International Journal of Sports Physiology and Performance, 2015, 10, 452 -457 http://dx.doi.org/10.1123/ijspp.2014-0235 © 2015 Human Kinetics, Inc.
Original Investigation
Two New Indexes for the Assessment of Autonomic Balance in Elite Soccer Players José Naranjo Orellana, Blanca de la Cruz Torres, Elena Sarabia Cachadiña, Moisés de Hoyo, and Sergio Domínguez Cobo Purpose: The application of Poincaré-plot analysis to heart-rate variability (HRV) is a common method for the assessment of autonomic balance. However, results obtained from the indexes provided by this analysis tend to be difficult to interpret. In this study the authors aimed to prove the usefulness of 2 new indexes: the stress score (SS) and the sympathetic:parasympathetic ratio (S:PS ratio). Methods: 25 professional Spanish soccer players from same team underwent 330 resting measurements of HRV. All subjects experienced 10 min of HRV monitoring through an R-R-interval recorder. The following parameters were calculated: (1) Poincaré-plot indexes: SD1 (transverse axis), which is proportional to parasympathetic activity; SD2 (longitudinal axis), which is inversely proportional to sympathetic activity; and the SD1:SD2 ratio; (2) time-domain parameters: standard deviation of R-R intervals (SDNN), root-mean-square differences of successive heartbeat intervals (rMSSD), and percentage of successive R-R-interval pairs differing in more than 50 ms in the entire recording divided by the total number of R-R intervals (pNN50); and (3) the proposed 2 new indexes: the SS and the S:PS ratio. Results: The study found a high negative correlation between the SS and SDNN (R2 = .94). The S:PS ratio correlated inversely to rMSSD (R2 = .95), SDNN (R2 = .94), and pNN50 (R2 = .74). The S:PS ratio showed a strong correlation with SD1 (R2 = .95) and SS (r = .87, R2 = .88). Conclusions: The application of the SS as sympathetic-activity index and the S:PS ratio as a representation of autonomic balance (SS:SD1) provides a better understanding of the Poincaré-plot method in HRV. Keywords: heart-rate variability, Poincaré plot, sympathetic, parasympathetic, SD1:SD2 ratio Heart-rate variability (HRV) has been defined as the time variation between consecutive heartbeats.1,2 HRV analysis is a noninvasive method that permits assessment of the interactions between sympathetic and parasympathetic systems. Thus, HRV reflects the reaction capacity of the heart to different physiological demands.3 According to all HRV guidelines and publications in the literature, there are several methods for the assessment of cardiac autonomic function by the examination of sympathetic–parasympathetic balance. Time-domain analysis1 comprises different parameters that reflect parasympathetic activity. They include the standard deviation of R-R intervals (RRIs), which is called SDNN; the root-mean-square differences of successive heartbeat intervals (rMSSD); and the percentage of successive RRI pairs differing in more than 50 milliseconds in the entire recording divided by the total number of RRIs (pNN50).1 On the other hand, the Poincaré-plot4 analysis is a nonlinear method that reflects sympathetic–parasympathetic fluctuations.5,6 To plot it, all consecutive RRIs are inserted in a 2-dimensional dispersion plot in such a way that every RRI point is represented against the previous point. Thus a Poincaré plot shows a qualitative picture of the variations between RRIs and can be adjusted to an ellipse.4
Naranjo Orellana is with the Dept of Sports and Computing, Pablo de Olavide University, Seville, Spain. de la Cruz Torres is with the Dept of Physical Therapy, and de Hoyo, the Dept of Physical Education and Sports, University of Seville, Seville, Spain. Cachadiña is with CES Cardenal Spínola, CEU San Pablo Andalucía, Seville, Spain. Cobo is with Sevilla Fútbol Club S.A.D., Seville, Spain. Address author correspondence to José Naranjo Orellana at [email protected]. 452
This plot has been widely used due to its simple visual interpretation.7,8 Furthermore, it has proved successful in the assessment of several diseases such as renal failure,9 diabetes,10 cardiac failure,11,12 and sleep apnea.13 Moreover, it is of great interest to apply this method to cardiovascular function at rest and during exercise.14–18 Initially, the Poincaré-plot analysis was a qualitative method,11 but the measurement of the major and minor diameters of the ellipse4 turned this method into a quantitative one. The transversal axis (SD1) reflects short time changes in the RRI and is directly linked to parasympathetic activity. However, the longitudinal axis (SD2) is not so well defined but seems to be inversely proportional to sympathetic activity. Although some studies4,17 reported SD2 alterations when atropine (parasympathetic block) or moxonidine (sympathetic block) was administered, most studies during exercise14 show a clear relationship between a decrease in SD2 and sympathetic stimulation. Finally, some authors9,19 have reported correlations of the diameters of the Poincaré plot with nonlinear parameters. The SD1:SD2 ratio is normally used to assess the interaction between parasympathetic and sympathetic activity. However, its interpretation is as yet unclear because both terms of the ratio increase or decrease simultaneously. This is clearly understood since the numerator is proportional to parasympathetic stimulation while the denominator is inversely proportional to sympathetic stimulation. For all of these reasons, the aim of the study was to propose 2 new indexes—the stress score (SS) and the sympathetic:parasympathetic ratio (S:PS ratio) from the Poincaré plot—that should aid a better understanding of the autonomic balance through HRV analysis. Furthermore, this study had a second objective, which was to assess reference values for the previously mentioned variables in elite soccer players.
