Effects of Alcohol Consumption on HRV and Sleep

Posted on March 27, 2013 by hrvtraining

Excessive alcohol consumption is not uncommon among high school, collegiate and professional athletes. This typically occurs after competitions during the season and likely with greater frequency throughout the offseason. Today I’d like to share what I’ve learned after reading through the available research pertaining to alcohol and HRV in healthy individuals. In addition I will post up some ithlete data I’ve collected showing the effects that excessive drinking has on HRV.

Bau and colleagues (2011) investigated the effects of 60g of ethanol ingestion on HRV in young men. HRV was measured before and during the following 17 hours after ingestion. Compared to the control group, the ethanol group saw a decrease in all time domain indices of HRV that persisted for 10 hours.

Koskinen et al (1994) tested the effects of ethanol consumption (1g/kg) on HRV in healthy young males (n=12). HRV was measured prior to ethanol ingestion and once each hour for 3 hours after ingestion. A significant decrease in RMSSD and HF was observed compared to control.

In healthy volunteers Weise et al. (1986) observed an immediate reduction in HRV after alcohol consumption (0.7 g/kg) with no change in HR or blood pressure.

Spaak et al. (2009) investigated the dose-related effects that red wine, ethanol and water have on HRV in a mixed group of healthy folks (n=12). Essentially, one drink of either red wine or ethanol had no effect. However, after the second drink HR increased and HRV decreased (Total HRV by 28-33%, HF by 32-45%, LF increased 28-34%).

The last study I’ll discuss is perhaps the most relevant. Sagawa et al. (2011) monitored HRV and sleep quality (polysomnography) after alcohol consumption in university aged healthy males (n=10). There was a control group, a low dose (LD) group (0.5g/kg) and a high dose (HD) group (1g/kg). As you can imagine, there was a dose related effect of alcohol on HRV and sleep. The HD group saw the lowest HRV value, highest RHR and poorest sleep quality. The LD group also saw reduced HRV, increased RHR and reduced sleep quality compared to control.

Below is a screen shot of mine from over the Christmas holidays. There is a marked drop in HRV on New Year’s day following a late night of NYE celebration that included several drinks.

The data set below is a re-creation in excel from an athlete/colleague who doesn’t know how to take a screen shot with his phone (c’mon man!). The three lowest dips on the trend all occur in March after nights out drinking on the 10^th, 16^th and 23^rd. The dip from the 12^th is reported to be caused by other stressors.

Wrap Up

For those who didn’t already know, alcohol has a negative effect on HRV and sleep quality in healthy individuals. Clearly this can impact recovery and performance and therefore should be avoided, or limited to time periods away from competition and/or rigorous training schedules.

References

Bau. P.F.D. et al. (2011) Acute ingestion of alcohol and cardiac autonomic modulation in healthy volunteers. Alcohol, 45: 123-9.

Koskinen, P. et al. (1994) Acute alcohol intake decreases short-term heart rate variability in healthy subjects. Clinical Science, 87(2): 225-30.

Sagawa,Y. et al. (2011) Alcohol has a dose-related effect on parasympathetic nerve activity during sleep. Alcoholism: Clinical & Experimental Research, 35(11): 2093-99

Spaak, J. et al. (2009) Dose-related effects of red wine and alcohol on heart rate variability. American Journal of Physiology, Heart & Circulatory Physiology, 298(6): H2226-31.

Weise, F. et al. (1986) acute alcohol ingestion reduces heart rate variability. Drug & Alcohol Dependence, 17(1): 89-91.

Why Assess the ANS?

Posted on March 20, 2013 by hrvtraining

I just finished watching a presentation by Andy O’Brien entitled “Modern Concepts in Program Design – A Systematic Approach to Individualization”. Andy O’Brien works with elite athletes including NHL star Syndey Crosby. His presentation is 28 minutes long and is truly worth watching if you work with athletes. After listening to his talk, you’ll understand why he works with such high level athletes. I’d also like to add that this is yet another great free resource put out by John Berardi and his team at PN. I have no problem endorsing a company that continually puts out top notch information for free. The thoughts in this post are inspired from the ideas and concepts discussed by Andy O’Brien.

In his presentation, Coach O’Brien essentially views program design as problem solving. Naturally, the first step in designing a program is assessing the athlete. An assessment allows us to form a needs analysis and determine limiting factors that impede progression.

An example was provided of a weight loss client who wanted to lose X amount of fat in time for a wedding. After the trainer decided that diet was not the limiting factor, emphasis was placed on increasing calorie expenditure. What would appear to be a very effective program for improving body composition was prescribed (resistance training, aerobic and anaerobic conditioning, plus a thermogenic supplement). The results however were quite surprising. The client in fact gained fat after several weeks. The reason? Incorrect identification of the limiting factor.

It turns out that the client had a significant ANS imbalance of sympathetic predominance. Even before the exercise program, the nature of her work and lifestyle was highly stressful. Adding intense exercise 5 days/week only further increased this imbalance resulting in unfavorable hormonal responses and poor adaptation to the program.

O’Brien mentions a related study by Messina et al. (2012) entitled “Enhanced parasympathetic activity of sportive women is paradoxically associated to enhanced resting energy expenditure”. Unfortunately I do not have access to this text at the moment but here is an excerpt from the abstract; “These findings demonstrate that resting energy expenditure is higher in the athletes than in sedentary women, despite the augmented parasympathetic activity that is usually related to lower energy expenditure.”

