Urban Form Lab Archives - Page 4 of 10

Does Neighborhood Walkability Moderate the Effects of Intrapersonal Characteristics on Amount of Walking in Post-Menopausal Women?

Perry, Cynthia K.; Herting, Jerald R.; Berke, Ethan M.; Nguyen, Huong Q.; Moudon, Anne Vernez; Beresford, Shirley A. A.; Ockene, Judith K.; Manson, Joann E.; Lacroix, Andrea Z. (2013). Does Neighborhood Walkability Moderate the Effects of Intrapersonal Characteristics on Amount of Walking in Post-Menopausal Women? Health & Place, 21, 39 – 45.

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Abstract

This study identifies factors associated with walking among postmenopausal women and tests whether neighborhood walkability moderates the influence of intrapersonal factors on walking. We used data from the Women's Health Initiative Seattle Center and linear regression models to estimate associations and interactions. Being white and healthy, having a high school education or beyond and greater non-walking exercise were significantly associated with more walking. Neighborhood walkability was not independently associated with greater walking, nor did it moderate influence of intrapersonal factors on walking. Specifying types of walking (e.g., for transportation) can elucidate the relationships among intrapersonal factors, the built environment, and walking. (C) 2013 Elsevier Ltd. All rights reserved.

Keywords

Self-talk; Postmenopause; Walking; Women's Health; Built Environment; Social Interaction; Regression Analysis; Postmenopausal Women; Walkability; Physical-activity; Older-adults; United-states; Us Adults; Exercise; Obesity; Transportation; Association; Attributes

The Spatial Clustering of Obesity: Does the Built Environment Matter?

Huang, R.; Moudon, A. V.; Cook, A. J.; Drewnowski, A. (2015). The Spatial Clustering of Obesity: Does the Built Environment Matter? Journal Of Human Nutrition & Dietetics, 28(6), 604 – 612.

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Abstract

BackgroundObesity rates in the USA show distinct geographical patterns. The present study used spatial cluster detection methods and individual-level data to locate obesity clusters and to analyse them in relation to the neighbourhood built environment. MethodsThe 2008-2009 Seattle Obesity Study provided data on the self-reported height, weight, and sociodemographic characteristics of 1602 King County adults. Home addresses were geocoded. Clusters of high or low body mass index were identified using Anselin's Local Moran's I and a spatial scan statistic with regression models that searched for unmeasured neighbourhood-level factors from residuals, adjusting for measured individual-level covariates. Spatially continuous values of objectively measured features of the local neighbourhood built environment (SmartMaps) were constructed for seven variables obtained from tax rolls and commercial databases. ResultsBoth the Local Moran's I and a spatial scan statistic identified similar spatial concentrations of obesity. High and low obesity clusters were attenuated after adjusting for age, gender, race, education and income, and they disappeared once neighbourhood residential property values and residential density were included in the model. ConclusionsUsing individual-level data to detect obesity clusters with two cluster detection methods, the present study showed that the spatial concentration of obesity was wholly explained by neighbourhood composition and socioeconomic characteristics. These characteristics may serve to more precisely locate obesity prevention and intervention programmes.

Keywords

Real Property; Ecology; Age Distribution; Anthropometry; Black People; Cluster Analysis (statistics); Communities; Computer Software; Epidemiological Research; Geographic Information Systems; Hispanic Americans; Mathematics; Obesity; Population Geography; Probability Theory; Race; Regression Analysis; Research Funding; Restaurants; Statistical Sampling; Self-evaluation; Sex Distribution; Shopping; Surveys; Telephones; Transportation; White People; Socioeconomic Factors; Body Mass Index; Data Analysis Software; Medical Coding; Statistical Models; Descriptive Statistics; Odds Ratio; Economics; Washington (state); Built Environment; Local Moran's I; Spatial Scan Statistic; Body-mass Index; Physical-activity; United-states; Risk-factors; Neighborhood; Association; Density; Disease; Disparities; Prevalence

Light Rail Leads to More Walking Around Station Areas

Huang, Ruizhu; Moudon, Anne V.; Zhou, Chuan; Stewart, Orion T.; Saelens, Brian E. (2017). Light Rail Leads to More Walking Around Station Areas. Journal Of Transport & Health, 6, 201 – 208.

