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）：加速度计监测已被誉为一个客观的，不显眼的工具，提供全天候观察身体活动的自由生活的情况。这些设备已经被大量研究采用和部署，其目标从确定儿童活动的决定因素到理解老年人口中与年龄相关的流动性下降。尽管加速度计监测固有的丰富性，它提供了跨多天的逐分钟的“活动计数”，但分析通常将观察到的数据简化为单个汇总测量，如总活动计数;这些分析因此隐含地假设，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。 我们的建议开发功能数据的方法加速度计数据。由于功能数据技术将时间结构作为分析的基本部分，我们试图识别日常活动模式，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，以及目前使用加速度计进行身体活动量化的许多其他研究。因此，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是必不可少的。 在本提案中，我们开发了一系列用于通用、多层次功能的工具 数据类型：这些数据是一般化的，因为它们不遵循高斯分布，是多层次的，因为对每个对象观察几天，并且是功能性的，因为在每天内几乎连续地监测活动。目前，很少或没有现有的工作，这种类型的数据。我们的目标是开发独特的功能主成分分析，并介绍统计上新颖的功能标量回归模型的方法，这种类型的数据。在整个过程中，我们使用贝叶斯方法，联合建模所有感兴趣的参数，并开发快速近似算法，以确保快速计算和可扩展性。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