Tag Archives: research

HRV Values: Indications of Training Readiness

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In my recent articles on HRV in Team Sports, I discussed the idea of having our athletes report to pre-season camp with favorable autonomic profiles prior to the initiation of intensive training. The goal of this being to enhance adaptation and reduce injury potential. Today I’d like to delve into this topic a little deeper.

First I’d like to review some important research that helped form the basis of this thought process. Other, more intelligent minds thought of this stuff way before I did and have produced what I consider to be, some pretty compelling research.

Research

Vesterinen and colleagues (2011) found that recreational endurance runners who had high baseline HRV levels prior to intensive training improved their performance significantly more than runners who had low baseline HRV levels prior to training.

Oliveira and colleagues (2012) found a strong correlation between parasympathetic indices of HRV (analyzed before training) with the performance improvement in Yo-Yo IR1 in soccer players during pre-season training.

Hedelin and colleagues (2001) set out to investigate relationships between HRV and central and peripheral performance measures in various trained endurance athletes over a 7 month period. The authors reported that; “higher parasympathetic activity, at least in these fit subjects, rather was a cause than an effect of a further increase in aerobic fitness.”

Kiviniemi et al (2007) found that in fit males, training when HRV levels are at baseline or above results in significantly higher improvements in maximum running velocity and greater improvements in vo2 max compared to a group that followed pre-planned training, of which saw insignificant changes in both measures.

In a repeat study Kiviniemi et al (2010) included female groups and found that females take longer to recover from a training session and that fitness can be improved with fewer high intensity training days when guided by HRV compared to the pre-planned training group

Hautala et al (2003) reported that baseline HF Power was the most powerful determinant of future training response in healthy subjects. I strongly urge interested readers to read through this review by Hautala et al (2009) for a thorough discussion on this topic.

I’m certain I’m leaving out some good research but I think you get the idea. There is evidence to suggest that HRV levels can be a good indicator of training response in athletes and fit individuals.

Discussion

A couple issues I’m having with the evidence as it applies to team sport settings;

  1. HRV measurement is different in much of the research. Some is nocturnal, some is morning, etc. Therefore, we can’t say for certain if we can draw similar conclusions based on a morning measurement if the researchers used nocturnal HRV measurements. Having said that, I do feel that morning measurements are sufficient, if not optimal.
  2. The research mostly pertains to aerobic athletes and aerobic training. However, given that most team sports require a sufficient level of aerobic capacity I still think the discussed research offers valuable information. Even in a sport like American Football, many of the drills are serial and repetitive in nature and thus places a greater dependence on energy production from aerobic metabolism. Further, repeated sprint ability is related to oxygen uptake during rest periods (Dupont et al. 2010).

It appears that having a high level of resting parasympathetic tone prior to intensive training results in more favorable responses and performance improvements in athletes. The research suggests that HRV levels appear to reflect adaptive potential. It should be of high priority to the coaching staff that players remain healthy throughout training. Keeping tabs on HRV levels throughout training, taken with other measures of training status, may reveal maladaptation and therefore a necessitation for intervention.

I’d personally like to see HRV levels monitored in Collegiate American Football players throughout pre-season training camp. It’s conceivable that injury risk is heightened in athletes showing consistent decrements in HRV. It surprises me that there is very little research on HRV and injury (risk, recovery, return to play, etc) in comparison to HRV and performance enhancement/monitoring.

Whether or not we can apply this to strength/power athletes is not clear as there is very little research on this. It’s been a personal goal of mine to investigate this issue and I hope to do this at some point in the future.

Provided that athletes are engaging in training throughout the off-season having a high level of parasympathetic tone at rest shouldn’t be an issue. Team sport athletes will generally have low resting heart rates and a high work capacity. The concern would be with athletes that are either not preparing themselves for intense training, or with those that may be over doing it.