This is one example of why it is important to assess the ANS. I think there are many folks who reject HRV as a useful metric in monitoring athletes or individuals. Perhaps this is because there is a misunderstanding of what the data is telling us or perhaps because interpretation of the data is difficult. Maybe it’s a compliance issue. Regardless, in my opinion, an objective measure of ANS status requires at the very least, periodic assessment for several reasons.

We measure strength, power, body comp, etc. yet ignore one major component of the body that largely acts as a moderator in training response and adaptation. HRV is likely the cheapest and most efficient non-invasive tool we can use to acquire ANS information.

To be clear, I’m not saying that HRV is first in the hierarchy of assessment (if one exists). I’m merely saying that the ANS plays a huge role in our health and performance and requires monitoring and assessing just as much as performance and body composition. How can we rule it out as a limiting factor if we don’t consider it at all?

HRV and Deload Periods

Posted on March 18, 2013 by hrvtraining

Before I review my own data from my overload and deload period, I first wanted to discuss some of the available research that I have pertaining to HRV response to overload training and following recovery.

Some Research Pertaining to HRV and Taper/Deload Periods

Pichot et al. (2000) monitored HRV in middle distance runners over 3 weeks of intensive training followed by a 1 week recovery week consisting primarily of moderate aerobic work. RMSSD decreased progressively over weeks 1-3 and rebounded to peak values during the recovery week.

Pichot et al. (2002) found that RMSSD increased after an aerobic training period in sedentary subjects. After transitioning to a 4 week overload period, RMSSD decreased significantly followed by an abrupt rebound reaching peak values during a 2 week recovery period.

In a study by Baumert et al. (2006), baseline HRV values were established prior to training camp in track and field athletes. After week 1 of a 2 week training camp, RMSSD declined significantly. At 3-4 day’s post-training camp, RMSSD started to return toward pre-camp basal values.

In elite rowers, Iellamo et al (2004) reported that HRV indices decreased as training load increased from 50% to 100%. However, during a taper for the World Championships, HRV values returned to baseline. “Reduction in training load during the World Championship resulted in a return of autonomic indices to the level observed at 50% training load”

Though not a comparison for pre and post HRV values following overload, Buchheit et al. (2004) showed that moderate training loads are better than no training or intensive training for the purposes of increasing vagal-related HRV indexes. Their data revealed that moderately trained individuals had higher basal HRV values compared to sedentary and highly trained individuals.

Reviewing my data

In older posts I discussed my experimentation with not taking planned deload weeks but rather reducing training loads on days when HRV was low. This method of managing training loads worked very well during times of consistent, albeit, relatively unchanging training. However, due to work/travel schedules and other set-backs I really didn’t plan any overload training. I was mostly doing my best at not losing strength. A feat much easier to accomplish than gaining strength. At the present time, I believe that one can get away without doing week long deloads at fixed intervals (every 4th week or so) if training is managed on a daily basis. However, by design, this set-up really doesn’t allow for overreaching as you would back off as soon as your trend declined for too long.

It was my goal in my latest training cycle to not focus on daily HRV changes but instead evaluate weekly changes. My training, though still manipulated slightly on a day to day basis (particularly in week 4 of the cycle) was much more pre-planned than I had been doing previously. My training set-up was designed so that HRV would return to above baseline after each weekend.. which it did. The purpose of this was to be fresh for the beginning of each week and to avoid premature overreaching. A deload was planned following the last week of the cycle.

Below is a screen shot of my HRV trend that includes interesting trend changes in response to different events/training. See my previous post here for a more extensive review of my 9 week training cycle. This post will focus primarily on the last 3 weeks of the trend (weeks 8, 9 and 10 of the cycle)

During week 6 and 7 of my trend HRV baseline reached peak values since the holidays. However, during weeks 8-9 HRV steadily decreases. In fact, in week 9, HRV remains below baseline until the weekend. Typically my HRV will come back up after a recovery day on Wednesdays. The difference between weeks 6-7, 8-9, and 10 are volume and intensity related.

During weeks 6-7 my training volume reduced and my intensity increased only slightly. In week 6, it is reasonable to say that I reduced more stress than I added based on volume and intensity change and sRPE. In week 7 however, there are 2 sRPEs of 9 which marks the initial decent in the trend. During weeks 8-9, volume reduced only slightly but intensity increased to near maximal in week 8 and as close to maximal as I could get in week 9. In that 14 day period I performed 8 workouts of near maximal or at maximal intensity on my main barbell lifts.

It was also during these last two weeks of the training cycle that I experienced nagging pains, high levels of soreness etc.

Though weeks 8 and 9 are the most taxing, my sRPE doesn’t change all that much (primarily 8’s with a rare 9). This does not do a good job of reflecting the change in volume/intensity. Perhaps I need to re-evaluate my current method of rating workouts and tracking training load.

Week 10 is a deload week and HRV returns to peak levels. Soft tissue problems progressively resolve and I’m anxious to start a new cycle.

Wrap Up

HRV will likely decline during intensive training and return to baseline following a recovery period of reduced training loads. Perhaps focusing more on weekly changes in HRV as opposed to daily acute changes is more meaningful during overload periods; permitting a more controllable approach to overreaching.

References

Baumert, M. et al. (2006) Changes in heart rate variability of athletes during a training camp. Biomed Tech, 51(4): 201-4.

