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About hrvtraining

Researcher and Professor. Former athlete and coach.

HRV Recording Methodology: Stabilization

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Smart phone app’s and other field tools have made HRV data collection relatively simple and affordable for coaches, athletes and recreational lifters. However, the shortened recording methodology utilized by these devices requires validation. Standardized guidelines recommend that short-term (i.e., 5-min) HRV be collected under physiologically stable conditions (Task Force). Most HRV papers will allow for 5 minutes or greater of supine rest prior to HRV recording to allow for stabilization. However, this 5+ minute pre-recording period is not practical for daily monitoring. A 1 – 2 minute HRV recording period with a very minimal stabilization period used by many app’s is still too long for some individuals to comply with daily measures.

The issue of “stabilization” was the topic of our latest research project that we just presented at the ACSM Annual Meeting in Orlando this past weekend. We looked at the time-course for stabilization of HRV across 5-min ECG segments (e.g., 0-5 min, 1-6 min, 2-7 min, 3-8 min, etc.)  in 12 endurance athletes (6 female) and 12 non-athletes (6 female). We included lnHFnu, lnLFnu, and lnRMSSD.

The figures from the poster are displayed below (Athletes on left)

Stab Poster title

stability figures

 

The full manuscript for this project (with a different methodological approach) is currently in review so I will not get into too much depth on the discussion of the results. However, it is quite clear that lnRMSSD demonstrates the most and earliest stability of the 3 HRV parameters. Therefore, for lnRMSSD assessment, a minimal stabilization period is likely a non-issue. When including spectral measures (e.g., HF, LF), a longer period for stabilization may be required, though lnHFnu was relatively stable in the athletic group in the current sample.

 

HRV: Means and Variation

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At this point, most of you are aware that a single HRV (lnRMSSD) score taken in isolation does not necessarily imply or reflect an acute change in performance, fatigue, recovery, etc (though it may sometimes).

Here’s why:

Below are two separate HRV trends I pulled from a training cycle I did last year at week 1 and week 8.

Week 1 and Week 8

If someone were taking once per week recordings, or pre and post training phase recordings on isolated days, you can see how they can get entirely different results based on which day they measured. Suppose measures were taken on Friday’s from the above trends. These values are 84 and 76.7, respectively. However, if we look at the weekly mean values, we would get 73.6 and 78.3. From the isolated readings, one would conclude that HRV decreased nearly 10 points. However, the weekly mean shows an entirely different change (HRV actually increased from 73.6 to 78.3).  Therefore, it’s quite clear that when averaged weekly, HRV scores allow for more meaningful interpretation.

  Isolated Measure (Friday) Weekly Mean
Week 1 84 73.6
Week 8 76.7 78.3

See the following papers for more on weekly mean vs. isolated recordings (Le Meur et al. 2013; Plews et al. 2012; Plews et al. 2013)

 

One limitation of the weekly mean value is that is does not reflect the fluctuation in scores throughout the 7 day period. A simple way of determining this is to calculate the coefficient of variation (CV) from the 7 day HRV values (see Plews et al. 2012 for more on CV).

The coefficient of variation is calculated as follows;

CV = (Standard Deviation/Mean)x100

Below is 9 weeks worth of data from a training cycle I performed early last year that resulted in some personal records (PR’s) and was discussed in this post. This time, in addition to the weekly mean values I have also calculated the CV for each week.

9 weeks CV and Mean

Without going into too much detail about the training cycle (see the original post for that), I will highlight a few keep observations.

HRV Avg HRV CV Brief Notes
73.6 7.5 1st week after detraining, Good
77.4 5.6  Good
77.5 2.3  Good
76.2 5.7 Stress, poor sleep, deload
79.37 3.0  Good
79.7 4.0  Good
77.9 11.4 Stressful week
77.8 6.8 ↑ intensity, ↓ Volume, Good
78.2 4.8 PR(1RMs)
81.1 4.7 Deload, Good

 

Below are the HRV trends from Week 1 – 4 of the cycle.

weeks 1 to 4

Week 1 was my first week training after about 10 days off from lifting (Christmas holidays). Clearly the trend from week 1 reflects the fatigue and recovery as I lifted on M W F that week. On week 2 I performed the same workouts on the same days but with a little more weight for each set. However, it appears (based on CV) that this may have been less stressful. In week 3, I moved to lifting 4 days/week with moderate loads and CV decreases further. Interestingly, the following week (week 4), the weights feel heavy, I feel pretty rough and I take an unplanned deload (CV increases, mean decrease).

Further analysis of the CV and weekly mean can include calculating the smallest worthwhile change (see Buchheit, 2014) to see if a change is practically meaningful. (Will do this in the future once I figure out how to display SWC on a chart).

The point of this post was to introduce the CV concept for those who may not be familiar. I believe that the CV likely provides information regarding stress, fatigue and adaptation that the weekly mean may not reflect. Therefore, the CV and mean values should be considered together.

