# Visitation-based obesogenic environment measurement: A novel instrument using Big Data approach > **NIH NIH R21** · PENNSYLVANIA STATE UNIVERSITY, THE · 2024 · $217,821 ## Abstract Abstract. Obesity is a predictor of multiple negative health outcomes, including type 2 diabetes, coronary heart disease, hypertension, various cancers, and premature death. Today, nearly two-thirds of US adults are overweight or obese, and one out of three is obese or morbidly obese. Obesity disproportionally affects African Americans, who have the highest age-adjusted prevalence of obesity (49.9%). Obesity disparities by race and geolocation result from complicated interactions between individual behaviors (e.g., physical activities, healthy food choices) and socioeconomic and environmental context (income, public infrastructure, neighborhood green lands). Individuals’ obesity-related behaviors are embedded in their communities and shaped by structural factors (e.g., racial segregation) and built environments (infrastructure and resources). The obesogenic environment produces conditions that encourage the overconsumption of calories and sedentary behaviors. For instance, distribution of different food resources (e.g., healthy food grocery stores, fast-food restaurants) within a residence area may influence people’s food choice and consumption. Likewise, neighborhoods with few walking or bike trails, poor street lighting, limited public transportation, and a lack of recreational spaces such as parks hinder physical activity and thus increase obesity risk. Traditional obesogenic environment indices are limited by a lack of timely monitoring the dynamic utilization of the infrastructures, challenges in integrating with behavioral data, and the potential bias due to self-report survey. To address these limitations and better assess and explore racial disparities of obesity, we propose to develop and test a novel measurement tool to assess obesity-related behaviors at multiple geographic levels (i.e., census blockgroup, tract, and county) in the US. First, a novel visitation-based obesogenic environment measurement (VOEM) will be developed using cellphone-based place visitation data to measure three types of obesity-related behaviors: physical activity (visitation to the exercise facilities such as parks and gyms), healthy food choices (visitation to healthy food outlets such as supermarkets and organic groceries), and less healthy food choices (visitation to fast-food restaurants and convenience stores). Second, the validity and performance of the VOEM will be assessed in terms of predicting adult obesity rates and explaining associated racial disparities at multiple geographic levels in the US by controlling for population-level social determinants of health and other sociodemographic factors based on public available datasets. This comprehensive, valid, and near real-time measurement instrument can be used as a surveillance tool to monitor population-level patterns and changes of proxy obesity-related behaviors over space and time (e.g., seasonal trends), map obesogenic environments at various geographic levels, and thus inform tailored and evide... ## Key facts - **NIH application ID:** 10888508 - **Project number:** 1R21MD018666-01A1 - **Recipient organization:** PENNSYLVANIA STATE UNIVERSITY, THE - **Principal Investigator:** Zhenlong Li - **Activity code:** R21 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $217,821 - **Award type:** 1 - **Project period:** 2024-07-06 → 2026-01-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10888508 ## Citation > US National Institutes of Health, RePORTER application 10888508, Visitation-based obesogenic environment measurement: A novel instrument using Big Data approach (1R21MD018666-01A1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10888508. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*