Buchheit, M., et al. (2004) Effects of increased training load on vagal-related indexes of heart rate variability: a novel sleep approach. American Journal of Physiology – Heart & Circulatory Physiology, doi:10.1152/ajpheart.00490.2004.

Iellamo, F., Pigozzi, F., Spataro, A., Lucini, D., & Pagani, M. (2004) T-wave and heart rate variability changes to assess training in world class athletes. Medicine & Science in Sports and Exercise, 36(8): 1342-1346.

Pichot, V., Busso, T., Roche, F., Gartet, M., Costes, F., Duverney, D., Lacour, J., & Barthelemy, J. (2002) Autonomic adaptations to intensive overload training periods: a laboratory study. Medicine & Science in Sports & Exercise, 34(10), 1660-1666.

Pichot, V., et al. (2000) Relation between heart rate variability and training load in middle-distance runners. Medicine & Science in Sport & Exercise, 32(10): 1729-36.

Reviewing HRV data after a 9 week training cycle

Posted on March 13, 2013 by hrvtraining

It’s been a quite a while since I can honestly say that I completed a successful training cycle with little interruption. After Christmas break I had a 9 week cycle tentatively planned out. As you’ll see, the plan changes due to unforeseen events, but training manipulations were made and the cycle was successful; resulting in some gym PR’s which haven’t been made in a long time!

Set up was as follows;

Monday – Squat

Tuesday – Bench

Wednesday – Active Recovery (20-30 mins of light aerobic work, mobility, stretching, etc.)

Thursday – Deadlift

Friday – Incline Bench

Saturday – Off or Active Recovery

Sunday – Off

Weeks 1-4 were of moderate intensity (75-85%) and higher volume. An example of a typical workout from this phase would be 5×5, 6×4, etc. However on deadlift day’s I’d rarely perform sets with more than 3 reps. Weights were selected based on RPE and guided by previous session’s working sets. If you look at my trend closely however, you’ll see that week 4 was a lousy week and my workouts were adjusted accordingly (more below).

Weeks 5-7 were of higher intensity (85-90%) and moderate volume such as 3×4, 4×3, etc.

Week 8 consisted of 1-2 sets of 2 reps with a weight that was near but not quite maximal

Week 9 was test week where I worked up to as close to a 1RM as I could get safely (I train alone).

Essentially I was blocking my training up into an “accumulation” period, a “transmutation” period and a “realization” period. I use those terms loosely however.

Below is a screen shot of my HRV/sRPE trend from the last 3 months. The training cycle began on Jan. 7. This is following a period of detraining over the holidays that you can clearly see early in the trend.

Week 1 – Post-Christmas holidays and I’m detrained. I began lifting 3 day’s/week to let my body get back into the swing of training with plans of moving to 4/days week in week 3. Though the workouts aren’t very intense, I experience large drops in HRV in response to workouts. My body is clearly adapting to the re-initiation of training.

Week 2 – My body appears to have adapted well as I experience very few low HRV days. HRV peaks on the weekend after some rest.

Week 3 –Switch to 4/day week lifting schedule. I was surprised that I didn’t see some lower drops this week. HRV peaks again on the weekend after rest.

Week 4 – I miss a workout due to snow day. My HRV is low practically all week and the weights were feeling heavy. I decided not to push it and essentially deloaded with sRPE’s of 7. Below is a screen shot of my data as it appears when I export it to excel from ithlete from weeks 2-4.

*Regarding the comments section, I document some random stuff sometimes. This is simply because I plan to review that data at a later date to see if I notice any trends. For example I note when I have ZMA before bed to see how it effects sleep score and next morning HRV. I’ll try and make note of any changes in nutrition, etc. Since I keep a training log I only document brief details about workouts on ithlete. Keep in mind that the comments , Sleep score and sRPE are all referring to the PREVIOUS day/night. So for example, when you see an sRPE of 8, it was from the workout on the day before. Lastly, I work days/evenings working with athletes so I typically stay up a bit late and therefore wake up later in the morning.

Weeks 5-7 run smooth. Training goes well and HRV responds well as my trend actually increases a bit. HRV reaches its lowest point on a Saturday morning at the end of week 7. This was after a long day of work, a workout and a football skills practice I helped coach. This practice beat the hell out of me as I was shouting the whole time so that my kids could hear me over all of the other groups. I was exhausted at the end of the day so I expected a low score the next day.

Weeks 8-9 both go well. HRV drops much lower than I had expected in response to the higher intensities. In the past, heavy workouts with low volume typically don’t create such marked drops. In week 9, my final week with 1RM attempts, HRV doesn’t even come above baseline. I’m also feeling beat up at this point with a sore left pec, tight lateral hamstring on my right side and overall wear and tear. HRV peaks again every weekend after rest.

Week 10 is a deload week and you can see at the very end of the trend that HRV starts to climb back up.

In my comments above from ithlete you can see when and where certain body parts start nagging, etc. It’s worth mentioning again (as I’ve mentioned this in previous posts), any time I spend time with my family (particularly my nieces and nephews) that I don’t see too often, my HRV is always high the next day.

The results of the training cycle – (all raw, vid’s of some of these in last post and on youtube page)

Squat – 540

– 11lbs shy of my Competition PR of 551 from back in 2010. I’m pretty confident I could’ve hit this if I had a spotter. I made this lift in a relatively relaxed state. Not a true 1RM.

