Andrew Vigotsky is a biomedical engineering PhD candidate and statistics MS student at Northwestern University, where he develops and applies analytical tools to neuroimaging, psychophysics, and self-report data to better understand acute and chronic pain neurophysiology. Before attending Northwestern, Andrew graduated with a B.S. in Kinesiology from Arizona State University (ASU). It was during his undergraduate studies that he started getting involved in research; in particular, biomechanics research.
Freelap USA: Stiffness is a confusing area, where coaches sometimes talk about estimations of joint stiffness and forget that it’s more than kinematic motion. Can you talk about actual stiffness and perhaps get into elastography so we can understand true stiffness?
Andrew Vigotsky: Sure! Stiffness is an object’s resistance to stretch. Importantly, this stretch must be elastic deformation: the energy used to stretch the object (e.g., tendon) must be stored, not dissipated, so that it can be returned. This also implies that additional energy is not added to the system. Stiffness (elastic deformation of a structure) is a very specific mechanical construct that differs from what many coaches, clinicians, and even researchers describe.
To draw a direct example, quantities such as “vertical stiffness” are reported throughout the running literature, and it is defined by the relationship between ground reaction forces and the position of the center of mass. It assumes that the body can be modeled as a point mass (its center of mass) on the end of a spring (the vertical position). When you strike the ground, the center of mass continues to travel downward, compressing the spring. The ground reaction force is assumed to be the force that this spring “experiences.”
When our foot strikes the ground, our muscles activate…In this scenario, the increase in muscle activation violates the definition of stiffness because it puts external energy into the system, says @avigotsky. Share on XIn actuality, when our foot strikes the ground, our muscles activate. This puts energy into the system. Of course, how our muscles activate will control to what extent our center of mass remains above the ground (spring length). In this scenario, the increase in muscle activation violates the definition of stiffness because it puts external energy into the system. Some have argued that this type of stiffness—strictly based on the force-deformation curve—be termed “quasi-stiffness.” Indeed, measures of quasi-stiffness can differ greatly from those of real stiffness. As a result, “quasi-stiffness” measures can be quite difficult to interpret, as they are not guaranteed to be related to a well-defined mechanical construct such as stiffness.
There are other quantities that further confound the force-deformation curve, independent of muscle activation. For example, biological tissues tend to also have viscosity components; this will result in energy dissipation, again violating the assumption of pure elastic deformation. Higher-order impedance terms (i.e., quantities that resist movement), such as inertia, will also confound stiffness measures. These concepts have been thoughtfully discussed at great length by Mark Latash and Vladimir Zatsiorsky,1 as well as Elliott Rouse and colleagues.2
The implications of the above are marked. First, a person’s stiffness cannot be judged simply by being watched; smaller joint ranges of motion can arise for reasons other than increased stiffness. Second, I cannot perceptually judge my own mechanical stiffness. Although there is “perceptual stiffness,” this will not necessarily map to true, mechanical stiffness.
Since stiffness is of such great interest, there have been many advances over the years to try to assess it. Shear wave elastography is one such technological advancement. The concept is fairly straightforward: By applying a perturbation to tissues, we can assess their mechanical properties. This is analogous to a guitar string. If you tighten a guitar string and pick it, it will vibrate faster and produce a higher sound. The string’s frequency is related to its tension, a mechanical quantity. Shear wave works like throwing a rock into a pond: The tissues are perturbed, and the rate at which waves travel away from that perturbation (shear wave speed) is related to the properties of the tissue.
Muscles and tendons are not ideal tissues for assessing stiffness through shear wave…, says @avigotsky. Share on XIn an ideal world, shear wave speed is purely a measure of shear modulus, which is related to Young’s modulus, which, finally, is related to stiffness. However, muscles and tendons are not ideal tissues for assessing stiffness through shear wave; they are in tension, they are anisotropic (not the same in every direction), they are nonhomogeneous (made of different materials), and they are viscous. Each of these violates an assumption that is required to go from shear wave speed to Young’s modulus (a tissue-level measure of stiffness; stiffness itself is a structure-level measure). We are still working to understand what exactly this means for muscle.