References:

Buchheit, M. (2014). Monitoring training status with HR measures: do all roads lead to Rome? Frontiers in physiology, 5. http://journal.frontiersin.org/Journal/10.3389/fphys.2014.00073/full

Le Meur, Y., Pichon, A., Schaal, K., Schmitt, L., Louis, J., Gueneron, J., … & Hausswirth, C. (2013). Evidence of parasympathetic hyperactivity in functionally overreached athletes. Medicine and Science in Sports and Exercise, 45(11), 2061-2071.

Plews, D. J., Laursen, P. B., Kilding, A. E., & 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), 3729-3741.

Plews, D. J., Laursen, P. B., Kilding, A. E., & Buchheit, M. (2013a). Evaluating training adaptation with heart-rate measures: a methodological comparison. International Journal of Sports Physiology & Performance, 8(6).

HRV Measurement Frequency: Comparing 3 day to 7 day per week recordings

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There is very little longitudinal HRV data within the research, particularly in team sport athletes. In many cases, HRV will be assessed pre and post or pre, mid and post of a pre-season camp or what have you. This is very likely due to the inconvenience of acquiring this type of data in athletic populations. For the study to be research quality, validated tools must be used (ECG, Polar, Suunto, Omegawave, etc.). In addition, standard measurement procedures are required to ensure that the data is of sufficient quality. This generally involves a 5 minute rest period followed by a 5 minute recording. However, some researchers have used shorter resting and recording durations. Measurement standards for athlete monitoring in the field need to be developed. This is an area my colleague Dr. Esco and I are working on in our lab. This includes cross-validating field HRV tools, assessing the suitability of shorter recording durations and determining the time course for stabilization of HRV (i.e., how long should the resting condition be prior to recording).

Apart from the issue of valid and reliable tools and measurement protocols, another major issue that prevents HRV from being widely used as a component of a comprehensive athlete monitoring program is compliance. Due to day to day variations it is highly unlikely that a single HRV recording per week is sufficient. A recent paper by Le Meur et al. (2013) found that:

“using mean weekly values obtained from daily HRV recordings, rather than isolated HRV assessments, may improve the diagnostic utility of HRV indices in endurance-trained athletes to assess training-induced adaptations of the autonomic nervous system. The present results suggest that the day-to-day variability of HRV values is too high to allow clear detection of autonomic modulations associated with F-OR using single-day values.”

Understanding that daily HRV recordings can be difficult to obtain from our athletes, Plews et al. (2013) sought to determine what the minimum measurement frequency is that still appropriately reflects the weekly mean value.

“We have previously demonstrated HRV values averaged over 1 week provide a superior representation of training-induced changes than HRV values taken on a single day. In the current study, we have shown that HRV values averaged at random over a minimum number of 3 days will allow for an equivalent representation of training adaptation than values averaged for up to 7 days in trained triathletes. Conversely, recreational athletes will need a slightly greater number of days averaging (~5 days) due to their greater day-to-day variations in Ln rMSSD values.”

Last March, I posted some data that assessed the trends of daily vs. once per week and twice per week HRV recordings here. Today I’d like to revisit this topic. However, in this post I will be comparing weekly mean values to 3 day/week values. In the study mentioned above by Plews et al., the 3 days were randomly selected and were compared with performance changes. I’ve elected to consistently use Mon-Wed-Fri recordings to assess how a more structured and consistent approach would work and compare to the weekly mean trend. Working with a team of athletes, it would be much easier to designate 3 specific week days as HRV recording days. However, which 3 days are selected should likely depend on the training/competition schedule. Selecting days that all fall after intense training/competition may skew the results and not sufficiently reflect the recovery seen on days after lower intensity or rest days.

Below is the first week of every month from the last year (Jan 2013 – Jan2014) of my ithlete data. I figured 1 week per month would sufficiently show trend changes due to changes in fitness, lifestyle etc, without having to spending too much time analyzing data in excel for every week of the year.

1 Year Trend

Below is HRV data from a Collegiate Cross Country athlete throughout the fall competitive season.

Cross Country Season

Below is some in-season data from a female Collegiate soccer player.

Female Soccer

Lastly, below I’ve posted a randomly selected week from each month over 9 months from a competitive male powerlifter with Cerebral Palsy.

powerlifter CP

It was specifically stated that non-elite athletes may require more than a 3 day average to sufficiently reflect performance changes. This is due to a higher degree of variation in day to day measures.

“Conversely, recreational athletes will need a slightly greater number of days averaging (~5 days) due to their greater day-to-day variations in Ln rMSSD values.” Plews et al. 

None of the above athletes are “elite” (as much as I’d like to think I am). Clearly there are a few weeks that do not match up for each data set, but the 3 day average (drawn from Mon-Wed-Fri in each set) appears to follow the weekly mean trend reasonably well. Having your athletes record HRV on a mobile device 3 days per week would certainly be more manageable than daily recordings. I intend to investigate this more officially in the future.

