how to estimate VO2max when heart rates don’t follow the norms!

One of the most frequent questions I get asked is how to deal with heart rates that don’t follow the norms. That is, how do you deal with abnormal heart rates and/or rhythms in response to exercise for anybody, not just for people with heart disease or people in cardiac rehabilitation programs?

Smartwatch algorithms pay no attention to heart rates that do not follow the norms

The problem with almost the proprietary algorithms (e.g. smartwatches and smartphones) is that they pay no attention or allowance to the very large number of people who do NOT follow the age-predicted look up tables. We don’t assume that every male is 80 kg or has brown eyes and we don’t assume that every female is 65 kg has blue eyes. Why then do these apps then do this for heart rates?

In another post, I talked about the updated guidelines as published by the National Heart Foundation of Australia, published in 2019 and one of the important things in the guidelines is that there’s a strong recommendation that participants in cardiac rehabilitation programs should be given a tailored, progressive and supervised exercise training program. fit.test is ideally suited to doing just that. A big part of individualising a program for your client is to use the person’s actual HRmax, or use a very close approximation to it, and not rely on assumptions or look up tables. fit.test does this beautifully.

Another recommendation by the National Heart Foundation of Australia is that each client when entering a cardiac rehabilitation program should have a symptom limited exercise test on a cycle ergometer or a treadmill or even a step bench.

Low exercise heart rates:

I want to start by giving an example of how fit.test caters for clients with low exercise heart rates. Go to 4:00 minutes on the video in this post for a clear illustration of this point. I have set up two sets of data side by side: on the left graph, I have inputted data from a client with low exercise heart rates into fit.test, and on the right graph I have inputted the same heart rates into a proprietary algorithm (smartwatch). fit.test estimates VO2max with precision but the smartwatch dangerously over-estimates VO2max, making the exercise assessment inaccurate and worse still, the exercise plan will be UNCOMFORTABLE and even UNSAFE for such a client.

Smartwatch algorithms pay no attention to heart rhythms that do not follow the norms

The second example is when heart rhythms do not follow the norm. In medicine, these are called arrhythmias and they are very common among people over 60 years, and even 50 years. I have provided two illustrations below as to how well fit.test caters for people with arrhythmias, compared to smartwatches.

Exercise physiologists: some causes of heart rates that do not follow the norms

For those of you who are interested, I have created a list of 14 exceptions as to why age-predicted HRmax can be inaccurate and it is better to measure it yourself:

  1. Beta blockers
  2. Calcium channel blockers
  3. ‘Funny channel’ blockers (e.g. Ivabradine)
  4. Chronotropic incompetence (e.g. sick sinus syndrome)
  5. SAN pauses / blocks
  6. AVN blocks (20 and 30)
  7. Wandering atrial pacemaker
  8. AEs, VEs, JEs
  9. PPM
  10. SVT, VT, JT
  11. AF and atrial flutter with variable AVN conduction
  12. Baroreflex failure / dysfunction