Our hormones, neurotransmitters, blood pressure, heart rate and other markers all vary in response to different situations we find ourselves in. Our bodies are continuously adjusting to different stressors to maintain a balanced state. One of the most important mediators of this delicate balance is the autonomic nervous system. Heart rate variability is directly regulated by the autonomic nervous system. This makes it an especially useful proxy to measure how our bodies respond to different stressors. For example when our bodies are under stress, this can be reflected in a decline in HRV. But its more complicated than that.
To understand HRV correctly, we must understand what stress is. What we mean by stress here is physiological stress. Which is any condition that challenges the internal balance of our bodies or what is called homeostasis. The cause of stress can be external or internal. How well our bodies react to that stressor is reflected in HRV. What I like about HRV is that it is an objective measure that can give us real time feedback on different situations and behaviours.
These are the 4 most common mistakes I see people make when interpreting their HRV:
As I explained earlier, a decline in HRV can mean our body is under stress and subsequently, a rise in HRV can mean the opposite. It is important to note that stress is not always a bad thing. In fact it is very often a good thing. Physiological stress that accompanies exercise is a necessary signal to our bodies to strengthen our tissues and improve performance.
What does this mean practically? When looking at your HRV data, too many consecutive high HRV readings (if you use Whoop this would be seen as a recovery that is always green), this can mean that you are not putting your body under enough stress from exercise or if you already exercise regularly this could indicate that you are not training hard enough. A sedentary life can be stress free, but that doesn’t make it a good idea.
On the other hand, a constant decrease in HRV could be an indication of overtraining and fatigue. This may be a signal to get more recovery time. There have been multiple studies showing that HRV guided training is better at increasing fitness and performance to training based on more conventional methods. Athletes use HRV as an objective measure to determine where they are on the training continuum below and make sure they don’t go too far to towards the end.
The absolute reading of HRV does not mean much on its own. Always compare your data with your historical data in mind. To do this correctly you must determine your baseline. Which is the average over a period of 7 days where you have had relatively no stressors that are out of the ordinary. Once you’ve determined your baseline you can start comparing your readings to it.
There are generally two types of trends to notice: short term and long term trends. Short term changes in HRV can show up after a strong stressor such as overtraining, sleep deprivation or drinking alcohol. These will take effect within 24-48 hours. I explained in detail how alcohol affects HRV in a previous article. If there is no obvious stressor, think about possible causes that can go undetected such as low sleep quality or certain lifestyle changes. It is important to know that the autonomic system can be very complex and there can sometimes be normal day to day variations in HRV that are difficult to explain.
Long term variations from your baseline can be seen as changes that last more than 7-days. These are often neglected when interpreting HRV data because they are not always reflected as a simple increase or decline in HRV. Long term negative trends Appear as increased fluctuation in day to day readings as well as a decline in average HRV. Positive trends are more likely to be reflected in a more stable HRV trend or a gradual increase.
Negative long term trends are more likely to reflect more insidious changes in lifestyle or behaviours that have a cumulative effect such as moving homes, starting a new job or too much overtraining. Examples of a positive trend are training the right amount or they may simply mean that you are adapting well to a new environment.
Averages from big data sets can be misleading. There is a huge variability from person to person in HRV. It is true however, that there is also a general decline in HRV observed with age when looking at large datasets. This means that there is some consistency between people.
But I believe when it comes to biomarkers such as HRV it is more useful to focus on what is actionable. If you have a naturally low HRV compared to others, this is most likely due to your unique physiology and genetics. Unless you can change your genetic makeup there is no point comparing your HRV with people other than yourself.
It is far more useful to compare your metrics to your historical data. When interpreting HRV, it's more useful to think of it as a guide to existing behaviours rather than try to alter it by introducing new behaviours that specifically target HRV.
Decrease in heart rate variability with overtraining: assessment by the Poincaré plot analysis
https://pubmed.ncbi.nlm.nih.gov/14717743/
Heart Rate Variability: An Old Metric with New Meaning in the Era of Using mHealth technologies for Health and Exercise Training Guidance. Part Two: Prognosis and Training
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6304793/
Daily exercise prescription on the basis of HR variability among men and women
https://pubmed.ncbi.nlm.nih.gov/20575165/