Refs:

Le Meur, Y., Pichon, A., Schaal, K., Schmitt, L., Louis, J., Gueneron, J., … & Hausswirth, C. (2013). Evidence of Parasympathetic Hyperactivity in Functionally Overreached Athletes. Medicine and Science in Sports and Exercise.

Plews, D. J., Laursen, P. B., Le Meur, Y., Hausswirth, C., Kilding, A. E., & Buchheit, M. (2013). Monitoring Training With Heart Rate Variability: How Much Compliance is Needed for Valid Assessment?. International Journal of Sports Physiology and Performance. Ahead of print.

Validity of the ithleteTM Smart Phone Application for Determining Ultra-Short-Term Heart Rate Variability

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Here’s our latest study testing the accuracy of the ithlete vs ECG.

“Validity of the ithleteTM Smart Phone Application for Determining Ultra-Short-Term Heart Rate Variability”

 

Training Load and Nutrition Impact on HRV: 10 Week Data Analysis

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Below is 10 weeks worth of my own training data that includes;

  • HRV – Collected daily on ithlete in standing position immediately after waking
  • HR  – Taken from the ithlete HRV measures
  • Load – Sets*Reps*Weight(lbs)
  • sRPE – Reps*RPE of session(1-10 scale)

All data is presented as weekly mean values.

HRV & Load

HRV & Load

HRV & sRPE

HRV & sRPEHR & LoadHR & Load

HR & sRPE

HR & sRPE

Data

Data

Training

– Training volume in weeks 1-5  involved 3 straight working sets for main lifts alternating between weeks of 5’s, triples and singles. Working weight for each set was predetermined based on previous week but would be adjusted if need be. Training volume progressively decreases as working sets were reduced from 3 top sets to 1 top set. Assistance work was mostly just maintained during the reduced load period. Week 10 was more of a  true deload where all working set weights were reduced but only to about 80% while assistance work was reduced slightly as well. Keep in mind that volume for each week would vary based on whether I was performing sets of 5, 3,  or 1 for top sets.

Thoughts

– Even prior to week 1 displayed in the data, I had not taken a deload in quite some time (end of August). Performance (strength) had progressively been increasing and I didn’t feel the need so I kept at it.  My HRV was consistently averaging in the low 70’s which is quite low compared to my typical average of  about 80 (based on several years of data).  Once I started having some nagging soft tissue problems accumulate I decided to taper the volume.  I was seeing how my body and HRV responded to deloading keeping intensity high but just cutting volume. HRV trended back towards baseline though soft tissue problems weren’t quite resolved.

– Week 10 was Thanksgiving week and I traveled to my folks place. Training was reduced yet HRV decreased. I attribute this entirely to the drastic change in my nutrition during this week. Fruit and Vegetable intake decreased significantly. Processed foods and carb intake increased dramatically. It was an atrocious but delicious week of eating. This is not the first time that I’ve seen HRV change due to similar changes in eating.

– In the chart below you can see HRV decline during the high volume/load period followed by a progressive increase during the taper. This is then disrupted with a progressive drop during Thanksgiving week of binge eating. HRV then trends back up this week as eating improves and regular training resumes.

Trend 9 to 12_2013

– HRV and HR need to be taken into context when being used to guide or monitor training. Other stressors always need to be considered. Neither will ever perfectly correlate with training load as this would assume that only training affects the ANS. It also worth mentioning that HR reflected training load better than HRV in this case and simple RHR should certainly not be dismissed or overlooked.

– Acute changes in HRV/HR won’t always “make sense” or correspond to perceptions of soreness, fatigue, mood etc. (though they do quite often). The weekly mean values tend to provide a better reflection of training/life style. I don’t adjust training on a day to day basis basis until I’m approaching my top sets.

HRV in a bit more detail: Part 2

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Part 1 of this series provided information on heart function, ECG basics, HRV basics and how the Autonomic nervous system influences heart rate. For Part 2, I’ll discuss and display basic HRV analysis concepts to try and enhance your understanding of HRV.  I’ll relate as much of this discussion as possible to smart-phone based HRV tools as it is unlikely that most readers of this site have access to an ECG.

Athlete vs. Non-Athlete

Fit individuals generally have lower resting heart rates and greater parasympathetic activity at rest. These adaptations to training may be a result of both intrinsic heart adaptations (SA node remodeling, increase in ventricle capacity) and autonomic adaptations (greater vagal activity).

Below is an ECG segment from a collegiate male endurance athlete. This sample is likely capturing the normal fluctuation in heart rate that occurs in response to respiration (breathing). Heart rate tends to speed up on inspiration and slow down on expiration. The technical term used to describe this phenomenon is “Respiratory Sinus Arrhythmia”.

athlete ECG RSA1

Endurance Athlete ECG

For comparison, below is a screen shot of a healthy non-athlete ECG. Here, you can clearly see a higher resting heart rate and less variability.

