the orthostatic test in young athletes, depending on the characteristics of the training process. Scientists notes university named after P.F. Lesgaft 3: 21-24.
29. Kudrya ON (2009) Influence of physical loads of different directions on heart rate variability in athletes. Bulletin Siberian medicine 8(1): 36-42.
30. Vikulov AD, Bocharov MV, Kaunina DV, Boykov VL (2017) Regulation of cardiac activity in highly qualified athletes. Herald sports science 2: 31-36.
31. Ivanova NV (2011) Evaluation of the functional state of the cardiorespiratory system of athletes with different specifics of muscle activity in the competitive period of training. Bulletin of sports science 1: 64-68.
32. Shlyk NI (2021) Norms of the variation range of cardiointervals at rest and orthostasis with different types of regulation in cross-country skiers in the training process. Science and sport: current trends 9(4): 35-50.
33. Rul EA, Kudrya ON (2022) Indicators of heart rate variability in cross-country skiers during training camps using transcrianial electrical stimulation. Modern questions biomedicine 6(1): 195-199.
34. Hedelin R, Wiklund U, Bjerle P, Larsén KH (2000) Pre- and post- season heart rate variability in adolescent cross-country skiers. Scand J Med Sci Sports 10(5): 298-303.
35. Kalsina VV, Kudrya ON, Reutskaya EA (2021) Evaluation of the functional state of highly qualified biathletes in terms of heart rate variability. Scientists notes university named after PF Lesgaft 8(198): 111-118.
36. Litvin FB, Brook TM, Terekhov PA, Osipova NV (2020) Features of anaerobic performance of biathletes depending on the type of autonomic regulation of the heart rate. Journ honey biol research 8(4): 368-377.
37. Rusanov VB (2011) Typological features of autonomic regulation of heart rhythm. Bulletin of the South Ural State Humanitarian Pedagogical University 6: 313-324.
38. Kudrya ON (2014) Evaluation of the functional state and physical fitness of athletes in terms of heart rate variability. Herald Novosibirsk state pedagogical university 4(1): 185-195.
39. Mishchenko IA, Volynskaya EV, Korobova SA (2021) Monitoring of the functional state of taekwondo wrestlers in terms of heart rate variability in the precompetitive microcycle. Human Sport Medicine 21(2): 42-50.
40. Krylova IF, Baltabaev FY, Novichenko AO, Kulikov VY, Pikovskaya NB (2015) Analysis of cardiointervalogram parameters in athletes involved in Brazilian jiu-jitsu during training. Medicine and education in Siberia 3: 1-8.
41. Korepanov AL, Bobrik YV, Titarenko AA, Ponomarev VA (2022) Dynamics of heart rate variability indicators in the process of attention training in high-skilled sportsmen-shooters. Theory and practice physical culture 4: 54-56.
42. Plews DJ, Laursen PB, Kilding AE, Buchheit M (2014) Heart-rate variability and training-intensity distribution in elite rowers. Int J Sports Physiol Perform 9(6): 1026-1032.
43. Grushin AA (2014) Sports training of highly qualified cross-country skiers at the stage of maximum realization of sports achievements; textbook for independent work of students M: Physical culture pp. 106.
44. Blásquez JCC, Font G, Ortis LC (2009) Heart-rate variability and precompetitive anxiety in swimmers. Psicothema 21(4): 531-536.
45. Palazzolo J (2020) Anxiety and performance. Encephale 46(2): 158-161.
46. Efremova RI, Spitsin AP, Voronina GA (2015) Reactivity of the regulatory systems of young skiers depending on the type of autonomic regulation. Vyatka Medical Bulletin 4: 15-18.
47. Shlyk NI (2015) Express assessment of the functional readiness of the athlete's body for training and competitive activities (according to the analysis of heart rate variability). Science and sports modern trends 9(4): 5-15.
48. Souza RA, Beltran OA, Zapata DM, Silva E, Freitas WZ, et al. (2019) Heart rate variability, salivary cortisol and competitive state anxiety responses during pre-competition and pre-training moments. Biol Sport 36(1): 39-46.
49. Moreno RA, García JPF, Mateo DC, Villafaina S (2020) Heart rate variability and pre-competitive anxiety according to the demanding level of the match in female soccer athletes. Physiol Behav 222: 112926.
50. Suzuki M, Nakamura T, Hirayama M, Ueda M, Hatanaka M, et al. (2022) Wearable sensor device-based detection of decreased heart rate variability in Parkinson's Disease. J Neural Transm (Vienna) 129(10): 1299-1306.
