Heart rate monitoring

During dynamic exercise, our heart rate increases to meet the demands of the musculature. But it also changes in response to other factors.

Here is an example of someone’s heart rate while sitting on a Swiss Ball for 5 minutes. The rate rises and falls rhythmically. Why would you think that is? Here’s a hint – he took a deep breath at 1 min 45 when there is  a big peak in HR. That’s right – the heart rate increases when breathing in, and decreases when breathing out. It is “entrained” to the breathing pattern. Some heart rate monitors derive breathing rate from this variation rather than directly measuring breathing. It is also affected by major muscle movements, like running, cycling, squatting and so on.

Sayers first reported the effect of breathing in  Analysis of Heart Rate Variability in Ergonomics Volume 16, Issue 1, 1973 DOI: 10.1080/00140137308924479 pages 17-32.

Heart rate reserve

 Our heart rate can go from about 60 beats a minute to about 200 beats a minute. The upper rate usually depends on our age and fitness. One formula to estimate it is to subtract our age from 220, so for a 40-year old it would be about 220-40 = 180 bpm. If the resting heart rate is 60 bpm then the “heart rate reserve” is 180-60 = 120 bpm. We can calculate 60% and 80% of this range (72 and 96) and add to the resting heart rate (giving 102 and 156 bpm). The lower rate would represent peak fat metabolism, the upper one would be about the anaerobic threshold.

Heart rate monitoring example

This example shows heart rate monitoring during exercise, with a warm-up at 180W on a cycle taking the heart rate to about 150 bpm. Then the peaks represent the heart rate response to a series of resistance exercises, such as clean-press. The last 20 minutes is another session of cycling at 140W, 180W and 140W, a final sprint at 300W and then cool-down.

This example shows the heart rate response to cycling with an immediate start at 150W and continuing at the same load. It takes 2 minutes for the heart rate to reach about 80% of the rate corresponding to this load, and another 3-4 minutes while the heart rate rises more slowly for oxygen uptake to match demand. For this 60-year old person, at 150W his heart rate reaches a first plateau at about 150 bpm.

The so-called “oxygen deficit” represents anaerobic metabolic components including hydrolysis of ATP and creatine phosphate stores and the adenylate kinase reaction, glycolysis, and even glycogenolysis. Oxygen uptake and heart rate continues to slowly rise even though power output is held constant. This extra energy expenditure has been called the “slow oxygen component”. When working at or above the anaerobic threshold, the combination of the “oxygen deficit” and the slow component quickly leads to exhaustion.

Another factor is the heart rate recovery after exercise. You can see the rapid decline after each weight lifting set or after cycling at 180W. Heart rate recovery in 1 minute is a good measure of fitness and predicts cardiovascular health.

If exercise is below the anaerobic threshold (AT) it can be continued for a longer time without the slow heart rate increase that occurs when the workload is above the AT (as shown above). In addition, the so-called oxygen deficit is “repaid” (e.g. glycolytic ATP and PC resynthesis) during exercise and the contribution of aerobic and anaerobic energy systems are balanced to demand. We can also estimate the anaerobic threshold, and relate it to blood lactate levels.

In conclusion, we can use heart rate monitoring to assess cardiovascular exercise intensity, training effect, and the rest interval between sets in resistance training. We can use HRM and HRV together to assess cardiovascular fitness including recovery and response to training. That is described in another article.

The heart rate gives a measure of intensity of exercise and recovery, a bit like the rev counter in a car. But it is much more than a rev counter.

The heart rate is controlled by the sinoatrial (SA) node of the heart, which itself adapts to signals from both the sympathetic and parasympathetic branches of the autonomic nervous system.

Imagine a local pacemaker that is fine-tuned by the central nervous system.

Heart Rate Variability

The interval or space between each blip in your heart rate is plotted vertically, against time on the horizontal axis.  The longer the time interval between beats, the higher the point is plotted.  When measured at rest, the more this line goes up and down, the better is your health.  If you measure HRV when working at a consistent workload with a fairly high heart rate, the variability will be less, as there is far less time between beats.  Imagine a heart rate of 60 beats per minute – that’s one second between each beat.  If your heart rate is 140 beats per minute, that’s just 0.4 seconds between each beat.  The heart beat itself takes almost that long.

