European Journal of Applied Physiology

, Volume 118, Issue 1, pp 175–184 | Cite as

Ultra-shortened time-domain HRV parameters at rest and following exercise in athletes: an alternative to frequency computation of sympathovagal balance

  • Michael R. EscoEmail author
  • Henry N. Williford
  • Andrew A. Flatt
  • Todd J. Freeborn
  • Fabio Y. Nakamura
Original Article



The primary purpose of this study was to determine the accuracy of the standard deviation of normal-to-normal intervals (SDNN) to root mean square of successive normal-to-normal interval differences (RMSSD) ratio from 1-min recordings (SDNN:RMSSD1−min) compared to criterion recordings, as well as its relationship to low-frequency-to-high-frequency ratio (LF:HF) at rest and following maximal exercise in a group of collegiate athletes.


Twenty athletes participated in the study. Heart rate variability (HRV) data were measured for 5 min before and at 5–10 and 25–30 min following a maximal exercise test. From each 5-min segment, the frequency-domain measures of HF, LF, and LF:HF ratio were analyzed. Time-domain measures of SDNN, RMSSD, and SDNN:RMSSD ratio were also analyzed from each 5-min segment, as well as from randomly selected 1-min recordings.


The 1-min values of SDNN, RMSSD, and SDNN:RMSSD provided no significant differences and nearly perfect intra-class correlations (ICCs ranged from 0.97 to 1.00, p < 0.001 for all) to the criterion measures from 5-min recordings. In addition, SDNN, RMSSD, and SDNN:RMSSD from the 1-min segments provided very large to nearly perfect correlations (r values ranged from 0.71 to 0.97, p < 0.001 for all) to LF, HF, and LF:HF, respectively, at each time point.


The findings of the study suggest that ultra-shortened time-domain markers may be useful surrogates of the frequency-domain parameters for tracking changes in sympathovagal activity in athletes.


Heart rate variability RMSSD Time-domain Cardiovascular-autonomic control Athlete monitoring 



Heart rate variability


Electrocardiogram or electrocardiographic


High frequency


Low frequency


LF-to-HF ratio


Root mean square of successive normal-to-normal interval differences


Standard deviation of normal-to-normal intervals


RMSSD-to-SDNN ratio


Pre-exercise resting period


Period between 5- and 10-min post-exercise


Period between 25- and 30-min post-exercise


Intra-class correlations


Pearson’s correlation coefficient


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

The study was approved by The University of Alabama’s Institutional Review Board.

Research involving human and animal participants

The research involved human participants. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.


