This past week, an excellent article by Charles Wallace, entitled “What’s Your Heart-Rate Variability? It May Be Time to Find Out,” was published in the Wall Street Journal. My good friend, exercise physiologist and coach, Alan Couzens, contributed on the practical aspects of using heart rate variability (HRV) in the training of endurance athletes generally, and his coached athlete, Inaki de la Parra, specifically.
Since then, several readers inquired about previous columns I’d written for Endurance Corner on the topic of heart rate variability, noting that links at my blog to those columns were no longer working. My apologies! I’d need my own IT person to keep track of the many old links.
I’ll reprint the Endurance Corner columns here, in 2 parts….
Part 1: The Basics
In a previous column, I wrote about the resting heart rate and heart rate recovery and how they can be used as indicators for monitoring athletes’ training status. At least 2 other heart rate-related indicators are also used for that purpose. I’ll leave the discussion about exercise heart rate to Alan Couzens, our resident Endurance Corner physiologist, but I wanted today to introduce the concept of heart rate variability (HRV).
In sports science circles, there has been a surge in interest recently in the use of HRV as a tool for monitoring athletes’ responses to training. The basic concepts have been around for decades, but technology—both software and hardware—is now becoming reasonably priced for individual athletes. The driving motivation has been a quest to identify physiologic markers that might help to optimize training and avoid overreaching or overtraining.
In today’s column, Part 1 of a 2-part series, I thought I’d offer a primer on heart rate variability for those of you who might be interested in this emerging technology. In Part 2, I’ll cover HRV applications in the endurance sports and describe some of the software and hardware tools that are now available.
Definitions and Terminology
First, we’ll need some definitions and terminology. Our starting point is the surface electrogram, or EKG, that reflects the electrical activity of the heart. The electrical activity for a couple heartbeats might look something like:
Entire textbooks are written about the EKG, but let’s simplify things here. By convention, with each heart beat there is a p-wave that corresponds to the electrical activation of the upper chambers of the heart, the atria. Next, there is a Q-R-S complex that corresponds to the electrical activation of the pumping chambers of the heart, the ventricles. The cycle is then completed with a T wave that corresponds to electrical repolarization of the ventricles before the next heartbeat. This cycle repeats over and over again.
The time between successive activations of the ventricles is reflected by the R-R interval, the time between successive R-waves of the EKG, and is usually expressed in msec. This is the quantity that most heart rate monitors measure to calculate the heart rate (in beats per minute) by:
Heart rate (beats per minute) = 60 / [R-R interval (in msec)/1000 ].
What’s interesting and relevant to our discussion here is that the R-R interval is not exactly constant. It varies from beat to beat, by a small amount. Said differently, the heart rate actually changes from beat to beat—thus the term heart rate variability.
Some Derived Quantities
We can record the EKG and measure each of the R-R intervals over any time period. A plot of these R-R intervals during the recording period is called a tachogram:
For purposes of athletes, we might do this for a few minutes, like shown in the example….or even for a whole day. Regardless, over a period of time the series of R-R intervals varies about a mean. In the example, the R-R interval varies about a mean of ~1000 msec, or a heart rate of 60 beats per minute. To better illustrate the distribution of the measured R-R intervals, we can generate a histogram of the R-R intervals:
In the so-called time domain, quantities such as the mean heart rate and standard deviation (SD), pRR50 (the percentage of R-R intervals that are >50 msec different from the previous beat), or rMSSD (the root mean square of differences between successive R-R intervals—the average absolute value change in R-R interval between beats—can each be determined. We say generically that HRV is increased when pRR50 or rMSSD are increased.
The time series of R-R interval measurements can be considered another way, though, in the so-called frequency domain. Using Fast Fourier Transformation (FFT), the original time series of R-R interval measurements (the tachogram) can be broken down into its time-dependent sinusoidal components:
The area beneath the curve is referred to as the power spectral density, expressed in msec2. By convention, in humans there are ranges termed low frequency (LF, 0.04-0.15 Hz) and high frequency (HF, 0.15-0.4 Hz) for which power spectral density can be determined separately (again, the area beneath the corresponding portion of the curve). These values are called simply LF and HF. The ratio of LF to HF, or LF/HF is also a relevant derived quantity, as we’ll see below.
Why is HRV Physiologically Relevant?
The beat-to-beat variability of the human heart rate is governed, at least in part, by the autonomic, or involuntary nervous system, which has 2 components. The sympathetic nervous system acts on the heart to increase the heart rate and the parasympathetic nervous system acts on the heart to decrease the heart rate. In terms of HRV, HF is a reflection of the parasympathetic activity and LF is a reflection of the sympathetic activity. By extension, the LF/HF ratio is generally reflective of the balance between the parasympathetic and sympathetic activity.
Despite the importance of the autonomic nervous system in clinical medicine, the use of HRV has found very few applications in the clinical setting. While HRV has been proposed for such purposes as early identification of infection, prediction of risk for developing arrhythmias, prediction of risk of death after heart attack, and risk stratification in patients with diabetes, among others, none has become a part of modern clinical practice because of practical difficulties with HRV measurements and poor correlations with important outcome measures.
In Part 2, we’ll talk about applications of HRV to endurance athletes’ training specifically and I’ll share some information about the software and hardware tools that are available today.