Accepted for/Published in: JMIR Formative Research
Date Submitted: Jan 18, 2024
Date Accepted: Jul 14, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
The Automatic Context Measurement Tool (ACMT): A tool to compile participant-specific built and social environment measures for health research
ABSTRACT
Background:
The environment shapes health behaviors and outcomes. Studies exploring this influence have been limited to research groups with the geographic information systems (GIS) expertise required to develop built and social environment measures (e.g., groups that include a researcher with GIS expertise).
Objective:
The goal of this study was to develop an open-source, user-friendly and privacy-preserving tool for conveniently linking built, social, and natural environmental variables to study participant addresses.
Methods:
We built the Automatic Context Measurement Tool (ACMT). The ACMT comprises two components: (1) a geocoder, which identifies a latitude and longitude given a United States street address and (2) a context measure assembler, which computes measures from publicly available data sources linked to a latitude and longitude. ACMT users access both of these components using an RStudio/RShiny-based web interface that is hosted within a Docker container. We illustrate ACMT with two use cases: one comparing population density patterns within five major cities in the US, and one identifying correlates of cannabis licensure status in Washington State.
Results:
Our use cases demonstrate that while Los Angeles and Seattle are roughly equally densely populated within 1000m of city hall, Los Angeles is far more densely populated at greater distances. No measured environment variables were associated with cannabis retail outlet licensure in Washington state.
Conclusions:
The ACMT can be used to compile environmental measures to study the influence of environmental context on population health. The portable and flexible nature of ACMT makes it optimal for neighborhood study research seeking to attribute environmental data to specific locations within the United States.
Citation
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Copyright
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