Resting Heart Rate and Heart Rate Variability (HRV): What’s the Difference? — Part 4
Individual-level data
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.
In this blog, we finally get to the most interesting aspect: individual-level data. Needless to say, both resting heart rate and HRV become a lot more useful when tracked over time within individuals, and this is exactly what I’ll be showing here. I’ll also try to highlight some of the differences between these two parameters, so that you can better understand what the data means when tracked in response to strong acute stressors (e.g. training, sickness, alcohol intake, the menstrual cycle) and in the longer run (e.g. changes in fitness).
You can find the other parts of this series at these links:
- Part 1: The Physiology
- Part 2: The Technology
- Part 3: Population-level data
- Part 4: Individual-level data (this post!)
- Part 5: Takeaways
Check out my Twitter (@altini_marco) for updates.
What happens when we face stress?
In part 1 of this guide, I’ve discussed the physiology of heart rhythm. However, we didn’t go into much detail about why we look at resting heart rate and HRV in the first place, when analyzing stress and recovery.
Let’s cover this aspect before we start looking at the data.
In a nutshell, the human body detects stress through its senses and sends information to the brain, which determines how to deal with them. Sources of stress (stressors) are disruptions that trigger specific responses as the body tries to maintain a state of balance, also called homeostasis, which is key to ensure optimal functioning. Impulses from the brain and spinal cord to smooth muscles and (among others) the heart, are conducted by the autonomic nervous system, which is regulated by the hypothalamus.
The autonomic nervous system controls and regulates many functions of our body, from the heart beating to respiration. We typically think of the autonomic nervous system in the context of its two branches, the sympathetic and parasympathetic nervous systems. While the sympathetic nervous system is responsible for stimulating the body’s fight or flight response, the parasympathetic nervous system is mainly responsible for the body’s resting functions.
As I’ve previously covered, both resting heart rate and HRV are mediated by neurons with parasympathetic and sympathetic origin. Thus, changes in resting heart rate and HRV can reflect our response to stress. However, the main difference between resting heart rate and HRV in this context is due to how the parasympathetic system works. In particular, acetylcholine release happens synchronously during respiration which means that during expiration vagal activity is higher, leading to a slowing down of heart rate that impacts HRV, but not necessarily average heart rate.
While this theoretical background is key, all of this would be of little use if we weren’t able to capture such stress responses using available tools able to measure resting heart rate and HRV accurately. Let’s recap this section and then look at some data to better understand some of these relationships.
Recap:
- Parasympathetic activity depends on breathing, with increased firing during expiration, and ~no firing during inspiration. This firing pattern originates in the brain and causes changes in HRV, but is not captured by resting heart rate
- A typical stress response results in reduced parasympathetic activity and increased sympathetic activity. Resting heart rate and HRV are both associated with resting parasympathetic activity. However, due to the point above, resting heart rate is a blunt instrument, able to capture mostly very large stressors. On the other hand, HRV is more sensitive to stress and is therefore able to better capture the body’s stress response
Acute stressors first
What’s an acute stressor? Acute stressors are events that impact your physiology in the immediate future. Think about an intense workout, an intercontinental flight, a night out with too many drinks, high caffeine intake, etc. — anything that has an effect on your physiology that lasts from a few minutes up to 24–48 hours.
Acute stressors are typically the easiest phenomena to interpret and reproduce, and looking at data in the context of acute stressors can help understanding how your physiology works. Looking at acute changes can also help in gaining confidence in the tools we use, as these changes should be captured and reproduced more easily.
It’s important to remember that physiology is complex, and while acute stressors and the resulting resting heart rate and HRV changes are often repeatable and easy to understand, there might be other factors behind the relationships that we are seeing (or not seeing) in our data. No stressor acts in isolation, there’s always something going on with our lifestyle, training, health, etc. — which is why resting physiology is so insightful.
Acute stressors are also a great framework to validate our previous assumptions, based on how the physiology of heart rhythm works. For example, by looking at the day-to-day change in resting heart rate and HRV, we can determine if a certain stressor (e.g. training, alcohol intake, traveling, sickness, etc.) has a stronger influence on HRV or resting heart rate.
The data
For this analysis, we look at heart rate and HRV (rMSSD) for 15 000 individuals as captured a few years back (in 2016 and 2017) using the HRV4Training app. That’s about a thousand times more people than the average sports science study and should give us some solid insights.
Acute stressors: Training intensity
The relation between physiological data and training can be analyzed by first computing day-to-day differences in heart rate and HRV (e.g. the difference between today’s score and yesterday’s) for each person. Subsequently, we can analyze the change in HR and HRV on days following training of different intensities for each user, answering the question: how did your HRV change between yesterday and today, considering that you trained hard (or rested)?
Here I clustered training intensities in two groups first, using self-reported intensities, and then also broke this down into 4 categories (rest, easy, average and hard). I additionally analyzed the relation between resting heart rate, HRV and training by age group (I had published an earlier analysis of this data, you can find the paper here, showing also the breakdown by sex).
The rationale behind monitoring recovery using HR or HRV is that heavy training shifts the autonomic nervous system towards a sympathetic drive, which is reflected in higher HR and lower HRV within 24 to 48 hours after training. Below you can see how morning measurements captured in free-living over a period of two years, clearly reflect the expected changes in resting physiology: increased resting heart rate and reduced HRV following harder workouts. However, we can also see that our assumption about which of the two metrics would be more sensitive to stress is confirmed, meaning that the change in resting heart rate is rather small, with respect to the change in HRV.
