New Study: Agreement between a smart-phone pulse sensor application and ECG for determining lnRMSSD

Here’s a brief overview of our latest study, in press with JSCR.  We compared the ithlete finger sensor with ECG in supine, seated and standing positions. We are continuing our testing with other popular smartphone HRV apps in the near future. Thanks to the Summer 2015 Alabama S&C interns for making up a large portion of the participants in this study.

Full text link:

Agreement between a smart-phone pulse sensor application and ECG for determining lnRMSSD


The purpose of this study was to determine the agreement between a smartphone pulse finger sensor (SPFS) and electrocardiography (ECG) for determining ultra-short-term heart rate variability (HRV) in three different positions. Thirty college-aged men (n = 15) and women (n = 15) volunteered to participate in this study. Sixty second heart rate measures were simultaneously taken with the SPFS and ECG in supine, seated and standing positions. lnRMSSD was calculated from the SPFS and ECG. The lnRMSSD values were 81.5 ± 11.7 via ECG and 81.6 ± 11.3 via SPFS (p = 0.63, Cohen’s d = 0.01) in the supine position, 76.5 ± 8.2 via ECG and 77.5 ± 8.2 via SPFS (p = 0.007, Cohen’s d = 0.11) in the seated position, and 66.5 ± 9.2 via ECG and 67.8 ± 9.1 via SPFS (p < 0.001, Cohen’s d = 0.15) in the standing positions. The SPFS showed a possibly strong correlation to the ECG in all three positions (r values from 0.98 to 0.99). In addition, the limits of agreement (CE ± 1.98 SD) were -0.13 ± 2.83 for the supine values, -0.94± 3.47 for the seated values, and -1.37 ± 3.56 for the standing values. The results of the study suggest good agreement between the SPFS and ECG for measuring lnRMSSD in supine, seated, and standing positions. Though significant differences were noted between the two methods in the seated and standing positions, the effect sizes were trivial.

Full Text on Research Gate

FS EKG data

New Study: Individual HRV responses to preseason training in D-1 women’s soccer players

Here’s a brief look at a new paper of ours in press with JSCR. This is a very small study that we submitted as “Research Note” that looked at changes in HRV (via finger pulse sensor) and training load (via Polar Team2) across preseason training in D-1 women’s soccer players.

Full text link:

Individual HRV responses to preseason training in D-1 women’s soccer players

The purpose of this study was to track changes in training load (TL) and recovery status indicators throughout a 2-week preseason and to interpret the meaning of these changes on an individual basis among 8 Division-1 female soccer players. Weekly averages for heart ratevariability (lnRMSSD), TL and psychometrics were compared with effect sizes (ES) and magnitude based inferences. Relationships were determined with Pearson correlations. Group analysis showed a very likely moderate decrease for total training load (TTL) (TTL week 1 = 1203 ± 198, TTL week 2 = 977 ± 288; proportion = 1/2/97, ES = -0.93) and a likely small increase in lnRMSSD (week 1 = 74.2 ± 11.1, week 2 = 78.1 ± 10.5; proportion = 81/14/5, ES = 0.35). Fatigue demonstrated a very likely small improvement (week 1 = 5.03 ± 1.09, week 2 = 5.51 ± 1.00; proportion = 95/4/1; ES = 0.45) while the other psychometrics did not substantially change. A very large correlation was found between changes in TL and lnRMSSD (r = -0.85) while large correlations were found between lnRMSSD and perceived fatigue (r = 0.56) and soreness (r = 0.54). Individual analysis suggests that 2 subjects may benefit from decreased TL, 2 subjects may benefit from increased TL and 4 subjects may require no intervention based on their psychometric and lnRMSSD responses to the TL. Individual weekly changes in lnRMSSD varied among subjects and related strongly with individual changes in TL. Training intervention based on lnRMSSD and wellness responses may be useful for preventing the accumulation of fatigue in female soccer players.


Full Text on Research Gate

Early HRV changes relate to the prospective change in VO2max in female soccer players

It’s been a good start to the Thanksgiving break with the  acceptance of our latest study entitled “Initial weekly HRV response is related to the prospective change in VO2max in female soccer players” in IJSM (Abstract below).

We’re currently working on supporting these findings with a much larger sample size in the new year.