51. Koch С, Wilhelm M, Salzmann S, Rief W, Euteneuer F (2019) A meta-analysis of heart rate variability in major depression. Psychol Med 49(12): 1948-1957.
52. Teen DB, Kaludercic N, Weissman D, Turan B, Maack C, et al. (2021) Mitochondrial ROS and mitochondria-targeted antioxidants in the aged heart. Free Radic Biol Med 167: 109-124.
53. Wongprayoon P, Govitrapong P (2021) Melatonin Receptor as a Drug Target for Neuroprotection. Curr Mol Pharmacol 14(2): 150-164.
54. Andreadou I, Daiber A, Baxter GF, Brizzi MF, Lisa FD, et al. (2021) Influence of cardiometabolic comorbidities on myocardial function, infarction, and cardioprotection: Role of cardiac redox signaling. Free Radic Biol Med 166: 33-52.
55. Singh RB, Fedacko J, Pella D, Fatima G, Elkilany G, et al. (2022) High exogenous antioxidant, restorative treatment (heart) for prevention of the six stages of heart failure: The heart diet Antioxidants (Basel) 11(8): 1464.
56. Zhou Y, Suo W, Zhang X, Lv J, Liu Z, et al. (2022) Roles and mechanisms of quercetin on cardiac arrhythmia: A review. Biomed Pharmacother 153: 113447.
57. Korotaeva KN, Tsirkin VI, Vyaznikov VA (2012) Positive inotropic effect of tyrosine, histidine, and tryptophan in experiments with isolated human heart myocardium. Bull Exp biology and medicine 153(1): 51-53.
58. Tsirkin VI, Nozdrachev AD, Korotaeva YV (2014) An endogenous sensitizer of β-adrenergic receptors and its analogs attenuate the inhibition of β-adrenergic receptors by propranolol and atenolol in the rat myocardium. Doklady Biological Sciences 456(1): 169-172.
59. Tsirkin VI, Nozdrachev AD, Sizova EN, Polezhaeva TV, Khlybova SV (2016) Endogenous sensitizer of beta-adrenergic receptors (ESBAR) as a component of the humoral link of the autonomic nervous system and its analogues (literature review). Uspekhi fiziologicheskikh nauk 47(4): 18-42.
60. Tsirkin V, Nozdrachev A, Sizova E, Polezhaeva T, Khlybova S, et al. (2018) Endogenous sensitizer of beta-adrenergic receptors (ESBAR) and its analogs (Review). J Cardiol Cardiovasc Med 3(3): 64-78.
61. Schindler CW, Thorndike EB, Rice KC, Parilla JS, Baumann MH (2019) The Supplement adulterant β-methylphenethylamine increases blood pressure by acting at peripheral norepinephrine transporters. J Pharmacol Exp Ther 369(3): 328-336.
62. Tapbergenov SO, Sovetov BS, Smailova ZHK (2022) Adrenergic receptors in the mechanism of regulation of mitochondrial and cytoplasmic enzymes of cardiomyocytes by catecholamines. Bull Exp Biol Med 173(3): 330-334.
63. Song Y, Xu C, Liu J, Li Y, Wang H, et al. (2021) Heterodimerization with 5-HT2BR is indispensable for β2AR-mediated cardioprotection. Circ Res 128(2): 262-277.
64. Smiljic S (2017) The clinical significance of endocardial endothelial dysfunction. Medicine (Kaunas) 53(5): 295-302.
65. Radi ZA, Khan KN (2019) Cardiorenal safety of nonsteroidal anti-inflammatory drugs. J Toxicol Sci 44(6): 373-391.
66. Ghasemi A, Jeddi S (2022) Quantitative aspects of nitric oxide production in the heart. Mol Biol Rep 49(11): 11113-11122.
67. Lukowski R, Cruz SM, Kuret A, Ruth P (2022) cGMP and mitochondrial K+ channels-Compartmentalized but closely connected in cardioprotection. Br J Pharmacol 179(11): 2344-2360.
68. Popov SV, Mukhomedzyanov AV, Voronkov NS, Derkachev IA , Boshchenko AA, et al. (2023) Regulation of cardiac autophagy in ischemia and reperfusion. Apoptosis 28(1-2): 55-80.
69. Kakinuma Y (2021) Characteristic effects of cardiac non-neuronal acetylcholine system augmentation on brain functions. Int J Mol Sci 22(2): 545.
70. Tan DX, Manchester LC, Qin L, Reiter RJ (2016) Melatonin: A mitochondrial targeting molecule involving mitochondrial protection and dynamics. Int J Mol Sci 17(12): 2124.