The Ultimate Guide to Heart Rate Variability (HRV): Part 3

Show me the data

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.).

  • Case study 1: 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 2: 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 3: 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 4: 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 5: Tight relationship between the menstrual cycle, subjective feeling, and resting heart rate data
  • Case study 6: Going beyond chronic training load using HRV to monitor how you respond to training: Serena’s first marathon.
  • Case study 7: 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 8: Guiding recovery post-race by Peter Glassford.
  • Case study 9: 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 10: 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 11: 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 12: 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 13: reducing intensity when HRV is suppressed, to avoid long term setbacks
  • Case study 14: Raul and the lockdown: another clear example of how psychological stress can have a large influence on our physiology, even in the absence of training

Case study 1: 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.

Case study 2: 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.

Case study 3: 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.

Case study 4: 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.

Case study 5: Tight relationship between the menstrual cycle, subjective feeling, and resting heart rate data

Below is a few months of data highlighting the relationship between the menstrual cycle, resting heart rate (HR) and perceived physical condition.

Case study 6: 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):

The top plot shows HRV data, typically a higher score is associated with a more rested physiological condition (less stress). On the other hand, lower values are associated with higher stress. HRV4Training Pro contextualizes your baseline (blue line, computed using the past week of data) with respect to your normal values (the larger band) so that you can easily spot periods of significantly higher stress (baseline below normal values). The second plot shows training load, as discussed in the previous section.
Automatically detected trend in HRV4Training Pro, learn more here.

Case study 7: 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.

Case study 8: Guiding recovery post-race

Word to Peter Glassford one last time for another really good case study.

Case study 9: 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).

Case study 10: 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).

Case study 11: 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 12: 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.

Case study 13: 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).

  • 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

Case study 14: Raul and the lockdown

We’ve talked many times about how HRV is a particularly useful marker as it captures stress regardless of the source. In the context of training, this is helpful because the way we respond to a stressor (for example a workout) can depend on what other stressors are present (poor sleep, traveling, work-related stress, family worries, etc.) — and needless to say, our capacity to handle stress is limited. Measuring HRV allows us to capture our individual response to everything that is going on from training to lifestyle, and make adjustments when necessary.

Wrap up

The data above should clearly show how important context is and how effective is HRV in capturing individual responses to various stressors.

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Marco Altini

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