The Ultimate Guide to Heart Rate Variability (HRV): Part 2

This is the second part of my series of educational posts on heart rate variability (HRV). You can find the other posts at these links:

Let’s dive right into it.

Part 2: You measured. Now what?

Once you start collecting some data, the most important thing to remember is that you are unique.

This means that you need to always interpret your HRV data with respect to your historical data, and there is no point in comparing your HRV to another person. Your normal might be different from your friend’s normal (most likely it will be), and that means very little. This difference in what we call the average baseline HRV is most likely due to genetics and other factors hardly modifiable (at least if we consider health-conscious individuals, if your lifestyle or health is not good, then you can also improve your baseline — but even in this case, the most meaningful way to use your data is to look at how things are changing over time).

Once we have understood that we should only look at relative changes over time, with respect to our historical data (our previous recordings), one of the most important points for data interpretation needs to be addressed: fluctuations between consecutive days. Let’s look into this point in more detail.

Fluctuations between consecutive days

HRV data has an inherently high day-to-day variability. This means that there can be large fluctuations between consecutive days, differently from parameters that you might be more familiar with (for example your heart rate or your body weight).

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 so that we can easily establish if a change in HRV is meaningful. In the next sections, I will cover in more detail all of these aspects.

Normal is good: determining your normal range

HRV analysis requires a mindset shift. As discussed in Part 1, we first need to understand the nature of the data and the constant re-adjusting of the autonomous nervous system, and therefore take all the necessary steps to acquire a reliable measurement. This is typically addressed by the morning routine: the importance of context, limiting external factors, measuring as soon as you wake up and in the same body position every day — with the only viable alternative being as previously introduced, the night measurement. Then, we need to shift from a “higher is better” to a “normal is better” mentality, as physiologically speaking, being in a stable condition is typically a good sign.

The inherent variability of HRV 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.

In the HRV4Training app we report daily if your score is within your normal day-to-day range, or not. You can learn more about the daily score, baseline and, normal range at this link. Below you can see for example a day in which HRV is particularly low, highlighting more stress on the body, due to traveling.

Resting heart rate is typically not as sensitive to stressors, as we will discuss below, and indeed in this case remained within normal range

Example of an acute reduction in HRV, representative of higher stress. In this case, the reduction was associated to travel. We’ll learn more about acute and chronic stressors in relation to HRV, in this blog post

You can see some more examples below, where an individual’s normal range is highlighted by a light blue band, which makes it clear when HRV is significantly suppressed (for example when the daily score or baseline is below the band). These visualizations become more helpful as you gather a few months of data, and you can try them on HRV4Training Pro.

Below we have two examples showing HRV changes in response to sleep and work-stress issues (more examples later).

Examples of physiological responses to training and lifestyle stressors. In the first figure, a disruption in sleep causes several days of poor condition and high stress, with the baseline below normal values. In the second figure, a large increase in work-related stress ends up causing a huge dip in HRV regardless of training. All stress matters: you can easily analyze significant changes in HRV and contextual data (training, subjective metrics) in HRV4Training Pro.

Start by observing

I’d recommend just measuring and collecting data for several weeks (or even months!) so that you can start getting a feel of how you respond to various stressors and how the data reflects your choices (in terms of both training and lifestyle) as well as how it relates to subjective parameters. There are various ways you can analyze your data without necessarily making changes, here are the two most common methods:

  • Acute changes (day to day variability in response to strong stressors)
  • Medium to long term trends (long term responses and adaptation to larger stressors)

Let’s look at both of them in more detail.

Acute changes: what happens after a strong stressor?

What’s an acute stressor? Acute stressors are events that affect your physiology in the immediate future. Think about an intense workout, an intercontinental flight, the menstrual cycle, sickness, 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 understand 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 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 HRV is such a useful tool to keep things in check, no matter what sport you do, there will be stress.

Let’s look at some data, for example, changes in HRV due to traveling and alcohol intake:

On the left, we can see how HRV is reduced during a month with frequent traveling (yellow distribution), with respect to another month at home (blue distribution). On the right, we can see how having no alcohol or a glass of wine does not impact the physiology of the person collecting these data, but having more consistently reduces HRV. Courtesy of Massi Milani.

These are simple examples of how you can analyze your response to acute stressors in isolation, for example in the HRV4Training app, which provides this analysis for you:

Acute HRV changes analysis in the HRV4Training app. You can analyze systematically responses to various factors such as alcohol intake, menstruation, training, travel and getting sick.

