| 基于身体形态、体力活动和超声骨指标的超重和肥胖男性骨密度预测模型的构建 |
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| 引用本文： | 赵晓光,黄晖明.基于身体形态、体力活动和超声骨指标的超重和肥胖男性骨密度预测模型的构建[J].武汉体育学院学报,2020,54(3):70-75. |
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| 作者姓名： | 赵晓光 黄晖明 |
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| 作者单位： | 1. 宁波大学 体育学院, 浙江 宁波 315211;2. 宁波大学 大健康研究院, 浙江 宁波 315211 |
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| 基金项目： | 国家社会科学基金项目（18BTY100）；宁波大学科研基金项目（XYW18003）。 |
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| 摘 要： | 目的利用身体形态、体力活动和超声骨指标来预测超重和肥胖者的下肢骨密度、脊柱骨密度和全身骨密度。方法选取超重和肥胖男性受试者138名(年龄50.9±9.6岁;BMI29.1±2.6 kg/m^2),随机测量其身体形态(身高、体重、BMI、胸围和腰围等)、体力活动(握力、纵跳和肺活量等)和超声骨指标(超声声速和传播指数)。骨密度使用双能X线吸收测定法进行测量。使用Stepwise逐步回归法构建下肢骨密度、脊柱骨密度和全身骨密度的预测模型。结果多元线性回归分析显示,所构建的模型都能够有效地预测受试者的骨密度。其中,下肢骨密度预测模型的拟合度最高。Bland-Altman定量测量结果一致性分析表明,下肢骨密度、脊柱骨密度和全身骨密度的实测值与预测值具有显著的相关性(r=0.35,r=0.60,r=0.31;P<0.001)。结论研究所构建的模型可以有效预测不同年龄、肥胖程度和体力活动水平男性的下肢骨密度、脊柱骨密度和全身骨密度,适用于大规模人群的骨质疏松症筛查与早期防治。
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| 关 键 词： | 运动人体科学 身体形态 体力活动 骨密度 肥胖 预测模型 |
| 收稿时间： | 2020-01-09 |
Bone Mineral Density Assessments in Evaluating Overweight or Obese Adult Men Based on Anthropometric,Physical Fitness and Quantitative Ultrasound Variables |
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| ZHAO Xiaoguang,HUANG Huiming.Bone Mineral Density Assessments in Evaluating Overweight or Obese Adult Men Based on Anthropometric,Physical Fitness and Quantitative Ultrasound Variables[J].Journal of Wuhan Institute of Physical Education,2020,54(3):70-75. |
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| Authors: | ZHAO Xiaoguang HUANG Huiming |
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| Institution: | 1. College of P. E, Ningbo Univ., Ningbo 315211, China;2. Research Academy of Grand Health, Ningbo Univ., Ningbo 315211, China |
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| Abstract: | The purpose of this study was to build bone mineral density assessments to evaluate overweight or obese male adults, based on anthropometric, physical fitness and quantitative ultrasound variables. 138 overweight or obese male adults(age:50.9±9.6 years; BMI:29.1±2.6 kg/m2) participated in the study. Anthropometric variables (e.g.:height, weight, BMI, chest and waist circumferences), physical fitness(e.g.:grip strength, side steps, vertical jump) and quantitative ultrasound variables were measured randomly. Bone mineral density was tested by dual energy X-ray absorptiometry. Leg, spine and whole body bone mineral density prediction models were built by stepwise regression analysis. Multiple linear regression analyses showed that all models could effectively assess bone mineral density, and the predictive model for leg bone mineral density revealed the highest model fitting. The Bland-Altman approaches demonstrated that there were significant correlations between measured and estimated bone mineral density (r=0.35, r=0.60, r=0.31; P<0.001). The constructed model could effectively predict bone mineral density of leg, spine and whole body for men with different age, obesity and physical fitness. It was suitable for osteoporosis screening, early prevention and treatment in large population. |
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| Keywords: | kinesiology anthropometrics physical fitness bone mineral density obesity predictive model |
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