What better way to spend your weekends than getting sprayed with color!
Last Sunday, eleven eager Lumo Team members participated in our first local running event: The San Francisco Color Run. A fun and festive 5k race, the color run consists of 5 “checkpoints” where runners dressed in white run through a crowd of people that throw colored powder at you. The run is described as “the Happiest 5k on the planet”, and with all of the bright colors, enthusiastic runners and loud music playing, we have to agree.
From the left: Andrew, Ellie (me!), Monisha, Catherina, Nevran, Sebastian, Tansy, and Gianluca
The race course is a flat loop that starts right around Pier 48 and takes you through AT&T Park onto Embarcadero to run alongside the water and back.
Of course, who would we be if we didn’t squeeze in a little R&D for Lumo Run. Out of the eleven, three members were wearing the Lumo Run sensor to collect some data. The three wearing our sensors were…
Andrew: Our CTO and co-founder of Lumo. He is our most seasoned runner amongst the group.
Gianluca: Our software engineer from Italy. He runs occasionally for a couple of miles but prefers to play a game of soccer.
Nevran: Our Head of Operations and another one of our more seasoned runners. She’s run several marathons in the past but has been taking a little break from training.
We captured lots of great data from our run, and our algorithms team is still going through all of it to for testing and validation. However, we did manage to extrapolate some interesting highlights for cadence, stride length, and pace.
On the three graphs below, you’ll see plotted line graphs for Nevran (blue), Andrew (red) and Gianluca (green) for cadence, stride length, and pace over time.
For this 5k, our team ran as a group until the very end where a few of us sprinted for the finish line (go team!). You can see this reflected in the data by looking at the bottom graph for pace, where Nevran, Andrew and Gianluca all have a similar pace of around 9 min/mile, until the end where all three of their pace decreases to about a 7 min/mile. Where it starts to get interesting is when you look at their corresponding cadence values and stride lengths.
Stride Length and Cadence
In a previous post, we talked about the relationship between stride length, cadence, and speed. Given that speed stays the same, cadence and stride length have an inverse relationship where increasing one will decrease the other. Looking at the three graphs, you can see this relationship perfectly across the three runners between times 9:05 and 9:10, where Nevran, Andrew, and Gianluca are all running at the same 9 min/mile pace.
Even though all three of them have the same pace, you’ll see that they all have different cadence values and stride lengths. Nevran, who maintains a steady 180 steps per minute has the shortest stride length of around 6 ft; Andrew, who averages just under at around 178 steps per minute and a median stride length of around 7.5 ft; and Gianluca, who runs at around 165 steps per minute with the longest stride length of around 7 ft.
In short, to run at the same 9 min/mile pace, Nevran takes the most frequent steps and has the shortest stride length, and Gianluca takes the least frequent steps has the longest stride length – which supports the relationship between stride length, cadence and pace.
Lumo Run Tips for Improvement
One of the exciting things that we do during our production phase for Lumo Run is that we can go out for runs to collect data, hand it over to our algorithms team to generate these insightful graphs, and then sit down with our in-house biomechanist to go over some insights and tips for improvement. Based on this the run data from the Color Run, these are the recommendations we would make for Nevran, Andrew and Gianluca.
Since Nevran is hitting her target range of 180 to 200 steps per minute, we recommend that she starts working on her push-offs to gradually increase her stride length for improved pace. Some exercises she can do are TRX Sprinters for cross-training or incorporating hill sprints to strengthen her glutes.
Looking at Andrew’s cadence values throughout the whole run, we can see that he is very close to target range for cadence. During his runs, he can focus on increasing his cadence slightly for improved pace, as well as work on his push-offs like Nevran for a more powerful stride.
A good in-run coaching tip for cadence is to “imagine you are running on a hot surface to quicken your steps”.
Our best recommendation for Gianluca for improving performance is to work on increasing his cadence to get closer to the target band of 180 to 200 steps. A low cadence value may be an indicator for overstriding, especially when speed increases but the frequency of steps do not. It’s difficult to determine whether Gianluca is overstriding at this pace without further analysis of his data (like pelvic movement). However, as a starter, our first recommendation would be to increase his cadence through in-run coaching cues like imagining running on a hot surface, or doing downhill running drills to practice fast turnover.
Additional resource: How to Boost Your Cadence
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