Zakeri Issa, Adolph Anne L, Puyau Maurice R, Vohra Firoz A, Butte Nancy F
United States Department of Agriculture, USA.
J Appl Physiol (1985). 2008 Jun;104(6):1665-73. doi: 10.1152/japplphysiol.01163.2007. Epub 2008 Apr 10.
Accurate estimation of energy expenditure (EE) in children and adolescents is required for a better understanding of physiological, behavioral, and environmental factors affecting energy balance. Cross-sectional time series (CSTS) models, which account for correlation structure of repeated observations on the same individual, may be advantageous for prediction of EE. CSTS models for prediction of minute-by-minute EE and, hence, total EE (TEE) from heart rate (HR), physical activity (PA) measured by accelerometry, and observable subject variables were developed in 109 children and adolescents by use of Actiheart and 24-h room respiration calorimetry. CSTS models based on HR, PA, time-invariant covariates, and interactions were developed. These dynamic models involve lagged and lead values of HR and lagged values of PA for better description of the series of minute-by-minute EE. CSTS models with random intercepts and random slopes were investigated. For comparison, likelihood ratio tests were used. Log likelihood increased substantially when random slopes for HR and PA were added. The population-specific model uses HR and 1- and 2-min lagged and lead values of HR, HR(2), and PA and 1- and 2-min lagged values of PA, PA(2), age, age(2), sex, weight, height, minimum HR, sitting HR, HR x height, HR x weight, HR x age, PA x weight, and PA x sex interactions (P < 0.001). Prediction error for TEE was 0.9 +/- 10.3% (mean +/- SD). Errors were not correlated with age, weight, height, or body mass index. CSTS modeling provides a useful predictive model for EE and, hence, TEE in children and adolescents on the basis of HR and PA and other observable explanatory subject characteristics of age, sex, weight, and height.
为了更好地理解影响能量平衡的生理、行为和环境因素，需要准确估计儿童和青少年的能量消耗（EE）。横断面时间序列（CSTS）模型考虑了同一个体重复观测值的相关结构，可能有利于EE的预测。通过使用Actiheart和24小时室内呼吸热量测定法，在109名儿童和青少年中开发了用于根据心率（HR）、加速度计测量的身体活动（PA）以及可观察到的受试者变量预测每分钟EE以及总EE（TEE）的CSTS模型。开发了基于HR、PA、时不变协变量和相互作用的CSTS模型。这些动态模型涉及HR的滞后和超前值以及PA的滞后值，以便更好地描述每分钟EE序列。研究了具有随机截距和随机斜率的CSTS模型。为了进行比较，使用了似然比检验。当添加HR和PA的随机斜率时，对数似然显著增加。特定人群模型使用HR以及HR的1分钟和2分钟滞后和超前值HR(2)、PA以及PA的1分钟和2分钟滞后值PA(2)、年龄、年龄(2)、性别、体重、身高、最低心率、静息心率、HR×身高、HR×体重、HR×年龄、PA×体重和PA×性别相互作用（P<0.001）。TEE的预测误差为0.9±10.3%（平均值±标准差）。误差与年龄、体重、身高或体重指数无关。CSTS建模为基于HR、PA以及年龄、性别、体重和身高的其他可观察解释性受试者特征的儿童和青少年的EE以及TEE提供了一个有用的预测模型。
