You are now easily able to spot high-level trends using our revamped HRV graphs!
We've also introduced the concept of "HRV Index", which is a normalized HRV value on a Natural Log scale, allowing easy spotting of significant changes.
Since HRV is affected by a variety of factors (movement, light, noise, temperature, food, and fluid ingestion, etc), sleep theoretically represents the optimal condition for HRV assessment.
In the image below, your nocturnal HRV is represented by the recovery rate line shown.
This line is a visual representation of the average increase in the plotted HRV values during sleep run through a linear regression calculation.
The endpoint of the line created is your HRV number during that session of sleep.
HRV Index is a derivative of rMSSD HRV number. The following bar graph shows the relationship of Index HRV Values with respect to rMSSD Values. Basically, HRV Index reduces the disparity between the rMSSD values.
Week, Month, and Year view
Green Section of the Graph: This is your desirable range for daily HRV Index values. You want your daily HRV Index values (as shown by white dots) to stay within this region.
Baseline: Center of the Green section is your baseline. Your baseline is the average Nocturnal Index HRV value for the past 5 to 28 days
CV (Coefficient of Variance): This number indicates how resilient you are to changes (physical, psychological, emotional, etc). The lower the number, the more resilient you are.
Daily Changes: This method involves reducing training intensity when the daily index HRV (shown as white dots) value is below baseline. Generally, the individual would opt for a rest or active recovery day when Index HRV is below baseline for a second consecutive day. Higher intensity training would be planned for days when HRV Index is within or above baseline. This method has been shown to be superior for improving markers of endurance performance than traditional pre-planned training.
Trend Changes: Normal training would be performed as long as the trend-graph remains within baseline. This means that isolated Index HRV values below baseline would be ignored as long as the trend is within baseline. This is why it is considered “less reactive” to use this method. However, when the trend falls below baseline thresholds, training intensity is reduced until the trend returns. This method has also been shown to be superior to traditional pre-planned training and is more commonly used with athletes.