基于步态触觉参数的青少年运动能耗估算公式的建立

目的:建立基于步态触觉参数的青少年体力活动估算公式,为能耗评估方法和青少年健康促进的研究提供参考。方法:11～14岁青少年为受试者,测量其身体基本数据(身高、体重、腿长、体脂率)后,穿着课题组自主研发的数字化跑鞋在可变速跑台上,分别以3、4、5、6、7、8 km/h的速度进行走或跑步运动,同时采用K4b2心肺功能测试仪采集数据,测定运动能量消耗。根据数字化跑鞋采集的运动足底压力和加速度信号参数测算步频,推算步频值与运动能量消耗的关联,再综合个体参数,采用多元逐步回归建立运动能耗代谢公式,最后将公式带入事先抽出、未参与建模的数据进行验证。结果:(1)步频值和单位体重运动能耗的代谢率的皮尔森指数为0.86,有显著性差异;两者之间线性拟合与二次曲线拟合的R2值均约为0.73。(2)通过多元逐步回归得到了基于青少年运动步频和身体参数的单位体重运动能耗代谢率的估算公式:AEE=1.913×SF-2.079×BMI+10.755×Sex-4.211×Age-42.312(R2=0.75),其中,AEE:每公斤体重每分钟运动消耗的能量,单位为cal/min/kg;SF:步频,单位为steps/min;BMI单位为kg/m2;Sex:性别,男生=1,女生=0;Age:年龄,单位为周岁。通过验证,该公式准确度为83%。结论:基于生物力学步态触觉参数得到的步频值和身体参数可较准确地估测初中阶段青少年的运动能量代谢量,并得到运动代谢计算公式,采用步态触觉参数评估能耗代谢有良好的应用发展前景。     

8种市售运动传感装置计步功能的效度研究

目的:评价国内8种市售不同品牌运动传感装置在不同运动条件下计步功能的准确性和变异性。方法:39名23～64岁健康受试者,同时在腹侧及背侧腰间佩戴8种不同品牌的运动传感装置,在室外100米塑胶跑道上,依照拟合的步频,先后进行慢走(90步/min)、快走(120步/min)、慢跑(150步/min)和中速跑(180步/min),人工记录实际步数、距离,并记录各传感装置的步数测量值。结果:各种装置计步功能的准确性均随步频升高而升高,而在接近自然步频的快走和慢跑时普遍更好,分别有6种和7种装置80%以上的测量值在-5～5%的误差范围内。结论:8种市售运动传感装置估计步数的准确性随步频增加而升高,接近自然步频的快走(120步/min)和慢跑(150步/min)时,各装置计步准确性普遍较好。     

养生无处不在

<正>坚持锻炼身体俗话说:"生命在于运动。"专家建议老人坚持每天运动30分钟,这样可以使心脏病风险降低一半。锻炼的方法有很多种,如慢跑、骑自行车、做保健操等。当然,有些老年人因年龄大体质弱、有慢性病,不适合做难度大、比较复杂的活动,可以根据自己的生理特点、身体状况,选择适合自己的锻炼项目,如打太极拳、练气功、慢走等。在锻炼的     

11～16岁肥胖青少年体力活动耗氧量推算方法实验研究

目的:采用多水平线性模型分析方法建立适用于肥胖青少年的耗氧量推算方程,为测量体力活动能量消耗提供简便有效的方法。方法:以60名11~16岁年龄段的肥胖青少年为研究对象,随机分为公式组(40名)和回代验证组(20名),男女各半。公式组在坡度为0的跑台上以2km/h、3km/h、4km/h、5km/h、6km/h、7km/h、8km/h的速度运动,每级负荷持续2.5分钟后继续增加,以心率达到80%HRmax为运动终止标准。采用气体代谢系统连续测量运动过程中耗氧量和心率,建立耗氧量多元线性回归方程,并进行回代验证。结果:所建回归方程R2男生为0.795,女生为0.75;耗氧量与负荷心率、瘦体重(LBM)具有线性关系(P<0.001);自变量系数及常数项均有极显著意义,得回归方程为男:耗氧量(ml/min)=20.098×负荷心率(bmp)+25.160×瘦体重(kg)-2935.835;女:耗氧量(ml/min)=19.863×负荷心率(bmp)+26.551×瘦体重(kg)-2675.676。回代验证组耗氧量预测值与实测值的平均相对误差男生为6.84%～8.72%,女生为6.53%～8.23%。结论:以负荷心率和瘦体重为自变量建立耗氧量的多元回归方程,可应用于肥胖青少年体力活动耗氧量的推算。     

