Heart Rate Variability (HRV) trends: going beyond daily scores

Examples of resting physiology analysis and trend detection in HRV4Training

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Heart rate variability (HRV) trends over long periods of time (e.g. from weeks to months) are one of the most interesting and complex aspects to analyze when it comes to resting physiology. While day-to-day (or acute) changes reflect well stressors such as training intensity, the menstrual cycle, sickness, alcohol intake, or travel in the day(s) before the measurement, in the long term things are quite different.

Here are two simple examples:

  • Your HRV after a hard session will tend to reduce (acute effect). However, a positive response to that session, as well as other factors resulting from consistent training, even when part of a high intensity or volume block, should increase your HRV in the long run (chronic effect, or trend). There is often a clear difference between the short (or acute) and long-term (or chronic) effects of a positive stressor.
  • Your HRV after rest will typically increase (acute effect). However, a bell-shaped HRV trend has been reported for athletes following a training program of 2–3 months before a competition. Thus, HRV does not simply increase with better physical condition and fitness but typically increases up to a point (e.g. upon reaching functional overreaching), and then decreases (e.g. during tapering) before the competition.

Thus, relations between HRV, training load, fitness, and recovery get more complex to analyze when we move beyond day-to-day acute changes.

In this post, I will cover our approach to trends analysis in HRV4Training, and cover some of the features in the app that should help you make sense of the data in the longer term.

The importance of trends

Day-to-day HRV guidance is very useful to make small changes to our overall training plan. If today we are down, we can easily adapt and move our intense training by a day, or make some other small adjustments to better balance the different stressors we face.

Day-to-day guidance based on acute changes can help better manage stress and avoid negative responses to stress in the long term. However, this kind of analysis cannot say much about the big picture. How is our overall condition? Are we at risk of non-functional overreaching? How are we adapting to a new training phase? Analyzing trends can help you answer these questions and provide a better understanding of your overall physiological response.

Note that while most research on HRV-guided training these days relies on trends and your normal range (covered here), this is not to say that acute changes should be ignored. Acute reductions can signal incoming sickness for example, and should always be contextualized with your own subjective feeling and all the additional information you have available (if it was just a bad dinner, nothing to worry about). There’s a place for both acute and chronic responses if we understand the differences and are able to properly contextualize the data.

Left: daily advice, or acute changes. After a race (23/10), HRV is suppressed below my normal for 2 days, then gradually climbs back. Right: detected trends, or longer-term analysis. It takes about a week for baseline HRV, heart rate, and coefficient of variation to re-normalize. These are some of the parameters we use for automatic trend detection. In this case, maladaptation was detected for several days. More on this, below


First of all, some definitions and clarifications:

  • Trends are changes or adaptations in physiological values (e.g. heart rate, HRV) over longer periods of time, typically weeks to months, during a training program. It is important to consider trends in the context of training load (e.g. high-intensity block, tapering, etc.) [5]. Our trends analysis uses between 40 and 60 days of data.
  • When talking about HRV I am referring to rMSSD (or ln rMSSD as reported in HRV4Training) baseline values, or in other words, 7 days moving averages. All other variables introduced later (resting heart rate, coefficient of variation of HRV, etc.) are also considered as 7 days moving averages, since we are not particularly interested in acute changes for this analysis.
  • Determining accurately the HRV baseline value requires daily recordings (or at least 3–5 recordings/week). So this analysis requires that you take your daily measurement on most days, or read your night data using the link between HRV4Training and Oura.
  • The HRV coefficient of variation (HRV CV) is a measure of the variability in our values. Analyzing both the baseline rMSSD value and its variation (CV) can provide more insights compared to looking at the baseline only. More details on this, below
  • Normal range: your normal range is the range where we expect your data to be, provided that no abnormal stressors are present. We show the normal range on the homepage of the app, and on the Baseline page for Pro users. You can learn more, here.

What does the research say?