  1. Abboud S, Barnea O (1995) Errors due to sampling frequency of electrocardiogram in spectral analysis of heart rate signals with low variability. Comput Cardiol 461–463Google Scholar
  2. Balocchi R, Cantini F, Varanini M, Raimondi G, Lagramante JM, Macerata A (2006) Revisiting the potential of time-domain indexes in short-term HRV analysis. Biomed Tech 51:190–193CrossRefGoogle Scholar
  3. Billman GE (2013) The LF/HF ratio does not accurately measure cardiac sympatho-vagal balance. Front Physiol 4:26. PubMedPubMedCentralGoogle Scholar
  4. Buchheit M (2014) Monitoring training status with HR measures: do all roads lead to Rome? Front Physiol 5:73. CrossRefPubMedPubMedCentralGoogle Scholar
  5. Buchheit M, Laursen PB, Ahmaidi S (2007) Parasympathetic reactivation after repeated sprint exercise. Am J Physiol Heart CircPhysiol 293:H133–H141CrossRefGoogle Scholar
  6. Buchheit M, Millet GP, Parisy A, Pourchez S, Laursen PB, Ahmaidi S (2008) Supramaximal training and postexercise parasympathetic reactivation in adolescents. Med Sci Sports Exerc 40:362–371. CrossRefPubMedGoogle Scholar
  7. Cataldo A, Zangla D, Cerasola D, Vallone V, Grusso G, Lo Presti R, Traina M (2016) J Sports Med Phys Fitness 56:491–496PubMedGoogle Scholar
  8. Ellis RJ, Zhu B, Koenig J, Thayer JF, Wang Y (2015) A careful look at ECG sampling frequency and R-peak interpolation on short-term measures of heart-rate variability. Physiol Meas 36:1827–1852CrossRefPubMedGoogle Scholar
  9. Esco MR, Flatt AA (2014) Ultra-short-term heart rate variability indexes at rest and post-exercise in athletes: evaluating the agreement with accepted recommendations. J Sports Sci Med 13:535–541PubMedPubMedCentralGoogle Scholar
  10. Esco MR, Flatt AA, Nakamura FY (2016) Initial weekly HRV response is related to the prospective change in VO2max in female soccer players. Int J Sports Med 37:436–441. CrossRefPubMedGoogle Scholar
  11. Esco MR, Flatt AA, Nakamura FY (2017b) Agreement between a smartphone pulse sensor application and electrocardiography for determining lnRMSSD. J Strength Cond Res 31:380–385. PubMedGoogle Scholar
  12. Flatt AA, Esco MR (2016a) Heart rate variability stabilization in athletes: toward more convenient data acquisition. Clin Physiol Funct Imagining 36:331–336. CrossRefGoogle Scholar
  13. Flatt AA, Esco MR (2016b) Evaluating individual training adaptation with smartphone-derived heart rate variability in a collegiate female soccer team. J Strength Cond Res 30:378–385. CrossRefPubMedGoogle Scholar
  14. Flatt AA, Esco MR, Nakamura FY (2017a) Individual heart rate variability responses to preseason training in high level female soccer players. J Strength Cond Res 31:531–538. PubMedGoogle Scholar
  15. Flatt AA, Hornikel B, Esco MR (2017b) Heart rate variability and psychometric responses to overload and tapering in collegiate sprint-swimmers. J Sci Med Sport 20:606–610. CrossRefPubMedGoogle Scholar
  16. Goulopoulou S, Fernhall B, Kanaley JA (2009) Hemodynamic responses and linear and non-linear dynamics of cardiovascular autonomic regulation following supramaximal exercise. Eur J ApplPhysiol 105:525–531. Google Scholar
  17. Hopkins WG (2000) Measures of reliability in sports medicine and science. Sports Med 30:1–15. CrossRefPubMedGoogle Scholar
  18. Hopkins WG (2002) A scale of magnitudes for effect statistics. A new view of statistics.
  19. Iallamo F, Legramante JM, Pigozzi F, Spataro A, Norbiato G, Lucini D, Pagani M (2002) Conversion from vagal to sympathetic predominance with strenuous training in high-performance world class athletes. Circulation 105:2719–2724CrossRefGoogle Scholar
  20. Javorka M, Zila I, Balharek T, Javorka K (2002) Heart rate recovery after exercise: relations to heart rate variability and complexity. Braz J Med Biol Res 35:991–1000CrossRefPubMedGoogle Scholar
  21. Kiviniemi AM, Tulppo MP, Eskelinen JJ, Savolainen AM, Kapanen J, Heinonen IH, Hautala AJ, Hannukainen JC, Kalliokoski KK (2015) Autonomic function predicts fitness response to short-term high-intensity interval training. Int J Sports Med 36:915–921. CrossRefPubMedGoogle Scholar
