Metabolic and biomechanical responses to running-related fatigue assessed with optimal control simulations tracking wearable sensor signals

Abstract Fatigue during distance running is a critical factor affecting performance, yet the complex interplay between physiological and biomechanical responses remains understudied. This study explored the relationships among fatigue, running economy and joint kinematics in six trained distance runners. The participants completed a treadmill-based fatigue protocol, which consisted of repeated time trials and high-intensity intervals. Inertial sensors captured continuous three-dimensional motion data, with joint kinematics and metabolic cost analysed pre- and post-fatigue. Optimal control models were used to track wearable sensor signals with musculoskeletal models, estimating joint kinematics and energetic cost. We found a statistically significant positive relationship between the increase in time-trial duration (i.e. reduced performance) and the metabolic cost of running (R² = 0.87). Biomechanical changes were primarily observed in the upper body, involving the torso and pelvis, while lower-limb kinematics showed minimal alterations, but with large inter-subject variation. These findings suggest that both physiological factors and upper-body motion are linked to performance decline from fatigue, with individual variability in biomechanical responses. This study provides new insights into the responses to fatigue in running and highlights the importance of personalized monitoring strategies that account for both biomechanical and metabolic factors.

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Publication Details

Journal
Royal Society Open Science
Published
2026-01-07
DOI
https://doi.org/10.1098/rsos.250668
Primary Topic
Lower Extremity Biomechanics and Pathologies
Type
article
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article

Metabolic and biomechanical responses to running-related fatigue assessed with optimal control simulations tracking wearable sensor signals

Dominic Thewlis, Grace Eva McConnochie, Clint Bellenger, Aaron Fox
Royal Society Open Science
Lower Extremity Biomechanics and Pathologies
article

Metabolic and biomechanical responses to running-related fatigue assessed with optimal control simulations tracking wearable sensor signals

Dominic Thewlis, Grace Eva McConnochie, Clint Bellenger, Aaron Fox
article en

Abstract

Abstract Fatigue during distance running is a critical factor affecting performance, yet the complex interplay between physiological and biomechanical responses remains understudied. This study explored the relationships among fatigue, running economy and joint kinematics in six trained distance runners. The participants completed a treadmill-based fatigue protocol, which consisted of repeated time trials and high-intensity intervals. Inertial sensors captured continuous three-dimensional motion data, with joint kinematics and metabolic cost analysed pre- and post-fatigue. Optimal control models were used to track wearable sensor signals with musculoskeletal models, estimating joint kinematics and energetic cost. We found a statistically significant positive relationship between the increase in time-trial duration (i.e. reduced performance) and the metabolic cost of running (R² = 0.87). Biomechanical changes were primarily observed in the upper body, involving the torso and pelvis, while lower-limb kinematics showed minimal alterations, but with large inter-subject variation. These findings suggest that both physiological factors and upper-body motion are linked to performance decline from fatigue, with individual variability in biomechanical responses. This study provides new insights into the responses to fatigue in running and highlights the importance of personalized monitoring strategies that account for both biomechanical and metabolic factors.

Royal Society Open ScienceVol. 13(1)
Deakin University (AU), University of South Australia (AU), The University of Adelaide (AU)
Openalex Percentile: Top 96%
Lower Extremity Biomechanics and Pathologies
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Metabolic and biomechanical responses to running-related fatigue assessed with optimal control simulations tracking wearable sensor signals — Dominic Thewlis, Grace Eva McConnochie, et al. · Royal Society Open Science (2026) | TGRS Research Map | TGRS