However, based on the literature, there are some key take homes that we should consider:
- What % LSI should your involved be of your uninvolved in order to be considered not at risk or for making return to play decisions? Based on what we have seen, this answer is not clear but 90% seems to be consensus from the field as well as what most of the researchers are aiming for.
- How should we measure LSI? Based on the literature, there is a very compelling argument to compare the involved limb to the baseline data for the uninvolved limb (measurement taken prior to the injury or prior to surgery on the uninvolved side).
- What test should we be using? Every study we looked at looked used different battery of tests. However, one thing is clear, single limb closed kinetic chain testing is what we should be looking at.
- Is the use of a battery of tests better? Based on what we have seen, the more tests you add in your battery of tests, the less likely your athlete is to pass.
- Does equal LSI tell us anything about risk? The studies are pretty clear that 100% of LSI does not equate to risk. You can have 100% of bad movement and still be symmetrical.
- What should we be looking for when assessing? The studies are clear, control of frontal plane motion (magnitude and speed) in single limb performance.
So despite creating a lot of questions, we did get some guidance on some aspects. Part of the
challenge with getting answers to LSI is historically, we have relied on open kinetic chain testing. Use of the biodex or similar device to get hamstring to quadriceps ratios right to left, strength, torque, etc. It was this kind of information that helps guide us on LSI and whether or not the athlete was ready to return to play. However, we would all agree that sitting in a machine and performing an activity in an open kinetic chain provides very different information than what is provided in a closed kinetic chain. As we all know, as soon as the feet come into contact with the ground and we are upright, there is a different level of core activation, lower kinetic chain muscle recruitment and changes in length tension relationships. In other words, they are more in sport like positions.
With closed kinetic chain testing comes the challenge of how do we quantify control of frontal plane motion? My interpretation may be different than yours which is different than the next person. However, with the advent of new technologies, there are now ways to quantify these motions in meaningful ways. What we know, is that we need to assess control of frontal plane motion and that means control of the magnitude of motion and the speed of motion. Why both?
If we were only looking at 16 year old female soccer players, we might have a better idea. But, when you collect across all athletic populations, from high school to professional your data pool to compare mass numbers of a subset population is hard. What happens when you compare a 16 year old female soccer player to a college lacrosse player to a professional football player to professional MMA fighter? Should these athletes move the same? What I can tell you is that there is a HUGE difference in the way a Division I Soccer Player, a Division II Soccer Player and Division III Soccer player moves. It is one of the things that separates a division I player from a division III player. Not only do they have better frontal plane control during single limb activities there is significantly less losses of balance during.
So this might lead us to the question, if you don't know what the passing score is then what does the data mean? What we are able to do is determine LSI in both magnitude of motion and speed of motion. We do have some normative data for frontal plane motion and speeds of valgus during a single leg squat, single leg hop and single leg hop plant. We know if you are significantly out of those ranges that you are at greater risk that it will impact your injury risk and your athletic performance. We also know that if your valgus speeds exceed 200 degrees per second that this has a high correlation to increased risk for non-contact hamstring strains, knee injuries and ankle injuries. Most of our teams and users do baseline tests so they can use this information to compare to following injury or post intervention. So the point of this whole discussion is that we are trying to use what the literature tells us we should be looking at and at the same time, using our real time data to drive how we interpret and use this information.
Stay tuned next week as we start to look at fatigue and impact this has on SL testing and return to play. If you enjoy this blog, please share with your colleagues and associates. You can also follow us on Instagram at bjjpt_acl_guy and on twitter at ACL_prevention. #ViPerformAMI