Tag Archives: training status

Correlation between HRV, sRPE and subjective fatigue in athletes

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Today I will review the research I’ve read that investigates the relationship between perceived exertion ratings of a workout session (sRPE), subjective levels of fatigue and HRV in effort to examine the usefulness of HRV in reflecting training load in athletic populations. Like all of my articles, this report is based on my interpretation of the research and perspectives from personal experience.

The Research

In a brand new study from the JSCR, Sartor and colleagues (2013) followed elite male gymnasts (n=6, age 16) over 10 weeks of training. HRV was monitored daily every other week while sRPE was collected immediately following each workout. HRV strongly correlated to previous day sRPE in both supine (HF%, HF%/LF%) and supine to seated measurements (mean RR, mean HR, HF%, SD1). Relationships were also seen between HRV, and perceived wellness (foster’s index). HRV correlated with training load (sRPE) and psychophysiological status.

Though sRPE wasn’t used in this next study, KeTien (2012) monitored HRV, blood-urine nitrogen (BUN) and profile of mood states (POMS) in 24 national level rugby players over an 8 week conditioning program. The program progressed from more aerobic based work to more anaerobic/interval based work. Spectral measures of HRV correlated with both POMS and BUN at each time point throughout the training period.

During the 2006 World Cup, Parrado and colleagues (2010) set out to determine if perceived tiredness could predict cardiac autonomic response to overload in elite field hockey players (n=8).  A strong correlation was found between per­ceived tiredness scores and HRV. Higher levels of perceived tiredness (acquired from questionnaire) were related to lower values of parasympathetic tone (RMSSD), pNN50 and higher LF/HF ratio. In order to discern changes in HRV brought on by fatigue from changes in HRV caused by pre-competitive anxiety, the researchers had the athletes complete anxiety questionnaires.

“Results show that cognitive anxiety and self-confidence sub­scales of the CSAI–2 were not related to perceived tiredness nor to heart rate variability. In the absence of a relation between cognitive anxiety and heart rate variability, it can be assumed that the relationship established between heart rate variability indexes and perceived tiredness scores are attributable to the fatigue state.”

Accounting for pre-game anxiety is very important as previous research has shown this to affect HRV (Edmonds et al. 2012, Mateo et al. 2012, Murray et al. 2008), thus making it difficult to distinguish fatigue from acute anxiety on the morning of a competition.

Edmonds et al. (2012) found that HRV (HF) correlated with sRPE in youth rugby players (n=9) during a one week microcycle of practices and a game. However, game day HRV values were lower which was attributed to the aforementioned pre-game anxiety since training loads were reduced before the competition.

Smith and Hopkins (2011) monitored performance, HRV, sRPE and subjective fatigue in elite rowers (n=10) throughout an intense 4 week training period. Interestingly, the most improved athlete and the only overtrained athlete both had statistically similar levels of perceived fatigue and changes in LF/HF ratio. However, after looking closely at the data, RMSSD showed a noticeable decline in the OT athlete compared to the most improved who had a moderate increase in RMSSD. The determining factor however in this case was performance changes.

Thiel at al. (2012) found that in 3 elite male tennis players, HRV, serum urea and psycho-physical state (assessed by EBF-52 questionnaire) each responded to overload training. As training load increased, HRV (RMSSD) decreased, perceived fatigue increased and serum urea increased. However, performance increased (V02 max, Single Leg CMJ, DJ index) and therefore performance metrics should always be considered when trying to discern functional overreaching (FOR) from non-functional overreaching (NFOR). HRV changes act as an early warning sign while performance decrements may represent the initial transition from FOR to NFOR.

Cipryan et al (2007) found that HRV correlated to performance in hockey players (age 17, n=4) but did not correlate to self-reported health status. Therefore, coaches should use caution when using perceived stress to predict ANS status and thus an objective measure (like HRV) is still recommended.

In elite female wrestlers, perceived stress (in the form of; excessive competition schedule, social, education, occupational, economical, travel, nutritional, etc) contributed to NFOR when HRV parameters were significantly increased (Tian et al. 2012). There was no mention of perceived stress/recovery in the NFOR group with significant decreases in HRV parameters. Regardless, subjective measures of stress including non-training related events require consideration when planning training. Monitoring the global stress of an athlete is more meaningful then simply training load.

Plews et al. (2012) monitored HRV and perceived measures of recovery (sleep, soreness, etc.) in two elite triathletes over a 77 day period leading up to competition. One athlete was considered NFOR. Perceived levels of recovery were not associated with HRV. However, the NFOR athlete admitted that she felt deterred from  reporting  low scores as anything below a certain score would be automatically sent to the coach. Therefore, when relying on perceptual measures from athletes, coaches must be prudent in ensuring honest reports. HRV was a better indicator of fatigue in this study.

