Where different tests and benchmarks fit along the Return to Sport Functional Road Map
When was the last time you ventured out on a trip using a good old fashioned roadmap and a pencil to mark out your route?
I can remember my parents working at the dining room table before a long road trip to trace out the route they would take. A bunch of working rules or heuristics guided their decision making. For example, if you were traveling with little kids, you would probably need to plan for a break every few hours and, ideally, those locations would have toilets and space for a good run.
A functional milestone roadmap provides a similar directional compass to inform and guide the rehabilitation process after injury. Importantly, rehabilitation goals shouldn’t be limited to just treating symptoms like reducing swelling and subjective movement assessments. We need to prepare athletes physically and mentally for the sport-specific demands they will encounter after they return from injury. Just like the route my parents sketched out, a functional roadmap is a pathway to success with planned stopping points along the way, a Plan B route in case something goes awry, and the ability to measure progression (or regression).
You may remember from our last post that a constraints-led approach to understanding human movement provides three key levers that practitioners can pull to shape an athlete’s movement strategies. We can pull the internal constraint lever by increasing the strength at a joint (e.g. increasing an athlete’s quadriceps strength); we can pull the task constraint lever by modifying the rules of an exercise (e.g. not using the arms during a vertical jump landing to restrict the contribution of the upper body to landing stability); and we can pull the environmental constraint lever by having the athlete perform a test in a contextually different manner (e.g. moving a change of direction test from the clinic to the field of play and adding pressure to perform).
Because human movement is shaped partly by constraints, these levers allow us to build a comprehensive functional roadmap that challenges the athlete and exposes their compensation patterns in different ways. Remember, just like Sherlock Holmes, we can modify the demands of a test and use objectively determined measurements to uncover movement compensation clues. These clues lead us towards more targeted rehabilitation and decision-making throughout the post-injury return to sport transition.
The Plantiga Baseline and Recovery Profiles harness the technological power of the AI-driven movement platform and the ubiquity of wearable technology so that practitioners can check every nook and cranny for information when it comes to return to sport testing.
Just like a 21-point inspection for your car, the Plantiga Baseline and Recovery Profiles provide a comprehensive audit of neuromuscular performance and movement strategy. For example, we can measure performance outcomes (e.g., how fast? how high?), braking movements (e.g., landing and stopping), propulsive movements (e.g., reactive strength index or limb speed), and movement quality (e.g., movement variability score for stride length).
Figure 1: A practitioner’s guide to the hierarchy of evidence guiding the Baseline and Recovery Profiles.
Importantly, the Baseline and Recovery Profiles allow us to assess functional movements like walking, running, and jumping so that we can track progression as an injured athlete gets back to health (e.g., learning to walk without a limp), returns to sport (e.g., achieves symmetrical single leg vertical jump height), and restores their pre-injury performance level (e.g., getting back to the pre-injury maximal running speed). Instead of discrete time point analysis, we can monitor an athlete’s long-term functional capacities to address the period for the highest risk for reinjury – which can often be up to two years out from the initial injury.
Figure 2: The Plantiga Baseline and Recovery Profiles guide return to health, return to sport, and return to performance decision making
In the next section, we’ll dig deeper into the top tier of functional milestone evidence, the n=1 benchmark, and look at some normative data that can support practitioners and clinicians in becoming more evidence-led and objective when it comes to return to sport / return to play decision making.
What is Normal Anyway?
If we agree that we live in an n=1 world, then the best benchmark for guiding our decision-making process after injury is a pre-injury baseline from a strong, fit, and high functioning athlete. However, in the absence of this, we need a comparative group to get our benchmarks. Group benchmarks will always need to be sport-specific, sex-specific, age-specific, and performance-level-specific.
But in the absence of benchmarks, we can still use a between-limb asymmetry index to track the functional recovery of athletes returning from injury.
We just need to remember a few things:
- When we think about lower limb capacities (e.g., strength, power, explosive ability), we should first ensure that the non-injured limb is strong enough to be considered an appropriate benchmark, and then work to achieve limb symmetry with a target goal of getting the injured limb to within 5% of the non-injured limb.