​In this section, I will try to highlight the main findings and structure them in a way that can ease trends interpretation. ​This post builds on top of a great paper published by Martin Buchheit [3], in which he already provided a generalization of the main trends seen in years of his research in sports and performance. I have combined his analysis with additional findings and considerations derived from the work of Daniel Plews [1, 6], Andrew Flatt [9], and others. Finally, I have added my own considerations of how all of these parameters can be analyzed together, which is what I ended up implementing in HRV4Training (covered below).

Research up to today is clear on at least two points: first, for trend analysis a multiparameter approach is key. Looking at HRV alone will not be sufficient to understand long-term trends and overall physical condition.

Secondly, the relation between HRV and cardiorespiratory fitness which is sometimes observed at the cross-sectional level over a population is much less obvious when we analyze HRV changes longitudinally within one person, especially for elite athletes following a specific training program. This is something I have also covered in my recent guide on resting heart rate vs HRV, showing how resting heart rate has a stronger association to cardiorespiratory fitness, with respect to HRV (learn more, here).

Let’s look at an example. Below are a few physiological trends after a hard effort (2h 40' at 85% of maximal heart rate, left picture), and a week after the race (right picture).

Example of automatic trend detection in HRV4Training. HRV baseline, heart rate baseline, and the coefficient of variation are used together with your normal range, to determine the most likely physiological response.

We can see how the HRV baseline is initially stable, with a slight increase over a period of consistent training, and dips post-race (decrease detected in the left picture). Similarly, resting heart rate is rather stable and shows an increase towards the end. The large jump in these metrics, due to a strong acute stressor such as racing, is also captured by the coefficient of variation, which spikes up (increase detected). The app detects maladaptation.

After about a week (right picture), HRV starts to climb back up again, and values are more similar on consecutive days, resulting in a stable physiological condition (and reduced coefficient of variation). The app detects coping well with training again. Let’s see below how these trends are analyzed to estimate the physiological response.

A multiparameter approach

In HRV4Training, we use resting heart rate, HRV, and the HRV coefficient of variation (HRV CV), for our trends analysis, adding training load as contextual information [1, 3]. Additionally, we also use your normal range, to determine if the physiological trend is causing your baseline to shift outside of what is considered normal variation for your physiology.

Analyzing both HRV and heart rate can be used to better understand fatigue, since a decrease of HRV alone is not necessarily associated with increased fatigue [6].

A reduction in HRV CV has been associated with an increased risk of non-functional overreaching [1]. This makes sense since less variation might mean that there is more stress. However, there is some controversy on this point since other authors interpret a reduction in HRV CV as a measure of better adaptation [9]. This also makes sense since we expect more day-to-day changes if we are not coping very well with the training and therefore HRV jumps around more, showing difficulty in maintaining homeostasis (e.g. when we start a new training phase with more high-intensity work). A multiparameter approach allows you to make sense of both trends, as a lower HRV CV becomes problematic only when HRV is also suppressed.

Typically, coping well with training was associated with higher HRV and lower resting heart rate[8], while not coping well was associated with the opposite. Finally, reductions in HRV and resting heart rate can be associated with parasympathetic saturation (i.e. parasympathetic activity is actually increasing even if we see reduced HRV) [3]. This is a bit of a more challenging situation to detect, which I discuss here.

HRV and training load in the longer term

Making some oversimplifications, in the average population or in recreational runners negative adaptations to training (non-functional overreaching, overtraining) are generally associated with reductions in parasympathetic activity (HRV), while better fitness and performance is typically associated with higher values of HRV [4]. However, in elite athletes, when training load is close to an individual maximal load, HRV can either be unchanged or reduced [6], while after a few weeks of reduced load it can increase again [5]. Therefore HRV reflects training load more than cardiorespiratory fitness level, consistently with what we see at the population level. I have discussed this a few times in the past, and I believe we should not use HRV as a marker of fitness, but a marker of our current response to stress.

