Stewart, Orion; Moudon, Anne Vernez; Claybrooke, Charlotte. (2012). Common Ground: Eight Factors that Influence Walking and Biking to School. Transport Policy, 24, 240 – 248.
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Abstract
The primary goals of Safe Routes to School (SRTS) programs are to increase the number and safety of children walking, biking or using other forms of active travel to school (ATS). This study reviewed quantitative and qualitative research and identified eight common factors that influenced the choice of ATS: distance to school, parental fear of traffic and crime, family schedule constraints and values, neighborhood and family resources and culture, weather, and school characteristics. Suggestions were made as to how these barriers and facilitators of ATS could be integrated into the decision to fund local SRTS programs and to improve their effectiveness. Published by Elsevier Ltd.
Keywords
Commuting; Transportation Of School Children; Transportation Safety Measures; Qualitative Research; Cycling; Walking; Bike; Child; Pedestrian; Safe Routes To School; Safety; Walk; Active Transportation; Physical-activity; Urban Form; Elementary-schools; Safe Routes; Travel Mode; Children; Prevalence; Trip; Environment
Cohen-Cline, Hannah; Lau, Richard; Moudon, Anne V.; Turkheimer, Eric; Duncan, Glen E. (2015). Associations between Fast-Food Consumption and Body Mass Index: A Cross-sectional Study in Adult Twins. Twin Research & Human Genetics, 18(4), 375 – 382.
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Abstract
Obesity is a substantial health problem in the United States, and is associated with many chronic diseases. Previous studies have linked poor dietary habits to obesity. This cross-sectional study aimed to identify the association between body mass index (BMI) and fast-food consumption among 669 same-sex adult twin pairs residing in the Puget Sound region around Seattle, Washington. We calculated twin-pair correlations for BMI and fast-food consumption. We next regressed BMI on fast-food consumption using generalized estimating equations (GEE), and finally estimated the within-pair difference in BMI associated with a difference in fast-food consumption, which controls for all potential genetic and environment characteristics shared between twins within a pair. Twin-pair correlations for fast-food consumption were similar for identical (monozygotic; MZ) and fraternal (dizygotic; DZ) twins, but were substantially higher in MZ than DZ twins for BMI. In the unadjusted GEE model, greater fast-food consumption was associated with larger BMI. For twin pairs overall, and for MZ twins, there was no association between within-pair differences in fast-food consumption and BMI in any model. In contrast, there was a significant association between within-pair differences in fast-food consumption and BMI among DZ twins, suggesting that genetic factors play a role in the observed association. Thus, although variance in fast-food consumption itself is largely driven by environmental factors, the overall association between this specific eating behavior and BMI is largely due to genetic factors.
Keywords
Diseases In Twins; Obesity; Adults; Diseases; Food Habits; Food Consumption; Body Mass Index; Cross-sectional Method; United States; Fast-food Consumption; Generalized Estimating Equations; Twin Studies; Fto Gene Variants; Physical-activity; Dietary-intake; Weight Status; Environment Interaction; Human Obesity; Young-adults; Zygosity; Patterns; Exercise
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
Moudon, Anne Vernez. (2019). Introducing Supergrids, Superblocks, Areas, Networks, and Levels to Urban Morphological Analyses. Iconarp International Journal Of Architecture And Planning, 7, 1 – 14.
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Abstract
Urban morphological analyses have identified the parcel (plot), the building type, or the plan unit (tessuto in Italian) as the basic elements of urban form. As cities have grown in geographic size disproportionately to their growth in population over the past seven decades, new elements have been introduced that structure their form. This essay describes these new elements and proposes that they be formally recognized in urban morphology. It introduces a conceptual framework for a multilevel structure of urban space using areas and networks and including supergrids and superblocks to guide morphological analyses.
Keywords
Morphological Elements; A Posteriori Approach; A Priori Approach
Kang, Bumjoon; Moudon, Anne V.; Hurvitz, Philip M.; Reichley, Lucas; Saelens, Brian E. (2013). Walking Objectively Measured: Classifying Accelerometer Data with GPS and Travel Diaries. Medicine & Science In Sports & Exercise, 45(7), 1419 – 1428.
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Abstract
Purpose: This study developed and tested an algorithm to classify accelerometer data as walking or nonwalking using either GPS or travel diary data within a large sample of adults under free-living conditions. Methods: Participants wore an accelerometer and a GPS unit and concurrently completed a travel diary for seven consecutive days. Physical activity (PA) bouts were identified using accelerometry count sequences. PA bouts were then classified as walking or nonwalking based on a decision-tree algorithm consisting of seven classification scenarios. Algorithm reliability was examined relative to two independent analysts' classification of a 100-bout verification sample. The algorithm was then applied to the entire set of PA bouts. Results: The 706 participants' (mean age = 51 yr, 62% female, 80% non-Hispanic white, 70% college graduate or higher) yielded 4702 person-days of data and had a total of 13,971 PA bouts. The algorithm showed a mean agreement of 95% with the independent analysts. It classified PA into 8170 walking bouts (58.5 %) and 5337 nonwalking bouts (38.2%); 464 bouts (3.3%) were not classified for lack of GPS and diary data. Nearly 70% of the walking bouts and 68% of the nonwalking bouts were classified using only the objective accelerometer and GPS data. Travel diary data helped classify 30% of all bouts with no GPS data. The mean + SD duration of PA bouts classified as walking was 15.2 + 12.9 min. On average, participants had 1.7 walking bouts and 25.4 total walking minutes per day. Conclusions: GPS and travel diary information can be helpful in classifying most accelerometer-derived PA bouts into walking or nonwalking behavior.
