Who, what and why?

Heart rate variability, or HRV, has long been considered to reflect overall health. Since the 1980s, Professor Steven Porges described HRV as an index not only of stress, but of vulnerability to stress. In other words, the higher someone’s HRV, the more headroom and ability their body has to cope with stress, whether that stress is coming from external (training, mental, emotional) sources or internal sources (chemical, inflammation, disease).

An international team of distinguished researchers led by Marc Jarczok at the Mannheim Institute of Public Health decided to analyse comprehensive health check data that included HRV and detailed blood tests as well as the answer to a very simple general health question to see where the strongest relations lay.

What did they do?

They looked at data from a total of 3,947 working people with an age span of 18-65 years who had volunteered for an extensive health check-up.  As well as detailed questionnaires and measurements, participants were asked to answer a simple question about their health over the past 4 weeks:

“In general, would you say your health is: 1. Excellent, 2. Very good, 3. Good, 4. Fair or 5. Poor?”

The researchers used the results of blood tests to determine biomarkers including glucose, lipids and CRP (inflammation), and used questionnaires to determine lifestyle, sleep quality and work stress.  24hr ECG recordings were used to calculate a range of HRV indices including time and frequency domain parameters.

What did they find?

hrv and health

  1. Self-rated health is associated with mean HRV in a linear fashion. The figure shows clearly that the average RMSSD (a measure of HRV) (ithlete equivalent numbers shown on the left) steps up in every category from Poor to Excellent health.
  2. Self-rated health was significantly associated with sleep quality, work stress, age and physical activity.
  3. Blood pressure measures were all significantly (negatively) related to HRV, and interestingly, more strongly so than heart rate. So knowing this surely measuring your HRV is both more important and predictive of overall health than measuring blood pressure.
  4. HRV was significantly negatively associated with LDL (bad cholesterol) and total cholesterol, whereas it was weakly positively associated with HDL (good) cholesterol.
  5. HRV was also significantly, negatively associated with fasting blood glucose.
  6. HRV was a stronger predictor of overall health than any other biomarker. Being in the lowest 30% of HRV made people three times more likely to report poor overall health.

What does it mean?

This is the first paper I have studied which paints such a comprehensive picture of the relationship between a simple holistic measure of overall health and many of the most common biomarkers used in health assessments, including blood pressure, cholesterol, fasting glucose and inflammation as well as questionnaires on mood and stress.

The fact that HRV (RMSSD, as used by ithlete) was the strongest overall predictor of self-rated health in a sample size of 4,000 working age people lends credibility to the claim for HRV to be a powerful and comprehensive barometer of overall health.

An interesting observation related to this is that the range on the ithlete scale from Poor to Excellent is only 12 points.  These scores are averages so it’s important to use the (blue) base line for assessments rather than just daily readings.  Of course it doesn’t mean that if you get a 60 score on ithlete on one day that your overall health has become poor, but if your baseline is continually declining, there is likely to be an underlying reason that requires attention.  Conversely, you can feel rightly proud of upward baseline trends over longer periods of time, which can be brought about by improved sleep patterns, lower stress levels, better diet and higher aerobic fitness.

By Simon Wegerif.

Read the full paper here:

Investigating the associations of self-rated health: heart rate variability is more strongly associated than inflammatory and other frequently used biomarkers in a cross sectional occupational sample. Jarczok et al.

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