Articolo su Ravenna e dintorni

Special thanks to Rob Wallace for inviting me to speak to triathlon coaches in Australia about HRV and our work with HRV4Training. In the one hour webinar, I cover:

• Theory about heart rate variability
• How to collect data (available technologies)
• Guidelines (measurements, confounding factors)
• Data analysis and interpretation (normal values, case studies)
• HRV4Training Pro & teams 

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The webinar was recorded and is available at the link below. Enjoy.

In this series of posts, I discuss Heart Rate Variability (HRV) Biofeedback, and in particular:

• Part One: Rationale for using HRV Biofeedback (this post)
• Part Two: Important metrics
• Part Three: Common Protocols
• Part Four: Expected physiological, psychological and performance outcomes

While part Four of this guide is mainly focused on athletic performance, Parts One, Two, and Three are generic and cover aspects applicable to any other population in terms of the benefits of improved emotional self-regulation for health and performance.

​Learn more at this link.

We have partnered with the Biomedical department of the University of Milan to provide free Heart Rate Variability apps in the context of a new study investigating the effect of home isolation on the cardiac autonomous nervous system.

Prolonged isolation studies during quarantines have focused mainly on the clinical effects of isolation, reporting emotional disorders, irritability, insomnia, poor concentration, deterioration of working capacity, stress symptoms and decision-making skills. The reduction in physical activity seems to contribute to the establishment of these dynamics.

The purpose of this project is instead to monitor the psycho-physiological impact of home isolation that is occurring in Italy as a consequence of the general lockdown aimed at containing the pandemic spread of covid-19.

Psychological monitoring will focus, through the remote administration of validated questionnaires, on sleep disorders (Pittsburg Sleep Quality, PSQI), affective and emotional states (UCLA Loneliness scale, Profile of Mood States, POMS), on trait and state anxiety (State-Trait anxiety Inventory, STAI) and on the level of physical activity (International Physical Activity Questionaire, IPAQ).

Physiological monitoring will evaluate the modulation of heart rate by the autonomic nervous system (HRV), as measured using the Camera HRV app, so that questionnaires data and objective physiological stress levels can both be analyzed.

I would like to thank professor Giampiero Merati of the University of Milan for involving us in this project and I hope our tools we'll be helpful to gather objective data on ANS activity. Our own data tells quite a clear story, as we still struggle to adapt to the new normal. 

In the past weeks, we have developed a free (and private) camera-based heart rate app to support self-awareness as well as data donation projects in the context of the current covid-19 pandemic. 

Since the start of the outbreak, I've been approached by many doctors and scientists that have been looking into simple and cost-effective methods to identify and manage individuals that might require additional care. Physiological measurements (for example resting heart rate, HRV or temperature), are all great candidates as these parameters have shown a consistent response to infections (you can find some data here).

At the same time, many research institutions and governments are starting data donation initiatives to track covid-19 symptoms, at a much larger scale. One of the main examples of this approach is the corona-datenspende app launched by the Robert Koch Institute (the National Health Institute in Germany), which also aims at integrating (anonymized) objective physiological data collected with wearables such as Fitbit and Garmin smart watches. 

I was recently made aware of the project by my former PhD supervisor, professor Oliver Amft, who is actively involved with the Robert Koch Institute and also familiar with our work on camera-based measurements. Needless to say, a free camera-based heart rate monitoring app could empower people from a self-awareness point of view, as well as give them the opportunity to contribute to these project. Hence, in the past week we have developed Camera Heart Rate, a free app that can be used to measure resting heart rate, and can share data only via the Health app, so that each user is in total control of their data and privacy.

​The app relies on HRV4Training's technology and is currently available on iOS in selected areas. Please read below to learn more.

Something as simple as measuring resting heart rate, is a very useful tool to capture changes in physiology due to our body fighting various forms of stress, including infections [1]. In particular, infections tend to show up in the data as increased heart rate (and reductions in HRV). You have probably noticed these changes in the past if you were ever sick while also collecting data longitudinally. This can happen a short time before we actually realize that something is going on (say a day or two in my experience).
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​Already years ago, Michael Snyder at Stanford University ran studies in free-living using commercially available sensors and found quite striking relationships between heart rate and infection (if you are into papers, I highly recommend this one [2]). We have also shown reductions in HRV and increases in HR with respect to self annotated sick days, in this blog post.

