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首页> 外文期刊>American Journal of Sports Science and Medicine >Multivariate Analysis of Vertical Jump Predicting Health-related Physical Fitness Performance
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Multivariate Analysis of Vertical Jump Predicting Health-related Physical Fitness Performance

机译:垂直跳的多元分析预测健康相关的身体素质表现

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摘要

Background: Health-related fitness tests measure one of five different traits: cardiorespiratory endurance, muscular strength, muscular endurance, body composition, and flexibility. To assess an individual on all five traits can be costly and time consuming. Thus, it would be useful to the fitness practitioner if one single test could be used as a proxy for overall fitness. Therefore, the purpose of this study was to employ multivariate data analyses to examine the ability of the vertical jump (VJ) to predict health-related fitness performance. Methods: This study used data from college students who completed both ten different health-related fitness tests and the VJ assessment. Three body composition measures were used: percent body fat (PBF, %), body mass index (BMI, kg/m2), and waist circumference (WC, cm). Four muscular fitness measures were used: 1RM bench press (BP, lb), 1RM leg press (LP, lb), maximal push-up repetition (PU, #), and flexed arm hang time (FAH, sec). Two cardiorespiratory endurance measures were used: maximal oxygen consumption (VO2, ml/kg/min) and physical activity rating score (PAR, 0 thru 10). One flexibility measure was used: sit-and-reach (SNR, cm). The countermovement vertical jump (VJ, in) was used as the single predictor variable and participants were categorized into high or low VJ groups using the sex-specific median. Results: Male-specific multivariate analysis of variance (MANOVA) results showed that VJ significantly predicts the linear combination of body composition (λ=0.85, F=4.8, p=.004), muscular fitness (λ=0.66, F=10.4, p<.001), and cardiorespiratory endurance (λ=0.85, F=7.3, p=.001). Female-specific MANOVA results also showed that VJ significantly predicts the linear combination of body composition (λ=0.43, F=17.6, p<.001), muscular fitness (λ=0.41, F=14.1, p<.001), and cardiorespiratory endurance (λ=0.61, F=13.0, p<.001). Univariate ANOVA models showed that VJ significantly predicts flexibility (F=5.0, p=.028) in males only. Overall fitness MANOVA models showed that VJ significantly predicts the linear combination of all ten fitness scores in both males (λ=0.61, F=4.8, p<.001) and females (λ=0.33, F=6.8, p<.001). Conclusion: Results from this study indicate that VJ is a valid predictor of health-related fitness performance in college students.
机译:背景:与健康相关的体能测试可测量以下五个不同特征之一:心肺耐力,肌肉力量,肌肉耐力,身体成分和柔韧性。评估一个人的全部五个特征可能既昂贵又费时。因此,如果一个单独的测试可以用作整体健康的替代指标,对健身从业者将是有用的。因此,本研究的目的是运用多元数据分析来检验垂直跳动(VJ)预测与健康相关的健身表现的能力。方法:本研究使用来自大学生的数据,他们完成了十种不同的健康相关健身测试和VJ评估。使用了三种身体成分测量方法:体脂百分比(PBF,%),体重指数(BMI,kg / m2)和腰围(WC,cm)。使用了四种肌肉健身措施:1RM卧推(BP,lb),1RM腿部按压(LP,lb),最大俯卧撑重复(PU,#)和屈臂悬吊时间(FAH,sec)。使用了两种心肺耐力测量方法:最大耗氧量(VO2,ml / kg / min)和体力活动评分(PAR,0至10)。使用了一种灵活性度量:坐着和到达(SNR,cm)。反向运动的垂直跳动(VJ,in)被用作单个预测变量,并且参与者使用性别特定的中位数分为高或低VJ组。结果:针对男性的方差多元分析(MANOVA)结果表明,VJ显着预测了身体成分(λ= 0.85,F = 4.8,p = .004),肌肉健康状况(λ= 0.66,F = 10.4, p <.001)和心肺耐力(λ= 0.85,F = 7.3,p = .001)。特定于女性的MANOVA结果还显示,VJ显着预测了身体成分(λ= 0.43,F = 17.6,p <.001),肌肉健康度(λ= 0.41,F = 14.1,p <.001)和心肺耐力(λ= 0.61,F = 13.0,p <.001)。单变量方差分析模型显示,VJ仅可预测男性的柔韧性(F = 5.0,p = .028)。总体适应性MANOVA模型显示,VJ显着预测了男性(λ= 0.61,F = 4.8,p <.001)和女性(λ= 0.33,F = 6.8,p <.001)中所有十个健身得分的线性组合。结论:这项研究的结果表明,VJ是大学生健康相关健身表现的有效预测指标。

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