The Ultimate Guide to Heart Rate Variability (HRV): Part 3
Show me the data
This is the third part of my series of educational posts on heart rate variability (HRV). You can find the other posts at these links:
- Part 1: Measurement setup, best practices, and metrics.
- Part 2: You measured, now what? (on interpreting your data).
- Part 3: Show me the data (case studies, this post!)
- Part 4: Common misconceptions (strength training, night data, etc.)
HRV is nothing new, and fairly simple to use effectively, but poor standardization and methodological inconsistencies make it difficult sometimes for people to make good use of the technology or understand what is reported in the scientific literature.
Make sure to follow the tips listed in part one, and you’ll be able to easily benefit from available technologies on the market. You can also find common ways to easily analyze and interpret your data with respect to training and lifestyle stressors, in part two. Let’s get to it.
Part 3: Show me the data
In previous posts, I’ve shown a few examples of what to expect in terms of the relation between HRV and acute stressors (for example traveling, alcohol intake, a hard workout) and longer-term stressors (positive adaptation to training, work stress, poor lifestyle choices, etc.).
This part is all about examples and case studies that highlight many of the aspects previously discussed so that you can intuitively see how morning HRV measurements are an effective way to capture changes in stress in response to training and lifestyle stressors.
In particular, in part 3 we’ll see:
- Case study 1: Going beyond chronic training load using HRV to monitor how you respond to training: Serena’s first marathon.
- Case study 2: Training and lifestyle stressors over a year. Here we look at data from triathlete Ricardo Mazzini highlighting HRV responses to a broken wrist, training at altitude and racing an ironman.
- Case study 3: HRV in response to steep increases in training load. Here we’ll analyze data from Shawn Watson (cancer survivor and cyclist), who is using HRV to learn how to better balance training to stay healthy and active while fundraising for charities.
- Case study 4: HRV response to a multi-day cycling event, as well as positive adaptations to training during other phases of the season. Data by Peter Glassford.
- Case study 5: HRV response to travel (work-related), consistent training, and racing (hard stressor), showcased again by Peter Glassford who is using HRV4Training Pro to monitor a few of his athletes.
- Case study 6: Cumulative stress: a tool to start a conversation, with data by Peter Glassford and some additional consideration based on my experience with coaches and athletes using the platform.
- Case study 7: Guiding recovery post-race by Peter Glassford.
- Case study 8: Exams and training. My own data, showing HRV in response to work-related stressors (exams) and how stressors pile up when combined with for example training stress (in this case preparing and running a marathon).
- Case study 9: Traveling and training. Data from Alessandra, showing HRV in response to traveling and how combined stressors can be managed (e.g. for example training load) to maintain a positive physiological response (HRV within normal values).
- Case study 10: HRV trends during the menstrual cycle. In this section, we’ll see what to expect in terms of common trends during the different phases of the cycle.
- Case study 11: HRV when getting sick. Here we will see how measurements of resting physiology can sometimes show us potential issues before we even notice.
- Case study 12: reducing intensity when HRV is suppressed, to avoid long term setbacks
All these case studies include several months to years of data because as we’ve learned, that’s the only meaningful way to analyze HRV data.
Hopefully, this post will help, but please feel free to ask questions below should you have any doubts.
Case study 1: Going beyond chronic training load using HRV to monitor how you respond to training
Standard training load monitoring tools are provided by pretty much all software out there, and all rely on the Banister model to calculate acute and chronic load which should be linked to fatigue and fitness. We also use a similar approach in HRV4Training (mainly to flag issues with lack of freshness or high risk of injury):
If you are not familiar with these models, they are pretty simple: you use what is typically called training impulse (even just the distance you run, or TSS and relative effort if you do it a bit better) to determine how much cumulative training you have been doing in the past 4–8 weeks (depending on the sport and model used). This is what we call chronic training load (or CTL), shown in blue above. In the data here Serena is building up towards her marathon, and training regularly, plus adding longer runs every few weeks, hence we see chronic training load increasing as we build up to the race.
