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Blog post by Marco Altini In the past few months, we’ve talked a lot about HRV during exercise. In this post, I’ll try to keep it simple and address some of the main motivations behind this approach, as well as provide practical tips and tools for the ones interested in trying it out. Keep reading, at this link  Blog post by Daniel Rowland  The goal of training is to apply the right amount of training stimulus at the optimal rate to help an athlete improve their performance. Within those two variables of quantity and rate there are a huge range of possibilities for structuring and designing training. Even after considering the specific qualities of the athlete, the nature of their race, and the training that has worked well for them in the past, there are many options that could still help an athlete improve. How a coach structures and describes a training plan is the prescription of training. Once armed with the plan, it is up to the athlete to implement the plan and to do the training. This post will look at a number of different ways to prescribe training, evaluating each method explaining when and for whom each is appropriate. The goal is to provide some ideas for planning and prescribing training for coaches and for self-coached athletes. When the coach has delivered the plan it is equally important for the athlete to know how to implement and execute the plan. There can be uncertainty and tension for an athlete when they're not sure how best to apply a training plan. Should they always follow the plan no matter what? When should they be flexible and adjust the plan to suit their schedule? How much interaction and guidance is needed from the coach to make these decisions? When reviewing the different methods of prescribing training, this post will also cover how best an athlete should follow each plan. A fixed microcycleThe simplest and probably the most common training plan consists of a microcycle of scheduled training sessions. The duration of the microcycle can vary from a week to 10 days to a fortnight, however, the most common duration is a week. The fixed aspect relates to having individual sessions allocated to specific days within the plan. An example could be a week-long microcycle with the goal of completing three high-demand sessions: a set of short intervals, a tempo run, and a long run. The structure of this microcycle could be:
In this example and this type of training prescription every day is scheduled and the athlete knows exactly what to do on each day.  The athlete in this example following a fixed microcycle would decide to skip any high-demand sessions on the weekend based on the HRV feedback. There is no flexibility in the plan and there is likely to be a high-demand session early in the next week which they would need to be fresh for.
Blog post by Marco Altini Another study just published using HRV4Training for data collection: "These data suggest that daily HRV monitoring performed at home upon waking, show very strong agreement with those taken prior to practice" (within 1 hour) Check out our earlier post, in which Sara Sherman discusses this study. Full text, here  Blog post by Marco Altini "Until recently no mobile-based product could display DFA a1 in real-time using an off the shelf consumer device" ... well, that has changed Check out how you can use the Heart Rate Variability Logger app to estimate the aerobic threshold non-invasively, in the British Journal of Sports Medicine: "From laboratory to roadside: Real-time assessment and monitoring of the aerobic threshold in endurance-typed sports" Thank you Thomas and Bruce for involving me in this project, I've really enjoyed working with you and making your scientific work more accessible via this app More to come..  Blog post by Marco Altini Here are two examples of strong stressors that impact in a similar way morning HRV (collected with HRV4Training's camera measurement) and night HRV (collected with Oura rings) In the first image: feeling a bit sick 🤒  In the second image: 2h 30' long run and menstrual cramps 🏃🏻♀️🩸:  As we have reported previously, as long as a few best practices are followed (using the whole night of HRV data for example, instead of just a few minutes), you should be able to gain the same insights from morning and night HRV readings.
