Movement variability can be considered normal variations of motor output across multiple repetitions of a task such as reaching and walking.  Bernstein described variability rather well in his famous quote “repetition without repetition”. Historically variability has been regarded as noise in the sample, but recently variability has received more attention, and is now regarded as a resource (van Emmerik & van Wegen, 2002).

There are numerous different measures that can be employed to quantify variability. Many of these measures are based upon rather complex non-linear dynamics (Decker, Moraiti, Stergiou, & Georgoulis, 2011). Furthermore, there are different models in regards to the interpretation of variability. One model is based upon a U-shaped relationship and skill. Initially there is a great deal of variability with low performance that decreases with improving performance. As the skilled is further enhanced variability will increase again. Another theory is the optimal movement variability proposed Stergiou and co-workers (Stergiou, Harbourne, & Cavanaugh, 2006). Their model is based upon variability as an inverted u-shape relationship related to complexity and predictability where a healthy system is based upon the largest possible effective complexity. Optimal movement variability is therefore the intermediate region between excessive order (maximum predictability) and excessive disorder (no predictability) (Stergiou et al., 2006).

The analysis of variability is very interesting as it comes to ACL injuries and what we consequently do in terms of training and rehabilitation. ACL deficient knees have been found to be more rigid and have decreased variability. That is a system that is in a state of decreased complexity and high predictability (Decker et al., 2011). Decker and co-workers hypothesized that this could be one reason why osteoarthritis develops over time. An ACL reconstructed knee has been found to have an increased variability, which is associated with a noisy state that has decreased complexity and low predictability (Decker et al., 2011).

Based upon that variability is controlled by the central nervous system (van Emmerik & van Wegen, 2002) one might want to consider this to a greater extent in injury prevention and rehabilitation. The mechanical model, based upon the mechanical constraints provided by the ACL, might be limited since the neurophysiological function of the ACL is ignored. Injury prevention and rehabilitation that has an even greater focus on proprioception might offer even better results than we currently have. Repeating the same strict movement of the knee at the same rate, such as a slow partial single leg squats with knees of the second toe, rather than challenging rate, position and speed to facilitate proprioception, might not be the optimal solution. Regardless of these perspectives, current models of injury prevention have good results.

In the future it seems that there is more to be gained from integrating different fields of study, such as biomechanics and motor control, to get an even better understanding of how to design optimal injury prevention, rehabilitation and training programs.

Jessica, Ali and Ola

References

Decker, L. M., Moraiti, C., Stergiou, N., & Georgoulis, A. D. (2011). New insights into anterior cruciate ligament deficiency and reconstruction through the assessment of knee kinematic variability in terms of nonlinear dynamics. Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA, 19(10), 1620-1633. doi: 10.1007/s00167-011-1484-2

Stergiou, N., Harbourne, R., & Cavanaugh, J. (2006). Optimal movement variability: a new theoretical perspective for neurologic physical therapy. J Neurol Phys Ther, 30(3), 120-129.

van Emmerik, R. E., & van Wegen, E. E. (2002). On the functional aspects of variability in postural control. Exercise and sport sciences reviews, 30(4), 177-183.