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Under normal conditions the heart is governed by parasympathetic tone, beats slower than its intrinsic rate and shows higher beat-to-beat variability. In stress parasympathetic tone is withdrawn an  sympathetic tone predominates leading to increased heart rate and decreased HRV. Thus assessment of HRV can give us insight into the ability to respond appropriately to stress and how stressed we are. There’s clearly a role of the HPA axis in this response and you might want to consider the endocrine changes this axis involves. In chronic stress and illness HRV is reduced and does not change greatly in response to an acute stressor (like exercise) indicating increased sympathetic tone. HRV is also greater in people who are fitter. This may seem paradoxical but higher HRV indicates an ability to cope with stress and exercise is a stress. Conceptually exercise training helps train you to cope with physical stress. A recent review on HRV and exercise can be found in (Michael, Graham, and Davis 2017). Recent studies also point to the usefulness of HRV in monitoring training load (Djaoui et al. 2017; Wallace, Slattery, and Coutts 2014).

HRV can be assessed in the time domain or the frequency domain. In this practical we will collect data on heart rate and we will use this to examine HRV in the time domain only. In particular we’ll calculate the Root Mean Square of Successive Differences (RMSSD) and the Standard Deviation of Normal-Normal (SDNN). In this context Normal-Normal means the same as R-R. These two metrics tell us about variability in the time between R waves. We will also display HRV data in a Poincaré plot. This is a scatter plot that has the time of the current R wave on the x-axis and the time of the t+1 (i.e.next) R wave on the y-axis.

How do we record heart electrical activity?

Why does the waveform appear as it does?

What is heart rate variability?

What time domain measures are used?

What about frequency domain measures?

What is a Poincaré plot?

What will you do?