Similarly, training phases of higher intensity reduced HRV in elite athletes, while training phases of reduced-intensity increased HRV [7]. Finally, optimal competition performance is often associated with a reduction in HRV in the week prior to competition [3, 8], during taper.

Key parameters and trends used in HRV4Training

We’ve seen in literature that looking at multiple parameters together is a must in the context of trying to understand long-term trends.

In HRV4Training, I have implemented the following parameters, which I report here together with a basic interpretation of their use:

  • HRV (ln rMSSD): an increase is typically associated with coping well with training and improved fitness level. A reduction is not necessarily bad, it could be associated with parasympathetic saturation or tapering. Looking at heart rate can help figure out the different situations. Most importantly, HRV should always be considered in the context of a specific training phase. For example, an increase in HRV during a high-load phase is typical when good athletes are responding well to the stimulus and might be associated with other physiological mechanisms such as increased blood plasma volume. Such an increase in HRV during high-volume training can be considered a sign of functional overreaching, that’s an ideal response. Proper planning should most likely include a deload phase at this point, where HRV should re-normalize. A reduction in HRV outside of tapering and with stable or increasing resting heart rate is typically a clear sign of a poor physiological response to either acute or chronic stressors.
  • Resting heart rate: in general, an increase is associated with more fatigue if acute, or less fitness if chronic unless it is occurring during tapering. A reduction is most of the time associated with coping well with training and better cardiorespiratory fitness level, especially if we talk about trends analyzed over several weeks. A stable trend is ideal. Note that seasonal patterns are present, and therefore changes should always be analyzed with respect to your normal values, as we do in the app.
  • Coefficient of variation of HRV (CV rMSSD): A decrease associated with higher HRV and lower resting heart rate can be representative of coping well with training, while a decrease associated with lower HRV is probably representative of the risk of non-functional overreaching. An increase in HRV CV might reflect some trouble in adapting to a new training block (or other stressors, such as travel or altitude) and if associated with reduced HRV might be a warning sign of inappropriate training load. I cover the coefficient of variation in more detail, here.
  • Normal range: one of the difficulties of analyzing trends is due to the choices we need to make in terms of the amount of data used for this analysis. For example, after a negative response, resulting in a reduction in HRV, we might have stable trends, but still far off from our normal range. This is why I recently included also the normal range in our multiparameter trends analysis, so that periods of long, abnormal responses, can be better quantified.

Let’s look at another example.

In the data shown below, we can see how we go from a situation of coping well with training to maladaptation and finally accumulated fatigue, for several weeks. This was a period of poor health due to allergies and resulting headaches, which was captured very well by HRV.

Daily HRV scores with respect to my normal range (left) and automatically detected trend (right) during a period of poor health (allergies, headaches).

Note that even in a down phase, which can last several weeks (on rare occasions), we can have good and bad days. This is shown clearly by the daily score (on the left). Despite the many “yellow days” (HRV below my normal range), there are also fewer bad days. However, if we take a step back and look at the big picture, this was clearly a negative phase, as properly captured by our new automated trend analysis (to be released by end of 2021 for both iOS and Android). Once again, both daily scores and long-term trends can be useful to manage stress and assess physiology, as long as we understand the difference.

The trends analysis shown so far was automated by detecting non-trivial changes in trends in each parameter separately and then analyzing how the different parameters change with respect to your normal values.

To determine non-trivial changes, we compute the slope of the relation between time and the parameter of interest (e.g. HRV) over time windows of 2 weeks. Then we consider only changes greater than e.g. 1 standard deviation from the mean, to make sure we are excluding non-trivial trends. We know that physiology is highly variable, so similarly to the whole concept of the normal range, it is important to also detect trends only when we have an actual change, and not just a small increase or decrease. To analyze your day-to-day variability and capture reliably only non-trivial trends, we use up to 60 days of historical data.

