Large individual variation in training adaptation has been observed after standardized endurance training. It is typical that some individuals show great improvements (even to 40%) in maximal oxygen uptake (V˙O2max) or endurance performance after standardized endurance training, whereas some individuals show no changes or sometimes even a decrement in endurance performance (5,7,36). In our previous study (34), a slightly smaller variation in improvement of endurance performance was observed after 8 wk of both high-intensity training (HIT) (0% to 10.2%) and low-intensity training (LIT) (−2.8% to 4.1%) when training volume and frequency were individualized according to individuals’ previous training history. However, some nonresponders (change in endurance performance ≤0%) were observed, which proposes that a more precisely and comprehensively individualized training program would be beneficial for achieving greater improvements and smaller variation in the adaptation to training.
Cardiac autonomic regulation is an important determinant of endurance training adaptation. Baseline vagal-mediated resting HR variability (HRV) has been shown to be related to greater improvements in V˙O2max and endurance performance after endurance training in different populations (2,12,13,33,34). In addition, increased resting HRV during a training period has been related to improvements of endurance performance or V˙O2max (6,7,10,12,22).
It has been proposed that training prescription according to the status of cardiac vagal activity may be a beneficial method to improve endurance training adaptation. Kiviniemi et al. (18,19) expressed promising results while using HRV in daily training prescription. Individuals who were prescribed HIT sessions according to HRV showed greater improvements in V˙O2max among moderately fit men (19) and in endurance performance among healthy individuals (18,19) compared with individuals who trained according to a traditional predefined training program. Women benefitted from HRV guidance by achieving the same significant improvement in endurance performance with a lower amount of HIT compared with predefined training (18,19). In the studies by Kiviniemi et al. (18,19), training prescription was based on daily single-HRV measurements. However, Plews et al. (24) suggested that it is more valuable to use longer trends of HRV, e.g., a week rolling average, compared with assessing its value on a single day. In addition, Plews et al. (25) proposed to use the individual’s own HRV profile, the individually determined smallest worthwhile change (SWC), which reflects normal values of HRV, in interpreting HRV values and training prescription.
Although determinants of endurance performance are widely accepted, the optimal training volume and intensity distribution is still indefinite (31). The majority of endurance training studies present a high proportion of high-volume LIT among endurance athletes, and also a polarized training model with a higher amount of HIT has been proposed to be an effective training model especially in high-level athletes (21,30). Furthermore, increasing training intensity also in sedentary adults would be more effective compared with increasing training volume (28). Recent studies have shown that changing the periodization of LIT and HIT without changing the training intensity distribution may have a positive effect on performance improvement (26,27). Traditionally, periodized endurance training includes usually single-HIT sessions, e.g., two times per week, simultaneous with LIT and moderate-intensity training (MOD) trying to develop many fitness components at the same time. On the contrary, block training includes shorter training periods (1–4 wk), when the aim is to develop a few selected fitness components (17). Recently, it has been suggested that block periodization of HIT may provide superior training adaptation compared with traditionally organized HIT (26,27). However, the timing of HIT blocks has been typically determined subjectively without objective information about the athlete’s training status. A good recovery status may be needed before an intense training block to avoid imbalance between training load and recovery.
Endurance training programs are typically predetermined based on the literature, general recommendations, and experience of coaches regardless of objective information about the athlete’s training and recovery status. In addition, adaptation to endurance training is individualized, and the same training program would not be optimal for everyone in spite of similar training background. Furthermore, more individualized, objective training prescription may improve training adaptation. The aim of this study was to investigate the effectiveness of using HRV averaged over 7 d in endurance training prescription for endurance training adaptation compared with traditional predefined endurance training in recreational endurance runners. It was hypothesized that HRV-guided training would result in greater adaptations to training (18,19).
