What is it?
Heart rate variability (HRV) is a measure of differences in the time intervals between heart beats. Heart rate by itself is the expression of how many contractions of the heart there are in a given unit of time; however, the rate itself is not constant. There is normal fluctuation of time between heartbeats, in a manner that speeds up and slows down heart rate. Therefore, HRV is a quantifiable measure that assesses these differences.
This variation in the time between heartbeats is thought to be a composite measure of parasympathetic and sympathetic neural inputs and hormonal inputs as regulated by the autonomic nervous system. While much is still unknown about the mechanism of action causing variability changes, many studies have shown correlations between HRV and diseased states, such as heart disease, Parkinson disease, and cardiovascular disease; emotional stress, such as depression; physical/mechanical stress, such as high-intensity or resistance training; sleep in the context of both acute stress and chronic stress; and meditation whether it’s “inward- attention” or Vipassana meditation. Therefore, HRV is becoming a more common non-invasive measure to examine the physiological state and responses.
In general, a higher HRV is considered better, as high stress and poorer health outcomes have been associated with low values of HRV.
How it’s measured
HRV can be measured by use of an electrocardiogram (ECG) or photoplethysmography (PPG). By referencing a common point in the ECG or PPG waveform, the time between each heart beat can be recorded in milliseconds (ms). Collecting each beat-to-beat interval in ms allows us to compute HRV, most commonly reported as rMSSD (root mean square of successive differences).
The rMSSD method of calculation takes each interval, squares the interval, takes the overall mean, and then the square root of that mean is taken. Biostrap computes the rMSSD using this method and remains the standard computational method for HRV.
More complex measures of HRV, including frequency domain analysis can be performed to get further information out of heart rate patterns, which will be covered in another review.
Correlation with health conditions
HRV is most notably correlated with stress conditions, such as anxiety disorders, depression, PTSD, and other psychological states, with lower HRV indicating higher-stressed states. The suggested mechanism is an increased sympathetic arousal, which affects HRV; HRV alone does not cause these states, but reflects and provides insight into the heightened stress on the physiological systems, which in turn have effects on other bodily systems, particularly the cardiovascular and endocrine systems.
Because of the chronic effects of stress, as previously mentioned, HRV has been noted to be a predictor of all-cause mortality and correlated with obesity, cardiovascular disease, cancer, and neurodegenerative diseases, among other health conditions.
What is a “normal” range?
Heart rate variability has a large individual component that has yet to be understood clinically, and therefore is more often used to assess changes in health over time (see “Interpreting Trends” below).
Heart rate variability can fluctuate day-to-day based on exposure to stress, sleep quality, diet, and exercise. This leads to low repeatability, and therefore makes normative data difficult to collect. In general, younger individuals, males, and more active individuals tend to have higher heart rate variability, but the inter-subject variability tends to be too high to suggest proper normative ranges.
However, Biostrap has collected HRV on over 3,800 regular users, and can make some general observations.
The distribution of HRV tends to follow a log-normal pattern with a median of 40.1 and a mean of 45.4 milliseconds, using the rMSSD method. In general, the average coefficient of variability in each individual is 25.8 % , suggesting high day-to-day variability. This demonstrates a need to track HRV over time to understand the ‘profile’ of an individual (mean, standard deviation, and coefficient of variability).
As previously mentioned, HRV is difficult to interpret and generally nonspecific using data from a single spot check. However, since it is a dynamic measure that responds to various lifestyle factors, tracking HRV over time allows for non-invasive insight into changes in health status or efficacy of certain interventions.
In general, since higher HRV is preferable, a greater ability to manage stress results in an increased HRV. The results of the studies demonstrating the relationship between stress and HRV suggest that interventions aimed at reducing mental and physical stress could increase HRV and minimize day-to-day fluctuations (coefficient of variation, CV%). The increase in HRV itself will not reduce risk and improve health over the long term, but rather, it reflects positive changes in an individual’s physiology.
In a 2018 study, the Biostrap sensor as a wrist-worn device was shown to produce high-quality signals which are useful for the estimation of heart rate variability.
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