Thinking In New Boxes: How Performance Therapy Shapes Perception
By Dr. Jas Randhawa
Sports Medicine/Performance Therapy Lead ALTIS
email: email@example.com Twitter: @jrsportperform
Creating The Process Driven Therapist
The ever-changing landscape of High-Performance sport lends itself to be volatile and complex in the best of times. To further compound the issue is the scarcity of time, which in the truest sense becomes a commodity amongst performance staff. Those who are in the trenches on a daily basis know all too well the challenges that come with trying to optimize an athlete’s full potential when the task occurs in real-time. As such, the need for efficient processes arises such that the performance staff, working as a collective, can impart a meaningful change in the limited time available.
With an understanding that it would require an interdisciplinary team of experts, all working under the premise that performance was to be the ultimate end goal, Performance Therapy was forged as the solution to this very problem. This process would allow performance teams to implement plans of actions based on identifying athlete specific Key Performance Indicators (KPIs) when their expression of them falls outside a normal bandwidth.
The ability to see and react to movement in realtime is at the heart of Performance Therapy and is what separates it from other schools of thought. Performance Therapy in and of itself is not a treatment protocol, nor is it a soft tissue system. Instead, it looks to create a deeper understanding of why our current toolbox works in the manner it does and the best ways to use it. However, for this process to function, we must first start to reframe what we have thought about current best practice. To this end, this article will serve to introduce two current topics within Performance Therapy: complexity and reductionist thought.
Reductionism in Sports Medicine
With athlete monitoring and data analytics playing crucial roles amongst many teams, it seems the search for the holy grail that is injury prediction is the direction medical staffs are headed. Despite the importance of these measures, injury numbers across the board have yet to reflect the efficacy of these measures. In the therapist’s mind this brings up a big question: do we measure what matters? To help us reframe what we are trying to measure, it is essential for us to understand some of the limitations that come with predictive analytics, especially when reductive logic is used.
Reductionist thought has long been utilized in the medical field, and for good measure. The germ theory of disease was a scientific breakthrough, which would alter the course of medicine forever. Through the discovery of a singular causative agent for a disease process, the medical community made it best practice to reduce illness down to its core element. Once an accurate diagnosis was made, a specific course of action could be taken against the causative agent. This process of discovery would shape the face of medicine for decades and is still woven into the fabric of Evidence-Based Medicine (EBM).
The use of EBM methodology has become the gold standard amongst many professions working outside the medical field. Predicated on three pillars of belief (clinical experience, research, patient preference), it would be surprising that some experts are now calling into question the limitations of earlier models. One of the significant limitations therapists will face within High-Performance environments is the lack of success we often have despite decisions being made on the shoulders of the best evidence available. With that being said, it may be fair to assume that we have been myopic in our quests to serve better the athletes we work with. In a sense, we are missing the forest from the trees because of this view.
Figure 2 depicts the hierarchy of EBM. The pyramid aligns such that RCTs and Meta-Analysis are the most reliable form of evidence we have. The controlled nature of these studies ensures that every variable has been accounted for such that statistically relevant results can be reported. There is no doubt a need for these studies, and no one would argue that, however, research seems to have thrown philosophy by the wayside. We must remember that the underpinnings of the scientific method have always been to show the repeatable nature of phenomenon, not necessarily to prove something is correct; the latter would belong to the field of mathematics. More importantly, we must understand that research done in a vacuum will, unfortunately, lack context in the world of High-Performance sport.
Context becomes crucial for Performance Therapists, as it provides a holistic manner in which the athlete, the environment and the task at hand become relevant drivers of how best to structure therapy. As will be discussed shortly, the problems we will encounter in High- Performance cannot be broken down into simple systems. Breaking down the whole into parts, through analysis, will not help us understand the big picture. Rather, we need to take a mindset of seeing the parts within the whole, through synthesis, if we are to make sense of why perturbations in performance are occurring.
Complex Adaptive Systems – Understanding Performance Biology
What makes predictive analytics so challenging to manage is the complexity of an athletes ecosystem, that is the external environment in which they must perform and the unique physiological environment within. Once more, by virtue, complex systems are inherently non- linear, chaotic environments in which uncertainty will always exist with the decision making process. With this, why is it then essential for us to understand complex adaptive systems?
Fig 3. The Cynefin framework
Figure 3 is taken from the excellent work of David Snowden, and it helps to serve as a framework for how Performance Therapists view problems and ultimately operate. Although delving into complexity science is well beyond the scope of this article, it is essential for therapists to understand how complexity effects what we do on a daily basis.
Simple systems represent models in which there are order and consistency. In simple systems, a given input will have a known and reproducible output. Following a recipe is one example of a simple system. Following a recipe may require a base level of technical knowledge, but following the sequential steps allows for standardized results. As such, the successful outcome of one recipe almost always guarantees a subsequent attempt will produce the same result. Thus, recipes are linear; they do not necessarily require content experts and have a high assurance of repeatable results.
Although this standardization makes implementation easy, there is a caveat to how much information we are gaining from such an approach. With a simplistic approach, the only perceived value is in solutions that “work”. At the surface, this may seem rational; however, it implicitly implies a closed system is being modeled. Working in the context of a binary system, questions that look to understand why an outcome is occurring are not answered.
Complicated problems are more involved than simple ones, although it is possible to have a subset of simple problems within a complicated one. An excellent example of this would be the current work being conducted by SpaceX, who is engaged in not only space travel, but the aim of one day colonizing Mars. The problems that the team at SpaceX will face are not readily reducible, scalable, simple problems. They will face issues which will require the coordination of multiple experts who must all function within the same constraints being placed on the team as a whole. Thus, complicated problems are not merely answered by assembling the components of simple systems. Taking a complicated approach to problem-solving also requires the acceptance of the multifaceted natures of problems and seeking to gain insight into the multiple mechanisms of action. This requires understanding how context can affect outcomes. Although complicated systems respect the multifaceted nature of problems, the framework does not allow for the explanation of inconsistent outcomes. For this, we must investigate complex systems.
Simple and complicated methods attempt to answer questions which from an outside perspective may seem to be complex. It is, therefore, necessary to clarify that although many incidences can be complex, complexity is much more specific.
Complexity is the study of complex adaptive systems which were defined by Plesk as, “a collection of individual agents with the freedom to act in ways that are not always totally predictable, and whose actions are interconnected so that one agent’s actions changes the context for other agents”. Complex systems will be non-linear, have feedback loops which can drastically alter the workings of the system and thus cause many variabilities. Although they may contain components of both simple and complicated systems, they can not be understood by merely investigating these individual components. The non-linearity of complex systems further adds a level of variability as past experience, formulas/recipes and precedence have limited applicability as prior success concerning complex systems does not ensure future success. For example, rearing a child can be thought of as a complex system with many interacting variables. In this case, the means and methods used to raise the first child may not work for a second. The reasons for this may be extensive, but from a heuristical perspective complex systems: will exist in unique local conditions, have an interdependency with other elements which form non-linear relations and can adapt as conditions change.