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Improved readability of your subjective data in HRV4Training Pro

11/14/2019

 
We have just released an improvement in HRV4Training Pro that lets you easily identify periods of significantly higher or lower values in the subjective tags you are tracking with HRV4Training.

In particular, the update adds normal values to the plots under the Overview page. The normal values represent the expected value for a certain parameter, given the past two months of historical data. This means that any values outside of this range will be easy to spot (for example days in which you slept much less than normal), and most importantly, you will be able to easily see when your baseline tag (7 days moving average) is outside of your normal values, highlighting how some major change is occurring. Without normal values, it can be difficult to understand if things are just fluctuating in a trivial way, or if there is a larger change that we should be more cautious about. 

Let's look at a few examples. Below is the data from an athlete that has been prioritizing sleep quality, and we can see that despite some normal variation and a few data points that are particularly high or low (in this case associated to traveling), the baseline never gets outside of normal values, hence confirming that sleep is going well and should not be a major issue or the cause of any significant changes in baseline physiology (e.g. changes in HRV):
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Sleep quality is rather stable despite some variability
Below is another example where we look at muscle soreness during marathon training, we can see some peaks here and there, followed by periods of recovery as we alternate long and easy runs, as well as the major impact of the race towards the end, and how long it took subjectively to go back to normal:
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Muscle soreness during marathon training
And finally, here is a complete example where we can look at changes in resting physiology (HRV), training load and subjectively annotate lifestyle stress, during 2 months that include a few business trips (color coded in the first plot), periods of higher lifestyle stress (due to work and traveling, as shown in the last plot), and marathon training (plus marathon day, the peak in acute load towards the end of the second plot).

Using the latest visualization in Pro, it is easy to see when lifestyle stress was much higher than normal for this person, and how only the combination of high load (e.g. the marathon) and high stress brought HRV below normal values, showing that we had significant stress on the body (and staying in that condition for several days, with a difficulty to getting back to homeostasis quickly).

On the other hand, periods with high stress earlier where managed better, for example by reducing training load:
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Overview page in HRV4Training Pro. HRV, training load and lifestyle stress are plotted during marathon preparation (and race day).
This case study above shows what we know very well already, stress is cumulative and we cannot isolate training and lifestyle stress or think that training is not affected by everything else going on at any given moment in our professional or personal life. 

Yet, a simple marker such as HRV, measured in a well defined context (first thing in the morning while in a rested state), can capture stress deriving from all sources and help us make meaningful adjustments to maintain things in check. 

​We hope you'll enjoy the latest update. 

If you have an HRV4Training account, you can try Pro for free by logging in here.

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    This blog is curated by
    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
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    1. Context & Time of the Day
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    ​7. HRV features
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    Data Analysis
    1a. Acute Changes in HRV
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    1c. Acute Changes in HRV & measurement consistency
    1d. Acute Changes in HRV in endurance and power sports​
    2a. Interpreting HRV Trends
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    14. Publication: VO2max & running performance
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    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
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    7. Acute HRV changes by sport
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    Other
    1. HRV normal values​
    ​2. HRV normalization by HR
    ​
    3. HRV 101

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