Tracking changes in aerobic endurance
Are you making any progress with your training?
In this post I’ll show two methods we have implemented in HRV4Training Pro to let you easily track changes in aerobic endurance while preparing a running or cycling event, so that you can analyze your progress:
- Aerobic efficiency
- Cardiac decoupling*
By analyzing how these metrics change over time, you can better understand if you are still making progress or if you have reached good levels with respect to your historical data, and are therefore ready to move towards a different phase in your training program.
You will also be able to see which aspect is your limiter so that you know what to address first. For example, poor cardiac decoupling could mean that you need to work on your longer workouts while you can most likely improve your aerobic efficiency by doing speedwork (obviously, it’s more complicated than that, and it all depends on your history, current abilities and desired goal — get a coach!).
*currently in Beta, reach out if interested
What’s aerobic efficiency?
Aerobic efficiency relates to your ability to sustain a given workload. Good endurance athletes tend to have high aerobic endurance, meaning that they can sustain a relatively high workload (for example pace or power), at a relatively low effort (typically measured in terms of heart rate).
To determine your aerobic endurance you can compute the relation between output (pace or power) and input (heart rate). Intuitively, a lower heart rate for the same output (pace or power), when consistently shown over periods of weeks, translates into better aerobic endurance.
Similarly, a higher power or faster pace at the same heart rate is linked to improved aerobic endurance. By analyzing the relationship between input and output for running or cycling activities, you can easily track aerobic endurance changes over time, as you progress with your training.
Below is an example of my own, data. You can see how aerobic efficiency improves as I progress with training for a few months, before abruptly dropping following an injury during my marathon preparation.
The plot above gave me confidence that training was going very well. On January 20th I ran my 10 km PR in 37'52", then I shifted training to maintenance mode in terms of aerobic base, and worked more on marathon-specific workouts (longer intervals, longer runs, less track), which resulted in a stable aerobic efficiency.
The ability to track over time this metric makes it easy to determine when significant improvements happened, and how long it took, which is useful information for the next cycle as well (or the next injury, as a matter of fact!).
Note that there is no ideal (absolute) value here, the whole point is to track progress relative to your historical data, and to see how training is progressing.
Everything is relative.
What’s cardiac decoupling?
Cardiac decoupling relates to your cardiac drift during an aerobic effort. What’s your cardiac drift? Basically, your heart rate increasing as a result of your body getting fatigued, during the second part of a workout.
To determine your cardiac decoupling, we compute the relation between output (pace or power) and input (heart rate) during the first and second half of a workout.
Intuitively, if heart rate increases at the same pace during the second part of a workout, or if your pace reduces in an attempt to keep your heart rate below a certain value, it means that your aerobic endurance for the distance is not well developed. Similarly, a ratio close to one or below 1.03–1.05 shows that your heart rate does not drift much during the second part of the workout, which is a sign of good aerobic endurance.
Here is once again my own data for the same 6 months shown above, which include about 4–5 months of very good training in between two injuries:
As you can see there is a quite long period in which decoupling is suboptimal (gray), which means my heart rate was drifting quite a bit.
Then, things get better with consistent training and towards February cardiac drift is constant at 1, which means there is no difference in heart rate and pace in the second half of the workout (for the selected workouts, more on this later). Here is an example of an easy run during that period, you can see pace and heart rate being quite constant:
Finally, as I got injured again, which resulted in about 6 weeks without running before getting back at it at the beginning of April, my cardiac decoupling became again pretty terrible (see the yellow area in the figure above).
Here is an example of a workout in this period, in which I had to slow down a lot despite running just 12 km, to keep my heart rate from going really high:
Knowing both your aerobic efficiency and your cardiac decoupling can provide you with quite a good picture of your current aerobic endurance abilities as you prepare a certain event.
Here is another example, this time using Alessandra’s data. We can see the same few months of consistent training, with decoupling reducing over time until race day (March 19th). Here we have no injuries, but a bit of post-race tiredness that shows as increased decoupling before things get back to normal:
For cardiac decoupling, we do have ideal values, as we are not comparing to anyone else but simply computing the ratio between your first and second half of a workout, hence the closer to 1, the better.
Knowing your average pace (or power) and heart rate for a workout is sufficient to compute your aerobic efficiency. Running the same analysis isolating the first and second half of a workout can provide you with more insights on cardiac drift or what we have called here cardiac decoupling.
Accounting for confounding factors
There are many factors that can affect the relationship between pace (or power) and heart rate.
A few examples are: running or cycling on trails or difficult terrains, (which reduces pace and makes your data not really representative of your fitness), very short workouts where heart rate does not reach a steady state, environmental factors such as hot days or training at altitude, etc. — the list goes on.
While many of these parameters are simply impossible to account for, what we can do is give you more control over what data is used to track changes in aerobic endurance. In particular, via the panel below you can filter workouts and environmental factors so that the resulting data is more representative of your aerobic endurance.
You can also select how much data you’d like to use for each data point, for example selecting light smoothing, only this week of data will be used, while using average smoothing, which I recommend, uses 3 weeks of data. The plots shown above use heavy smoothing, 6 weeks of data per data point.
For example, using the parameters selected above, and my recent training log which includes just a few runs post-injury, I get the following list of workouts:
Note that to use this feature you need to use HRV4Training linked to Strava, so that your workout summaries and laps can be analyzed.
Aerobic efficiency or VO2max?
If you are familiar with our work on VO2max estimation (or any other VO2max estimate), you might know that the same principle just explained, is also the principle behind VO2max estimates. In particular, the ratio between heart rate and pace or power is used as one of the main predictors in our VO2max estimation model. You can learn more about VO2max estimation here.
What’s the difference then? While VO2max is a good marker of cardiorespiratory fitness and aerobic endurance, the estimate depends also on parameters that have very little to do with actual aerobic endurance and performance, for example, body weight. Losing weight will increase your VO2max without necessarily improving your aerobic endurance or performance. Similarly, you never have control on which workouts are included for the estimate, making results difficult to read over time.
While these days VO2max is a quite common marker, and I’ve spent years during my PhD to get to this point, I feel like in the context of endurance athletes it’s difficult to properly track progress using it. While many confounding factors are simply impossible to account for, giving you more control over what data is used to track changes and isolating changes in physiology can be really beneficial.
Alright, that’s all.
Using these two simple methods and analyzing changes systematically over time with respect to your historical data, it should be easy to track improvements (or lack thereof) over time and make meaningful adjustments to your training plan. Learn more about HRV4Training here.
Take it easy!
Marco holds a PhD cum laude in applied machine learning, a M.Sc. cum laude in computer science engineering and is currently enrolled in a M.Sc. in human movement sciences and high-performance coaching.
He has published more than 50 papers and patents at the intersection between physiology, health, technology and human performance.
He is the co-founder of HRV4Training and loves running.