Autonomic Balance Indexes in Soccer 453
Methods Subjects During this study we performed 330 measurements of heart rate during a soccer season to 25 Spanish male professional soccer players from the same team (age 25 ± 3 y, weight 74 ± 7 kg, height 179 ± 8 cm). This team ended the season in the sixth position of the Spanish League (Liga BBVA) and as champions of the UEFA Europa League. The local ethics board approved this study, which followed all principles in the Declaration of Helsinki. All subjects signed an informed written consent to participate in this study.
Design Every subject underwent 13 measurements over 7 months, in a sitting position, early morning under fasting conditions. This was a descriptive longitudinal cohort study.
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Methodology The heart-rate monitor Firstbeat Bodyguard (Firstbeat Technologies, Jyväskylä, Finland) recorded heart-rate data during 10 minutes
in every measurement. The device-proprietary software (Firstbeat Uploader) downloaded the data from the devices to a computer, and all HRV parameters imported from all RRI series were made in the software Kubios (University of Eastern Finland, Kuopio, Finland).
Table 3 Pearson Correlation Coefficient (r) and R2 for the Exposed Variables Variable SD1 SD2 Stress score SD1:SD2 S:PS
rMSSD 1.00, 1.00 .81, .65 –.67, .69 .74, .55 –.61, .95
SDNN .89, .78 .99, .98 –.85, –.98 .44, .16 –.68, .94
pNN50 .77, .76 .74, .59 –.73, .59 .55, .38 –.74, .75
Abbreviations: rMSSD, root-mean-square differences of successive heartbeat intervals; SDNN, standard deviation of R-R intervals; pNN50, percentage of successive RR-interval pairs differing in more than 50 milliseconds in the entire recording divided by the total number of RR intervals; SD1, transverse axis; SD2, longitudinal axis; S:PS, sympathetic:parasympathetic ratio.
Table 1 Values for All Variables of the Study Variable Heart rate SDNN (ms) rMSSD (ms) pNN50 (%) SD1 SD2 SD1:SD2 Stress score S:PS
Mean 59.98 131.55 97.67 39.84 69.16 170.98 0.39 6.87 0.17
SD 7.77 6.67 62.95 19.19 44.59 70.08 0.14 2.88 0.18
Lowest 38.20 35.66 16.60 0.65 11.75 44.68 0.12 2.21 0.01
Highest 92.20 350.96 467.03 82.19 330.55 453.02 1.06 22.38 1.33
Abbreviations: SDNN, standard deviation of R-R intervals; rMSSD, root-meansquare differences of successive heartbeat intervals; pNN50, percentage of successive RR-interval pairs differing in more than 50 milliseconds in the entire recording divided by the total number of RR intervals; SD1, transverse axis; SD2, longitudinal axis; S:PS, sympathetic:parasympathetic ratio.
