As a self-proclaimed science nerd, I enjoy the data junkie side of our sport. We can track and measure more parameters now then we ever have been able to. While this satisfies the science nerd and research junkie side of me, I’m always left with asking the question of so what? What actionable change do these measurements lead us to.
In the world of quantified self and tracking devices galore, most of them lead to cool data that really doesn’t transfer into practical action. So when I came across the RunScribe I reached out to the guys behind it to see if I could check it out.
After testing the device for several months, they are finally up on kickstarter, and I would highly recommend checking it out:
So what is it?
RunScribe is a an accelerometer that clips onto the back of your shoe. It’s simple and easy to use. You just clip it and forget it. The real beauty in it is the data and program that comes behind it. The data you get out of it includes:
-Foot strike type
-Ground Contact Time
-Peak Impact G’s
-Peak Breaking G’s
and a few more factors. It gives you raw data, as seen below, and should be familiar for those used to working with biomechanics/accelerometers. As well as processed and refined data.
But what can you do with it?
Let me take you through a few quick runs and workouts to show you the power of the device.
If you look at the graphs above, it’s simple a plot of stride length, rate, pace, and ground contact as I progressively picked up the speed every 45sec going from around 6:45 pace down to 4:35 pace.
This is a test I refer to as the gear-change test. What we are looking for is to understand how you change gears as you accelerate. So we know that speed is dependent on a certain mixture of these three variables. How you go about accomplishing that matters. So by doing this simple test, we get an idea of how the variables interact.
So in the above chart, you can see a steady increase in stride rate and a somewhat variable increase in length throughout, while ground contact time steadily drops and then stabilizes as I get fast enough.
The idea would also be to look at change in speeds as we change gears in workouts or races. So understanding what an athlete does when they decide to kick. Will they increase their speed via rate or length changes. If we understand how an athlete is doing this, then we can better train and cue them to accomplish this.
Another particularly interesting use is to look at some of these parameters during workouts to see changes in pacing and fatigue.
The above graph shows a workout I did with Sara Hall in which we were dong some 400m repeats. So above you see the last 4 400’s of the workout. The interesting thing is that you can see the variance in how I run the intervals. So stride rate takes slightly more precedence early on in the workout while I shift to length increases to maintain speed in the last 100-200m of the 400m repeat. The pace is almost the exact same throughout the interval, as I’m a pretty dang good pacer (years of pacing fast women will do that to you!). It’s an interesting settling effect that seems to occur as each interval progresses. Similarly, we can look at braking G’s and see an increase as the interval progresses.
Although these weren’t maxed out intervals, my belief is that we should be able to see changes in mechanics such as ground contact time and pronation velocities as fatigue occurs. We can paint a picture of how fatigue manifests itself biomechanically. Whether we start to rely on stride length changes or spend more time on the ground, for example.
We then can develop training plans to combat this.
Lastly, one of the coolest things we can now simply do is compare the effects shoes have on our mechanics. No longer do we have to guess to see if a shoe actually does what it says. We can simply slap one of these things on, try a bunch of shoes and see what the results are.
Here’s an example from another runner in comparing various shoes on all of the parameters. As you can see this is an easy way to visualize and instantly recognize the inherent differences that shoes cause.
For the more numbers based, here’s a few of my data points
As you can see using these three different shoes running at the SAME speed on the treadmill we see markedly different ways to run. The pronation excursion actually goes in the opposite direction for the racing flats for example. And the ground contact and stride rate varies significantly too.
The point is when you start to look at how shoes change your mechanics, it becomes rather interesting.
As you can see, just using this data we get readily available info on how shoes change things. Instead of relying on shoe companies jazzed up lingo on what a shoe should be, we actually get to see what their effects are on YOU. From an injury standpoint, this could be huge.
Using the RunScribe for just this short amount has led to many other practical applications. Right now, I’m putting my 30+ college kids through a variety of experiments to see how their mechanics change over the course of a run and workout. We’re trying to come up with a baseline “stride signature” for a few paces and then see how much that variance changes when fatigue occurs.
The applications are almost endless. And what I love about it is that it gives both hardcore/in-depth data for us science nerds but also easy visualizations for the less scientifically inclined.
Again, I don’t get anything from the company, they were kind enough to let me test these things out 6 months ago or so. I just think it’s a fascinating device that allows us to quantify variables that were once reserved for the lab.
We get to actually see how people “pace” workouts, how fatigue effects our mechanics, and how shoes and/or injuries impact mechanics. In all, it gives us a much better look at why things happen.
Not many devices allow for practical change based on data, but I believe RunScribe gives us the tool to translate some of these nice metrics into practical change that influences performance