共查询到19条相似文献，搜索用时 140 毫秒
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一、问题的提出: 目前国内外预测青少年身高的方法,概括起来有以下一些:用父母身高预测;根据自身身高预测;用骨龄预测;也有用脚长、手长预测等等。然而不同国家,不同地区、不同民族和不同时代的青少年生长发育的特点是有差异的。也就是说,上述一些预测身高的方法其应用范围应有一定的区域性和局限性。本文将通过对捷克的哈费利采克、东德的瓦尔克尔氏和我国城市男子身高的预测方法进行比较、分析,从而验证每种预测方法在具体实施中的不足。在此基础 相似文献
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曾倩 《上海体育学院学报》1986,(1)
现代竞技体育中的许多竞赛项目对运动员的身高要求越来越高,而运动员出成绩的年龄又有普遍提前的趋势,因此,少年运动员的选拔、预测工作显得尤为重要。目前国内外较多采用的是利用骨龄进行预测,但拍摄骨龄片费用昂贵,不可能广泛使用;一些学者经研究,提出了检查第二特征推导骨龄预测身高。此外,还有根据本人目前身高推算最终身高;根据脚的大小预测身高;根据父母身高推算子女身高等简便易行的方法来进行身高预测。上海体院科学选材研究组关于头型的研究结果报导:“人的头型与男少年预测身高(应用骨龄推算的)间的相关关系有显 相似文献
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一、选材选材是训练中的一个重要问题。篮球运动员需要身材高大,不仅要看现在身高,还要了解将来的身高。对筛选出来的队员的身高还要进行预测,以便了解他们身高发展趋势,给评价提供依据。预测的方法我们采用以下几种。1.根据父母身高预 相似文献
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预测不仅是体质研究的重要课题,而且也是广大家长,体育教师和教练员们十分关注的问题。长期以来,国内外许多学者在这方面作了不少研究并取得了一些成果。如根据父母身高预测子女身高;用足长预测身高及骨令预测身高等。本文试图 相似文献
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一、选好体育后管苗子
1、身体形态的选择
（1）合适的身高,短跑运动员所应具备的条件。短跑运动员预测,身高男子1．75至1．80米,女子1．67至1．73米均为理想身高,预测长成后身高办法可用已见专著和广泛使用的从父母身高和现在身高相结合预测将来身高。 相似文献
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对众多的身高预测方法，根据其预测的原理和理论依据进行分类。对各类预测方法进行了初步的分析与评价，指出了不同预测法的适用范围。随着社会环境的变化，有的预测方法已失去了原有的实用价值。 相似文献
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身高预测对运动员的选材具有很重要的意义,在利用遗传和年龄因素预测身高方面,国外已有资料报道,捷克专家总结为下列公式: 男儿身高=(交亲高+母亲高)×1.08/2 女儿身高=(父身高×0.923+母亲高)/2瓦尔克尔利用年龄因素建立了身高预测的二元回归方程:=b_0+b_1x_1其中:表示预测的身高,b_1为回归系数,b_0为截矩,x_1为某一年龄的身高。我们知道,影响身高增长的因素较多,由于他们总是 相似文献
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Beunen GP Malina RM Freitas DL Thomis MA Maia JA Claessens AL Gouveia ER Maes HH Lefevre J 《Journal of sports sciences》2011,29(15):1683-1691
The purpose of this study was to validate and cross-validate the Beunen-Malina-Freitas method for non-invasive prediction of adult height in girls. A sample of 420 girls aged 10-15 years from the Madeira Growth Study were measured at yearly intervals and then 8 years later. Anthropometric dimensions (lengths, breadths, circumferences, and skinfolds) were measured; skeletal age was assessed using the Tanner-Whitehouse 3 method and menarcheal status (present or absent) was recorded. Adult height was measured and predicted using stepwise, forward, and maximum R (2) regression techniques. Multiple correlations, mean differences, standard errors of prediction, and error boundaries were calculated. A sample of the Leuven Longitudinal Twin Study was used to cross-validate the regressions. Age-specific coefficients of determination (R (2)) between predicted and measured adult height varied between 0.57 and 0.96, while standard errors of prediction varied between 1.1 and 3.9 cm. The cross-validation confirmed the validity of the Beunen-Malina-Freitas method in girls aged 12-15 years, but at lower ages the cross-validation was less consistent. We conclude that the Beunen-Malina-Freitas method is valid for the prediction of adult height in girls aged 12-15 years. It is applicable to European populations or populations of European ancestry. 相似文献
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Gaston P. Beunen† Robert M. Malina Duarte L. Freitas Martine A. Thomis José A. Maia Albrecht L. Claessens 《Journal of sports sciences》2013,31(15):1683-1691
