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Mechanical, Hormonal, and Anabolic Effects of Hypertrophy-Oriented Cluster Sets

It has been proposed that muscle cross-sectional area (CSA) plays a large role in a muscle’s shortening velocity and ability to produce force, making hypertrophy extremely important for all populations. To induce hypertrophy for novice and intermediate lifters, it is recommended that resistance training be performed 2-3 days per week, using moderate loads (70–85% of 1 RM) for 8– 12 repetitions per set for 1–3 sets of single- and multiple-joint free-weight and machine exercises, resting 1-2 minutes between sets. However, hypertrophy can also be stimulated by training with loads that are well below and above these recommendations. An underlying theme within many hypertrophy-based studies is that increasing the amount of mechanical work increases the amount of time under tension, yielding a hypertrophic response. By utilising cluster sets, which involve resting between repetitions within a set, partial recovery of PCr stores may allow greater training loads for a given volume, or a greater volume for a given load. This may allow for a greater amount of work to be accomplished, effectively increasing the time that the muscle is under tension. Studies that examine cluster sets are scarce and many use programs that are not designed for developing skeletal muscle hypertrophy.

Therefore, the purpose of these four proposed studies is to investigate the acute and chronic effects of hypertrophy-oriented cluster sets in relation to a muscle’s time under tension, hormonal responses, and cross-sectional area. The acute effects of set-structure (Study 1), training load (Study 2), and rest intervals (Study 3) will be determined in order to create a training paradigm that “optimally” utilises cluster sets to induce skeletal muscle hypertrophy. This paradigm will then be investigated over an 8-week training study (Study 4) to compare the hypertrophic responses of training using cluster sets to traditional sets.

Project duration

2013-2016


Researchers

Mr James Tufano
Associate Professor Greg Haff

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