However, while I believe these plots and analysis are informative, they do not consider how our body is responding to the stress stimulus. If the input is a long run, the output is going to be the same plot no matter how your body responds to that run — which can differ greatly as we will soon see.
Here is when heart rate variability (or HRV) data comes to the rescue. As HRV reflects physiological stress, we can determine an individual’s response to training and make adjustments when necessary. Let’s look at Serena’s HRV data during the three months leading to the marathon:
Above we can see how things go really well for the first month (increasing or stable HRV baseline highlighting positive adaptation) until we really step it up with training. Around February 17th, HRV starts to sink. We had just gone through 2 big weeks, with 26 km and 30 km runs over two weekends, all uncharted territory for Serena. By the time we reach February 22nd, the baseline is below normal values, a clear red flag.
You can see here how proper periodization (an easy week was planned right after this block) and feedback from HRV4Training was used to take a longer break before getting back into that kind of very demanding training (the next acute peak is at the beginning of March).
While your baseline going below normal values is something you can easily spot, HRV4Training combines that information with your recent trend in heart rate data, the coefficient of variation of your HRV and training load data. The result is the automatically detected physiological trend, which is shown color-coded below:
We can see how HRV4Training determined that there was an issue in adapting to this high-intensity and higher volume work (flagged as maladaptation, shown in orange above) around February the 21st, and for several days afterward.
Here you can see a clear case of how “not killing yourself with training” when your body is really stressed, is a good idea. After a period of reduced acute load (no orange spikes), HRV bounced back and stayed within normal values until the marathon, despite some really hard sessions in the following weeks (a half marathon race and a 35 km long run).
During these weeks and for a long period, the automatically detected trend reported that Serena was “coping well with training” (shown in green above) which gave us confidence it was all going in the right direction in terms of physiological adaptations.
This is in accordance with what has been shown multiple times in recent research on HRV-guided training, which typically consists in holding back when your body is not in good condition to assimilate the additional stressors (in practical terms this is done by checking your baseline against your normal values, exactly as shown above) and in performing high-intensity training when the athlete is in optimal conditions (HRV baseline within normal values — I’ll say it once more: HRV baseline within normal values means it’s all good because when working with these data, normal is good). This approach resulted in improved performance in both runners and cyclists, with respect to standard training periodization (which even included more high-intensity workouts — but without considering an individual response to training — timing matters!).
Case study 2: Training and lifestyle stressors over a year
In this section, we report a blog post by triathlete Ricardo Mazzini, who analyzed one year of data with a lot of useful insights that are clearly showcased in the plots below.
“My season started strong, with consistent and improving trends, but a bicycle accident and a broken wrist created a big dip in my trends. This makes sense as the stressors of trauma and the psychological stress of recovery can take a toll in your body.”
“While I didn’t really stop training, I was limited to indoor biking and some running. Soon after recovery, however, trends rapidly improved and remained stable for most of the first half of the season. This was a good indicator that my body was adapting well to training loads, and I was managing stress appropriately. It is worth noting that changes in the environment can also create stressors that can worsen your HRV, such as altitude training. A good example is the training camp I went to in June at Boulder. Changes in altitude and increase training load created a small dip in my HRV”
“When looking at my race performances, and comparing them against my HRV, my worst performance was probably during Ironman 70.3 Santa Cruz (September 9th). While Ironman 70.3 Muskoka (July 7th) was soon after the training camp, the rapid, but acute training loads had a better effect on me than long, sustained stressors from work prior to 70.3 Santa Cruz.”
“Not surprising. This was probably due to lack of sleep, and increased fatigue towards the end of the season. Changes in HRV also happened because of sickness and holidays, though I wasn’t as concerned during this time since this happened after IM Arizona (November 24th)”
Case study 3: HRV in response to steep increases in training load
Here we’ll analyze data from Shawn Watson (cancer survivor and cyclist), who is using HRV to learn how to better balance training to stay healthy and active while fundraising for charities. The screenshot below shows the past 6 months of Shawn’s data.
“In the screenshot below we can see my overtraining and how my body reacted … poorly. The latter data (last circle) shows that my tapering is helping bring my numbers back up.”
