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Date Permissions Signed


Date of Award

Summer 2017

Document Type

Masters Thesis

Degree Name

Master of Science (MS)


Health and Human Development

First Advisor

Suprak, David N. (David Nathan)

Second Advisor

Brilla, Lorraine R., 1955-

Third Advisor

Harrison, Alex, 1987-


Maximal recoverable volume (MRV) has been described as the maximal volume load an individual can accumulate, recover from, and respond to positively. There has been little research conducted in attempts to quantify this load. The purpose of this study was to determine the relative MRV of resistance training in total volume load, and employ a multiple regression model consisting of initial volume load (VL_initial) and maximum relative strength (RS) (1RM back squat divided by body weight) to define a method to accurately predict this upper tolerable limit. VL_initial for each exercise was calculated as the product of the number of sets performed, the load on the bar, and three repetitions. The subjects performed sets of three repetitions at 80% 1RM until the average barbell velocity decreased by 7% compared to the fastest velocity collect during that session. While volume load was increased by 17% each week after determining the VL_initial, subjects performed weekly performance tests until overreaching was seen, marked as a 10% decrease in any two or three performance tests. These performance tests included vertical jump height, single rep peak velocity and average velocity of two sets of three repetitions of back squats at 80% of 1RM. Multiple regression analyses were conducted, with both VL_initial and RS as predictors for total volume load (VL_total), and volume load of the final week (VL_final). This model revealed that VL_initial and RS significantly predicted both VL_total (F[2,11] = 52.88, p < 0.001, R2-adj. = 0.89), and VL_final (F[2,11] = 59.54, p 2-adj. = 0.90).





Western Washington University

OCLC Number


Subject – LCSH

Muscle strength--Measurement; Exercise--Physiological aspects--Measurement




masters theses




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Kinesiology Commons