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Abstract

Areas around Light Rail Transit (LRT) stations offer ideal conditions for Transit-Oriented Development (TOD). Relatively dense, mixed-use neighborhoods can have positive impacts on mobility, health, and perceptions of neighborhood safety among nearby residents, primarily through walking activity for both transit and other purposes. To examine how station areas may attract new activity, this study analyzed changes in walking around station areas among people living close to an LRT station before and after the opening of a new transit system. This study examined walking behavior among the subset of 214 participants living within one mile of one of 13 LRT stations from among a sample of residents living close or further away from a new LRT line in Seattle. They completed a survey and a travel log and wore an accelerometer and a GPS for 7 days both before (2008) and after the opening of the Seattle area LRT (2010). Walking bouts were derived using a previously developed algorithm. The main outcome was the individual-level change in the proportion of daily walking within one quarter Euclidean mile of an LRT station. Overall walking decreased from before to after the LRT opening while station area walking did not change significantly, indicating a shift in walking activity to the station areas after the introduction of LRT. Increases in the proportion of station area walking were negatively related to participants' distance between home and the nearest LRT station, peaking at .0.75 mile. Male gender, college education, normal weight status, less access to cars, and frequent LRT use were also significantly associated with greater positive changes in the proportion of station area walking. The shift in walking to station areas after the completion of light rail provides evidence that the local proximate population is attracted to station areas, which may potentially benefit both transit use and TOD area economic activity. The residential catchment area for the shift in LRT area walking was < 0.75 mile of the LRT stations. (C) 2017 Elsevier Ltd All rights reserved.

Keywords

Body-mass Index; Physical-activity; Built Environment; Travel Behavior; Transit; Stop; Transit Oriented Development (tod); Behavior Change; Global Positioning Systems; Geographic Information Systems

Beyond the Bus Stop: Where Transit Users Walk

Eisenberg-Guyot, Jerzy; Moudon, Anne V.; Hurvitz, Philip M.; Mooney, Stephen J.; Whitlock, Kathryn B.; Saelens, Brian E. (2019). Beyond the Bus Stop: Where Transit Users Walk. Journal Of Transport & Health, 14.

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Objectives: Extending the health benefits of public-transit investment requires understanding how transit use affects pedestrian activity, including pedestrian activity not directly temporally or spatially related to transit use. In this study, we identified where transit users walked on transit days compared with non-transit days within and beyond 400 m and 800 m buffers surrounding their home and work addresses. Methods: We used data collected from 2008 to 2013 in King County, Washington, from 221 non-physically-disabled adult transit users, who were equipped with an accelerometer, global positioning system (GPS), and travel diary. We assigned walking activity to the following buffer locations: less than and at least 400 m or 800 m from home, work, or home/work (the home and work buffers comprised the latter buffer). We used Poisson generalized estimating equations to estimate differences in minutes per day of total walking and minutes per day of non-transit-related walking on transit days compared with non-transit days in each location. Results: We found that durations of total walking and non-transit-related walking were greater on transit days than on non-transit days in all locations studied. When considering the home neighborhood in isolation, most of the greater duration of walking occurred beyond the home neighborhood at both 400 m and 800 m; results were similar when considering the work neighborhood in isolation. When considering the neighborhoods jointly (i.e., by using the home/work buffer), at 400 m, most of the greater duration of walking occurred beyond the home/work neighborhood. However, at 800 m, most of the greater duration of walking occurred within the home/work neighborhood. Conclusions: Transit days were associated with greater durations of total walking and non-transit related walking within and beyond the home and work neighborhoods. Accordingly, research, design, and policy strategies focused on transit use and pedestrian activity should consider locations outside the home and work neighborhoods, in addition to locations within them.

Keywords

Physical-activity; Public-transit; Accelerometer Data; Combining Gps; United-states; Travel; Transportation; Health; Time; Neighborhood

Does the Built Environment Have Independent Obesogenic Power? Urban Form and Trajectories of Weight Gain

Buszkiewicz, James H.; Bobb, Jennifer F.; Hurvitz, Philip M.; Arterburn, David; Moudon, Anne Vernez; Cook, Andrea; Mooney, Stephen J.; Cruz, Maricela; Gupta, Shilpi; Lozano, Paula; Rosenberg, Dori E.; Theis, Mary Kay; Anau, Jane; Drewnowski, Adam. (2021). Does the Built Environment Have Independent Obesogenic Power? Urban Form and Trajectories of Weight Gain. International Journal Of Obesity, 45(9), 1914 – 1924.