Let’s look at training as well, from an acute-response point of view. 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.

To analyze this relationship between training and physiology, we can first compute day-to-day differences in resting HRV for a person. Subsequently, we can analyze the change in HRV on days following training of different intensities.

Here is, for example, a paper we published showing that measurements taken first thing in the morning, in unsupervised free-living settings (real life, not the lab!), clearly capture the different recovery demands of training of various intensities, across a broad population (almost 30 000 individuals).

As you can see from the figure below, both heart rate and HRV capture recovery needs following training of different intensities, but HRV is more sensitive, and therefore more useful (the percentage change in HRV is larger, and remains the same across different age groups, while this is not the case for heart rate).

Relation between HR, HRV, and training load split into two categories, analyzed on the entire dataset and grouped by age group. HR is consistently increased on days following higher intensity training load, while rMSSD is consistently reduced. Relative changes in rMSSD are larger, highlighting how HRV can be more discriminating for training intensity. Additionally, percentage changes in heart rate reduced with age while remaining constant for rMSSD. Error bars indicate the standard error.

This analysis is also part of the acute HRV changes analysis shown before in the HRV4Training app, shown above.

If you are interested in this research, in the paper we also analyzed systematically the impact of training intensities, menstrual cycle, sickness, and alcohol intake.

As you can see from the figure above, there is quite a strong relationship between intense workouts and reductions in HRV on the following day. This is a typical acute stressor, and the reduction in HRV can be used to quantify recovery and understand if we need an extra day off.

However, this relationship does not tell us much about long-term adaptations to a training block or training program (as well as other lifestyle stressors). That’s what we will learn by looking at medium and long-term trends.

One last important point to discuss here. The view that training should cause a dip in HRV is very simplistic, HRV is a measure of physiological stress — or even better: a measure of how we are adapting and responding to stress. While higher stress is typically highlighted by a reduction in HRV, positive adaptation to stress (think for example about an intense training block) should result in a stable or increased HRV.

Let’s look at some data from a different angle to clarify these points.

Long-term changes: how are you responding and adapting to training and lifestyle stressors?

HRV trends over long periods of time (e.g. from weeks to months) are one of the most interesting and complex aspects to analyze. While day-to-day acute changes reflect rather well training load in the day(s) before the measurement, which is one of the principles behind using HRV to quantify recovery needs (as we have seen in the previous section), in the long term things get much less linear. However, analyzing long-term trends is a very powerful way to better understand adaptation to training and determine if it is the case to implement changes to our planning.

Due to the availability of more practical tools, much research has been carried out in the field in the last few years, showing consistently a few aspects that are applicable across a wide range of sports and athletes.

Positive and negative responses

In particular, in this section, I will focus on the most important parameter, the HRV baseline (7-days moving average, shown as the blue line below) with respect to the normal values (historical data, shown as a light blue band below).

Let’s look at an example. Physiological stress comes from different sources, all having an impact on our ability to deal with additional stress and therefore maintaining or improving our health and performance. In the figure below we can see how both running a marathon and a few days out of the ordinary for new years trigger a baseline change below normal values, a clear sign of high stress and difficulty coping.

See how much variability we have on a day-to-day basis? The gray bars (or actual daily scores) jump a lot, this is why we should rely on statistical representations that are able to provide a more clear view of what is going on. In our software, we show your historical data as a band that is built using the past 60 days of data and highlight where your data is expected to be if there were no major disruptions. The baseline, or 7 days average, is normally within this band, showing that it’s all good, and we can proceed as planned. When the daily scores or the baseline are below the band, then it means we have significant stress, as we are outside of the range that is considered normal for our own physiology.

These visualizations are helpful when we look at the big picture, beyond simple acute responses. In this case, you should not expect your HRV to reduce if you are responding well to stress in the medium / long term, even during a high-volume or high-intensity training block. On the contrary, a positive adaptation is shown as a stable HRV or even increasing HRV for a few weeks.

HRV4Training combines multi-parameter data to help you better understand the big picture. Looking at baseline changes in HRV, heart rate and the coefficient of variation of HRV, the app can automatically determine if your recent trends are changing in a trivial way, or if the change is something to take more seriously, based on your historical data​. ​Once the various trends have been analyzed, HRV4Training will determine your physiological response to training as one of the following categories: stable physical condition, coping well with training, maladaptation to the current stimulus, or accumulated fatigue.