Apart from aiming to have high HRV levels prior to training we may also want to use HRV as an indicator of recovery status day to day. During intense training periods, recovery and restoration modalities can aid in parasympathetic re-activation and therefore more rapid recovery. Paying closer attention to nutritional strategies, active recovery, cold water immersion (a controversial topic at the moment it seems) sleep quality and duration, etc. may help us in maintaining favorable ANS activity; perhaps a topic for another day.

References:

Dupont, G., et al. (2010) Faster oxygen uptake kinetics during recovery is related to better repeated sprint ability. European Journal of Applied Physiology, (110)3: 627-34

Hautala, A.J., et al. (2003) Cardiovascular autonomic function correlates with the response to aerobic training in healthy sedentary subjects. American Journal of Heart & Circulatory Physiology, 285(5): H1747–52.

Hautala AJ, et al. (2009)Individual responses to aerobicexercise: the role of the autonomicnervous system. Neuroscience & Biobehavioral  Reviews, 33(2): 107–115.

Hedelin, R. et al. (2001) Heart Rate Variability in athletes: relationship with central and peripheral performance. Medicine & Science in Sports & Exercise, 33(8), 1394-1398.

Kiviniemi, A.M., Hautala, A., Kinnumen, H., & Tulppo, M. (2007) Endurance training guided by daily heart rate variability measurements. European Journal of Applied Physiology, 101: 743-751.

Kiviniemi, A.M., Hautala A.J., Kinnunen, H., Nissila, J., Virtanen, P., Karjalainen, J., & Tulppo, M.P. (2010) Daily exercise prescription on the basis of HR variability among men and women. Medicine & Science in Sport & Exercise, 42(7): 1355-1363.

Oliveira, RS. et al. (2012b) The correlation between heart rate variability and improvement in soccer player’s physical performance. Brazilian Journal of Kinanthropometry, 14(6)

Vesterinen, V. et al. (2011) Heart rate variability in prediction of individual adaptation to endurance training in recreational endurance athletes. Scandinavian Journal of Medicine & Science in Sports, DOI: 10.1111/j.1600-0838.2011.01365.x

5 New HRV Studies

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There’s plenty of great research being done on HRV and its application to sport’s training. I’ll do my best to keep you apprised of the latest findings by periodically compiling abstracts of relevant studies. Unfortunately, I don’t have access to many of these newer studies and therefore will reserve comments until I do. In the meantime, check out the abstracts of some of the most recent research on HRV and athletes.

1.

Leti, T., & Veronique, AB. (2012) Interest of analyses of heart rate variability in the prevention of fatigue states in senior runners. Autonomic Neuroscience: Basic & Clinical, Ahead of print

Background The use of heart rate variability (HRV) in the management of sport training is a practice which tends to spread, especially in order to prevent the occurrence of fatigue states.

Objectives To estimate the HRV parameters obtained using a heart rate recording, according to different exercise impacts, and to make the link with the appearance of subjective fatigue.

Methods Ten senior runners, aged 51 ± 5 years, were each monitored over a period of 12 weeks in different conditions: (i) after a resting period, (ii) after a day with training, (iii) after a day of competition and (iv) after a rest day. They also completed three questionnaires, to assess fatigue (SFMS), profile of mood states (POMS) and quality of sleep.

Results The HRV indices (heart rate, LF (n.u.), HF (n.u.) and LF/HF) were significantly altered with the competitive impact, shifting toward a sympathetic predominance. After rest and recovery nights, the LF (n.u.) increased significantly with the competitive impact (62.1 ± 15.2 and 66.9 ± 11.6 vs. 76.0 ± 10.7; p<0.05 respectively) whereas the HF (n.u.) decreased significantly (37.9 ± 15.2 and 33.1 ± 11.6 vs. 24.0 ± 10.7; p<0.05 respectively). Positive correlations were found between fatigue and frequency domain indices and between fatigue and training impact.

Conclusion Autonomic nervous system modulation-fatigue relationships were significant, suggesting the potential use of HRV in follow-up and control of training. Furthermore, the addition of questionnaires constitutes complementary tool that allow to achieve a greater relevance and accuracy of the athletes’ fitness and results.

2.

