Sensitivity of movement features to fatigue during an exhaustive treadmill run

ABSTRACT

The objective of the present study was to examine the sensitivity of several movement variables during running to exhaustion adopting a cross-sectional design. Thirteen recreational runners, that systematically trained and competed, performed an exhaustive running protocol on an instrumented treadmill. Respiratory data were collected to establish the ventilatory threshold in order to obtain a reference point regarding the gradual accumulation of fatigue. A machine learning approach was adopted to analyse a set of 29,650 data points (individual steps) of kinetic and kinematic data, using a random forest classifier for the region pre and post the ventilatory threshold. The overall accuracy of the model was 0.914 (95% CI: 0.907–0.919). The four most important variables, and more sensitive in predictive ability, as it was concluded from the variable importance procedure and the partial dependence (PD), were the angular range in AP axis of upper trunk C7, the maximum loading rate, the angular range in LT axis of the C7 and the maximum value of the ground reaction force. Two-dimensional PD revealed considerable interactions for certain areas of the joint distributions between kinetic and kinematic data. These results provide a direction towards understanding the interconnections of kinetics and kinematics of the torso to maintain the coordinated running pattern under fatigue conditions.

Highlights

• Trunk frontal plane kinematics is the most sensitive parameter to fatigue. Practitioners should consider this finding during endurance training.

• Kinetics exhibit a stable linear increase in mean values but a non-linear increase in variance during an exhaustive incremental treadmill run. This may affect training at a submaximal fatigued state.

• Specific areas in the joint distributions of kinetics and kinematics during treadmill running exhibit increased sensitivity in predicting fatigue state.

KEYWORDS:

• Endurance
• data
• biomechanics
• methodology

Acknowledgements

The authors would like to thank Professor Georgios Mavromatis, Associate Professor Argyris Toubekis for their consulting, Mr Georgios Manakis for his help during the testing processes, and the participants for taking part in the study.

Disclosure statement

We wish to confirm that to our knowledge there are no conflicts of interest related to this study that could have influenced its outcome.