- Unlike limb symmetry in lower limb capacities, movement asymmetries are inherently variable on a stride by stride or jump by jump basis, so we have to measure them over multiple movement cycles – here the question is not whether any given stride was asymmetrical but whether or not there is a pattern showing excessively high movement asymmetry (e.g., > 10%) or low-grade non-variable movement asymmetry (e.g., the athlete always loading a particular limb in a particular phase of movement, like avoiding the injured limb in a bilateral vertical jump landing).
- Movement asymmetries are task-specific and movement-specific, so we can’t necessarily extend the observation of elevated movement asymmetry in a vertical jump to the possibility of elevated movement asymmetry in walking.
- And finally, because we can have two symmetrical but weak limbs, we should always consider the capacity of the reference limb (e.g., how strong, how powerful) alongside our measures of movement asymmetry.
With that said, asymmetry testing can be hugely helpful for tracking the return to health, return to sport, and return to performance transitions after injury. We will consider a few case studies below.
Plantiga’s machine learning algorithms are powerful and extract a multitude of biomechanical variables from walking and running.
Figure 3 below gives us some normative values for walking and running movement asymmetries. These data were obtained from more than 10 ACL injured athletes at various stages of recovery compared to more than 300 non-injured athletes. There are a few limitations with this observational cross-sectional analysis. First, this is a preliminary analysis that is descriptive. Second, ACL-injured athletes tend to demonstrate a recovery in their between-limb asymmetry over time, but grouping together all ACL-injured athletes (at various stages of recovery) doesn’t account for these time-dependent differences. Third, the participants were heterogenous across a range of measures (e.g., age, sex, sport, performance-level).
Figure 3: Normative between-limb asymmetry data for male and female athletes obtained for walking and running.
However, there are still some interesting observations to note. First, notice how the bulk of the asymmetries for the non-injured athletes fall between ± 10% for running and walking and that the median asymmetry (solid horizontal black line) is less than 5% across the board. If we look at the ACL injured group, we will note a higher median asymmetry in the walking impact force compared to the non-injured group and higher variation owing to some athletes who were demonstrating asymmetries > 20% early in their recovery.
Due to limited data, there are no observable group differences for the impact force asymmetry in running, but this is primarily due to the lack of control in terms of how the tests were conducted (i.e., the tests for the ACL injured group included very short stretches of jogging). We also can note that the variation in the between-limb asymmetry for the stride length asymmetry and swing time asymmetry is lower for the ACL injured group. Upon first glance, we may assume that a lower variation in movement asymmetry is a positive feature but there is literature that suggests ACL injury may lead to a reduction in movement variability, which is actually a sign of an altered neuromuscular control strategy. This is further reflected in Figure 4 that shows a comparison of the the stride length limb variation for the non-injured control limbs and the injured limbs of the ACL group.
Figure 4: Normative between-limb coefficient of variation data for male and female athletes obtained for stride length in control subjects and those with ACL injuries.
Finally, the bilateral and unilateral vertical jump asymmetries are shown in Figure 5. Here, we see considerable separation between the ACL injured group and the non-injured group in terms of the between-limb asymmetry index. Notably, the ACL group displayed a higher between-limb asymmetry index compared to the non-injured group across the countermovement jump impact force, the single leg jump height asymmetry, and the single leg jump impact force. These results are consistent with the scientific literature.
Figure 5: Normative between-limb asymmetry data for male and female athletes countermovement jumps and single leg jumps for height.
In summary, the aim of this blog post was to build off Parts I and II of the Sherlock Holmes series and present some actual data obtained from the Plantiga system. This can help sport practitioners and clinicians contextualize their findings with the Plantiga system. There are limitations that need to be addressed in future work and this provides only a preliminary descriptive analysis. But, given the importance of evaluating functional recovery after ACL injury with objective testing, Plantiga can help practitioners build a recovery profile using the functional road map approach to help inform the return to health, return to sport, and return to performance transitions. Stay tuned for the next blog post where we will take a deeper dive into some case studies and highlight Plantiga’s Movement Health Score, which provides a predictive score on movement quality.
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