11-14岁青春期少年常见体力活动能耗测量的方法学研究
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摘要
研究目的
运用间接测热法（Cosmed K4b2）研究11-14岁青春期少年静息能量消耗和常见体力活动能量消耗，探讨影响青春期少年静息和运动能量消耗的因素。
以间接测热法（Cosmed K4b2）为效标，建立适合于11-14岁青春期少年的三轴运动加速度计（ActiGraph GT3X）的体力活动能耗预测方程，并进行验证。
以间接测热法（Cosmed K4b2）为效标，验证Actiheart Child: GroupAct/Group HR（Actiheart RA+HR）联合能耗预测方程的有效性。
确立不同运动强度下的活动计数、心率和心率百分率切点，便于今后对青少年常见体力活动水平进行评估。
研究方法
随机抽取120名11-14岁在校中学生，佩戴便携式气体代谢分析仪（Cosmed K4b2）对静息能耗和八种常见体力活动能耗（全国中学生第三套广播体操运动、一分钟尽力跳绳、3～8km/h走跑运动）进行测试。
80名11岁～14岁的在校中学生，随机分成实验组（60人）和验证组（20人），命被试完成上述九种常见体力活动测试。采用逐步回归法建立以间接测热法（K4b2）实测值为因变量，以AC、年龄、性别、身高、体重等为自变量的能耗预测回归方程；通过ROC曲线的约登指数确定低、中、高运动强度的单轴（垂直轴）AC、VM2、VM3的切点，并通过ROC曲线下面积评估其对运动强度的归类能力。
66名在11岁～14岁的在校中学生，命被试完成上述静息能耗和八种常见体力活动能耗的测试。以间接测热法（Cosmed K4b2）为效标，验证心率加速度传感器（Actiheart）儿童Actiheart RA+HR预测方程的有效性和准确性；运用ROC曲线诊断心率和心率百分率区分运动强度的准确性，同时确定不同运动强度的心率和心率百分率切点。
研究结果
1青春期少年静息能量消耗高于成年人，其1MET均高于3.5ml/min/kg推荐值，REE最高是在较年轻的年龄段和那些处于生长发育较早阶段的人群，性别和年龄的差异只在较低年龄段出现。
2年龄对常见体力活动能耗（相对耗氧量、 AEE等）无显著影响；性别对常见体力活动能耗（相对耗氧量、AEE等）的影响较小；青春期发育阶段对走跑运动能耗（相对耗氧量、AEE等）有显著影响；肥胖对常见体力活动能耗（相对耗氧量、AEE）有显著影响。
3第三套中学生广播操，4km/h、5km/h、6km/h速度的步行运动均属于中等强度体力活动，METs分别为4.0、3.0、3.6、4.6；7km/h、8km/h跑的运动属于大强度运动，METs分别为6.3、6.9；3km/h慢走则属于低强度运动（METs=2.6）；一分钟跳绳属于剧烈运动（METs=14.0）。
4METs这个指标受年龄、性别、发育阶段、身体质量等差异的影响较小。
5以站立姿势为主的日常活动中，主要以垂直轴活动占优势；日常活动中垂直轴与VM2和VM3变化趋势相一致。
6以K4b2实测的EE或AEE的均值为因变量，以GT3X的三个轴的活动计数（ACxis1、ACxis2、ACxis3）/VM2/VM3为自变量，运用逐步回归法所获得的六个能耗预测方程均有效，方程4、方程5和方程6预测不同活动类型运动的能量消耗的准确性高（预测绝对误差为0.57～0.65kcal/min；相对误差为9.14～13.70%； r=0.591～0.700， P<0.01）。
7Actiheart儿童群体能耗预测方程对11～14岁青春期少年日常生活中的常见运动项目表现出较高的预测能力，预测准确性优于Actiheart RA和Actiheart HR，但对于日常活动中的完全静态的活动（预测绝对误差为1.57kcal/min；95.61%；r=0.154，P>0.05）、持续时间较短的单纯性的跳跃活动（预测绝对误差为5.20kcal/min；相对误差为22.52%；r=0.736，P<0.001）和极慢速的步行运动（预测绝对误差为0.55kcal/min；相对误差为23.09%；r=0.658，P<0.001）预测能力较差；对于静态活动的预测运用Actiheart HR公式可能更为合适。
8基于运动加速度计和心率的Actiheart预测方程能够较好的预测三种类型的日常综合活动的能量消耗（预测绝对误差为0.55～0.98kcal/min；相对误差为9.91～12.31%；r=0.832～0.854，P<0.001）。
9研究所获得的AC和HR百分率能很好地区分不同运动强度的体力活动，3METs、6METs和9METs所对应的垂直轴切点分别为2151counts/min、4935counts/min和9600counts/min； VM2切点分别为3761counts/min、6009counts/min和9900counts/min； VM3切点分别为3783counts/min、6169counts/min和10296counts/min；所对应的心率切点分别为125beat/min、159beat/min、172beat/min；所对应的HR%切点分别为54.75%、97.16%、118.79%。
结论
1METs是衡量体力活动运动强度的较理想指标；成年人活动纲要METs不适用于青春期少年。
2成年人能耗预测方程不适用于青春期少年能耗预测，本研究所建方程Y=-0.657+0.10522*W+5.06548E-4*ACxis1、 Y=-1.579+0.10531*W+6.54427E-4*VM2和Y=-1.471+0.10440*W+6.15209E-4*VM3可作为11-14岁青春期少年各类活动能耗的有效预测方程。
3Actiheart儿童群体能耗预测方程可以有效预测青春期少年常见运动项目和各种活动类型综合活动的能耗，但对于日常活动中的趋于静态的活动、持续时间短暂的单纯性跳跃活动和极慢速的步行等运动的预测能力较差。
411-14岁青春期少年体力活动监测中，可利用单轴（垂直轴）AC、VM2、VM3、心率和心率百分率切点区分低、中、高运动强度的体力活动。
Purpose
