Blog post by Marco Altini
Below is a few months of data highlighting the relationship between the menstrual cycle, resting heart rate (HR) and perceived physical condition. For the visualization, we are using the Overview page in HRV4Training Pro, which makes it easier to spot longer term trends as it displays normal values, baseline, training load and subjective metrics, all in the same page.
In particular, we can see an increase in resting heart rate in the luteal phase of the menstrual cycle, which anticipates menstruation and a reduction in perceived physical condition.
This is a good example of the importance of looking at physiology + context, to better understand the full picture. As physical as well as mental stressors continuously change over time, the relationship can change. Yet, analyzing these physiological changes and properly contextualizing them (which in this case means understanding that the menstrual cycle can drive much of the change in resting physiology), can help deriving the right conclusions.
The data below was collected using HRV4Training's camera-based measurement every morning.
I hope you've found this short case study informative!
Our latest paper, titled "Real-time estimation of aerobic threshold and exercise intensity distribution using fractal correlation properties of heart rate variability: A single-case field application in a former Olympic triathlete" was just accepted for publication in Frontiers in Sports and Active Living: Elite Sports and Performance Enhancement.
In this paper, we show a case study of our real-time implementation of DFA alpha-1 in the HRV Logger, which you can find at this link
Learn more about the paper, here
We have just released an update for the dashboard in HRV4Training Pro, comparing the current week to your past month for subjective and physiological data
In the new version, you will find:
This way it should be easier to quantify how much each parameter has changed in the past 7 days, with respect to the previous month, and how you are trending in general
See an example below, and try it for your data at http://HRV4T.com
In this podcast, we chat with ultrarunners Jason Brooks and Jason Schlarb about HRV and HRV4Training, what to expect in terms of acute changes and long term trends, and how to use the data
We also touch on some of our more experimental tools for training intensity estimation such as DFA alpha 1 in the HRV Logger and deep breathing exercises with HRV4Biofeedback
You can find the full episode at this link
Thank you for having me and enjoy the podcast!
Below you can find a brief overview of the recent updates released for our heart rate variability tools, which we hope you have been enjoying
Thank you for providing your feedback and helping us make these tools better, and more useful to you
The latest HRV4Training update includes normal values in the Baseline page of the app for Pro users. Normal values are a representation of your historical data to help you better understand when variations in heart rate and HRV are outside your normal range, so that you can focus on important changes
You can try this visualization by logging in at HRV4T.com and starting your free trial or purchasing a Pro plan (use code NORMALISGOOD for a 25% discount). You can learn more about normal values, how we build them, other features in Pro and a few case studies and examples, at this link
We made quite a few improvements in our biofeedback app, in particular:
If you are already using HRV4Training and are interested in trying out these deep breathing techniques, you should be able to get HRV4Biofeeback with a little discount, by using the app bundle we put together for you
We are continuing our research on the effect of deep breathing on HRV, you can see an interesting follow-up of last week's article, here, where we look at the dose-response relationship between deep breathing and session HRV changes
The HRV Logger now includes heart rate as a feature. This way you can also see it averaged over the feature computation window (for example 2 minutes for DFA), which makes it easier to compare with other features
Bruce Rogers has been doing some comparisons of the HRV Logger and Kubios to compute DFA alpha 1, showing great agreement between the two for the Android version as well, check out some data, here
Blog post by Marco Altini
In this post, I look at the long-term effects of deep breathing on heart rate variability (HRV) as measured during deep breathing practice
While there is plenty of data and published literature on the acute effect of deep breathing on HRV (basically the difference between resting conditions and practice), we know much less about long-term effects. Looking at this data might help us better understand the relationship between deep breathing and long-term physiological changes (if any!)
Enjoy the read
The latest HRV4Training update includes normal values in the Baseline page of the app for Pro users.
You can try this visualization by logging in at HRV4T.com and starting your free trial or purchasing a Pro plan (use code NORMALISGOOD for 25% off)
Overview page in HRV4Training Pro
What are normal values?
