A Survey of Resistance Training Practices Among Physique Competitors During Peak Week

Homer, KA, Cross, MR, and Helms, ER. A survey of resistance training practices among physique competitors during peak week. J Strength Cond Res 38(10): 1745-1752, 2024-Physique athletes are ranked by their on-stage presentation of muscle size, proportionality, and leanness. To acutely maximize muscle size, competitors manipulate resistance training (RT) variables in the days before the contest, commonly referred to as peak week (PW). Resistance training manipulations during PW may act synergistically with nutrition strategies such as carbohydrate loading. However, because little information exists on changes made to RT during PW, the purpose of this research was to determine the current practices of physique athletes and whether competitor characteristics were predictive of the RT variables manipulated. A total of 104 responses to the RT section of a survey on PW nutrition and training were analyzed through a series of multiple logistic regression models to examine the relationship between RT manipulations and competitor characteristics. Furthermore, to determine the magnitude of differences between PW and the week before PW (WBPW) for these variables, a McNemar-Bowker test, paired t-tests, and Wilcoxon signed-rank tests were conducted for nominal, continuous, and discrete outcomes, respectively. For all statistical analyses, p <0.05 was deemed significant. Competitors generally adjusted RT in a variety of ways, where proximity-to-failure was the most frequently manipulated and training frequency was the least; however, no competitor characteristic predicted any of the RT variables manipulated. Within those who manipulated RT variables during PW, frequency, volume, and intensity decreased while repetition ranges of compound exercises increased, empirically confirming that competitors seek to reduce training stress during PW. Such findings can be incorporated in future experimental designs examining the efficacy of peaking strategies to enhance the generalizability of results.