Edmonds, RC., Sinclair, WH., and Leicht, AS. (2012) The effect of weekly training and a game on heart rate variability in elite youth Rugby League players. Proceedings of the 5th Exercise & Sports Science Australia Conference and 7th Sports Dietitians Australia Update. 5th Exercise & Sports Science Australia Conference and 7th Sports Dietitians Australia Update Research to Practice , 19-21 April 2012, Gold Coast, QLD, Australia , p. 183.

Introduction: To date, the majority of research related to rugby league has investigated movement patterns, injury mechanisms and the effects of training workload and a game on player fatigue. Interest in monitoring player workloads and recovery has increased recently, with heart rate variability (HRV) proposed as an important monitoring tool in both individual and team sports [1, 2]. Due to the high physical demands associated with rugby league, monitoring alterations in cardiac autonomic control via HRV may lead to improved player management and enhanced performance. The aim of this study was to investigate the influence of weekly training and a competitive game on HRV in elite youth rugby league players, and to identify the importance of HRV as a monitoring tool for Rugby League player preparation.

Methods: Youth rugby league players (n=9) were monitored during supine rest (10 min) at 2 days prior to a game (Pre-2), day of the game (Game Day), and 1 (Post-1), 2 (Post-2) and 4 (Post-4) days following a game. Heart rate (HR) recordings were recorded via a chest strap transmitter with beat-by-beat intervals during the last 5 min of supine rest analysed for time domain, frequency domain (low frequency [LF], high frequency [HF]) and non-linear measures of HRV. Player daily training load was calculated from players’ rating of perceived exertion and session duration as previously described (Foster, 1998). Significant (p<0.05) differences in HRV over the monitoring days were identified via 1-way ANOVA and post-hoc pairwise comparisons with a Bonferroni correction or a Friedman’s test with a Conover post-hoc comparison, where appropriate. Relationships between HRV variables and training loads were identified using Spearman’s rank rho (ρ) correlation coefficients.

Results: All time domain and nonlinear measures of HRV were similar over the 5 monitoring days except for mean HR, which was significantly greater on Game Day and Post-1 compared to Pre-2 (73.0 ± 5.7 and 80.1 ± 8.1 vs. 64.9 ± 8.7 beats per minute). On Game Day, LF and the ratio between LF and HF were significantly increased and remained elevated until Post-2 (Figure 1). In contrast, HF was significantly reduced on Game day and remained low until Post-2 (Figure 1). A strong negative correlation was identified between mean HR and training load on Pre-2 (ρ = -0.783, p < 0.05) with a strong positive correlation identified between HF and training load on Pre-2 (ρ = 0.700, p < 0.05).

Conclusion/Discussion: Prior to a competitive game, elite youth, Rugby League players exhibited a significant reduction in HRV that was sustained for at least 24 hours post-game. This withdrawal of parasympathetic and/or increased sympathetic control of HR possibly may result from pre-match anxiety as well as the physical demands of the game. Strong relationships between HRV and training load at Pre-2 indicate that early monitoring may assist in identifying training workloads for the upcoming week. The current results support HRV as an important monitoring tool for managing training workload.

3.

Plews, DJ., Laursen, PB., Kilding & Buchheit, M. (2012) Heart rate variability in elite triathletes, is variation in variability the key to effective training? A case comparison. European Journal of Applied Physiology, 112(11): 3729-41.

ABSTRACT: Measures of an athlete’s heart rate variability (HRV) have shown potential to be of use in the prescription of training. However, little data exists on elite athletes who are regularly exposed to high training loads. This case study monitored daily HRV in two elite triathletes (one male: 22 year, VO2max 72.5 ml kg min−1; one female: 20 year, VO2max 68.2 ml kg min−1) training 23 ± 2 h per week, over a 77-day period. During this period, one athlete performed poorly in a key triathlon event, was diagnosed as non-functionally over-reached (NFOR) and subsequently reactivated the dormant virus herpes zoster (shingles). The 7-day rolling average of the log-transformed square root of the mean sum of the squared differences between R–R intervals (Ln rMSSD), declined towards the day of triathlon event (slope = −0.17 ms/week; r 2 = −0.88) in the NFOR athlete, remaining stable in the control (slope = 0.01 ms/week; r 2 = 0.12). Furthermore, in the NFOR athlete, coefficient of variation of HRV (CV of Ln rMSSD 7-day rolling average) revealed large linear reductions towards NFOR (i.e., linear regression of HRV variables versus day number towards NFOR: −0.65%/week and r 2 = −0.48), while these variables remained stable for the control athlete (slope = 0.04%/week). These data suggest that trends in both absolute HRV values and day-to-day variations may be useful measurements indicative of the progression towards mal-adaptation or non-functional over-reaching.

