The relationship between physical activity race ethnicity an

The relationship between physical activity, race/ethnicity, and income is complex, and differs depending on physical activity domain (e.g., leisure activity versus active transportation; Kruger, Ham, Berrigan, & Ballard-Barbash, 2008), age group (Cain et al., 2014), neighborhood type (urban versus suburban; Parks, Housemann, & Brownson, 2003), and racial/ethnic background (Li & Wen, 2013). For example, data from the 2005 National Health Interview Survey (n=31,482) showed that leptomycin b walking for transport was most prevalent among Asian women and non-Hispanic black men, but leisure walking was most prevalent among non-Hispanic White women and Asian men (Kruger et al., 2008). In a previously published study using the current data (Cain et al., 2014) we found that microscale pedestrian features explained differences in physical activity across four age groups. The present study expands upon the previous analysis by examining whether income or racial/ethnic disparities exist in microscale environments. Only a few studies have examined whether microscale features differed based on neighborhood income (Gibbs, Slater, Nicholson, Barker, & Chaloupka, 2012; Sallis et al., 2011) or racial/ethnic composition (Neckerman et al., 2009; Yu, 2014; Zhu & Lee, 2008). Several studies suggest that low-income neighborhoods or neighborhoods with a high proportion of racial/ethnic minorities have worse microscale pedestrian environments (Gibbs et al., 2012; Lovasi, Hutson, Guerra, & Neckerman, 2009). A study of 2199 US adults found that residents in low-income neighborhoods perceived their microscale built and social environments to be worse than those living in high-income neighborhoods (Sallis et al., 2011). Gibbs et al. (2012) found that low income neighborhoods had fewer sidewalks, traffic calming devices, and marked crosswalks than high income neighborhoods. Other studies found that low income neighborhoods had more complete and wider sidewalks, but fewer esthetic features (Neckerman et al., 2009; Zhu & Lee, 2008). Focusing on active travel to school, Zhu and Lee (2008) concurrently examined income and race/ethnicity for routes (N=73) in Austin, Texas that were near public schools, had a posted speed limit of 30 mph, and had a sidewalk on at least one side of the street. They found that, around public schools in Austin, the relationship between microscale features and neighborhood income varied by neighborhood racial/ethnic composition. Additional evidence is needed to examine income and race/ethnicity interactions in a bigger and more varied sample of microscale environments. Most existing studies focused on a single city (Neckerman et al., 2009; Yu, 2014; Zhu & Lee, 2008) and therefore did not examine how microscale environments or disparities can vary across cities. Moreover, existing studies did not examine within-city variations in microscale environments based on neighborhood type (i.e., residential versus mixed-use neighborhoods). Given that national and international authorities have identified the elimination of health disparities as a priority (World Health Organization, 2014; USDHHS, 2010; USDHHS, 2014), examining disparities in microscale pedestrian features is a first step toward addressing potential inequities in physical activity environments (LaVeist, 2005). As compared to changing macroscale urban features, such as the layout of roads, improving microscale features may provide a more feasible and affordable approach to creating activity-friendly environments (Cain et al., 2014). The objective of the present study was to conduct an in-depth examination of microscale pedestrian environments and the degree to which the quality of these environments is associated with the income and racial/ethnic composition of the neighborhood in which peptide bond exist. Data were collected from three US regions with markedly different SES levels, racial/ethnic composition, and walkability characteristics. This study adds to the literature by examining income and racial/ethnic disparities in three diverse regions, observing a large sample of routes, assessing residential and mixed-use neighborhoods, and using a validated direct observation tool (Cain et al., 2014).