Forty recreational endurance runners (20 women and 20 men) were recruited to participate in the study through advertisement in the local newspaper and social media. The minimal sample size was determined based on the data of Vesterinen et al. (35) and Stöggl and Sperlich (30) (expected changes in maximal running speed, 3% ± 3% and 5% ± 3%). A priori power analysis suggested that 16 subjects were required for both groups to achieve 80% power and a significance level of 5%. The subjects did not have any diseases or use regular (e.g., daily) medication for any kind of chronic or long-term diseases. They had at least 2 yr of regular endurance running training background. Age, V˙O2max, regular training background, and weekly running kilometers during the previous 2 months were 34 ± 8 yr, 49 ± 4 mL·kg−1·min−1, 13 ± 8 yr, and 34 ± 22 km, respectively, for women and 35 ± 7 yr, 56 ± 5 mL·kg−1·min−1, 15 ± 8 yr, and 39 ± 19 km, respectively, for men. After being fully informed about the study design and the possible risks, all subjects provided a written informed consent document. The study was approved by the ethics committee of the local university.
The study protocol included two training periods: a 4-wk preparation period (PREP) and an 8-wk intensive training period (INT). All measurements were performed before and after both training periods on weeks 0, 5, and 14. For INT, the subjects were matched into pairs according their baseline background (sex, age, and training background), endurance performance characteristics (V˙O2max, 3000-m running time), and HRV. Thereafter, within each pair, the runners were randomized into the HRV-guided experimental training group (EXP) and traditional predefined training group (TRAD).
Maximal running tests
The subjects were asked not to do any vigorous physical activity 2 d before the running tests. The subjects performed an incremental treadmill test, starting at 7 km·h−1 for women and at 8 km·h−1 for men, and were followed by an increase of 1 km·h−1 every third minute until volitional exhaustion. The incline was kept at 0.5° during the whole test. HR was recorded continuously using an HR monitor (Suunto t6; Suunto Ltd., Vantaa, Finland). Oxygen consumption was measured breath by breath throughout the test using a portable spiroergometer (Oxycon mobile; Viasys Health Care, Würzburg, Germany). After each 3-min stage, the treadmill was stopped for about 15–20 s for fingertip blood samples (20 μL) and blood lactate analysis. Blood lactate was determined using a Biosen S_line Lab + lactate analyzer (EKF Diagnostic, Magdeburg, Germany). The highest 60-s V˙O2 value during the treadmill test was considered as the maximal oxygen uptake (V˙O2max). The determination of lactate thresholds was based on a rise and change in the inclination of the blood lactate curve during the test (11). Lactate threshold 1 (LT1) was set at 0.3 mmol·L−1 above the lowest lactate value. Lactate threshold 2 (LT2) was set at the intersection point between 1) a linear model between LT1 and the next lactate point and 2) a linear model for the lactate points with a lactate increase of at least 0.8 mmol·L−1.
For the determination of maximal endurance performance, the subjects performed the 3000-m running test on a 200-m indoor track before and after both training periods. Time and a mean running speed of 3000 m (RS3000m) were calculated. At least two easy training days were prescribed between the treadmill test and the 3000-m running test.
During PREP, all subjects were asked to maintain the same training volume as that before the study. The period followed a periodization model of three hard training weeks followed by an easy training week with progressively increasing intensity throughout the period (Table 1). Endurance training was mainly running, but the subjects were encouraged to do at least one easy session per week in some other training mode than running (i.e., cycling or cross-country skiing). In addition, muscle endurance circuit training was instructed to be performed once a week.
For INT, all subjects were instructed to maintain the same training volume as that during PREP. TRAD trained according to a predefined training program, which included approximately 50% of weekly training sessions performed at LIT (below LT1) and the other 50% at MOD/HIT, with the week periodization 3/1 as that during PREP (Table 1). The main training sessions at moderate and high intensities were similar to the training sessions during PREP in both groups.
All subjects in EXP were instructed to measure their R–R interval data at home every morning after awakening and emptying their urinary bladder. Omegawave Pro Mobile System, with a two-lead chest belt (Omegawave Ltd., Helsinki, Finland), was used to record R–R intervals. Four minutes of recording was performed in supine position, and the subjects were allowed to breathe spontaneously. R–R interval data were analyzed using the Omegawave cloud service, and the vagal-mediated square root of the mean squared differences of successive R–R intervals (RMSSD) was selected to be used in training prescription because of its greater reliability than other HRV spectral indices (1). Furthermore, a 7-d rolling average of RMSSD (RMSSD7d) was calculated because it has been proposed to be more sensitive to track changes in the training status compared with single-day values (24).