Table 2 Reference Values for All Variables of the Study Percentile Variable Heart rate SDNN (ms) rMSSD (ms) pNN50 (%) SD1 SD2 SD1:SD2 Stress score S:PS
10th 51.40 71.95 39.31 13.39 27.82 99.11 0.24 3.75 0.03
25th 54.85 89.50 54.04 24.57 38.24 119.88 0.30 4.66 0.06
50th 59.30 116.90 85.08 39.83 60.23 150.09 0.36 6.66 0.11
75th 64.70 160.80 118.03 55.79 83.54 214.77 0.47 8.34 0.21
90th 69.32 213.96 182.08 65.59 129.41 266.89 0.55 10.09 0.36
Abbreviations: SDNN, standard deviation of R-R intervals; rMSSD, root-meansquare differences of successive heartbeat intervals; pNN50, percentage of successive RR-interval pairs differing in more than 50 milliseconds in the entire recording divided by the total number of RR intervals; SD1, transverse axis; SD2, longitudinal axis; S:PS, sympathetic:parasympathetic ratio.
Figure 1 — R2 correlation of SD1 with (A) the root-mean-square differences of successive heartbeat intervals (rMSSD), (B) standard deviation of R-R intervals (SDNN), and (C) percentage of successive RR-interval pairs differing in more than 50 milliseconds in the entire recording divided by the total number of RR intervals (pNN50).
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454 Naranjo Orellana et al
Thus, Kubios software provided SD1, SD2, and SD1:SD2 ratio; SDNN; rMSSD; and pNN50. From the obtained variables, the 2 new indexes were calculated as follows: • SS: 1000 × 1/SD2. Given that SD2 is an inverse function of sympathetic activity, we expressed the inverse of SD2 to obtain a value that is directly proportional to the sympathetic activity (multiplied by 1000 to make it a more manageable number). • S:PS: The ratio of SS to SD1 (SS:SD1), whose meaning is to express a relationship between sympathetic and parasympathetic activity.
also analyzed the correlation between parasympathetic indicators and sympathetic indicators. This investigation applied the following criteria to determine the magnitude of the correlation (r): .80) and healthy subjects (r > .90). Furthermore, Kleiger et al2 and Garrido et al15 reported similar findings. Changes in SD2 could sometimes be difficult to understand because it represents the inverse of sympathetic activity. Nevertheless, if we take the inverse of SD2, we have a direct index for sympathetic activity, and its physiological interpretation would be easier, especially when it is included in a relationship to evaluate the sympathetic–parasympathetic balance. The current study indeed tried to improve this understanding using SS instead of SD2; in this process we found a high inverse correlation between SS and SDNN (Table 3). In the same direction, Figure 3(B) shows that 94% of changes in SDNN match with the behavior of the SS. The interpretation of SD1:SD2 as an indicator of autonomic balance is even less clear due to the presence of an inverse function at the denominator of the ratio. In fact, physical exercise or a state of fatigue and lack of recovery could produce very similar changes in SD1:SD2 or even no change. In both cases there would be an increase in the sympathetic activity (decrease of SD2) and a decrease in parasympathetic activity (reduction of SD1), resulting in a ratio unchanged and unable to differentiate between the 2 situations. The current study proposes a new assessment of autonomic balance from HRV through the ratio between SS (as an indicator of sympathetic activity) and SD1 (as an indicator of parasympathetic activity) and shows an inverse large to very large correlation to all time-domain parameters (Table 3). Moreover, the relationships of the S:PS ratios with time-domain parameters are stronger than the relationships between SD1:SD2 and the same variables (Figure 4). If we analyze the correlation of S:PS ratio to its 2 components, SS and SD1, we can observe an inverse large correlation with SD1 and a large correlation with SS (Table 3, Figure 5). To evaluate the participation of both components in the changes of the S:PS ratio, Figure 5 provides important information. Here it is shown that an increase in the sympathetic–parasympathetic relationship at rest should be more strongly related to a decrease in parasympathetic function (SD1) than to an increase in sympathetic activity (SS). On the other hand, this study provides useful reference values given that HRV values in athletes are very far from those provided by the ESC/NASPE task force1 for patients. To our knowledge, no other study provides a sample with players at this high level of competition and with such long-term follow-up. The number of measurements made allows these values to be used as references for monitoring soccer players. It is also particularly important to provide references for the new indexes proposed. To this extent the percentile distribution shows that SS values higher than 10 (p90) should be considered indicators of a high level of sympathetic stress, while values between 8 and 10 (p75–p90) should be considered an alert level. In the same way, an S:PS ratio higher than 0.3 (p90) could indicate an alteration of the autonomic balance due to an increased sympathetic activity or a lack of recovery in parasympathetic activity at rest.