To research energy expenditure and to explode influence factor aboutenergy expenditure of the rest and command physical activity for11～14years old adolescents using the Cosmed K4b2portable indirect calorimetrysystem.
Base on calibration method of the Indirect Calorimetry (Cosmed K4b2),we attempt to establish and verify physical activity energy expenditurepredictive equation of triaxial accelerometer (ActiGraph GT3X) applying to11～14yr adolescents
We validate the validation and accuracy of the energy expenditurepredictive equation based on Actiheart Child: Group Act/Group HR bycomparing with the calibration method of the Indirect Calorimetry (CosmedK4b2).
We establish the cut-points of activity counts, HR and HR%todistinguish the exercise intensity, aiming to evaluate the common physicalactivity lever of adolescents in future.
Methods
Rest and nine activities energy cost including Third Series of NationalBroadcast Gymnastics for Middle School Students (Flourishing Youth), ropeskipping for one minute and walking/running at the speed of3～8km/h on atreadmill were monitored in120Junior middle school students,11～14yr,subjects need wear simultaneously the portable indirect calorimetry system(Cosmed K4b2), during the whole experiment.
The8011–14yr Junior middle school students were random divide toexperimental group (n=60) and validation group (n=20) by gender and age,and finished the upper nine common activities. Predictive energyexpenditure equation were established by stepwise regression analysisbasing on measured value of K4b2is dependent variable and AC, age,gender, height, mass were independent variable. We determine the ACcut-points of vertical axis, VM2and VM3for low, moderate, hard and veryhard excise intensity by the Youden value of ROC curve.
The samples are66Junior middle school students age11–14yr andrequired to finish the upper rest and nine common physical activityexperiments. The research aims to verify the veracity and accuracy ofActiheart predictive equation (Child: Group Act/Group HR) by calibrationmethod of indirect calorimetry (Cosmed K4b2). Under areas of ROC curvewere using to assessment the classifying ability of HR and HR%for exciseintensity. ROC curve were using to evaluate the dividing ability of HR andHR%, and to conduct the cut-points of HR and HR%for various sportintensity.
Results
1These data demonstrate that EE at rest, estimated as1.0MET(recommended value3.5ml/min/kg) in adults, is higher in children and young adolescents than in adults. The highest REE were found in theyounger children and in those at an earlier stage of physical development(lower developmental stage); the gender and age difference of the REE onlyshow in the lower developmental stage.
