Conversations with Paul Wu: Countermovement jumps and sports performance

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Paul Wu is an engineer turned statistician at Queensland University of Technology. His work has involved in a number of fields including ecology, social services, and even managing the flow of passengers in airports. His latest work entails sports and fitness training. Specifically, he has explored the predictive modelling of fatigue using PCA and the countermovement jump, analysis of posture control, and addressing the challenges of longitudinal data with wearable technologies.

SCIENTIFIC INQUIRER: First, can you provide some back ground? What are the differences between neuromuscular and metabolic fatigue?

PAUL WU: Neuromuscular fatigue is longer term, present for upwards of 48 hours whilst metabolic fatigue is short term, typically gone after 3 hours. The latter arises when energy stores in the muscle have been depleted whereas the former is attributable to both muscular and nervous system effects of fatigue.

SI: What exactly is a countermovement jump? What is going on at the muscular level?
PW:  A countermovement jump is where the person bends down then jumps up, see for example part 1 of this: https://www.youtube.com/watch?v=DZV_RlzfSIY.
The jump itself is a compound exercise and is a good measure of lower body dynamic performance. It is the kind of performance that underpins many sports including football, athletics, basketball and so on.

SI: How did you design your experiment?

PW: Our paper used a somewhat unique experimental design. In order to study how jump performance varied with fatigue, jump measurements were taken before training, immediately after training, and then at intervals of 30min, 60min, 3 hours, 6 hours, 24 hours and 48 hours after training. This was repeated for high, medium and low training intensity over a number of athletes. Such an unique dataset gave us the ability to: (i) establish a baseline of performance for each athlete, (ii) model their performance and hence better understand the magnitude and duration of fatigue effects, subsequently relating that to neuromuscular and metabolic fatigue.

SI: What did your results tell you about using CMJs as a way to gauge fatigue?

PW: Looking at the entire data set of jump performance features across all our athletes, we found that 68% of the variation in jump performance features were due to differences between athletes. However, approximately 10% of the variation emerged to be attributable to neuromuscular versus metabolic fatigue and could be uniquely identified using principal components. In addition, using functional principal components analysis, differences between onset of metabolic versus neuromuscular fatigue was also found in different parts of the jump itself.

Using these findings, we were then able to build predictive models of peak force output that could be used to predict whether a specific athlete would be fatigued or not after training, and if so by how much. The mean squared error in the predicted peak force was 0.013 and 0.015 for predicting metabolic and neuromuscular fatigue, respectively.

SI: What does using CMJ with Principal Component Analysis tell you about the individual athlete?

PW: The Principal Components Analysis allows us to build a baseline picture of the capability of athletes and how they respond to different training intensities. Specifically, it tells us about how much neuromuscular or metabolic fatigue they develop given different training loads. This baseline is essential for predicting how they would respond to future training loads.

SI: How can your findings be applied in the sports and fitness world? Can you imagine a scenario?

PW: What we envisage is that an athlete would first complete something akin to what was done in our study to establish a baseline. Having established this baseline, they can now use the model to:
a) Understand better what training regime to use by seeing where they lie on the capability map in terms of whether they develop metabolic (short-term) or neuromuscular (long-term) fatigue to different training intensities. This could be useful in team sports for instance.
b) By doing some jump measurements before and up to 30min after training, they can more accurately predict whether they will have neuromuscular or metabolic fatigue.

SI: Finally, what is next for you in terms of research?

PW: We are currently engaging with collaborators around Australia, including those at institutes of sports in two states, to validate and refine the model. With a robust model, we then look to work together with practitioners to apply, test and refine it in day-to-day management of athletes.

For more information follow @PaulPWu, @QUTMedia, and @ACEMathStats

IMAGE SOURCE: Creative Commons

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