Non-athlete ECG

Non-athlete ECG

The Excel snap shot below is what R-R interval data looks like once exported from the ECG software (Acqknowledge in this case) to a workbook. Though specialized HRV software is much more functional, it’s certainly possible to perform some time series (statistical) analysis on the R-R interval data with basic excel functions (i.e. Standard Deviation, Mean R-R Interval, RMSSD, etc.). Today, we’ll focus primarily on RMSSD as this parameter appears to be the preferred HRV index for athlete monitoring (See Plews et al. 2013 and this for more on RMSSD).

R-R Intervals

R-R Intervals

Tachogram

With HRV software analysis, ECG recordings are converted to a tachogram, which plot the successive R-R intervals on the y-axis and the number of beats within the ECG segment on the x-axis. This provides a nice visual representation of heart rate variability over a given time and makes for easy software analysis.

Below is a 5 minute ECG segment from an endurance athlete converted to a tachogram with our Nevrokard HRV software. Notice how the R-R intervals vary considerably over a broad range. The time domain values follow.

Endurance Athlete Tachogram

Endurance Athlete Tachogram

EAstats1

In contrast, below is the tachogram and time domain analysis of an age matched non-endurance athlete for comparison. Note how the R-R intervals are relatively stable and within a narrow range, demonstrating less variability.

Non-athlete Tachogram

Non-athlete Tachogram

NAstats1

To put this in perspective for the good folks using ithlete or BioForce, I’ll convert the raw RMSSD values (displayed in the “Summary Statistics” screen shots above) to ithlete/BioForce values. To do this, we simply log transform the raw RMSSD and multiply it by 20 (lnRMSSDx20). Keep in mind that the ithlete uses a 55-sec test and BioForce uses 2.5-min test. The values shown here are from 5-min ECG samples, but you get the idea.

rmssd conversion

Ectopic Beats and Artifacts

Last post, I discussed “normal” beats originating from the SA node. Any beats originating from outside the SA node disturb cardiac rhythm and can therefore impact HRV. These are called ectopic beats. Electrical interference, or excessive noise or movement can create “artifacts” which can also affect the data. It’s important to manually inspect ECG data for ectopic beats or artifacts and correct them (replace with the adjacent “normal” cycle) or discard the ECG sample entirely if there are excessive disturbances. Most smart-phone HRV tools do not provide R-R interval data and therefore manual inspection for ectopic beats is impossible. Conveniently, devices such as ithlete and BioForce are designed to automatically detect and correct irregular beats. For example, the application will detect and replace R-R intervals that are unlikely to occur in healthy, resting individuals (e.g., R-R intervals below 500ms or above 1800ms). I’d assume OmegaWave Pro, SweetBeat and other devices also have this feature.

Below is an example of an ectopic beat from an ECG which appears to be a premature atrial contraction. You’ll see this again in a moment on the tachogram as well.

ectopic1

Measurement Protocol

For athlete monitoring, HRV data is ideally collected as soon after waking in the morning as possible after bladder emptying.  We want as close to resting conditions as possible. It would be wise to avoid checking e-mails, text messages and anything else that can alter mood, excite you, upset you, etc. Even water consumption will have an acute impact on HRV. Consistency of measurement protocol and time of measurement are important for longitudinal monitoring. In addition, being as motionless as possible and undisturbed is equally as important.

In the screen shot below towards the right hand side, we can clearly see when the individual gets restless and moves or adjusts his position. We can also see the ectopic beat that occurs toward the start of the measure (same ectopic beat shown above in the ECG). Slight and subtle movements can clearly impact heart rate so be as still as possible when you perform your measures at home.

ectopic_disturbed tachogram

In the tachogram below, notice how heart rate changes considerably at around the 10 minute mark. This is a result of a researcher entering the exam room where the subject was resting during an ECG recording. Clearly, the resting condition was disturbed as the subject was excited/stressed from the person entering the room. Thus, do your best to remain as undisturbed as possible when performing a measure at home.

disturbed measure 10min

All data shown today were from supine ECG recordings. Next post I’ll discuss and show HRV changes in response to postural change (i.e., from supine to standing).

HRV in a bit more detail

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Over the next several posts I will attempt to provide a little more depth to the typical explanations of heart rate variability that I’ve provided in the past. I will be displaying ECG data and HRV software screen shots to provide a better visual representation of HRV analysis. I will present and discuss things like;

  • How HRV data is often collected and analyzed
  • ECG basics
  • What respiratory sinus arrhythmia looks like
  • What an ectopic beat looks like
  • What a tachogram is and looks like (HRV software)
  • Comparing athlete to non-athlete ECG/HRV data
  • Looking at supine and standing ECG/HRV data
  • Looking at paced vs. spontaneous breathing data and how it affects HRV
  • Showing how subtle errors can impact an HRV measurement
  • Discussing HRV research questions that my colleague and I are investigating here in our lab
  • Whatever else seems  relevant as I get writing

Today’s post will serve as a brief, but slightly more in depth introduction to heart rate physiology. To really get a handle on HRV, it’s important to have an understanding of the interplay between the brain and heart and the details therein. I encourage interested readers to check out an actual physiology text for a more elaborate and detailed discussion for which I’ll provide a few recommendations at the end.

