Statistical methods for analyzing objectively measured physical activity data

项目总结/摘要 这个项目的主要重点是开发新的统计方法，客观地衡量身体活动 (PA)数据通过加速度计或其他可穿戴监测器，并应用这些方法来了解如何具体 活动强度、频率和持续时间的模式与健康结果有关，如心血管疾病， 更大的疾病。随着技术的快速进步，现在越来越普遍地记录加速度计数据 在大规模的流行病学研究中虽然这类数据的规模和复杂性正在爆炸式增长，但 在很大程度上缺乏对分析方法的阐述。新颖的分析方法是可解释的，强大的，但 计算效率是迫切需要的，特别是随着技术的迅速发展。 受妇女健康倡议（WHI）和其他PA研究的启发，我们提出了新的统计方法， 总结并有效利用客观体力活动测量结果进行健康关联分析。 具体而言，我们将首先开发一个功能性数据分析框架，从 标准分辨率加速度计数据，并灵活地建模PA模式和健康之间的关联。 接下来，我们考虑高分辨率的原始加速度计数据，并提出了一个系统的分析框架， 识别自由生活环境中的行走，描述行走模式，并研究它们与 健康成果。最后，我们提出了新的测量误差校正方法的大规模研究 采用复杂的抽样设计，其中仅在研究的一小部分中提供客观测量 人口拟议的方法论研究的动机是PA研究的WHI，将直接 适用于这些项目。将这些方法应用于这些研究将提高我们对PA的理解， 这将最终为未来的PA指南奠定基础。这些方法普遍适用 收集客观PA测量的其他流行病学研究，软件将公开 容易接近

Project Summary/Abstract The major focus of this project is to develop novel statistical methods for objectively measured physical activity (PA) data by accelerometers or other wearable monitors, and to apply these methods to understand how specific patterns of activity intensity, frequency and duration are associated with health outcomes, such as cardiovascu- lar diseases. With rapid technological advances, it is now increasingly common to record accelerometer data in large-scale epidemiological studies. While the size and complexity of this type of data is exploding, the de- velopment of analytic methods is largely lacking. Novel analytic methods that are interpretable, robust, and yet computationally efficient are urgently needed, especially with the technology rapidly evolving. Motivated by the Women's Health Initiative (WHI) and other PA studies, we propose novel statistical meth- ods to summarize and effectively utilize objective physical activity measurements for health association analysis. Specifically, we will first develop a functional data analysis framework to extract novel summary indices from standard resolution accelerometer data and flexibly model the association between PA patterns and health. We next consider high-resolution raw accelerometer data and propose a systematic analytical framework for identifying walking in free-living environment, describing walking patterns and studying their association with health outcomes. Finally, we propose novel measurement error correction approaches for large-scale studies with complex sampling designs, where objective measurement is available only in a small subset of the study population. The proposed methodological research is motivated by PA studies of the WHI and will be directly applied to these projects. Applications of these methods to these studies will improve our understanding of PA, which will eventually help lay the foundation for future PA guidelines. These methods are generally applicable to other epidemiological studies that collect objective PA measurements, and software will be made publicly accessible.