The basic idea of using HRV in endurance training prescription was to decrease training intensity when cardiac vagal activity differed meaningfully from the regular level based on the findings of Plews et al. (24,25). Thus, the SWC of RMSSD7d for each subject in EXP was determined, as mean ± 0.5 SD, from RMSSD values of 4 wk over PREP based on observations by Plews et al. (24,25) and Kiviniemi et al. (18,19) (Fig. 1). When RMSSD7d was within the SWC, only MOD and HIT sessions were performed. If RMSSD7d fell outside the SWC, the subjects trained only at low intensities or rested. When RMSSD7d returned to the mean level of the SWC, the subjects started to perform MOD and HIT sessions and continued until RMSSD7d fell outside the SWC. The subjects maintained the same number of training sessions and rest days every week compared with PREP regardless of HRV. The SWC was updated after the first 4 wk of INT based on the values over the previous 4 wk because of previous findings about a relationship between an increment of resting cardiac autonomic activity and improved endurance performance (6,7,22).
The subjects controlled their training intensity by measuring their HR and RS during all exercises using the Garmin FR 610 HR monitor with GPS (Garmin Ltd., Schaffhausen, Switzerland). In addition, the subjects rated their perceived exertion (RPE) and recovery feelings using the scale of 0–10 after each training session (3) and reported possible other stress factors, e.g., sicknesses, injuries, and work/school stress, in their training diary via a cloud service. HR data were used to determine the times at the three different training intensity zones: low (below LT1), moderate (between LT1 and LT2), and high (above LT2) intensities. Weekly main training sessions were supervised by experienced members of the research group.
The results are expressed as means ± SD. The Gaussian distribution of the data was assessed using the Shapiro–Wilk goodness-of-fit test. The adaptation to training was analyzed using repeated measures of ANOVA followed by Bonferroni as a post hoc test. Differences in the training adaptation between EXP and TRAD were analyzed using Student’s t-test for independent samples and between sexes using the Kruskal–Wallis test. In addition, the magnitudes of changes after training and differences between groups were expressed as standardized effect sizes (ES), calculated from pooled means and SD (15). The threshold values for Cohen’s ES statistics were <0.2 (trivial), 0.2–0.5 (small), 0.5–0.8 (moderate), and >0.8 (large) (8). Pearson’s product–moment correlation coefficient was used to determine the relationships between the amount of MOD/HIT sessions and the training adaptation (change in RS3000m and V˙O2max). Statistical significance was accepted as P ≤ 0.05. Statistical analyses were carried out using SPSS Statistics 20 software (IBM, Armonk, NY).
A total of 31 subjects (13 in EXP and 18 in TRAD) completed the whole study. Nine subjects dropped out because of injuries (n = 2), sicknesses (n = 2), and insufficient compliance with the training program (i.e., <90% of all training sessions in EXP and more than two main training sessions missing in TRAD during INT, n = 5).
No differences were observed between the groups in training during PREP (Table 2). Training volume remained similar between PREP and INT. In addition, proportions of times in different training zones remained similar. The number of MOD and HIT sessions over INT was significantly lower (P = 0.021, ES = 0.98) in EXP (13.2 ± 6.0 sessions) compared with TRAD (17.7 ± 2.5 sessions). Individual ranges in the number of MOD/HIT sessions were from 5 to 24 sessions in EXP and from 11 to 21 sessions in TRAD.
RS3000m improved by 2.7% ± 2.5% (P < 0.001) and V˙O2max by 2.9% ± 4.4% (P = 0.003) over PREP. Time in the 3000-m running test improved in EXP (−14.3 ± 14.1 s, P = 0.005) but not in TRAD (−7.8 ± 19.8 s, P = 0.111) during INT. A small between-group difference was observed in the change in RS3000m (Table 3 and Fig. 2). V˙O2max and RSLT2 improved in both groups (a small between-group difference), whereas RSLT1 improved only in EXP (a moderate between-group difference). No differences were observed in the changes of the endurance performance variables between sexes.