Practical Applications First, we have described 2 indices to facilitate the analysis of the relationship between sympathetic and parasympathetic activity at rest and the understanding of its physiological significance. These indices are strongly coherent, and the percentile distribution in elite players indicates that an SS higher than 10 and/or an S:PS
Figure 5 — R2 correlation of the sympathetic:parasympathetic ratio (S/ PS) with (A) the transverse axis (SD1) and (B) the stress score (SS).
ratio higher than 0.3 at rest could indicate an excess of sympathetic activity or a lack of recovery of parasympathetic activity. Second, this study provides reference values of HRV for elite soccer players through a percentile distribution. These references should be particularly useful when HRV is used for the control of workloads in soccer. In a weekly follow-up, these findings may be used to plan the workload of training sessions, and they can be a good indicator of how each player is assimilating the accumulated workloads.
Conclusions The application of the SS index as a direct calculation for the assessment of sympathetic activity, and S:PS as an indicator of autonomic balance (SS:SD1) improves the physiological meaning of HRV by the Poincaré-plot-analysis method. This study provides reference values in elite soccer players, not only from the 2 proposed new indexes but also from the variables of the Poincaré-plot and time-domain methods.
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Autonomic Balance Indexes in Soccer 457
Acknowledgments The authors would like to thank all the players in the squad of Sevilla FC for their participation in this study. We also want to thank to Mr Felipe Del Valle for his help in data collection during the season.
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References 1. Heart rate variability, standards of measurement, physiological interpretation, and clinical use. Task force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation. 1996;93(5):1043–1065. PubMed doi:10.1161/01.CIR.93.5.1043 2. Kleiger RE, Stein PK, Bigger JT Jr. Heart rate variability: measurement and clinical utility. Ann Noninvasive Electrocardiol. 2005;10(1):88– 101. PubMed doi:10.1111/j.1542-474X.2005.10101.x 3. Akselrod S, Gordon D, Ubel F, Shannon D, Berger A, Cohen R. Power spectrum analysis of heart rate fluctuation: a quantitative probe of beatto-beat cardiovascular control. Science. 1981;213(4504):220–222. PubMed doi:10.1126/science.6166045 4. Tulppo MP, Mäkikallio TH, Takala TES, Seppänen T, Huikuri HV. Quantitative beat-to-beat analysis of heart rate dynamics during exercise. Am J Physiol. 1996;271(1 Pt 2):H244–H252. PubMed 5. Brennan M, Palaniswami M, Kamen P. Do existing measures of Poincaré plot geometry reflect nonlinear features of heart rate variability? IEEE Trans Biomed Eng. 2001;48:1342–1347. PubMed doi:10.1109/10.959330 6. Karmakar CK, Gubbi J, Khandoker AH, Palaniswami M. Analyzing temporal variability of standard descriptors of Poincaré plots. J Electrocardiol. 2010;43(6):719–724. PubMed doi:10.1016/j.jelectrocard.2010.09.001 7. Kamen PW, Krum H, Tonkin AM. Poincaré plots of heart rate variability allows quantitative display of parasympathetic nervous activity in humans. Clin Sci. 1996;91:201–208. PubMed 8. Toichi M, Sugiera T, Murai T, Sengoku A. A new method of assessing cardiac autonomic function and its comparison with spectral analysis and coefficient of variation of R-R interval. J Auton Nerv Syst. 1997;62(1-2):79–84. PubMed doi:10.1016/S0165-1838(96)00112-9 9. Lerma C, Infante O, Pérez-Groyas H, José MV. Poincaré plot indexes of heart rate variability capture dynamic adaptations after haemodialysis in chronic renal failure patients. Clin Physiol Funct Imaging. 2003;23(2):72–80. PubMed doi:10.1046/j.1475-097X.2003.00466.x 10. Guzik P, Bychowiec B, Piskorski J, et al. Heart rate variability by Poincaré plot and spectral analysis in young healthy subjects and patients with type 1 diabetes. Folia Cardiol. 2005;12(Suppl D):64–67.