Heart Rate

The human heart is equipped with an intrinsic pacemaker within the wall of the right atrium called the sinoatrial node (SA node).  The SA node randomly depolarizes, generating action potentials that ultimately result in a contraction (heart beat). All heart beats that originate from the SA node are “normal” beats and provide normal cardiac rhythm. However, as we’ll get into eventually when I display some ectopic beats, depolarization also regularly occurs in other areas within the myocardium, which if reach threshold, can initiate a contraction on its own. Non SA node action potentials disturb cardiac rhythm that is ideally dictated by the SA node (more on ectopic beats in future). Left alone, the SA node would give you a resting heart rate of about 100 beats per minute. Obviously, healthy individuals have much lower heart rates while at rest. Other times, we can experience quite high elevations in heart rate to facilitate blood distribution requirements (e.g., during physical activity). We’ll get into how these changes in heart rate occur momentarily.

In the lab, we can evaluate heart beat information with electrocardiographic (ECG) recordings. An ECG detects electrical currents at the surface of the skin generated by the action potentials that propagate through the heart. In our lab, since we’re mainly interested in heart rate variability and not intricate ECG analysis, we use a simple, modified lead II electrode placement. From the ECG we can observe 3 distinct patters that represent the electrical conductivity involved in the cardiac cycle;

pqrst

P wave – Displays as a small upward deflection and represents atrial depolarization. The P wave indicates that the impulse originated from the SA node and therefore results in a “normal” beat.

QRS Complex – Begins with a shallow downward deflection (Q), followed by a tall upward deflection (R) and ends with another downward deflection (S). Collectively, this represents ventricular depolarization.

T wave – Oftend described as dome-shaped in appearance and represents ventricular repolarization

As you can see, the R wave has a high peak making measurements between cardiac cycles rather easy. The elapsed time between two R waves creates an R-R interval. The time between R-R intervals varies across successive R-R intervals and is termed heart rate variability. In the screen shot below of the AcqKnowledge software, notice how the space between R waves (the tall peaks) is inconsistent as some intervals are wider and some are more narrow.

ECG

 

Centrally Mediated Cardiac Control

Now we’ll return to our discussion on heart rate control. Heart rate is influenced by both intrinsic and extrinsic mechanisms, however for this discussion, our interest is primarily with central nervous system regulation of cardiac control via autonomic innervations of the heart. Heart rate is largely mediated by both sympathetic and parasympathetic influence which originates in the cardiovascular center of the brain. The cardiovascular center is located on the lower portion of the brain stem at the medulla oblongata. From here, sympathetic neurons extend from the brain, through the spinal cord and directly into the heart. Increased sympathetic activity increases the release of norepinephrine which speeds up SA node depolarization (increases heart rate) and increases the force of contraction. This response occurs to facilitate increased blood distribution requirements that may arise due to physical activity, stress, standing up, etc.

Parasympathetic influence of the heart occurs via the Vagus nerve (10th cranial nerve) which originates in the medulla and has axons that terminate directly into the heart. Vagal stimulation elicits an inhibitory effect on the SA node via release of acetylcholine, effectively reducing heart rate and is associated with “rest and digest” activity. Since vagal activity inhibits SA node activity, vagal withdrawal will result in less SA node inhibition and allow the heart to beat faster. At the onset of exercise, the initial increase in heart rate is a result of vagal withdrawal with a progressive increase in sympathetic activity as exercise persists (Yamamoto et al. 1991).

Since heart rate is directly affected by autonomic activity, it serves as a relatively simple marker for us to monitor to assess autonomic status. Increased parasympathetic activity will reduce heart rate and result in greater variability between R waves. In contrast, a higher heart rate with less variability (think more metronomic) is the result of reduced parasympathetic activity and possibly increased sympathetic activity.  HRV has thus become a valuable metric to monitor in athletes as it provides information regarding the relative balance of “stress” in the individual. Though I’m a proponent of HRV monitoring in athletes, its interpretation requires caution as nothing is black and white when it comes to determining an athlete’s training status from HRV, particularly from isolated measurements. Rather, taken with performance trends, psychometrics (perception of mood, soreness, fatigue, etc.), and training load, HRV becomes more meaningful.

In the next post, I will start to get into HRV analysis with some software screenshots to provide a good visual representation of HRV.

 

References/Recommended Reading:

Smith, D. & Fernhall, B. (2010) Advanced Cardiovascular Exercise Physiology. Human Kinetics. http://www.amazon.com/Advanced-Cardiovascular-Exercise-Physiology/dp/0736073922

Tortora, G. & Derrickson, B. (2006) Principles of Anatomy and Physiology 11th Edition. Biological Sciences Textbooks Inc.