The last study I’d like to mention only appears to be available in German at the moment. I translated the paper with google, however it was very rough to say the least. Therefore I will simply quote the pertinent information from the abstract:

“6 endurance athletes measured morning heart rate, heart rate variability (HRV) and mood state during a normal training period, a 17 day ultrarace (Deutschlandlauf) and following a recovery period. 4 out of 6 runners could not finish the race due to injury or exhaustion. 3 of them showed diagnostically relevant criteria of overreaching. All runners who quit the race showed increased morning heart rate, decreased HRV and a decreased mood state during competition. The studied parameters showed individually different adaptations but there were early changes that preceded the abortion of the run that gave diagnostically relevant information.” (Bossmann 2012)

Thoughts

Though there appears to be a strong tendency for HRV to reflect perceived training load and subjective fatigue, an objective measure of ANS status should still be considered. Subjective measures from athletes are only meaningful if honestly reported.

I’ve personally seen a strong correlation between morning HRV score and session rating of perceived exertion (sRPE) of the previous day’s workout. However, I’ve learned that this relationship isn’t perfect. I’ve experienced situations where;

–          Perceived exertion may be high but HRV response may be minimal if the workout is familiar (exercise selection, order, intensity, etc.).

–          In direct contrast to the above, perceived exertion may be moderate but HRV response may be significant if the workout is unfamiliar.

–          Non-training related factors affect HRV. Sleep, aerobic fitness, mental stress, nutrition, etc. can all impact ANS activity, possibly obscuring the relationship between training load and HRV.

–          Stress from travel, illness, occupation, etc. may have a larger impact on ANS than is perceived and reported.

–          More on other factors effecting HRV here.

In conclusion, obtaining both objective and subjective measures of fatigue along with performance indicators will provide a more accurate indication of training status. Monitoring of these variables regularly should enable the coach to better manipulate training loads to ensure progression and avoid unintentional overreaching.

References

Bossman, T. (2012) Effects of ultra-long-distance running on selected physiological and psychological parameters as a possible marker of overloading. Swiss Journal of Sports Medicine, 60(1): 21-5. Full Text

Cipryan, L., Stejskal, P., Bartakova, O., Botek, M., Cipryanova, H., Jakubec, A., Petr, M., & Řehova, I. (2007)  Autonomic nervous system observation through the use of spectral analysis of heart rate variability in ice hockey players.  Acta Universitatis Palackianae Olomucensis. Gymnica, 37(4): 17-21. Free Full-Text

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. Research to Practice  Abstract

Ke-Tien, Y.(2012) Effects of Cardiovascular Endurance Training Periodization on Aerobic performance and Stress Modulation in Rugby Athletes. Life Science Journal, 9(2): 1218-25. Full-Text

Mateo, M. et al. (2012) Heart rate variability and pre-competitive anxiety in BMX discipline. European Journal of Applied Physiology, 112(1): 113-23.

Murray, N. P. et al. (2008) Heart rate variability as an indicator of pre-competitive arousal. International Journal of Sport Psychology, 39: 346-355.

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.

Parrado, E.  et al. (2010)Perceived tiredness and HRV in relation to overload during a field hockey world cup. Perceptual and Motor Skills, 110(3): 699-713 Abstract

Sartor, F. et al. (2013) Heart rate variability reflects training load and psychophysiological status in young elite gymnasts. Journal of Strength & Conditioning Research, Published ahead of print.

Smith, T.B., & Hopkins, WG. (2011) Heart rate variability and psychological stress in an elite female rower who developed over-training syndrome. New Zealand Journal of Sports Medicine, 38(1): 18-20.

Thiel, C. et al. (2012) Functional overreaching in preparation training of elite tennis professionals. Journal of Human Kinetics, DOI: 10.2478/v10078-011-0025-x

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 & Conditioning Research, Published ahead of print

 

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

HRV response to perceived training load – Observations from 2.5 months of data

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About two months ago the new version of iThlete was released with some really cool new features. These new features included;

  • The ability to rate your sleep on a score of 1-5
  • A comment section that allows you to make notes about the previous day’s events, stressors, etc.
  • The ability to input training loads that appear on your HRV trend chart so you can see how your HRV responds to your training
  • The ability to export data to drop box

Here is a video that shows the updated features;

The most significant addition in my opinion is the ability to track your training loads with your HRV trend. This really puts into perspective how stressful your workouts are. There is no specific method or formula that you have to use for your training load data. There are several methods that have been used in research to quantify training load, some of which I’ll describe below.

Training Impulse (TRIMP) – this is calculated using training duration, maximal heart rate, resting heart rate and average heart rate during the session

Session Rating of Perceived Exertion (RPE) for Endurance Athletes – Session RPE score x duration of exercise in minutes (for endurance training)

Session Rating of Perceived Exertion (RPE) for Strength/Power Athletes – Session RPE score x repetitions

*See Borrensen & Lambert (2009) for a more elaborate review and explanation of the above methods.

       Training Volume – Weight Used x Sets x Reps

Other methods exist, but these tend to be the most commonly used. In deciding how I would monitor my training I simply decided to use an RPE of the session, however, not like the method listed above. Instead, I simply rated my workout on a scale of 1-10 based on how hard, or how much effort I put into the session. I would consider volume, strain, RPE of my main sets, how hard I pushed my assistance work and so forth. I realize this isn’t the most valid or reliable measure of training load, but it’s been working well for me.

