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：大多数美国人未达到推荐的身体活动水平，导致糖尿病、高血压和心脏病等众多可预防的健康问题。这引发了通过监测人类活动来引导干预措施，以识别与更高身体活动水平相关的环境特征。可穿戴设备（如连续记录受试者粗大运动活动的腕戴式加速度计）能生成大量高分辨率测量数据。分析加速度计数据需考虑佩戴设备受试者轨迹或路径的时空信息。推断目标包括：估计受试者沿给定轨迹的身体活动水平；识别可能使特定受试者产生更高身体活动水平的轨迹；以及针对给定健康属性集，预测任何新建议轨迹中的预期身体活动水平。本文为时空加速度计数据设计了一个贝叶斯分层建模框架，在考虑受试者个体健康属性和时空依赖性的同时，对轨迹进行基于完整模型的推断。我们对来自洛杉矶可持续交通方式身体活动研究（PASTA-LA）的原始数据集进行全面分析，以在考虑各种异质性来源的同时，确定显示显著更高身体活动水平的空间区域和轨迹。