Normal values are a representation of your historical data. They are built using the past 60 days of data and help you better understand when variations in heart rate and HRV are outside your normal range, so that you can focus on important changes.
It is normal for heart rate and HRV to fluctuate quite a bit on a day to day basis, and it is key to be able to determine what changes are just part of normal day to day variability, and what changes are important.
For example, a daily score or baseline below normal values for HRV data, identifies a day or period of increased stress, something to be cautious about. On the other hand, if your score is just a little lower than yesterday, but still within normal day to day variability, that's typically nothing to worry about. HRV4Training makes it easy to differentiate between these two conditions.
Example of the new Baseline page for Pro users in HRV4Training. Data can also be color-coded using different annotations, to better contextualize your trends. In particular, we can see how both HRV and heart rate go outside normal values, clearly highlighting a period of higher stress
How do we build normal values?
Currently, we use the past 60 days of your measurements to build your normal values. As soon as you start using the app, HRV4Training will start learning what day to day changes are normal for you, and what changes are outside your normal range. As you gather more data, the app will get better at this job, eventually giving you the best estimate when having all the 60 days of data.
The normal values are always kept current, so that you are not stuck in older data, but at any given time, the most recent 60 days are always used. Over the years, we made a few adjustments to this method, but we believe that 60 days is a great trade-off between 1) not being too reactive, or adjusting your normal values too quickly for example using just the past few weeks of data 2) not getting stuck in very old measurements or in seasonal changes that might have little to do with your current status, which is what would happen when using more data. For heart rate and HRV, 60 days of data seems to be just right.
How do we use normal values?
Collecting high quality data using either our validated camera-measurement, a chest strap or an Oura ring, is only the first step. Once we have collected high quality data we need to be able to properly interpret it with respect to our own historical data.
On a daily basis HRV4Training already compares your daily score with your normal values, you can see this comparison and a visualuzation in the homepage of the app. Additionally, you can also see the color coded advice in the History page, which also relies on normal values.
The text in the homepage will also report if your score is within your normal values or not, as you can see below. What you aim for typically, is a stable physiological condition, so scores within normal on most days.
Making adjustments such as trying to reduce training load or other stressors when your HRV is below your normal values, can be an effective strategy to better balance stress, and improve outcomes in the longer term. This is also the approach used in HRV-guided training.
Homepage and History page in HRV4Training. The color coding and the bar at the bottom of the homepage highlight if your daily score is within your normal values
However, in the long term, once you have used the app for a few months, it can be helpful to visually look at the normal values band to better understand the full picture and analyze baseline deviations that highlight longer periods of higher stress. This is what you can do now in the app if you have a Pro account, similarly to what you already see on the web dashboard in the Overview page.
Additionally, the new visualization provides color-coded annotations so that it is a bit easier to contextualize stressors in the medium-long term. See for example here annotations related to the menstrual cycle or getting sick.
How can you try it?
You can try Pro for free by logging in at HRV4T.com
Once you have started your free trial or purchased a plan, the Baseline page of the app will update automatically.
We hope you'll enjoy this update.
Thank you for supporting our work.
The Heart Rate Variability (HRV) Logger app is finally available on both the Apple Store and Google Play, including DFA alpha 1 to analyze exercise intensity with respect to the aerobic threshold
What's the DFA alpha 1?
A non-linear HRV feature which can be used to estimate the aerobic threshold. In particular, when DFA alpha 1 is below 0.75, it means the intensity is higher than the aerobic threshold (so it defines "low intensity work", as typical in a polarized training program). If alpha 1 is above 0.75, the intensity is considered low, or below the aerobic threshold
How can you try it?
The HRV Logger app requires an external sensor, and for this specific analysis, we highly recommend a Polar H7 or 10 for its accuracy. Garmin dual straps should also work well.
