PURPOSE: this study examined the effects of heavy resistance training on the relationships between power loss and surface EMG (sEMG) indices and blood metabolite concentrations on dynamic exercise-induced fatigue with the same relative load as in pretraining. METHODS: twelve trained subjects performed five sets consisting of 10 repetitions in the leg press, with 2 min of rest between sets before and after a strength training period. sEMG variables (the mean average voltage, the median spectral frequency, and the Dimitrov spectral index of muscle fatigue) from vastus medialis and lateralis muscles and metabolic responses (i.e., blood lactate, uric acid, and ammonia concentrations) were measured. RESULTS: the peak power loss after the posttraining protocol was greater (61%) than the decline observed in the pretraining protocol (46%). Similar sEMG changes were found for both protocols, whereas higher metabolic demand was observed during the posttraining exercise. The linear models on the basis of the relations found between power loss and changes in sEMG variables were significantly different between pretraining and posttraining, whereas the linear models on the basis of the relations between power loss and changes in blood metabolite concentrations were similar. CONCLUSIONS: linear models that use blood metabolites to map acute exercise-induced peak power changes were more accurate in detecting these changes before and after a short-term training period, whereas an attempt to track peak power loss using sEMG variables may fail after a strength training period.
PURPOSE: this study examined the effects of heavy resistance training on the relationships between power loss and surface EMG (sEMG) indices and blood metabolite concentrations on dynamic exercise-induced fatigue with the same relative load as in pretraining. METHODS: twelve trained subjects performed five sets consisting of 10 repetitions in the leg press, with 2 min of rest between sets before and after a strength training period. sEMG variables (the mean average voltage, the median spectral frequency, and the Dimitrov spectral index of muscle fatigue) from vastus medialis and lateralis muscles and metabolic responses (i.e., blood lactate, uric acid, and ammonia concentrations) were measured. RESULTS: the peak power loss after the posttraining protocol was greater (61%) than the decline observed in the pretraining protocol (46%). Similar sEMG changes were found for both protocols, whereas higher metabolic demand was observed during the posttraining exercise. The linear models on the basis of the relations found between power loss and changes in sEMG variables were significantly different between pretraining and posttraining, whereas the linear models on the basis of the relations between power loss and changes in blood metabolite concentrations were similar. CONCLUSIONS: linear models that use blood metabolites to map acute exercise-induced peak power changes were more accurate in detecting these changes before and after a short-term training period, whereas an attempt to track peak power loss using sEMG variables may fail after a strength training period.
Authors: Rafael Torres-Peralta; José Losa-Reyna; Miriam González-Izal; Ismael Perez-Suarez; Jaime Calle-Herrero; Mikel Izquierdo; José A L Calbet Journal: High Alt Med Biol Date: 2014-12 Impact factor: 1.981
Authors: Moritz Schumann; Simon Walker; Mikel Izquierdo; Robert U Newton; William J Kraemer; Keijo Häkkinen Journal: Eur J Appl Physiol Date: 2014-01-17 Impact factor: 3.078
Authors: John A Korak; Max R Paquette; Dana K Fuller; Jennifer L Caputo; John M Coons Journal: Eur J Appl Physiol Date: 2018-04-11 Impact factor: 3.078
Authors: Gabriel R Neto; Heleodório H Santos; Juliana B C Sousa; Adenilson T A Júnior; Joamira P Araújo; Rodrigo R Aniceto; Maria S C Sousa Journal: J Hum Kinet Date: 2014-07-08 Impact factor: 2.193