Narrow Grip Floor Press with Pause – 385

– Due to left pec soreness I decided to test with a narrow grip floor press instead of bench press. This was probably a stupid idea. I’m glad I didn’t hurt it even more. This was a floor press PR. Pec’s already feeling better now.

Deadlift – 565

– This went up pretty easy. I opted to not go heavier because I’ve had back issues in the past as I’ve discussed several times in previous posts. I did not want to push it just in case. Again, not a true PR (which is 600), but it’s been a while since I’ve deadlifted this heavy due to injury.

Incline Bench – 350

– I was pretty happy with this since I don’t always include this lift in my training.

My bodyweight throughout this cycle was around 235lb.

I’m moving to Alabama real soon to get started on some HRV research at Auburn. I expect that this will affect my training. I’m hopeful however that the move will be a smooth transition and that I can continue on without too much issue. Unlikely though.

Making HRV More Practical For Athletes: Measurement Frequency?

Posted on March 6, 2013 by hrvtraining

Perhaps the biggest limitation with HRV monitoring in a team setting is obtaining and maintaining compliance from athletes. Daily HRV measurements can become monotonous, particularly for athletes who may not fully understand the value of the data. One question I’ve had in mind for a while now is; what is the minimal frequency of HRV measurements we can acquire that can still offer meaningful information regarding training status in athletes?

If you’ve read any of the research on HRV and athletes, you’d note that HRV is rarely measured daily. This is likely because having each subject report to the lab everyday to have their HRV measured on an ECG is impractical. However, with the advent of valid and reliable devices such as the Polar RS800, R-R intervals can be collected in the field making more frequent measurements a little more practical in the research setting. However, for the practitioner in the field, an even more practical, economical and user friendly device is desired. Thankfully smart phone app’s such as ithlete were created to accommodate this.

So now we have very affordable, very user friendly smart phone applications that can provide us with HRV data. The trick is getting the athletes to use them often enough so that we can use the data for monitoring purposes. Is it more of a reasonable expectation of our athletes to collect only one or two HRV measurements per week as opposed to every day? Will this provide us with enough information to draw meaningful interpretations from?

After giving it some thought, I decided to review some data over a 3 month period. With my own HRV data, I recreated trends in excel with; once per week, twice per week and daily measurements. The purpose of this is to see what these varying frequencies of measurement reveal in the trend. I’ve also included sleep score data which is graded 1-5 based on perceived quality and quantity after waking.

Daily Measurement 3 Month Trend

– There is a period of time between the 4^th week of December to the 2^nd week of January that my HRV trend declines and I rarely see scores over 80. During this time (the Christmas Holidays)I was not lifting regularly and experienced some detraining.

– Daily measurements allow for sRPE to be recorded providing the coach with a good indication of how the athlete is perceiving and responding to the workouts. Conversely, the sRPE allows the coach to see when the athlete is experiencing high stress in the absence of a high load training day.

– Daily measurements allow the coach to see acute changes in HRV which can be important in planning or manipulating training.

– It’s worth mentioning that my highest levels of strength were displayed over the last week of February and early March (early March not included). This is expected as I am nearing the end of my training cycle which has transitioned from moderate intensity/high volume to high intensity/low volume. Coincidentally, my HRV is reaching peak heights. I’m not entirely sure what to attribute these high scores to as I have been doing less aerobic work than normal. This may or may not have any meaning. Some vid’s are posted below from this “realization” phase.

Once Per Week HRV Measurement 3 Month Trend

– I chose Monday as the reference day because it is the day of the week furthest from training stress that can influence HRV. My goal was to find a day that gives me the best indication of baseline HRV. Since I train Mon-Fri and rest on weekends this left Sunday or Monday as the best options. I selected Monday over Sunday because Saturday nights can be social, late, etc. and therefore affect Sunday morning results.

– This trend clearly shows my detraining period over the holidays.

– Given that I adjust my training when necessary to avoid excessive fatigue accumulation, my baseline HRV is relatively consistent apart from the detraining period. Training is being well tolerated because I’m intentionally adjusting my training for that purpose. However, in a more pre-planned setting such as a collegiate weight room the result/trend would likely differ; particularly in a preparation phase (pre-season, early off-season, etc)

– Coaches should be cautious when using weekly measurements due to potentially low scores caused by non training related stressors that may obscure interpretation. For example, if an athlete has a rough sleep Sunday night, HRV may be lower than usual Monday morning. This does not mean the athlete is fatigued or should have training loads reduced. Therefore, coaches need to keep tabs on performance and feedback from the comments section. Clearly, weekly measurements have its limitations however it still may offer some value.

Twice Per Week HRV Measurements 3 Month Trend

– I chose Monday and Saturday as my reference days because Monday represents HRV at rest while Saturday represents HRV after fatigue has been accumulated all week from training. This may provide some insight as to how stressful the training was based on Mon-Fri change in HRV. I am a bad example for this as I try and allow for HRV to reach baseline at least once during the week using Wednesday as an active recovery day. Data from an athlete involved in training, practices, class, etc. would have a different trend.

– This trend allows for comparison of Sleep quality pre and post microcycle. In my trend, Mondays sleep scores never fall below 4 while there are 2’s and a 3 from Friday night’s sleep.

– As with the weekly measurement, this trend fails to capture major acute changes (highs and low’s).