Yamamoto, Y., Hughson, R. L., & Peterson, J. C. (1991). Autonomic control of heart rate during exercise studied by heart rate variability spectral analysis. Journal of Applied Physiology, 71(3), 1136-1142.

Reviewing HRV, RPE, 1RM and Grip Strength Data Over 6 Weeks

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I’ve been continuing to collect data on a competitive powerlifter that trains out of our facilities here at AUM. This athlete has cerebral palsy and therefore only competes in raw bench press. Currently, he can press approximately 2.21x his bodyweight (265lbs at 120lb).  I’ve posted his older training cycle data previously here and here. This time around, I’ve been tracking a few different variables that are listed and described below. The purpose of this was to see if any of the monitored variables were able to reflect or predict daily variations in 1RM strength.

1RM – Unlike previous cycles, I calculated his 1RM bench press each session based on reps performed and RPE. For example; on his first working set of the day, if he performed 3 reps at an RPE of 9 (1 rep left in the tank), this was considered a 4RM weight and approximately 85% of 1RM using Mike Tuchscherer’s 1RM formula/table. I’ve chosen this specific formula because it is designed for powerlifters. We pause each bench press rep at the bottom which obviously decreases the total amount of reps that can be performed. After trying a few different formulas I found that most were under-predicting his actual strength.

Example:

230×3 RPE @9 = 4RM

Tuchsherer’s Formula: 1RM = 271.4

ExRx.net Calculator:  1RM = 251

Obviously, since many of these are calculated and not true 1RM’s, there is some give or take with these values.

sRPE – Following his workout, I asked him to rate the entire session on a 10 point scale. I do not multiply this by total reps performed but rather just use the rating as a general indicator of how hard the workout was for him.

Hand Dynamometry – Grip strength for each hand was assessed prior to each session via hand dynamometer (starting after week 2). You’ll note that there is a difference between his right and left grip strength due to his condition.

HRV – The athlete measured HRV each morning after waking on his iPod Touch with ithlete in a seated position.

Details of First Training Cycle (Weeks 1-3):

  •  3 weeks in duration
  • Trained 3 days/week (M-W-F)
  • Monday:  sets of 3 progressing from approximately 82% in week 1 to 87% by week 3
  • Wednesday: sets of 5 progressing from approximately 75% in week 1 to 80% in week 3
  • Friday: Singles progressing from approximately 92% in week 1 to 100% in week 3

Details of Second Training Cycle (Weeks 4-6):

  • 3 weeks in duration
  • Trained 3 days/week (M-W-F)
  • Monday: Same as previous cycle
  • Wednesday: Speed work progressing from 60-70% from week 4 to week 6 (no 1rm calculations on these days)
  • Friday: Same as previous cycle

Assistance work progressed each week and would consist of rowing/pull ups, dumbbell pressing variations and some lower body exercise.

Data is presented below:

Daily HRV and sRPE

Daily HRV and sRPE

Daily HRV & 1RM

Daily HRV & 1RM

*Regarding the last two 1RM’s on the above chart, 26o is likely lower than his true 1RM that day. He smoked it but I cut him off there. The 277 1RM was based off a 3RM calculation that is probably a little higher than his current ability.

Z_1rm_HRV_sRPE_10_2013

  • Daily sRPE shows a progressive increase from week 1 -3 which accurately reflects the progressive increase in intensity for his main work.  A decrease in HRV in week 3 along with high RPE’s and a slight decline in 1RM suggests some fatigue accumulation.
  • Week 4-6 is the second training cycle. Day one of week 4 is missed and therefore this cycle doesn’t start until the Wednesday. This missed workout caused us to slightly extend the cycle to fit one more lift in on a Monday of the last week.
  • Since Wednesday’s are speed focused in the second cycle, intensity is reduced and therefore, RPE was expected to be lower. However, Wednesday of week 6, the workout is rated quite high with an 8 which also happens to be on his lowest HRV day of the entire 6 weeks. The speed emphasis prevents me from collecting a good 1RM estimation and therefore average values are based on only Mon and Fri lifts in contrast to the previous cycle that allowed for 1RMs to be calculated on all three days.
  • In week 6, HRV peaks which is in complete contrast to the first cycle where HRV bottomed out in week 3. Interestingly, session RPE’s are lower in week 6 vs. week 3. As HRV declined in week 3, RPE increased, whereas in week 6, though intensity increased, HRV continued to climb and RPE did not increase. There are several instances where HRV relates to RPE (high RPE on low HRV days and vice versa).
  • 1RM avg peaked in week 6 along with HRV avg, however I included an extra workout (the last Monday) in this average as this was the day that made up for the missed workout at the beginning of the second cycle. Therefore the average is of 8 days (4 lifts) rather than the typical 7 days (3 lifts).
  • HRV on a given day doesn’t particularly appear to be a good predictor of the subtle variation in 1RM strength in this athlete, however weekly mean values showed a strong relationship. This of course needs to be taken in context with where one is within a training cycle. You won’t magically set a PR because your HRV is high or your weekly mean is high.