上海城区部分轻体力劳动者能量摄入和体力活动关系研究

目的:探讨上海城区部分轻体力劳动者能量摄入和体力活动关系。方法:选取上海市中心城区部分轻体力劳动者52名,其中男性24名,女性28名,年龄40±12岁。全部实验持续5天。第1天,清晨空腹状态下,测定受试者静息代谢率(RMR),进行医学检查和身体基本形态、机能指标(身高、体重和身体成分)测定,采血检测葡萄糖、甘油三酯、总胆固醇、高密度脂蛋白和低密度脂蛋白。第2天起至第4天,连续3天分别填写24小时活动记录表(AR)和饮食记录表(DR),第5天回收表格并依此计算受试者的能量消耗和能量摄入。结果:(1)受试对象能量摄入大于能量消耗。(2)能量摄入及能量正平衡(PB)与血液相关指标无相关。(3)能量摄入和体力活动水平存在线性相关(r=0.80,P=0.0001),在控制年龄、性别、BMI等因素后,仍然存在显著相关(r=0.77,P=0.00001)。PB与体力活动水平(PAL)呈显著负相关(r=-0.66,P=0.0001)。结论:(1)上海城区轻体力劳动者饮食结构不合理,存在能量正平衡现象。(2)一定的生活方式可能会导致摄入增加,同时也可能促进体力活动的增加。     

空降兵部队新兵伞训期间的营养调查

目的 调查空降兵部队新兵伞训期间膳食营养状况,探讨营养保障问题。方法 采用生活观察法、记帐法计算新兵伞训时每人每日热能消耗量、体力活动时间,结合实验室检查结果评定新兵营养状况。结果 每人每日的热能消耗量为17.04±1.31MJ,全训日净体力活动时间560.2±48.2 min。每人每日能量摄入为16.4MJ,不能满足需要。蛋白质摄入量偏低,膳食结构不合理。训练强度大、时间长,体重减轻2kg以上者占78％。维生素、无机盐摄入量不足,视黄醇当量不足者占54％,核黄素缺乏症12％,抗坏血酸缺乏症占36％。实验室检查,血清总蛋白、白蛋白、血红蛋白、血清钙普遍降低。结论 新兵伞训期间热能摄入不足,膳食结构不合理,蛋白质、维生素偏低,造成体重下降。应改进营养保障,合理平衡膳食,调整营养供给量。     

12～14岁正常和肥胖男性青少年运动能耗的比较研究

目的:分析比较肥胖和正常青少年静息能量消耗(RMR)和进行相同运动时的耗氧量、代谢当量(MET)及自觉疲劳感觉(RPE)。方法:通过采集身高、体重等信息,抽取12～14岁肥胖和正常青少年各15名,在2天内采用Cosmed K4b2便携式气体代谢分析仪测试被试完成两部分测试过程中的氧耗量。第一部分是在餐后2小时以上,先测试30分钟静息能量消耗,随后测试第三套全国中学生广播体操(舞动青春)的能量消耗。第二部分在餐后1小时以上,在实验室分别进行3、4、5、6、7、8km/h的走或跑递增跑台运动,每级5分钟,中间无间断。采用RPE分级表,在广播体操运动后即刻和每级跑台运动结束即下一速度开始加速前,记录被试相应的RPE值。计算上述各项运动MET。结果:肥胖和正常青少年1 MET分别为3.88 ml/min/kg和5.45 ml/min/kg。肥胖青少年在进行广播体操和3 km/h、4 km/h、5 km/h、6 km/h、7 km/h、8 km/h跑台运动时的总能量消耗、MET值均高于正常青少年;排除体重影响,肥胖青少年在进行广播体操和3 km/h、4 km/h、5 km/h、6 km/h、7 km/h、8 km/h跑台运动时单位体重能量消耗低于正常青少年。肥胖(r=0.963,P<0.01)和正常(r=0.986,P<0.001)青少年RPE和MET值呈高度相关。结论:正常青少年静息能量消耗(以ml/min/kg表示)高于肥胖青少年。肥胖和正常青少年从事相同定量负荷运动时,运动能耗和代谢当量不同。RPE可以作为反映青少年运动强度的有效指标。     

9～11岁肥胖小学生体力活动与膳食热量摄入的抽样研究

目的:对比9~11岁肥胖、超重和正常体重小学生自然条件下体力活动情况和膳食摄入的差异,探寻引起小学生发生肥胖的因素。方法:北京市、武汉市三所小学高年级9~11岁小学生中肥胖、超重儿童共57名为研究对象,正常体重的同性别同班级儿童为对照。测定受试者的身高、体重、体脂百分比、1周体力活动能耗,计算各组受试者日均体力活动能量消耗(PAEE)、每时体力活动能量消耗、代谢当量(MET)、日均步数和平均每分钟计步数等数据;采用膳食调查法,记录受试者1周的饮食情况,根据中国食物成分表计算日均能量摄入、日均热能营养素摄入量和三餐热能占每日热能摄入的百分比。结果:1周体力活动监测发现,肥胖、超重小学生日均PAEE显著高于正常体重组小学生(P<0.05),但日均每公斤体重能量消耗(PAEE/W)、MET、日均步数各组小学生无显著差异。选取小学生在校日和休假日分别分析,结果与1周体力活动监测反映的情况基本相同,但各组小学生休假日各项反映体力活动水平的数据较在校日有所下降。肥胖组小学生日均膳食能量摄入量显著高于超重组、正常体重组(P<0.05),晚餐能量摄入量占日膳食能量摄入量的百分比显著高于正常体重组,早餐、午餐能量摄入量所占百分比显著低于正常体重组(P<0.05),日均摄入的热能营养素(糖类、脂类、蛋白质)的绝对量均高于正常体重组,但其中只有脂类热量摄入量占总热量摄入量的百分比显著高于正常体重组(P<0.05)。结论:早餐热能摄入量所占比例较低,而晚餐热能摄入量所占比例过高;热能营养素绝对摄入量高,且比例不合理,脂类的摄入量过高;休假日体力活动水平下降可能是生长发育期儿童发生肥胖的因素。     

加速度传感器能耗预测模型的建立与验证北大核心CSCD

目的:根据不同思路建立Actigraph加速度传感器能耗预测模型并加以验证,为更好地应用加速度传感器监测体力活动提供依据。方法:以男女大学生各30人为实验组,进行静坐、看书、整理书桌、扫地、慢走(4 km/h)、快走(6 km/h)和慢跑(8 km/h)共7项活动;另以男女大学生各10人作为验证组,进行约4 h日常体力活动。分别以Actigraph GT3X加速度传感器和Cosmed K4b2气体代谢分析仪监测垂直轴加速度记数(accelerometry counts,AC)和能耗。采用线性回归法,根据实验组数据建立3个以AC为自变量的能耗预测模型。以验证组数据,采用配对t检验和Altman-Bland图验证上述3个模型和Freedson模型的有效性。结果:本研究建立的分段线性模型为:如AC<1630 counts/min,则METs=1.419+0.005644×AC-5.927×10-6×AC2+1.993×10-9×AC3;如AC≥1630 counts/min,则METs=1.818+0.000752×AC。经验证,使用Actigraph加速度传感器监测日常体力活动时,可应用上述模型推算体力活动总能耗。     

