The majority of Americans fail to achieve recommended levels of physical activity, which leads to numerous preventable health problems such as diabetes, hypertension, and heart diseases. This has generated substantial interest in monitoring human activity to gear interventions toward environmental features that may relate to higher physical activity. Wearable devices, such as wrist-worn sensors that monitor gross motor activity (actigraph units) continuously record the activity levels of a subject, producing massive amounts of high-resolution measurements. Analyzing actigraph data needs to account for spatial and temporal information on trajectories or paths traversed by subjects wearing such devices. Inferential objectives include estimating a subject's physical activity levels along a given trajectory; identifying trajectories that are more likely to produce higher levels of physical activity for a given subject; and predicting expected levels of physical activity in any proposed new trajectory for a given set of health attributes. Here, we devise a Bayesian hierarchical modeling framework for spatial-temporal actigraphy data to deliver fully model-based inference on trajectories while accounting for subject-level health attributes and spatial-temporal dependencies. We undertake a comprehensive analysis of an original dataset from the Physical Activity through Sustainable Transport Approaches in Los Angeles (PASTA-LA) study to ascertain spatial zones and trajectories exhibiting significantly higher levels of physical activity while accounting for various sources of heterogeneity.
翻译:大部分美国人未能达到所建议的体育活动水平,这导致糖尿病、高血压和心脏病等许多可预防的健康问题,这引起了人们对监测人类活动的巨大兴趣,以便针对可能与较高体能活动有关的环境特征采取干预措施。可穿戴的装置,例如手腕式传感器,监测运动总活动量(活体单位),不断记录某一主题的活动水平,产生大量高分辨率测量数据。分析活性数据需要考虑到关于穿透这些装置的主体的轨道或路径的空间和时间信息。推断目标包括按照特定轨迹估计一个对象的物理活动水平;查明更有可能为某一主题产生较高体能活动水平的轨迹;预测任何拟议新轨迹中某一特定健康属性的预期体力活动水平。这里,我们为空间-时尚行为数据设计了一个巴耶斯等级模型框架,以提供完全基于模型的轨迹推断,同时计算主题一级的健康属性和空间-时空依赖性可靠性水平;我们从空间-时空空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-空间-