4.

Tian, Y., He, ZH., Zhao, JX., Tao, DL., Xu, KY., Earnest, CP. & McNaughton, LR. (2012) Heart rate variability threshold values for early-warning non-functional overreaching in elite women wrestlers. Journal of Strength and Conditioning Research, Ahead of print

ABSTRACT: Functional overreaching (FOR) represents intense training followed by a brief reduction in performance, then a rapid recovery (<2 wk) and performance super-compensation. Non-functional overreaching (NFOR) occurs when the reduced performance continues ≥ 3 wk. Heart rate variability (HRV) is a promising tool for detecting NFOR. In this study, we examined HRV thresholds in 34 elite women wrestlers (mean ± SD: age 23±3 yr; height 165.6±6 cm, weight 63±8 kg) for FOR/NFOR during training before 11 major competitions. Supine HRV was analyzed weekly at the same time of day using time and frequency domain methods. We observed that the time domain index, square root of the mean of the sum of the squares of differences between adjacent RR intervals (rMSSD, ms), denoting parasympathetic tone, showed those responding normally to training (82.76 ms, 95% CI 77.75, 87.78) to be significantly different to those showing a decrease (45.97 ms, 95% CI, 30.79, 61.14) or hyper-responsiveness (160.44 ms, 95% CI, 142.02, 178.85; all, P< 0.001). Similar results were observed for mixed sympathetic and parasympathetic signal standard deviation of the NN intervals (SDNN, ms): Normal (65.39; 95% CI, 62.49, 68.29), decrease (40.07; 95% CI, 29, 51.14), and hyper-response (115.00; 95% CI, 105.46, 124.54; all, P< 0.001) and synonymous frequency domain components. An examination of the 95% CI shows a narrow band surrounding a normal response compared to broader bands accompanying adverse responses. Thus, severe perturbations both above and below normal responses lasting >2 weeks indicated an athlete’s transition to NFOR and, hence, are useful for assessing possible overreaching/training.

5.

Maior, AS., Carvalho, AR., Marques-Nesto, SR., Menezes, P., Soares, PP. & Nascimento, JH. (2012) Cardiac autonomic dysfunction in anabolic steroid users. Scandinavian Journal of Medicine & Science in Sports, Ahead of print

ABSTRACT

This study aimed to evaluate if androgenic-anabolic steroids (AAS) abuse may induce cardiac autonomic dysfunction in recreational trained subjects. Twenty-two men were volunteered for the study. The AAS group (n = 11) utilized AAS at mean dosage of 410 ± 78.6 mg/week. All of them were submitted to submaximal exercise testing using an Astrand-Rhyming protocol. Electrocardiogram (ECG) and respired gas analysis were monitored at rest, during, and post-effort. Mean values of VO(2) , VCO(2) , and V(E) were higher in AAS group only at rest. The heart rate variability variables were calculated from ECG using MATLAB-based algorithms. At rest, AAS group showed lower values of the standard deviation of R-R intervals, the proportion of adjacent R-R intervals differing by more than 50 ms (pNN50), the root mean square of successive differences (RMSSD), and the total, the low-frequency (LF) and the high-frequency (HF) spectral power, as compared to Control group. After submaximal exercise testing, pNN50, RMSSD, and HF were lower, and the LF/HF ratio was higher in AAS group when compared to control group. Thus, the use of supraphysiological doses of AAS seems to induce dysfunction in tonic cardiac autonomic regulation in recreational trained subjects.