Raw Data Below

Z_rawdata_10_2013

  • Grip strength testing did not start until week 3. In this athlete, it does not seem to provide any insight as to daily performance potential, fatigue etc. Perhaps this assessment is more useful for lifts directly involving grip requirements (e.g. deadlifts, Olympic lifts, etc.).
  • Though not presented, sleep ratings never really dropped below 4 out of 5 and therefore sleep did not seem to be impacted by nor affect the other variables.

This data set has a laundry list of limitations. The main one being that 1RM’s were mostly calculated based on the athletes reported RPE of a set and not a true RM attempt, thus leaving plenty of room for error.

I attribute the adjustment in cycle 2 to its success compared to cycle 1. Adding in the speed work and removing the sets of 5 resulted in less fatigue and allowed for more recovery.

This data set convinces me of nothing, but simply encourages me to continue to explore the relationship between HRV and strength in athletes. Though no conclusions should be drawn, the main findings of this small case study are as follows;

  1. In this athlete, weekly average changes in 1RM Bench Press strength were related to weekly average changes in HRV (in all but week 5)
  2. On many instances, low HRV days corresponded to higher ratings of perceived exertion, however this didn’t necessarily affect strength performance.
  3. Grip strength assessed via hand dynanometer did not appear to be a useful indicator of anything in particular (other than grip strength of course) in this athlete.
  4. The peak in HRV and Strength in week 6 along with lower than expected sRPE suggests that the second cycle was well tolerated and fatigue was minimal (likely due to the programming adjustment). This is in contrast to week 3 from the end of the first cycle where HRV fell to lowest values, as did strength, while sRPE’s peaked.

Reviewing Survey Monkey as a free tool for Daily Wellness Questionnaires

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Working with athletes in a team setting versus one on one or in small groups limits our ability to engage in small talk with each athlete before training or during warm-up. Being able to ask the athlete how they’re feeling, how they slept, how sore they are, what they’ve been eating, etc., provides insight as to the general state of the athlete and may be used to guide training on that particular day. Small modifications based on insight garnered from these conversations can help you determine if you’ll be pushing it a little harder or reducing the volume a little. Essentially, this is the simplest form of monitoring and managing training. However, it’s difficult to have 20 of these conversations before a workout with an entire team.

A popular method for acquiring this information without having to have individual conversations is to have your athletes respond to a “Wellness” questionnaire that surveys the athletes on their perceived quality of sleep, stress levels, soreness, etc. A brand new study from the JSCR by Gastin et al (2013), demonstrate the effectiveness of daily Wellness questionnaires (among many others). A team of Australian Football players were surveyed daily throughout their season with a brief questionnaire asking the athletes to rate levels of sleep quality, soreness, muscular strain, stress and so forth. Results showed that subjective ratings of physical and psychological wellness responded to weekly training adjustments. Scores reflected improved wellness (less strain, better sleep, etc) throughout the week allowing for optimal states for competition followed by significant decreases in overall wellness following competition. Scores also showed improvement during periods of unloading. Perhaps most interestingly, questionnaire scores discriminated individual differences for muscle strain following a competition as players with higher maximum speed reported higher levels of muscle strain. Evidently, simple daily questionnaires can prove to be quite insightful and useful for monitoring athletes.

If you’re part of a well funded program, you can purchase fancy software that will allow for easy data collection, interpretation and visualization of the data. Unfortunately this is not a luxury that most coaches have, particularly those involved in youth and amateur sports. In discussing this topic with Carl Valle a while back, he suggested Survey Monkey as a simple and free tool for collecting this data. So I created an account and have been testing it out over the last little while. Below are screen shots of its features, and some brief descriptions of how it works, pros, cons, etc.

Below is a screen shot of the survey I’ve been using. Thanks to Mladen Jovanovic, John Fitzpatrick, Aiden Oakley, Rhys Morris and Josh Dixon for their insights earlier this summer on survey options and collection methods. To my knowledge, this survey was created by Martin Buchheit.

wellness questionnaire

Creating a free account with Survey Monkey simply requires the user to provide an email address and create a password. However, the free account has restrictions (discussed later) but can be unlocked with upgrading and paying for premium accounts.

Admittedly, I am not the most tech savvy guy, so the number one thing I was hoping for was that it would be user friendly and intuitive… and it was.

Below is the screen for creating a new survey. Once you’ve created a survey you can re-create it easily with “Copy an existing survey”.

SM1

Creating survey questions:SM2

Once you’ve created your survey:SM3

Sending options for your survey:SM4

Add e-mail addresses of your athletes for email collection:SM5

Personalize the email (Default Shown):SM6

Schedule your survey delivery:SM7

Analyze results as a group:SM8

Or analyze results by individual:SM9

And the feature you’re likely wondering about… SM10

Sorry folks, need to upgrade your account to export data. They need to make money somehow, right?

