I was invited to Tampere university of technology for Julia Pietilä’s PhD defense where I had the honor to act as the “opponent”, according to Finnish protocol.

Julia did great work on HRV data and its relation to physical activity, sleep, alcohol intake and more (a few extra words on her work can be found below, while the full text of the thesis is available here).

Special thanks to Ilkka Korhonen, who is (among other things) CTO at Firstbeat, a company I’ve always admired for the sound scientific approach. I’m grateful for the invitation and the opportunity to talk about my work and learn more about the research being done here. I really had a great time and it is always nice to find like minded people who understand the value of collaboration and research even when working in a similar business or addressing the same market.

Here is a very brief overview of the thesis:
In this thesis, a very large dataset comprising thousand of individuals and multiple days of data per individual were used to analyse the relationship between resting physiological parameters such as HRV and population level parameters (age, BMI, etc.) as well as acute stressors such as alcohol intake and physical activity. In particular, the thesis focused on methodological aspects linked to the use of large datasets collected in unsupervised conditions, and provides useful insights on various aspects. Most notably, on the difficulty of determining PA levels in relative and absolute manners, as well as on the impact of alcohol intake and PA on HRV and sleep quality. 

The work builds incrementally on what is known from smaller studies, validating the use of data collected in unconstrained free living settings for the analysis of PA, alcohol intake and sleep and then takes this further by exploring aspects that could hardly be investigated in regular clinical or more controlled studies.

I believe an important contribution here is the analysis of the relation between acute bouts of PA, overall fitness or habitual PA, and HRV as a marker of both parasympathetic activity in response to acute stressors (in relative terms) but also as a marker of health and potentially of fitness. Another important contribution is certainly the analysis of the difference in terms of acute responses depending on individual characteristics, for example recovery time in terms of HRV in response to alcohol intake or PA, and how these aspects can depend on a person’s age or BMI. These are all aspects very difficult to analyse or extrapolate from published literature given the small sample size or limited data. 

This work is of great relevance at this time, as there seem to be a gap between the large amounts of data collected by digital health companies and the methods and studies carried out in controlled research settings where often just a few subjects take part in such studies.​

​🇮🇹 Piccola intervista per gli amici italiani sulla variabilità cardiaca e l'utilizzo di HRV4Training per il canale YouTube di Massi Milani (2 volte vincitore di categoria della maratona di New York)

Perchè è importante misurarla? quando va fatta la misura? come utilizzare i valori? a chi consigliamo utilizzare questa tecnologia? ▶️

Grazie Massi!

In this series of posts, I’ll provide an overview of best practices for your Heart Rate Variability (HRV) measurements (part 1), and tips on how to analyze and interpret your data over the short and long term (response to acute stressors, longer-term trends, etc. — in part 2). I’ll include quite a few case studies so that you can clearly see how you can too make use of the data (in part 3). Finally, the last post will cover a few misconceptions (utility with respect to subjective scores, non-training related use, strength training, etc. — part 4).

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. Hopefully, these posts will help, but please feel free to ask questions should you have any doubts.

You can find the other parts of this series 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)
• Part 4: Common misconceptions (strength training, night data, etc.)

I have been invited to give a talk about my research at Tampere University of Technology.

This was a nice opportunity for me to go over what is now more than ten years of work I have done in the field. I started back in 2009 developing hardware, firmware and software for wearable sensors, and then went into a PhD in data science applied to physical activity. Eventually, this work led me to the entrepreneurial route, starting companies with the goal of furthering our knowledge on complex relations between physiology, health and performance, thanks to large-scale studies and user generated data. This process brought me to my current goal, which is to empower the individual decision making process with accurate, transparent and effective tools such as HRV4Training.

​I tried to provide a decent overview in this talk, you can find the slides below or at this link.

James Witts wrote a piece on polarized training (or 80/20 running) for the June 2019 issue of Runners World UK. My case study was featured, you can find it below. 

