Description
We developed the Automatic Context Measurement Tool (ACMT), a piece of R code to compile 193 measures of neighborhood features including demographics, commute mode, educational attainment, and land cover from publically available data sources with 833 meters (½ mile) of each participant’s home address. A prediction model using principal components from ACMT measures more accurately predicted total number of walk bouts than an a priori model did.
Learning Objective:
After viewing this poster, the learner should be better able to:
- Understand barriers to quantifying neighborhood characteristics’ relationship to health behaviors and outcomes
- Understand the principles of the Neighborhood Environment-Wide Association Study (NE-WAS) design
- Apply the Automatic Context Measurement Tool (ACMT) in his or her own work
Authors:
Stephen Mooney (Presenter)
University of Washington
Philip Hurvitz, University of Washington
Anne Moudon, University of Washington
Brian Saelens, Seattle Children’s Research Institute
Presentation Materials: