A mathematical model for analyses of muscle oxygenation measurements using NIR spectroscopy

Area of Science:

• Exercise Physiology
• Biomedical Engineering
• Metabolic Research

Background:

• Near-infrared spectroscopy (NIRS) offers noninvasive muscle oxygenation monitoring.
• NIRS cannot directly measure muscle metabolism, necessitating complementary analysis.
• Understanding NIRS data requires linking oxygenation dynamics to metabolic processes during exercise.

Purpose of the Study:

• To develop a computational model of muscle metabolism.
• To simulate and interpret near-infrared spectroscopy (NIRS) data during exercise.
• To elucidate the relationship between muscle oxygenation and metabolic systems.

Main Methods:

• A computational model integrating aerobic, anaerobic, and O(2) transport systems was created.
• The model was used to reproduce temporal muscle oxygenation profiles measured by NIRS.
• Simulations were conducted for varying exercise intensities (20%, 40%, 70% MVC) and recovery.

Main Results:

• The developed model accurately reproduced NIRS-measured muscle oxygenation kinetics across different exercise intensities.
• Simulations provided insights into the dynamic responses of aerobic and anaerobic metabolic systems.
• Intensity-dependent differences in temporal responses during exercise and recovery were estimated.

Conclusions:

• The muscle metabolism model is effective for simulating NIRS data kinetics.
• The model facilitates a systematic interpretation of NIRS measurements in relation to muscle metabolism.
• This approach enhances understanding of physiological responses during physical exertion.