You can learn more about the coefficient of variation, long term trends and physiological responses to different training phases, in this blog:

Context is key

Remember that training is hardly the only stressor in anyone’s life, no matter if you are a professional athlete or just went for your first run yesterday. There’s work, family, expectations, etc. — we need to deal with a lot more than just training, and it all affects us physiologically.

Below you can see an example of altered physiology during the menstrual cycle, typically resulting in a suppression of HRV and increased resting heart rate during the luteal phase (second half of the cycle):

if you are a HRV4Training Pro user, you will also see the normal values on the Baseline page, together with some of your annotations, as shown above

This is the whole point of HRV monitoring, it’s naive to think that the only thing that matters is training, and no matter what sport you do (or do not do), measuring your response to stress can help you get back on track.

It makes no sense to look at your HRV data decontextualized. HRV represents your response to training and lifestyle stressors, and you need to look at how things are changing with respect to those stressors, as shown in HRV4Training Pro for example.

Make sure to collect data properly, as explained in Part 1, and you’ll be able to see useful trends over time.

How do you feel?

How you feel is an important part of capturing context around your measurements. Sometimes your subjective feeling will be in line with what you see in the data (for example, your HRV is within normal range and you are feeling good). Other times, the data might capture stressors that are not obvious yet (for example, a suppression in HRV before getting sick, or more chronic stressors slowly creeping in). Subjective feel and HRV are not supposed to reflect the same aspects or align over time.

Despite how some try to antagonize subjective feel and objective data, it is fairly obvious that subjective metrics and being able to assess how we are feeling is very important. This is why we include a questionnaire after the measurement so that you can take a minute to pause, and self-assess how you are feeling subjectively, a key part of the process.

The data can typically aid this process. As you gather more data, you can learn to fine-tune your subjective feel and assessment based on your objective response to the various stressors you face.

A smart coach, educator or athlete, understands that training load, HRV, and subjective metrics all provide important information that needs to be integrated daily, to decide the better course of action.

There is no winner between objective and subjective metrics, they all serve a purpose.

Now let’s make some adjustments

So far we have seen how HRV data can be used to better understand how we are responding and adapting to various stressors. While this is a key first step towards improved health and performance, we have not made any changes yet.

In my opinion, HRV should be used as a continuous feedback loop, so that in the long term, what you improve is health and performance (by for example providing the right stressor at the right time, as shown by research on HRV-guided training), and not HRV itself. This is different from for example even just resting heart rate (or even better, submaximal heart rate during exercise), which typically is highly correlated with changes in cardiorespiratory fitness and reduces as you get more fit. This is also why HRV is not a good predictor of fitness (we don’t even use it in our VO2max estimate, as explained here).

How do you integrate HRV with your training plan? Should you have a plan at all?

Let’s revise the basics: HRV, is simply a way to capture parasympathetic activity, or in other words, level of physiological stress. As we apply stress to trigger certain adaptations, measuring our body’s response to such stressors, as well as to all other forms of stress we are affected from (e.g. simply life happening, work stress, family, etc.), is very helpful as it can provide objective feedback and help us make meaningful adjustments.

The simpler adjustments are probably just being a little more honest with ourselves, and slowing down from time to time, especially when our body is already too stressed. The example we’ve just highlighted is something we all understand quite well, higher stress as shown by lower HRV highlights how it might be a good idea to take it easy and avoid excessive stress which might lead to overtraining or slower recoveries, hindering improvements in performance.

When everything is within your normal values or in other words a green light, should give you confidence that everything is going well and in general, you are coping well with your current training and lifestyle. Yet, if your training plan says you are due for a rest day, take it. If you are due for a low-intensity workout, do it. It is important to understand that HRV and physiological measurements are tools for awareness, which allow you to understand how you respond to a particular plan, not to replace your plan entirely​.

What’s next?

Now that you have seen how you can interpret your measurements by looking at relative changes over time you should be able to make the most of your data.

Most importantly, the data above should clearly show how important context is. It makes little sense to analyze HRV just in relation to training or to measure it without properly contextualizing data. This is why HRV4Training includes a simple questionnaire after the measurement and provides the visualizations shown above to let you explore your data in relation to all other factors (travel, subjective parameters, training load, your normal range, etc.).

In the next posts, we’ll look at more data with a few extra case studies and also learn about some common misconceptions:

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.

He is the co-founder of HRV4Training and loves running.

Twitter: @altini_marco

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Marco Altini

Marco Altini

2.6K Followers

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