Submitted: 04 Dec 2016
Accepted: 17 Jun 2017
First published online: 30 Jun 2017
EndNote EndNote

(Enw Format - Win & Mac)

BibTeX BibTeX

(Bib Format - Win & Mac)

Bookends Bookends

(Ris Format - Mac only)

EasyBib EasyBib

(Ris Format - Win & Mac)

Medlars Medlars

(Txt Format - Win & Mac)

Mendeley Web Mendeley Web
Mendeley Mendeley

(Ris Format - Win & Mac)

Papers Papers

(Ris Format - Win & Mac)

ProCite ProCite

(Ris Format - Win & Mac)

Reference Manager Reference Manager

(Ris Format - Win only)

Refworks Refworks

(Refworks Format - Win & Mac)

Zotero Zotero

(Ris Format - FireFox Plugin)

Abstract View: 1266
PDF Download: 973
Full Text View: 1163
The Effects of Heart Rate Versus Speed-Based High-Intensity Interval Training on Heart Rate Variability in Young Females

Int J Basic Sci Med, 2(2), 90-94; DOI:10.15171/ijbsm.2017.17

Original article

The Effects of Heart Rate Versus Speed-Based High-Intensity Interval Training on Heart Rate Variability in Young Females

Maryam Rabbani1, Effat Bambaeichi2 ,*, Fahimeh Esfarjani2, Alireza Rabbani3

1 MSc, Department of Exercise Physiology, Faculty of Sport Sciences, University of Isfahan, Isfahan, Iran
2 Associate Professor, Department of Exercise Physiology, Faculty of Sport Sciences, University of Isfahan, Isfahan, Iran
3 PhD Student, Department of Exercise Physiology, Faculty of Sport Sciences, University of Isfahan, Isfahan, Iran

*Correspondence to Effat Bambaeichi, Tel: +989132050472 Email: e.bambaeichi@yahoo.com

Copyright © 2017 The Author(s);


Introduction: The aim of this study was to compare the effects of high-intensity interval training (HIT) prescription by heart rate (HR-based) and running speed (speed-based) methods on natural logarithm of the square root of the mean of the sum of the squares of differences between adjacent normal R-R intervals (Ln rMSSD) as a measure of heart rate variability (HRV) in young female student athletes.

Methods: Seventeen female student athletes participated in this study and were divided into HR-based (n=9, age: 16.7 years) and speed-based (n=8, age: 16.9 years) HIT groups. 30-15 Intermittent Fitness Test was used for the speed-based group to detect the reference maximum speed (VIFT) for prescribing the HIT intensity accordingly. Age predicted maximal HR was used for the HR-based group as the reference value. All subjects performed similar training protocol for 5 weeks, except the method of individualizing HIT sessions (2 weekly sessions of HIT=3 sets of 3 minutes work interspersed with 3 minutes passive recovery with the 15-15 seconds format during each working set); either according to 90%-95% of maximal HR or VIFT.

Results: HR- and speed-based HIT groups showed the most likely large improvements in Ln rMSSD of +7.9%, 90% confidence limits [CL] (5.9; 10.0); standardized change: +1.75 (1.32; 2.19) and +5.5%, (2.8; 8.3); +1.41 (0.72; 2.09), respectively. In between group analyses, HR-based HIT produced likely a small greater improvement in Ln rMSSD than speed-based HIT (+1.9%, [-5.0; 4.4]; +0.50 [-0.14; 1.14], chances for greater/similar/lower values of 79/17/4).

Conclusion: It is concluded that both HIT prescription strategies were effective in Ln rMSSD elevation, but using maximal HR as a reference may elicit higher parasympathetic dominance with small effect in young female student athletes.

Keywords: Heart Rate, High-intensity interval training, Fitness, Heart rate variability, Females

Please cite this article as follows: Rabbani M, Bambaeichi E, Esfarjani F, Rabbani A. The effects of heart rate versus speed-based high-intensity interval training on heart rate variability in young females. Int J Basic Sci Med. 2017;2(2):90- 94. doi:10.15171/ijbms.2017.17.