Final Thoughts

Weekly measurements performed after a day or two of rest to allow for a true measure of baseline HRV can be useful in determining how an individual is coping with training on a week to week basis. However, I would urge you to be very cautious when interpreting trends as a low score caused by poor sleep or something other than training fatigue can provide a false sense of training response. This is where subjective measures, performance indications and regular communication is important.

Twice per week measurements might be the frequency which provides us with the most meaningful information from the least amount of data and therefore demand from the athlete. Seeing how HRV changes from pre to post training over a one week period likely provides much more meaningful information about training status verses weekly measures. It goes without saying that this needs to be manipulated according to the team’s training/practice/competition schedule. I used myself as the example today but most teams will not have Saturday and Sunday completely off from training.

Perhaps starting with weekly or twice weekly measurements is sufficient for getting athletes started and comfortable with the device. The goal should be to eventually get them to take daily measurements as this will provide more complete information including sRPE, daily sleep score and comments. The comments section is highly underrated and I intend to elaborate more on it’s value in a future post.

New HRV Research: Vol. 3

Posted on March 3, 2013 by hrvtraining

Here are 7 new studies pertaining to HRV and training.

New HRV Research: Vol. 1

New HRV Research: Vol. 2

Dupuy, O. et al. (2013) Night and postexercise cardiac autonomic control in functional overreaching. Applied Physiology, Nutrition, and Metabolism, 2013, 38(2): 200-208, 10.1139/apnm-2012-0203

ABSTRACT

The purpose of this study was to evaluate the effect of a 2-week overload period immediately followed by a 1-week taper period on the autonomic control of heart rate during the night or after exercise cessation. Eleven male endurance athletes increased their usual training volume by 100% for 2 weeks (overload) and decreased it by 50% for 1 week (taper). A maximal graded exercise test and a constant-speed test at 85% of peak treadmill speed, both followed by a 10-min passive recovery period, were performed at baseline and after each period. Heart rate variability was also measured during a 4-h period in the night or during estimated slow-wave sleep. All participants were considered to be overreached based on performance and physiological and psychological criteria. We found a decrease in cardiac parasympathetic control during slow-wave sleep (HFnu = 61.3% ± 11.7% vs 50.0% ± 10.1%, p < 0.05) but not during the 4-h period, as well as a faster heart rate recovery following the maximal graded exercise test (τ = 61.8 ± 14.5 s vs 54.7 ± 9.0 s, p < 0.05) but not after the constant-speed test, after the overload period. There was a return to baseline for both measures after the taper period. Other indices of cardiac autonomic control were not altered by the overload period. Care should be taken in selecting the most sensitive heart rate measures in the follow-up of athletes, because cardiac autonomic control is not affected uniformly by overload training.

Vargas, W. et al. (2013) Higher mean blood pressure is associated with autonomic imbalance but not with endothelial dysfunction in young soccer players. American Journal of Hypertension, doi: 10.1093/ajh/hps034

BACKGROUND Blood pressure (BP) should be kept within a narrow range to allow adequate tissue perfusion. In particular, heart-rate variability (HRV) can be used to assess autonomic cardiovascular modulation, and flow-mediated dilation (FMD) can provide valuable information about the ability of the cardiovascular system to adapt to different pressures. Our objective in the study described here was to investigate the effect of a difference of 10mm Hg in mean arterial pressure (MAP) on endothelial function and autonomic balance in young and normotensive soccer players.

METHODS Twenty-nine young male soccer players (mean age 17.7 years) were divided into two groups according to their MAP (mm Hg): MAP-84 and MAP-94. The BP, FMD, HRV and maximum oxygen uptake (VO_2max) of each group were measured.

RESULTS Systolic BP (SBP) and diastolic BP (DBP) were significantly higher (P < 0.0001 and P < 0.006, respectively) in the MAP-94 group. There were no differences in VO_2max and endothelial function in the two groups (P < 0.7699). However, the standard deviation (SD) of normal RR intervals (SDNN) and the square root of the mean squared differences in successive RR intervals (RMSSD) were significantly lower in the MAP-94 than in the MAP-84 group (P < 0.0001 and P < 0.005, respectively). In the MAP-94 group, both the high-and low-frequency components were significantly (P< 0.001, P < 0.021, P < 0.017, respectively) lower in both absolute and normalized units, whereas the LF/HF ratio was significantly (P < 0.012) higher.

CONCLUSIONS Collectively, our findings indicate that in young soccer players, autonomic cardiovascular modulation is impaired when MAP is increased by 10mm Hg, even within an optimal range of BP and regardless of endothelial function and VO_2max.

Heydari, M., Boutcher & Boutcher. (2013) High-intensity intermittent exercise and cardiovascular and autonomic function. Clinical Autonomic Research, 23(1): 57-65

Objective

The effect of 12 weeks of high-intensity intermittent exercise (HIIE) on cardiac, vascular, and autonomic function of young males was examined.

Methods

Thirty-eight young men with a BMI of 28.7 ± 3.1 kg m⁻² and age 24.9 ± 4.3 years were randomly assigned to either an HIIE or control group. The exercise group underwent HIIE three times per week, 20 min per session, for 12 weeks. Aerobic power and a range of cardiac, vascular, and autonomic measures were recorded before and after the exercise intervention.