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Abstract

Objective To determine whether selected features of the built environment can predict weight gain in a large longitudinal cohort of adults. Methods Weight trajectories over a 5-year period were obtained from electronic health records for 115,260 insured patients aged 18-64 years in the Kaiser Permanente Washington health care system. Home addresses were geocoded using ArcGIS. Built environment variables were population, residential unit, and road intersection densities captured using Euclidean-based SmartMaps at 800-m buffers. Counts of area supermarkets and fast food restaurants were obtained using network-based SmartMaps at 1600, and 5000-m buffers. Property values were a measure of socioeconomic status. Linear mixed effects models tested whether built environment variables at baseline were associated with long-term weight gain, adjusting for sex, age, race/ethnicity, Medicaid insurance, body weight, and residential property values. Results Built environment variables at baseline were associated with differences in baseline obesity prevalence and body mass index but had limited impact on weight trajectories. Mean weight gain for the full cohort was 0.06 kg at 1 year (95% CI: 0.03, 0.10); 0.64 kg at 3 years (95% CI: 0.59, 0.68), and 0.95 kg at 5 years (95% CI: 0.90, 1.00). In adjusted regression models, the top tertile of density metrics and frequency counts were associated with lower weight gain at 5-years follow-up compared to the bottom tertiles, though the mean differences in weight change for each follow-up year (1, 3, and 5) did not exceed 0.5 kg. Conclusions Built environment variables that were associated with higher obesity prevalence at baseline had limited independent obesogenic power with respect to weight gain over time. Residential unit density had the strongest negative association with weight gain. Future work on the influence of built environment variables on health should also examine social context, including residential segregation and residential mobility.

Keywords

Body-mass Index; Physical-activity; Food Environment; Structural Racism; Obesity; Neighborhoods; Associations; Health; Walkability; Exposure; Environment Models; Minority & Ethnic Groups; Urban Environments; Regression Analysis; Regression Models; Residential Density; Body Mass Index; Property Values; Body Weight Gain; Government Programs; Body Weight; Electronic Medical Records; Electronic Health Records; Fast Food; Buffers; Real Estate; Body Mass; Body Size; Socioeconomics; Health Care

Access to Supermarkets and Fruit and Vegetable Consumption

Aggarwal, Anju; Cook, Andrea J.; Jiao, Junfeng; Seguin, Rebecca A.; Moudon, Anne Vernez; Hurvitz, Philip M.; Drewnowski, Adam. (2014). Access to Supermarkets and Fruit and Vegetable Consumption. American Journal Of Public Health, 104(5), 917 – 923.

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Abstract

Objectives. We examined whether supermarket choice, conceptualized as a proxy for underlying personal factors, would better predict access to supermarkets and fruit and vegetable consumption than mere physical proximity. Methods. The Seattle Obesity Study geocoded respondents' home addresses and locations of their primary supermarkets. Primary supermarkets were stratified into low, medium, and high cost according to the market basket cost of 100 foods. Data on fruit and vegetable consumption were obtained during telephone surveys. Linear regressions examined associations between physical proximity to primary supermarkets, supermarket choice, and fruit and vegetable consumption. Descriptive analyses examined whether supermarket choice outweighed physical proximity among lower-income and vulnerable groups. Results. Only one third of the respondents shopped at their nearest supermarket for their primary food supply. Those who shopped at low-cost supermarkets were more likely to travel beyond their nearest supermarket. Fruit and vegetable consumption was not associated with physical distance but, with supermarket choice, after adjusting for covariates. Conclusions. Mere physical distance may not be the most salient variable to reflect access to supermarkets, particularly among those who shop by car. Studies on food environments need to focus beyond neighborhood geographic boundaries to capture actual food shopping behaviors.

