【作者】 刘丹松；

【导师】 郑伟涛；

【作者基本信息】 武汉体育学院 ， 体育教育训练学， 2014， 博士

【摘要】 研究目的：根据《大学生体质健康标准》（以下简称为《标准》）内容，对大学生体质进行测试，探讨体力活动与体质健康关系。通过间接测热法对常见体力活动项目进行能量消耗测量，构建基于三维加速度的能量消耗预测方程，探索准确、便捷、高效的能量消耗监测方法。研究方法：1、以800名非体育专业大学生为测试对象，依据《标准》内容分别对身体形态、身体机能和身体素质三个方面共九项项目进行测试，同时使用2013年《国家学生体质健康标准》测试卡片（大学），对大学生个人课余活动与参加体育锻炼情况对大学生进行问卷调查。2、从上述人群中抽取70人，随机划分为测试组50人（男=25，女=25），验证组20人（男=10，女=10）。受试人员需同步佩戴MetaMax3B便携式气体分析仪、三维加速度传感器仪SWA和polar心率表进行体力活动。测试项目共分为七项，依次为平躺、步行（3.2km/h、4.0km/h、4.8km/h和5.2km/h）、跑步（6.4km/h、7.0km/h、8.1km/h和9.0km/h）、伏案、爬楼梯（100-120步/分钟和全力冲刺）、自行车（10km/h、13km/h、15km/h和20km/h四个速度）、俯卧撑，记录每项活动的三维加速度值等数据。以间接测热（IC）法为标准，以垂直轴记数（ACz）、三轴综合记数（VM）和心率（HR）为自变量建立能耗预测方程。建立3METs为区分点所对应的能量消耗公式。验证组以同样顺序完成7项体力活动，以其测得体力活动的能耗结果对方程准确性进行验证。研究结果：1、《标准》测试结果体现出高校大学生的体质整体表现不佳，除身体形态外，身体机能和素质是导致体质下滑的主要因素。问卷分析来看，体力活动不足、课业压力大和不良生活习惯是导致体质下滑的主要原因。其次，体育锻炼环境不好、督促力度不够、运动评估手段的缺失，也是造成体力活动缺乏和体质下降的原因之一。2、不同测试项目，垂直加速度值ACz和综合加速度值VM有显著性差异；同一测试项目内不同速度，ACz和VM没有显著性差异。相关性分析得知，以性别、个体特征BMI值、心率、ACz和VM的作为自变量，能耗模型具有高拟合度，其R2值高达0.7。3、依据项目构成，本研究基于综合能耗、分类项目能耗和METs构建了10个能量消耗模型。综合能耗模型和走跑类能耗模型可以较好预测体力活动能量消耗，非走跑类和基于METs建立的能耗模型，预测准确度有所下降。综合能耗模型和走跑类能耗模型如下：⑴综合能耗模型：W/min=－9.125288+0.004357*ACz+1.097599*SEX+0.2270181*BMI+0.057226*HRW/min=－12.27049+0.008815*VM+0.953711*SEX+0.2318533*BMI+0.0586115*HR⑵走跑类能耗模型：W/min=－18.78608+0.008212*ACz+2.058352*SEX+0.2000157*BMI+0.0474637*HRW/min=－8.849811+0.014097*VM+2.034297*SEX+0.1546967*BMI+0.0202001*HR研究结论：1、大学生需提高体力活动量，促进体质健康发展。2、基于三维加速度值、BMI、HR、性别为基础构建的体力活动能量消耗预测方程具有较高准确度，可应用于大学生日常体力活动的监测。更多还原

【Abstract】 Purpose: The purpose of this study was to analyze the relationship between physical activityand body health. The physical statues of college students were tested according to the contentof “college students’ physical health standards”. The energy expedition of several normalphysical activities were obtained by IC method, and physical activity volume predictionequations were established by using three-axis accelerometer, which to explore convenient,accurate and effective method to monitor physical activities.Methods:1.800no PE college students participated in the research. According to the“college students physical health standards”, the analysis was mainly from three aspects: bodyshape, bodily functions and physical quality, the test contents were divided into9subjects.The questionnaire was also used to investigate the whole situation of after class activities andphysical fitness.2.70students from the group were in the following test,50were chosen randomly in testgroup(male=25, female=25) and another20were in validation group(male=10, female=10).Participants wore MetaMax3B portable indirect calorimetry (IC) system, SensewareArmband three-axis accelerometer and polar heart rate monitor (HR) during performingphysical activities. Seven subjects were tested in the experiment, which were lie flat,walking(3.2km/h、4.0km/h、4.8km/h&5.2km/h), running(6.4km/h、7.0km/h、8.1km/h&9.0km/h), bend over a table, climbing steps(100-120steps/min), riding(10km/h、13km/h、15km/h&20km/h) and push up, accelerometry data was recorded in every test subject.Using the IC method as standard to measure energy expenditure (EE), accelerometry countsof axis Z (ACz), vector magnitude (VM) and heart rate (HR)as independent variables toestablish EE prediction equations. EE prediction equations also established based on3METs.The students in validation group pursued the accuracy testing in same order to verified theprediction equations.Results:1Results from “college students physical health standards” indicated collegestudents in bad physical condition, the bad performance of bodily functions and physicalquality were the main cause for the decline of physical healthy. By statistic of questionnaire, the lack of physical activities, pressure on studying and unhealthy living habits alsonegatively infected physical healthy. On the other hand, the incomprehensive of exerciseenvironment, lack of supervision and judgment facilities was another reason for theinsufficient of physical activities and decline of physical healthy.2The correlation between ACz and VM were significant different between activities, but ACzand VM of different speed in same activity had no significant difference. By correlationanalysis, EE prediction equations had high degree of fitting with independent variable of sex,BMI, HR, ACz and VM, and R2=0.7.3In this research, ten EE prediction equations were established based on overall EE,classified EE and METs EE. The equations of overall EE and walk/run EE were obviouslyimproved accuracy of physical activities EE prediction. The equations of overall EE andwalk/run EE were as follow:(1) overall EE prediction equations：W/min=－9.125288+0.004357*ACz+1.097599*SEX+0.2270181*BMI+0.057226*HRW/min=－12.27049+0.008815*VM+0.953711*SEX+0.2318533*BMI+0.0586115*HR(2) walk/run EE prediction equations：W/min=－18.78608+0.008212*ACz+2.058352*SEX+0.2000157*BMI+0.0474637*HRW/min=－8.849811+0.014097*VM+2.034297*SEX+0.1546967*BMI+0.0202001*HRConclusion:1College students need joint more physical activities to improve body healthy.2Based on three axis accelerometry data, BMI, HR and sex, the prediction equations hadhigher accuracy on physical activities EE prediction, which can be widely used in supervisionof college students daily physical activities.更多还原