绝经女性体力活动特征及其与骨量关系的研究

目的:观察1年健骨操运动前后绝经女性日常活动特征、能量消耗、骨密度、体成分及肌力变化特点,探讨绝经女性体力活动与机体骨量变化的关系。方法:以47名骨量低下的绝经女性为志愿研究对象,分为运动组(EG组)和非运动组(OG组),EG组进行1年60min/天、3～4天/周的中等强度健骨操运动。分别对实验1年前后两组受试者各项指标进行测定:以DXA(GE Lunar Prodigy,美国)测定主要部位骨密度(BMD)和体成分,以加速度计(OMRON hja-350it,日本)测定能量消耗,以等速肌力仪(ISOMED 2000,德国)测定肌肉力量。采用SPSS16.0统计分析数据。结果:EG组运动1年后,步行活动能耗(包括健骨操运动能耗)显著高于OG组,视为有效体力活动能耗。与OG组相比,EG组活动步数、步行时间及3～6 MET活动时间均显著升高,体重、BMI及体成分均发生了积极改变,各主要部位BMD均呈升高趋势,左股骨颈BMD显著升高。EG组运动1年后肌肉力量发生显著变化,其中髋等速90°伸、180°伸、180°屈及腰等速120°屈肌力均较1年前明显升高,实验1年后EG组髋等速180°伸、180°屈及腰等速120°伸肌力均显著高于OG组。结论:健骨操运动(有氧耐力、抗阻、冲击性练习等)通过增加机体能耗,改善体成分,增加肌肉力量,进而增加对骨骼的力学负荷刺激,是绝经女性改善和维持骨量的适宜运动。     

Predictive validity of three ActiGraph energy expenditure equations for children

PURPOSE: This study evaluated the predictive validity of three previously published ActiGraph energy expenditure (EE) prediction equations developed for children and adolescents. METHODS: A total of 45 healthy children and adolescents (mean age: 13.7 +/- 2.6 yr) completed four 5-min activity trials (normal walking, brisk walking, easy running, and fast running) in an indoor exercise facility. During each trial, participants wore an ActiGraph accelerometer on the right hip. EE was monitored breath by breath using the Cosmed K4b portable indirect calorimetry system. Differences and associations between measured and predicted EE were assessed using dependent t-tests and Pearson correlations, respectively. Classification accuracy was assessed using percent agreement, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve. RESULTS: None of the equations accurately predicted mean energy expenditure during each of the four activity trials. Each equation, however, accurately predicted mean EE in at least one activity trial. The Puyau equation accurately predicted EE during slow walking. The Trost equation accurately predicted EE during slow running. The Freedson equation accurately predicted EE during fast running. None of the three equations accurately predicted EE during brisk walking. The equations exhibited fair to excellent classification accuracy with respect to activity intensity, with the Trost equation exhibiting the highest classification accuracy and the Puyau equation exhibiting the lowest. CONCLUSIONS: These data suggest that the three accelerometer prediction equations do not accurately predict EE on a minute-by-minute basis in children and adolescents during overground walking and running. The equations maybe useful, however, for estimating participation in moderate and vigorous activity.     

Blood lipid and lipoprotein adaptations to exercise: a quantitative analysis.