A screenshot of what the athlete see’s in their email on their smart phone (this can be customized):

photo 1

A view of the actual survey after following the link from the email on Smart Phone:

photo 2

Pros:

  • Easy to create and send surveys
  • Nice visualization of the data
  • Can assess results as a team or individual with several options of how you want the data presented
  • Can schedule when the survey is to be delivered
  • All of these features are free
  • This can also be used to collect sRPE info by scheduling the survey to be sent at the appropriate time, provided you know session duration or total reps.

RPE

Cons:

  • As far as I’m aware, you must re-create the survey and schedule to send it every day. This takes about 2 minutes. If any of you are Survey Monkey experts and know how to automatically re-send the same survey with a new collector please tell me.
  • Data can be exported easily to excel but you must pay to upgrade your account for this feature
  • A numeric value is not assigned to a given rating e.g., 1-5 points. This must be done manually in excel so that you can create daily totals (out of 25 possible points). See Mladen’s free spreadsheet for more on this here

Hopefully this was helpful.

HRV and Training Cycle Review of Powerlifter with CP

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I’ve been continuing to work with Zarius out of our lab here at AUM. I provided a detailed account of his  recent powerlifting meet prep and competition data in this post. Zarius is 22 years old, weighs 120lbs and has Cerebral Palsy. Due to travel/work schedules we have had a hard time completing a solid training cycle since his last competition earlier this summer. We were finally able to get a good 1 month of training in before one of us had to travel again. Here is an overview of the training and HRV data from our most recent training cycle.

The Plan:

Our schedules allow for 3 training days per week. We train full body every Mon-Wed-Fri. Zarius is able to do some lower body exercises and we always finish each session with some walking laps around the track. (As an aside, prior to getting involved with the Human Performance Lab here at AUM about a year ago, Zarius’ main mode of transportation was his wheel chair. Now he walks most of the time. A testament to the great work done by Dr. Esco and the staff here at AUM as well as the effort put forth by Zarius.) Since we’re not currently preparing for a meet, we only performed the competition press once per week and performed incline and narrow-grip press on the other two training days as his main movements. I also started recording RPE for each set of his main lifts. There is definitely a learning curve to using RPE so I take the earlier values with a grain of salt. Volume for the main lifts remained constant while intensity increased each week. For his assistance work we just did some basic progressive overload. Since we wouldn’t be training for at least a week after the training cycle I wanted to overreach him a little. Zarius recorded his HRV each morning after waking with ithlete in a seated position.

The Data and Analysis:

Z_Data_07_2013

* Training Load (TL) above is simply referring to the average intensity (%1RM) that we used for his main lifts that week. 7.4=74% , etc. For his future cycles, I’ll be collecting sRPE as he gets more comfortable with the rating system.

Week 1:

–          Lowest intensity week

–          Avg. RPE for main lift this week was 8.4

–          Previous week mean HRV was 85.2 to serve as a baseline, though not on chart. Near +2 increase (86.9) in HRV after week 1.

Week 2:

–          Intensity increases

–          Avg. RPE for main lifts this week increases slightly to 8.6

–          +1 increase in week mean HRV. Training is well tolerated and apparently not very stressful.

Week 3:

–          Highest intensity week

–          Avg. RPE for main lifts this week increases to 9.1. This week he had four RPE@10 compared to week 2 which had 1 RPE@10 and 0 in week 1. (RPE@10 means it was a maximal set, no further reps could be performed)

–          As expected and consistent with his previous meet prep cycle, HRV declines this week -2.3 to 85.7. You’ll note that on weekends his HRV tends to climb back up, indicating good recovery even though the week was quite physically stressful.

Week 4:

–          Low intensity lift on Monday

–          Bench 1RM Test with competition rules (pause and rack command) on Wednesday. Zarius pressed 260lbs cleanly (at 120lbs bodyweight) which is a 10lb Gym personal record and is 5lbs over his competition best of 255lbs.

–          – 3.5 change in HRV this week down to 82.2. This is consistent with his competition cycle where HRV reached lowest values the week of competition. However, we deloaded after week 3 of his competition prep which allowed HRV to recover and peak going in to competition. In this case, due to time constraints, we tested before the deload. It’s possible he may have been stronger with a proper deload before testing.

Week 5:

–          No training this week due to travel

–          HRV week mean returns to baseline at 84.9 (the week before the start of the cycle with no training had a week mean HRV of 85.2).

Overall, this was a successful training cycle based on the result of his 1RM test. Training to at or very near failure with high intensity appeared to have a large effect on his HRV during training evidenced by the downward trend starting in week 3. Though volume was reduced considerably, the 1RM test day appeared to be very taxing as it  had the biggest effect on his HRV (consistent with his previous competition). During week 5 where there were no training sessions, HRV returned to baseline values.

Here is a little video interview that the local media did on Zarius. Take a second and check it out. http://www.montgomeryadvertiser.com/apps/pbcs.dll/article?AID=2013130726027