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Training (mostly) slow to race (kind of) fast

Physiological adaptations to 80/20 running: the data

Disclaimer: This post is a short case study on my personal data. I wasn’t blessed with much athletic talent, but I am really passionate about running and data science, which is what motivated me in the first place to build HRV4Training, a platform to measure and interpret physiological data. You will not read about any world record, but decent improvements backed up by solid data, and hopefully it will be useful to some.

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10 km PR and 5th place at the Fort to Fort 10 km run organized by the DSE running group. Special thanks to all the good runners for not showing up, so I could get my ribbon. If you are in the bay area, check out this running group, they organize 5 to 10 km runs weekly, for only 5 USD.

The research

The problem with the average recreational runner is that we tend to get stuck into too much moderate training, having hard time recovering and increasing training volume as well as being more prone to injuries.

I was exactly that kind of runner. After some initial progress mainly due to basic adaptations to running as a sport, I’ve spent the past five years stuck with zero progress in running performance (sometimes inevitably as life got in the way). Last summer I’ve started reading more on running and I eventually decided to give this different type of training a try. Surely I was running slow trainings before as well, or at least I thought I did, but things changed a lot when I started with 80/20 running.

Below I’ll go through my data and provide a few tips on tracking adherence and progress as well as show physiological adaptations in both resting measurements (heart rate and HRV) and heart rate during workouts.

Trying it out

Here is how it went for my first few runs since I switched, about 6 months ago (August 3rd, 2016):

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What you can see above is pace over time. Light blue dots indicate trainings after I started with 80/20 running, the dot size is my average heart rate for the run, so you can see it gets smaller as I start 80/20 running, as I was trying to stay below threshold. The circle indicates my first 80/20 training, a.k.a. the slowest training in my life. There are still bigger dots at faster (smaller numerically) pace, as quality work (tempo, intervals) is still present.

The first run was so slow, that it ended up being my slowest training in 7 years since I started running.

It definitely took some patience and commitment at the beginning.

Tracking adherence

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Same data as above (pace over time, dot size this time is the inverse, so bigger = lower heart rate) but for the entire year. The dot color this time is determined based on my average heart rate for a workout being either below or above the pre-defined threshold of 150 beats per minute.

Similarly, we can look at the heart rate distribution for my data before and after August, showing again a much lower intensity (lower heart rate) for my trainings after starting 80/20 running:

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Running below threshold often turns out as running at threshold, as we can see from the second distribution peaking at 150 bpm, but that’s good enough for me :)

Even more revealing is the maximal heart rate plot. In this one I plotted my heart rate max for any given training, before and after starting 80/20 running:

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Apart from potential outliers (HR max >200 bpm), in this plot we can see that 1) I was getting close to my maximal HR basically in each training before I started training at low intensities — i.e. always too intense 2) after starting low intensity training the distribution gets bimodal, we have two main peaks, which quite clearly separate low intensity trainings and quality work (tempo, intervals).

The plots above show you how you can keep track of your trainings and making sure you follow the 80/20 or mostly low intensity training. The main question I had however was another one, as I knew I was sticking to the plan, was I making any progress?

Making any progress?

Racing short distances regularly to track progress is an option, but also can be a big disruption in training and stress on the body. The end game is improving racing performance, however there are other simple ways to track progress as we go.

In my opinion a good approach is the MAF test, which consists in running at your threshold heart rate (same you picked for your low intensity trainings) for 1 mile, check the time it takes, repeat every month or so. By keeping two parameters constant (your heart rate and the distance you run) you will be able to track progress as it will take less time to cover the same distance at the same intensity. I do this for 1 km before my interval sessions as I am rested (I take the day before off) and I function on the metric system, but it’s pretty much the same thing.

Alternatively you can simply check your typical low intensity run. As creatures of habit all runners tend to often run the same routes, so pick the one you run the most often, and check if your pace gets better over time, at the same threshold heart rate. Here are my pace and heart rate for my most typical training, a 10 km, 110 meters elevation gain “recovery loop”:

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A couple of points from the plot above: pace decreased (higher values in the plot) dramatically when I started the 80/20 running. However, as aerobic fitness improves and your body adapts, pace started increasing again (lower values at the right end side of the plot), highlighting improved fitness. There is a gap in the data as I was in Europe in September/October and I couldn’t run this route.

It’s important to use always the same route as you want most parameters to remain unchanged (elevation gain for example). Clearly other factors will also have an impact over long periods of time, for example temperature, yet this is the most practical way I found to track my progress. As heart rate on a daily basis can be affected also by many other parameters (training, stress, etc.), I would recommend using a route that you run weekly, so that you can have more data points and can spot trends instead of just noise in the data.