The aim of this study was to determine if the early response in weekly measures of HRV, when derived from a smart-phone application, were related to the eventual change in VO2max following an off-season training program in female soccer athletes. Nine female collegiate soccer players participated in an 11-week off-season conditioning program. In the week immediately before and after the training program, each participant performed a test on a treadmill to determine maximal oxygen consumption (VO2max). Daily measures of the log-transformed root mean square of successive R-R intervals (lnRMSSD) were performed by the participants throughout week 1 and week 3 of the conditioning program. The mean and coefficient of variation (CV) lnRMSSD values of week 1 showed small (r = -0.13, p= 0.74) and moderate (r = 0.57, p = 0.11), respectively, non-significant correlations to the change in VO2max at the end of the conditioning program (∆VO2max). A significant and near-perfect correlation was found between the change in the weekly mean lnRMSSD values from weeks 1 and 3 (∆lnRMSSDM) and ∆VO2max (r = 0.90, p = 0.002). The current results have identified that the initial change in weekly mean lnRMSSD from weeks 1 to 3 of a conditioning protocol was strongly associated with the eventual adaptation of VO2max.


RMSSD: The HRV Value provided by ithlete and BioForce

Most individuals who take their sport or training very seriously have likely heard of heart rate variability (HRV). Thanks to devices such as the Polar RS800 (Formerly S810) wrist-watch/heart rate monitor and eventually ithlete, the first (to my knowledge) commercially available smart phone HRV application, HRV data can be collected easily and affordably. The recent accessibility of HRV tools has resulted in greater usage, more data and of course greater popularity.

What most folks aren’t aware of however is that HRV is not a solitary figure or value. In fact, numerous HRV parameters exist that are supposedly representative of different autonomic variables. Below is a brief list and description of popular HRV analysis methods and values (many more values exist than described).

Time Domain Analysis: This method includes statistical and geometrical analysis of R-R interval data. Common statistical time domain values include:

  • SDNN – Standard Deviation of Normal to Normal intervals.
  • RMSSD – The square root of the mean squared difference between adjacent N-N intervals.

*Note:  NN or “normal to normal” is used to denote that only “normal” beats originating from the sinus node are measured. Impulses from other areas within the myocardium (non-sinus node impulses) are termed ectopic beats. Ectopic beats disturb normal cardiac rhythm and can therefore affect HRV. Generally 3 or more ectopic beats within a short-term measurement meets criteria for exclusion in many research papers.

Frequency Domain Analysis: This method is considerably more complex than time domain analysis and often requires longer measurement durations. It assesses how variance is distributed as a function of frequency.

  • HF – High Frequency Power: A marker of Parasympathetic Activity
  • LF – Low Frequency Power: A marker of both Parasympathetc and Sympathetic Activity
  • LF/HF – Low Frequncy/High Frequency Ratio: Once thought to represent the balance between sympathetic and parasympathetic activity however this remains a hot topic of debate.

As you can see, saying something along the lines of “My HRV is low today” is really vague. I’m sure I’ve been guilty of this in the past. More often than not, most people are referring to their RMSSD value as this is the same parameter provided by ithlete and BioForce (among other HRV tools).

The RMSSD is commonly used as an index of vagally (Vagus Nerve) mediated cardiac control which captures respiratory sinus arrhythmia (RSA), the frequent changes in heart rate occurring in response to respiration (Berntson et al. 2005). During inhalation, heart rate speeds up. During exhalation, heart rate slows down. RMSSD is an accepted measure of parasympathetic activity and correlates very well with HF of frequency domain analysis (discussed above).

PhD candidate and HRV researcher James Heathers provides a good explanation of why we would want to track changes in RMSSD vs. other HRV values throughout training here. I’d like to add that RMSSD is one of the few meaningful values that we can acquire with ultra-short measurement durations. It’s generally accepted that a 5 minute recording is the gold standard for HRV analysis (Task Force 1996). However, 5 minutes is entirely too long if we expect compliance from athletes or individuals. Thankfully, ample research exists that shows that ultra-short (60 seconds or less) RMSSD values (randomly selected from within a 5 minute recording) highly correlate with RMSSD from the standard 5 minute ECG recording (Katz et al. 1999; Mackay et al. 1980; Nussinovitch et al. 2012; Nussinovitch et al. 2011; Salahuddin et al. 2007; Smith et al. 2013; Thong et al. 2003). Unfortunately no research exists that tested the suitability of ultra-short RMSSD in athletic populations so my colleague Dr. Mike Esco and I went ahead and did this very recently in athletes at rest and post-exercise (paper currently in peer review). I will let you know what we found once it gets published.

Why does my HRV score (from ithlete or BioForce) look different from the values in research?

