Uncertainty in the relevant spatial context may drive heterogeneity in findings on the built environment and energy balance. To estimate the effect of this uncertainty, we conducted a sensitivity analysis defining intersection and business densities and counts within different buffer sizes and shapes on associations with self-reported walking and body mass index. Linear regression results indicated that the scale and shape of buffers influenced study results and may partly explain the inconsistent findings in the built environment and energy balance literature.

University students are exposed to many stressors, necessitating opportunities for restoration. Research has indicated that actual experiences in nearby green spaces are associated with restorative psychological and physiological health benefits. However, the perception of greenness and restorativeness of environments might also impact health outcomes. Can green campus spaces provide restorative potential to university students? Do students perceive the greenness and restorative benefits? To explore these questions, students at three universities (convenience sample) were surveyed with items on perceived greenness of campus, perceived restorativeness of campus, and the World Health Organization Quality-of-Life Scale. Results indicate that those with higher perceived campus greenness report greater quality of life, a pathway significantly and partially mediated by perceived campus restorativeness. Future research should help identify effective ways in which university green spaces can be developed as health resources for students.

Background Physical inactivity has been associated with obesity and related chronic diseases. Understanding built environment (BE) influences on specific domains of physical activity (PA) around homes and workplaces is important for public health interventions to increase population PA. Purpose To examine the association of home and workplace BE features with PA occurring across specific life domains (work, leisure, and travel). Methods Between 2012 and 2013, telephone interviews were conducted with participants in four Missouri metropolitan areas. Questions included sociodemographic characteristics, home and workplace supports for PA, and dietary behaviors. Data analysis was conducted in 2013; logistic regression was used to examine associations between BE features and domain-specific PA. Results In home neighborhoods, seven of 12 BE features (availability of fruits and vegetables, presence of shops and stores, bike facilities, recreation facilities, crime rate, seeing others active, and interesting things) were associated with leisure PA. The global average score of home neighborhood BE features was associated with greater odds of travel PA (AOR=1.99, 95% CI=1.46, 2.72), leisure PA (AOR=1.84, 95% CI=1.44, 2.34), and total PA (AOR=1.41, 95% CI=1.04, 1.92). Associations between workplace neighborhoods’ BE features and workplace PA were small, but in the expected direction. Conclusions This study offers empirical evidence on BE supports for domain-specific PA. Findings suggest that diverse, attractive, and walkable neighborhoods around workplaces support walking, bicycling, and use of public transit. Public health practitioners, researchers, and worksite leaders could benefit by utilizing worksite domains and measures from this study for future BE assessments.

Rest-activity patterns provide an indication of circadian rhythmicity in the free-living setting. We aimed to describe the distributions of rest-activity patterns in a sample of adults and children across demographic variables. A sample of adults (N=590) and children (N=58) wore an actigraph on their non-dominant wrist for 7 days and nights. We generated rest-activity patterns from cosinor analysis (MESOR, acrophase and magnitude) and non-parametric circadian rhythm analysis (IS: intradaily stability; IV: interdaily variability; L5: least active 5-hour period; M10: most active 10-hour period; and RA: relative amplitude). Demographic variables included age, sex, race, education, marital status, and income. Linear mixed effects models were used to test for demographic differences in rest-activity patterns. Adolescents, compared to younger children, had: 1) later M10 midpoints (β=1.12 hours [95% CI: 0.43, 1.18] and lower M10 activity levels; 2) later L5 midpoints (β=1.6 hours [95% CI: 0.9, 2.3]) and lower L5 activity levels; 3) less regular rest-activity patterns (lower IS and higher IV); and 4) lower magnitudes (β=−0.95 [95% CI: −1.28, −0.63]) and relative amplitudes (β=−0.1 [95% CI: −0.14, −0.06]). Mid-to-older adults, compared to younger adults (ages 18 to 29 years), had: 1) earlier M10 midpoints (β=−1.0 hours [95% CI: −1.6, −0.4]; 2) earlier L5 midpoints (β=−0.7 hours [95% CI: −1.2, −0.2]); and 3) more regular rest-activity patterns (higher IS and lower IV). The magnitudes and relative amplitudes were similar across the adult age categories. Sex, race and education level rest-activity differences were also observed. Rest-activity patterns vary across the lifespan, and differ by race, sex and education. Understanding population variation in these patterns provides a foundation for further elucidating the health implications of rest-activity patterns across the lifespan.

IntroductionInformation on the relationship between diabetes prevalence and built environment attributes could allow public health programs to better target populations at risk for diabetes. This study sought to determine the spatial prevalence of diabetes in the United States and how this distribution is associated with the geography of common diabetes correlates.MethodsData from the Centers for Disease Control and Prevention and the US Census Bureau were integrated to perform geographically weighted regression at the county level on the following variables: percentage nonwhite population, percentage Hispanic population, education level, percentage unemployed, percentage living below the federal poverty level, population density, percentage obese, percentage physically inactive, percentage population that cycles or walks to work, and percentage neighborhood food deserts.ResultsWe found significant spatial clustering of county-level diabetes prevalence in the United States; however, diabetes prevalence was inconsistently correlated with significant predictors. Percentage living below the federal poverty level and percentage nonwhite population were associated with diabetes in some regions. The percentage of population cycling or walking to work was the only significant built environment–related variable correlated with diabetes, and this association varied in magnitude across the nation.ConclusionSociodemographic and built environment–related variables correlated with diabetes prevalence in some regions of the United States. The variation in magnitude and direction of these relationships highlights the need to understand local context in the prevention and maintenance of diabetes. Geographically weighted regression shows promise for public health research in detecting variations in associations between health behaviors, outcomes, and predictors across geographic space.