In the plot below you can see the same data, but color-coded by detected trend. The detected trend in HRV4Training is a combination of resting heart rate, resting HRV, coefficient of variation of your HRV, and training load.
We can see how the detected trend captures maladaptation to training, even a bit earlier than training load is reduced, showing how this information could be used to better manage training intensity and training volume so that we can avoid ending up in a situation of maladaptation to training.
Case study 4: HRV response to a multi-day cycling event
In this section, we will show HRV response to a multi-day cycling event, as well as positive adaptations to training during other phases of the season. The data was collected by Peter Glassford and the original post is available here.
“This athlete raced their A-priority race in September (far left) and then wanted to keep riding while the weather was good and build towards a November event AND got coaxed into a big multi-day bike packing trip with riding most of each day.”
“You can see the big spike in the training stress (bottom) and the corresponding drop in the top image (HRV). The athlete’s November race was ok but there was missing the snappy workouts and lower volumes typical ahead of short/hard events that seem to lead to great race days.”
“A trade-off that is perhaps captured in the drop in HRV and training load prior to the event (and in this case likely worth it for the great 4-day adventure!)”
Case study 5: HRV response to travel (work-related), consistent training, and racing (hard stressor)
This is another great case study showcased by Peter Glassford who is using HRV4Training Pro to monitor a few of his athletes.
“This athlete is a busy parent and professional. So travel, illness, work stress all contribute but we had a really good January and February that led to nice high confidence, recovery and form for a ski race. HRV supported bike power and ski numbers (and race performance).”
“Life/travel work happened after the race, which then guides how much training we do (less)”
Case study 6: Cumulative stress: a tool to start a conversation
When working with elite athletes and coaches of elite athletes, I often hear that the data provided is a great tool to start a conversation. What does that mean? Elite athletes tend to be in great tune with their body, as they develop and train over the years, they get to understand very well how they respond to different stressors.
In many cases, the data simply shows them that everything is going well, and gives confidence in the process. We saw this also in Ricardo’s data and other case studies here.
Yet, there can be situations in which things do not go as expected, maybe some non-training related stressor popping up (again work, family, getting sick, etc.) — and in this case, having hard data on their physiological response can be of great help.
This is especially true when there is nothing out of the ordinary (no crazy travel or altitude camp), but some more subtle changes. Looking at the data and seeing a negative trend, two, three, or four days of low scores, can be an effective way for the coach to try to dig a bit deeper and investigate what is going on so that changes can be implemented to improve the situation.
This is of course even more so for non-elite athletes that tend to be less in tune with their body, but my point is that we can clearly all benefit. Let’s look at some data, again from Peter Glassford and one of his athletes.
“This athlete has been fairly consistent until this spring when lifestyle stress and poor weather made for a period where HRV dropped (see below the red arrow) which I noticed due to the HRV drop and then this started the discussion of what was up with life-stress and how we could adjust training to match”
Case study 7: Guiding recovery post-race
Word to Peter Glassford one last time for another really good case study.
“The athlete’s HRV is shown below as school ends and we tried a big volume block with the newly available time ahead of a recovery week and a stage race. HRV seemed to reflect a good response to training and also matched a decent recovery/form for the stage race.”
“Post-stage race recovery coupled with some crazy travel days made for a bunch of yellow days with low HRV that can help guide the recovery strategy in the week(s) after the race”
Case study 8: Exams and training
Below we can see some of my data, showing HRV in response to work-related stressors (exams) and how stressors pile up when combined with for example training stress (in this case preparing and running a marathon).
Both me and Alessandra were preparing the New York City marathon (her data is shown in the next post). In my case, the two months leading to the marathon have been quite stressful, but hardly because of training (an important detail on this will follow). Some context first: last September I started a new Master’s degree, and therefore the past months were quite busy (running a business, going to lectures, doing assignments, and trying to prepare a marathon). In particular, at the beginning of October university started taking much of my time, with exams approaching around the 20th.
You can see in the plot below how “lifestyle stress” (third row), a subjective annotation in the HRV4Training app that I use to track how stressed I feel at a given point in time, gets quite a bit higher than my normal (above normal values, derived from my previous 2 months of data).
