Resting Pulse Rate

A Measure Of Neurological Fitness
Originally published in Pace Magazine Vol. 6 No. 4 Fall 2017

Runners naturally like to measure their progress, such as keeping track of their running times. Another measure of progress for runners is resting heart rate. The measure is also known as resting pulse rate (RPR) when obtained by palpating a peripheral artery as is commonly done with the radial artery at the wrist.

There is a lot of research showing that RPR is a powerful predictor of health outcomes, where a lower RPR is healthier than a higher RPR. For example, people with a lower RPR tend to live longer than their counterparts who have a higher RPR.

In addition, RPR is a reflection of nervous system fitness. This is important because the nervous system controls so many vital functions in the body, including RPR. Indeed, as a practitioner who seeks to improve spinal nerve health in my patients, I use RPR to measure the success of my care. In runners, as with my patients, the expectation is that RPR will improve (decrease) over time. I have found this to be the case in my own running experience and encourage my curious running colleagues to monitor their RPR as well.

Reliability of RPR as a scientific measure is good. For example, there is good agreement between RPR and resting heart rate obtained by an electrocardiogram (ECG). A current-day popular method for measuring RPR among runners is the use of smartwatch technology. There is emerging research showing that some of this technology has acceptable agreement with the gold standard methods of ECG and RPR.

Since RPR is user-friendly, I teach my patients how to self-measure their RPR, using the method of radial artery palpation. In this way, my patients can participate in determining the neurological effectiveness of the care they receive. Studies indicate that self-measured RPR by patients provides important and useful information for clinicians and researchers. Runners can also self-measure their RPR to track their progress in neurological fitness.

There are a number of factors that need to be considered when measuring RPR, regardless of who is measuring it. Consistency is key in order to obtain valid comparisons of RPR over time. For example, the position during measurement should be the same for all measurements being compared, in either the seated position or lying on the back, but not both. A comparison of RPR measured while on the back to RPR measured while seated would not be a valid comparison since seated RPR tends to be a slightly higher than when lying on the back. Other things to be considered for purposes of consistency include: a) Time of day (should be within an hour of two). b) Time since ingestion of food, caffeine, or alcohol. c) Rest period prior to measurement (should be at least five minutes).

A commonly recommended method of measurement is early in the morning, before getting out of bed, before breakfast, while lying on the back, counting for a full minute to achieve a beats per minute (BPM) value. Then record the number along with date and time. Once you have collected a number of measurements, you could enter the data into a spreadsheet such as Excel and construct a chart such as the one below, in which I use mock data. You can also begin to average the RPR readings, e.g., average of the first 10 readings you have taken, on different days compared to the average of the second 10 readings.









The chart below is based on the mock data above. In this scenario, RPR is generally improving (decreasing) over time. The arrow indicates the point at which an intervention was applied in this scenario, such as a spinal adjustment and/or the beginning of a running program. The “before” and “after” are in reference to the intervention.


For these data, the average of the first 10 measurements (July 1-July 10) is 82.5 BPM while the average of the remaining 20 readings (July 11 – July 30) = 76.9 BPM, an improvement (decrease) of about 5 BPM. For those familiar with the statistical functions in Excel, a statistical test could be performed to determine the probability that the difference between the two averages happened by chance alone versus possibly attributable to the intervention(s). This probability is represented by what is known as a p-value. In this scenario, the p-value is very small (p < 0.0001) meaning the decrease probably did not happen by chance. If these were real data, this would suggest that the intervention(s) may be associated with the RPR improvement.

Dr. Hart is a chiropractor, researcher, and runner in Greenville, SC. He has been doing research on resting pulse rate since 2011, and has a dozen papers published on the topic in peer-reviewed science journals (list available upon request). Contact Dr. Hart at:
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