Simple Concept, Convoluted Journey
To many, being able to quantify your body’s state of stress and recovery appears something of science fiction. The complex processes that run in your body are often redundant, synergistic, and tangled, making it very difficult to determine causality. One the more controversial topics in exercise science is the use of heart-rate variability (HRV) to determine an athlete’s state of stress and recovery; a measurement of how much an athlete can push themselves before injury. This essay explores the physiology behind HRV, how it is being used in athletics, and whether this is something to invest in.
The nervous system is the interface between the body and its environment. The autonomic nervous system (ANS) is a branch of the nervous system and one of several other systems (such as the Hypothalamus-Pituitary Axis) that dictates responses to injury and recovery in the body. Within the ANS are two more branches: the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS). The innervation of both the SNS and PNS reach the heart, thus have major influence on heart rate and its oscillations. HRV can be described simply as the variations between each heart beat measured from an electro-cardiogram. Common knowledge postulates that the SNS and PNS operate linearly and discordantly. For example the SNS excites and the PNS inhibits- but this is an oversimplification. Consider the following by Gernot Ernst’s 2017 review published in Frontiers.
“In a linear, reductionist world, a change in one condition will have a straightforward effect on the connected system. Increase adrenaline twice, and the heartbeat increases twice. Take away some blood, and the blood pressure decreases, take away twice as much, and the blood pressure decreases twice as much. The reality is different. A change of internal or external factor will often only induce minor changes. Losing a half liter of blood will probably induce small changes in blood pressure and slightly increase heart frequency. Removing more will eventually lead to sudden changes, at the end to a breakdown of circulation”
So due to the redundancies and complementary interactions, perturbations are allowed up to a certain point. Likewise, the complex oscillations in HRV are better described by chaos theory, something beyond the topic of this article. What scientists like Gisselman are saying that despite all the noise from HRV, during injury or inflammation in the body, there is a heightened response from the ANS and this is reflected by a change in HRV. This is because the branches of the ANS innervate the heart. Thus the long-winded hypothesis of relating injury towards HRV can be seen in the diagram below. One of Gisselman’s key questions follows — Can HRV detect subtle changes to the body’s state of stress and injury before a big injury happens?
How Useful is A Seismograph?
A rough metaphor of HRV can be drawn to a seismograph, a device used to measure earthquakes. A seismograph cannot predict earthquakes, it only can calculate the likelihood an earthquake will occur in a certain area within a certain time-frame. And those time frames are often beyond our lifetimes. Similarly HRV lacks the predictive power compared to other measures for disease pathologies. The Atherosclerosis Risk in Communities study (ARIC) followed 11, 654 people between the ages of 45–64. Each person had a 2 minute measurement of the heart rate and then followed for 8 years. The results had the following hazard ratios:
- Myocardial Infarction (Heart Attack)-2.03 (1.28–3.23)
- Incident Coronary Heart Disease-1.60 (1.12–2.27)
- Fatal Coronary Heart Disease-1.50 (0.65–3.42)
- Non-Coronary Heart Disease Deaths-1.27 (0.84–1.91)
These are the risk ratios for myocardial infarction from AMORIS study of 175, 553 people for at least 60 months so the study is much more powered to detect differences than the ARIC study. Here are the following risk ratios for the highest levels of ApoB in the cohort for male sex (1) and female sex (2)
- Myocardial Infarction 1.33 (1.17–1.51) p value <.0001
- Myocardial Infarction 1.33 (1.17–1.51) p value <.0001
Despite having some predictive power regarding the risk of developing coronary artery disease, HRV is not an accessible modifiable risk factor compared to other known risk factors such as controlling diet, body weight, physical activity, sleep, and smoking. Yes, the paper has found HRV as coronary heart disease risk factor independent of the metabolic and traditional derangements such as glucose, cholesterol and blood pressure, but because the relationship is very chaotic, it is very different to know what change to HRV will meaningfully decrease the risk of coronary heart disease. It is also comparatively, under-powered to the AMORIS study (~n = 11, 000 vs 175, 000). Referring back to our seismograph metaphor, if the device tells you that an earthquake will occur in your city in the next 200 years how likely is it worth investing in moving away from that city?
While Gisselman’s hypothesis is intriguing, to prematurely extrapolate HRV towards predicting sports injury and recovery is quite a leap. One study in 2017 has attempted to explore HRV’s relationship to injury risk found that a high workload combined with a very low HRV had a 2.61 risk ratio 90% CI: (1.38–4.93). The wide range between the ratios is because the study only looked at 6 subjects. I’ll leave you with the following points.
- The relationship between the autonomic nervous system and HRV is non-linear
- There are many extraneous and noisy inputs contributing to HRV, including biological sex, age, fatigue, genetics…all of which hides the signal of HRV. Meaning that if you have a low HRV, it is difficult to deduce what exactly is causing it.
- Individual variability is high and the statistical effects are only profound on a group level-making clinical application very difficult
In short, the effort to sift through all the noise to make a judgement whether a HRV reading is meaningful to your body is not worth the reward. However, HRV is an interesting reflection of the internal inputs in the body and further and complex research may one day yield meaningful clinical use for the athlete and patient.
- Gisselman, A. S., Baxter, G. D., Wright, A., Hegedus, E., & Tumilty, S. (2016). Musculoskeletal overuse injuries and heart rate variability: Is there a link?. Medical Hypotheses, 87, 1–7.
- Liao, D., Carnethon, M., Evans, G. W., Cascio, W. E., & Heiss, G. (2002). Lower heart rate variability is associated with the development of coronary heart disease in individuals with diabetes: the atherosclerosis risk in communities (ARIC) study. Diabetes, 51(12), 3524–3531.
- Ernst, G. (2017). Heart-rate variability — More than heart beats?. Frontiers in public health, 5, 240.
- Kemp, A. H., Quintana, D. S., Quinn, C. R., Hopkinson, P., & Harris, A. W. (2014). Major depressive disorder with melancholia displays robust alterations in resting state heart rate and its variability: implications for future morbidity and mortality. Frontiers in psychology, 5, 1387.
- Walldius, G., Jungner, I., Holme, I., Aastveit, A. H., Kolar, W., & Steiner, E. (2001). High apolipoprotein B, low apolipoprotein AI, and improvement in the prediction of fatal myocardial infarction (AMORIS study): a prospective study. The Lancet, 358(9298), 2026–2033.