If you’re not assessing (the ANS), you’re guessing

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“If you’re not assessing, you’re guessing” is a phrase often used by strength and conditioning professionals to explain the importance of movement assessment prior to exercise prescription. Prescribing a program that doesn’t consider the athlete’s movement ability (or lack thereof) can end up causing problems.Essentially, you would be guessing that your exercise prescription is helpful when in fact it could be exacerbating a problem. I wholeheartedly agree with this. However this article has nothing to do with movement assessment. This was just my way of illustrating what my next point is.

I am going to apply the same logic we use for why we assess movement (to influence program design) with monitoring the function of the autonomic nervous system (ANS); if you’re not assessing the ANS, you’re guessing.

If you’re unfamiliar with what the ANS is and why it’s important I suggest you read this. In a nutshell the ANS governs “rest and digest” and “fight or flight” responses in the body. This is done without our conscious control. The two components of the ANS are the parasympathetic branch and sympathetic branch. Sympathetic activity is elevated in response to stress be it physical, or mental. Adrenaline is secreted and catabolic activity (the breakdown of structures) ensues. Parasympathetic activity is elevated in the absence of stress and functions to heal and repair the body.

We can monitor our ANS status non-invasively and inexpensively through heart rate variability (HRV). I explain how you can do this here.

HRV as an indicator of autonomic function can tell you a tremendous amount about your athlete’s responsiveness to training. I shared plenty of research in this post that lends support to HRV as an effective tool for; reflecting recovery status, showing better adaptation to training and even predicting performance. In a separate post I shared my thoughts on HRV as a predictor for injury.

Let me summarize what I shared in my initial research review post;

HRV reflects recovery status in elite Olympic weightlifters (Chen et al 2011), national level rowers (Iellamo et al 2004) and untrained athletes (Pichot et al 2002).

Cipryan et al (2007) showed that hockey players performed better when HRV was high while performance was rated lower when HRV was low.

Endurance athletes who improved vo2 max had consistently high HRV while athletes who did not improve vo2 max had low HRV (Hedelin et al 2001).

Endurance athletes who trained using HRV to determine their training loads had a significantly higher maximum running velocity compared to athletes in a pre planned training group (Kiviniemi et al 2007, Kiviniemi et al 2010).

Female athletes who used HRV to guide their training increased their fitness levels to the same level as females in a pre planned training group but the HRV group had fewer high intensity training days (Kiviniemi et al 2010).

(references for the above articles can be found in my original post here.

I’d now like to show some more research that lends support to the usefulness of HRV in monitoring athletes.

Mourot, L (2004) saw decreased HRV in overtrained aerobic athletes. Uusitalo et al (2000) also saw decreased HRV in overtrained female aerobic athletes.

Huovinen et al (2009) measured HRV and testosterone to cortisol (T-C) ratio in army recruits during their first week of basic training. The training was class room based (not physical) and therefore all stress can be considered mental. The authors found that HRV declined in several soldiers, though not all. This demonstrates that, what can be interpreted as stress is highly variable and dependent on the individual. The authors used the terms “high responders” and “low responders” to describe the differences among soldiers. Immediately I thought about the differences among athletes and how their bodies perceive stress. You can’t assume everyone is responding in kind to a training program. What is stressful for one athlete may not be as stressful to another.

All soldiers that showed decreases in HRV also showed lower T-C ratios. In contrast, soldiers with higher HRV had higher T-C ratio’s. Baseline T-C levels were not recorded so we shouldn’t draw any concrete conclusions however it appears that low HRV (increased sympathetic activity with parasympathetic withdrawl) is associated with a reduced T-C ratio.