Individual ranges in the change in RS3000m were from −1% to 6% in EXP and from −4% to 8% in TRAD. One subject in EXP and five subjects in TRAD showed decrements in RS3000m, whereas other subjects improved or maintained their running performance over INT. Figure 3 presents three examples of HRV-based individualization in training prescription. Subject A responded well to a high amount of MOD/HIT. The training program included three MOD/HIT blocks (a total of 34 d) during INT. Subject B performed also three MOD/HIT periods, but those were much shorter (a total of 15 d). However, the response to the high amount of LIT was good. Subject C performed two 7-d periods of MOD/HIT at the beginning of INT. After that, HRV was below the SWC because of work stress and the training was solely LIT. Subject C was the only subject who showed a decrement in the 3000-m running performance in EXP during INT. No significant correlations were found between the number of MOD/HIT sessions and the change in RS3000m (r = 0.42, P = 0.17) or V˙O2max (r = 0.26, P = 0.43).
The aim of the present study was to compare training adaptation induced by HRV-guided and predefined endurance training. The main finding was that 3000-m running performance improved only in the HRV-guided training group over the 8-wk INT by performing less MOD and HIT sessions compared with predefined training.
Changes in endurance performance
Vagal activity of the autonomic nervous system has widely been observed to be related to individual adaptation to endurance training, for example (6,7,10,12,22). To our best knowledge, only a few previous studies have examined the use of HRV in endurance training prescription (4,18,19). In the study by Kiviniemi et al. (19), the HIT session was programmed if high frequency power (HFP), measured every morning, was increased or remained the same compared with the reference value of HFP (an average of 10 d − SD). Otherwise, LIT or rest was programmed. The HRV-guided group showed 7% improvement in V˙O2max and 6% improvement in maximal running performance, whereas the predefined group improved by 4% in running performance and maintained V˙O2max after 4 wk of training. The change in running performance was greater in the HRV group (19). The present study expressed similar findings. Maximal running performance and RSLT1 improved in EXP but not in TRAD, whereas V˙O2max and RSLT2 improved in both groups. The group difference in the change in running maximal performance (2.1% in EXP vs 1.1% in TRAD) was rated as small in qualitative and as nonsignificant in the t-test analyses. However, in practice, a difference of 1% in sport performance can make the difference between winning and losing (9). The finding may suggest that it may be possible to identify more optimal timing to receive vigorous training stimulus according to HRV compared with subjectively predefined training.
It is worth considering that a slightly greater improvement in 3000-m running performance was achieved with a lower frequency of MOD and HIT sessions in EXP compared with TRAD. Furthermore, training focused more on LIT in EXP, which may explain the greater improvement in RSLT1 compared with TRAD. The previous finding is in line with the results of Kiviniemi et al. (18) among moderately trained women. The authors did not observe any differences in the training adaptation between the HRV-guided and the predefined groups, but the HRV group performed less HIT sessions (1.8 vs 2.8 sessions per week) during 8 wk of training. In addition, they proposed that a different HRV-guided program is needed for women because they are more susceptible to longer recovery of HRV (18). In the present study, no differences were observed between sexes in the training adaptation. Kiviniemi et al. (18) proposed that the gender differences in HRV responses to HIT could be explained by a lower relative fitness level in women compared with men. In the present study, the relative fitness level was even a bit higher in women (29), which can explain the contradictory findings in gender differences between the present study and the study by Kiviniemi et al. (18). However, clarification of possible sex differences needs more investigations with larger sample sizes.
