New Podcast: Discussing Smartphone HRV Apps

I recently had a chance to sit down and discuss all things HRV monitoring with James Darley of the Historic Performance Podcast. There’s also a number of great interviews in the podcast archives worth checking out.

Topics discussed:

  • Background
  • Physiological basis for HRV as a recovery status metric
  • Preferred HRV parameter for athletes
  • HRV recording methodology (position, conditions, time of day, etc.)
  • Considerations for chosing the right HRV app for your situation
  • Recent research
  • Interpreting HRV data

Link to Podcast with show notes 

Show in Overcast App

 

 

Interpreting HRV Trends in Athletes: High Isn’t Always Good and Low Isn’t Always Bad

This article was written for the FreelapUSA site. The intro is posted below. You can follow the link for the full article. Thanks to Christopher Glaeser from Freelap for inviting my contribution as I’ve found this site to be a great resource.

Interpreting HRV Trends in Athletes: High Isn’t Always Good and Low Isn’t Always Bad

Heart rate variability (HRV) monitoring has become increasingly popular in both competitive and recreational sports and training environments due to the development of smartphone apps and other affordable field tools. Though the concept of HRV is relatively simple, its interpretation can be quite complex. As a result, considerable confusion surrounds HRV data interpretation. I believe much of this confusion can be attributed to the overly simplistic guidelines that have been promoted for the casual-end, non-expert user.

In the context of monitoring fatigue or training status in athletes, a common belief is that high HRV is good and low HRV is bad. Or, in terms of observing the overall trend, increasing HRV trends are good, indicative of positive adaptation or increases in fitness while decreasing trends are bad, indicative of fatigue accumulation or “overtraining” and performance decrements. In this article I address the common notions of both acute and longitudinal trend interpretation, and discuss why and when these interpretations may or may not be appropriate. We will briefly explore where these common interpretations or “rules” have come from within the literature, and then discuss some exceptions to these rules.

Continue reading article on FreelapUSA

HRV Monitoring Interview

Here’s a recent interview I had the pleasure of doing with Chris Beardsley of the Strength and Conditioning Research site:

http://www.strengthandconditioningresearch.com/2014/11/18/andrew-flatt-hrv/

We go over;

  • What HRV is and why we measure it
  • Practical and valid recording methodology as it pertains to shortened measurement duration and stabilization periods
  • HRV data interpretation for training and athlete monitoring
  • Practical recommendations

Trend Analysis: Importance of Context

It’s pretty well documented that regular aerobic exercise can result in an increase in resting HRV. Generally, you can expect no change or even a slight increase in HRV the day following low to moderate intensity aerobic exercise. However, with higher intensity training, HRV can take up to 48-72 hours to return to baseline, depending on intensity, duration, training status, fitness level, age, gender, etc. (Stanley et al. 2014). I’ve seen this numerous times with my own data where HRV decreases significantly 24 hours following interval sessions (particularly when they are not performed regularly) and increase beyond baseline by 48 hours.

Because low-moderate aerobic work tends to have an acute stimulatory effect of parasympathetic activity, it has been suggested that this would be useful as active recovery following high intensity sessions.

“because (at least) autonomic supercompensation following low-intensity training may occur within 24 h and since cardiac parasympathetic reactivation is delayed by the build-up of metabolites, inclusion of low intensity training subsequent to a high-intensity session may accelerate metabolite breakdown [88]. Athletes who train twice daily may also benefit from the accelerated recovery (metabolic recovery, as reflected by autonomic recovery) afforded by a low-intensity training session” (Stanley et el. 2013)

I recently included moderate intensity aerobic work on off days (approx.10 mins each on treadmill, cycle and rower for a total of 30 mins) and following my training sessions (10-12 mins on cycle) over about a three week period. Previous to this, very little aerobic work was being done, at least not consistently. During this time I lifted on Mon-Tue-Thurs-Fri each week. Below is my HRV data (lnRMSSDx20, standing) that includes a few weeks prior to the inclusion of regular aerobic work as well as the few weeks that followed.

daily trend aerobic weeks Next, I’ve included the weekly mean HRV and %CV (coefficient of variation) values.

mean and CV o2 weeks

 

I started performing the aerobic work midway through week 3 and continued until week 6. The trends both clearly show an increase in HRV during this time. We also see quite a large change in %CV with the regular aerobic work. In the weeks before and following the aerobic work, there are much bigger day to day changes in HRV which is quite typical for me. The inclusion of regular moderate aerobic work attenuated the daily changes I’d typically see following heavy training sessions. Clearly the post-workout aerobic work and active recovery work on off days was effective at promoting recovery. However,it’s important to clarify that HRV parameters are reflective of cardiovascular-autonomic activity, which does not necessarily include neuromuscular ability, CNS potential, etc.

“changes in cardiac parasympathetic activity are useful for monitoring aspects of recovery that are dependent on cardiovascular function. By contrast, changes in cardiac parasympathetic activity are less useful for monitoring other aspects of recovery such as restoration of muscle and liver glycogen, or repair of damaged muscle tissue” (Stanley et al. 2013)

Therefore, the lack of day to day changes in my HRV following heavy resistance training workouts does not imply that I was fully recovered within 24 hours and could repeat performances (e.g., heavy squats), only that that particular system was recovered. Thus, for strength athletes in particular, HRV is only one marker to consider when assessing daily recovery status. More work needs to be done in this area to determine how useful HRV monitoring is in this population and how it can be used effectively.

This data also shows how interpretation of a trend is context dependent, as mean and %CV values are affected by exercise mode and intensity. Thus, if working with team sport athletes, we may expect larger fluctuation and a lower mean when less aerobic exercise is prescribed and vice versa. Even endurance athletes will experience similar HRV changes when preparing for competition as the amount of high intensity/interval training increases and low-moderate intensity/steady state work decreases. This is often characterized with a bell-shaped HRV trend (example below).

bell shaped trend

The above data is taken from a case study we did of a collegiate endurance athlete over his competitive season (will be in a future edition of JASC). There is clearly a progressive increase in HRV up to a peak, at which point there is a progressive decrease. This is likely a result of more high intensity training and lower volumes of moderate/steady state  work as the athletes prepares to peak, further supporting the need to assess HRV changes in context to training phase, goal, structure, etc. (Buchheit,2014).

Changes in HRV are always context dependent. Decreases in the trend are not always associated with fatigue, nor are increases always associated with higher “readiness”. Nothing is ever as black and white as we’d like it to be. Additional reference to training load, psychometrics and performance will help with interpretation and if necessary intervention.

Refs:

Buchheit, M. (2014). Monitoring training status with HR measures: do all roads lead to Rome?Frontiers in Physiology5.

Stanley, J., Peake, J. M., & Buchheit, M. (2013). Cardiac parasympathetic reactivation following exercise: implications for training prescriptionSports Medicine43(12), 1259-1277.

HRV: Means and Variation

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 physiology5. 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 Exercise45(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 physiology112(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 & Performance8(6).