Results

The exercise, compared to the control group, recorded a significant reduction in heart rate accompanied by an increase in stroke volume. For the exercise group forearm vasodilatory capacity was significantly enhanced, P < 0.05. Arterial stiffness, determined by pulse wave velocity and augmentation index, was also significantly improved, after the 12-week intervention. For the exercise group, heart period variability (low- and high-frequency power) and baroreceptor sensitivity were significantly increased.

Conclusion

High-intensity intermittent exercise induced significant cardiac, vascular, and autonomic improvements after 12 weeks of training.

Souza, G., et al. (2013) Resting vagal control and resilience as predictors of cardiovascular allostasis in peacekeepers. Stress, doi:10.3109/10253890.2013.767326

Abstract

The body’s adaptive reaction to a stressful event, an allostatic response, involves vigorous physiological engagement with and efficient recovery from stress. Our aim was to investigate the influence of individual predispositions on cardiac responses to and recovery from a standardized psychosocial stress task (Trier Social Stress Task) in peacekeepers. We hypothesized that those individuals with higher trait resilience and those with higher resting vagal control would be more likely to present an allostatic response: a vigorous cardiac response to stress (i.e., reduction in interbeat intervals and heart rate variability (HRV)) coupled with a significant cardiac recovery in the aftermath. Fifty male military personnel with a mean age of 25.4 years (SD ± 5.99) were evaluated after returning from a peacekeeping mission. Electrocardiogram recordings were made throughout the experimental session, which consisted five conditions: basal, speech preparation, speech delivery, arithmetic task, and recovery. Mean interbeat intervals and HRV were calculated for each condition. An Ego-Resilience Scale and resting vagal control assessed individual predispositions. Stress tasks reduced interbeat intervals (tachycardia) and HRV in comparison with basal, with return to basal in the aftermath (p < 0.001, for all comparisons). Resilience and resting vagal control correlated positively with cardiac parameters for both stress reactivity and recovery (r ≥ 0.29; p < 0.05). In conclusion, peacekeepers showing higher trait resilience and those with higher resting vagal control presented a more adaptive allostatic reaction characterized by vigorous cardiac response to stress (i.e., tachycardia and vagal withdrawal) and efficient cardiac recovery after stress cessation.

Lujan, H.L. & DiCarlo. (2013) Physical activity, by enhancing parasympathetic tone and activating the cholinergic anti-inflammatory pathway, is a therapeutic strategy to restrain chronic inflammation and prevent many chronic diseases. Medical Hypotheses, doi:10.1016/j.mehy.2013.01.014

Abstract

Chronic diseases are the leading cause of death in the world and chronic inflammation is a key contributor to many chronic diseases. Accordingly, interventions that reduce inflammation may be effective in treating multiple adverse chronic conditions. In this context, physical activity is documented to reduce systemic low-grade inflammation and is acknowledged as an anti-inflammatory intervention. Furthermore, physically active individuals are at a lower risk of developing chronic diseases. However the mechanisms mediating this anti-inflammatory phenotype and range of health benefits are unknown. We hypothesize that the “cholinergic anti-inflammatory pathway” (CAP) mediates the anti-inflammatory phenotype and range of health benefits associated with physical activity. The CAP is an endogenous, physiological mechanism by which acetylcholine from the vagus nerve, interacts with the innate immune system to modulate and restrain the inflammatory cascade. Importantly, higher levels of physical activity are associated with enhanced parasympathetic (vagal) tone and lower levels of C-reactive protein, a marker of low-grade inflammation. Accordingly, physical activity, by enhancing parasympathetic tone and activating the CAP, may be a therapeutic strategy to restrain chronic inflammation and prevent many chronic diseases.

Luque-Casado A, Zabala M, Morales E, Mateo-March M, Sanabria D (2013) Cognitive Performance and Heart Rate Variability: The Influence of Fitness Level. PLoS ONE 8(2): e56935. doi:10.1371/journal.pone.0056935

Abstract

In the present study, we investigated the relation between cognitive performance and heart rate variability as a function of fitness level. We measured the effect of three cognitive tasks (the psychomotor vigilance task, a temporal orienting task, and a duration discrimination task) on the heart rate variability of two groups of participants: a high-fit group and a low-fit group. Two major novel findings emerged from this study. First, the lowest values of heart rate variability were found during performance of the duration discrimination task, compared to the other two tasks. Second, the results showed a decrement in heart rate variability as a function of the time on task, although only in the low-fit group. Moreover, the high-fit group showed overall faster reaction times than the low-fit group in the psychomotor vigilance task, while there were not significant differences in performance between the two groups of participants in the other two cognitive tasks. In sum, our results highlighted the influence of cognitive processing on heart rate variability. Importantly, both behavioral and physiological results suggested that the main benefit obtained as a result of fitness level appeared to be associated with processes involving sustained attention.

Oliveira, T.P. et al. (2013) Absence of parasympathetic reactivation after maximal exercise. Clinical Physiology and Functional Imagine, 33(2): 143-149.