Heart rate variability (HRV) is referred to a measure of interactions between sympathetic and parasympathetic nervous systems or a general marker of autonomic function.1HRV, in brief, is derived from the mathematical properties of cardiac inter-beat intervals (i.e, R-R interval).1The importance of HRV has been highlighted in the literature by its relationship with several diseases and conditions.2-4Furthermore, HRV is known as a risk factor for all-cause mortality.5 It has also been shown that different training interventions particularly high-intensity interval training (HIT) can improve HRV profile in both patient and athletes’ populations by enhancing parasympathetic dominance.4,6-9,

When individualizing HIT programs, different variables including the training intensity need to be taken into account.10,11 Accordingly, the use of maximal heart rate (HRmax) or the final speed reached in 30-15 Intermittent Fitness Test (30-15IFT, VIFT) as a reference, are two approaches known as HR-based and speed-based methods, respectively.10 Although HR-based method has several proposed limitations including difficulties for practitioners when controlling exercise intensity, it is yet the most common method implemented in the field.10

Furthermore, there are a number of proposed theoretical superiorities of speed-based HIT by using VIFT as a reference over the HR-based methods including its higher sensitivity to the athlete’s locomotor profile, acceleration, deceleration, and change of direction abilities.12 It has also been shown that VIFT-based method is more effective than HR-based HIT to improve maximal high-intensity running performance among the athletes.13 However, differences between these two approaches (i.e., speed-based vs. HR-based) in improving the physiological adaptations like HRV have not been investigated yet.

In addition, while analyzing HRV includes a myriad of variables in three different aspects (i.e., time domain, frequency domain and spectral analyses), the use of the square root of the mean of the sum of the squares of differences between adjacent normal R-R intervals (Ln rMSSD) has been recommended as a valid and sensitive marker for monitoring training-induced physiological adaptations in athletes.14 Fortunately, advancements in smart phone apps have recently provided ultra-short-time Ln rMSSD as a valid index.15 However, while it has been shown that ultra-short-time Ln rMSSD, is sensitive to team sport training interventions,16 the differences between the effects of different HIT protocols on this marker have yet to be determined.

Following these lines, there seems to be a lack of evidence on differences between the effects of HR-based and speed-based HIT interventions on ultra-short-time Ln rMSSD in the literature. Therefore, the purpose of this study was to investigate the within group changes and between group differences in changes of ultra-short-time Ln rMSSD following 5 weeks of speed-based and Heart-based HIT interventions in young female student athletes.



Seventeen female student athletes participated in this quasi-experimental study and were divided into HR-based (n = 9, mean ± SD; age: 16.7 ± 0.3 years, weight: 59.8± 2.6 kg, height: 162.4 ± 2.7 cm) and speed-based (n = 8, age: 16.9 ± 0.3 years, weight: 58.7 ± 3.2 kg, height: 164.3 ± 3.3 cm) HIT groups. Before the study, pre levels of resting HRV were used to allocate the participants in 2 homogenous experimental groups. First explanations about the experimental risks of the study were given and later informed consent was obtained from all the participants and their parents. Furthermore, local research ethics committee approved the protocol and the study conformed to the Declaration of Helsinki.17

Testing and Training

The speed-based group performed 30-15IFT12 before HIT intervention. 30-15IFT is an incremental high-intensity intermittent running performance field test. The final speed derived from this test (i.e., VIFT) is a reference to individualize speed-based HITs.18 30-15IFT was performed at the same time of day (10 am) with similar temperature ranging between 26ºC to 28ºC. The intensity in both groups was individualized to train the subjects in their red zone (i.e., >90% HRmax). Age predicted HRmax was used for the HR-based group as the reference value.19 Participants in the HR-based group had to check the Polar BeatTM application installed on their smart-phone regularly during HIT sessions while using a HR Bluetooth sensor (Polar H7, Finland) to assure exercising in the prescribed zone. The individualized running pace using VIFT has been calculated for each subject in the speed-based group. All VIFT-based subjects were guided to set their running pace according to an audio signal. While it was not mandatory for the HR-based group to set their running speed according to auditory signal, the running speed for the VIFT-based group was adjusted according to maximal test speed explained in detail previously.13 The experimental period lasted for 5 weeks with 2 weekly HIT sessions. The training protocol during the entire experimental period is shown in Table 1.

Table 1 . Training Program
Week Protocol HR-Based Intensity VIFT-Based Intensity
1 3 sets (6 reps of 15"-15" HIT) 85-90% HR max 90% VIFT
2 3 sets (6 reps of 15"-15" HIT) 85-90% HR max 90% VIFT
3 3 sets (8 reps of 15"-15" HIT) 90-95% HR max 95% VIFT
4 3 sets (8 reps of 15"-15" HIT) 90-95% HR max 95% VIFT
5 4 sets (6 reps of 15"-15" HIT) 90-95% HR max 95% VIFT

Note: In all HIT (high-intensity interval training) sessions, work periods were interspersed with 3 min of passive recovery.