2The age element dose not effect significantly the common physicalactivity energy expenditure (relative oxygen uptake, AEE, etc); the gendereffects minor the common physical activity energy expenditure; thedevelopment stage of puberty is the significant influence factors to thewalking/running energy expenditure; the degree of obese impactssignificantly the energy expenditure of the common physical activity.
3The Third Series of National Broadcast Gymnastics for Middle SchoolStudents (Flourishing Youth) and the walking at speed4km/h,5km/h,6km/hare moderate PA, and METs values are4.0、3.0、3.6、4.6, respectively; therunning at7km/h and8km/h speed are hard PA and METs values are6.3、6.9,respectively; the walking at very slow pace belongs to low PA (METs=2.6);the best-effort rope skipping for one minute is very hard PA (METs=14.0).
4The index of METs isn’t influenced significantly by the effect factors ofage, gender, development stage, body mass, etc.
5Vertical axis activity count was priority to the stand posture daily physicalactivity and its variation trend is similar with that of VM2and VM3.
6The researcher based on dependent variable that is measured value EE orAEE mean by K4b2and the independent variable that (ACxis1、ACxis2、ACxis3)/VM2/VM3measured by GT3X acquainted six predictive energyexpenditure equations. The six predictive energy expenditure equations arevalid by stepwise regression method and apply to assess stand postureenergy expenditure for adolescents(11-14yr). The fourth， fifth and sixthpredictive equations show higher accuracy (absolute error=0.57～0.65kcal/min; relative error=9.14～13.70%； r=0.591～0.700， P<0.01) forevaluating various activity types.
7Actiheart child group energy expenditure predictive equation can validlypredict the common physical exercises EE for11～14yr adolescents and thepredictive accuracy are better than Actiheart RA and Actiheart HR, butshow poor predict veracity in sedentary activity(absoluteerror=1.57kcal/min； relative error=95.61%； r=0.154， P>0.05), pure jumpaction for short time (absolute error=5.20kcal/min； relative error=22.52%；r=0.736， P<0.001） and walking at dead slow speed(absoluteerror=0.55kcal/min； relative error=23.09%； r=0.658， P<0.001). TheActiheart HR predictive equation can valid assesses the sedentary activity.
8The Actiheart predictive equation based on RA and HR can valid forecastthe EE of various the daily integrated activity(absolute error=0.55～0.98kcal/min；relative error=9.91～12.31%；r=0.832～0.854，P<0.001), butcannot estimate validly the physical activity that AC equal proximity to0,for example sedentary activity.
9HR, HR%and AC were the valid index to divide the exercise intensity, HR cut-points for3METs,6METs, and9METs were125,159,172beat/minrespectively; HR%cut-points for3METs,6METs, and9METs were54.75%、97.16%、118.79%; AC cut-points of vertical axis for3METs,6METs, and9METs were2151,4935,9600counts/min; VM2and VM3for3METs,6METs, and9METs were respectively3761and3783,6009and6169,9900and10296counts/min.
Conclusions
1METs is the perfect indicator to evaluate the physical activity intensity;the METs values of activity compendium for adults were not fit theadolescents.
2The energy expenditure predictive equation for adults were not apply toadolescents; the equations (Y=-0.657+0.10522*W+5.06548E-4*ACxis1、Y=-1.579+0.10531*W+6.54427E-4*VM2和Y=-1.471+0.10440*W+6.15209E-4*VM3)were valid to estimate adolescents (11-14y)energyexpenditure concluding the daily activity type, the walk-run-jump activitytype, the irregular activity type and the walk-run activity type.
3The predictive equation of Actiheart child group energy expenditure canpredict effectively the energy expenditure of the daily activity type and theapparatus daily common physical exercise, but the accuracy of the equationis poor to evaluate the energy cost of sedentary activity, pure jump actionfor short time and walking at dead slow speed.
4The researcher can apply the HR cut-points, the HR%cut-points and theAC cut-points of uniaxial, biaxial, triaxial to distinguish the low, moderate,hard PA during monitoring the physical activity level for11-14year-oldadolescents.
