Color-coded correlation analysis in HRV4Training Pro

Blog post by Marco Altini

​We have just released an improvement to the correlations analysis in HRV4Training Pro, which now adds color coding to make it easier to spot stronger relationships between your annotated tags and your physiological data.

​You can try the new feature on HRV4Training Pro for free at this link, or use code SCIENCE for 15% off any package.

What are correlations about?

Citing Wikipedia: ​"Correlation refers to any of a broad class of statistical relationships involving dependence. Familiar examples of dependent phenomena include the correlation between the physical statures of parents and their offspring, and the correlation between the demand for a product and its price. Correlations are useful because they can indicate a predictive relationship that can be exploited in practice."

In other words, looking at correlations can help us to pinpoint which parameters have a stronger impact on our physiology, and potentially make adjustments (e.g. if there is a strong negative correlation between work stress and HRV, maybe we should try to reduce work stress). 

How should I configure this analysis?

The correlation analysis in HRV4Training Pro lets you pick any timeframe between 30 days and 2 years. However, in general, we think that using a time frame between 60 and 90 days is ideal. 

Why  is that? Most likely the stressors you face will change over time, and similarly your response to certain stressors will change, therefore we believe it can be more helpful to look at these relationships in the relatively short time frame (e.g. 60-90 days), to get a better idea of what factors are influencing your physiology the most. Shorter windows (e.g. 30 days) might not have enough data, unless some really large stressor was present (for example if you go from sea level to 2000m / 6000ft of altitude, then you will certainly see a strong correlation between resting heart rate and altitude), otherwise it might be better to extend the window. On the other hand, longer windows (e.g. a whole year) might fail to capture more complex, multidimensional relationships between various training and lifestyle aspects, and your physiology.  

Finally, we would recommend to look at baseline correlations, more than day-to-day correlations. Baseline correlations are computed on the 7 days moving average of each variable, and therefore provide a more stable trend of the data. Typically, this is more insightful than to look at the individual data points, especially in the longer term.

Below you can seen an example:

• Negative correlations are shown in yellow. Remember that negative means that for example your HRV goes up while the other parameter goes down. It does not mean that this is a bad thing! If you have a negative relationship between life stress and HRV it simply means that your HRV goes up when  your life stress goes down, as show here for my own data.
• Positive correlations are shown in blue, in this case a higher HRV is associated with a higher value for a given parameter, for example mental energy in my case

and here is the actual data for the strongest correlation in my data, which is 'lifestyle stress', a tag I use to represent work-related stress:

The important part after you start looking at these correlations, is not to jump to conclusions. For example, it could be that the relation you are seeing is actually caused by another variable excluded by the analysis. However, this can be a useful starting point to explore your data, and we hope the new color-coding will make it a bit easier.

Enjoy