Keywords

Confidence Intervals; Correlation (statistics); Fruit; Geographic Information Systems; Ingestion; Multivariate Analysis; Population Geography; Questionnaires; Regression Analysis; Research Funding; Sales Personnel; Shopping; Travel; Vegetables; Predictive Validity; Cross-sectional Method; Statistical Models; Descriptive Statistics; Null Hypothesis; Washington (state); Local Food Environment; Diet Quality; Socioeconomic Position; Atherosclerosis Risk; Stores; Associations; Obesity; Adults; Availability; Communities

Health Implications of Adults’ Eating at and Living Near Fast Food or Quick Service Restaurants

Jiao, J.; Moudon, A. V.; Kim, S. Y.; Hurvitz, P. M.; Drewnowski, A. (2015). Health Implications of Adults’ Eating at and Living Near Fast Food or Quick Service Restaurants. Nutrition & Diabetes, 5.

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Abstract

BACKGROUND: This paper examined whether the reported health impacts of frequent eating at a fast food or quick service restaurant on health were related to having such a restaurant near home. METHODS: Logistic regressions estimated associations between frequent fast food or quick service restaurant use and health status, being overweight or obese, having a cardiovascular disease or diabetes, as binary health outcomes. In all, 2001 participants in the 2008-2009 Seattle Obesity Study survey were included in the analyses. RESULTS: Results showed eating >= 2 times a week at a fast food or quick service restaurant was associated with perceived poor health status, overweight and obese. However, living close to such restaurants was not related to negative health outcomes. CONCLUSIONS: Frequent eating at a fast food or quick service restaurant was associated with perceived poor health status and higher body mass index, but living close to such facilities was not.

Keywords

Body-mass Index; Socioeconomic-status; Built Environment; Obesity; Association; Consumption; Weight; Proximity; Outlets; Establishments

GPS-Based Exposure to Greenness and Walkability and Accelerometry-Based Physical Activity

James, Peter; Hart, Jaime E.; Hipp, J. Aaron; Mitchell, Jonathan A.; Kerr, Jacqueline; Hurvitz, Philip M.; Glanz, Karen; Laden, Francine. (2017). GPS-Based Exposure to Greenness and Walkability and Accelerometry-Based Physical Activity. Cancer Epidemiology Biomarkers & Prevention, 26(4), 525 – 532.

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Background: Physical inactivity is a risk factor for cancer that may be influenced by environmental factors. Indeed, dense and well-connected built environments and environments with natural vegetation may create opportunities for higher routine physical activity. However, studies have focused primarily on residential environments to define exposure and self-reported methods to estimate physical activity. This study explores the momentary association between minute-level global positioning systems (GPS)-based greenness exposure and time-matched objectively measured physical activity. Methods: Adult women were recruited from sites across the United States. Participants wore a GPS device and accelerometer on the hip for 7 days to assess location and physical activity at minutelevel epochs. GPS records were linked to 250mresolution satellitebased vegetation data and Census Block Group-level U.S. Environmental Protection Agency (EPA) Smart Location Database walkability data. Minute-level generalized additive mixed models were conducted to test for associations between GPS measures and accelerometer count data, accounting for repeated measures within participant and allowing for deviations fromlinearity using splines. Results: Among 360 adult women (mean age of 55.3 +/- 10.2 years), we observed positive nonlinear relationships between physical activity and both greenness and walkability. In exploratory analyses, the relationships between environmental factors and physical activity were strongest among those who were white, had higher incomes, and who were middle-aged. Conclusions: Our results indicate that higher levels of physical activity occurred in areas with higher greenness and higher walkability. Impact: Findings suggest that planning and design policies should focus on these environments to optimize opportunities for physical activity. (C) 2017 AACR.

Keywords

Built Environments; Health Research; Breast-cancer; Obesity; Neighborhood; Validation; Validity; Walking; Risk; Energetics

Higher Residential and Employment Densities Are Associated with More Objectively Measured Walking in the Home Neighborhood

Huang, Ruizhu; Moudon, Anne, V; Zhou, Chuan; Saelens, Brian E. (2019). Higher Residential and Employment Densities Are Associated with More Objectively Measured Walking in the Home Neighborhood. Journal Of Transport & Health, 12, 142 – 151.