运用间接测热法（Cosmed K4b2）研究11-14岁青春期少年静息能量消耗和常见体力活动能量消耗，探讨影响青春期少年静息和运动能量消耗的因素。
以间接测热法（Cosmed K4b2）为效标，建立适合于11-14岁青春期少年的三轴运动加速度计（ActiGraph GT3X）的体力活动能耗预测方程，并进行验证。
以间接测热法（Cosmed K4b2）为效标，验证Actiheart Child: GroupAct/Group HR（Actiheart RA+HR）联合能耗预测方程的有效性。
确立不同运动强度下的活动计数、心率和心率百分率切点，便于今后对青少年常见体力活动水平进行评估。
研究方法
随机抽取120名11-14岁在校中学生，佩戴便携式气体代谢分析仪（Cosmed K4b2）对静息能耗和八种常见体力活动能耗（全国中学生第三套广播体操运动、一分钟尽力跳绳、3～8km/h走跑运动）进行测试。
80名11岁～14岁的在校中学生，随机分成实验组（60人）和验证组（20人），命被试完成上述九种常见体力活动测试。采用逐步回归法建立以间接测热法（K4b2）实测值为因变量，以AC、年龄、性别、身高、体重等为自变量的能耗预测回归方程；通过ROC曲线的约登指数确定低、中、高运动强度的单轴（垂直轴）AC、VM2、VM3的切点，并通过ROC曲线下面积评估其对运动强度的归类能力。
66名在11岁～14岁的在校中学生，命被试完成上述静息能耗和八种常见体力活动能耗的测试。以间接测热法（Cosmed K4b2）为效标，验证心率加速度传感器（Actiheart）儿童Actiheart RA+HR预测方程的有效性和准确性；运用ROC曲线诊断心率和心率百分率区分运动强度的准确性，同时确定不同运动强度的心率和心率百分率切点。
研究结果
1青春期少年静息能量消耗高于成年人，其1MET均高于3.5ml/min/kg推荐值，REE最高是在较年轻的年龄段和那些处于生长发育较早阶段的人群，性别和年龄的差异只在较低年龄段出现。
2年龄对常见体力活动能耗（相对耗氧量、 AEE等）无显著影响；性别对常见体力活动能耗（相对耗氧量、AEE等）的影响较小；青春期发育阶段对走跑运动能耗（相对耗氧量、AEE等）有显著影响；肥胖对常见体力活动能耗（相对耗氧量、AEE）有显著影响。
3第三套中学生广播操，4km/h、5km/h、6km/h速度的步行运动均属于中等强度体力活动，METs分别为4.0、3.0、3.6、4.6；7km/h、8km/h跑的运动属于大强度运动，METs分别为6.3、6.9；3km/h慢走则属于低强度运动（METs=2.6）；一分钟跳绳属于剧烈运动（METs=14.0）。
4METs这个指标受年龄、性别、发育阶段、身体质量等差异的影响较小。
5以站立姿势为主的日常活动中，主要以垂直轴活动占优势；日常活动中垂直轴与VM2和VM3变化趋势相一致。
6以K4b2实测的EE或AEE的均值为因变量，以GT3X的三个轴的活动计数（ACxis1、ACxis2、ACxis3）/VM2/VM3为自变量，运用逐步回归法所获得的六个能耗预测方程均有效，方程4、方程5和方程6预测不同活动类型运动的能量消耗的准确性高（预测绝对误差为0.57～0.65kcal/min；相对误差为9.14～13.70%； r=0.591～0.700， P<0.01）。
7Actiheart儿童群体能耗预测方程对11～14岁青春期少年日常生活中的常见运动项目表现出较高的预测能力，预测准确性优于Actiheart RA和Actiheart HR，但对于日常活动中的完全静态的活动（预测绝对误差为1.57kcal/min；95.61%；r=0.154，P>0.05）、持续时间较短的单纯性的跳跃活动（预测绝对误差为5.20kcal/min；相对误差为22.52%；r=0.736，P<0.001）和极慢速的步行运动（预测绝对误差为0.55kcal/min；相对误差为23.09%；r=0.658，P<0.001）预测能力较差；对于静态活动的预测运用Actiheart HR公式可能更为合适。
8基于运动加速度计和心率的Actiheart预测方程能够较好的预测三种类型的日常综合活动的能量消耗（预测绝对误差为0.55～0.98kcal/min；相对误差为9.91～12.31%；r=0.832～0.854，P<0.001）。
9研究所获得的AC和HR百分率能很好地区分不同运动强度的体力活动，3METs、6METs和9METs所对应的垂直轴切点分别为2151counts/min、4935counts/min和9600counts/min； VM2切点分别为3761counts/min、6009counts/min和9900counts/min； VM3切点分别为3783counts/min、6169counts/min和10296counts/min；所对应的心率切点分别为125beat/min、159beat/min、172beat/min；所对应的HR%切点分别为54.75%、97.16%、118.79%。
结论
1METs是衡量体力活动运动强度的较理想指标；成年人活动纲要METs不适用于青春期少年。
2成年人能耗预测方程不适用于青春期少年能耗预测，本研究所建方程Y=-0.657+0.10522*W+5.06548E-4*ACxis1、 Y=-1.579+0.10531*W+6.54427E-4*VM2和Y=-1.471+0.10440*W+6.15209E-4*VM3可作为11-14岁青春期少年各类活动能耗的有效预测方程。
3Actiheart儿童群体能耗预测方程可以有效预测青春期少年常见运动项目和各种活动类型综合活动的能耗，但对于日常活动中的趋于静态的活动、持续时间短暂的单纯性跳跃活动和极慢速的步行等运动的预测能力较差。
411-14岁青春期少年体力活动监测中，可利用单轴（垂直轴）AC、VM2、VM3、心率和心率百分率切点区分低、中、高运动强度的体力活动。
Purpose
