Data on your running has become easy to get. But it remains hard to act on, especially when it comes to using wearable tech (such as sports watches) to improve your gait.
Alex Hutchinson recently wrote about a 4-stage framework for using the training data from wearable tech. The stages are:
- descriptive–what happened
- diagnostic–why did it happen
- predictive–what will happen
- prescriptive–how do we make it happen
He argues that, when it comes to making training decisions, sports watches etc. only really work for stages 1-2.
Tech has become quite reliable and accurate in gathering data, and Hutchinson sees no difficulty in moving from there to diagnosis:
I’m pretty confident that a modern GPS watch can accurately describe how far and how fast I’ve run in training, which allows me to move to the next stage and try to diagnose whether a good or bad race resulted from training too much, too little, too hard, too easy, and so on.
As for stages 3 and 4, hopes for accurate prediction and the possibility of correct prescription as a result of analysis of very large quantities of data still yield disappointing outcomes, according to Hutchinson. The best that’s possible so far–and maybe for the long term–is using data to support decisions about training, but not as the sole criteria for making those decisions.
Using Gait Data vs. Training Data
In my experience, the usefulness of wearable tech for improving gait falls apart one stage sooner, at diagnosis.
Whether we’re looking at cadence, stride length, ground contact time, right/left balance, vertical oscillation, or any other gait parameter a data-hungry runner or triathlete might gather, it is impossible to diagnose based on the description the data gives us.
Take the example of an imbalance in ground contact time between the right and left feet. That tells us the obvious, that one foot (say, the left) is on the ground longer than the right. That asymmetry probably affects the runner’s performance and may correlate to injury risk, so it’s probably worth doing something about.
But what? How do you get off your left foot faster?
Do you try to land differently on that foot–a little lighter so you won’t be on it so long? Do you try to take a shorter stride forward with your right foot so you can get off your left sooner? Do you just try to feel somehow springier on the left?
Or do you go to the gym or pull out your exercise mat and do some strengthening for your left leg, and then hope to see a trend of improvement over time? That’s probably the best of the options, but knowing what exactly to strengthen is usually a puzzle, and then getting those strength gains to translate to a change in movement is far from guaranteed.
After all, if some of the muscles you should be using when you run aren’t strong, it’s because you don’t know how to run in a way that uses them. Going to the gym to strengthen them might make them stronger but doesn’t generally change the fact that you don’t know how to use that strength in your running. So it fails to change your gait much if at all.
What I’ve just described is an attempt to jump from stage 1 (description) to stage 4 (prescription) without noticing there were two steps missing. That’s what most runners try to do with this data.
And that second step is a doozy. Going from description of a gait parameter–right/left ground contact time imbalance–to diagnosis simply by using your Garmin is impossible. There is nothing in that watch that will tell you what you’re doing with your whole body to cause you to spend more time on your left foot than your right.
In other words, you cannot get from what to why with the kind of wearable tech athletes use in training. It’s like trying to create a picture of a body through connect-the-dots when there are only two dots–one for each foot.
Is More Data the Answer?
Could you get to why if you had more dots to connect? That’s what’s done in gait labs, where markers are put in many places on the body (check out this example). That’s maybe enough for a diagnosis, but as in Hutchinson’s training example, AI is not going to be generating that diagnosis–you’ll be depending on a person to analyze your gait. This is of course why many runners turn to expert gait analysis–in hopes of a diagnosis, followed by a prescription (trusting the practitioner to have mastered the prediction step–also not guaranteed).
For instance, if you came to me with your ground contact data and your video, I would probably find one of two things. Either you keep your left hip joint more flexed than your right, causing you to sit back and making it more difficult to spring off your left foot, or you keep the weight of your trunk shifted somewhat to the left throughout your gait cycle, making it harder to take off from the left foot.
No one, however expert, is going to be able to diagnose which of those two things is happening solely from your watch data. Prediction and prescription are completely out of the question.
This leaves you a frustrated runner with a lot of numbers you have no idea how to move, and possibly a running injury as well.
Someday there will probably be some kind of wearable suit that creates a 3-D model of your body when you run, so the description is sufficient to lead to diagnosis. By then, however, the suit will also probably be doing some or all of the running for you anyway, so you won’t need the data and it probably won’t tell you. Talk about wearable tech to improve gait!
If you like that vision of the future, by all means look forward to the day it comes. I can’t join you in that. (Think I’m crazy? I assure you, I’ve spoken to people developing these things.)
In any case, this will not come to pass between now and your next goal race.
The Highest-Quality Actionable Data
But here’s the thing that really matters: you don’t need to wear devices to measure your movement. You are a movement measuring device. And whatever the future holds, it’s going to be a long time before any technology is developed that’s anywhere near as good as your own nervous system.
Let’s look for a moment at the proprioceptive system, which gives you internal feedback on your body. It includes mechanoreceptors in your skin, joints, connective tissue, ligaments, tendons, and muscles. They give you an abundance of sensation on how you’re moving overall and even how parts of you are moving relative to other parts of you within your body. No gait lab collects even a fraction as much.
Your brain interprets that data, deciding what’s important and what it means. And then your movement is shaped by that information, a process that can involve prediction and adjustment in advance or in real time.
Description, diagnosis, prediction, prescription. Boom.
So if your nervous system can already do everything that wearable tech struggles and largely fails to deliver (for a hefty price tag), why are you still spending more time on your left foot?
The answer is that there is a lot of learning involved. The process of taking the raw data–sensation–and interpreting its significance is something you started working on as soon as you were born. You were very busy with this as a baby, and it’s a lifelong project. You could be doing it better, and working on it pays off enormously for any athlete.
And then the process of organizing your movement itself based on your perceptions is also something you learned and can–and should!–keep on learning and perfecting.
In fact, that’s actually the reason you’re spending more time on your left foot now. At some point you learned to do that to solve some other problem, and you’ll keep doing it until you learn something better. If you don’t choose to keep learning, you’ll be stuck with just what you already know.
This is the heart of running form. Not a set of correct movements that you master, make perfectly symmetrical, and repeat like a robot, but a sensory-motor dance of perception and action that–when you have developed enough skill–allows you to adjust and respond to the gnarliest trail, the most unpredictable winds, and all the possible situations that can arise in every footstep.
The Bottom Line
So getting back to the promise in the title of this blog post… how do you use wearable tech to improve your gait? The answer is you can use it after the fact to identify problems and document improvements.
In our example of the right/left imbalance, when you learn how to feel whether your weight is in the right place over each leg, and how to shift it well no matter whether you’re on a flat road or a camber or a rocky trail, then you’ll see your GCT even out in your watch data. And a whole bunch of other parameters–stride length, vertical oscillation, etc–will improve at the same time, and you’ll probably also see your heart rate go down for a given speed compared to your past data.
Some runners find this kind of confirmation reassuring. Feel free to use your watch for that if you like, but I think you’ll find the great feeling of your body in movement to be much more powerful and relevant data.
Excited by the possibilities? Dive in and start learning here, or dip your toe in here.