For a deep(er) dive into my data from a few years back, at this link. 
​

In this post, I'd like to show some data to highlight a few important aspects when analyzing your heart rate variability (HRV) data. In particular, I'd like to cover some misconceptions about the relationship between training and HRV as well as the importance of lifestyle and psychological aspects (context!).

We'll use my own data collected between January and April 2018, so 3 months in which I went from best shape of my life to injured and then back to training regularly post-injury, but in poor shape (detrained). We'll look at:

1. The basics: acute response to high-intensity training
2. The more interesting stuff: positive long term adaptation to high intensity and high volume training
3. Context: negative response to poor lifestyle choices following an injury
4. Feedback loop vs fitness marker: assessing full (physiological) recovery
5. Putting it all together

I hope this case study can be a good starting point to identify useful ways to look at your data using HRV4Training Pro.

I​n this post we’ll show two methods we have implemented in HRV4Training Pro to let you easily track changes in aerobic endurance while preparing a running or cycling event, so that you can analyze your progress:

• Aerobic efficiency
• Cardiac decoupling (currently in Beta)

Using these two methods and analyzing changes systematically over time with respect to your historical data, it should be easy to track improvements (or lack thereof) over time and make meaningful adjustments to your training plan.

​Learn more at this link.

​In this post, we go over the 12 weeks leading to Serena's first marathon. 

We'll see how HRV data can be used to analyze positive adaptations (increasing or stable HRV baseline) and to determine when to hold back if necessary (HRV baseline below normal values, or maladaptation detected).

We'll also see how to analyze training intensity distribution and how to determine race pacing strategy using HRV4Training Pro.

As always, while this post is about data, there is no use in data without common sense. Data is not here to replace our brain. Data is here to help us improve our understanding of our body and perception of stress and effort - something we are really bad at, especially as recreational athletes. 

Hopefully, the tools we have developed as well as this case study will help you to learn more about how you respond to stress and to manage things better. 

Thank you again Serena for working with me in these three months and congratulations again on your sub-4 marathon.

Train smart, run faster

​Today's blog post is about a fun project I had the pleasure to take part in thanks to Dan Plews and Rob Arkell. The project was ran by buyagift, and the idea was to determine which spa treatment is more relaxing, so that you can gift the most relaxing treatment for Mother's Day.

Obviously, we used HRV4Training to assess physiological stress. HRV4Training is the first and only validated app that can measure heart rate variability (HRV) without requiring anything more than your phone, and hence it provides an easy way to measure stress non-invasively.

While we normally work with athletes, our body responds to training and lifestyle stressors in the same way (which is why we cannot look at just one or the other), and therefore by using HRV4Training we could capture stress (and relaxation) resulting from a specific treatment, in an objective way.

Ten UK mummy bloggers were each given a spa treatment and asked to measure their stress levels – or Heart Rate Variability (HRV) – once during the most stressful moment of their day and again after their spa treatment. Buyagift then compared the different readings to determine the most improved stress rates and unveil which spa treatments truly reduce stress levels the most.

​The experiment setup is quite similar to what we would do in a pre / post experiment in clinical settings, trying to figure out the impact of a particular stressor or relaxation exercise (e.g. meditation, or in our case, a spa treatment) - what we also call acute HRV changes. 

Dr Daniel Plews, Physiologist and Buyagift’s stress experiment consultant comments: “The survey reveals that mums need to take more time to relax as too little sleep and too much stress can have serious long-term physical and mental health implications. As 4 in 5 families don’t know which treatment to get their mother, this experiment was designed to reveal which treatments help mums de-stress the most by analysing mums’ stress levels. We did this by measuring HRV, which is an accurate, non-invasive measurement of the variation between consecutive heart beats intervals. It reveals signs of physiological stress, as HRV is typically higher (more variation between heart beats) during relaxing activities and decreases (less variation between heart beats) during stressful activities.”