Let The Pleasure Guide Your Resistance Training Intensity

MEDICINE AND SCIENCE IN SPORTS AND EXERCISE(2018)

引用 39|浏览10
暂无评分
摘要
Purpose The purpose of this study was to evaluate the feasibility and reliability of the Feeling Scale (FS) to self-regulate resistance training (RT) intensity.Methods Sixteen sedentary men (39.7 7.5 yr) performed 3 familiarization sessions, 2 one-repetition maximum (1RM) testing, and 16 RT sessions (four sessions for each FS descriptor; randomized). The FS descriptors were very good (FS + 5), good (FS + 3), fairly good (FS + 1), and fairly bad (FS - 1). Resistance exercises were leg press, chest press, knee extension, and seated biceps curl. Participants were instructed to select a load associated with the verbal/numerical descriptor of the FS to perform three sets of 10 repetitions.Results Participants lifted a significantly greater %1RM as the FS level decreased from FS + 5 to FS - 1 (P < 0.001). The mean %1RM values for the FS descriptors of +5, +3, +1, and -1, respectively, were as follows: leg press, 42.5% +/- 9.5%, 58.2% +/- 7.4%, 69.9% +/- 7.0%, and 80.7% +/- 5.4%; knee extensor, 37.4% +/- 9.6%, 54.5% +/- 9.3%, 65.3% +/- 8.7%, and 78.2% +/- 5.9%; chest press, 42.4% +/- 11.3%, 54.9% +/- 11.4%, 66.4% +/- 12.6%, and 78.2% +/- 13.5%; and biceps curl, 39.0% +/- 8.1%, 54.0% +/- 9.7%, 68.4% +/- 5.9%, and 83.2% +/- 3.0%. The interclass correlation coefficient over the four experimental sessions ranged from 0.73 to 0.99 for %1RM and from 0.77 to 0.99 for weight lifted, with a coefficient of variation of approximately 7%, 4%, 2%, and 2% for FS descriptors of +5, +3, +1, and -1, respectively.Conclusion This study is the first to demonstrate that the FS can be used to self-regulate exercise intensity in RT. The lower the FS descriptor, the higher the weight lifted. In addition, the load self-selected for each FS descriptor was reliable across the four sessions.
更多
查看译文
关键词
AFFECTIVE RESPONSES, AFFECT-REGULATED EXERCISE, EXERCISE PRESCRIPTION, SELF-REGULATION, STRENGTH TRAINING, RELIABILITY
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要