Bayesian Hierarchical Modeling and Analysis for Actigraph Data from Wearable Devices

Details

• Personal Author:
  Banerjee S ; Batteate CM ; Di Loro PA ; Jerrett M ; Lipsitt J ; Mingione M
• Description:
  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. [Description provided by NIOSH]
• Subjects:
  Occupational Health Safety
• Keywords:
  Activity Levels Author Keywords: Bayesian Hierarchical Models Directed Acyclic Graph Gaussian Processes Motion Studies Physical Activity Physical Fitness Sensors Sparsity Spatial-temporal Statistics Statistical Analysis Wearable Technologies

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• ISSN:
  1932-6157
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  California ; OSHA Region 9
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  17
• Issue:
  4
• NIOSHTIC Number:
  nn:20069677
• Citation:
  Ann Appl Stat 2023 Dec; 17(4):2865-2886
• Federal Fiscal Year:
  2024
• Performing Organization:
  University of California Los Angeles
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  The Annals of Applied Statistics
• End Date:
  20270630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:d8384648d33cf8fb49ea1a7b25d5eca0709c590817b210bee8371823be29d19e5f1def1c148e7f7b48f6d729a4ee17812e55d7c8d7be605976c9b128c1aa19d6
• Download URL:
  https://stacks.cdc.gov/view/cdc/208057/cdc_208057_DS1.pdf
• File Type:
  [PDF - 8.63 MB ]