​Infections or getting sick in general, are one of the few conditions in which I would almost consider heart rate superior to HRV (or at least equal). Why is that? First, heart rate does not change much on a day to day basis, and is less sensitive than HRV to changes in physiological stress due to for example training, lifestyle stress, etc. (we have previously quantified these differences, with heart rate changing only 0.5–1% in response to hard training sessions for example, and HRV changing on average 5–8%, full paper here [3]). Secondly, heart rate changes to a much greater extent when facing an infection. Hence heart rate can be a simpler marker to look at, less affected by other stressors, and we could look at changes in heart rate to make sure things are staying within a normal range (based on your historical data).

Camera Heart Rate is powered by HRV4Training, the first validated camera-based heart rate variability (HRV) app. You can learn more about resting heart rate and measurement guidelines at the Camera Heart Rate FAQ page.

Camera Heart Rate is currently available on iOS and supports English, Italian and German. We have released the app only in selected areas, but if you are interested in potentially using it also for your initiative, please feel free to reach out. 

[1] Buchan, C. A., Bravi, A., & Seely, A. J. (2012). Variability analysis and the diagnosis, management, and treatment of sepsis. Current infectious disease reports, 14(5), 512–521.
Chicago.
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[2] Li, X., Dunn, J., Salins, D., Zhou, G., Zhou, W., Rose, S. M. S. F., … & Sonecha, R. (2017). Digital health: tracking physiomes and activity using wearable biosensors reveals useful health-related information. PLoS biology, 15(1), e2001402.
Chicago.
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[3] Altini, M., & Amft, O. (2016, August). Hrv4training: Large-scale longitudinal training load analysis in unconstrained free-living settings using a smartphone application. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 2610–2613). IEEE.

Special thanks to Pro triathlete Scott Bayvel for inviting me for a chat on his YouTube channel. In the video, we cover the following:

1. What is HRV and why do we care
2. What’s HRV4Training and how do you use it
3. How do you use the data
4. Who can benefit from monitoring HRV

Link here. Enjoy

EPISODE LINK
You can find the episode here.

EPISODE SUMMARY
HRV4Training founder Marco Altini is on the show this week to explain heart rate variability (HRV) and how monitoring this metric can be used to guide endurance training.
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EPISODE NOTES

• 1:00 what is HRV (heart rate variability) and how is it relevant to training?
• 2:00 a summary of the parasympathetic branch of the autonomic nervous system
• 3:00 the state of the parasympathetic branch allows us to monitor global stress on the body
• 5:30 on the usefulness of trying to quantify training and non-training stresses and the care that must be taken when doing so
• 8:00 factors affecting HRV and the autonomic nervous system: sleep, alcohol, illness, travel, training, work, relationships. But the effects are very individual!
• 12:30 how quickly do we see responses to stressors? Acute response occurs within 24-48 hours. Chronic response takes longer: 1-2 weeks or more.
• 14:45 HRV measurement best practices
  • Measure daily, at the same time of day / night, use proven measuring methods, 
• 17:30 what can you do knowing your acute and chronic HRV data?
• 18:30 if acute reading is low, then the capacity for more stress is reduced. Marco recommends reducing intensity on that day of training.
• 19:30 a case study in HRV-guided training - where training modification was only made when the HRV baseline (long-term trend) was below normal - demonstrated that this form of training was more effective than traditional programming with no accounting for HRV.
• 21:30 stable HRV is the goal! It is not meant to ‘improve’ over time.
• 23:00 interpreting low acute HRV score on race day. Lower is not necessarily bad.
• 23:30 understanding atypically high acute scores
• 26:15 interpreting chronic HRV data and trends
• 27:00 stable or increasing baseline is a sign that you are coping well, whereas a decreasing baseline suggests accumulation of stress that is not being resolved
• 28:00 coefficient of variation monitors the magnitude of variations between measurements. A high CoV  could signal some trouble adapting to a novel training stimulus or life stressor.
• 30:15 a low CoV combined with an abnormally low HRV trend is a sign of a system struggling with global stress and may be a signal for a state of chronic high stress - which, of course, is to be avoided.
• 33:30 is HRV-guided training a substitute for a periodized plan? 
• 36:15 is it useful to try to measure HRV during training?
• 38:15 what about live, but not-during-training HRV monitoring like Garmin’s Body Battery feature?
• 42:15 Michael’s struggles with a single daily measurement
• 45:30 body position in testing: all okay so long as the position is the same from one day to the next. 
• 46:45 the technology used by HRV4Training
• 50:30 using the Big Data collected by HRV apps for studies and new features
• 55:00 how does the athlete HRV4Training app work and what does it do?
• 58:00 the HRV4Training Pro platform

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Learn more about HRV4Training or Marco at their respective websites  and follow them on Instagram and Facebook. 
Read part 1 of Marco's guide to HRV-guided training on Medium. Then use the links in the article to access parts 2 and 3.

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.