Individual variation in the adaptation
Large individual differences in training adaptation after standardized endurance training are common (5,7,36). In the present study, EXP showed a slightly smaller range (−1% to 6%) in the change in RS3000m compared with the predefined training group (−4% to 8%), whereas the individual range in the amount of HIT sessions was larger in EXP (5 to 24 sessions) compared with TRAD (11 to 21 sessions). This finding suggests that HRV-based timing of HIT may be beneficial for diminishing variation in the adaptation. However, one subject in EXP failed to improve running performance during INT (Fig. 3C). The subject trained only at low intensities during the last 5 wk of INT because his RMSSD values were below the SWC. Reduced HRV was mainly induced by work stress, and it caused challenges to the function of the HRV-based training prescription method. Regardless, it might be assumed that the predefined HIT in that kind of stress state would not result in optimal training outcomes; thus, LIT is recommended to enhance the recovery process. On the other hand, the insignificant correlation between the training adaptation and the number of MOD/HIT sessions showed that the adaptation to training was independent of the amount of vigorous sessions. Some individuals responded well to the high amount of MOD and HIT (Fig. 3A), whereas other individuals (Fig. 3B) achieved good responses to the smaller dose of MOD/HIT sessions. This can be partly explained by our previous finding (34), which showed that some individuals respond better to LIT and some individuals to HIT. It seems also that individuals’ recovery of HRV and further capacity to cope vigorous training are different. Subject A (Fig. 3) performed three HIT blocks during INT. The longest one continued 21 d and included 15 MOD/HIT sessions. During the blocks, recovery feeling decreased and HRV changed relatively slowly from the regular level, whereas some individuals’ HIT blocks were remarkably shorter. Thus, individualized training program is needed for diminishing variation in the adaptation and increasing effectiveness of training.
Methodological issues to use HRV in training prescription
In the present study, the HRV-based method to prescribe training was different compared with the method by Kiviniemi et al. (18,19). We used the vagal-mediated, time-domain index, RMSSD instead of the HFP spectral density because it has been proposed that RMSSD has greater reliability compared with HRV spectral indices (1). Furthermore, in the present study, a 7-d rolling average was used because it provides a better representation of training adaptation by diminishing the large day-to-day variation in HRV compared with single-day values (24), which were used in the studies by Kiviniemi et al. (18,19). The averaged value of HRV and SWC together shows that one single-training session does not typically change HRV more than SWC. Thus, the present method prescribes the timing of MOD/HIT blocks rather than the single-MOD/HIT sessions. It is closer to practical endurance athletes’ training, which includes vigorous training periods, such as training camps, and not only single-training sessions. In addition, block periodization of HIT has been proposed to provide superior training adaptation compared with traditionally organized HIT (26,27). However, exhaustive HIT block training has mainly been used among high-level athletes. Individually determined duration and timing of HIT block according to HRV may be a more approachable training model also for untrained individuals. It may be easier to avoid the imbalance between training load and recovery when training prescription is based on the status of cardiac vagal activity. However, further studies are needed in different populations.
Instead of using a strict limit for the HRV value to determine the intensity of training, which was used in the studies by Kiviniemi et al. (18,19), we used the individually determined SWC, which reflects the regular values of HRV (24,25). When the HRV value falls either below or above the SWC, it is reflecting a sign of an abnormal situation. When HRV is below the SWC, it is indicating increased sympathetic activity, which has been observed to occur after vigorous training periods reflecting an insufficient recovery state (16,23). When HRV is above the SWC, it is indicating increased vagal activity called parasympathetic hyperactivity, which could also be a sign of functional overreaching (20) or overtraining (14,32). The imbalance of cardiac autonomic regulation may indicate an unfavorable situation to adapt to HIT sessions. It is also worth considering that HRV response to training is individualized, and it can vary in different situations. For example, it is shown in Figure 3A that HRV decreased during the first and third MOD/HIT blocks, but during the second block, it oppositely increased above the SWC. As it is unknown, whether HRV should be expected to increase or decrease in the case of overreaching and overtraining, it is recommended that the HRV-based training prescription method includes upper and lower limits for normal area of HRV.
Regardless of the encouraging results to use the present HRV method for training prescription, there are some uncertain issues to solve in future studies. First, baseline HRV recordings for determining the SWC should not be performed under extensive stress factors, like work stress, sicknesses, etc. Otherwise, the SWC does not represent a normal situation. The second issue is related to the magnitude of the SWC. In the present study, it was determined as mean ± 0.5 SD based on the previous findings by Plews et al. (25) and Hopkins et al. (15). The SWC should be wide enough for allowing an individual to train enough at high intensities to achieve adequate disturbances in homeostasis so that further training adaptation can be attained. On the other hand, it should give enough recovery time after HRV falls outside the SWC. That is the reason why LIT was performed until HRV returned to the mean level of the SWC, not only to the area of SWC. Performance changes and recovery feelings during INT did not reveal any signs of overtraining in the present study. Future studies should examine whether a shorter recovery time results in greater adaptations. In that case, LIT should continue until HRV is close to the mean level (e.g., within the area of mean ± 0.1 SD). In addition, an uncertain issue is the updating of SWC. Previous studies have expressed that resting HRV can increase as a positive result of training, especially in previously untrained and moderately trained subjects (6,7,10,12,22). Therefore, the SWC was decided to be updated after the first 4 wk of INT. Although the change in the SWC was relatively small in most subjects in the present study, it is recommended to update the SWC after certain time spans, especially during longitudinal training periods.