I hope you are not comparing your ithlete or BioFroce scores to data you see in published research. Simon, the creator of ithlete, decided to modify the RMSSD value collected by ithlete to make for a more intuitive and easily interpretable figure for non-expert users. The value you see from the app is the natural log transformed RMSSD multiplied by 20 (lnRMSSDx20). This modification essentially provides a figure on a 100 point scale (though ithlete scores above 100 are possible in highly fit individuals, though not common).

*Note: lnRMSSDx20 is a patented formula and therefore those interested in using this commercially must acquire a licence.


To be clear, RMSSD is only one HRV parameter. By no means was this article suggesting that other HRV values are meaningless. The purpose of this blog was to simply provide an explanation of the what and why of RMSSD since so many people are using ithlete and BioForce lately. Certainly, ECG derived HRV remains the gold standard and likely multiple HRV parameters provide a more complete picture of training status verses just one. However, for the purposes of convenience in non-expert users, the RMSSD provides an easily acquired and interpretable figure in a short period of time that reflects parasympathetic activity which is quite useful for monitoring the effects of training and in the manipulation of training loads.


Berntson, G. G., Lozano, D. L., & Chen, Y. J. (2005). Filter properties of root mean square successive difference (RMSSD) for heart rate. Psychophysiology,42(2), 246-252.

Camm AJ, Malik M et al. (1996) Heart rate variability: Standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circ 93(5): 1043-1065

Katz A, Liberty IF, Porath A, Ovsyshcher I, Prystowsky E (1999) A simple bedside test of 1-minute heart rate variability during deep breathing as a prognostic index after myocardial infarction. Am Heart J 138(1): 32-38

Mackay JD, Page MM, Cambridge J, Watkins PJ (1980) Diabetic autonomic neuropathy. Diabetol 18(6): 471-478

Nussinovitch U, Cohen O, Kaminer K, Ilani J, Nussinovitch N (2012) Evaluating reliability of ultra-short ECG indices of heart rate variability in diabetes mellitus patients. J Diabetes Complic 26(5): 450-453

Nussinovitch U, Elishkevitz KP, Katz K, Nussinovitch M, Segev S, Volovitz B, Nussinovitch N (2011) Reliability of ultra‐short ECG indices for heart rate variability. Ann Noninvasive Electrocardiol 16(2): 117-122

Salahuddin L, Cho J, Jeong MG, Kim D (2007) Ultra short term analysis of heart rate variability for monitoring mental stress in mobile settings. Conf Proc IEEE Eng Med Biol Soc 4656-4659

Smith AL, Owen H, Reynolds KJ (2013) Heart rate variability indices for very short-term (30 beat) analysis. Part 2: validation. J Clin Monit Comput E-Pub Ahead of Print

Thong T, Li K, McNames J, Aboy M, Goldstein B (2003) Accuracy of ultra-short heart rate variability measures. Conf Proc IEEE Eng Med Biol Soc 3, 2424-2427

Reviewing HRV data after a 9 week training cycle

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?

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 4th week of December to the 2nd 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.

HRV Data from a High School Sprinter

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.




  •  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

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.


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.



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



– 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)



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



– 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 


data table avg

– 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.

Reflections, Thoughts & Some HRV Data Analysis from 2 Athletes

This week Carl Valle had a great article posted on Mladen’s site here. It’s definitely worth the read if you train athletes. This article inspired me to reflect on where HRV fits in to training, for whom it may work best for and why. I monitor HRV in a very small number of athletes who are the minority of the overall pool of athletes I work with.

To get the most out of HRV tracking, I believe it should be measured daily, in the morning after waking. With ithlete this requires less than 2 minutes of your time to perform the measurement and make any comments, input training load, etc. Though this is a simple task, it is not easy to get full compliance from individuals. Therefore, I don’t even consider getting an athlete taking measurements unless he possesses a great deal of intrinsic motivation, is responsible, reliable, and perhaps most importantly, is interested. Though I would prefer they know nothing about the device, it’s hard to convince people to commit to using it every day if they don’t understand why. After a few sessions I will mention it to them and give them some basic details. If they appear interested or ask if they can use it then it’s a go.

I have several motivations for tracking HRV in select athletes. Below, these motivations are listed with some follow-up thoughts and elaborations.

  • To observe ANS response to training, daily stressors, recovery modalities, etc.