The term “Heart Rate Variability” has become accepted to describe variations of instantaneous heart rate and RR intervals, even though it is the interval between consecutive beats that is being analysed rather than the heart rate.

Heart rate variability (HRV) shows a feedback-controlled adjustment of the heart rate on a beat-by-beat basis imposed on top of the normal cardiac cycle and superimposed on a daily circadian pattern. The inter-beat interval (IBI) gets shorter as heart rate increases. Some heart-rate monitors assess your fitness from heart rate variability while you rest. HRV at rest increases as you get fitter.

The RR interval variations during resting conditions represent a fine tuning of the beat-to-beat control mechanisms.  Some Polar HRM watches measure this to assess fitness.  Heart rate and cardiac cycle can be altered by factors such as respiration, changes in arterial pressure, muscle action, stress and so on.

The most frequently presented data are:

• Standard deviation of normal-to-normal (NN) intervals (SDNN);
• Root mean square of successive differences between NN intervals (rMSSD);
• Proportion of successive NN intervals greater than 50 ms (pNN50);
• Total spectral power (TP), low-frequency (LF), and high-frequency power (HF) in both ms and normalized units (nu) and the ratio of LF power to HF power (LF:HFnu).

For tracking purposes, rMSSD is a more “robust” measure than pNN50.

Power spectral analysis shows distinct regions of which the high frequencies (>0.25 Hz) are mainly associated with parasympathetic nervous system activity, and the low frequencies (<0.10 Hz) are associated with both sympathetic nervous system and PNS. Recently it was reported that very low frequency may reflect vasomotor control and it changes in response to cold exposure or spicy food, and perhaps energy metabolic regulation.

Frequency-Domain Results give us total spectral power (TP), low-frequency (LF), and high-frequency power (HF) in both ms and normalized units (nu) and the ratio of LF power to HF power (LF/HFnu).

Overtraining shows a marked predominance of sympathetic activity (e.g. a higher value of LF/HF).

The non-linear Poincaré plot analysis provides a visual indicator of fatigue.  We can also derive the standard deviation of beat-to-beat R-R interval variability (SD1) and the standard deviation of continuous long-term R-R interval variability (SD2) and the SD1/SD2 ratio.  Overtraining shows up as a depressed SD1 from your establish your normal range of values you can quickly check whether you need to take more rest.

If you just had a hard training week, you would expect SD1 to drop.  Each day that you recover, SD1 will increase.  Here are some example results at rest and at constant 150W workload.  You can track rMSSD, LF/HFnu and SD1 to determine personal training and recovery profile.

Data	Jul 5	Jul 14	Jul 4	Jul 8	Jul 11
mean RR	1050	1012	404	425	430
SDNN	29	29	4.5	5.9	5.1
mean HR	59	57	148	141	140
rMSSD	15.7	15.6	3.2	3.6	2.8
LF/HF	11.36	5.86	0.87	1.55	1.5
SD1	11	11	2.2	2.6	2.0
SD2	40	40	6.0	7.9	7.0
	Rest	Rest	150W	150W	150W

For example his HR_rest is 57 bpm, HR_AT (at anaerobic threshold) is about 146 bpm, HR_peak 182 bpm, P_AT (power at anaerobic threshold) 180W and P_peak 350W, VO_2AT 35 and VO₂max about 52 ml•kg•min^-1.

Lying down mean RR 1012 ms; mean HR 57 bpm SDNN 29 ms; rMSSD 15..6 ms; LF 311 ms²; HF 53 ms²; LFnu 85.4; HFnu 14.6 and LF/HFnu 5.866.  What do you notice? That’s right, it is lower than it was on July 5.  He took three programmed “rest days”.

We take HRV data from the inter-beat intervals (IBIs) measured by a Suunto t6 heart rate monitor. If we do this at rest we can get an idea of someone’s fitness, or we might detect possible overtraining. We can do the same with some Polar HRMs which include a fitness test.

We can also keep track of training intensity based on heart rate zones across multiple training sessions.

Orthostatic test.

Some feedback mechanisms provide a quick adjustment to heart rate, one of which is the arterial baroreflex. This senses stretching (and thus blood pressure) within large arterial blood vessels and quickly increases the heart rate, for example when we stand up after lying down or squatting. We can check someone’s heart rate response to a squat test (just body weight, with no added weight), which is an example of an orthostatic test.