Blog post by Marco Altini.
rMSSD for non-trivial trends is highlighted in yellow (increases) and red (decreases) and shows that over these 2 months of data three trends were automatically detected. The trends are associated to the data right before the highlighted segment starts (see arrows in previous image).
For HR data, an initial decrease as well as a subsequent significant increase have been detected.
The CV in this case can be used to discriminate between maladaptation to training, that we expect to be raising CV, and periods of fatigue, which might be associated with reduced CV. As we can see below in this case we associate the rMSSD, HR and CV trends to maladaptation to the very intense training phase.
The rMSSD/AVNN ratio does not tell us much in this specific example.
The automated trend analysis shows that for almost the entire training program I was adapting and coping well with training. This can be seen also by the performance plot, showing improvements in performance over time.
However, during the main training cycle consisting of more intense interval trainings, I had consistent physiological changes representative of maladaptation to training (higher HR, lower rMSSD, higher CV). This is rather normal for someone that is not doing intense trainings too often and did not cause major issues later on (no fatigue or decrease in performance). However, I probably should've slightly reduced the intensity of my trainings.