Heart Rate Variability Recording

Home based resting ultra-short-term HRV recording was asked from all subjects in both groups.16 The subjects were asked to collect HRV using HRV application installed on their smart phones using HR Bluetooth sensor (H7, Polar, Finland) around fastened their trunk near to xiphoid area as well according to the previous investigations guidelines.16 The subjects were guided to record their HRV in the morning before eating and in the supine position. The R-R intervals derived from Elite HRVTM smart phone application were exported to be analyzed later by Kubios HRV software to calculate Ln rMSSD.20 Weekly averages of Ln rMSSD for the first and last week of training intervention were used as pre and post values for statistical analyses.

Statistical Analyses

Data in the figures are shown as means with 90% of CI in the case. All data were first log-transformed to reduce bias arising from non-uniformity error. Within-group changes and between-groups differences in changes of Ln rMSSD were analyzed. The percentage changes and standardized differences or effect size (ES) with 90% confidence limits (CL) were used to express the results.21 The Hopkins scale was used for standardized change/difference interpretation: < 0.2: Trivial; 0.2 – 0.6: Small; 0.6 – 1.2: Moderate; > 1.2: Large. Magnitude-based inference approach was used to analyze the chance that the true changes were clear or trivial.22 Probabilities were also calculated to establish whether the true changes/differences were lower than, similar to, or higher than the smallest worthwhile changes/differences (SWC, 0.2 × between-subjects SD).21


Weekly average of Ln rMSSD values are shown in Table 2. Within-group analyses showed that subjects in the HR-based and VIFT-based groups had the most likely large improvements in Ln rMSSD of +7.9%, 90% CL (5.9; 10.0); standardized change: +1.75 (1.32; 2.19) and +5.5%, (2.8; 8.3); +1.41 (0.72; 2.09), respectively (Figure1A). In between group analyses, HR-based HIT produced likely a small greater improvement in Ln rMSSD than speed-based HIT (+1.9%, 90% CL [-5.0; 4.4%]; standardized difference: +0.50 [-0.14; 1.14], chances for greater/similar/lower values of 79/17/4) (Figue 1B).

Table 2. Weekly Average of Heart Rate Variability
Groups Pre-test Post-test
HR-based HIT 3.15 ± 0.13 3.40 ± 0.12
VIFT-based HIT 3.17 ± 0.11 3.34 ± 0.09

Figure 1. Training-Induced Standardized Changes (90% CI) in Heart Rate Variability of Speed-Based and HR-Based Groups (A) and Standardized Differences Between Changes (90% CI) (B).


The aim of the present study was to quantify and compare the respective effects of two methods of individualizing HIT on HRV (i.e., using percentages of either HRmax or the maximum speed reached during the 30-15IFT (VIFT) as a reference value) for the first time. The main result is that while both methods allowed for substantial gains in HRV after 5 weeks, the HR-based approach produced likely a small greater improvement in Ln rMSSD compared with the speed-based approach. These results show that using the HR for individualizing HIT in young female students might be a more efficient choice to develop HRV.

Increases of Ln rMSSD suggest the increased vagal tone activity, parasympathetic dominance and in general the HRV improvement.9,14 Although the mechanisms responsible for vagal tone increase are not yet cleared, the angiotensin II and nitric oxide (NO) are potential mediators. Angiotensin II is known as an inhibitor of cardiac vagal tone.23 The suppression of angiotensin II expression by training has been addressed by Buch et al.24 Documented low levels of angiotensin II inferred from lower levels of plasma renin,25 has been referred to be responsible, at least in part, for the higher cardiac vagal tone in athletes. Therefore, there is a possibility of increased cardiac vagal tone activity due to the training-induced suppression of angiotensin II.24 The result of our study, confirming the beneficial effect of HIT interventions on Ln rMSSD, is in line with previous investigations reporting the positive outcomes4,6-8, and in contrast with studies showing unchanged values in HRV.26,27 Discrepancies between the present study result and previous unchanged HRV reports might be due to the subjects’ characteristics27 or health conditions.26 In fact, in the studies of Currie et al26 and Gamelin et al27 the subjects had coronary artery disease and were prepubescent children, respectively. While the subjects undergoing HIT intervention in the present study were healthy young females.