Hellard et al. (2011) found that in national level swimmers, as HRV dropped (sympathetic predominance) there was an increased risk of illness. The drops in HRV that lead to illness were preceded by a sudden increase in parasympathetic activity the week prior to illness. The authors speculated that the preceding increase in HRV (parasympathetic/vagal activity) was a reflection of the body experiencing the first incubation period and that an increase in vagal activity was a protective response trying to modulate the magnitude of early immune responses to inflammatory stimuli. The subsequent increase in sympathetic activity and decrease in HRV occurs during the symptomatic phase of the illness.

In humans, increased sympathetic activity is generally associated with inflammatory responses while parasympathetic predominance actually inhibits inflammation. At this point in time I will not elaborate on this for the simple fact that I don’t fully understand it. However, we can speculate that if we’re seeing consistently low HRV scores in ourselves or our athletes there is probably an increase in inflammation occurring. Check out Thayer (2009) for more information regarding HRV and inflammation. Simon from iThlete sent me that paper and I’m still processing it.

When dealing with a team or if we train multiple athletes at the same time we need to be aware of how they are adapting and recovering from training. Work by Hautala et al (2001) shows that athletes will recover from exercise at different rates according to fitness levels (obviously). Basically, fit individuals recover faster and show less HRV fluctuation compared to less fit individuals. In a team setting, some individuals who are highly fit may not be getting a sufficient training stimulus while other athletes who are less fit can be overworked.

Kiviniemi et al (2010) found that females take longer to recover from aerobic training than males. This needs to be considered if you are training a mixed gender group.

Buchheit et al (2009) and Manzi et al (2009) both found HRV to be a predictor of aerobic performance.

I’m well aware that the development of athletes has been taking place without the use of HRV monitoring. There are many great coaches and trainers who have their own systems and methods of monitoring recovery in their athletes that work well.

HRV is a tool to use within your own systems. I have thoughts about how I would implement this in a team setting that I will share another time.

To truly autoregulate the training of ourselves or of athletes, we need as much information about present physiologic status as possible. Based on the research and my own personal experience with HRV, this technology takes much of the guesswork out of load/volume manipulation and training prescription. Training hard when HRV is low can be counterproductive and delay recovery. Training hard when HRV is chronically low can lead to illness, injury, overtraining syndrome and suppressed testosterone. Alternatively, increasing load/volume on days when HRV is high can lead to more favourable adaptation. HRV can tell us how stressful the training was for our athletes based on how long it takes HRV to reach baseline in subsequent days. HRV can indicate how much stress your athlete is experiencing outside of training. There are several indications one can take from a simple HRV measurement. Further research will reveal more correlation between HRV and sports performance.

I believe that to train an athlete optimally, we need to be assessing the state of the autonomic nervous system… otherwise we’re guessing.

References:

Buchheit, M. et al (2009) Monitoring endurance running performance using cardiac parasympathetic function. European Journal of Applied Physiology, DOI 10.1007/s00421-009-1317-x

Hellard, P., et al. (2011) Modeling the Association between HR Variability and Illness in Elite Swimmers. Medicine & Science in Sports & Exercise, 43(6): 1063-1070

Huovinen, J. et al. (2009) Relationship between heart rate variability and the serum testosterone-to-cortisol ratio during military service. European Journal of Sports Science, 9(5): 277-284

Kiviniemi, A.M., Hautala A.J., Kinnunen, H., Nissila, J., Virtanen, P., Karjalainen, J., & Tulppo, M.P. (2010) Daily exercise prescription on the basis of HR variability among men and women. Medicine & Science in Sport & Exercise, 42(7): 1355-1363.

Manzi, V. et al (2009) Dose-response relationship of autonomic nervous system responses to individualized training impulse in marathon runners. American Journal of Physiology, 296(6): 1733-40

Mourot, L. et al (2004) Decrease in heart rate variability with overtraining: assessment by the Poincare plot analysis. Clinical Physiology & Functional Imaging, 24(1):10-8.

Thayer, J. (2009) Vagal tone and the inflammatory reflex. Cleveland Clinic Journal of Medicine, 76(2): 523-526

Uusitalo, A.L.T., et al (2000) Heart rate and blood pressure variability during heavy training and overtraining in the female athlete. International Journal of Sports Medicine, 21(1): 45-53