Assessments of residential and global positioning system activity space for food environments, body mass index and blood pressure among low-income housing residents in New York City

Abstract

Research has examined how the food environment affects the risk of cardiovascular disease (CVD). Many studies have focused on residential neighbourhoods, neglecting the activity spaces of individuals. The objective of this study was to investigate whether food environments in both residential and global positioning system (GPS)-defined activity space buffers are associated with body mass index (BMI) and blood pressure (BP) among low-income adults. Data came from the New York City Low Income Housing, Neighborhoods and Health Study, including BMI and BP data (n=102, age=39.3±14.1 years), and one week of GPS data. Five food environment variables around residential and GPS buffers included: fast-food restaurants, wait-service restaurants, corner stores, grocery stores, and supermarkets. We examined associations between food environments and BMI, systolic and diastolic BP, controlling for individual- and neighbourhood-level sociodemographics and population density. Within residential buffers, a higher grocery store density was associated with lower BMI (β=- 0.20 kg/m2, P<0.05), and systolic and diastolic BP (β =-1.16 mm Hg; and β=-1.02 mm Hg, P<0.01, respectively). In contrast, a higher supermarket density was associated with higher systolic and diastolic BP (β=1.74 mm Hg, P<0.05; and β=1.68, P<0.01, respectively) within residential buffers. In GPS neighbourhoods, no associations were documented. Examining how food environments are associated with CVD risk and how differences in relationships vary by buffer types have the potential to shed light on determinants of CVD risk. Further research is needed to investigate these relationships, including refined measures of spatial accessibility/exposure, considering individual’s mobility.

Obesity and the Built Environment: A Reappraisal

“…No such associations were observed using fast food outlet density captured in the 0.5‐mile street network buffer . However, two studies conducted in New York City produced counterintuitive results, with one finding that higher noise density in AS‐defined neighborhoods was associated with lower systolic and diastolic pressure and the other finding that home‐centric food environments were more predictive of BMI and blood pressure .…”

“…Acknowledging that an individual’s home neighborhood cannot capture the full extent of health‐related BE exposure, researchers turned to more advanced GPS technologies to track movements in space and time . Cumulative mobility over a given time period is the basis for constructing AS areas .…”

“…The literature examining whether BE features captured in an individual’s AS are more predictive of health than home‐centric‐defined features is relatively nascent . In some cases, AS‐defined food and structural environment features have been shown to be more predictive of health behaviors than those captured in the home neighborhood.…”

“…Accordingly, such BE measurement tools have become increasingly more sophisticated using wearable GPS devices, often augmented with travel diaries, travel logs, Google street views, or other mobile technologies . These approaches allow the capture of near instantaneous measures of exposure to the BE not only around the home location but also at work, school, and along travel routes . Mobile technologies have become increasingly atomized, allowing researchers to capture the minutiae of daily activities in time and space, sometimes at time resolutions of 10 seconds or less.…”

Neighborhood Social Environment and Cardiovascular Disease Risk

“…Two recent studies using GPS coordinates linked to accelerometer data tracked PA that occurred in a wide variety of spaces not restricted to residential areas [69••, 70••]. Two additional studies quantified the density of fast-food restaurants via GPS-defined food environments (i.e., quantifying food environment based upon an individual’s movements in space) [71, 72]. More work is needed to develop the social environment measures around GPS-defined activity space.…”