11. Woo MA, Stevenson WG, Moser DK, Trelense RB, Harper RM. Patterns of beat-to-beat heart rate variability in advanced heart failure. Am Heart J. 1992;123:704–710. PubMed doi:10.1016/00028703(92)90510-3 12. Kamen PW, Tonkin AM. Application of the Poincaré plot of heart rate variability: a new measure of functional status in heart failure. Aust N Z J Med. 1995;25:18–26. PubMed doi:10.1111/j.1445-5994.1995. tb00573.x 13. Aljadeff G, Gozal D, Schechtman VL, Burrell B, Harper RM, Ward SL. Heart rate variability in children with obstructive sleep apnea. Sleep. 1997;20:151–157. PubMed 14. de la Cruz Torres B, López López C, Naranjo Orellana J. Analysis of heart rate variability at rest and during aerobic exercise: a study in healthy people and cardiac patients. Br J Sports Med. 2008;42(9):715– 720. PubMed doi:10.1136/bjsm.2007.043646 15. Garrido A, De la Cruz B, Medina M, Garrido MA, Naranjo J. Heart rate variability after three badminton matches: are there gender differences? Arch Med Deporte. 2011;XXVIII(144):257–264. 16. Tulppo MP, Kiviniemi AM, et al. Sympatho-vagal interaction in the recovery phase of exercise. Clin Physiol Funct Imaging. 2011;31(4):272–281. PubMed doi:10.1111/j.1475-097X.2011.01012.x 17. De Vito G, Galloway SD, Nimmo MA, Maas P, McMurray JJ. Effects of central sympathetic inhibition on heart rate variability during steady-state exercise in healthy humans. Clin Physiol Funct Imaging. 2002;22:32–38. PubMed doi:10.1046/j.1475-097X.2002.00395.x 18. Wallace LK, Slattery KM, Coutts AJ. A comparison of methods for quantifying training load: relationships between modelled and actual training responses. Eur J Appl Physiol. 2014;114(1):11–20. PubMed doi:10.1007/s00421-013-2745-1 19. Hoshi RA, Pastre CM, Vanderlei LC, Godoy MF. Poincaré plot indexes of heart rate variability: relationships with other nonlinear variables. Auton Neurosci. 2013;177(2):271–274. PubMed doi:10.1016/j. autneu.2013.05.004 20. Stanley J, Peake JM, Coombes JS, Buchheit M. Central and peripheral adjustments during high-intensity exercise following cold water immersion. Eur J Appl Physiol. 2014;114(1):147–163. PubMed doi:10.1007/s00421-013-2755-z 21. Mourot L, Bouhaddi M, Perrey S, et al. Decrease in heart rate variability with overtraining: assessment by the Poincaré plot analysis. Clin Physiol Funct Imaging. 2004;24(1):10–18. PubMed doi:10.1046/ j.1475-0961.2003.00523.x 22. Mourot L, Bouhaddi M, Perrey S, Rouillon JD, Regnard J. Quantitative Poincaré plot analysis of heart rate variability: effect of endurance training. Eur J Appl Physiol. 2004;91(1):79–87. PubMed doi:10.1007/ s00421-003-0917-0
IJSPP Vol. 10, No. 4, 2015
Original Investigation
Two New Indexes for the Assessment of Autonomic Balance in Elite Soccer Players José Naranjo Orellana, Blanca de la Cruz Torres, Elena Sarabia Cachadiña, Moisés de Hoyo, and Sergio Domínguez Cobo Purpose: The application of Poincaré-plot analysis to heart-rate variability (HRV) is a common method for the assessment of autonomic balance. However, results obtained from the indexes provided by this analysis tend to be difficult to interpret. In this study the authors aimed to prove the usefulness of 2 new indexes: the stress score (SS) and the sympathetic:parasympathetic ratio (S:PS ratio). Methods: 25 professional Spanish soccer players from same team underwent 330 resting measurements of HRV. All subjects experienced 10 min of HRV monitoring through an R-R-interval recorder. The following parameters were calculated: (1) Poincaré-plot indexes: SD1 (transverse axis), which is proportional to parasympathetic activity; SD2 (longitudinal axis), which is inversely proportional to sympathetic activity; and the SD1:SD2 ratio; (2) time-domain parameters: standard deviation of R-R intervals (SDNN), root-mean-square differences of successive heartbeat intervals (rMSSD), and percentage of successive R-R-interval pairs differing in more than 50 ms in the entire recording divided by the total number of R-R intervals (pNN50); and (3) the proposed 2 new indexes: the SS and the S:PS ratio. Results: The study found a high negative correlation between the SS and SDNN (R2 = .94). The S:PS ratio correlated inversely to rMSSD (R2 = .95), SDNN (R2 = .94), and pNN50 (R2 = .74). The S:PS ratio showed a strong correlation with SD1 (R2 = .95) and SS (r = .87, R2 = .88). Conclusions: The application of the SS as sympathetic-activity index and the S:PS ratio as a representation of autonomic balance (SS:SD1) provides