HRV measurements performed at home may not be as highly standardized as those performed in laboratory conditions. However, it is impractical to perform the measurements in the laboratory every morning during the training period. In addition, it is possible to avoid psychophysiological effects of laboratory conditions on HRV by performing the measurements at home. In addition, the present study is limited by its relatively small number of subjects. Unfortunately, the relatively big number of dropouts due to sickness and injuries reduced statistical power, especially in comparisons between sexes. It does not allow adequate statistical power to investigate possible sex differences with parametric analyses. Furthermore, future studies should aim to examine the usefulness of HRV in endurance training prescription among different populations from sedentary individuals to high-level endurance athletes.
The present study expressed that the 3000-m running performance improved only in the HRV-guided training group over the 8-wk INT by performing less MOD and HIT sessions compared with the predefined training. In addition, individual range in the training adaptation was smaller in the HRV-guided group compared with the TRAD. The findings may suggest that the timing of MOD and HIT sessions according to HRV is more optimal compared with the subjectively predefined training. Therefore, HRV shows a potential tool in endurance training prescription by optimizing the timing of vigorous training sessions, although there is a need to solve some methodological issues related to the use of HRV in training prescription in future studies.
The authors wish to thank Sirpa Vänttinen and David Leith for their expert assistance during this study, as well as the subjects for their participation.
This study was financially supported by the Finnish Ministry of Education and Culture. The study was conducted in KIHU—Research Institute for Olympic Sports in collaboration with the Department of Biology of Physical Activity of the University of Jyväskylä.
The authors have no conflict of interest to disclose. The results of the present study do not constitute endorsement by the American College of Sports Medicine.
1. Al Haddad H, Laursen PB, Chollet D, Ahmaidi S, Buchheit M. Reliability of resting and postexercise heart rate measures. Int J Sports Med
2. Baynard T, Goulopoulou S, Sosnoff RF, Fernhall B, Kanaley JA. Cardiovagal modulation and efficacy of aerobic exercise training in obese individuals. Med Sci Sports Exerc
3. Borg GA. Psychophysical bases of perceived exertion. Med Sci Sports Exerc
4. Botek M, McKune AJ, Krejci J, Stejskal P, Gaba A. Change in performance in response to training load adjustment based on autonomic activity. Int J Sports Med
5. Bouchard C, Rankinen T. Individual differences in response to regular physical activity. Med Sci Sports Exerc
. 2001;33(6 Suppl):S446–51; discussion S452–3.
6. Boutcher SH, Park Y, Dunn SL, Boutcher YN. The relationship between cardiac autonomic function and maximal oxygen uptake response to high-intensity intermittent-exercise training. J Sports Sci
7. Buchheit M, Chivot A, Parouty J, et al. Monitoring endurance running performance using cardiac parasympathetic function. Eur J Appl Physiol
8. Cohen J. Statistical Power Analysis for the Behavioral Sciences
. New Jersey: Lawrence Erlbaum; 1988.