What was HRV score the day following a workout? What else did the athlete do that day that may influence this score? What has the overall trend been that week (positive or negative)?  I like to compare HRV score to other training status markers like strength levels (did he hit target weights for the day?), movement ability (how does he look during warm-ups, jumps, etc.?), perceived recovery/readiness levels (Does he feel great when HRV is high, when its low?), etc.

This motivation serves two purposes.

  1. It gets the athlete more engaged in his life style and training (more on this in a bit)
  2. It satisfies my curiosity. I’ve got questions I want answered.
  • To observe HRV trends over times of illness, injury, etc. to determine if there were early warning signs in the trend and if the trend reflects recovery/return to play readiness.

In the event of an injury during practice or competition, what was the trend indicating? In the past year or so I hurt myself once during training and it happened with 60% of my 1RM during squats (hardly a threatening situation). My HRV that day was well below baseline. Possibly a coincidence, or possibly injury risk is heightened when HRV is really low. To my knowledge, there is no research on this in human athletes, but this seems to be the case in race horses. I discussed some very interesting research by Dr. Christine Ross in this post from last winter.

Here’s an excerpt from that post.

“Dr. Christine Ross monitored the HRV of 16 competitive race horses, all of which were in training. Of the 16, 13 had HRV readings that were associated with pain, fatigue, illness or injury. It was stated that even though the horses appeared healthy and energetic, they were considered “at risk” based on their HRV. There were no outward signs or symptoms to suggest these horses were currently sick or hurt. Within 3 months, 12 of the 13 at-risk horses got injured or sick requiring veterinary intervention and cessation of race training.”

Furthermore, I work with plenty of football players and hockey players who by nature are at risk of concussion. What insight can HRV provide regarding recovery and return to play after concussion? (Perhaps a post on this in the future)

  • In rare cases, to manipulate training if HRV has been consistently below baseline and the athlete displays signs of fatigue.

This is an interesting topic. Working with an athlete is rarely long term. In many cases you may only have 6-8 straight weeks of consistent training before interruption. That means we need to get them better quickly. Getting better can be defined in many ways but in the training realm this means improving strength, speed, power, work capacity, etc. To do this we need to apply stress. In some cases, a lot of stress, of various kinds. Naturally, HRV will drop. The organism has to work hard to adapt to the stress (and thus improve). We don’t have time to wait for “optimal” recovery and this is likely not even desirable.

Let me use an example. Below is the HRV trend of a 25 year old hockey player I’m working with. He’s come to me to get in shape for a try-out he’s been invited to for a pro team in Germany.


He is a former NCAA hockey player and has been training relatively consistently throughout school. After this summer he thought he was done with competitive hockey and stopped training however he did start playing men’s league hockey.  Since he hasn’t been training I knew we’d probably see some pretty big downward deflections after our first few workouts. He missed a few mornings of HRV measurements but it’s been about 2 weeks since we started. The “week change” is -8 and his HRV trend is steadily decreasing. His strength is steadily improving as is his conditioning. He’s adapting fast and re-acquiring lost strength and fitness. Training loads are steadily increasing every week. Now that it’s Christmas I expect to see his HRV bump back up due to some extra rest and likely extra calorie intake. So long as HRV approaches baseline levels after a few days of rest then I think things are looking good. However, if HRV continues downward I will evaluate performance markers and make adjustments if necessary. The physical stress load is high as reflected by his HRV but it’s only been 2 weeks and his performance markers are improving. The weekly trends will likely continue to decrease until about 2 weeks out from the try-out at which point I’ll steadily reduce loads. HRV should climb back up and fatigue should dissipate. This is what happens when I have a relatively short period of time to work with an athlete.

In contrast, the trend below is of a high school sprinter I’m working with. He trains with his sprint coach and works with me for recovery/restoration, mobility, etc. He has a sub 11s 100m time and is one of the fastest high school sprinters in Canada. He is much more long term and his training load reflects that. His weight training volume has been reduced quite a bit and has transitioned into more sprint work and power development in the weight room (controlled and implemented by his Sprint Coach).

ZW Trend

This is an athlete who takes care of himself and is extremely motivated to get better, to say the least. He reports that training is going well, he’s hitting PR’s and it looks as though he’s handling training almost too well. Higher loads would be likely well tolerated. If I can just start getting him to get to bed at a decent hour on weekends he’ll be doing everything right.

