Generalized, multilevel functional response models applied to accelerometer data.

应用于加速度计数据的广义多级功能响应模型。

• 批准号: 8891025
• 负责人: Arthur J Goldsmith
• 金额: $ 19.76万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2017-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8891025
• 关键词: Accounting; Address; Adopted; Age; Aging; Algorithms; Baltimore; Bayesian Method; Behavioral; Birth; Child; Childhood; Cohort Studies; Collection; Complex; Computer software; Data; Data Analyses; Data Set; Devices; Elderly; Elements; Enrollment; Ensure; Environmental Health; Etiology; Future; Gender; Goals; Head Start Program; Health; Height; Home environment; Intervention; Joints; Life; Literature; Longitudinal Studies; Measurement; Measures; Methodology; Methods; Modeling; Monitor; Motivation; National Health and Nutrition Examination Survey; New York City; Normal Statistical Distribution; Outcome; Pattern; Physical activity; Population; Principal Component Analysis; Public Health; Research; Research Methodology; Sampling; Skinfold Thickness; Social Environment; Source; Statistical Methods; Structure; Techniques; Time; Uncertainty; Unit of Measure; Variant; Visit; Weight; Work; age related; base; data reduction; design; effective intervention; frailty; improved; insight; interest; novel; obesity in children; public health relevance; response; software development; theories; tool

 DESCRIPTION (provided by applicant): Accelerometer monitoring has been heralded as an objective, unobtrusive tool to provide around the clock observation of physical activity in free-living situations. These devices have been adopted and deployed in a large number of studies already, with goals ranging from identifying activity determinants of childhood to under- standing age-related declines in mobility in elderly populations. Despite the richness inherent to accelerometer monitoring, which provides minute-by-minute "activity counts" spanning multiple days, analyses often simplify the observed data to single summary measures like the total activity count; these analyses thereby implicitly assuming that timing, intensity and duration of activity are unimportant aside from the contribution to the total. Our proposal develops functional data approaches to accelerometer data. Because functional data techniques incorporate temporal structure as a fundamental part of the analysis, we seek to identify daily activity patterns and understand associations between covariates and complete activity time courses. These tools are critically important to understanding etiologies of childhood obesity, a key interest in our motivating dataset, as well as to the many other studies currently using accelerometry for physical activity quantification. Consequently, new findings using the proposed techniques are essential for designing effective interventions to promote physical activity. In this proposal we develop a collection of tools for generalized, multilevel functional data: these data are generalized in that they do not follow a Gaussian distribution, multilevel in that several days are observed for each subject, and functional in that activity is monitored nearly continuously within each day. Currently, there is little or no existing work for data of thi type. Our aims are to develop unique functional principal components analysis and to introduce statistically novel function-on-scalar regression models approaches for data of this type. Throughout, we use a Bayesian approach that jointly models all parameters of interest and develop fast approximate algorithms to ensure rapid computation and scalability. All new methods will be implemented in robust, publicly available software, be validated on simulated datasets designed to mimic real-data scenarios, and be deployed on the motivating dataset to generate insights into the mechanisms behind childhood obesity. 1

 描述（由申请人提供）：加速度计监测被誉为一种客观、不引人注目的工具，可以在自由生活的情况下全天候观察身体活动，这些设备已经在大量研究中得到采用和部署，并有目标。范围从确定儿童活动决定因素到了解老年人群中与年龄相关的活动能力下降，尽管加速度计监测本身具有丰富的内容，可以提供跨越多天的每分钟“活动计数”，但分析通常是这样的。将观察到的数据简化为单一的汇总测量，例如总活动计数；这些分析隐含地假设除了对总数的贡献之外，活动的时间、强度和持续时间并不重要，因为功能数据。技术将时间结构作为分析的基本部分，我们寻求识别日常活动模式并了解协变量和完整活动时间课程之间的关联，这些工具对于理解儿童肥胖的病因至关重要，这是我们激励数据集的关键兴趣，以及对很多人来说目前使用加速度测量进行身体活动量化的其他研究表明，使用所提出的技术的新发现对于设计有效的干预措施以促进身体活动至关重要。 数据：这些数据是概括性的，因为它们不遵循高斯分布，多层次性的，因为对每个受试者都观察了几天，并且功能性的，因为每天几乎连续监测该活动，目前，几乎没有或没有现有的工作。我们的目标是开发独特的函数主成分分析，并为此类数据引入特别新颖的标量函数回归模型方法，我们使用贝叶斯方法对所有感兴趣的参数进行联合建模并开发。快速近似算法，保证快速所有新方法都将在强大的公开软件中实施，在旨在模拟真实数据场景的模拟数据集上进行验证，并部署在激励数据集上以深入了解儿童肥胖背后的机制1。