Summary

The ability of the human organism to recover its autonomic balance soon after physical exercise cessation has an important impact on the individual’s health status. Although the dynamics of heart rate recovery after maximal exercise has been studied, little is known about heart rate variability after this type of exercise. The aim of this study is to analyse the dynamics of heart rate and heart rate variability recovery after maximal exercise in healthy young men. Fifteen healthy male subjects (21·7 ± 3·4 years; 24·0 ± 2·1 kg m⁻²) participated in the study. The experimental protocol consisted of an incremental maximal exercise test on a cycle ergometer, until maximal voluntary exhaustion. After the test, recovery R-R intervals were recorded for 5 min. From the absolute differences between peak heart rate values and the heart rate values at 1 and 5 min of the recovery, the heart rate recovery was calculated. Postexercise heart rate variability was analysed from calculations of the SDNN and RMSSD indexes, in 30-s windows (SDNN_30s and RMSSD_30s) throughout recovery. One and 5 min after maximal exercise cessation, the heart rate recovered 34·7 (±6·6) and 75·5 (±6·1) bpm, respectively. With regard to HRV recovery, while the SDNN_30s index had a slight increase, RMSSD_30s index remained totally suppressed throughout the recovery, suggesting an absence of vagal modulation reactivation and, possibly, a discrete sympathetic withdrawal. Therefore, it is possible that the main mechanism associated with the fall of HR after maximal exercise is sympathetic withdrawal or a vagal tone restoration without vagal modulation recovery.

HRV and Adaptation: Insights from a hockey player

Posted on February 27, 2013 by hrvtraining

One of the first things I learned from using ithlete was that my HRV trend reflected my progressive adaptation to exercise. After my first conditioning session in two years I saw an immense drop in my HRV. Each session thereafter resulted in smaller fluctuations in my HRV score until eventually my trend was virtually unchanged from it. In the screenshot below you can see that in early October of 2012, I experienced a huge drop in HRV with a red indication. That was the morning after my first conditioning session (stair intervals). These workouts were performed each Wednesday thereafter with a progressive increase in sets and duration however you’ll see that HRV isn’t nearly as effected as it initially was. The next major drop in my HRV occurred in late November but wasn’t the result of a conditioning session.

I also learned that HRV reflects illness, as well as the time it takes to fully recover from it. In the screen shot below you can see when I get sick and how long it took for my HRV to return to baseline levels (discussed in much more depth here).

Based on the sRPE levels, you can see that moderate workouts were causing large drops in HRV; a clear indication that my body was still fighting the infection even though I was symptom free. I was able to adjust my training accordingly and not push it too hard until my body was ready to handle it.

See also a case study by Botek et al (2012) who used HRV to guide an elite athlete back to competition after having infectious mononucleosis.

HRV has also been shown to reflect acclimatization to heat (Dranitsin, 2008; Epstein et al. 2010).

Hockey Player

About 8 weeks ago I posted the HRV trend of a hockey player that I’m working with. Below in his screen shot you can see that the first two weeks of workouts were very taxing. However, we only had a short period of time to train and therefore waiting for optimal recovery wasn’t an option.

Keeping tabs on HRV, subjective measures of fatigue and performance markers I would make small changes in his training. For example I would maintain intensity but reduce volume when his HRV was low and he felt fatigued. I’ve found volume to affect HRV much more than intensity.

At this point his HRV baseline has improved 8 points and his HRV is not nearly as affected after intense training sessions. He’s put on about 10lbs of lean body mass and has increased both strength and fitness considerably; clear indications of positive adaptation. I’ve found that with this particular athlete HRV is generally in line with his perceived levels of fatigue. For example, he’ll report some soreness but that he feels great and highly motivated to train. This will typically corresponds with a good HRV score. However, on days where he reports feeling fatigued, unmotivated etc, HRV is almost always below baseline. Sleep duration and quality will also correlate with his HRV and perceived fatigue.

In each of the above examples, HRV has been a valuable tool in reflecting adaptation. Acute changes (daily change) in HRV are valuable in that they reflect a transient response to a significant stressor (response to a workout, high emotional stress, awful sleep, extreme deviation in nutrition, etc.). Monitoring the weekly and monthly trends provides insight as to how training and global stress is being tolerated over time (cycle to cycle). At no point did this athletes strength performance drop during our training. Even on days with low HRV he was able to hit or match a PR in strength exercises. However, strength levels weren’t too high to begin with so I wouldn’t read too much into that.

I’ve had some compliance issues with this athlete and taking daily measurements so getting him to fill in the comments section or use the training load function was out of the question. Therefore that data is not posted.

In closing, taken with subjective measures of fatigue, performance and global stress, HRV can potentially reveal meaningful information about adaptation and training response.

References

Botek, M. et al. (2012) Return to play after health complications associated with infection mononucleosis guided on ANS activity in elite athlete: a case study. Gymnica, 42(2)

Dranitsin, O. (2008) The effect on heart rate variability of acclimatization to a humid, hot environment after a transition across five time zones in elite junior rowers. European Journal of Sport Science, 8(5): 251-258 Abstract

Epstein, Y. et al. (2010) Acclimation to heat interpreted from the analysis of heart rate variability by the Multipole Method. Journal of Basic & Clinical Physiology Pharmacology, 21(4): 315-23 Abstract

HRV Data from a High School Sprinter

Posted on February 24, 2013 by hrvtraining

Here is some more data and analysis from a nationally ranked high school sprinter (Junior) that I have using ithlete. Please note that the sprinter trains primarily with his sprint coach. I work with him roughly 3 days/week on mobility, restoration, etc. He was an ideal candidate for monitoring HRV as he is an extremely motivated and dedicated athlete and there was no doubt in my mind that he could handle the daily measurements. The data stops in early January because he somehow broke the HRV receiver I gave him. A new one has been ordered recently I’ve been told. This data collection is primarily for observational purposes since I do not control or manipulate his training as mentioned above.

