Guidelines to Classify Subject Groups in Sport-Science Research

术语 主题(文档) 工作量 数学 自行车 统计 计算机科学 语言学 地理 哲学 图书馆学 操作系统 考古
作者
Kevin De Pauw,Bart Roelands,Stephen S. Cheung,Bas de Geus,Gerard Rietjens,Romain Meeusen
出处
期刊:International Journal of Sports Physiology and Performance [Human Kinetics]
卷期号:8 (2): 111-122 被引量:560
标识
DOI:10.1123/ijspp.8.2.111
摘要

The aim of this systematic literature review was to outline the various preexperimental maximal cycle-test protocols, terminology, and performance indicators currently used to classify subject groups in sport-science research and to construct a classification system for cycling-related research.A database of 130 subject-group descriptions contains information on preexperimental maximal cycle-protocol designs, terminology of the subject groups, biometrical and physiological data, cycling experience, and parameters. Kolmogorov-Smirnov test, 1-way ANOVA, post hoc Bonferroni (P < .05), and trend lines were calculated on height, body mass, relative and absolute maximal oxygen consumption (VO(2max)), and peak power output (PPO).During preexperimental testing, an initial workload of 100 W and a workload increase of 25 W are most frequently used. Three-minute stages provide the most reliable and valid measures of endurance performance. After obtaining data on a subject group, researchers apply various terms to define the group. To solve this complexity, the authors introduced the neutral term performance levels 1 to 5, representing untrained, recreationally trained, trained, well-trained, and professional subject groups, respectively. The most cited parameter in literature to define subject groups is relative VO(2max), and therefore no overlap between different performance levels may occur for this principal parameter. Another significant cycling parameter is the absolute PPO. The description of additional physiological information and current and past cycling data is advised.This review clearly shows the need to standardize the procedure for classifying subject groups. Recommendations are formulated concerning preexperimental testing, terminology, and performance indicators.
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