Early work from the lab in which I completed my BME MS thesis suggests that tension itself plays a larger role for passive muscle than it does active muscle.3,4 Nevertheless, there may be useful insights that can be gleaned from elastography. For instance, my MS thesis used elastography to help tease apart the contributions of different muscles to ankle stiffness.5 I hope to see more work in the future to help further our understanding of elastography and how it directly relates to stiffness. At present, the exact implications of violating its assumptions remains unclear to me.
More generally, I think it is immensely important to understand the first principles of constructs to understand measurements and their assumptions. With stiffness, it seems like much of the literature has misrepresented and continues to misrepresent a well-established mechanical construct. With shear wave, many call findings “stiffness” that are confounded by violations of basic assumptions. Perhaps most importantly, I think it is necessary to provide a strong rationale as to why these constructs are of interest and should be measured. I find many of these justifications to be lackluster.
Freelap USA: You are known as having knowledge of EMG (electromyography) and exercise interpretation. Can you explain how high muscle activity may not mean it’s a great exercise for an athlete? Many coaches are chasing high MVIC %, and this may not be a perfect road.
Andrew Vigotsky: Surface electromyography (sEMG) is really just a fancy voltmeter that’s placed over muscles. It is a proxy for muscle excitation—the depolarization of a muscle fiber wherein an action potential travels across the sarcolemma. This excitation begets muscle activation and force production, which triggers adaptation (e.g., hypertrophy). Although straightforward in theory, there are many aspects about this process that make it questionable to rely on for practical inferences.
Just as an example, we have pretty strong evidence that sEMG amplitudes are far from perfect proxies for muscle excitation. For instance, lengthening a muscle will change the sEMG amplitude, independent of neural input.6 This is just one issue that arises in the first step of the logical chain that supposes sEMG amplitudes are indicative of stimulus potency. There are further questions that arise at each step of this logical chain that we touch on in our 2018 review,7 and more is forthcoming on this.
Perhaps most importantly, the idea of using sEMG as a surrogate for adaptation has, to my knowledge, not been directly validated, says @avigotsky. Share on XPerhaps most importantly, the idea of using sEMG as a surrogate for adaptation has, to my knowledge, not been directly validated. (I have searched extensively and asked those who disagree with my conservatism for evidence.) Admittedly, there is some indirect evidence that sEMG may be informative; for instance, rectus femoris growth in single versus multi-joint exercises. However, there is also indirect counterevidence, such as including concentric versus eccentric exercise. In the absence of direct validation, we do not know when sEMG amplitudes may be informative for longitudinal adaptations and to what extent they are.
If the logical chain has not one, but several, weak links, and there is no direct evidence, I think coaches and scientists alike should be critical of the use of sEMG to infer longitudinal adaptations. To be clear, sEMG amplitudes may very well be informative; however, there remain several unknowns.
Freelap USA: Statistics are important for coaches to know so they can interpret data carefully. Do you have some simple recommendations for those coaches who may not have a rich background in statistics so they can use available research better?
Andrew Vigotsky: This is a great, albeit difficult, question. I think these three points can go a long way.
- Avoid hard categorizations that are not ontologically grounded. By this, I mean that most data in sports science is continuous and interval or ratio scale. This means that there are likely not true distinct groups (e.g., high responders, low responders) or dichotomous outcomes (e.g., effect versus no effect as determined via a p-value). Data that is continuous should be treated as such.
- There will be variation and noise in data; embrace this uncertainty. Statistics serve to quantify this variation, not get rid of it. Moreover, data and statistics from studies and meta-analysis serve to inform decisions, not make them. Those who make decisions must consider many other factors—this is where decision analysis comes into play.
- Rather than focusing on p-values and standardized effect sizes, focus on the raw effects when possible. Look at the point estimates and the confidence (compatibility) intervals (CI). The latter tell you a range of values that are compatible with the data. Evaluate both the upper bound and lower bound of a CI—do not simply look to see if it crosses zero. Remember, values at the upper bound are just as consistent with the data as those on the lower bound.