November

After 1 week of using ithlete, I had him start using the comments section and sleep score.
His resting heart rate was higher than I expected. I had him perform his measurements standing but in hindsight I should’ve had him do them seated based on his RHR.
HRV average is mid 70’s which is what I expect from an anaerobic athlete. Still would expect his HR to be at least in the high 60’s in standing position.
Clearly he stays up super late on weekends and sleeps in late. Been on his case about this.

First Half of December

HR/HRV average remains consistent. Coping with training well.
Race day on 12/7, hit a PB in his part of the relay. Not a hard race, treated as practice.
Reports of back soreness that persisted long enough for him to seek treatment (documented in next table).

Christmas Break – Second Half of December & Early January

This last section of data is from his Christmas break. Interestingly his HRV average drops and his RHR increases. I attribute this to the change in routine (off of school), staying up late regularly, etc. I also notice changes in my HRV when my routine is interrupted. The body likes consistency.
Things appear to be going well though as he seldom gets below baseline scores (amber).
Race day on 1/6 and hits a PB on 60m.

Given that this athlete is still young and taking advantage of “newbie” strength gains, I would expect him to hit PB’s relatively consistently on the track. Based on his trend, fatigue was never really an issue. More training may have been well tolerated.

I’d like to get him to start using the training load feature too now to get a better idea of how hard his workouts are (perceptively).

4 Months of HRV, sRPE, Tap Test and Sleep score: Charts, Tables and Analysis

Posted on February 17, 2013 by hrvtraining

Since about mid-September of 2012 I started using a CNS Tap Test to see if it provided any indication of training fatigue or if it correlated with my HRV. In addition to tracking my tap test and HRV, I’ve also documented sRPE and sleep score.

Descriptions

Tap Test – On the tap test app, perform as many taps as possible in 10 seconds with right index finger and left index finger. I charted these values both separately as Right and Left as well as there total (sum). Tap test was performed immediately following morning HRV test.

HRV – Standard ithlete HRV measurement performed immediately after waking and bladder emptying. The measurements were all performed in the standing position. The ithlete uses the following formula for the HRV value: 20 x Ln (RMSSD). RMSSD is a time domain measure that reflects parasympathetic tone and has been shown to correlate reliably with the high frequency component of frequency domain measures (Sinnreich et al. 1998).

sRPE – Following a workout session I would rate perceived exertion on a scale of 1-10. Generally, active recovery/aerboic work would fall between 1-5 while resistance sessions fell between 6-10.

Sleep Score – I used the ithlete sleep rating score to track sleep quality. On a scale of 1-5 I would rate sleep quality after HRV measurement. Generally, an uninterrupted 7-8 hour sleep was rated as 5. One disturbance/wake was given a 4, etc.

Not Discussed – Today I will not be including discussion on strength performance in relation to HRV or Tap test as I did not really keep track of this. However, in the future I will do this once I determine the best way to quantify this.

Below are the charts with brief comments regarding training/stress for that month.

OCTOBER

– High stress and lack of training in early October due to work related trip over 3-4 days.

NOVEMBER

– Most consistent training month, most sessions completed, most stable HRV, highest HRV Avg, highest Tap Test Avg, highest sleep score. (more on averages and sleep at the end)

DECEMBER

– Highest strength demonstrated in this month out of the 4. Training interruption over the Christmas holiday.

JANUARY

– HRV effected by NYE party but Tap Test appears unaffected (alcohol, late night, etc.). Detrained slightly from lack of training of holidays. Training resumes, transitioning to lifting 4 days/week. Lowest HRV avg, lowest tap sum avg, fewest aerobic sessions.

Comparison of HRV, Sleep, and Tap Test Averages

– HRV and Tap Test both peak during November which also has the highest average sleep rating. However from the table above you can see that these are by very small percentages.

– In the table and chart below you can see that peak HRV and peak Tap average also occur during the month of most consistent training, most aerobic sessions and most overall training sessions.

– HRV, Tap Test Left, Right and Sum all reach lowest averages in January. January also has the fewest aerobic sessions and comes after a period of detraining (discussed in depth here) in late December.

Comparison of HRV, # of Aerobic Sessions, # of Resistance Sessions & Sum of all sessions

Main Findings

Highest HRV avg, highest sleep avg, highest tap sum avg, highest left tap avg all occur in November. This corresponds with most total and most aerobic training sessions.

Conversely, lowest HRV avg, lowest tap left, right and sum average occur during January which also corresponds with fewest aerobic sessions but not with lowest sleep avg.

As you can clearly see, there is very little variation in month to month values and therefore no significant or meaningful conclusions can really be made. However, my HRV data does fall inline with the overwhelming amount of research that shows HRV increases in response to aerobic exercise.

In a future experiment I will track performance ratings in addition to all of the other variables to see if there is any correlation. I will also plan some overload training to see how these markers respond. My training was relatively static during these 4 months.

Correlation between HRV, sRPE and subjective fatigue in athletes

Posted on February 11, 2013 by hrvtraining

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

HRVtraining

HRV research and consulting by Andrew Flatt Ph.D.

Effects of Alcohol Consumption on HRV and Sleep

Why Assess the ANS?

HRV and Deload Periods

Reviewing HRV data after a 9 week training cycle

Making HRV More Practical For Athletes: Measurement Frequency?