You can also see that training does not change much in this period (second row), as I am unable to increase load due to my previous injury firing up frequently. While training load is not high, training does have an impact on me here psychologically. When I go out I sometimes file pain, the marathon is approaching, and as any runner knows, no matter how slow we are, we all have our goals and have to deal with them mentally.
Despite no changes in training load, I felt very stressed with exams approaching (must be getting too old for that!), and you can see how my HRV goes down, below normal values, and stays there for several weeks.
Post exams, I have a bit of stress relief (highlighted by higher HRV around October 28th for three days), right before flying to NYC and getting all stressed for the marathon. Obviously, running a marathon is hard, and preparing it while recovering from an injury is far from ideal. While I’m aware this is not the smartest choice, what are the odds that both I and Alessandra get extracted in the NYC marathon lottery together? (I did the math, about 0.81%). So while I’ve paid attention and trained the minimum possible, I still went out for my sessions and long runs, hoping to be able to run the distance without additional issues.
In the last week shown above, you can see how weeks of HRV below normal values, and chronic stress due to university exams and self-imposed expectations or fears related to the marathon disappear really quickly. After the marathon, I have pretty much no stressor bothering me, and this is reflected in a rapid increase in HRV (reduction in stress) back to normal values.
The drop I experienced during the weeks preceding the race brought my HRV baseline to a two years low (!) and honestly got me worried a bit. I gave priority to sleep and other recovery measures, but I also felt really overwhelmed by everything I was doing (I’m an anxious person). On the other hand, it was nice to see that I was able to bounce back quickly once the stress was gone. Clearly, it was all in my head.
What this tells me is that I certainly need to work on better coping strategies when different stressors pile up. On to the next case study.
Case study 9: Traveling and training
In this case study, we look at data from Alessandra, showing HRV in response to traveling and how stressors can be managed (e.g. for example training load) to maintain a positive physiological response (HRV within normal values).
In Alessandra’s case, we also have a few important stressors. In terms of training, this was her first marathon, and obviously it comes with a little self-doubt and anxiety. Alessandra also started a PhD earlier this year and is running the business with me, meaning there are frequently up and downs in work-related stress. Additionally, she teaches and travels a lot to give lectures and workshops all around the world, making traveling an important aspect to consider.
Let’s look at the data. We can see below two different situations in which training and lifestyle stressors are managed differently, resulting in a good physiological response in one case, and a negative one in the second case (but after the race).
In particular, during an earlier business trip to Belgrade (shown in red in the first plot) we have a decrease in HRV for a few days. We can see how Alessandra also subjectively annotates these days as stressful (the third plot, increase in ‘lifestyle stress’).
Yet, as we knew about the trip, here we kept training load very low to avoid additional stress that could impair recovery and hinder longer-term adaptations and performance. This is a simple principle used by most HRV-guided research protocols: when your body is already stressed, it is not a good time to add high-intensity training.
In the second part of the figure, we can see how once again there is high lifestyle stress (traveling to the first marathon), and this time, of course, there is a huge spike in training load (the race!). The combination of the two triggers a large reduction in HRV, below normal values.
While it is fairly obvious that after a race some recovery is needed, each of us is different, and as shown in these examples, different stressors can pile up. Hence, looking at the data post-event can be a useful objective method to determine when physiologically we are back to normal.
Case study 10: HRV trends during the menstrual cycle
In this section, we’ll see what to expect in terms of common trends during the different phases of the cycle.
In literature, the relation between the menstrual cycle and HRV is investigated to understand if the menstrual cycle can act as a confounding factor when analyzing HRV data, for example, because of changes during the different phases of the cycle that would require to interpret the data differently. HRV analysis in women may be inconsistent if HRV cannot be considered stable across the menstrual cycle or if the expected differences are not accounted for. This can be an issue as interpretation may lead to inappropriate conclusions.
Patricia K. Doyle-Baker’s group (Kokts-Porietis et al. The Effect of the Menstrual Cycle on Daily Measures of Heart Rate Variability in Athletic Women), used HRV4Training to collect data daily and properly analyze the relationship between HRV and menstrual cycle. The results reported in the paper, are consistent with what most literature has shown, quoting the abstract: “daily HRV associated with the parasympathetic nervous system was observed to decrease nonlinearly across the menstrual cycle”.
