The Plantiga Return to Sport Functional Milestone Road Map (FMR) Program
In the last blog post, we discussed how much ado has been made over injury prediction, how it fits in an athletic environment and whether or not it is even a feasible prospect in sport.
To make injury prediction a feasible endeavor, we might have to start treating it like the weather. This means we need to study the athlete in their environment like it is a complex-adaptive system (because it is).
We also need to get as close as possible to the inciting event to capture relevant, time-sensitive data that can be used to reduce the uncertainty in a model that assigns a probability of injury to the athlete.
Wearables Unlock the Potential to Treat Injuries like the Weather
Wearables allow us to obtain ubiquity with our data collection and injury prediction requires us to get better biomechanical and functional data on the field of play, where it matters most.
Plantiga puts a high-grade inertial measurement unit (IMU) sensor pod in an insole that can go in all types of footwear. Machine learning is then used to extract insights from the data that can improve decision making.
Plantiga’s artificial intelligence (AI) algorithm is called Norman, named after Plantiga CEO Quin Sandler’s father who passed away a few years ago. The name is fitting because Norman and Quin conceived of Plantiga and built the company with a whole lot of passion and vision (that’s what impressed me the most when I first met them).
I think it’s fair to say that Norman is still looking over Quin’s shoulder to provide guidance and support, and now Norman the AI is looking underneath our feet in a similar way.
Just like a human who is born from the womb, almost entirely helpless and dependent on adult humans for survival, Norman is learning to roll over, sit up, crawl, walk, run and maybe one day win an Olympic medal. These are called functional milestones and they provide a continuous pathway of development for humans across the lifespan.
As we will discuss below, functional milestones are also incredibly important when it comes to rehabilitation after orthopaedic and musculoskeletal (MSK) injuries, like anterior cruciate ligament (ACL) tears.
Back to functional milestones: I concede, winning an Olympic medal isn’t on the pathway of functional milestones for humans but it is a single example of the pinnacle of functional movement. And if you’re an Olympian and suffered an MSK injury, defending your Olympic medal might be a key milestone in returning to health and performance.
I also have to concede that Norman isn’t trying to learn how to roll over and sit up per se. But Norman is learning how to read our movement patterns all by himself.
Norman is quite smart already.
He can distinguish walking from running from jumping all by himself. This is called movement classification.
He can measure the mass of a backpack on our backs within a few kilograms. Imagine the applications for firefighters and military personnel who have to carry heavy loads around, and with that, carry a tremendous burden of injury (Heir & Glomsaker, 2007).
Norman is also learning when our foot is on and off the ground during walking and running. This is called automatic event detection.
I’m sure you are thinking that accurately detecting foot-ground-interaction is a trivial task, right?
I mean, how hard is it to write an analytical algorithm to detect when the foot is on and off the ground during running. Let me tell you, as a guy who spent many years writing his own analytical algorithms to extract metrics from vertical jump force-time data, this is a staggeringly difficult task especially for complex movements like walking and running out of the laboratory in the real world.
“Analytical algorithms” involve a human writing instructions for the computer with a whole bunch of rules and exceptions. This is challenging when it comes to detecting foot-ground interaction during walking and running because there are so many different ways that humans move.
Human movement is variable.
Machine learning, on the other hand, means that the computer learns to do this all by itself.
Machine learning is the key to unlocking the full potential of wearables aimed at measuring human movement because it learns to read the nuance and variability in how we move.
Norman is getting so much more intelligent that he can now provide us with incredible insight from walking and running including spatial and time-based (temporal) biomechanical parameters like stride length, stride frequency and ground contact time.
Norman is even learning how to measure your ability to put on the “brakes” or decelerate after a forward sprint.
Norman makes mistakes sometimes and his adult parents, the team of computer scientists at Plantiga led by CTO Sean Ross-Ross, have to help him learn what’s right and what’s wrong. But just like a human baby who is moving through functional milestones, Norman is getting exponentially better every single day.
How Norman Can Help Us with Functional Milestones
Now, here’s where the magic happens.
Norman isn’t learning to walk, run, jump and decelerate in a literal sense, he’s learning to read our movement patterns when we are walking, running, jumping and decelerating.
He’s learning how to read our functional milestones.
This becomes hugely valuable when it comes to rehabilitation after MSK injuries because the gold-standard recommendation is that clinicians move away from a time-based approach toward an approach that is based on an individual’s progression over a set of increasingly demanding functional milestones (Jordan et al., 2020; Myer, Paterno, Ford, Quatman, & Hewett, 2006).
Despite this recommendation, many sports medicine practitioners still rely on a time-based approach to guide the return to health, sport and performance transition after serious MSK injuries like an ACL injury and, in this example, the follow-up surgery that is often required to restore knee joint stability (this is called an ACL reconstruction – ACLR) (Barber-Westin & Noyes, 2011).
What does a time-based approach mean?
Timelines are appropriate after injury especially surrounding biological processes like tissue healing or the ligamentization that occurs after an ACL reconstruction whereby the tissue graft starts to become more like a ligament.
But timelines don’t necessarily reflect functional capacities and abilities after injury. When you see the word capacity, think about outputs like maximal muscle strength, maximal muscle power, and explosive strength measured as the rate of force development (RFD). When you see the word ability, think about functional movements like walking, running and jumping.
Timelines may lead us to the notion of a pre-set functional plan for determining when an athlete is ready to start walking, resume higher force activities like strength training, begin jumping and jogging, and eventually make their way back to sport. For anyone how has suffered and ACL injury, you might have heard the magic number of 9-10 months.
To be sure, timelines do have their place.
But could you imagine applying the same timeline-based criteria for a human baby as they develop in their first two years of life? This would be like saying that babies should be walking at 12 months so regardless of whether a baby can walk at 12 months, this remains a safe timeline to progress all babies onto more demanding functional tasks. As a parent of a child who did not walk until he was just over 18 months, the prospect of a blind and somewhat arbitrary time course for functional development just doesn’t work.
And it doesn’t work for return to sport decision making after injury either.
We know babies will learn to walk sometime in the first two years of life but there is huge variability in this time course.
It’s an n=1 game and it doesn’t matter what the average is. What matters is where you fit on the normal curve.
The same is true of an athlete coming back from an ACL injury.
There is no preset timeline that can determine exactly when an athlete is ready to progress from one stage to the next. Again, it’s an n=1 game.
Here is where we have to measure and monitor the things that matter!
Making things even more challenging is that many of the field-based functional tests we use to evaluate an athlete’s readiness for return to sport after MSK injuries like an ACL tear are either (a) assessed subjectively by the practitioner (I call this the coach’s or practitioner’s eye) or (b) are performance based like the single leg hop for distance, a test that athletes are able to “cheat” to achieve the benchmark while masking the deficits that really make them vulnerable to another injury (Grindem, Snyder-Mackler, Moksnes, Engebretsen, & Risberg, 2016).
Experienced practitioners know this narrative well.
The athlete passes the functional criteria at a single time point (e.g. 9 months) while masking deficits, gets the greenlight to return to a high-risk activity and then becomes one of the statistically unfortunate many who go onto suffer a reinjury (Barber-westin & Noyes, 2020).
Worse yet, athletes rarely get assessed in a sport-specific or contextual manner and instead receive their greenlight for returning to a high-risk sport solely based on testing that happens in a clinic environment or a follow-up visit with their orthopaedic surgeon.
We need to do better. But how?
Stay tuned for part 2.
Dr Matt Jordan PhD
My name is Matt Jordan. My PhD is in Medical Science. I’m an applied sport scientist working with elite athletes. Head to my website: www.jordanstrength.com