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The impact of high- vs. low-load resistance training on measures of muscle activation, strength, body composition, and hormonal markers

Resistance training has shifted towards a high- vs low-load training approach. Heavier loads are suggested to maximally recruit motor units and optimize strength adaptations, whereas lower loads stimulate hypertrophy. However, a majority of the research has not used a true strength range when assessing load. Therefore, the purpose of this investigation was to examine and determine significant differences in strength, body composition, and hormonal markers over nine weeks of high- or low-load resistance training. Secondary purposes of the current investigation were to assess and quantify training load for resistance training using sEMG sensor-embedded compression shorts. 17 recreationally-trained males were randomized into two groups with training loads of 30 or 85% 1-RM. Both groups completed nine weeks of whole-body resistance training three days per week, with exercises performed as three sets to failure per movement. Measures were collected at baseline and every three weeks after, including muscle thickness, body composition, isometrics/isokinetic strength, and hormonal status (testosterone and cortisol). Predicted 1-RM testing was performed pre- and post-training. Both groups demonstrated significant hypertrophy and strength, although the 85% showed greater improvements in the predicted 1-RM and the isokinetic peak torque values. There were also significant differences between groups for muscle load and training load as measured by the wearables, indicating the technology was able to differentiate between resistance training intensities. However, there were no changes in any of the hormonal markers either in basal levels or acutely post-exercise. Overall, our results suggest a similar hypertrophy and hormone response occurs in both low- and high-load groups when training to failure, but the high-load results in greater strength improvements and higher muscle load output when measured by wearable technology.

Identiferoai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-6408
Date12 May 2022
CreatorsBello, Marissa Laina
PublisherScholars Junction
Source SetsMississippi State University
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceTheses and Dissertations

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