Above you can see a few months of data with annotations corresponding to menstruation days over an entire year. Note that dips and lower scores during menstruation and reductions during the luteal phase are not always low points at the absolute level, but still lower the current baseline, which might be higher than a month earlier, hence the importance to measure every day and always analyze data with respect to your current baseline and normal values (as discussed in part 2 of this guide).
Most importantly, remember that no stressor acts in isolation, there’s always something going on with our lifestyle, training, health, and so on — keep this in mind as you look at your data, it can very well be that other stressors have a larger impact. Finally, as you can expect a small cyclical pattern with HRV reducing across the cycle, you can use this information as part of your decision-making process. For example, should your HRV being particularly low during the follicular phase (when you are supposed to be more parasympathetic), then it might be a good idea to slow down.
Case study 11: HRV when getting sick
One of the people I keep an eye on using HRV4Training Pro is my old man. He’s been a runner all his life, but like all of us, he’s not getting any younger, and at age 65 a few health issues are showing up from time to time.
Despite his adversity towards technology, he’s been really keen on measuring his HRV daily in the past few years. As a runner and psychologist, he gets very well how important it is to keep stress in check.
Here is some useful data to look at. He messaged me saying he had low values for days and couldn’t figure out why. Then he woke up sick after a few days.
This is exactly why measurements of resting physiology matter, they can tell us a lot about what is going on in our body before we notice (getting sick, but also how we are responding or adapting to training and lifestyle stressors).
Personally, living far from my family, I find the objective feedback helpful in checking in, and potentially start a conversation about what might be going on, which might be difficult otherwise.
Case study 12: reducing intensity when HRV is suppressed, to avoid long term setbacks
Here is a simple and yet common example. Over the weekend my body was fighting a bug, I felt a bit down but good enough to train, however HRV was significantly suppressed (daily score below normal values as shown in HRV4Training), hence I went out but took it really easy (65% of heart rate max). The day after, again HRV was suppressed, so I went for another easy run (75% of heart rate max).
On Monday everything was back to normal and I went for my postponed hard session, as you can see below a long run with some intensity:
And here is my HRV during those days, as well as my normal values and baseline. You can see how there is always some variability, but the two days in which I was not feeling well, are clearly below normal (yellow bars on the right end side):
What are the takeaways?
• When HRV is suppressed, your ability to take additional stress is limited. Reduce intensity to avoid long term setbacks
• Reducing intensity doesn’t mean resting, everything is always relative to your normal. If you train daily, an easy session will do
• Reducing intensity when HRV is suppressed showed to improve performance both in runners and cyclists in a few research studies. Timing matters!
• Other aspects need to be considered, if you have suffered from overuse injuries (🙋🏻♂️), it might be wise to take that day off from time to time
The data above should clearly show how important context is and how effective is HRV in capturing individual responses to various stressors.
Note that we all respond differently to all kinds of stressors and also we can respond differently to the same stressor over time. The notion that increased load should trigger a reduction in HRV is very simplistic, and as we have seen we can have stable or increased HRV when increasing load (a sign of positive adaptation) as well as reduced HRV with low load because of other stressors (travel, work, etc.). The point is that by measuring your resting physiology first thing in the morning, you can understand easily how things are going, and use that information as part of your decision-making process.
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, etc.).
These case studies show that we cannot isolate training and lifestyle stress or think that training is not affected by everything else going on at any given moment in our professional or personal life. Physiological stress comes from different sources, all having an impact on our ability to deal with additional stress and therefore of maintaining or improving our health and performance.
A simple marker such as HRV, measured in a well-defined context using a validated app, can capture stress deriving from all sources and help us make meaningful adjustments to maintain things in check.
I hope you found these case studies useful, and I’ll try to add more over time.
Take it easy.
Marco holds a PhD cum laude in applied machine learning, a M.Sc. cum laude in computer science engineering and is currently enrolled in a M.Sc. 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.