Learn more and get the app at this link
Enjoy and stay aerobic
Blog post by professional ultrarunner and coach Daniel Rowland. You can learn more about Daniel on his website, and reach him on Twitter
I have always enjoyed my base training. This time of year is different to the rest of the season as it is about the simple goal of running more miles and getting fitter, rather than doing race-specific training. In this period, I like to build up my fitness from the starting point that I find myself at. Instead of planning backwards from goal races and setting milestones that I need to achieve in training, I can progress at a natural rate and in accordance with how quickly my body adapts to the training. I can also do consistent training without needing to taper or recover from races or to adjust for training camps or travel. For me this means that base training is natural, fun, and that I usually see great progress in my fitness and running performance.
During this period of training I place an emphasis on training below the aerobic threshold (AeT) or in zone 1 and zone 2 of a five-zone model. This is consistent with many training systems as it follows a polarized approach and incorporates the fundamentals of building an aerobic base.
There are two key challenges for performing this training effectively:
The first challenge of intensity control requires knowing the right intensity and then having the discipline to execute it in training. Traditionally, the AeT has been difficult to define. Unless the athlete goes to a laboratory for testing, the coach or athlete only have the "talk test" or heart rate-based estimates such the MAF heart rate which are not very accurate. The second challenge of assessing progress can be more easily addressed through using repeated routes or time trials, but this can be difficult depending on the weather and conditions over the winter.
This case study will explain how I have used the tools available from HRV4Training to address these two challenges and to show the progress from 60 days of base training.
Intensity control with Heart Rate Variability Logger
In January, Marco shared colab project  with a way to determine the AeT using DFA (1 alpha). I was interested in it because it addressed the first challenge I listed above. However, when I looked at the process it seemed a little too complicated for me and not worth the effort given how confident I felt about my current set of heart rate zones. Then Marco added the DFA (alpha 1) feature to the HRV Logger app which made it a much easier project and something that I could understand and do myself. I set out to "confirm" my AeT rather than to determine it because I felt so confident that I already knew what it was.
My process involved going for a run with two means of recording: my Polar OH1+ recording heart rate and transmitting that to my Polar Vantage M watch, and my Polar H10 recording heart rate and transmitting that to my phone which was recording using HRV Logger. I only looked at my current heart rate on my watch during the run and set an upper limit for heart rate during each session. When I returned home I reviewed the HRV Logger data to see how much time was below the DFA (1 alpha) threshold of 0.75. It took me multiple trials, each time reducing the upper limit of heart rate, until I reached a point where I did not exceed the DFA (1 alpha) threshold. You can see three of the tests in the following screenshots which were tests using heart rate upper limits of 135bpm, 130bpm, and 125bpm.
HRV Logger DFA (alpha 1) values from three runs. On the left, a run with a maximum heart rate target of 135bpm. 30% of the run was above AeT. In the middle, values from a run with a maximum heart rate target of 130bpm. 25% of the run was above AeT. On the right, values from a run with a maximum heart rate target of 125bpm. 0% of the run was above AeT.
I was surprised to see that my AeT was much lower than I had expected. This meant that my previous training was probably too hard and that I had an opportunity to train with better intensity control in this base period.
The steps I took above were my own simple method of determining AeT using HRV Logger. If you're interested in doing this yourself, I would recommend following this more thorough and more detailed practical guide from Marco .
There's a new independent validation out looking at the accuracy of commercially available HRV apps and sensors
Great to see the results for the lowest median error:
This is confirmation of the quality of the work we have been doing for the past 8 years, starting with early analysis of the accuracy, then our validation paper, and finally with this independently run study confirming the accuracy of the methods we have developed for both the camera based acquisition and artifact removal for RR intervals acquired from external sensors
Some thoughts at this link
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 microcycle
The 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.
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
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.
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"
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 yourself
1) 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)
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.
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
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.
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.
Data 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.
We 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.
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
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This blog is curated by
Marco Altini, founder of HRV4Training
The Ultimate Guide to HRV
1: Measurement setup
2: Interpreting your data
3: Case studies and practical examples
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
1. Context & Time of the Day
3. Paced breathing
4. Orthostatic Test
5. Slides HRV overview
6. Normal values and historical data
7. HRV features
1a. Acute Changes in HRV
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
9. Samsung Galaxy
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
1. HRV normal values
2. HRV normalization by HR
3. HRV 101