# The Automatic Context Measurement Tool: bringing environmental data to non-specialists

> **NIH NIH R00** · UNIVERSITY OF WASHINGTON · 2021 · $225,257

## Abstract

PROJECT SUMMARY/ABSTRACT
Recent studies reaffirming geographic disparities in the United States showed that they are not fully explained
by socioeconomic differences, suggesting context or environment strongly impacts health. Yet research on
environmental influences on health, particularly environmental effect modification of behavioral interventions,
has been limited by the cost of computing subject-specific measures of environmental context. We propose to
put environmental measures within the reach of non-specialist researchers and health promotion experts by
building and validating the Automatic Context Measurement Tool (ACMT). ACMT is a software tool that
researchers and practitioners can use to efficiently compile, attribute to individuals, and analyze environmental
measures drawn from free and nationally available datasets such as US Census and the National Land Cover
Database. After building ACMT, we will take five steps to validate and promote it. First, we will quantify how
well these nationally available measures capture health-relevant aspects of study participants' environments by
comparing nationally available environment measures predictive of physical activity to locally-available
environment measures predictive of physical activity for a cohort based in King County, WA. Second, we will
demonstrate how ACMT might apply to multi-site trials by contrasting environment measures predictive of
physical activity in King County with those predictive of physical activity in Salt Lake City, UT and Portland, OR.
Third, we will use ACMT with large electronic health record (EHR) datasets from Kaiser Permanente
Washington (formerly Group Health) patients and UW Medicine patients to explore which environmental
measures best predict BMI trajectories in healthy adults. Fourth, we will establish that ACMT can be used to
identify environmental modifiers of health intervention effectiveness by comparing environmental predictors of
BMI trajectories among adults receiving bariatric surgery compared with obese adults not receiving surgery.
Finally, we will ensure ACMT is available on the web with an interface that is usable by non-specialists,
recruiting project coordinators from weight management programs and other patient care projects to usability
test ACMT and provide feedback allowing us to improve it. Once made publically available, the ACMT will
unlock the use of environment measures for researchers and practitioners without geospatial training who had
previously been hindered by the considerable expertise (and related expense) required to collect and analyze
potential environmental influences on health. The steps we will take to validate ACMT will also provide
additional insight into environmental influences on physical activity and obesity. Finally, the complementary
training plan comprising coursework, structured mentoring, and experiential learning will let me develop the
skills to launch my career as an independent scientist working at the i...

## Key facts

- **NIH application ID:** 10189696
- **Project number:** 5R00LM012868-04
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Stephen John Mooney
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $225,257
- **Award type:** 5
- **Project period:** 2019-07-05 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10189696

## Citation

> US National Institutes of Health, RePORTER application 10189696, The Automatic Context Measurement Tool: bringing environmental data to non-specialists (5R00LM012868-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10189696. Licensed CC0.

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