Improvements to Xert by digesting HR data

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1

I have many bikes. Too many to fit power meters to all of them as its so…expensive. In addition I do other exercise sometimes. What about Tri-Athletes cycling is on 1/3rd at best for them. Also, of late I have found myself doing long rides in other countries on rental bikes.

Xert throws away my HR sessions. I fully understand why you base your work on Power, but to totally ignore the HR data seems to weaken your product significantly.

What could you use HR data for? Could you use it to give some sense of fatigue? Yes. I am sure with some of the same creative thought that lead to Xert you could use it to bolster the power backed rides. Could it help show that you were dragged down on a ride, due to HR loading and tiredness?

I would like to see HR data play its part to improve accuracy and to fill in the gaps when its all that is available. I would not mind at all if you could toggle the HR data supplements in and out. It would not matter (it would help in fact) if HR fatigue was kept as separate lines on the graph. It would be interesting to see some anal ysis of the ramp steepness as the HR responds to both increased and decreased efforts.

It would be great if HR could optionally be used to supplement the fatigue and training advice.

All this would be much better than massive gaps in my plan that make it look like I have done nothing, when in reality I have just completed one of the biggest weeks in the quarter.

What do you think?

Thanks for continuing to deliver the best cycling effort analysis out there.

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Thanks Martin. HR data is very valuable (it’s how the Xert model was established in fact … see our first blog…) but it’s extremely difficult to analyze precisely. There are many variables that make using HR data on a broad basis for everyone in every situation, very challenging. In controlled testing, HR data yields more information. But in an uncontrolled setting, such as when heat/humidity changes your HR response or when the HRM is acting up and is providing invalid data, analysis of HR data can lead you astray. You don’t get fitter by eating during exercise and seeing your HR go up a few beats in order to process the food in your stomach. You don’t lose fitness by turning up your fan and seeing your HR go down. So although HR is extremely valuable, analyzing it without knowing exactly the conditions and the quality of the data a priori, will inevitably lead to incorrect results.