Resting Heart Rate and Heart Rate Variability (HRV): What’s the Difference? — Part 5

A brief history of stress and physiology

In part 1 of this series, I covered the basic physiology of heart rhythm regulation. In part 2, I discussed the technology required for these measurements, why some sensors can be trusted, and why others can be used just for resting heart rate, and not for HRV. In part 3, we started looking at the data, with an analysis of population-level differences in resting heart rate and HRV. Finally, in part 4 we covered the most interesting aspect: individual-level data.

In this blog, I will report the main takeaways of the previous four parts, as a final wrap-up.

You can find the other parts of this series at these links:

Check out my Twitter (@altini_marco) for updates.

1. The Physiology

Heart rhythm (both HR and HRV) is influenced by the autonomic nervous system in response to stress. At rest, parasympathetic activity is predominant, which results in lowered HR and increased HRV, with respect to the heart’s intrinsic firing rate. When we inhibit parasympathetic influence on the heart, HR increases dramatically, almost reaching the intrinsic firing rate (100 bpm), and HRV at higher frequencies almost disappears. On the other hand, when we inhibit sympathetic influence on the heart, HR decreases, but just a tiny bit, showing how at rest, the heart is mostly under parasympathetic influence.

Parasympathetic influence on heart rhythm is quick, and therefore captured by high-frequency HRV changes (in terms of common HRV features, both HF and rMSSD are able to capture these changes, mathematically speaking). Additionally, parasympathetic activity is coupled to breathing, with increased firing during expiration, and ~no firing during inspiration. This firing pattern originates in the brain and causes increased HRV, but is not captured by HR alone. This is a key peculiarity of HRV as low modulation of heart rhythm during breathing (read: low HRV) is associated with a number of adverse outcomes.

Thus, HRV analysis captures information not present in average HR alone, highlighting an important difference at the physiological level in response to stress.

Pharmacological methods have been used for over 40 years to investigate autonomic modulation of the heart (see this paper,). Here the authors administered the same drugs (propranolol and atropine) in different orders, but the outcome is always the same. When atropine was administered, HR jumps up (the parasympathetic system is inhibited), while when propranolol was administered, HR slightly reduces. This data, together with results from several other groups, has consistently shown how the body at rest is predominantly under parasympathetic influence.

2. The Technology

Heart activity can be measured in different ways, using electrodes to pick up electrical current, capturing body movement as a result of the heart pumping blood, or using light absorption or reflection to detect blood flow. All methods are potentially capable of measuring HRV correctly.

However, all technologies are affected by artifacts, either due to measurement error or actual ectopic beats. Using ECG technology does not make the data better quality. On the contrary, chest straps do not do any artifact processing and the app you use becomes even more important to ensure correct HRV analysis.

You have different options and trade-offs available in terms of sensing modality, cost, and ease of use. Chest straps, wristbands, rings and apps using the phone cameras can all be reliable, but make sure they have been developed and validated for the purpose of HRV analysis. Most sensors out there can only measure reliably resting heart rate. Validations are key to prove that a given sensor can not only measure HRV accurately but also deal with artifacts. Ideally, validations should cover a very broad range of HRV values collected in realistic settings (see our validation of HRV4Training, here).

The timing of the measurement will also matter, for interpretability, regardless of the accuracy of the measurement. Sensors that are accurate might still provide less useful data when automatically sampling during the night, especially if they report only 5 minutes of data or a few data points. Resting heart rate is less affected by this problem, as it is a more stable (less variable) signal. On the other hand, HRV is highly affected by this issue, and therefore I would highly recommend using a sensor that averages at least 4–5 hours of data during the night or taking a morning measurement.

A final note on arrhythmia. Unfortunately, if your arrhythmia is frequent during the night, there is no point using a device that measures as you sleep. In this case, the only way to measure HRV is to take a morning measurement during a period in which you have no or fewer ectopic beats. These issues should be carefully evaluated in sports settings as athletes tend to have a higher prevalence of ectopic beats, especially in the context of endurance sports.

In the figure above, each row represents a different sensor, each column a minute of RR interval data. In this case, the Scosche rhythm+ (not to be confused with the Scosche rhythm24), in row 3, shows smoothed oscillations in RR intervals. The sensor is therefore unable to pick up beat-to-beat differences and variability (HRV), while still providing accurate heart rate. Rows 1 and 2 show reference Polar H7 data, and camera-based acquisition using HRV4Training, both capturing beat-to-beat differences with high accuracy. This is the typical behavior of many sensors and should highlight how an accurate measurement of resting heart rate, does not guarantee an accurate measurement of HRV. Note how the issue is not the technology per se (PPG), but how the data is processed.

