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Determining The Electromyographic Fatigue Threshold Following a Single Visit Exercise Test
Published on: July 27, 2015
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EMG amplitude, fatigue threshold, and time to task failure: A meta-analysis
J Matt McCrary1, Bronwen J Ackermann2, Mark Halaki3
1School of Medical Sciences, Sydney Medical School, The University of Sydney, Australia; Prince of Wales Clinical School, UNSW, Australia.
Journal of Science and Medicine in Sport
|December 14, 2017
Summary
The electromyographic fatigue threshold (EMGFT) may not accurately reflect exercise intensity, as neuromuscular fatigue occurs even below this threshold. New models are proposed for better EMGFT calculation and task failure prediction.
Area of Science:
- Exercise Physiology
- Neuromuscular Physiology
- Biomedical Engineering
Background:
- The electromyographic fatigue threshold (EMGFT) is a theoretical exercise intensity marker.
- It's used as a correlate for critical power, torque, and force thresholds.
- Recent findings question the construct validity of EMGFT due to fatigue occurring below critical thresholds.
Purpose of the Study:
- To evaluate the construct validity of the electromyographic fatigue threshold (EMGFT).
- To aggregate data on the rate of change of EMG amplitude (ΔEM¯G) and time to task failure (Tlim).
- To assess the relationship between neuromuscular fatigue and established exercise intensity thresholds.
Main Methods:
- A meta-analysis was conducted using data from MEDLINE, SPORTDiscus, Web of Science, and Cochrane databases.
- Studies included reported agonist muscle EMG amplitude data during constant force voluntary isometric contractions to task failure.
- Linear and nonlinear regression models were employed to analyze the relationship between ΔEM¯G and Tlim.
Main Results:
- Data from 837 healthy adults across 43 studies were analyzed.
- Strong relationships were observed between ΔEM¯G and Tlim in both nonlinear (R2=0.65) and linear (R2=0.82) models.
- The ΔEM¯G at EMGFT was significantly nonzero overall and in several cohorts, indicating fatigue below the threshold.
Conclusions:
- The current calculation of EMGFT lacks face validity.
- More precise models for EMGFT calculation are proposed.
- A new framework for predicting task failure using only EMG amplitude data is presented, showing consistency across sexes and task types.