a better understanding of the Poincaré-plot method in HRV. Keywords: heart-rate variability, Poincaré plot, sympathetic, parasympathetic, SD1:SD2 ratio Heart-rate variability (HRV) has been defined as the time variation between consecutive heartbeats.1,2 HRV analysis is a noninvasive method that permits assessment of the interactions between sympathetic and parasympathetic systems. Thus, HRV reflects the reaction capacity of the heart to different physiological demands.3 According to all HRV guidelines and publications in the literature, there are several methods for the assessment of cardiac autonomic function by the examination of sympathetic–parasympathetic balance. Time-domain analysis1 comprises different parameters that reflect parasympathetic activity. They include the standard deviation of R-R intervals (RRIs), which is called SDNN; the root-mean-square differences of successive heartbeat intervals (rMSSD); and the percentage of successive RRI pairs differing in more than 50 milliseconds in the entire recording divided by the total number of RRIs (pNN50).1 On the other hand, the Poincaré-plot4 analysis is a nonlinear method that reflects sympathetic–parasympathetic fluctuations.5,6 To plot it, all consecutive RRIs are inserted in a 2-dimensional dispersion plot in such a way that every RRI point is represented against the previous point. Thus a Poincaré plot shows a qualitative picture of the variations between RRIs and can be adjusted to an ellipse.4
Naranjo Orellana is with the Dept of Sports and Computing, Pablo de Olavide University, Seville, Spain. de la Cruz Torres is with the Dept of Physical Therapy, and de Hoyo, the Dept of Physical Education and Sports, University of Seville, Seville, Spain. Cachadiña is with CES Cardenal Spínola, CEU San Pablo Andalucía, Seville, Spain. Cobo is with Sevilla Fútbol Club S.A.D., Seville, Spain. Address author correspondence to José Naranjo Orellana at [email protected]. 452
This plot has been widely used due to its simple visual interpretation.7,8 Furthermore, it has proved successful in the assessment of several diseases such as renal failure,9 diabetes,10 cardiac failure,11,12 and sleep apnea.13 Moreover, it is of great interest to apply this method to cardiovascular function at rest and during exercise.14–18 Initially, the Poincaré-plot analysis was a qualitative method,11 but the measurement of the major and minor diameters of the ellipse4 turned this method into a quantitative one. The transversal axis (SD1) reflects short time changes in the RRI and is directly linked to parasympathetic activity. However, the longitudinal axis (SD2) is not so well defined but seems to be inversely proportional to sympathetic activity. Although some studies4,17 reported SD2 alterations when atropine (parasympathetic block) or moxonidine (sympathetic block) was administered, most studies during exercise14 show a clear relationship between a decrease in SD2 and sympathetic stimulation. Finally, some authors9,19 have reported correlations of the diameters of the Poincaré plot with nonlinear parameters. The SD1:SD2 ratio is normally used to assess the interaction between parasympathetic and sympathetic activity. However, its interpretation is as yet unclear because both terms of the ratio increase or decrease simultaneously. This is clearly understood since the numerator is proportional to parasympathetic stimulation while the denominator is inversely proportional to sympathetic stimulation. For all of these reasons, the aim of the study was to propose 2 new indexes—the stress score (SS) and the sympathetic:parasympathetic ratio (S:PS ratio) from the Poincaré plot—that should aid a better understanding of the autonomic balance through HRV analysis. Furthermore, this study had a second objective, which was to assess reference values for the previously mentioned variables in elite soccer players.
Autonomic Balance Indexes in Soccer 453
Methods Subjects During this study we performed 330 measurements of heart rate during a soccer season to 25 Spanish male professional soccer players from the same team (age 25 ± 3 y, weight 74 ± 7 kg, height 179 ± 8 cm). This team ended the season in the sixth position of the Spanish League (Liga BBVA) and as champions of the UEFA Europa League. The local ethics board approved this study, which followed all principles in the Declaration of Helsinki. All subjects signed an informed written consent to participate in this study.