Freelap USA: A lot of sport scientists using GPS systems went to charting the ACWR to manage training volume. While the idea was elegant conceptually, how can team coaches understand the recent research refuting this as a magic bullet?
Andrew Vigotsky: While I do not follow the training load literature closely, I can speak to my recent collaboration with the astute Professor Franco Impellizzeri and the implications of our findings.
ACWR is the ratio of the previous week’s workload (acute workload) to an average of several of the preceding weeks’ workloads (chronic workload). It is assumed that the chronic workload is necessary for putting the acute workload into context, answering the question, “How much stress am I putting my body through relative to what it is used to?” However, does chronic workload actually add anything informative?
We went on to show that the ACWR model is not good for prediction; it doesn’t perform appreciably better than assigning every player the same (average) probability of injury, says @avigotsky. Share on XTo assess the informativeness of chronic workload, quite simply, we took existing ACWR and injury data, and we recalculated an acute-to-random chronic workload. That is, each player’s acute workload was divided by a random chronic workload. Strikingly, the model results were nearly identical—we observed similar odds ratios and statistically significant p-values. This upends the idea of ACWR.
Finally, we went on to show that the ACWR model is not good for prediction; it does not perform appreciably better than assigning every player the same (average) probability of injury (or an intercept-only model).
A preprint of our article can be found on SportRxiv, and the final article was recently published in Sports Medicine.
Freelap USA: Research can benefit from more transparency, but many of the recommendations between the communities of scientists and medical professionals are internal. How would you think coaches could be used to make the research more ecologically valid?
Andrew Vigotsky: Quite simply, I think researchers should be collaborating with coaches. Coaches know what is needed in practice, what questions can directly translate, and what questions may not be as informative. They can encourage researchers to think about practical questions in a way they normally would not. As a researcher who is not a practitioner, I always find conversations with clinicians to be insightful.
To this end, I think long-term relationships between practitioners and researchers could be mutually beneficial. Creating a research program and application loop could make for ecologically valid, practically insightful, and theoretically interesting work. Finally, practitioners may even be able to help recruit and carry out the studies, in turn overcoming one of the largest practical burdens of a study: recruitment.
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References
1. Latash, M. L. and Zatsiorsky, V. M. “Joint stiffness: Myth or reality?” Human Movement Science. 1993;12(6):653-692.
2. Rouse, E. J., Gregg, R. D., Hargrove, L. J., and Sensinger, J. W. “The difference between stiffness and quasi-stiffness in the context of biomechanical modeling.” IEEE Transactions on Biomedical Engineering. 2012;60(2):562-568.
3. Bernabei, M., Lee, S. S., Perreault, E. J., and Sandercock, T. G. “Shear wave velocity is sensitive to changes in muscle stiffness that occur independently from changes in force.” Journal of Applied Physiology. 2020;128(1):8-16.
4. Bernabei, M., Lee, S. S., Perreault E. J., and Sandercock T. G. “Muscle stress provides a lower bound on the magnitude of shear wave velocity.” International Society of Biomechanics/American Society of Biomechanics. 2019, Calgary, Canada, p. 1021.
5. Vigotsky, A. D., Rouse, E. J., and Lee, S. S. “Mapping the relationships between joint stiffness, modeled muscle stiffness, and shear wave velocity.” Journal of Applied Physiology. 2020;129(3):483-491.
6. Vieira, T. M., Bisi, M. C., Stagni, R., and Botter, A. “Changes in tibialis anterior architecture affect the amplitude of surface electromyograms.” Journal of NeuroEngineering and Rehabilitation. 2017;14(1):81.
7. Vigotsky, A. D., Halperin, I., Lehman, G. J., Trajano, G. S., and Vieira, T. M. “Interpreting signal amplitudes in surface electromyography studies in sport and rehabilitation sciences.” Frontiers in Physiology. 2018;8:985.
