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Mechanical Deviations in Stride Characteristics During Running in the Severe Intensity Domain Are Associated With a Decline in Muscle Oxygenation.

Christos ChalitsiosThomas NikodelisVassilis Mougios
Published in: Scandinavian journal of medicine & science in sports (2024)
We explored the impact of running in the severe intensity domain on running mechanics and muscle oxygenation in competitive runners by investigating the relationship between mechanical deviations from typical stride characteristics and muscle oxygen saturation (SmO 2 ) in the quadriceps muscle. Sixteen youth competitive runners performed an 8-min exhaustive running test on an outdoor track. Running mechanics were continuously monitored using inertial measurement units. Rectus femoris SmO 2 and total hemoglobin (a measure of blood volume) were continuously monitored by near-infrared spectroscopy. One-class support vector machine (OCSVM) modeling was employed for subject-specific analysis of the kinematic data. Statistical analysis included principal component analysis, ANOVA, and correlation analysis. Mechanical deviations from typical stride characteristics increased as the running test progressed. Specifically, the percentage of outliers in the OCSVM model rose gradually from 2.2 ± 0.8% at the start to 43.6 ± 28.2% at the end (p < 0.001, mean ± SD throughout). SmO 2 dropped from 74.3 ± 8.4% at baseline to 10.1 ± 6.8% at the end (p < 0.001). A moderate negative correlation (r = -0.61, p = 0.013) was found between the average SmO 2 and the percentage of outlier strides during the last 15% of the run. During high-intensity running, alterations in running biomechanics may occur, linked to decreased quadriceps muscle oxygenation. These parameters highlight the potential of using running kinematics and muscle oxygenation in training to optimize performance and reduce injury risks. Our research contributes to understanding biomechanical and physiological responses to endurance running and emphasizes the importance of individualized monitoring.
Keyphrases
  • high intensity
  • resistance training
  • skeletal muscle
  • deep learning
  • mental health
  • big data
  • human health