Novel Models to Predict Energy Expenditure and Physical Activity in Preschoolers

预测学龄前儿童能量消耗和身体活动的新模型

• 批准号: 7765707
• 负责人: NANCY F. BUTTE
• 金额: $ 36.95万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-05-01 至 2014-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7765707
• 关键词: Accounting; Age; Algorithms; Behavior; Calibration; Calorimetry; Characteristics; Child; Classification; Complex; Computer software; Data; Dose; Effectiveness of Interventions; Energy Metabolism; Expenditure; Health; Heart Rate; Indirect Calorimetry; Label; Light; Machine Learning; Measures; Methods; Modeling; Monitor; Nature; Nursery Schools; Outcome; Pattern; Physical activity; Population; Posture; Preschool Child; Prevalence; Research; Sampling; Series; Sleep; Structure; Techniques; Technology; Time; Time Series Analysis; Validation; Water; awake; base; cost; heart rate variability; improved; mathematical model; miniaturize; model development; novel; novel strategies; obesity in children; phase 1 study; phase 2 study; public health relevance; response; sedentary

DESCRIPTION (provided by applicant): Novel approaches to assess physical activity (PA) and predict energy expenditure (EE) are essential for quantifying the characteristically sporadic PA patterns and variable rates of EE of preschool-aged children. Because of methodological limitations, there is a paucity of comprehensive quantitative data on the habitual PA patterns and normative rates of EE in preschoolers. Accelerometers or miniaturized heart rate (HR) monitors are used to assess PA and predict EE, however, the mathematical modeling of accelerometer counts (AC) and HR has been limited to regression models that do not take into account the interdependence of the data and do not exploit all the information in the raw data. In this proposal, we will apply advanced technology (fast- response room calorimetry, doubly labeled water (DLW), accelerometers and miniaturized HR monitors) and sophisticated mathematical modeling techniques to develop and validate prediction models that capture the dynamic nature of PA and EE in preschool-aged children. Cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS) models will be developed in 88 preschool-aged children using 12-h room respiration calorimetry as the criterion method and validated in an independent sample (n=50) against12-h room respiration calorimetry and the 7-d DLW method. Specific Aims: 1. For assessment of PA using unaxial and triaxial accelerometry (ActiGraph GT1M and GT3X), develop CSTS and MARS models for prediction of minute-to-minute activity energy expenditure (AEE) based on subject characteristics and the functional relationship between AC and AEE, measured by 12-h room respiration calorimetry in 88 preschool-aged children. 2. For classification of sedentary, light, moderate and vigorous levels of PA and awake/sleep periods, develop, evaluate, and compare algorithms using statistical and machine learning methods. 3. For prediction of EE using accelerometry and HR monitoring (Actiheart), develop CSTS and MARS models for prediction of minute-by-minute EE and hence TEE based on subject characteristics and the relationship between AC+HR and EE as measured by 12-h calorimetry in the same 88 preschoolers. 4. Validate the classification algorithms for PA levels and awake/sleep periods developed in Aim 2. 5. Validate the use of uniaxial and triaxial accelerometers for the prediction of AEE based on AC and subject characteristics, against 12-h calorimetry and the DLW method in an independent sample of 50 preschoolers. 6. Validate the CSTS and MARS models for the prediction of minute-by-minute EE and hence TEE, AEE, awake EE and sleep EE from AC and HR and subject characteristics against 12-h calorimetry and the DLW method in the same independent sample of 50 preschoolers. PUBLIC HEALTH RELEVANCE: In the US, childhood obesity has been increasing at alarming rates, particularly among preschool-aged children. Cost-effective, non-intrusive, valid and precise methods for the quantitative assessment of energy expenditure and physical activity are essential to determine patterns of physical activity, prevalence and determinants, dose-response relationships between physical activity and health outcomes, and intervention effectiveness in preschool-aged children. We will develop models to assess physical activity and to predict energy expenditure in preschoolers using advanced technology (room calorimetry, doubly labeled water, accelerometers, miniaturized heart rate monitors) and sophisticated mathematical modeling techniques that capture the dynamic nature of physical activity and energy expenditure in preschool-aged children.

描述（由申请人提供）：评估体育活动（PA）和预测能量消耗（EE）的新方法对于量化特征性零星的PA模式和幼儿园儿童EE的可变率至关重要。由于方法上的局限性，学龄前儿童中EE的习惯性PA模式和规范率很少。加速度计或微型心率（HR）监测器用于评估PA和预测EE，但是，加速度计计数（AC）和HR的数学模型限于回归模型，这些模型不考虑数据的相互依存关系，并且并未利用原始数据中的所有信息。在该提案中，我们将应用先进的技术（快速响应室量热法，双重标记的水（DLW），加速度计和微型的HR监控器）和精致的数学建模技术来开发和验证预测模型，从而捕获学龄前儿童中PA和EE的动态性质的预测模型。横截面时间序列（CSTS）和多元自适应回归条带（MARS）模型将在88名学龄前儿童中开发，使用12-H房间呼吸量热法作为标准方法，并在独立样品（n = 50）中对12-H房间呼吸量表进行验证，并使用7-D DLW方法进行验证。 Specific Aims: 1. For assessment of PA using unaxial and triaxial accelerometry (ActiGraph GT1M and GT3X), develop CSTS and MARS models for prediction of minute-to-minute activity energy expenditure (AEE) based on subject characteristics and the functional relationship between AC and AEE, measured by 12-h room respiration calorimetry in 88 preschool-aged children. 2。用于久坐，轻度，中度和剧烈的PA以及清醒/睡眠期的分类，使用统计和机器学习方法开发，评估和比较算法。 3。用于使用加速度测定法和HR监测（ACTIHEART）预测EE，开发了CST和MARS模型，用于预测逐分钟的EE，因此基于主题特征以及AC+HR和EE之间的TEE以及通过同一88个学龄前儿童测量的AC+HR和EE之间的关系。 4。验证AIM2。5中开发的PA水平和清醒/睡眠期的分类算法。验证使用基于AC和主题特征的AEE预测AEE的单轴和三轴加速度计的使用，针对12-h量热法和DLW方法在50个独立样本中使用DLW方法。 6。验证CSTS和MARS模型的预测EE分钟的预测，因此可以通过AC和HR的TEE，AEE，AWAKE EE和睡眠EE和对象特征和主题特征，以及在50个学龄前儿童的同一独立样本中，对12小时的热量法和DLW方法。 公共卫生相关性：在美国，儿童肥胖症的速度一直在增加，尤其是在学龄前儿童中。具有成本效益，非侵入性，有效和精确的方法来定量评估能量消耗和体育锻炼对于确定身体活动，患病率和决定因素，身体活动和健康结果之间的剂量反应关系以及对学龄前儿童的干预效果至关重要。我们将开发模型来评估体育活动并使用先进技术（室温量热法，加速度计，加速度计，微型心率监测器）和精致的数学建模技术来预测学龄前儿童的能源消耗，从而捕获学龄前儿童体育活动和能量的动态性质。