The interesting finding of the present study when analyzing between group differences in changes was the superiority of HR-based method in improving Ln rMSSD which showed likely a small effect. In fact, VIFT has been shown to be related not only to the maximal oxygen uptake (VO2max), but also to the athlete’s locomotor profile (i.e., maximal sprinting speed, anaerobic speed reserve, change of direction, acceleration and deceleration).12 It seems that HR-based approach targets mainly VO2max and accordingly elicits higher cardiac related physiological adaptations including HRV.28

Although speed-based HIT method is an easier method of controlling the intensity in practice, it may have lower longitudinal practicality for over loading the athlete. In fact, HR-based approach ensures that red zone (>90% of maximal HR) is always targeted during any HIT session. However, when using speed-based approach, there is not clear perspective on how athlete has been improved in terms of general fitness or VIFT.29 Therefore, there was not any assurance of accurate over loading the athletes and training them in their red zones in the last weeks of the plan which may explain less improvement of a physiological marker like HRV in speed-based group.

There is just one similar study in which the effects of these 2 HIT interventions on performance improvement have been compared and the superiority of speed-based approach has been suggested which is different from our study result showing the more effectiveness of HR-based method on HRV.13 However, Rabbani and Buchheit13 used performance marker (high-intensity running performance) as the training outcome different from our study in which, physiological marker (HRV) was used. Moreover, the limitation of not training both experimental groups in similar time frame in the previous study may further explain such discrepancies.13


This study showed that both speed- and HR-based HIT methods can enhance HRV after 5 weeks in young female student athletes. However, a small greater cardiac physiological adaptation may be elicited when using HR-based approach due to its specificity.

Ethical Approval

Local ethical committee of Sport Sciences College in the University of Isfahan approved the study protocol as a master proposal in 2016.

Competing Interests

Authors declare that they have no potential conflict of interest.


The authors thank all participants for their meaningful contributions during the study.