To give you an idea of how I grade my workouts, see below. This will make interpreting the charts I attach below of my trends much easier.

Session RPE of 10 – 3 or more top sets for my main exercise, RPE of 9-10 for each set, high volume of assistance work (3+ sets to failure), complete exhaustion by workouts end. I have yet to perform a 10 workout and likely never will.

Session RPE of 9 – 2-3 top sets for my main exercise, RPE of 8-10 for each set, moderate volume of assistance work (2-3 sets not to failure), considerable fatigue at end but not exhaustion.

  • I’ll typically perform these workouts when HRV is above baseline

Session RPE of 8 – 1-2 top sets for my main exercise, RPE of 8-9 for each set, low to moderate volume of assistance work (1-3 sets not to failure), moderate fatigue at end

  • I’ll typically perform these workouts when HRV is at the lower end of baseline

Session RPE of 7 – 1 top set for main exercise with an RPE of 8 or less, low volume of assistance work with reduced weight, minimal fatigue at end.

  • I’ll perform this workout when HRV is below baseline with an amber indication (deload)

Session RPE of 5 – No main exercise performed, light weight, moderate volume

  • This is what I’ve been doing on Sunday’s to hit delts and arm’s since I don’t do much work for them during my main sessions on Mon-Wed-Fri

Session RPE of 3 – Active recovery work for 20-40 minutes. This can be in the form of light jogging, sled dragging, circuits, etc.

  • I try and perform these workouts the day after each workout to facilitate recovery and maintain an aerobic base level of conditioning

Session RPE of 1 – Leisurely walk for 30-40 minutes. This can hardly be described as a workout but it’s more than a zero so I will log it when it happens.

  • This happens sometimes instead of an active recovery session.. usually when I’m visiting my folks as we’ll take a lot of walks.

So as you can see there is no sexy formula (I’ve never been a math guy anyway), but I’m pretty consistent and I’ve noticed some fairly common trends in my recovery (based on HRV). Below I have attached a couple screen shots of my HRV Trends with Training Load (Session RPE ala Andrew Flatt). The purple bars reflect training load (9 being the highest you’ll see) while the horizontal trend is my HRV daily fluctuations with the blue line representing my baseline.

Observations:

  • See here and here for previous posts about observations I’ve made from monitoring my HRV
  • A session rated as 9 is almost always going to cause a pronounced drop in HRV the following day. This is why I don’t typically train on consecutive days.
  • If circumstance causes me to train two days in a row, I’ll use a Session RPE of 8. My HRV will usually drop moderately after the first workout out and drop even more after the second one.
  • During the passed 2.5 months I experienced approximately 16 instances where my HRV dropped enough causing an amber or red indication. The majority of these occurred the day after a session and therefore fell on a recovery day.
  • There were 5 days in which a red or amber indication fell on a training day and therefore out of the 2.5 months, I only deloaded for a total of 5 days. In the past I would typically take a week off after every 3 week cycle however with my new system of training I simply deload on a given day when my HRV is well below baseline.
  • The lowest dip on the graph (around 04/20) I purposefully trained harder than normal on a below baseline day (amber indication) to see how my body would react. The next day my HRV dropped even lower with a red indication. This, as well as other incidences from the past solidifies my stance that training hard when HRV is low delays recovery. You’ll see that it takes several days until my HRV gets back up to previous levels. This negatively effects future training sessions. In my opinion, it’s much better to reduce loads for one day to improve the effect of your following sessions as opposed to just training through a bad day and ruining the next few sessions. This is also what has inspired me to stop deloading at pre-determined times for pre-determined periods. There certainly is value in doing this as the body needs time to recover and adapt to weeks of hard training. However, with HRV monitoring, it seems (atleast to me, for right now) that you can get away with just reducing loads on days when HRV is low.
  • I’m presently the leanest I’ve ever been at my current body weight. I’m about 232lbs at 17%. The leanest I’ve ever been is 14.8% at 218 while the heaviest I’ve ever been was nearly 270lbs when I played collegiate football (I’m the ogre in purple below from back in 2006).

  • I’m presently the strongest I’ve ever been at this body weight.
  • I’ve been able to remain injury and illness free since using HRV to guide my training. I no longer experience any tendonitis in my elbows either which used to be a big problem.

Final Thoughts:

I realize that I may appear overly biased towards HRV’s usefulness in my writing. However, I feel that I’ve been training long enough to know when something’s all in my head (placebo) or when it’s actually making a difference. The science supports HRV (see here) and my experience up to and including the present also seems to support it. The whole concept of planning training in advance and sticking to it no matter what is not as effective as manipulating training on a day to day basis according to an objective measure of your body’s current adaptive capacity. This doesn’t mean you can’t have a general plan, it just means that you need to be prepared to make adjustments along the way to ensure the quickest and safest way to reach your training goal. HRV provides, in my opinion, the simplest and most accurate information to allow you to do this. I will continue with this method of monitoring and training since it has been so successful. I’ll be sure to provide another update in a few months.

Thanks for reading.