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（由适用提供）：加速度计监视已被宣布为一种客观的，不引人注目的工具，可在自由生活的情况下全面观察体育锻炼。这些设备已经被采用并部署在大量研究中，目标从确定童年的活动决定者到老年人口的流动性下降。尽管对加速度计监测的丰富性继承了多天的逐渐划分的“活动计数”，但分析通常将观察到的数据简化为单个摘要措施，例如总活动计数。因此，这些分析隐含地假设活动的时间，强度和活动持续时间除了对总数的贡献外不重要。我们的提案开发方法可以使用功能数据方法来加速度计数据。由于功能数据技术将临时结构作为分析的基本组成部分，因此我们试图确定日常活动模式并了解协变量和完整的活动时间课程之间的关联。这些工具对于理解儿童观察的病因至关重要，这是我们激励数据集的关键兴趣，以及目前使用加速度计用于体育活动定量的许多其他研究。因此，使用拟议技术的新发现对于设计有效的干预措施以促进体育锻炼至关重要。该建议我们开发了用于广义多级功能的工具集合 数据：这些数据是普遍的，因为它们不遵循高斯分布，在每个受试者中观察到几天的多级分布，并且该活动中的功能几乎在每天在每天几乎连续监测。当前，对于Thi类型的数据，几乎没有现有的工作。我们的目的是开发独特的功能主成分分析，并引入统计上新颖的尺度回归回归模型方法，以进行此类数据。在整个过程中，我们使用贝叶斯方法，该方法共同对所有感兴趣的参数进行建模并开发快速近似算法以确保快速计算和可伸缩性。所有新方法都将在旨在模仿真实数据方案的模拟数据集上验证可靠的公开软件，并在激励数据集中部署，以生成对儿童对象背后的机制的见解。 1