  1. Acharya UR, Joseph KP, Kannathal N, Lim CM, Suri JS. Heart rate variability: a review. Med Biol Eng Comput 2006;44(12):1031-51‏. doi:10.1007/s11517-006-0119-0. [Crossref]
  2. Green KT, Dennis PA, Neal LC, et al. Exploring the relationship between posttraumatic stress disorder symptoms and momentary heart rate variability. J Psychosom Res 2016;82(31):31-4‏. doi:10.1016/j.jpsychores.2016.01.003. [Crossref]
  3. Vinik AI, Ziegler D. Diabetic cardiovascular autonomic neuropathy. Circulation 2007;115(3):387-97. doi:10.1161/CIRCULATIONAHA.106.634949. [Crossref]
  4. GuiraudT, Labrunee M, Gaucher-Cazalis K, et al. High-intensity interval exercise improves vagal tone and decreases arrhythmias in chronic heart failure. Med Sci Sports Exerc 2013;45(10):1861-7‏. doi:10.1249/MSS.0b013e3182967559. [Crossref]
  5. Achten J, Jeukendrup AE. Heart rate monitoring. Sports Med 2003;33(7):517-38. doi:10.2165/00007256-200333070-00004. [Crossref]
  6. Gamelin F, Berthoin S, Sayah H, Libersa C, Bosquet L. Effect of training and detraining on heart rate variability in healthy young men. Int J Sports Med 2007;28(07):564-70‏. doi:10.1055/s-2007-964861. [Crossref]
  7. Kiviniemi AM, Tulppo MP, Eskelinen JJ, et al. Cardiac autonomic function and high-intensity interval training in middle-age men. Med Sci Sports Exerc 2014;46(10):1960-7‏. doi:10.1249/MSS.0000000000000307. [Crossref]
  8. Rennie KL, Hemingway H, Kumari M, Brunner E, Malik M, Marmot M. Effects of moderate and vigorous physical activity on heart rate variability in a British study of civil servants. Am J Epidemiol 2003;158(2):135-43‏. doi:10.1093/aje/kwg120. [Crossref]
  9. Aubert AE, Seps B, Beckers F. Heart rate variability in athletes. Sports Med 2003;33(12):889-919‏.
  10. Buchheit M, Laursen PB. High-intensity interval training, solutions to the programming puzzle. Sports Med 2013;43(5):313-38. doi:10.1007/s40279-013-0029-x. [Crossref]
  11. Ghalavand A, Motamedi P, Deleramnasab M, Khodadoust M. The Effect of interval training and Nettle supplement on glycemic control and blood pressure in men with type 2 diabetes. Int J Basic Sci Med. 2017(1):33-40. doi:10.15171/ ijbms.2017.08. [Crossref]
  12. Buchheit M. The 30-15 intermittent fitness test: accuracy for individualizing interval training of young intermittent sport players. J Strength Cond Res 2008;22(2):365-74‏. doi:10.1519/JSC.0b013e3181635b2e. [Crossref]
  13. Rabbani A, Buchheit M. Heart rate-based versus speed-based high-intensity interval training in young soccer players. Paper presented at: 4th World Congress on Science and Football VII; October 29 2014; Portland, USA. https://wordpress.up.edu/wcss2014usa/html. Accessed January 15, 2015.
  14. Buchheit M. Monitoring training status with HR measures: do all roads lead to Rome? Front Physiol 2014;5:7. doi:10.3389/fphys.2014.00073. [Crossref]
  15. Flatt AA, Esco MR. Validity of the ithleteTM Smart phone application for determining ultra-short-term heart rate variability. J Hum Kinet 2013;1(39):85-92‏. doi:10.2478/hukin-2013-0071. [Crossref]
  16. Nakamura FY, Flatt AA, Pereira LA, Ramirez-Campillo R, Loturco I, Esco MR. Ultra-short-term heart rate variability is sensitive to training effects in team sports players. J Sports Sci Med 2015;14(3):602‏. doi:10.1001/JSS.203.1142. [Crossref]
  17. World Medical Association. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA 2013;310(20):2191. doi:10.1001/jama.2013.281053. [Crossref]
  18. Buchheit M. Individualizing high-intensityinterval training in intermittent sport athletes with the 30-15 Intermittent Fitness Test. NSCA Hot Topic Series website. http://www.nsca-lift.org/. Published 2011‏.
  19. Tanaka H, Monahan KD, Seals DR. Age-predicted maximal heart rate revisited. J Am Coll Cardiol 2001;37(1):153-6.
  20. Tarvainen MP, Niskanen J-P, Lipponen JA, Ranta-Aho PO, Karjalainen PA. Kubios HRV–heart rate variability analysis software. Comput Methods Programs Biomed 2014;113(1):210-20‏. doi:10.1016/j.cmpb.2013.07.024. [Crossref]
  21. Hopkins W, Marshall S, Batterham A, Hanin J. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc 2009;41(1):3‏. doi:10.1249/MSS.0b013e31818cb278. [Crossref]
  22. Batterham AM, Hopkins WG. Making meaningful inferences about magnitudes. Int J Sports Physiol Perform 2006;1(1):50-57. doi:10.1123/ijspp.1.1.50. [Crossref]
  23. Townend JN, Al-Ani M, West JN, Littler WA, Coote JH. Modulation of cardiac autonomic control in humans by angiotensin II. Hypertension 1995;25(6):1270-5‏. doi:10.1161/01.HYP.25.6.1270. [Crossref]
  24. Buch AN, Coote JH, Townend JN. Mortality, cardiac vagal control and physical training-what’s the link? Exp Physiol 2002;87(4):423-35‏. doi:10.1111/j.1469-445X.2002.tb00055.x. [Crossref]
  25. Fagard R, Grauwels R, Groeseneken D, et al. Plasma levels of renin, angiotensin II, and 6-ketoprostaglandin F1 alpha in endurance athletes. J Appl Physiol 1985;59(3):947-52‏. doi:10.1152/japplphysiol.01051. [Crossref]
  26. Currie KD, Rosen LM, Millar PJ, McKelvie RS, MacDonald MJ. Heart rate recovery and heart rate variability are unchanged in patients with coronary artery disease following 12 weeks of high-intensity interval and moderate-intensity endurance exercise training. Appl Physiol Nutr Metab 2013;38(6):644-50. doi:10.1139/apnm-2012-0354. [Crossref]
  27. Gamelin F-X, Baquet G, Berthoin S, et al. Effect of high intensity intermittent training on heart rate variability in prepubescent children. Eur J Appl Physiol 2009; 105(5):731-8‏. doi:10.1007/s00421-008-0955-8.‏. [Crossref]
  28. Franklin BA, Hodgson J, Buskirk ER. Relationship between percent maximal O2 uptake and percent maximal heart rate in women. Res Q Exerc Sport 1980;51(4):616-26‏. doi:10.1080/02701367.1980.10609322. [Crossref]
  29. Mann TN, Lamberts RP, Lambert MI. High responders and low responders: factors associated with individual variation in response to standardized training. Sports Med 2014;44(8):1113-24. doi:10.1007/s40279-014-0197-3. [Crossref]