PhD E.F. Surina-Marysheva¹
Dr.Biol., Professor V.V. Erlikh¹
Yu.B. Korableva¹
Associate Professor, PhD S.A. Kantyukov^2
1South Ural State University (National Research University), Chelyabinsk
²South Ural State Medical University, Chelyabinsk

Keywords: heart rate variability, athletes, puberty, ice hockey, forecasting

Introduction. The competitive performance forecast in ice hockey is not informative enough. At the same time, some measures of effectiveness of the athletes’ adaptation during intense periods of ontogenesis should be statistically significant to forecast success in their future professional career. The narrow age range from 15 to 16 years is characterized by active morphofunctional restructuring, which is associated with the peculiarities of pubertal development and affect young hockey players’ physical development [4, 13]. In terms of sports selection, the importance of morphological [10], functional and other indicators [7] ensuring a high level of special physical working capacity of hockey players increases. Of great importance is the level of development of their aerobic endurance [11].

The functional state of athletes has been successfully rated using the heart rate variability (HRV) analysis [1, 5, 6, 9, 12], therefore, a working hypothesis has been formulated on the applicability of the cardiorhythmographic test data for long-term forecasting of hockey players’ career success.

Objective of the study was to determine the informativeness of the HRV rates in elite ice hockey players of 15-16 years of age to forecast success in their future professional career in youth hockey.

Methods and structure of the study. The study was carried out at the premises of the Sports School of the Olympic Reserve on Hockey "Traktor". The HRV rates in the 15-16 year-old ice hockey players were obtained twice during the 2010/2011 competitive season: 1) in July; 2) in February. Success in the professional career of the 15-16 year-old ice hockey players was rated 4 years since graduation from the Olympic Reserve Sport School (2016) [3].

Electrocardiogram was recorded at rest and during the transitional period of active orthostatic test. VNS-MICRO Test System (made by NeuroSoft, Russia) was applied for the study purposes. The HRV profiles were obtained using the standard HRV timing method, spectral analysis [2, 5] and pulsometry; plus the vegetative reactivity in parasympathetic department (30:15) [2] and vegetative reactivity typing methods [5].

The test data were processed using Statistica 10.0 software toolkit with its R-language. The individual sport career forecasts and scenarios were generated using "Random Forest" modeling tools with the relevant set of the most informative progress criteria [8]. Binary variables were introduced: 1 - career success after 4 years of training (the level of youth hockey league and above), 0 - no success. The advantages of each model were compared using the "Area Under Curve" (AUC) indicator, which varies from 0.5 ("useless model") to 1.0 ("ideal model"). We built three alternative models that included standard HRV parameters. Model 2 additionally included K30:15, and Model 3 - K30:15 and type of regulation. For the purpose of descriptive statistics of HRV in the ice hockey players, 2 groups were singled out: Group 1 – with no progress; Group 2 – with good progress.

Results and discussion. The working hypothesis was tested by means of the appropriately built models. In July, the predictive value of the models in terms of AUC increased from Model 1 to Model 3: AUC 1=0.873; AUC 2(+K30:15)=0.879; AUC 3(+ K30:15+type of regulation)=0.885. However, in July, there was still a small number of significant HRV indicators used to forecast success in the future professional career. Among the most important indicators were the spectral analysis rates (VLF, LF, HF, LF/HF) measured in relative units and K30:15. The heart rate regulation typing data obtained in July was not statistically significant for the career success forecasting model, but its inclusion in the model increased its prognostic value.

In February, the predictive value of the models in terms of AUC also increased from Model 1 to Model 3: AUC 1=0.893; AUC 2(+K30:15)=0.910; AUC 3(+K30:15+type of regulation)=0.917. In February, of all the spectral analysis data, the most significant ones turned out to be total power (TP) and degree of impact of the parasympathetic division of the ANS - HF (ms2). The variational pulsometry indicators (SI, AMo, RPAI, VRI) and those of the HRV timing method (RMSSD, СV) became significant in the model. Regardless of the period of training, the K30:15 indicator stood out in the model of impact of HRV indicators on success in the future professional career. In a state of optimal readiness for competitive activity, along with HRV and K30:15, it is also the type of heart rate regulation that became statistically significant.

In July, all HRV parameters remained the same in each study group (р˃0.05 in all cases; Mann-Whitney U test). In February, K30:15 turned out to be higher in Group 1 than in Group 2 (1.40±0.22 and 1.21±0.18, respectively; p=0.016).

The dynamics of HRV parameters differed between the groups: in February, the HRV rates in the hockey players from Group 1 remained the same as compared to July (р˃0.05 in all cases), while in Group 2 there were certain changes (see Table 1): the mean values of SDNN (p=0.045), VLF (p=0.047), LF (p=0.003) and CV (p=0.005) decreased.

Table 1. Dynamics of changes in HRV, HR and K30:15 in 15–16 year-old hockey players (July – February)

Note. р₁ – significance of differences in Group 1 (July– February); р₂ – significance of differences in Group 2 (July– February). Wilcoxon test.