Abstract The purpose of this study was to validate and cross-validate the Beunen-Malina-Freitas method for non-invasive prediction of adult height in girls. A sample of 420 girls aged 10–15 years from the Madeira Growth Study were measured at yearly intervals and then 8 years later. Anthropometric dimensions (lengths, breadths, circumferences, and skinfolds) were measured; skeletal age was assessed using the Tanner-Whitehouse 3 method and menarcheal status (present or absent) was recorded. Adult height was measured and predicted using stepwise, forward, and maximum R 2 regression techniques. Multiple correlations, mean differences, standard errors of prediction, and error boundaries were calculated. A sample of the Leuven Longitudinal Twin Study was used to cross-validate the regressions. Age-specific coefficients of determination (R 2) between predicted and measured adult height varied between 0.57 and 0.96, while standard errors of prediction varied between 1.1 and 3.9 cm. The cross-validation confirmed the validity of the Beunen-Malina-Freitas method in girls aged 12–15 years, but at lower ages the cross-validation was less consistent. We conclude that the Beunen-Malina-Freitas method is valid for the prediction of adult height in girls aged 12–15 years. It is applicable to European populations or populations of European ancestry. 相似文献
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目的：建立蹦床运动员竞技能力结构评价指标体系,在此基础上构建基于人工神经网络的蹦床运动员竞技能力结构评价模型,为蹦床运动员竞技能力结构的个性化诊断及针对性训练提供参考。方法：以上海市体操运动中心16名蹦床运动员为研究对象,对受试者进行3次跨度6个月以上的初选指标测试。基于因子分析建立蹦床运动员竞技能力结构评价指标体系。在此基础上以竞技能力结构评价指标为自变量,运动员成绩为因变量,构建运动员竞技能力结构的人工神经网络评价模型,并开发运动员竞技能力结构评价系统。结果：蹦床运动员竞技能力结构指标体系由身体形态、身体素质、专项技术和心理素质4个维度构成,包括腿长、腿长/身高比、纵跳高度、原地立臂角度、60 s悬垂举腿、立卧撑、网上腾空高度、空跳高度/原地纵跳高度比、着网瞬间立臂角度、30次空跳高度下降率、状态焦虑水平和特质焦虑水平共12个指标。所构建的Elman人工神经网络模型由12个输入节点、9个隐含层节点和1个输出层节点组成,模型预测精度在95.87%~99.37%,平均预测精度高达97.66%。结论：构建了基于人工神经网络的蹦床运动员竞技能力结构评价模型,模型具有较好的预测精度。在训练中,可应用人工神经网络对竞技能力结构进行评价,动态获知竞技能力结构改变对总体运动成绩的影响作用。该研究对于蹦床运动员竞技能力结构的综合评价和针对性训练可提供科学性指导意见。 相似文献
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AbstractThis study attempted to validate an anthropometric equation for predicting age at peak height velocity (PHV) in 198 Polish girls followed longitudinally from 8 to 18 years. Maturity offset (years before or after PHV) was predicted from chronological age, mass, stature, sitting height and estimated leg length at each observation; predicted age at PHV was the difference between age and maturity offset. Actual age at PHV for each girl was derived with Preece–Baines Model 1. Predicted ages at PHV increased from 8 to16 years and varied relative to time before and after actual age at PHV. Predicted and actual ages at PHV did not differ at 9 years, but predicted overestimated actual age at PHV from 10 to 16 years. Girls of contrasting maturity status differed in predicted age at PHV from 8 to 14 years. In conclusion, predicted age at PHV is dependent upon age at prediction and individual differences in actual age at PHV, which limits its utility as an indicator of maturity timing in general and in sport talent programmes. It may have limited applicability as a categorical variable (pre-, post-PHV) among average maturing girls during the interval of the growth spurt, ~11.0–13.0 years. 相似文献
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Caleb R. Marriner John B. Cronin Adam Storey 《European Journal of Sport Science》2017,17(9):1101-1109
A popular method to improve athletic performance and lower body power is to train with wearable resistance (WR), for example, weighted vests. However, it is currently unknown what training effect this loading method has on full-body explosive movements such as the power clean. The purpose of this study was to determine what effects WR equivalent to 12% body mass (BM) had on the power clean and countermovement jump (CMJ) performance. Sixteen male subjects (age: 23.2?±?2.7 years; BM: 90.5?±?10.3?kg) were randomly assigned to five weeks of traditional (TR) power clean training or training with 12% BM redistributed from the bar to the body using WR. Variables of interest included pre and post CMJ height, power clean one repetition maximum (1RM), peak ground reaction force, power output (PO), and several bar path kinematic variables across loads at 50%, 70%, and 90% of 1RM. The main findings were that WR training: (1) increased CMJ height (8.7%; ES?=?0.53) and 1RM power clean (4.2%; ES?=?0.2) as compared to the TR group (CMJ height?=??1.4%; 1RM power clean?=?1.8%); (2) increased PO across all 1RM loads (ES?=?0.33–0.62); (3) increased barbell velocity at 90% 1RM (3.5%; ES?=?0.74) as compared to the TR group (?4.3%); and (4) several bar path kinematic variables improved at 70% and 90% 1RM loads. WR power clean training with 12% BM can positively influence power clean ability and CMJ performance, as well as improve technique factors. 相似文献
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This study examined a method of predicting body density based on hydrostatic weighing without head submersion (HWwithoutHS). Donnelly and Sintek (1984) developed a method to predict body density based on hydrostatic weight without head submersion. This method predicts the difference (D) between HWwithoutHS and hydrostatic weight with head submersion (HWwithHS) from anthropometric variables (head length and head width), and then calculates body density using D as a correction factor. We developed several prediction equations to estimate D based on head anthropometry and differences between the sexes, and compared their prediction accuracy with Donnelly and Sintek's equation. Thirty-two males and 32 females aged 17-26 years participated in the study. Multiple linear regression analysis was performed to obtain the prediction equations, and the systematic errors of their predictions were assessed by Bland-Altman plots. The best prediction equations obtained were: Males: D(g) = -164.12X1 - 125.81X2 - 111.03X3 + 100.66X4 + 6488.63, where X1 = head length (cm), X2 = head circumference (cm), X3 = head breadth (cm), X4 = head thickness (cm) (R = 0.858, R2 = 0.737, adjusted R2 = 0.687, standard error of the estimate = 224.1); Females: D(g) = -156.03X1 - 14.03X2 - 38.45X3 - 8.87X4 + 7852.45, where X1 = head circumference (cm), X2 = body mass (g), X3 = head length (cm), X4 = height (cm) (R = 0.913, R2 = 0.833, adjusted R2 = 0.808, standard error of the estimate = 137.7). The effective predictors in these prediction equations differed from those of Donnelly and Sintek's equation, and head circumference and head length were included in both equations. The prediction accuracy was improved by statistically selecting effective predictors. Since we did not assess cross-validity, the equations cannot be used to generalize to other populations, and further investigation is required. 相似文献
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The aim of this study was to assess the relationship between several commonly used aerobic and anaerobic cycle ergometer tests and performance during a treadmill cycling hill climb. Eight competitive cyclists (age 27+/-7 years; body mass 73.2+/-5.2 kg; height 177+/-6 cm; mean +/- s) completed six tests in random order: a lactate minimum test; a Wingate anaerobic power test; and two 6-km climbs at 6% and two 1-km climbs at 12% gradient performed on a motorized treadmill. The mean times and power outputs for the 6-km and 1-km climbs were 16:30+/-1:08 min: s and 330+/-17.8 W, and 4:19+/-0:27 