February recap 🗓️
hi there 👋
I hope all is well.
As mentioned earlier, I’ve decided to switch to a different format for my updates and I will email you only once per month, with an overview of what I’ve been writing in the previous month.
Here is my February recap, broken down by topic, with links to the individual articles.
I hope you’ll find them useful. Please feel free to comment below or in the individual articles should you have any questions.
Heart rate variability (HRV) 🫀
Heart rate variability (HRV) analysis before and after exercise. This article is based on Stephen Seiler's research, which shows that by using pre and post-exercise HRV we can better understand the impact of different workouts. In turn, this might help us fine-tune exercise intensity or duration, e.g. to make sure we are not overdoing it on our easy days. Is our easy training really easy? at what intensity does a workout require a much longer recovery time? does it need to be hard? what about just a bit harder than "aerobic threshold"? and what about duration? In the article, I cover protocols, tools and provide examples.
Low Heart Rate Variability (HRV). In this blog I try to address a common concern, i.e. having a relatively low HRV. It is only normal to get worried considering the amount of misinformation out there, and our poor understanding of what different values might mean. The first thing we need to realize is that there is great uncertainty on the topic, which - if anything - highlights how being on the lower side of the spectrum certainly doesn’t determine our destiny. What’s a low HRV? Why is it low? What does it mean? Can we change it? How can we use HRV regardless of its absolute value? Find out, here.
Should Heart Rate Variability (HRV) be associated with training load? It is a common misconception that HRV should track training load, for example reducing when training load is higher. Studies looking at the relationship between HRV (and other metrics) with training load over time, look at how these metrics correlate. However, there are various issues with this approach. Learn more here.
How to include Heart Rate Variability (HRV) in your training plan. The aim of this blog is to answer the simple question above, i.e. how do you include HRV in your training plan? There are plenty of articles on the basics of HRV and how it might be used to quantify recovery. However, there seems to be still a disconnect between the theory, and getting more practical in using the data effectively. In particular, it is not uncommon for self-coached recreational athletes or beginners to use HRV as their guide to determine day-to-day load management without having a plan, which is in my opinion, a recipe for disaster. Find out how to do it right, here.
Long-term trends in resting heart rate and heart rate variability (HRV). 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. Analyzing our responses acutely (e.g. right after a stressor) shows clear patterns, however, there is often a difference between the short (or acute) and long-term (or chronic) effects of a stressor. For example, our HRV immediately after a hard session will reduce (acute effect, see also this blog). 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 result in a stable or even higher HRV in the long run (chronic effect, or trend). In this post, I cover the current research on trends and our analysis in HRV4Training, hoping it will help you make sense of the data in the longer term.
Wearables ⌚️
Apple Watch and Heart Rate Variability (HRV): a complicated relationship. The Apple Watch is the best-selling wearable sensor out there. It packs great technology but falls short when it comes to heart rate variability (HRV) analysis. As a result, it is of limited practical utility in this context, unless we take care of a few important aspects. In this blog, I discuss the main limitations and show you how you can make better use of the Watch data for your own analysis of physiological stress in response to training and lifestyle stressors
A note on continuous heart rate variability (HRV) measurements. As it becomes easier and easier to capture HRV data (or at least PRV, the version of HRV captured when using optical methods via PPG), it follows that the interest grows in measuring HRV all the time. In this blog, I argue against this approach or at least provide some pointers related to why we should be very cautious. Some of my reasons have to do with physiology, others with the technology, and yet some more with the interpretation of the data. Learn more, here.
Building 🛠️
I have been busy building the new HRV4Training Pro and have started putting together various articles that will serve as a user guide and overview of the various features. Here is what I have so far:
Main principles: from data can come awareness. However, we need to be able to interpret data correctly, in order to gain insights and make meaningful adjustments. It’s easy to get overwhelmed when things are not put in the right context, and just add to the confusion. In HRV4Training Pro, analytics and insights are typically built by combining physiological data, subjective annotations, and workout data. To analyze and interpret the data, we relied on the following principles: 1) everything is relative 2) big picture first (going beyond day-to-day variability) 3) multiparameter is key. Learn more, here.
User guide: here you will find an index of the various features as I build them, with pointers to the individual pages.
Coach panel: features for coaches and teams are discussed here.
Overview: probably my favorite page, it shows the normal range, and daily and weekly averages for HRV and heart rate data. You can optionally highlight each recording with different annotations, and add training load and subjective parameters as context in additional plots. Correlations are reported for the selected parameters. See what’s new, here.
Dashboard: the dashboard provides you with a simple but effective tool to analyze trends and changes in multiple parameters at the same time, the radar plot. In particular, you can pick between 3 and 6 parameters, including resting physiology and subjective data, and the system will analyze how your data is trending in the current week, with respect to the prior month. This way, you can easily track progress or relative changes.
Calendar: the calendar page, as the name implies, is a calendar view of your historical data, so that you can easily browse through your data and look at a number of parameters: physiology, subjective data, and training information. You can change the size and color of each individual day based on the parameters you log, so that you can quickly explore and make sense of the data
Training talk 🏃🏻♂️
It has been a rough month for my health and training. I’m getting better this week but I’m still not out of the woods. With a half marathon planned for this weekend and a marathon two weeks later, my racing season is just around the corner. Let’s see how it goes.
Training log. As the name says, this is simply my training diary for the year.
That’s all for today, see you next month, and thank you for reading!
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 co-founder of HRV4Training, advisor at Oura, guest lecturer at VU Amsterdam, and editor for IEEE Pervasive Computing Magazine. He loves running.
Twitter: @altini_marco