In Group 1, regardless of the training period, the heart rate in the majority of ice hockey players was characterized by the predominance of parasympathicotonia (July - 82%; February - 91%). As opposed to Group 1, the number of ice hockey players with pronounced parasympathicotonia was lower in Group 2 (July - 67%; February - 54%). Initially, in Group 2, there was a relatively great number of athletes with a predominance of central regulation mechanisms - 33%. By February, the number of ice hockey players with such types of regulation increased by 13%. In Group 2, HRV decreased against the background of constant reactivity of the parasympathetic division of ANS (K30:15). In the group of 15-16 year-old ice hockey players achieving no career success, the constant HRV rate is accompanied by an increase in K30:15. The significance of this coefficient is confirmed by the correlation analysis data: there is a correlation between the HRV indicators and the indicator of progress in the future professional career in youth hockey at the end of the competitive period (r=-0.48; p<0.05).

Conclusion. It is only possible to forecast success in the future professional career of the 15-16 year-old ice hockey players based on the analysis of the dynamics of changes in the HRV parameters combined with the vegetative reactivity rates in the parasympathetic department of ANS and type of HRV regulation. These indicators were found to have the greatest predictive value at the end of the competitive training period (the optimal level of special physical fitness). At the same time, the 15-16 year-old ice hockey players holding promise in youth hockey were found to have a more pronounced decrease in HRV by the end of the competitive period (optimal readiness) against the background of relatively stable reactivity of the parasympathetic division of ANS in the orthostatic test.

The article was supported by the government of the Russian Federation (Act No. 211 of 03/16/2013), Contract No. 02.A03.21.0011. The study was performed as a part of the State Assignment of the Ministry of Education and Science of the Russian Federation (Grant No. 19.9731.2017/БЧ) and the State Assignment of the Ministry of Education and Science of the Russian Federation (Grant No. 19.9733.2017/БЧ).

References

1. Gavrilova E.A. Sport, stress, variabelnost [Sport, stress, variability]. Moscow: Sport, 2015. 168 p.
2. Mikhaylov V.M. Variabelnost ritma serdtsa. Opyt prakticheskogo primeneniya [Heart rate variability. Experience of practical application]. Ivanovo: ISMA publ., 2000, 200 p.
3. Rossiyskiy khokkey [Russian hockey]. http://www.r-hockey.ru (date of access 08.05.2017).
4. Surina-Maryisheva E.F., Erlikh V.V., Korableva Yu.B. et al. Fizicheskoe razvitie yunykh khokkeistov [Physical development of junior hockey players]. Chelovek. Sport. Meditsina. 2017. no. 17. pp. 21-31.
5. Shlyk N.I. Serdechny ritm i tip regulyatsii u detey, podrostkov i sportsmenov [Heart rate and type of regulation in children, adolescents and athletes]. Izhevsk: UU publ., 2009. 259 p.
6. Cipryan L. , Laursen P.B., Plews D.J. Cardiac autonomic response following high-intensity running work-to-rest interval manipulating. Eur. J. Sports Sci. 2016, no.16. P. 808-817.
7. Fumarco, L. , Gibbs B.G. , Jarvis J.A., Rossi G. The relative age effect reversal among the National Hockey League elite. PLos One. 2017. no.12(8). P. 27.
8. Kabacoff R.I. R in Action: Data Analisys and Graphics with R. Island: Manning Publications Co. 2015, 579 p.
9. Koenig, J. , Jarczok M.N. , Wasner M., Hillecke T.K. Heart rate variability and swimming. Sport Med. 2014. no.44. pp. 1377-1391.
10. Lauren, B. Do physical maturity and birth date predict talent in male youth ice hockey players? Journal of Sports Sciences. 2007. no.25(8). pp. 879-886.
11. Montgomery D.L. Physiological profile of professional hockey players – a longitudinal comparison. Appl. Physiol. Nutr. Metab. 2006. no.31. pp. 181-185.

Corresponding author: surina-marysheva2015@yandex.ru

Abstract

The study was designed to rate benefits of the heart rate variability profiling tests to forecast competitive progress of 15-16 year old elite ice hockey players, with VNS-MICRO Test System (made by NeuroSoft, Russia) applied for the study purposes. The heart rate variability profiles were obtained using the standard HRV timing method, spectral analysis and pulsometry; plus the vegetative reactivity in parasympathetic department (30:15) and the vegetative reactivity typing methods. The individual competitive progress was rated for 4 years since graduation from the Olympic Reserve Sport School. The test data were processed using Statistica 10.0 software toolkit with its R-language. The individual sport career forecasts and scenarios were generated using Random Forest modeling tools with the relevant set of the most informative progress criteria. The study showed benefits of the heart rate variability profiling tests for the competitive progress forecasts in youth elite ice hockey; and gives analyses of the heart rate variability variation profiles of the leading junior players in the competitive periods.