  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.