Without the need to go through the trouble of creating the plot above, an easy way to track progress on your most common route is simply to go on Strava, and check your pace and heart rate columns, as they already match all your runs on the same route for you:

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Here the pace plot is the other way around with respect to mine, however you see the same, which is a much slower pace the day I started 80/20 running, highlighted in the plot, and then slow improvement over time. It’s important that you check only trainings with a HR below threshold, as you cannot filter out faster trainings you might be doing from time to time on the same route.

Physiological adaptations

Below are my heart rate and heart rate variability (HRV) over the past year, measured each morning using HRV4Training:

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Heart rate (top) and heart rate variability (rMSSD, bottom) during this year. A reduction in heart rate is typically associated to improved cardiorespiratory fitness. Similarly, an increase in HRV can be associated to higher fitness as well as reduced physiological stress on the body.

The quick reduction in resting heart rate and increase in HRV put my mind at ease as I thought something positive (physiologically speaking) was happening rather fast.

I wonder if these are common adaptations, and I will probably try to understand more about this using HRV4Training’s data. I believe part of these changes might be due to the less overall intensity / load I was putting on my body, which might have been constantly overworked before. Here is another bit of anecdotal evidence in this direction, similar findings from another HRV4Training user.

Two other main adaptations that I believe should show up in most people were quite obvious. Training at low intensity allowed for much faster recovery, you go less hard not only on the cardiorespiratory system, but also on the muscles, less DOMS (delayed onset muscle soreness), etc. which in turn allowed me to increase significantly training load, something I’ve always struggled with (50% more running in 6 months, from 30–35 km / week to 70+ km / week, peaking last week with 92 km and a 37 km run at 5:04 min/km). Below you can see chronic and acute training load over time (also available in HRV4Training):

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Chronic and acute training loads are computed on 42 and 7 days moving windows, according to the Banister model.

These data are particularly interesting especially when we look at them together with resting physiological data. Both heart rate and HRV show signs of reduced stress (high HRV and low HR) while training load was significantly increasing. This might be an effect of the more polarized training as the body is given more time to recover, due to all the low intensity training, with respect to the previous “always moderate” regime.

The second adaptation, that influenced both resting physiological data and heart rate during exercise, is improved aerobic capacity / cardiorespiratory fitness. It took some time to see these changes coming, but they are clear now as in the past month I PR’d all distances from 1 km to the half marathon, peaking with a 10 km in 40:22, something I almost had given up on.

How do we track changes in cardiorespiratory fitness? I’ve done quite some work on this topic during my PhD, the key is to contextualize sub-maximal heart rate. Intuitively, a person that can keep heart rate lower (with respect to her/his maximal) during a certain effort (e.g. running at a certain pace), can potentially perform better than a person that is already maxing out during the same effort (e.g. at the same running pace). In HRV4Training we use the speed over heart rate ratio as a variable representative of changes in cardiorespiratory fitness. We basically contextualize HR at different speeds so that you can track how your body adapts over time, without the need of performing a specific exercise or protocol. Here is how the data looks for my year:

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Speed over heart rate for each workout. This parameter is representative of cardiorespiratory fitness and performance.

We can see above that I first plateau, then switch training to 80/20 and slowly build up to being able to sustain a higher speed a the same heart rate, which eventually resulted in my 10 km PR. This plot provides very similar information to the one shown above where we look at changes in pace for the same route when keeping heart rate at threshold (constant), with the advantage that here we can put together all trainings, as heart rate is contextualized by pace.

Closing remarks

We have also brought this analysis to HRV4Training, so that you can track both adherence and progress similarly to what is shown above, within the app and web platform.

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James Witts’ piece on training polarization featuring my case study. Runners World UK, June 2019.

How can you track training polarization?

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Above you can see how my data looked before starting a more polarized training program, when I was stuck in moderate intensity training (always training at way to high heart rates, only 23% of my trainings were easy at that stage), and how it looks now that I mainly train at low intensities (78% of my trainings in the past month are now easy), the last screenshot shows improvements in estimated VO2max following 6 months of polarized training (as VO2max is estimated using as one of the main parameters sub-maximal heart rate normalized by pace, changes in aerobic capacity resulting in the ability to run at low intensities faster, will reflect in improvements in VO2max)

HRV4Training Pro provides some additional filters that also let you explore in more detail moderate and high-intensity work:

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Training intensity breakdown in HRV4Training Pro. Try it at HRV4T.com

Other useful links:

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Marco holds a PhD cum laude in applied machine learning, a M.Sc. cum laude in computer science engineering, and 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.

Written by

Founder HRV4Training.com, Data Science @ouraring Lecturer @VUamsterdam. PhD in Machine Learning, 2x MSc: Sport Science, Computer Science Engineering. Runner

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