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Resumen de Validity of Electromyographic Fatigue Threshold as a Noninvasive Method for Tracking Changes in Ventilatory Threshold in College-Aged Men

Kristina L. Kendall, Abbie E. Smith, Jennifer L. Graef, Ashley A. Walter, Jordan R. Moon, Christopher Lockwood, Travis W. Beck, Joel T. Cramer, Jeffrey R. Stout

  • The submaximal electromyographic fatigue threshold test (EMGFT) has been shown to be highly correlated to ventilatory threshold (VT) as determined from maximal graded exercise tests (GXTs). Recently, a prediction equation was developed using the EMGFT value to predict VT. The aim of this study, therefore, was to determine if this new equation could accurately track changes in VT after high-intensity interval training (HIIT). Eighteen recreationally trained men (mean ± SD; age 22.4 ± 3.2 years) performed a GXT to determine maximal oxygen consumption rate ([latin capital V with dot above]o2peak) and VT using breath-by-breath spirometry. Participants also completed a discontinuous incremental cycle ergometer test to determine their EMGFT value. A total of four 2-minute work bouts were completed to obtain 15-second averages of the electromyographic amplitude. The resulting slopes from each successive work bout were used to calculate EMGFT. The EMGFT value from each participant was used to estimate VT from the recently developed equation. All participants trained 3 days a week for 6 weeks. Training consisted of 5 sets of 2-minute work bouts with 1 minute of rest in between. Repeated-measures analysis of variance indicated no significant difference between actual and predicted VT values after 3 weeks of training. However, there was a significant difference between the actual and predicted VT values after 6 weeks of training. These findings suggest that the EMGFT may be useful when tracking changes in VT after 3 weeks of HIIT in recreationally trained individuals. However, the use of EMGFT to predict VT does not seem to be valid for tracking changes after 6 weeks of HIIT. At this time, it is not recommended that EMGFT be used to predict and track changes in VT.


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