As you may have noticed, my blogging rate has diminished this week. It’s not that I haven’t been thinking about blogging. It’s been on my mind a lot but I’ve had a hard time thinking about how to approach the article you are about to read (note – I’ve included some practical coach’s tips to help you apply some of the concepts). I had to remind myself several times of my goals and mission.
Goal #1 is to provide credible, honest and unbiased information on health, fitness and performance. I think we all need to be prudent about those who spread mistruth for financial gain, and unfortunately this is quite common in the fitness industry. I think it’s important to have a source to turn to for some truth especially when it comes to your health and fitness goals, and hopefully I can be a part of this process; Goal #2 is to teach you how to think like a sport scientist so that you can become the expert. From my experience, the more an athlete or client knows the better equipped they are to overcome challenges, and to adapt their training to meet the new fitness and health goals that emerge over the lifespan. It’s like the old adage “give me a fish and I eat for a day, teach me to fish and I eat for a lifetime”.
A big part of this is learning how to assess the relevance of information and data. Just because a scientific study finds something novel or interesting doesn’t make it a truth, and furthermore there are many different ways to analyze and assess data from the natural world. For example, I’m a big proponent of individualizing the training and nutrition process. I described one way of approaching this in a previous blog on the responder-factor, which generated a lot of interest with those who have been following my blog. Briefly, the responder-factor involves the clustering of individuals in a population into an extreme-responder, responder, and non-responder group. As the science around genetic testing emerges, I can guarantee you this will be a major consideration for health professionals because it is becoming increasingly evident that our genetic makeup greatly affects whether or not a particular medical treatment will be effective.
In addition to the responder-factor, I think another tendency in assessing data is to focus solely on whether or not a particular variable improved in response to a treatment. Take vertical jump for example. As I mentioned in a previous blog, vertical jump is often considered as a performance measure. Often a coach or trainer will evaluate the effectiveness of a training method for improving vertical jump and may find that vertical jump decreased so they will conclude the method was ineffective. However, vertical jump is very sensitive to fatigue and tends to have considerable variability over a training cycle, and a depression in vertical jump may actually reveal that a training method is working! In fact, evaluating the variability of vertical jump over a training cycle can provide tremendous information to the coach or sport scientist for peaking strategies and for evaluating an athlete’s adaptive potential.
Variability is a significant part of the natural world, and can often be an indicator for health or peak physiological function. For example, it has been shown that elite pistol shooters display considerably better end point stability (i.e. gun control) than novices; however, the elite shooters display considerably more variability in muscle activation compared to the novices. It has also been shown that individuals with patello-femoral pain syndrome and lower back pain exhibit considerably less variability in muscle activation compared to their healthy counterparts. I went into great detail in my blog from Day 4 at the ECSS Conference, and you can hear Matt Price and I discussing the importance of this for elite sport in our powercast available on iTunes.
In my recent presentation on the Quotidian Movement Screen (QMS), I also discussed how an athlete’s quality of movement is highly variable and sensitive to training stress. Our group at the Canadian Sport Centre-Calgary integrates the QMS movement screen into our daily routine, and we use it to properly prescribe activation and mobilization exercise. It adds a further layer into our assessment of an athlete’s adaptive potential. In this instance movement screening is not seen as a “pass/fail” test as it is in so many movement screens but instead is assessed with an expectation that movement will change in response to training stress, and that detecting this change is a critical part of the training process.
Looking for trends in the normal variability that can be observed in the natural world can be complex but it’s applications are seemingly endless. A great example of this is the science of Fractal Analysis, which was pioneered by a famous mathematician named Benoit Mandelbrot. Mandelbrot expanded on his observation that many physiological processes and aspects of the natural world with seemingly chaotic patterns could actually be described mathematically. In simple terms, an object with a fractal dimension has an element of self-similarity from a large scale down to a small scale. Consider a small branch of a branch on a tree in a forest. Upon first glance, the little branch appears to be nothing more than a random set of bifurcations but when examined with respect to a fractal dimension, not only is this branching pattern similar for the entire tree but also for the surrounding area around the tree. This gives ecologists a tremendous advantage because by studying the branching that occurs on a small scale, which is relatively easy, it tells them about what is happening on a much larger scale in the forest, which is relatively hard to do (for more information on this, check out the PBS Series Nova, and the episode on Fractals, Hunting the Hidden Dimension).
The self similarity in branching of a tree is also paralleled in the human body where the branching of blood vessels and capillary network in organs has been shown to have a fractal dimension (Figure 1). Interestingly, if a cancerous tumour is present, the tissue loses this fractal dimension, which gives physicians a powerful tool for detecting cancer.
Figure 1. Representation of the fractal dimension, and the application to human physiology [From: Glenny et al., (1991). Applications of Fractal Analysis to Physiology. J App Phys]
In addition to blood flow, many other physiological variables possess a fractal dimension. For example, heart rate displays considerable variability, and the analysis of heart rate variability (HRV) can help detect morbidity, and even overtraining and overreaching in elite athletes. Postural sway also has a fractal dimension. To get a picture of postural sway, stand up and close your eyes. You will notice that you don’t stand still, instead there is a gentle sway in your position. If you were to stand on a force plate and measure this sway, you would obtain a very random looking pattern (Figure 2). Interestingly, individuals who have sustained a concussion lose this fractal dimension, making the analysis of postural sway of interest to those returning from concussive injury.
Figure 2. Postural sway measured on a force plate. Analysis of this sway reveals a fractal dimension, meaning there is self-similarity across multiple scales of magnitude [From: Duarte & Zatsiorsky (2000). On the Fractal Properties of Human Standing. Neuroscience Letters]
In summary, it’s important to understand how to look at data regardless of your level of fitness or performance. Let’s face it, we are bombarded with all sorts of information on a daily basis and making educated decisions for ourselves is key. Finally, remember that the variability that occurs in our seemingly chaotic and random world can actually have great meaning, and can provide us with tremendous insight into the adaptive process.