  22. Mahdiani S, Jeyhani V, Peltokangas M, Vehkaoja A (2015) Is 50 Hz high enough for ECG samply frequency for accurate HRV analysis?. Conf Proc IEEE Eng Med BiolSoc 5948–5951Google Scholar
  23. Mourot L, Bouhaddi M, Perrey S, Cappelle S, Henriet MT, Wolf JP, Rouillon JD, Regnard J (2004) Decrease in heart rate variability with overtraining: assessment by the Poincare plot analysis. Clin Physiol Funct Imaging 24:10–18CrossRefPubMedGoogle Scholar
  24. Nakamura FY, Flatt AA, Pereira LA, Ramirez-Campillo R, Loturco I, Esco MR (2015) Ultra-short-term heart rate variability is sensitive to training effects in team sports players. J Sports Sci Med 14:602–605PubMedPubMedCentralGoogle Scholar
  25. Nussinovitch U, Elishkevitz KP, Katz K, Nussinovitch M, Segev S, Volovitz B, Nussinovitch N (2011) Reliability of ultra-short ECG indices for heart rate variability. Ann Noninvasive Electrocardiol 16:117–122. CrossRefPubMedGoogle Scholar
  26. Otzenberger H, Gronfier C, Simon C, Charloux A, Ehrhart J, Piguard F, Brandenberger G (1998) Dynamic heart rate variability: a tool for exploring sympathovagal balance continuously during sleep in men. Am J Physiol 275(3 Pt 2):H946-950Google Scholar
  27. Pagani M, Lonbardi F, Guzzetti S, Sandrone G, Rimoldi O, Malfatto G, Cerutti S, Malliani A (1984) Power spectral density of heart rate variability as an index of sympatho-vagal interaction in normal hypertensive subjects. J HypertensSuppl 2:S383-S385Google Scholar
  28. Parekh A, Lee CM (2005) Heart rate variability after isocaloric exercise bouts of different intensities. Med Sci Sports Exerc 37:599–605CrossRefPubMedGoogle Scholar
  29. Saboul D, Pialoux V, Hautier C (2014) The breathing effect of the LF/HF ratio in the heart rate variability measurements of athletes. Eur J Sport Sci 14(Suppl 1):S282-8. PubMedGoogle Scholar
  30. Saboul D, Balducci P, Millet G, Pialoux V, Hautier C (2016) A pilot study on quantification of training load: The use of HRV in training practice. Eur J Sport Sci 16:172–181CrossRefPubMedGoogle Scholar
  31. Salahuddin L, Cho J, Jeong MG, Kim D (2007) Ultra short term analysis of heart rate variability for monitoring mental stress in mobile settings. Conf Proc IEEE Eng Med BiolSoc 4656–4659Google Scholar
  32. Seiler S, Haugen O, Kuffel E (2007) Autonomic recovery after exercise in trained athletes: intensity and duration effects. Med Sci Sports Exerc 39:1366–1373CrossRefPubMedGoogle Scholar
  33. Senthinathan A, Mainwaring LM, Hutchison M (2017) heart rate variability of athletes across concussion recovery milestones: a preliminary study. Clin J Sport Med 27:288–295CrossRefPubMedGoogle Scholar
  34. Sollers JJ 3rd, Buchanan TW, Mowrer SM, Hill LK, Thayer JF (2007) Comparison of the ratio of the standard deviation of the R-R interval and the root mean squared successive differences SD/rMSSD) to the low frequency-to-high frequency (LF/HF) ratio in a patient population and normal healthy controls. Biomed Sci Instrum 43:158–163PubMedGoogle Scholar
  35. Tarvainen MP, Niskanen JP, Lipponen JA, Ranta-aho PO, Karjalainen PA (2014) Kubios HRV—heart rate variability analysis software. Comput Methods Programs Biomed 113:210–220CrossRefPubMedGoogle Scholar
  36. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (1996) Heart rate variability: standards of measurement, physiological interpretation and clinical use. Circulation 93:1043–1065CrossRefGoogle Scholar
  37. Wang HM, Huang SC (2012) SDNN/RMSSD as a surrogate for LF/HF: a revised investigation. Model Simulat Eng 16:

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Exercise Physiology Laboratory, Department of KinesiologyUniversity of AlabamaTuscaloosaUSA
  2. 2.Department of KinesiologyAuburn University MontgomeryMontgomeryUSA
  3. 3.Biodynamics Laboratory, Department of Health SciencesArmstrong State UniversitySavannahUSA
  4. 4.Department of Electrical and Computer EngineeringUniversity of AlabamaTuscaloosaUSA
  5. 5.Department of Medicine and Aging Sciences“G. d’Annunzio” University of Chieti-PescaraChietiItaly
  6. 6.The College of Healthcare SciencesJames Cook UniversityDouglasAustralia

Personalised recommendations