# Developing AI-measures of Pedestrian Environment Features for Physical Activity and Cancer Prevention in Rural Communities

> **NIH CA R21** · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · 2026 · $181,963

## Abstract

7. Project Summary/Abstract
Residents of rural areas often exhibit lower rates of physical activity (PA), correlating to elevated cancer
incidence and mortality rates compared to urban dwellers. The lack of PA resources significantly contributes to
the lower rates of PA observed among racially and ethnically diverse and lower-income rural populations. A
major obstacle to addressing urban-rural PA and cancer disparities is an insufficient understanding of the
neighborhood environment—specifically, the pedestrian environment features that inhibit or promote PA—
which can be cost-effectively modified. Existing, publicly available pedestrian environment measures assess
macroscale walkability features (e.g., land use mix, street intersection density) that are costly and infeasible to
improve in rural areas. While smaller-scale research has identified more affordable, microscale Pedestrian
Environment Features (PEFs) (e.g., sidewalks, crosswalks, lighting), person-led, microscale audits of PEFs
show limited feasibility across expansive rural geographies. Machine learning algorithms have been developed
using data from urban and suburban areas to audit microscale PEFs, but these can introduce bias when
scaled up for use across expansive rural areas to study their relationship with PA. Addressing urban-rural
cancer disparities necessitates assessing the association between microscale PEFs and PA in both urban and
rural areas of the US. Therefore, we propose three specific aims: 1) further validate existing machine learning
algorithms to assess 9 microscale PEFs (sidewalks, sidewalk buffers, curb ramps, zebra and line crosswalks,
walk signals, bike symbols, benches, and lighting) for rural areas, 2) test the relationship between rural
microscale PEFs and middle to older age adult PA, and 3) identify disparities in microscale PEFs by income
levels, racial and ethnic composition, & geographic location across the US. We will retrain and leverage
existing deep learning classifier

## Key facts

- **NIH application ID:** 11291287
- **Project number:** 5R21CA293976-02
- **Recipient organization:** ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
- **Principal Investigator:** Marc A Adams; Marilyn Elizabeth Wende
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** CA
- **Fiscal year:** 2026
- **Award amount:** $181,963
- **Award type:** 5
- **Project period:** 2025-03-01T00:00:00 → 2027-02-28T00:00:00

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11291287

## Citation

> US National Institutes of Health, RePORTER application 11291287, Developing AI-measures of Pedestrian Environment Features for Physical Activity and Cancer Prevention in Rural Communities (5R21CA293976-02). Retrieved via AI Analytics 2026-07-08 from https://api.ai-analytics.org/grant/nih/11291287. Licensed CC0.

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