인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
The aim was to evaluate the concurrent validity and reliability of the Resistance Intensity Scale for Exercise [RISE], which uses verbal descriptors, to quantify the intensity in velocity-based training with elastic bands. Eighteen trained volunteers performed parallel squats at maximum speed at 40%, 55%, 70%, and 85%1RM in four sessions, two for familiarization and two for reliability. Each set was stopped at a 10% intra-set velocity loss. Participants reported the perceived effort (easy-low-moderate-hard-maximal) at the first and last repetition. The concurrent validation was conducted with external load (i.e., mean propulsive velocity, weight, repetitions, and maximum power) and internal load parameters (i.e., heart rate). Participants' relative strength was calculated to assess its influence on the dependent variable. Acceptable concurrent validity and reliability (ICC>0.77, CV<21%) were observed, with the perceived effort being appropriate to differentiate between intensities and not being influenced by the participants' relative strength (<i>p</i> = 0.88). A categorical linear regression showed significant (<i>p</i> < 0.001) associations between the RISE scores and the weight, repetitions, and mean propulsive velocity (<i>r</i> = 0.43-0.63). The findings certify the usefulness of the perceived exertion for quantifying the intensity during velocity-based training with elastic bands. The perceived exertion of the first and last repetition favors a proper dosage of the training load.
#Reliability (semiconductor)
#Intensity (physics)
#Perceived exertion
#Resistance training
#Linear regression
#Concurrent validity
#Strength training
#Categorical variable
#Mathematics
#Validity
#Psychology
#Statistics
#Exercise intensity
#Physical therapy
#Heart rate
#Medicine
#Physics
#Power (physics)
#Psychometrics
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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