The algorithm we implemented reports one of the following conditions:

  • Stable: values are fluctuating normally but without strong changes. Normally, this means that you can proceed as planned, but keep an eye on any acute drops.
  • Coping well with training: typically associated with unchanged or increased HRV and unchanged or decreased resting heart rate, together with a lower HRV CV, within your normal range. This means that it’s all good, and you should proceed according to your plans.
  • Maladaptation to training: associated with increased resting heart rate and HRV CV, reductions in HRV. This is a clear sign of a poor response to the current stressors (training or other), and typically it would be a good idea to reduce training intensity.
  • Accumulated fatigue: decreased HRV and increased resting heart rate, with reduced HRV CV or value suppressed below your normal values. This is what is shown above for my own data, and is often associated with longer periods of poor health or poor responses that require slowing down and trying to prioritize recovery.
  • Saturation: saturation is often associated with reduced HRV and resting heart rate, in particular for individuals with very low resting heart rates or elite athletes. Important to analyze this trend in the context of a training program and training history. This is an experimental feature present only online in HRV4Training Pro, under the resting physiology analysis.

The next version of the HRV4Training app, will also include the detected trend in the Baseline page, for Pro users, as shown here below

Trends analysis in HRV4Training, for Pro users


In this post, I’ve introduced the main parameters and findings reported in the scientific literature in the context of analyzing physiological data (resting heart rate, HRV, HRV CV) for longer-term trends.

By looking at trends, we can better understand the big picture and how different training phases and other stressors are affecting our physiology beyond acute day-to-day changes.

This analysis is part of HRV4Training, which can help you determine how you are responding to training and other lifestyle stressors. Get the app, here.

I hope you have found it useful, take it easy

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, a data science advisor at Oura, an Editor at IEEE Pervasive Computing (Wearables), and a guest lecturer at VU Amsterdam.

He loves running.

Twitter: @altini_marco


[1] Plews, D. J., Laursen, P. B., Kilding, A. E., & Buchheit, M. (2012). Heart rate variability in elite triathletes, is variation in variability the key to effective training? A case comparison. European journal of applied physiology, 112(11), 3729–3741.
[2] ​Stanley, Jamie, Shaun D’Auria, and Martin Buchheit. “Cardiac Parasympathetic Activity and Race Performance: An Elite Triathlete Case Study.” IJSPP 10.4 (2015).
[3] Buchheit, M. (2014). Monitoring training status with HR measures: do all roads lead to Rome?. Frontiers in physiology, 5. Chicago
[4] Buchheit, M., Chivot, A., Parouty, J., Mercier, D., Al Haddad, H., Laursen, P. B., & Ahmaidi, S. (2010). Monitoring endurance running performance using cardiac parasympathetic function. European journal of applied physiology, 108(6), 1153–1167.
[5] ​Pichot, V., Roche, F., Gaspoz, J. M., Enjolras, F., Antoniadis, A., Minini, P. & Barthelemy, J. C. (2000). Relation between heart rate variability and training load in middle-distance runners. Medicine and science in sports and exercise,32(10), 1729–1736.
[6] ​Plews, D. J., Laursen, P. B., Stanley, J., Kilding, A. E., & Buchheit, M. (2013). Training adaptation and heart rate variability in elite endurance athletes: Opening the door to effective monitoring. Sports Medicine, 43(9), 773–781.
[7] ​Plews, D. J., Laursen, P. B., Kilding, A. E., & Buchheit, M. (2014). Heart Rate Variability and Training Intensity Distribution in Elite Rowers. International journal of sports physiology and performance.
[8] ​Stanley, J., D’Auria, S., & Buchheit, M. (2015). Cardiac Parasympathetic Activity and Race Performance: An Elite Triathlete Case Study. IJSPP, 10(4).
[9] Andrew Flatt blog. http://hrvtraining.com/2015/05/19/hrv-stress-and-training-adaptation/

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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