Given the positive results above, we took things one step further, and looked at changes in HRV for more categories:rest, easy, average or high intensity. We can see how HRV decreases the greatest for high intensity, a bit less for average training intensity, and increases for rest and easy ones, by a lesser extent. Again really interesting data and very consistent for both resting heart rate and HRV, but with larger changes for HRV data.
Acute stressors: Getting sick
In the plot below, we look at things a bit differently. When getting sick, it’s not really the day-to-day difference that matters, but it’s more interesting to look at the difference between the two conditions, so ‘not sick’ and ‘sick’.
Hence here I considered ‘not sick’ the normal, and normalized the data with respect to that ‘not sick’ value, which is why the bars are at 1.00 — then I looked at how much things change within an individual when sick, for example rMSSD is about 90% of a person’s normal when sick. Again these changes are consistent for all metrics, with the largest change shown for rMSSD (HRV).
These are simple but yet effective methods to analyze the relationship between acute stressors and resting physiology. In the HRV4Training app, you can explore acute changes in resting physiology for the parameters listed above and a few more (traveling, menstruation, and alcohol intake).
Recap:
- Looking at the data, HRV confirms to be a more sensitive metric when analyzing responses to acute stressors
- 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
What about longer-term changes in resting physiology and HRV?
As previously discussed, the analysis of acute stressors can be a useful framework to analyze differences in stress response between heart rate and HRV.
However, in the long run, many factors will play a role, some due to stress, and some due to other aspects. For example, both resting heart rate and HRV show seasonal patterns, with lower values in resting heart rate reported during summer, and slightly higher values during winter. Similarly, cardiorespiratory fitness changes will play a role, with improved fitness resulting in clear changes in resting heart rate, and less marked changes in HRV, something I have discussed in part 3 of this guide, when looking at population-level data.
While the analysis of acute stressors shown above can be informative, this is not really how we use the data normally. While data analysis and interpretation is something I cover more in detail in my HRV guide, I’d like to cover the basics here, so that I can also show a couple of examples confirming the effectiveness of HRV data in capturing changes in physiological stress, independently of resting heart rate.
Day to day variability: what changes are worth paying attention to?
Resting physiology (both heart rate and HRV) has an inherently high day-to-day variability. This means that there can be large fluctuations between consecutive days, which is different from parameters that you might be more familiar with.
What are the implications? To make effective use of the data, we need to be able to determine what changes are trivial, or just part of normal day to day fluctuations, and what changes do matter and might require more attention or simply truly represent a positive (or negative) adaptation to training and other stressors. In other words, to make effective use of the data, we need to determine your normal range.
Normal is good: determining your normal range
When looking at physiological data, stability is typically a good goal. Stable data is often associated with positive responses to stressors. The inherent variability of physiological measurements is something that your app or software of choice, needs to deal with. This is something we have spent a lot of time researching and designing in HRV4Training, starting with the way the daily advice is built.
A software that interprets any HRV increase as a good sign, or any HRV decrease as a bad sign, is failing to correctly represent the fact that there are normal variations in physiology, and that only variations outside of this normal range, should trigger concern or more attention or simply be interpreted as actual changes.
Below 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).
One last example.
I’ve shared data earlier highlighting how for me, work stress tends to be the main driver of changes in resting physiology, as work is where “I need to perform”, more than training.
Once again, the relationship between self-reported perceived stress and resting physiology, is much stronger for HRV than for heart rate (0.65 vs 0.14 in terms of baseline correlation)
These differences are quite important and can better contextualize what we have seen above in terms of acute stressors. While resting heart rate is often capable of capturing acute stressors to a certain extent, the variation due to daily stressors is so small that ends up being within an individual normal range most of the time. As a result, when collecting data, resting heart rate tends to provide less useful information when it comes to tracking stress responses and potentially implementing changes to maintain or improve health and performance.
Some important counterexamples are e.g. very strong stressors that are captured equally well by resting heart rate and HRV (the acute phase of sickness, or excessive alcohol intake), as well as the menstrual cycle, which normally shows similar oscillations in resting heart rate and HRV.
Additionally, as we have seen in part 3 of this guide, resting heart rate can still be extremely valuable to track other aspects, for example, improved cardiorespiratory fitness level in response to an exercise program.
Recap:
- 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
- Changes in resting heart rate are typically 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
Takeaways
As the body tries to maintain balance so that it can function optimally, heart rhythm is influenced by a series of processes going from the brain to the heart, processes that reflect the level of stress on the body. Both HR and HRV are key health and performance markers influenced by stress responses. However, as this series tries to point out from different angles, they do differ (which is why it is worth looking at both).
The differences in resting heart rate and HRV are linked to their physiological origin and can be observed at the population level and within individuals, in response to both acute and chronic stressors. 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 this blog, we’ve been looking at plenty of data, confirming that HRV is a more sensitive metric when analyzing responses to acute stressors, while resting heart rate is a more blunt instrument. I’ve also introduced the concept of the normal range, and shown a few examples of how changes in resting heart rate are typically within an individual’s normal range, due to less sensitivity to daily stressors. 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.
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. As always, understanding the physiology and looking at the data can help us make better use of the available tools. I hope this series of blogs has been helpful in this context.
That’s all for now. In the last part of this guide, we’ll summarize the main takeaways on the differences between resting heart rate and HRV.
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
