Heart Rate Variability (HRV): What is it, What is it for, How to Interpret it

Heart Rate Variability (HRV): What is it, What is it for, How to Interpret it

Today we will explain a parameter that can be very useful as a way to rest: HRV or Heart Rate Variability.

We tell you what it is, what it’s for, and how we can interpret it.

What is HRV or Cardiac Frequency Variability?

HRV is Heart Rate Variability, or “Cardiac Frequency Variability” and it measures the time interval that separates cardiac beats and how constant the period of time between them is.

Variability

In contrast to what you might think, a large variability is a good sign..

How does HRV work?

HRV is a cardiac reflection of the activity of our independent nervous system.

Our nervous system is divided into:

  • Central nervous system (brain and spinal function: something like the control centre and the information highway); and
  • Peripheral Nervous System (Peripheral Nerves and Nodes). This is further divided into:
    • The somatic nervous system (that controls voluntary actions, such as moving a finger); and
    • The autonomic nervous system (which controls involuntary actions, such as breathing or visceral movements).
This autonomic nervous system is divided into:
  • Sympathetic nervous system (“fight or flight”, that respond to stress stimulus); and
  • Parasympathetic nervous system (“rest and digest”, it attenuates the activation of the organism).

Nervous system

Both subsystems act on our cardiac pacemakers and affect the beat, therefore also your HRV.

The interval between two heart beats (between the R waves) decreases with activation of the sympathetic nervous system, and increases with activation of the parasympathetic nervous system.

Hearthbeats

Both are always on and their predominance depends on a complex regulation system, but we could say that those who suffer from generalised anxiety will have a higher overall heart rate, while those who are more relaxed will have a lower heart rate.

Figure 1

In this image, you can see inverse correlation between heart rate and distance between beats.

What is it for

Heart rate has been validated as a reliable marker to infer a person’s psychophysiological state.

Why does the heart beat faster when you are speaking in public?

The different heart status controlled through heart rate has been linked to the state of health (physical, mental, and emotional) of a subject, on numerous occasions.

Graphic

An increase of 10 beats per minute increases the risk of death from any cause by 12% (Zhang, Shen, and Qui, 2016).

Because HRV responds to changes in nerve activation, it has been studied as a simple, inexpensive, non-invasive measure to assess a person’s physical and emotional state.

Recovery in Sport and HRV

HRV is a useful tool for assessing the physical condition of a subject after training.

However, the more adapted it is to the type of work being done, the less time it takes for your recovery (Hautala et al., 2001).

Graphic 1

In this graph we can see that the more cardiorespiratory capacity the athlete has (X axis), the less time it takes to return to the pre-training state (Y axis).

Seems logical, no? Well, we will see what the real life application of this is:

TimeBefore5′ post10′ post15′ post1h post24h post48h post
R-R (ms)1036.9675.7709.3741.0900.31011.21002.2

As we can see, in moderately trained subjects (no professionals or similar), the R-R interval recovered almost completely within the hour after the training was completed, at 24 hours it was already completely normal (Maurot et al., 2004).

Graphic 2

And the more trained they are, the faster it is. In fact, in highly trained subjects, within 30 minutes of training, they were already recovered to 100% (Seiler, Haugen, Kuffel, 2007).

What does this mean?

Well, that HRV IS NOT a good measure for estimating fatigue in moderately trained subjects exposed to normal training, to which they are already accustomed, and although many athletes use HRV in the mornings, a one-day measurement is not representative, as we must observe trends.

Plews et al., (2014) tell us that to make reliable changes in HRV we must measure at least 3 days of heart variability if we are trained; but possibly even a week.

Acute variations

So acute variations are not relevant, i.e. a reduced HRV on a one-time day, is not worrying and should not alter your training schedule.

HRV is not a useful method for assessing the recovery of bodybuilders or Powerlifters, i.e. if you train in the gym to look and/or be strong, forget about HRV.

Image 1

In trained gym users, both in hypertrophy and strength training, HRV recovered within 30 minutes of completing the training.

Image 3

However, 48 hours later markers of muscle damage remained elevated.

Image 2

And capacity to generate strength, too.

So imagine you’re measuring your HRV the next morning and it’s normal, perfect, we’re going to train, and you note that you feel weird, what’s wrong: As the HRV doesn’t measure this, it’s useless.

Note for geeks

HRV is a compendium that includes time analysis (which is the most studied, and which I have referred to throughout the article), and spectral analysis.

In the field known as “time-domain analysis” and “frequency-domain analysis”.

Each of these analysis, in turn, has a huge number of values that can be measured and gives us different information.