3. Population-level data

Population data covers a huge range, highlighting how looking at your own individual variability over time becomes more relevant for decision making. This is the case for both resting heart rate and HRV, but especially for HRV, due to the broader range of possible values.

In terms of differences between subgroups of the population, a slightly higher resting heart rate in women is well documented. However, HRV is fairly similar between men and women, with potential differences disappearing after menopause.

Due to well-known training adaptations, resting heart rate decreases with increased cardiorespiratory fitness level, while HRV shows a weaker association with fitness level and is less predictive of changes in fitness.

On the other hand, HRV is tightly coupled to aging and could be a marker of aging, as shown by the association between e.g. lower HRV and negative health outcomes. Resting heart rate shows no link to aging, and most likely reflects changes associated with training habits, more than other factors.

Finally, Higher BMI is associated with lower HRV and higher resting heart rate, with comparable differences between the two metrics.

In the figure above you can see how resting heart rate does not change across age groups but is tightly coupled to physical activity level. On the other hand, we can see how HRV reduces with age no matter the physical activity level, even though slightly higher HRV is associated with higher fitness across age groups

4. Individual-level data

Looking at the data, HRV confirms to be a more sensitive metric when analyzing responses to acute stressors. In particular, very strong stressors such as sickness (and excessive alcohol intake) can result in similar responses in resting heart rate and HRV, while more subtle stressors are better captured by HRV.

Another key aspect concerns data interpretation. When analyzing physiological data, high day-to-day variability can make it challenging to understand when a change is within normal variation and when a change is outside what we’d normally expect (and could therefore highlight a negative stress response). Normal values, built using between 30 to 60 days of previously collected data, effectively capture abnormal variations in resting physiology. You should not overthink changes within your normal range, as they do not reflect any particularly meaningful change.

When looking at resting heart rate, typically day to day changes are within an individual’s normal range. Thus, even in the long run or in the context of chronic stressors, HRV is often a better marker of changes in physiological stress, with respect to resting heart rate.

Above you can see for example a period of more chronic stress, in which I had several weeks of headaches due to allergies and other issues. The normal range (light gray band) is present for both HRV (left pic) and heart rate (right pic), but only HRV captured these issues, as highlighted by a clear suppression (baseline, or light blue line, below my normal range — remember that a low HRV is a negative response to increased stress)

That’s all

I hope you have found this series useful. Understanding the basic physiological mechanisms, the strengths and limitations of currently available technologies, and looking at the data both at the population and at the individual level, we can make better use of measurements of resting physiology such as heart rate and HRV.

In particular, we have seen in part 1 of this series how when we count beats over a period of time (that is, resting heart rate), we completely ignore the timing of parasympathetic influence on heart rhythm and therefore miss key information. This explains why parasympathetic activity is better captured by HRV, and why HRV is, in turn, more tightly coupled to most stressors, especially when the stressor is more subtle.

In part 3, we have seen how resting heart rate and HRV tell very different stories at the population level, highlighting once again important differences. Finally, in part 4, we’ve been looking at plenty of individual-level data, confirming that HRV is a more sensitive metric when analyzing responses to acute stressors, while resting heart rate is a more blunt instrument.

Capturing more subtle stressors or stress responses before they develop into negative chronic states can be key in making adjustments leading to improved health and performance. For these reasons, HRV is in broader terms a better tool for day-to-day stress management (exceptions always apply).

This is not to say that resting heart rate is not a useful parameter. On the contrary, in the context of illness detection, it might even be a better marker, as it is less affected by more subtle stressors. Similarly, resting heart rate is a much better marker of cardiorespiratory fitness.

That’s all for now. Feel free to reach me here for any questions.

Marco holds a PhD cum laude in applied machine learning, a M.Sc. cum laude in computer science engineering, and a M.Sc. cum laude in human movement sciences and high-performance coaching.

He has published more than 50 papers and patents at the intersection between physiology, health, technology, and human performance.

Marco is the founder of HRV4Training, data science advisor at Oura, and guest lecturer at VU Amsterdam. He loves running.

Twitter: @altini_marco

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Marco Altini

Marco Altini

Founder HRV4Training.com, Data Science @ouraring Lecturer @VUamsterdam. PhD in Machine Learning, 2x MSc: Sport Science, Computer Science Engineering. Runner