To research energy expenditure and to explode influence factor aboutenergy expenditure of the rest and command physical activity for11～14years old adolescents using the Cosmed K4b2portable indirect calorimetrysystem.
Base on calibration method of the Indirect Calorimetry (Cosmed K4b2),we attempt to establish and verify physical activity energy expenditurepredictive equation of triaxial accelerometer (ActiGraph GT3X) applying to11～14yr adolescents
We validate the validation and accuracy of the energy expenditurepredictive equation based on Actiheart Child: Group Act/Group HR bycomparing with the calibration method of the Indirect Calorimetry (CosmedK4b2).
We establish the cut-points of activity counts, HR and HR%todistinguish the exercise intensity, aiming to evaluate the common physicalactivity lever of adolescents in future.
Methods
Rest and nine activities energy cost including Third Series of NationalBroadcast Gymnastics for Middle School Students (Flourishing Youth), ropeskipping for one minute and walking/running at the speed of3～8km/h on atreadmill were monitored in120Junior middle school students,11～14yr,subjects need wear simultaneously the portable indirect calorimetry system(Cosmed K4b2), during the whole experiment.
The8011–14yr Junior middle school students were random divide toexperimental group (n=60) and validation group (n=20) by gender and age,and finished the upper nine common activities. Predictive energyexpenditure equation were established by stepwise regression analysisbasing on measured value of K4b2is dependent variable and AC, age,gender, height, mass were independent variable. We determine the ACcut-points of vertical axis, VM2and VM3for low, moderate, hard and veryhard excise intensity by the Youden value of ROC curve.
The samples are66Junior middle school students age11–14yr andrequired to finish the upper rest and nine common physical activityexperiments. The research aims to verify the veracity and accuracy ofActiheart predictive equation (Child: Group Act/Group HR) by calibrationmethod of indirect calorimetry (Cosmed K4b2). Under areas of ROC curvewere using to assessment the classifying ability of HR and HR%for exciseintensity. ROC curve were using to evaluate the dividing ability of HR andHR%, and to conduct the cut-points of HR and HR%for various sportintensity.
Results
1These data demonstrate that EE at rest, estimated as1.0MET(recommended value3.5ml/min/kg) in adults, is higher in children and young adolescents than in adults. The highest REE were found in theyounger children and in those at an earlier stage of physical development(lower developmental stage); the gender and age difference of the REE onlyshow in the lower developmental stage.
