AffiliationUniv Arizona, Coll Med, Dept Physiol
MetadataShow full item record
PublisherPUBLIC LIBRARY SCIENCE
CitationA motor unit-based model of muscle fatigue 2017, 13 (6):e1005581 PLOS Computational Biology
JournalPLOS Computational Biology
Rights© 2017 Potvin, Fuglevand. This is an open access article distributed under the terms of the Creative Commons Attribution License.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractMuscle fatigue is a temporary decline in the force and power capacity of skeletal muscle resulting from muscle activity. Because control of muscle is realized at the level of the motor unit (MU), it seems important to consider the physiological properties of motor units when attempting to understand and predict muscle fatigue. Therefore, we developed a phenomenological model of motor unit fatigue as a tractable means to predict muscle fatigue for a variety of tasks and to illustrate the individual contractile responses of MUs whose collective action determines the trajectory of changes in muscle force capacity during prolonged activity. An existing MU population model was used to simulate MU firing rates and isometric muscle forces and, to that model, we added fatigue-related changes in MU force, contraction time, and firing rate associated with sustained voluntary contractions. The model accurately estimated endurance times for sustained isometric contractions across a wide range of target levels. In addition, simulations were run for situations that have little experimental precedent to demonstrate the potential utility of the model to predict motor unit fatigue for more complicated, real-worldapplications. Moreover the model provided insight, into the complex orchestration of MU force contributions during fatigue, that would be unattainable with current experimental approaches
NoteOpen access journal.
VersionFinal published version
SponsorsAuto21 Network of Centres of Excellence [A506-AWH]; National Institutes of Health [R01NS079147]