PURPOSE: This study aimed to analyze the acute mechanical and metabolic response to resistance exercise protocols (REP) differing in the number of repetitions (R) performed in each set (S) with respect to the maximum predicted number (P). METHODS: Over 21 exercise sessions separated by 48-72 h, 18 strength-trained males (10 in bench press (BP) and 8 in squat (SQ)) performed 1) a progressive test for one-repetition maximum (1RM) and load-velocity profile determination, 2) tests of maximal number of repetitions to failure (12RM, 10RM, 8RM, 6RM, and 4RM), and 3) 15 REP (S × R[P]: 3 × 6[12], 3 × 8[12], 3 × 10[12], 3 × 12[12], 3 × 6[10], 3 × 8[10], 3 × 10[10], 3 × 4[8], 3 × 6[8], 3 × 8[8], 3 × 3[6], 3 × 4[6], 3 × 6[6], 3 × 2[4], 3 × 4[4]), with 5-min interset rests. Kinematic data were registered by a linear velocity transducer. Blood lactate and ammonia were measured before and after exercise. RESULTS: Mean repetition velocity loss after three sets, loss of velocity pre-post exercise against the 1-m·s load, and countermovement jump height loss (SQ group) were significant for all REP and were highly correlated to each other (r = 0.91-0.97). Velocity loss was significantly greater for BP compared with SQ and strongly correlated to peak postexercise lactate (r = 0.93-0.97) for both SQ and BP. Unlike lactate, ammonia showed a curvilinear response to loss of velocity, only increasing above resting levels when R was at least two repetitions higher than 50% of P. CONCLUSIONS: Velocity loss and metabolic stress clearly differs when manipulating the number of repetitions actually performed in each training set. The high correlations found between mechanical (velocity and countermovement jump height losses) and metabolic (lactate, ammonia) measures of fatigue support the validity of using velocity loss to objectively quantify neuromuscular fatigue during resistance training.
PURPOSE: This study aimed to analyze the acute mechanical and metabolic response to resistance exercise protocols (REP) differing in the number of repetitions (R) performed in each set (S) with respect to the maximum predicted number (P). METHODS: Over 21 exercise sessions separated by 48-72 h, 18 strength-trained males (10 in bench press (BP) and 8 in squat (SQ)) performed 1) a progressive test for one-repetition maximum (1RM) and load-velocity profile determination, 2) tests of maximal number of repetitions to failure (12RM, 10RM, 8RM, 6RM, and 4RM), and 3) 15 REP (S × R[P]: 3 × 6[12], 3 × 8[12], 3 × 10[12], 3 × 12[12], 3 × 6[10], 3 × 8[10], 3 × 10[10], 3 × 4[8], 3 × 6[8], 3 × 8[8], 3 × 3[6], 3 × 4[6], 3 × 6[6], 3 × 2[4], 3 × 4[4]), with 5-min interset rests. Kinematic data were registered by a linear velocity transducer. Blood lactate and ammonia were measured before and after exercise. RESULTS: Mean repetition velocity loss after three sets, loss of velocity pre-post exercise against the 1-m·s load, and countermovement jump height loss (SQ group) were significant for all REP and were highly correlated to each other (r = 0.91-0.97). Velocity loss was significantly greater for BP compared with SQ and strongly correlated to peak postexercise lactate (r = 0.93-0.97) for both SQ and BP. Unlike lactate, ammonia showed a curvilinear response to loss of velocity, only increasing above resting levels when R was at least two repetitions higher than 50% of P. CONCLUSIONS: Velocity loss and metabolic stress clearly differs when manipulating the number of repetitions actually performed in each training set. The high correlations found between mechanical (velocity and countermovement jump height losses) and metabolic (lactate, ammonia) measures of fatigue support the validity of using velocity loss to objectively quantify neuromuscular fatigue during resistance training.
Authors: Alejandro Benavides-Ubric; David M Díez-Fernández; Manuel A Rodríguez-Pérez; Manuel Ortega-Becerra; Fernando Pareja-Blanco Journal: J Sports Sci Med Date: 2020-08-13 Impact factor: 2.988
Authors: Ricardo Morán-Navarro; Carlos E Pérez; Ricardo Mora-Rodríguez; Ernesto de la Cruz-Sánchez; Juan José González-Badillo; Luis Sánchez-Medina; Jesús G Pallarés Journal: Eur J Appl Physiol Date: 2017-09-30 Impact factor: 3.078
Authors: Alejandro Martínez-Cava; Ricardo Morán-Navarro; Alejandro Hernández-Belmonte; Javier Courel-Ibáñez; Elena Conesa-Ros; Juan José González-Badillo; Jesús G Pallarés Journal: J Sports Sci Med Date: 2019-11-19 Impact factor: 2.988
Authors: José L Maté-Muñoz; Juan H Lougedo; Manuel Barba; Ana M Cañuelo-Márquez; Jesús Guodemar-Pérez; Pablo García-Fernández; María Del C Lozano-Estevan; Rosa Alonso-Melero; María A Sánchez-Calabuig; Monserrat Ruíz-López; Fernando de Jesús; Manuel V Garnacho-Castaño Journal: J Sports Sci Med Date: 2018-11-20 Impact factor: 2.988