Changes in muscle oxygenation and activity during cumulative isometric muscle contraction: new insight into muscle fatigue

This study aimed to investigate the progression of muscle fatigue during submaximal efforts by examining alterations in muscle activation and oxygen saturation, employing surface electromyography ( _<i>S</i> EMG) and near-infrared spectroscopy (NIRS) measurements. Participants performed intermittent voluntary isometric knee extension tasks at 50% of maximal voluntary contraction to induce muscle fatigue. This was conducted consecutively until they could no longer generate the target torque. Knee extension torque, _<i>S</i> EMG, and NIRS data from the vastus lateralis were collected. Torque variability, the magnitude and frequency of the _<i>S</i> EMG signal, and NIRS-derived parameters of the tissue oxygen saturation index (TSI) were analyzed. An increase in the magnitude (<i>p</i> < 0.001) and a decrease in the spectrum (<i>p</i> < 0.001) of the _<i>S</i> EMG signal were observed, followed by a rise in torque variability (<i>p</i> < 0.001), despite the average magnitude of knee extension torque remaining constant across the trials. The NIRS measurements indicated alterations in TSI parameters, reflecting increased metabolic demand and diminished oxygen supply in the fatigued muscle (<i>p</i> < 0.001). Furthermore, significant interrelationships were found between changes in torque, _<i>S</i> EMG, and NIRS variables due to the development of muscle fatigue. Our findings provide a comprehensive understanding of the development of muscle fatigue, highlighting the interconnectedness of mechanical, electrical, and metabolic responses during submaximal efforts. The reduction in force-generation capacity due to muscle fatigue is reflected in the _<i>S</i> EMG signal and manifests as an increase in motor variability. This study identified changes in the EMG and NIRS parameters, and significant interrelation between the two metrics during the process of fatigue accumulation. These findings have the potential to provide crucial knowledge for the prediction of fatigue from either EMG signal or hemodynamic signals of the muscles.