Dose-response relationships between exercise training volume and blood lipid changes suggest that exercise can favourably alter blood lipids at low training volumes, although the effects may not be observable until certain exercise thresholds are met. The thresholds established from cross-sectional literature occur at training volumes of 24 to 32 km (15 to 20 miles) per week of brisk walking or jogging and elicit between 1200 to 2200 kcal/wk. This range of weekly energy expenditure is associated with 2 to 3 mg/dl increases in high-density lipoprotein-cholesterol (HDL-C) and triglyceride (TG) reductions of 8 to 20 mg/dl. Evidence from cross-sectional studies indicates that greater changes in HDL-C levels can be expected with additional increases in exercise training volume. HDL-C and TG changes are often observed after training regimens requiring energy expenditures similar to those characterised from cross-sectional data. Training programmes that elicit 1200 to 2200 kcal/wk in exercise are often effective at elevating HDL-C levels from 2 to 8 mg/dl, and lowering TG levels by 5 to 38 mg/dl. Exercise training seldom alters total cholesterol (TC) and low-density lipoprotein-cholesterol (LDL-C). However, this range of weekly exercise energy expenditure is also associated with TC and LDL-C reductions when they are reported. The frequency and extent to which most of these lipid changes are reported are similar in both genders, with the exception of TG. Thus, for most individuals, the positive effects of regular exercise are exerted on blood lipids at low training volumes and accrue so that noticeable differences frequently occur with weekly energy expenditures of 1200 to 2200 kcal/wk. It appears that weekly exercise caloric expenditures that meet or exceed the higher end of this range are more likely to produce the desired lipid changes. This amount of physical activity, performed at moderate intensities, is reasonable and attainable for most individuals and is within the American College of Sports Medicine's currently recommended range for healthy adults.     

Field evaluation of energy expenditure in women using Tritrac accelerometers

PURPOSE: To investigate the use of Tritrac accelerometers to measure energy expenditure (EE) of various activities for women in the field setting, as compared with portable indirect calorimetry. METHODS: Twenty women (age 20-29) performed a choreographed routine of six activities (walking, jogging, stair climbing, walking on an incline, stationary cycling, and arm ergometry) while wearing a Tritrac-R3D accelerometer (Hemokinetics Inc., Madison WI) and the Cosmed K4b(2) portable metabolic cart (Cosmed, Rome, Italy). RESULTS: Comparing the mean error scores (K4b(2) - Tritrac), the Tritrac overestimated the EE (kcal x min(-1)) of walking (-1.45) and jogging (-1.75), whereas underestimating the EE of stair climbing (2.76), stationary cycling (2.75), and arm ergometry (1.20). Walking on an incline showed the lowest mean error score (-0.11). Intraclass correlations were moderate for walking (r = 0.568, < 0.05), jogging (r = 0.666, < 0.05), and stairs (r = 0.503, < 0.05) but for the other activities ranged from r = 0.290 ( > 0.05) to r = 0.480 ( < 0.05). The raw data from the Tritrac was applied to a previously developed nonlinear model to adjust the Tritrac scores to the standard of whole-room indirect calorimetry. This resulted in statistically significant improvements in the agreement between the adjusted Tritrac value and the K4b for walking, jogging, and walking on an incline ( < 0.05). CONCLUSION: When compared with portable indirect calorimetry, the Tritrac overestimates the EE of walking and jogging, whereas underestimating that of stair climbing, stationary cycling, and arm ergometry. This limits the use of such a technique to measure EE in the field. The main issues appear to be the type and intensity of the activity and the need for movement in order for the Tritrac to register EE. Activity specific linear regression equations are proposed as a tool to improve the measurement of EE using the Tritrac in the field.     

吸烟大学生体力活动与烟草依赖相关分析

目的:分析大学生体力活动水平与烟草依赖性之间的相关性,探讨不同体力活动水平对大学生烟草依赖性的影响.方法:以133名吸烟男性大学生为研究对象,运用国际体力活动问卷(IPAQ)和尼古丁依赖检验量表(FTND)等测量工具,分别评估体力活动水平和烟草依赖程度.采用Spearman相关分析的方法检验体力活动与烟草依赖的相关性,...     