Pick what works for you, and use the same method consistently over time, in order to learn how your physiology changes in response to various stressors. Blog post by Marco Altini I've enjoyed writing this one with Jamie and Caroline Grateful for the opportunity to showcase HRV4Training and how you can use physiological data to monitor training and other stressors (in this case, a bee sting! 🐝) Check it out on Oura's blog, here   Blog post by Marco Altini Always great to see HRV4Training used in state of the art research “Large relationships between seasonal changes in measured HRV parameters and critical speed provide further evidence for incorporating a HRV-guided training approach." Thank you Eva, Sean and all co-authors. Full text here.  Blog post by Marco Altini In my latest blog post, I go over the basics of heart rate variability (HRV) biofeedback and show changes in baseline physiology (resting HR, HRV) potentially linked to practicing deep breathing at resonant frequency consistently for the past month Learn more, here Get breathing!  Blog post by Marco Altini The recording of yesterday's live interview on Wild Ginger Running is available at this link. Thank you Jen and Marcus for the invite, it was a really fun chat with great questions. Enjoy!  Blog post by Marco Altini Thank you to BJSM and the authors of this post for featuring our work "using HRV4Training we can accurately monitor training-related physiological adaptations integrating internal and external load together with recovery-related variables that are key for athlete’s performance" Article here  Blog post by Marco Altini Since I started working with Oura, I've been looking more closely at morning vs night HRV data. I collect my morning data using HRV4Training's camera measurement See my last 30 days below. Great consistency in baseline changes for both heart rate and HRV between Oura’s night data and HRV4Training’s morning measurements 👌 I've tried to annotate the stressors, both positive and negative:
Morning and night measurements are both valid ways to capture changes in baseline physiological stress deriving from training and lifestyle stressors, as long as a few simple best practices are followed I cover the basics of morning (and night) measurements, here, and go deeper on some of the nuances of night measurements, here  Blog post by Marco Altini A few weeks ago we discussed a new HRV-based approach to estimate the aerobic threshold. A new paper from Bruce Rogers and Thomas Gronwald validates the method with respect to VT1 The paper is titled: "A New Detection Method Defining the Aerobic Threshold for Endurance Exercise and Training Prescription Based on Fractal Correlation Properties of HRV" and shows some neat data (male participants only), see for example VT1 vs DFA alpha 1 in the image below You can find the full text of the paper at this link  Two methods to try it yourself1) fit file and colab code: You can try this method with your .fit file (if it includes RR intervals) by loading it in my Colab (that you can find here) 2) Use the Heart Rate Variability Logger app with a chest strap to collect data The Heart Rate Variability Logger is currently the only app able to provide DFA alpha 1 in real-time.  If you try the app, make sure to configure it as follows, in Settings:
At that point, any value below 0.75 in alpha 1 as computed every 2 minutes, highlights an intensity higher than the aerobic threshold (zone 1 in a 3 zones system, or zones 1 and 2 in a 5 zones system, basically low-intensity work as typically present in polarized plans) Enjoy! Blog post by Marco Altini A new remote study investigating how the heart responds to endurance training with and without the use of recovery breaks during the exercise is about to start. HRV4Training will be used by the study participants to monitor resting heart rate and HRV each morning. Please read below in case you are interested in participating. The study is performed by Dr Neil Eves and John Sasso from the Centre for Heart, Lung and Vascular Health, within the School of Health and Exercise Sciences at the University of British Columbia. Participant Details The study needs thirty-six healthy males and females (pre-menopausal), between the ages of 30 and 48, who have been participating in endurance sport training for at least 3 years, are currently exercising at least 5 times per week, use indoor cycle training with a powermeter and heart rate monitor, and have daily access to a smart-device. You are not eligible to participate if you: have a heart, lung or brain condition, have diabetes, have a muscle or bone condition that limits vigorous exercise, or smoke Study Details The study will be performed entirely remotely (home-based)- it will involve tracking your typical endurance training over 4 weeks and performing 7 cycling-based tests of your heart’s response to exercise. The study will start and finish by asking you to measure how your heart rate responds to a maximal exercise test, a moderate intensity cycling bout and a 20-minute cycling trial. For the first 2 weeks of the study, you will perform your usual training and send your heart rate and training data files to the researchers. For the next 2 weeks, you will either repeat the exact same training, or include short rest breaks within the same training sessions. From this study, you will gain insight into how your heart responds to forms of endurance training and will have an opportunity to speak with an exercise specialist about your training programming and goals.
Blog post by Marco Altini I was recently contacted by Kow Ping, co-founder of Well Being Digital Limited (WBD101), a company which makes heart rate sensing technology for hearables, as we have been in touch regarding PPG-based technologies for HRV analysis. WBD101 makes technology which can be embedded in different products, with the goal of providing high quality heart rate data. The technology has been embedded in the Hera Leto Two earphones, sold by Actywell. Kow has been kind enough to send me a device for testing, and therefore I report here an initial validation showing really good RR intervals when compared to both our camera-based solution and our trusted reference chest strap, the Polar H7. Measurement setupData was acquired using the following devices at the same time:
During data acquisition, I collected data a few minutes while breathing freely, and a few minutes while deep breathing, to elicitate higher HRV due to RSA. You will see in the plots below visually the effect of deep breathing as we get greater swings in RR intervals. A final note on data synchronization: data cannot be perfectly synchronized because it is not timestamped by the sensors. What we can do is either to log real time and then to split data in windows based on when data was collected, then compute HRV features on these windows or to sum up RR intervals over time. For this analysis we went with the second option and also tried to visually align the data streams. As usual, if you use the camera for your measurements, please double check our camera based measurement best practices, to make sure you'll collect high quality data on which meaningful analytics can be derived.  RR intervalsWe will start by looking at RR intervals, the basic unit we need to compute HRV features. RR intervals (peak to peak differences in consecutive heart beats) are provided by the two sensors directly, so we don't really need to do much to collect them, apart from linking the sensor to the HRV Logger app and export the csv files. As a third comparison, we will add also an RR intervals time series collected using the phone camera, which is the method we have introduced and normally recommend using.  What can we derive from these data? You can see clearly almost perfect correlation between Polar H7 and Hera Leto Two and Phone Camera for all conditions (relaxed vs paced breathing as highlighted by bigger oscillations in RR intervals or instantaneous heart rate), meaning that the sensor works really well.
Blog post by Marco Altini Below we report two case studies highlighting aspects of potential physiological responses to COVID infection, which hopefully can help others identifying promptly potential issues, or tracking recovery (or impaired recovery) in the long run. 1. "Long COVID" and what happens when recovery takes a lot longer. Here is an example with HRV4Training data and rMSSD still suppressed 2 months after infection  2. acute COVID infection in a pro athlete. You can see here how large is the drop with respect to their normal values, this is how data can help to identify a problem, despite lack of specificity for a condition:  Take care and stay safe
Blog post by Marco Altini Welcome to the HRV4Training team to all our new ambassadors Full list at this link Looking forward to working together in the next months. Thank you for your support and all the best for the new year  Blog post by Marco Altini We wrote a fair amount in 2020, and I hope you have found the blogs informative. Here is my favorite 5:
Enjoy and all the best for the new year. Hopefully we'll all have a better one Take care  Blog post by Marco Altini Our work on HRV and altitude adaptation in elite triathletes is available on Sport Performance and Science Reports, at this link. This work is based on my thesis, and was carried out in collaboration with the Dutch Triathlon Federation and Vrije Universiteit Amsterdam We hope you will find it interesting  Blog post by Marco Altini In this post, I provide a short overview of HRV features. When getting started with HRV, things can get overwhelming and confusing pretty quick. One of the main reasons is probably the many different ways HRV can be computed from RR intervals, or what we call HRV features. While we have dedicated many words (and many examples) in this blog to our favorite HRV feature (rMSSD), it can be helpful to provide a short overview of the most frequently used features, so that we can better understand why rMSSD is a good choice, and what are the limitations of other methods. Keep in mind that the motivations are both physiological (what is happening in the body in terms of heart rhythm changes in response to stress) and mathematical (how the physiological processes can be captured by processing the RR intervals differently). Physiologically speaking, and making a few oversimplifications, the autonomic nervous system regulates heart rhythm, and therefore we can monitor changes in heart rhythm (HRV) as a way to measure how we react to stressors (training, lifestyle, etc.). In the context of monitoring the effect of different stressors on our physiology, both in the short term and chronically, we are interested in quantifying parasympathetic activity. The parasympathetic branch of the autonomic nervous system is the one in charge of rest functions and recovery, and therefore we are interested in monitoring it because reduced parasympathetic activity is a clear sign of increased stress and poor recovery. For a deep primer on the physiology of resting HR and HRV, check out this blog. For now, remember that this will have implications in terms of the math used to compute HRV, as I'll show later on. The main features of interest can be split into time and frequency domain features, a distinction that has to do with how the RR intervals are mathematically processed. The most common time domain features are the following:
Let's get started.  Blog post by Marco Altini In this blog I briefly discuss parasympathetic saturation and introduce a new feature we have released in HRV4Training Pro to allow you to determine the likelihood of parasympathetic saturation in your data or your athlete's data. What's parasympathetic saturation?Parasympathetic saturation refers to a situation in which parasympathetic activity is particularly high, but this is not reflected accurately in HRV data. As Kiviniemi et al. explain in [1], "possible physiological mechanisms underlying saturation could be due to the dose response of the heart to the acetylcholine secreted by vagal nerve ending. The dose response to acetylcholine has been considered to be linear until its concentration reaches the level at which a further increase in acetylcholine concentration does not produce a change in the response", or in Daniel Plews' words [3]: "a heightened vagal tone may give rise to sustained parasympathetic control of the sinus node, which may eliminate respiratory heart modulation and reduce HRV". It follows that as reported in another of Daniel's papers [2], "in some circumstances, such as vagal saturation, decreases in cardiac parasympathetic indices of HRV during this particular training phase can be related to positive performance outcomes and consequently reductions in HRV, so should not be viewed negatively". How can we identify parasympathetic saturation?Looking at the relationship between HRV and RR interval length you can identify possible parasympathetic saturation. Parasympathetic saturation is a rare event which can happen in elite endurance athletes during high load training blocks. In particular, parasympathetic saturation typically requires:
Under these circumstances, you can look at the relationship between HRV and the average RR interval length (basically the inverse of heart rate), to determine the likelihood of saturation. Normally, higher HRV is associated to lower HR (see data below for an example), and therefore we expect a linear relationship. However, if you are in a period of high training load and HRV is low, together with low HR, and therefore the correlation between HRV and the average RR interval length is small or negative (we lose the linear relationship that we were just discussing), parasympathetic saturation is plausible (below I'll show how you can look at this in our platform). What can you do about it?Depending on how you measure your HRV, you could be pro-active and collect data that is less likely to be affected by the issue of parasympathetic saturation. In particular:
New feature in HRV4Training Pro to determine likelihood of parasympathetic saturation for you and your athletesAs mentioned earlier, if you are in a period of high training load and HRV is low, together with low HR, and therefore the correlation between HRV and the average RR interval length is small or negative, parasympathetic saturation is plausible. We have developed a new feature in HRV4Training Pro to help you analyze this relationship. In the plot below, you would see the darker dots in the lower right corner (low HRV, low HR or high RR interval length). In this case, the suppression in HRV should not be interpreted negatively, as reported by Plews et al.: "the lack of correlation between the R-R interval and Ln rMSSD indicate that athletes are more likely to undergo parasympathetic saturation". You can find the plot below under Insights / Resting Physiology in HRV4Training Pro, we also report the correlation between HRV and the RR interval length for the past 2 and 6 weeks, so that you can more easily spot any recent changes:  Finally, we have added Possible parasympathetic saturation as an automatically detected trend, hence the system will try to do the math for you:  Is this something that should concern you?In general, parasympathetic saturation is a rare event. Over the years, I have received a few emails from users reading online that they should be sitting instead of lying down when measuring, and I just want to make something clear here: this is typically a non-issue. If you are an elite endurance athlete, and you have experienced periods of suppressed HRV, low HR, and performed well in training or racing under high loads while your HRV was low, then you are a good candidate and it would be beneficial to measure while sitting or standing. Otherwise, please do not obsess over something that most likely will never happen, and keep taking your measurements in a consistent manner, so that you can benefit the most from analyzing the data in the long term, as shown in many case studies here. If you have any doubts, you can login in Pro at HRV4T.com and check your own data. I hope you will enjoy the new feature, take care!  References[1] Kiviniemi AM, Hautala AJ, Seppanen T, Makikallio TH, Huikuri HV, Tulppo MP. Saturation of high-frequency oscillations of RR intervals in healthy subjects and patients after acute myocardial infarction during ambulatory conditions. American Journal of Physiology-Heart and Circulatory Physiology. 2004 Nov;287(5):H1921-7
[2] Plews DJ, Laursen PB, Buchheit M. Day-to-day heart-rate variability recordings in world-champion rowers: appreciating unique athlete characteristics. International journal of sports physiology and performance. 2017 May 1;12(5):697-703 [3] Plews DJ, Laursen PB, Stanley J, Kilding AE, Buchheit M. Training adaptation and heart rate variability in elite endurance athletes: opening the door to effective monitoring. Sports medicine. 2013 Sep 1;43(9):773-81 [4] Andrew Flatt's blog: https://hrvtraining.com/ Blog post by Marco Altini Quick update that HRV4Training, HRV4Biofeedback and Camera Heart Rate Variability currently support also all new iPhones from the 12 mini to the 12 Pro Max For iPhones with three cameras, the apps will use the corner camera, as shown below  As usual, please double check our camera based measurement best practices, to make sure you'll collect high quality data on which meaningful analytics can be derived. Below you can also see two minutes of RR intervals data collected with the phone and a Polar chest strap, showing the usual agreement between the two, as reported in earlier validation studies:  Here are the same RR intervals, but showing a bit better how they match:  And finally the computed rMSSD (HRV value) for the two minutes:  Enjoy!  Useful links:
Blog post by Marco Altini
We recorded our latest webinar that was put together for the Spanish Paralympic Committee. In this talk, we cover:
¡gracias! Blog post by Marco Altini As more devices are able to collect high-quality Heart Rate Variability (HRV) data during the night, a few questions come up:
In this post, I will try to answer these questions and show data that should clarify a few important aspects. In the process, we’ll have to debunk some common misconceptions.  Blog post by Marco Altini In the past few weeks I had the pleasure to get to know Francesco Cuzzolin thanks to our partnership with the EuroLeague Players Association Francesco needs no introductions, but I’m gonna do it anyway: first European to become Head Strength and Conditioning Coach in NBA (@ Toronto Raptors), formerly at Benetton Treviso, Virtus Bologna, the Russian National Basketball Team and Italian National Basketball Team, lecturer in training / re-training and injury prevention at the University of Verona and of Padova, Research and Innovation Director for Technogym, currently personal trainer for Andrea Dovizioso Francesco is also an expert in HRV monitoring, and has been using the available technologies for decades. Needless to say, we clicked In the first episode of his new podcast, we tried to break down the science and application of HRV and stress monitoring with a lot of practical insights. I hope this will be a useful resource for anyone getting started with our technology and striving to get their athletes to peak performance Link here  Blog post by Marco Altini We are starting a new research project together with CUS Torino Women's Rugby, and the University of Turin. In particular, this work aims at quantifying training load and resting physiology (HR, HRV) in the context of the menstrual cycle, looking at the relationships between these variables over several months in rugby athletes. More updates will follow, special thanks to Luca Beratto for involving us!  |
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Marco Altini, founder of HRV4Training Blog Index The Ultimate Guide to HRV 1: Measurement setup 2: Interpreting your data 3: Case studies and practical examples How To 1. Intro to HRV 2. How to use HRV, the basics 3. HRV guided training 4. HRV and training load 5. HRV, strength & power 6. Overview in HRV4Training Pro 7. HRV in team sports HRV Measurements Best Practices 1. Context & Time of the Day 2. Duration 3. Paced breathing 4. Orthostatic Test 5. Slides HRV overview 6. Normal values and historical data 7. HRV features Data Analysis 1a. Acute Changes in HRV (individual level) 1b. Acute Changes in HRV (population level) 1c. Acute Changes in HRV & measurement consistency 1d. Acute Changes in HRV in endurance and power sports 2a. Interpreting HRV Trends 2b. HRV Baseline Trends & CV 3. Tags & Correlations 4. Ectopic beats & motion artifacts 5. HRV4Training Insights 6. HRV4Training & Sports Science 7. HRV & fitness / training load 8. HRV & performance 9. VO2max models 10. Repeated HRV measurements 11. VO2max and performance 12. HR, HRV and performance 13. Training intensity & performance 14. Publication: VO2max & running performance 15. Estimating running performance 16. Coefficient of Variation 17. More on CV and the big picture 18. Case study marathon training 19. Case study injury and lifestyle stress 20. HRV and menstrual cycle 21. Cardiac decoupling 22. FTP, lactate threshold, half and full marathon time estimates 23. Training Monotony Camera & Sensors 1. ECG vs Polar & Mio Alpha 2a. Camera vs Polar 2b. Camera vs Polar iOS10 2c. iPhone 7+ vs Polar 2d. Comparison of PPG sensors 3. Camera measurement guidelines 4. Validation paper 5. Android camera vs Chest strap 6. Scosche Rhythm24 7. Apple Watch 8. CorSense 9. Samsung Galaxy App Features 1. Features and Recovery Points 2. Daily advice 3. HRV4Training insights 4. Sleep tracking 5. Training load analysis 6a. Integration with Strava 6b. Integration with TrainingPeaks 6c. Integration with SportTracks 6d. Integration with Genetrainer 6e. Integration with Apple Health 6f. Integration with Todays Plan 7. Acute HRV changes by sport 8. Remote tags in HRV4T Coach 9. VO2max Estimation 10. Acute stressors analysis 11. Training Polarization 12. Lactate Threshold Estimation 13. Functional Threshold Power(FTP) Estimation for cyclists 14. Aerobic Endurance analysis 15. Intervals Analysis 16. Training Planning 17. Integration with Oura 18. Aerobic efficiency and cardiac decoupling Other 1. HRV normal values 2. HRV normalization by HR 3. HRV 101 |
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