In both cases the athletes have learned how lifestyle factors outside of training effect their recovery, soreness levels, etc. This is directly attributed to seeing their HRV trend, recognizing what events may have caused the additional stress and re-evaluating there decision making. One of the main things I like about HRV is that it forces you (and the athlete) to be more engaged in the process. It allows them to see how their actions (good or bad) can effect the quality of their training and their progress.

Final Thoughts

Having HRV records as an objective measure of training status helps guide the training process when taken with other markers of performance and fatigue. If the athlete is a high level athlete, mature enough to handle daily measurements and wants to use it then I am all for it. I don’t use it with many athletes because it would be a waste of time and energy for both parties. However, with the right athletes it can be a great tool to for monitoring training.

Recent HRV trend analysis and a new collaboration

As I try and further my understanding of the seeming incomprehensible autonomic nervous system I try to simplify the role HRV may play in monitoring athletes. There is one main issue I’m having; I don’t yet fully grasp the ANS (does anyone?) and therefore I still have a ton of unanswered questions.

I’ve noticed that there are some extremely intelligent people who are strong advocates of HRV usage as a monitoring tool. I’ve also noticed there are equally as intelligent people who are very skeptical and even doubtful of its efficacy and applicability. I’m doing my best to understand both sides of this argument. The best I can do to contribute to this discussion (at the moment) is draw attention to research and offer personal experience.

It’s been a while since I’ve posted and discussed some of my HRV trends so today I will do this as well as share some observations a colleague of mine has made at McMaster University.

Below is a screen shot of my HRV trend from the last 30 days:

  • Horziontal Blue Line = HRV Baseline
  • Vertical Purple Bars = sRPE (absence of these indicate no training)
  • White Lines = Day to day HRV scores

Training structure has been as follows:

  • Monday – Squat
  • Tuesday – Active Recovery
  • Wednesday – Bench Press
  • Thursday – Active Recovery
  • Friday – Deadlift
  • Saturday – Off
  • Sunday – Off

Strength workouts range from an RPE rating of 7-9 while the low intensity “recovery” days range between 3-5.

dec 2012 trend


  • Much of what I’ve seen is consistent with what I documented in this post so I won’t discuss these in too much depth again.
  • Normally my HRV will be at or above baseline after a weekend (no training). In the first weekend you see my HRV dropped quite a bit Monday morning. I assume this is because I was away that weekend and I spent much of Sunday in the car and then was frantically trying to get caught up on things once I got home before Monday.
  • I trained at an sRPE of 8 on Monday and as expected another drop and a red indication for Tuesday. Active recovery typically will bump HRV back up the next day however Tuesday night I unknowingly went to sleep with my friends cat hiding under my bed. Around 2am I got a startling wake up as the animal tried to snuggle with my face. It took me nearly 2 hours to fall back asleep after. HRV that morning is another red and I feel like crap. I take a deload day on Bench  (sRPE 7), sleep well and HRV comes back up the next morning.
  • Things remain consistent during the week shown in the middle of the trend. Moderate dips in HRV in response to sRPE 8’s with returns to baseline after low intensity days. HRV is high after a restful weekend.
  • The following week I start doing a little more work in my workouts (more heavy sets) and therefore a higher sRPE rating (of 9). Along with higher amounts of soreness and perceived fatigue I saw larger dips in HRV the following day. On Friday (deadlift day) I keep things conservative due to previous lower back injuries and perform an sRPE of 8 and see less of a drop in HRV the next day. I’m happy to report that the back has been feeling good and I have started deadlifting again recently. I stopped deadlifting  for a while as I was experiencing pain during the lift (no surprise it was an underactive multifidus) Video below of a recent deadlift.
  • HRV is high after a restful weekend. sRPE of 9 on Monday (squat) of the last week shown on the image and I again see a larger dip in HRV (today). Will do some low intensity stuff later on after work.

Collaborating with Steve Lidstone at McMaster University

Since moving back to Canada I’ve been working on getting an HRV project going with Steve Lidstone, the head strength coach at McMaster University (a huge rival of mine in my football days). After some e-mail discussions I sent Steve an ithlete to try out. After a few weeks Steve sent me this update;

“I’ve been monitoring my HRV for 3 weeks now every morning.

I started off with HRV at 88 with a HR of 60bpm.

In times of poor sleep (we have 2 kids ages 2 & 4) or high stress my HRV has plummeted to 55 and resting HR of 79.

It is also interesting to me as I am in my 5th week of post concussion symptoms. When my HRV is low my symptoms are escalated.”

At this point we’re looking at getting two of his teams started with ithlete (about 8 players in total). Should make for some good data to discuss.