min: s and 411+/-24.4 W, respectively. The best individual predictor of 6-km and 1-km performance times was the time for the corresponding climb at the other distance (r = 0.97). The next strongest predictor of both hill climb performances was the average power produced during the Wingate test divided by body mass. Stepwise regression analysis showed that the two variables contributing most to the prediction equation for both climbs were the Wingate average power per unit of body mass and maximal aerobic power divided by total mass (rider + bike), which together accounted for 92 and 96% of the variability in the 6-km and 1-km climbs. In conclusion, among competitive cyclists, the Wingate average power per unit of body mass was the best single predictor of simulated cycling hill climb performance at the distance and gradient used. 相似文献
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Anthony D. Mahon Andrea D. Marjerrison Jonah D. Lee Megan E. Woodruff Lauren E. Hanna 《Research quarterly for exercise and sport》2013,84(4):466-471
In this study, we compared measured maximal heart rate (HRmax) to two different HRmax prediction equations [220 — age and 208 — 0.7(age)] in 52 children ages 7-17 years. We determined the relationship of chronological age, maturational age, and resting HR to measured HRmax and assessed seated resting HR and HRmax during a graded exercise test. Maturational age was calculated as the maturity offset in years from the estimated age at peak height velocity. Measured HRmax was 201 ± 10 bpm, whereas predicted HRmax ranged from 199 to 208 bpm. Measured HRmax and the predicted value from the 208 — 0.7(age) prediction were similar but lower (p < .05) than the 220 — age prediction. Absolute differences between measured and predicted HRmax were 8 ± 5 and 10 ± 8 bpm for the 208 — 0.7 (age) and 220 — age equations, respectively, and were greater than zero (p < .05). Regression equations using resting HR and maturity offset or chronological age significantly predicted HRmax, although the R2 < .30 and the standard error of estimation (8.2-8.5) limits the accuracy. The 208 — 0.7(age) equation can closely predict mean HRmax in children, but individual variation is still apparent. 相似文献
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Mahon AD Marjerrison AD Lee JD Woodruff ME Hanna LE 《Research quarterly for exercise and sport》2010,81(4):466-471
In this study, we compared measured maximal heart rate (HRmax) to two different HRmax prediction equations [22 - age and 208 - 0.7(age)] in 52 children ages 7-17 years. We determined the relationship of chronological age, maturational age, and resting HR to measured HRmax and assessed seated resting HR and HRmax during a graded exercise test. Maturational age was calculated as the maturity offset in years from the estimated age at peak height velocity. Measured HRmax was 201 +/- 10 bpm, whereas predicted HRmax ranged from 199 to 208 bpm. Measured HRmax and the predicted value from the 208 - 0.7(age) prediction were similar but lower (p < .05) than the 220 - age prediction. Absolute differences between measured and predicted HRmax were 8 +/- 5 and 10 +/- 8 bpm for the 208 - 0.7 (age) and 220 - age equations, respectively, and were greater than zero (p < .05). Regression equations using resting HR and maturity offset or chronological age significantly predicted HRmax, although the R2 < .30 and the standard error of estimation (8.2-8.5) limits the accuracy. The 208 - 0.7(age) equation can closely predict mean HRmax in children, but individual variation is still apparent. 相似文献