2The age element dose not effect significantly the common physicalactivity energy expenditure (relative oxygen uptake, AEE, etc); the gendereffects minor the common physical activity energy expenditure; thedevelopment stage of puberty is the significant influence factors to thewalking/running energy expenditure; the degree of obese impactssignificantly the energy expenditure of the common physical activity.
3The Third Series of National Broadcast Gymnastics for Middle SchoolStudents (Flourishing Youth) and the walking at speed4km/h,5km/h,6km/hare moderate PA, and METs values are4.0、3.0、3.6、4.6, respectively; therunning at7km/h and8km/h speed are hard PA and METs values are6.3、6.9,respectively; the walking at very slow pace belongs to low PA (METs=2.6);the best-effort rope skipping for one minute is very hard PA (METs=14.0).
4The index of METs isn’t influenced significantly by the effect factors ofage, gender, development stage, body mass, etc.
5Vertical axis activity count was priority to the stand posture daily physicalactivity and its variation trend is similar with that of VM2and VM3.
6The researcher based on dependent variable that is measured value EE orAEE mean by K4b2and the independent variable that (ACxis1、ACxis2、ACxis3)/VM2/VM3measured by GT3X acquainted six predictive energyexpenditure equations. The six predictive energy expenditure equations arevalid by stepwise regression method and apply to assess stand postureenergy expenditure for adolescents(11-14yr). The fourth， fifth and sixthpredictive equations show higher accuracy (absolute error=0.57～0.65kcal/min; relative error=9.14～13.70%； r=0.591～0.700， P<0.01) forevaluating various activity types.
7Actiheart child group energy expenditure predictive equation can validlypredict the common physical exercises EE for11～14yr adolescents and thepredictive accuracy are better than Actiheart RA and Actiheart HR, butshow poor predict veracity in sedentary activity(absoluteerror=1.57kcal/min； relative error=95.61%； r=0.154， P>0.05), pure jumpaction for short time (absolute error=5.20kcal/min； relative error=22.52%；r=0.736， P<0.001） and walking at dead slow speed(absoluteerror=0.55kcal/min； relative error=23.09%； r=0.658， P<0.001). TheActiheart HR predictive equation can valid assesses the sedentary activity.
8The Actiheart predictive equation based on RA and HR can valid forecastthe EE of various the daily integrated activity(absolute error=0.55～0.98kcal/min；relative error=9.91～12.31%；r=0.832～0.854，P<0.001), butcannot estimate validly the physical activity that AC equal proximity to0,for example sedentary activity.
9HR, HR%and AC were the valid index to divide the exercise intensity, HR cut-points for3METs,6METs, and9METs were125,159,172beat/minrespectively; HR%cut-points for3METs,6METs, and9METs were54.75%、97.16%、118.79%; AC cut-points of vertical axis for3METs,6METs, and9METs were2151,4935,9600counts/min; VM2and VM3for3METs,6METs, and9METs were respectively3761and3783,6009and6169,9900and10296counts/min.
Conclusions
1METs is the perfect indicator to evaluate the physical activity intensity;the METs values of activity compendium for adults were not fit theadolescents.
2The energy expenditure predictive equation for adults were not apply toadolescents; the equations (Y=-0.657+0.10522*W+5.06548E-4*ACxis1、Y=-1.579+0.10531*W+6.54427E-4*VM2和Y=-1.471+0.10440*W+6.15209E-4*VM3)were valid to estimate adolescents (11-14y)energyexpenditure concluding the daily activity type, the walk-run-jump activitytype, the irregular activity type and the walk-run activity type.
3The predictive equation of Actiheart child group energy expenditure canpredict effectively the energy expenditure of the daily activity type and theapparatus daily common physical exercise, but the accuracy of the equationis poor to evaluate the energy cost of sedentary activity, pure jump actionfor short time and walking at dead slow speed.
4The researcher can apply the HR cut-points, the HR%cut-points and theAC cut-points of uniaxial, biaxial, triaxial to distinguish the low, moderate,hard PA during monitoring the physical activity level for11-14year-oldadolescents.
引文
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