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Abstract

Introduction: Understanding where people walk and how the built environment influences walking is a priority in active living research. Most previous studies were limited by self-reported data on walking. In the present study, walking bouts were determined by integrating one week of accelerometry, GPS, and a travel log data among 675 adult participants in the baseline sample of the Travel Assessment and Community study at Seattle, Washington in the United State. Methods: Home neighborhood was defined as being within 0.5 mile of each participants' residence (a 10-min walk), with home neighborhood walking defined as walking bout lines with at least one GPS point within the home neighborhood. Home neighborhood walkability was constructed with seven built environment variables derived from spatially continuous objective values (SmartMaps). Collinearity among neighborhood environment variables was analyzed and variables that were strongly correlated with residential density were excluded in the regression analysis to avoid erroneous estimates. A Zero Inflated Negative Binomial (ZINB) served to estimate associations between home neighborhood environment characteristics and home neighborhood walking frequency. Results: The study found that more than half of participants' walking bouts occurred in their own home neighborhood. Higher residential density and job density were the two neighborhood walkability measures related to higher likelihood and more time walking in the home neighborhood, highest tertile residential density (22.4-62.6 unit/ha) (coefficient= 1.43; 95% CI 1.00-2.05) and highest tertile job density (12.4-272.3 jobs/acre) (coefficient= 1.62; 1.10-2.37). Conclusions: The large proportion of walking that takes place in the home neighborhood highlights the importance of continuing to examine the impact of the home neighborhood environment on walking. Potential interventions to increase walking behavior may benefit from increasing residential and employment density within residential areas.

Keywords

Body-mass Index; Built Environment; Physical-activity; Land Uses; Epidemiology; Selection; Location; Obesity; Travel Assessment And Community; Smartmaps; Neighborhood Environment; Physical Activity; Walking

Differential Associations of the Built Environment on Weight Gain by Sex and Race/Ethnicity but Not Age

Buszkiewicz, James H.; Bobb, Jennifer F.; Kapos, Flavia; Hurvitz, Philip M.; Arterburn, David; Moudon, Anne Vernez; Cook, Andrea; Mooney, Stephen J.; Cruz, Maricela; Gupta, Shilpi; Lozano, Paula; Rosenberg, Dori E.; Theis, Mary Kay; Anau, Jane; Drewnowski, Adam. (2021). Differential Associations of the Built Environment on Weight Gain by Sex and Race/Ethnicity but Not Age. International Journal Of Obesity, 45(12), 2648 – 2656.

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Abstract

Objective To explore the built environment (BE) and weight change relationship by age, sex, and racial/ethnic subgroups in adults. Methods Weight trajectories were estimated using electronic health records for 115,260 insured Kaiser Permanente Washington members age 18-64 years. Member home addresses were geocoded using ArcGIS. Population, residential, and road intersection densities and counts of area supermarkets and fast food restaurants were measured with SmartMaps (800 and 5000-meter buffers) and categorized into tertiles. Linear mixed-effect models tested whether associations between BE features and weight gain at 1, 3, and 5 years differed by age, sex, and race/ethnicity, adjusting for demographics, baseline weight, and residential property values. Results Denser urban form and greater availability of supermarkets and fast food restaurants were associated with differential weight change across sex and race/ethnicity. At 5 years, the mean difference in weight change comparing the 3rd versus 1st tertile of residential density was significantly different between males (-0.49 kg, 95% CI: -0.68, -0.30) and females (-0.17 kg, 95% CI: -0.33, -0.01) (P-value for interaction = 0.011). Across race/ethnicity, the mean difference in weight change at 5 years for residential density was significantly different among non-Hispanic (NH) Whites (-0.47 kg, 95% CI: -0.61, -0.32), NH Blacks (-0.86 kg, 95% CI: -1.37, -0.36), Hispanics (0.10 kg, 95% CI: -0.46, 0.65), and NH Asians (0.44 kg, 95% CI: 0.10, 0.78) (P-value for interaction <0.001). These findings were consistent for other BE measures. Conclusion The relationship between the built environment and weight change differs across demographic groups. Careful consideration of demographic differences in associations of BE and weight trajectories is warranted for investigating etiological mechanisms and guiding intervention development.

Keywords

Body-mass Index; Socioeconomic-status; Food Environment; Obesity; Health; Outcomes; Scale; Risk; Minority & Ethnic Groups; Urban Environments; Etiology; Demographics; Sex; Residential Density; Supermarkets; Age; Race; Ethnicity; Property Values; Body Weight Gain; Electronic Medical Records; Fast Food; Electronic Health Records; Real Estate; Subgroups; Demography; Trajectory Analysis; Weight