Bayesian Hierarchical Modeling and Analysis for Actigraph Data from Wearable Devices

TL;DR

This paper introduces a Bayesian hierarchical model for analyzing high-resolution actigraph data from wearable devices, enabling inference on physical activity trajectories while considering spatial, temporal, and individual health factors.

Contribution

It develops a novel Bayesian hierarchical framework specifically designed for spatial-temporal actigraphy data analysis, incorporating subject health attributes and dependencies.

Findings

Identified spatial zones with higher physical activity levels.

Quantified the influence of health attributes on activity trajectories.

Provided a comprehensive analysis of the LA dataset.

Abstract

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.