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The Automatic Context Measurement Tool (ACMT) to Compile Participant-Specific Built and Social Environment Measures
Weipeng Zhou1, Amy Youngbloom2, Xinyang Ren1
1Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States.
JMIR Formative Research
|October 4, 2024
Summary
A new tool links environmental data to participant addresses, aiding health behavior research. This open-source Automatic Context Measurement Tool (ACMT) simplifies environmental data collection for studies in the United States.
Area of Science:
- Environmental Health
- Geographic Information Systems
- Public Health Research
Background:
- Environmental factors significantly influence health behaviors and outcomes.
- Previous studies linking environment and health were limited by the need for specialized geographic information systems (GIS) expertise.
- This limitation restricted the development of built and social environment measures to research groups with in-house GIS capabilities.
Purpose of the Study:
- To develop an open-source, user-friendly, and privacy-preserving tool for linking environmental variables to participant addresses.
- The tool aims to facilitate the integration of built, social, and natural environment data into health studies.
- The objective is to make environmental data more accessible for health research.
Main Methods:
- The Automatic Context Measurement Tool (ACMT) was developed, featuring a geocoder for address-to-coordinate conversion (US only) and a context measure assembler.
- It utilizes publicly available data sources linked to geographic coordinates.
- A web interface built with RStudio/RShiny, hosted in a Docker container on a local computer, ensures user-friendly access and data privacy.
Main Results:
- The ACMT was illustrated with two use cases: population density analysis in major US cities and correlates of cannabis licensure status in Washington State.
- Population density generally decreased with distance from city centers, with notable variations among cities.
- While neighborhood characteristics were explored for cannabis licensure, no significant associations were found after Bonferroni correction.
Conclusions:
- The ACMT enables the compilation of environmental measures to study the impact of environmental context on population health.
- Its portable and flexible design is optimal for neighborhood-based research requiring location-specific environmental data.
- The tool supports research attributing environmental data to specific locations within the United States.