That is, it is not a single marker, there are many (rMSSD, AVNN, SDNN, pNN50, pNN20 UVLF, VLF, LF, HF and their ratios; in addition to non-linear analysis and correlation dimensions), HRV is really complex.

Spectral analysis has been shown to possess mixed evidence: In the systematic review of Bosquet et al. (2008) frequency domain trends were found to be altered when subjects were subjected to different training loads for short (<2 weeks) and long (>2 weeks) periods.

That is, the more training load, the more the frequency domain was altered with HRV.

However, Hedelin et al., (2000) did not observe changes in highly trained subjects who were recruited for a 6-day camp and increased their training load day by day to maximum efforts. Yes, they were overtraining on purpose (rather, looking for overreaching)

We continue with the unknown of whether, as proposed by certain authors such as Massaro and Pecchia (2016), if we are going to do ultra-short or short measurements (around 5 minutes), if it is better to look at the spectrum whereas in 24-hour measurements, the rMSSD is superior.

Heart Rate Variability to measure Overtraining

HRV is a useful way to know if a subject is overtraining.

Their values are reduced compared to untrained athletes, but still, they remain better than sedentary subjects, even if they are healthy (Mourot et al., 2004). But it is not a measurement, but a trend of measurements over time.

HRV domain temporal

Health and HRV

Heart rate variability has been linked to health status on numerous occasions.

In fact, low heart variability has been associated with development of a large number of diseases.

HRV is being reviewed as a measure with potential (predictor) to detect the risk of death from cardiovascular or other pathologies (Sen & McGill, 2017) which seems very promising, but we need even more studies.

Practical application

I suppose you think the theory is very good, but how do we apply this:

First of all, I will say that for now, resting heart rate is a much more valid measure for estimating both the recovery status (Bosquet et al., 2008), and the health (Zhang et al., 2016) of a person.

If you still want to learn how to use HRV I recommend that you read the reviews of Massaro and Pecchia (2017), and Draghici and Taylor (2016) to understand how a spectral analysis is performed. Then get acquainted with the use of the Kubios program.

If you prefer to use time domain HRV, even if you know that (in the absence of further research) it is inferior to other simpler measurements such as heart rate, you can do the following:

  1. Download HRV4Training or EliteHRV, personally I recommend the first.
  2. Take measurements as you are asked to do until this screen appears:

Measure HRV

From there you can take your measurements daily, remember to do them (if it’s to see how you progress) as you wake up, lie back on top of the bed (the position is important), and sync a chest heart rate band with the APP.

Evaluate your health status

  1. Click on the “insights” sections of the APP.

rMSSD

  1. Look for the field where you can enter your rMSSD values.

rMSSD 2

  1. Look at the article published by Sammito et al. (2019) that I have left in the description, look in the “rMSSD” section for your sex and age range in Table 3. You’ll see that “5th/25th…” appears above, those are your percentiles, the closer you are to 95th the better, because this indicates more heart variability and health
  2. Measure your rMSSD every 2 weeks under the same conditions to see if you are improving your heart variability as a result of your workout.
If you don’t do it, you might want to think about adding some aerobic exercise, do you know that “cardio” is positive at any time of the year? We tell you here.

Evaluate Training Recovery

Although heart variability is not an interesting variable to control fatigue, if you have chosen to use HRV4training you can be guided moderately well by its indications.

  1. The APP asks you in the logs for stressors (travel, rest, eating, health status…) to be able to computerise the variables and generate their own logarithms.

HRV training

  1. On days when after you measure your heart rate your values are out of the desired range (as shown in the image below), the app itself will tell you that it’s a good idea to rest or take it easy.

Conclusions

Personally if you see just what you see in the image “Your HRV is below your normal values. However, your subjective scores are trending positively”, I wouldn’t give it a lot of thought, train as scheduled and as per how you feel.

App HRV result

If your subjective perception gets worse, or the rMSSD (in insights) falls drastically and does not recover in at least 3 days, maybe it’s a good idea to rest, don’t you think?

With this information you can now do your basic cardiac variability monitoring to know how healthy you are!

And you, the rMSSD tells you you’re fit? 😉

Bibliography Sources

  1. Bauer, A., Camm, A. J., Cerutti, S., Guzik, P., Huikuri, H., Lombardi, F., … Yamamoto, Y. (2017). Reference values of heart rate variability. Heart Rhythm, 14(2), 302–303.
  2. Bosquet, L., Merkari, S., Arvisais, D., & Aubert, A. E. (2008). Is heart rate a convenient tool to monitor overreaching? A systematic review of the literature. British Journal of Sports Medicine, 42(9), 709–714.
  3. Castaldo, R., Melillo, P., Bracale, U., Caserta, M., Triassi, M., & Pecchia, L. (2015). Acute mental stress assessment via short term HRV analysis in healthy adults: A systematic review with meta-analysis. Biomedical Signal Processing and Control, 18, 370–377.
  4. Dobkin, P. L., & Pihl, R. O. (1992). Measurement of psychological and heart rate reactivity to stress in the real world. Psychotherapy and Psychosomatics, 58(3–4), 208–214.
  5. Dong, J. G. (2016). The role of heart rate variability in sports physiology (Review). Experimental and Therapeutic Medicine, 11(5), 1531–1536.
  6. Draghici, A. E., & Taylor, J. A. (2016). The physiological basis and measurement of heart rate variability in humans. Journal of Physiological Anthropology, 35(1), 22.
  7. Hautala, A., Tulppo, M. P., Mäkikallio, T. H., Laukkanen, R., Nissilä, S., & Huikuri, H. V. (2001). Changes in cardiac autonomic regulation after prolonged maximal exercise. Clinical Physiology, 21(2), 238–245.
  8. Hedelin, R., Kentta, G., Wiklund, U., Bjerle, P., & Henriksson-Larsen, K. (2000). Short-term overtraining: Effects on performance, circulatory responses, and heart rate variability. Medicine and Science in Sports and Exercise, 32(8), 1480–1484.
  9. Kim, H. G., Cheon, E. J., Bai, D. S., Lee, Y. H., & Koo, B. H. (2018). Stress and heart rate variability: A meta-analysis and review of the literature. Psychiatry Investigation, 15(3), 235–245.
  10. Massaro, S., & Pecchia, L. (2019). Heart Rate Variability (HRV) Analysis: A Methodology for Organizational Neuroscience. Organizational Research Methods, 22(1), 354–393.
  11. Michael, S., Graham, K. S., & Oam, G. M. D. (2017). Cardiac autonomic responses during exercise and post-exercise recovery using heart rate variability and systolic time intervals-a review. Frontiers in Physiology, 8(MAY), 301.
  12. Mourot, L., Bouhaddi, M., Perrey, S., Cappelle, S., Henriet, M. T., Wolf, J. P., … Regnard, J. (2004). Decrease in heart rate variability with overtraining: Assessment by the Poincaré plot analysis. Clinical Physiology and Functional Imaging, 24(1), 10–18.
  13. Mourot, L., Bouhaddi, M., Tordi, N., Rouillon, J. D., & Regnard, J. (2004). Short- and long-term effects of a single bout of exercise on heart rate variability: Comparison between constant and interval training exercises. European Journal of Applied Physiology, 92(4–5), 508–517.
  14. Plews, D. J., Laursen, P. B., Le Meur, Y., Hausswirth, C., Kilding, A. E., & Buchheit, M. (2014). Monitoring training with heart-rate variability: How much compliance is needed for valid assessment? International Journal of Sports Physiology and Performance, 9(5), 783–790.
  15. Schmitt, L., Regnard, J., & Millet, G. P. (2015). Monitoring fatigue status with HRV measures in elite athletes: An avenue beyond RMSSD? Frontiers in Physiology, 6(NOV), 343.
  16. Seiler, S., Haugen, O., & Kuffel, E. (2007). Autonomic recovery after exercise in trained athletes: Intensity and duration effects. Medicine and Science in Sports and Exercise, 39(8), 1366–1373.
  17. Sen, J., & McGill, D. (2017). Fractal Analysis of Heart Rate Variability as a Predictor of Mortality: A Systematic Review. Heart, Lung and Circulation, 26, S165–S166.
  18. Sessa, F., Anna, V., Messina, G., Cibelli, G., Monda, V., Marsala, G., … Salerno, M. (2018). Heart rate variability as predictive factor for sudden cardiac death. Aging, 10(2), 166–177.
  19. Thamm, A., Freitag, N., Figueiredo, P., Doma, K., Rottensteiner, C., Bloch, W., & Schumann, M. (2019). Can heart rate variability determine recovery following distinct strength loadings? A randomized cross-over trial. International Journal of Environmental Research and Public Health, 16(22).
  20. Zhang, D., Shen, X., & Qi, X. (2016). Resting heart rate and all-cause and cardiovascular mortality in the general population: A meta-analysis. Cmaj, 188(3), E53–E63.

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About Alfredo Valdés
Alfredo Valdés
A specialist in Pathophysiology and biomolecular effects on nutrition and sportive activity who will show you the elaborate world of sports nutrition in his articles, employing a simple and critical writing.
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