9. Currell K, Jeukendrup AE. Validity, reliability and sensitivity of measures of sporting performance. Sports Med
10. Da Silva DF, Verri SM, Nakamura FY, Machado FA. Longitudinal changes in cardiac autonomic function and aerobic fitness indices in endurance runners: a case study with a high-level team. Eur J Sport Sci
11. Faude O, Kindermann W, Meyer T. Lactate threshold concepts: how valid are they? Sports Med
12. Hautala AJ, Makikallio TH, Kiviniemi A, et al. Cardiovascular autonomic function correlates with the response to aerobic training in healthy sedentary subjects. Am J Physiol Heart Circ Physiol
13. Hedelin R, Bjerle P, Henriksson-Larsen K. Heart rate variability in athletes: relationship with central and peripheral performance. Med Sci Sports Exerc
14. Hedelin R, Wiklund U, Bjerle P, Henriksson-Larsen K. Cardiac autonomic imbalance in an overtrained athlete. Med Sci Sports Exerc
15. Hopkins WG, Marshall SW, Batterham AM, Hanin J. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc
16. Iellamo F, Legramante JM, Pigozzi F, et al. Conversion from vagal to sympathetic predominance with strenuous training in high-performance world class athletes. Circulation
17. Issurin V. Block periodization versus traditional training theory: a review. J Sports Med Phys Fitness
18. Kiviniemi AM, Hautala AJ, Kinnunen H, et al. Daily exercise prescription on the basis of HR variability among men and women. Med Sci Sports Exerc
19. Kiviniemi AM, Hautala AJ, Kinnunen H, Tulppo MP. Endurance training guided individually by daily heart rate variability measurements. Eur J Appl Physiol
20. Le Meur Y, Pichon A, Schaal K, et al. Evidence of parasympathetic hyperactivity in functionally overreached athletes. Med Sci Sports Exerc
21. Munoz I, Seiler S, Bautista J, Espana J, Larumbe E, Esteve-Lanao J. Does polarized training improve performance in recreational runners? Int J Sports Physiol Perform
22. Nummela A, Hynynen E, Kaikkonen P, Rusko H. Endurance performance and nocturnal HRV indices. Int J Sports Med
23. Pichot V, Busso T, Roche F, et al. Autonomic adaptations to intensive and overload training periods: a laboratory study. Med Sci Sports Exerc
24. Plews DJ, Laursen PB, Kilding AE, Buchheit M. Evaluating training adaptation
with heart-rate measures: a methodological comparison. Int J Sports Physiol Perform
25. Plews DJ, Laursen PB, Kilding AE, Buchheit M. Heart rate variability in elite triathletes, is variation in variability the key to effective training? A case comparison. Eur J Appl Physiol
26. Rønnestad BR, Hansen J, Ellefsen S. Block periodization of high-intensity aerobic intervals provides superior training effects in trained cyclists. Scand J Med Sci Sports
27. Ronnestad BR, Hansen J, Thyli V, Bakken TA, Sandbakk O. 5-week block periodization increases aerobic power in elite cross-country skiers. Scand J Med Sci Sports
28. Ross R, de Lannoy L, Stotz PJ. Separate effects of intensity and amount of exercise on interindividual cardiorespiratory fitness response. Mayo Clin Proc
29. Shvartz E, Reibold RC. Aerobic fitness norms for males and females aged 6 to 75 years: a review. Aviat Space Environ Med
30. Stöggl T, Sperlich B. Polarized training has greater impact on key endurance variables than threshold, high intensity, or high volume training. Front Physiol
31. Stöggl TL, Sperlich B. The training intensity distribution among well-trained and elite endurance athletes. Front Physiol
32. Uusitalo AL, Uusitalo AJ, Rusko HK. Heart rate and blood pressure variability during heavy training and overtraining in the female athlete. Int J Sports Med
33. Vesterinen V, Hakkinen K, Hynynen E, Mikkola J, Hokka L, Nummela A. Heart rate variability in prediction of individual adaptation to endurance training in recreational endurance runners. Scand J Med Sci Sports
34. Vesterinen V, Häkkinen K, Laine T, Hynynen E, Mikkola J, Nummela A. Predictors of individual adaptation to high-volume or high-intensity endurance training in recreational endurance runners. Scand J Med Sci Sports
. 2015. Epub ahead of print. doi: 10.1111/sms.12530.
35. Vesterinen V, Hokka L, Hynynen E, Mikkola J, Hakkinen K, Nummela A. Heart rate-running speed index may be an efficient method of monitoring endurance training adaptation
. J Strength Cond Res
36. Vollaard NB, Constantin-Teodosiu D, Fredriksson K, et al. Systematic analysis of adaptations in aerobic capacity and submaximal energy metabolism provides a unique insight into determinants of human aerobic performance. J Appl Physiol (1985)