Design Every subject underwent 13 measurements over 7 months, in a sitting position, early morning under fasting conditions. This was a descriptive longitudinal cohort study.
Downloaded by ETSU on 09/19/16, Volume 10, Article Number 4
Methodology The heart-rate monitor Firstbeat Bodyguard (Firstbeat Technologies, Jyväskylä, Finland) recorded heart-rate data during 10 minutes
in every measurement. The device-proprietary software (Firstbeat Uploader) downloaded the data from the devices to a computer, and all HRV parameters imported from all RRI series were made in the software Kubios (University of Eastern Finland, Kuopio, Finland).
Table 3 Pearson Correlation Coefficient (r) and R2 for the Exposed Variables Variable SD1 SD2 Stress score SD1:SD2 S:PS
rMSSD 1.00, 1.00 .81, .65 –.67, .69 .74, .55 –.61, .95
SDNN .89, .78 .99, .98 –.85, –.98 .44, .16 –.68, .94
pNN50 .77, .76 .74, .59 –.73, .59 .55, .38 –.74, .75
Abbreviations: rMSSD, root-mean-square differences of successive heartbeat intervals; SDNN, standard deviation of R-R intervals; pNN50, percentage of successive RR-interval pairs differing in more than 50 milliseconds in the entire recording divided by the total number of RR intervals; SD1, transverse axis; SD2, longitudinal axis; S:PS, sympathetic:parasympathetic ratio.
Table 1 Values for All Variables of the Study Variable Heart rate SDNN (ms) rMSSD (ms) pNN50 (%) SD1 SD2 SD1:SD2 Stress score S:PS
Mean 59.98 131.55 97.67 39.84 69.16 170.98 0.39 6.87 0.17
SD 7.77 6.67 62.95 19.19 44.59 70.08 0.14 2.88 0.18
Lowest 38.20 35.66 16.60 0.65 11.75 44.68 0.12 2.21 0.01
Highest 92.20 350.96 467.03 82.19 330.55 453.02 1.06 22.38 1.33
Abbreviations: SDNN, standard deviation of R-R intervals; rMSSD, root-meansquare differences of successive heartbeat intervals; pNN50, percentage of successive RR-interval pairs differing in more than 50 milliseconds in the entire recording divided by the total number of RR intervals; SD1, transverse axis; SD2, longitudinal axis; S:PS, sympathetic:parasympathetic ratio.
Table 2 Reference Values for All Variables of the Study Percentile Variable Heart rate SDNN (ms) rMSSD (ms) pNN50 (%) SD1 SD2 SD1:SD2 Stress score S:PS
10th 51.40 71.95 39.31 13.39 27.82 99.11 0.24 3.75 0.03
25th 54.85 89.50 54.04 24.57 38.24 119.88 0.30 4.66 0.06
50th 59.30 116.90 85.08 39.83 60.23 150.09 0.36 6.66 0.11
75th 64.70 160.80 118.03 55.79 83.54 214.77 0.47 8.34 0.21
90th 69.32 213.96 182.08 65.59 129.41 266.89 0.55 10.09 0.36
Abbreviations: SDNN, standard deviation of R-R intervals; rMSSD, root-meansquare differences of successive heartbeat intervals; pNN50, percentage of successive RR-interval pairs differing in more than 50 milliseconds in the entire recording divided by the total number of RR intervals; SD1, transverse axis; SD2, longitudinal axis; S:PS, sympathetic:parasympathetic ratio.
Figure 1 — R2 correlation of SD1 with (A) the root-mean-square differences of successive heartbeat intervals (rMSSD), (B) standard deviation of R-R intervals (SDNN), and (C) percentage of successive RR-interval pairs differing in more than 50 milliseconds in the entire recording divided by the total number of RR intervals (pNN50).
