Muscle hypertrophy is correlated with load progression delta, climb volume, and total load volume in rodents undergoing different ladder-based resistance training protocols

2022 ◽  
pp. 101725
Author(s):  
Walter Krause Neto ◽  
Wellington de Assis Silva ◽  
Tony Vinicius Apolinário de Oliveira ◽  
Alan Esaú dos Santos Vilas Boas ◽  
Adriano Polican Ciena ◽  
...  
2014 ◽  
Vol 46 ◽  
pp. 353-354
Author(s):  
Sukho Lee ◽  
Aram Yoon ◽  
Soon-Mi Choi ◽  
Junyoung Hong ◽  
Dongwoo Hahn ◽  
...  

2020 ◽  
Vol 45 (5) ◽  
pp. 463-470 ◽  
Author(s):  
Scott J. Dankel ◽  
Zachary W. Bell ◽  
Robert W. Spitz ◽  
Vickie Wong ◽  
Ricardo B. Viana ◽  
...  

The objective of this study was to determine differences in 2 distinct resistance training protocols and if true variability can be detected after accounting for random error. Individuals (n = 151) were randomly assigned to 1 of 3 groups: (i) a traditional exercise group performing 4 sets to failure; (ii) a group performing a 1-repetition maximum (1RM) test; and (iii) a time-matched nonexercise control group. Both exercise groups performed 18 sessions of elbow flexion exercise over 6 weeks. While both training groups increased 1RM strength similarly (∼2.4 kg), true variability was only present in the traditional exercise group (true variability = 1.80 kg). Only the 1RM group increased untrained arm 1RM strength (1.5 kg), while only the traditional group increased ultrasound measured muscle thickness (∼0.23 cm). Despite these mean increases, no true variability was present for untrained arm strength or muscle hypertrophy in either training group. In conclusion, these findings demonstrate the importance of taking into consideration the magnitude of random error when classifying differential responders, as many studies may be classifying high and low responders as those who have the greatest amount of random error present. Additionally, our mean results demonstrate that strength is largely driven by task specificity, and the crossover effect of strength may be load dependent. Novelty Many studies examining differential responders to exercise do not account for random error. True variability was present in 1RM strength gains, but the variability in muscle hypertrophy and isokinetic strength changes could not be distinguished from random error. The crossover effect of strength may differ based on the protocol employed.


2020 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Rodrigo C. R. Diniz ◽  
Frank Douglas Tourino ◽  
Lucas T. Lacerda ◽  
Hugo C. Martins-Costa ◽  
Marcel Bahia Lanza ◽  
...  

Author(s):  
Witalo Kassiano ◽  
Bruna Daniella de Vasconcelos Costa ◽  
João Pedro Nunes ◽  
Andreo Fernando Aguiar ◽  
Belmiro F. de Salles ◽  
...  

AbstractSpecialized resistance training techniques (e.g., drop-set, rest-pause) are commonly used by well-trained subjects for maximizing muscle hypertrophy. Most of these techniques were designed to allow a greater training volume (i.e., total repetitions×load), due to the supposition that it elicits greater muscle mass gains. However, many studies that compared the traditional resistance training configuration with specialized techniques seek to equalize the volume between groups, making it difficult to determine the inherent hypertrophic potential of these advanced strategies, as well as, this equalization restricts part of the practical extrapolation on these findings. In this scenario, the objectives of this manuscript were 1) to present the nuance of the evidence that deals with the effectiveness of these specialized resistance training techniques and — primarily — to 2) propose possible ways to explore the hypertrophic potential of such strategies with greater ecological validity without losing the methodological rigor of controlling possible intervening variables; and thus, contributing to increasing the applicability of the findings and improving the effectiveness of hypertrophy-oriented resistance training programs.


2019 ◽  
Vol 6 ◽  
Author(s):  
Gary John Slater ◽  
Brad P. Dieter ◽  
Damian James Marsh ◽  
Eric Russell Helms ◽  
Gregory Shaw ◽  
...  

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