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）：评估体力活动 (PA) 和预测能量消耗 (EE) 的新方法对于量化学龄前儿童特有的零星 PA 模式和 EE 的可变率至关重要。由于方法学的局限性，关于学龄前儿童习惯性 PA 模式和 EE 规范率的全面定量数据很少。加速度计或小型心率 (HR) 监测器用于评估 PA 和预测 EE，但是，加速度计计数 (AC) 和 HR 的数学建模仅限于回归模型，这些模型没有考虑数据的相互依赖性，并且不利用原始数据中的所有信息。在本提案中，我们将应用先进技术（快速响应室内量热法、双标记水 (DLW)、加速度计和小型心率监测仪）和复杂的数学建模技术来开发和验证预测模型，以捕获 PA 和 EE 的动态性质。学龄前儿童。将使用 12 小时室内呼吸量热法作为标准方法，在 88 名学龄前儿童中开发横截面时间序列 (CSTS) 和多元自适应回归样条 (MARS) 模型，并在独立样本 (n=50) 中针对 12- h 室内呼吸量热法和 7 天 DLW 方法。具体目标： 1. 使用单轴和三轴加速度计（ActiGraph GT1M 和 GT3X）评估 PA，开发 CSTS 和 MARS 模型，根据受试者特征和 AC 之间的函数关系预测每分钟的活动能量消耗 (AEE)和 AEE，通过 88 名学龄前儿童的 12 小时室内呼吸量热法进行测量。 2. 对于久坐、轻度、中度和剧烈的 PA 水平以及清醒/睡眠时间的分类，使用统计和机器学习方法开发、评估和比较算法。 3. 为了使用加速度测量和 HR 监测 (Actiheart) 预测 EE，开发 CSTS 和 MARS 模型，用于预测每分钟 EE，从而根据受试者特征以及通过 12- 测量的 AC+HR 和 EE 之间的关系来预测 TEE h 热量测定同样针对 88 名学龄前儿童。 4. 验证目标 2 中开发的 PA 水平和清醒/睡眠周期的分类算法。 5. 根据 12 小时量热法和 DLW 方法，验证使用单轴和三轴加速度计根据 AC 和受试者特征预测 AEE在 50 名学龄前儿童的独立样本中。 6. 验证 CSTS 和 MARS 模型，以预测每分钟 EE，从而根据 AC 和 HR 以及同一独立样本中的 12 小时量热法和 DLW 方法预测 TEE、AEE、清醒 EE 和睡眠 EE 以及受试者特征50 名学龄前儿童。 公共卫生相关性：在美国，儿童肥胖症正在以惊人的速度增长，尤其是学龄前儿童。具有成本效益、非侵入性、有效和精确的能量消耗和体力活动定量评估方法对于确定体力活动模式、患病率和决定因素、体力活动与健康结果之间的剂量反应关系以及干预措施的有效性至关重要。学龄前儿童。我们将开发模型来评估学龄前儿童的体力活动和预测能量消耗，利用先进技术（室内量热法、双标记水、加速计、微型心率监测器）和复杂的数学建模技术，捕捉学龄前儿童体力活动和能量消耗的动态性质。学龄前儿童。