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454 Naranjo Orellana et al
Thus, Kubios software provided SD1, SD2, and SD1:SD2 ratio; SDNN; rMSSD; and pNN50. From the obtained variables, the 2 new indexes were calculated as follows: • SS: 1000 × 1/SD2. Given that SD2 is an inverse function of sympathetic activity, we expressed the inverse of SD2 to obtain a value that is directly proportional to the sympathetic activity (multiplied by 1000 to make it a more manageable number). • S:PS: The ratio of SS to SD1 (SS:SD1), whose meaning is to express a relationship between sympathetic and parasympathetic activity.
also analyzed the correlation between parasympathetic indicators and sympathetic indicators. This investigation applied the following criteria to determine the magnitude of the correlation (r): .80) and healthy subjects (r > .90). Furthermore, Kleiger et al2 and Garrido et al15 reported similar findings. Changes in SD2 could sometimes be difficult to understand because it represents the inverse of sympathetic activity. Nevertheless, if we take the inverse of SD2, we have a direct index for sympathetic activity, and its physiological interpretation would be easier, especially when it is included in a relationship to evaluate the sympathetic–parasympathetic balance. The current study indeed tried to improve this understanding using SS instead of SD2; in this process we found a high inverse correlation between SS and SDNN (Table 3). In the same direction, Figure 3(B) shows that 94% of changes in SDNN match with the behavior of the SS. The interpretation of SD1:SD2 as an indicator of autonomic balance is even less clear due to the presence of an inverse function at the denominator of the ratio. In fact, physical exercise or a state of fatigue and lack of recovery could produce very similar changes in SD1:SD2 or even no change. In both cases there would be an increase in the sympathetic activity (decrease of SD2) and a decrease in parasympathetic activity (reduction of SD1), resulting in a ratio unchanged and unable to differentiate between the 2 situations. The current study proposes a new assessment of autonomic balance from HRV through the ratio between SS (as an indicator of sympathetic activity) and SD1 (as an indicator of parasympathetic activity) and shows an inverse large to very large correlation to all time-domain parameters (Table 3). Moreover, the relationships of the S:PS ratios with time-domain parameters are stronger than the relationships between SD1:SD2 and the same variables (Figure 4). If we analyze the correlation of S:PS ratio to its 2 components, SS and SD1, we can observe an inverse large correlation with SD1 and a large correlation with SS (Table 3, Figure 5). To evaluate the participation of both components in the changes of the S:PS ratio, Figure 5 provides important information. Here it is shown that an increase in the sympathetic–parasympathetic relationship at rest should be more strongly related to a decrease in parasympathetic function (SD1) than to an increase in sympathetic activity (SS). On the other hand, this study provides useful reference values given that HRV values in athletes are very far from those provided by the ESC/NASPE task force1 for patients. To our knowledge, no other study provides a sample with players at this high level of competition and with such long-term follow-up. The number of measurements made allows these values to be used as references for monitoring soccer players. It is also particularly important to provide references for the new indexes proposed. To this extent the percentile distribution shows that SS values higher than 10 (p90) should be considered indicators of a high level of sympathetic stress, while values between 8 and 10 (p75–p90) should be considered an alert level. In the same way, an S:PS ratio higher than 0.3 (p90) could indicate an alteration of the autonomic balance due to an increased sympathetic activity or a lack of recovery in parasympathetic activity at rest.
Practical Applications First, we have described 2 indices to facilitate the analysis of the relationship between sympathetic and parasympathetic activity at rest and the understanding of its physiological significance. These indices are strongly coherent, and the percentile distribution in elite players indicates that an SS higher than 10 and/or an S:PS
Figure 5 — R2 correlation of the sympathetic:parasympathetic ratio (S/ PS) with (A) the transverse axis (SD1) and (B) the stress score (SS).
ratio higher than 0.3 at rest could indicate an excess of sympathetic activity or a lack of recovery of parasympathetic activity. Second, this study provides reference values of HRV for elite soccer players through a percentile distribution. These references should be particularly useful when HRV is used for the control of workloads in soccer. In a weekly follow-up, these findings may be used to plan the workload of training sessions, and they can be a good indicator of how each player is assimilating the accumulated workloads.
Conclusions The application of the SS index as a direct calculation for the assessment of sympathetic activity, and S:PS as an indicator of autonomic balance (SS:SD1) improves the physiological meaning of HRV by the Poincaré-plot-analysis method. This study provides reference values in elite soccer players, not only from the 2 proposed new indexes but also from the variables of the Poincaré-plot and time-domain methods.
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Autonomic Balance Indexes in Soccer 457
Acknowledgments The authors would like to thank all the players in the squad of Sevilla FC for their participation in this study. We also want to thank to Mr Felipe Del Valle for his help in data collection during the season.
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