上海市11-16岁青少年体力活动能量消耗参考值研究

目的:建立上海市青少年体力活动能量消耗的参考值。方法:以上海市11-16岁青少年为调查对象,进行体质健康项目测试,并进行体力活动问卷调查。在BMI正常、肺活量和耐力、柔韧力量、速度灵巧项目在及格以上的青少年中,按每2岁一个年龄段分组,每组随机选取男女各200人,用统计学方法分别建立中等强度和大强度体力活动能量消耗的95%置信区间;每年龄组再随机选取男女各20人进行回代检验。结果:11-12岁、13-14岁、15-16岁中等强度体力活动能量消耗(kcal/w)95%置信区间分别为:男生971.19-1271.58、1081.16～1492.94、1324.56-1712.37,女生1139.82-1437.37、1046.69-1398.77、1113.15-1427.22;大强度体力活动能量消耗(kcal/w)95%置信区间分别为:男生1167.75-1486.57、1226.24-1715.26、1117.23-1453.33,女生843.53～1141.61、676.01-1006.54、1142.53-1392.51。回代检验时,各年龄组有75%-87%的对象在所建立的95%置信区间内。结论:初步建立了上海市11-16岁青少年中等强度和大强度体力活动能量消耗的参考值。     

Classifying household and locomotive activities using a triaxial accelerometer

The purpose of this study was to develop a new algorithm for classifying physical activity into either locomotive or household activities using a triaxial accelerometer. Sixty-six volunteers (31 men and 35 women) participated in this study and were separated randomly into validation and cross-validation groups. All subjects performed 12 physical activities (personal computer work, laundry, dishwashing, moving a small load, vacuuming, slow walking, normal walking, brisk walking, normal walking while carrying a bag, jogging, ascending stairs and descending stairs) while wearing a triaxial accelerometer in a controlled laboratory setting. Each of the three signals from the triaxial accelerometer was passed through a second-order Butterworth high-pass filter to remove the gravitational acceleration component from the signal. The cut-off frequency was set at 0.7 Hz based on frequency analysis of the movements conducted. The ratios of unfiltered to filtered total acceleration (TAU/TAF) and filtered vertical to horizontal acceleration (VAF/HAF) were calculated to determine the cut-off value for classification of household and locomotive activities. When the TAU/TAF discrimination cut-off value derived from the validation group was applied to the cross-validation group, the average percentage of correct discrimination was 98.7%. When the VAF/HAF value similarly derived was applied to the cross-validation group, there was relatively high accuracy but the lowest percentage of correct discrimination was 63.6% (moving a small load). These findings suggest that our new algorithm using the TAU/TAF cut-off value can accurately classify household and locomotive activities.     

Energy expenditure during walking and jogging

BACKGROUND: The aim of this investigation was to compare the physiological and subjective responses during treadmill walking and jogging at several corresponding speeds in physically active young women. METHODS: Experimental design: Maximal oxygen uptake was determined during a continuous treadmill test to exhaustion. The walking protocol consisted of treadmill walking for five min at each of the following speeds: 4.0, 5.6, 7.2, 8.0, 8.8, 9.6 and 10.4 km.hr(-1). The jogging protocol consisted of treadmill walking for five min at 4.0, and 5.6 km.hr(-1) and treadmill jogging for five min at each of the following speeds: 7.2, 8.0, 8.8, 9.6 and 10.4 km.hr(-1). Setting: This research was performed at Washington University School of Medicine. Participants: Fifteen healthy women (mean+/-SE, age; 26.9+/-1.4 yrs, BMI; 22.5+/-0.70, VO2max; 41.9+/-1.9 ml.hr(-1).min(-1)) performed a maximal treadmill exercise test, a walking test and a jogging test. RESULTS: The rate of oxygen consumption, calculated energy expenditure per distance (kJ.hr(-1).mile(-1)) and heart rates were significantly higher during walking compared to jogging at treadmill speeds > or =8.8 km.hr(-1). Plasma lactate concentration and respiratory exchange ratio were significantly higher at treadmill speeds > or =8.0 km.hr(-1) during walking as compared to jogging. Subjects subjectively rated their exertion during walking as being significantly greater when compared to jogging across the range of overlapping treadmill speeds. CONCLUSIONS: These findings demonstrated that walking at speeds > or =8.0 km.hr(-1) resulted in rates of energy expenditure that were as high or higher than jogging at the same speeds. Also, the higher rates of energy expenditure during walking as compared to jogging at speeds greater than 8.0 km.hr(-1) were associated with higher heart rates, RER, RPE and plasma lactate response.     

