PDF(Pubmed)
Abstract：
Physical activity (PA) is significantly associated with many health outcomes. The wide usage of wearable accelerometer-based activity trackers in recent years has provided a unique opportunity for in-depth research on PA and its relations with health outcomes and interventions. Past analysis of activity tracker data relies heavily on aggregating minute-level PA records into day-level summary statistics in which important information of PA temporal/diurnal patterns is lost. In this paper we propose a novel functional data analysis approach based on Riemann manifolds for modeling PA and its longitudinal changes. We model smoothed minute-level PA of a day as one-dimensional Riemann manifolds and longitudinal changes in PA in different visits as deformations between manifolds. The variability in changes of PA among a cohort of subjects is characterized via variability in the deformation. Functional principal component analysis is further adopted to model the deformations, and PC scores are used as a proxy in modeling the relation between changes in PA and health outcomes and/or interventions. We conduct comprehensive analyses on data from two clinical trials: Reach for Health (RfH) and Metabolism, Exercise and Nutrition at UCSD (MENU), focusing on the effect of interventions on longitudinal changes in PA patterns and how different modes of changes in PA influence weight loss, respectively. The proposed approach reveals unique modes of changes, including overall enhanced PA, boosted morning PA, and shifts of active hours specific to each study cohort. The results bring new insights into the study of longitudinal changes in PA and health and have the potential to facilitate designing of effective health interventions and guidelines.
摘要：
身体活动（PA）与许多健康结果显着相关。近年来，基于可穿戴加速度计的活动跟踪器的广泛使用为深入研究PA及其与健康结果和干预措施的关系提供了独特的机会。过去对活动跟踪器数据的分析在很大程度上依赖于将分钟级PA记录汇总为日级汇总统计数据，其中丢失了PA时间/昼夜模式的重要信息。在本文中，我们提出了一种基于黎曼流形的新型功能数据分析方法，用于对PA及其纵向变化进行建模。我们将一天的平滑分钟级PA建模为一维黎曼流形，并将不同访问中PA的纵向变化建模为流形之间的变形。一组受试者中PA变化的变异性通过变形的变异性来表征。进一步采用功能主成分分析对变形进行建模，和PC评分用作模拟PA变化与健康结果和/或干预措施之间关系的代理。我们对两项临床试验的数据进行了全面分析：接触健康(RfH)和代谢，UCSD的运动和营养（菜单），重点关注干预措施对PA模式纵向变化的影响，以及PA变化的不同模式如何影响体重减轻，分别。所提出的方法揭示了独特的变化模式，包括整体增强PA，增强上午PA，以及每个研究队列特有的活动时间的变化。该结果为PA和健康的纵向变化研究带来了新的见解，并有可能促进有效的健康干预措施和指南的设计。