Validation of SenseWear Armband in children,adolescents, and adults

SenseWear Armband (SW) is a multisensor monitor to assess physical activity and energy expenditure. Its prediction algorithms have been updated periodically. The aim was to validate SW in children, adolescents, and adults. The most recent SW algorithm 5.2 (SW5.2) and the previous version 2.2 (SW2.2) were evaluated for estimation of energy expenditure during semi‐structured activities in 35 children, 31 adolescents, and 36 adults with indirect calorimetry as reference. Energy expenditure estimated from waist‐worn ActiGraph GT3X+ data (AG) was used for comparison. Improvements in measurement errors were demonstrated with SW5.2 compared to SW2.2, especially in children and for biking. The overall mean absolute percent error with SW5.2 was 24% in children, 23% in adolescents, and 20% in adults. The error was larger for sitting and standing (23%‐32%) and for basketball and biking (19%‐35%), compared to walking and running (8%‐20%). The overall mean absolute error with AG was 28% in children, 22% in adolescents, and 28% in adults. The absolute percent error for biking was 32%‐74% with AG. In general, SW and AG underestimated energy expenditure. However, both methods demonstrated a proportional bias, with increasing underestimation for increasing energy expenditure level, in addition to the large individual error. SW provides measures of energy expenditure level with similar accuracy in children, adolescents, and adults with the improvements in the updated algorithms. Although SW captures biking better than AG, these methods share remaining measurements errors requiring further improvements for accurate measures of physical activity and energy expenditure in clinical and epidemiological research.     

Aspects of activity behavior as a determinant of the physical activity level

This study investigated which aspects of the individuals' activity behavior determine the physical activity level (PAL). Habitual physical activity of 20 Dutch adults (age: 26–60 years, body mass index: 24.5±2.7 kg/m²) was measured using a tri‐axial accelerometer. Accelerometer output was used to identify the engagement in different types of daily activities with a classification tree algorithm. Activity behavior was described by the daily duration of sleeping, sedentary behavior (lying, sitting, and standing), walking, running, bicycling, and generic standing activities. Simultaneously, the total energy expenditure (TEE) was measured using doubly labeled water. PAL was calculated as TEE divided by sleeping metabolic rate. PAL was significantly associated (P<0.05) with sedentary time (R=?0.72), and the duration of walking (R=0.49), bicycling (R=0.77), and active standing (R=0.62). A negative association was observed between sedentary time and the duration of active standing (R=?0.87; P<0.001). A multiple‐linear regression analysis showed that 75% of the variance in PAL could be predicted by the duration of bicycling (Partial R²=59%; P<0.01), walking (Partial R²=9%; P<0.05) and being sedentary (Partial R²=7%; P<0.05). In conclusion, there is objective evidence that sedentary time and activities related to transportation and commuting, such as walking and bicycling, contribute significantly to the average PAL.     

Energy cost of activity assessed by indirect calorimetry and a 13CO2 breath test

PURPOSE: The aim of this study was to assess the accuracy of a 13CO2 breath test for the prediction of short-duration energy expenditure. METHODS: Eight healthy volunteers walked at 1.5 km.h-1 for 60 min followed by 60-min recovery. During this time, the energy cost of physical activity was measured via respiratory calorimetry and a 13C bicarbonate breath test. A further eight subjects were tested using the same two methods during a 60-min cycle at 0.5 kp, 30 rpm followed by a 60-min recovery. The rate of appearance of 13CO2 (RaCO2) was measured and the mean ratio, VCO2/RaCO2, was used to calculate energy expenditure using the isotopic approach. RESULTS: As would be expected, there was a significant difference in the energy cost of walking and cycling using both methods (P < 0.05). However, no significant differences were observed between respiratory calorimetry and the isotope method for measurement of energy expenditure while walking or cycling. CONCLUSIONS: These data suggest that the 13C breath test is a valid method that can be used to measure the energy cost of short duration physical activity in a field setting.