Keywords
Walking; Algorithms; Decision Trees; Geographic Information Systems; Research Funding; Travel; Accelerometry; Diary (literary Form); Descriptive Statistics; Algorithm; Classification; Physical Activity; Walk Trip; Global Positioning Systems; Physical-activity; Environment; Behaviors; Validity; Location
Drewnowski, Adam; Aggarwal, Anju; Tang, Wesley; Moudon, Anne Vernez. (2015). Residential Property Values Predict Prevalent Obesity but Do Not Predict 1-year Weight Change. Obesity, 23(3), 671 – 676.
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Abstract
ObjectiveLower socio economic status (SES) has been linked with higher obesity rates but not with weight gain. This study examined whether SES can predict short-term weight change. MethodsThe Seattle Obesity Study II was based on an observational cohort of 440 adults. Weights and heights were measured at baseline and at 1 year. Self-reported education and incomes were obtained by questionnaire. Home addresses were linked to tax parcel property values from the King County, Washington, tax assessor. Associations among SES variables, prevalent obesity, and 1-year weight change were examined using multivariable linear regressions. ResultsLow residential property values at the tax parcel level predicted prevalent obesity at baseline and at 1 year. Living in the top quartile of house prices reduced obesity risk by 80% at both time points. At 1 year, about 38% of the sample lost >1 kg body weight; 32% maintained ( 1 kg); and 30% gained >1 kg. In adjusted models, none of the baseline SES measures had any impact on 1-year weight change. ConclusionsSES variables, including tax parcel property values, predicted prevalent obesity but did not predict short-term weight change. These findings, based on longitudinal cohort data, suggest other mechanisms are involved in short-term weight change.
Keywords
Body-mass-index; Socioeconomic-status; United-states; Physical-activity; King County; Association; Health; Trends; Gain; Income
Kang, Bumjoon; Moudon, Anne V.; Hurvitz, Philip M.; Saelens, Brian E. (2017). Differences in Behavior, Time, Location, and Built Environment between Objectively Measured Utilitarian and Recreational Walking. Transportation Research: Part D, 57, 185 – 194.
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Abstract
Objectives: Utilitarian and recreational walking both contribute to physical activity. Yet walking for these two purposes may be different behaviors. We sought to provide operational definitions of utilitarian and recreational walking and to objectively measure their behavioral, spatial, and temporal differences in order to inform transportation and public health policies and interventions. Methods: Data were collected 2008-2009 from 651 Seattle-King County residents, wearing an accelerometer and a GPS unit, and filling-in a travel diary for 7 days. Walking activity bouts were classified as utilitarian or recreational based on whether walking had a destination or not. Differences between the two walking purposes were analyzed, adjusting for the nested structure of walking activity within participants. Results: Of the 4905 observed walking bouts, 87.4% were utilitarian and 12.6% recreational walking. Utilitarian walking bouts were 45% shorter in duration (-12.1 min) and 9% faster in speed (+0.3 km/h) than recreational walking bouts. Recreational walking occurred more frequently in the home neighborhood and was not associated with recreational land uses. Utilitarian walking occurred in areas having higher residential, employment, and street density, lower residential property value, higher area percentage of mixed-use neighborhood destinations, lower percentage of parks/trails, and lower average topographic slope than recreational walking. Conclusion: Utilitarian and recreational walking are substantially different in terms of frequency, speed, duration, location, and related built environment. Policies that promote walking should adopt type-specific strategies. The high occurrence of recreational walking near home highlights the importance of the home neighborhood for this activity.
Keywords
Walking; Utilitarianism; Recreation; Behavioral Assessment; Built Environment; Physical Activity Measurement; Accelerometer; Active Transportation; Gps; Home And Non-home Based Walking; Pedestrian; Physical-activity; Us Adults; Accelerometer Data; Trip Purpose; Urban Form; Travel; Neighborhood; Distance; System
Moudon, Anne Vernez; Huang, Ruizhu; Stewart, Orion T.; Cohen-Cline, Hannah; Noonan, Carolyn; Hurvitz, Philip M.; Duncan, Glen E. (2019). Probabilistic Walking Models Using Built Environment and Sociodemographic Predictors. Population Health Metrics, 17(1).
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Abstract
BackgroundIndividual sociodemographic and home neighborhood built environment (BE) factors influence the probability of engaging in health-enhancing levels of walking or moderate-to-vigorous physical activity (MVPA). Methods are needed to parsimoniously model the associations.MethodsParticipants included 2392 adults drawn from a community-based twin registry living in the Seattle region. Objective BE measures from four domains (regional context, neighborhood composition, destinations, transportation) were taken for neighborhood sizes of 833 and 1666 road network meters from home. Hosmer and Lemeshow's methods served to fit logistic regression models of walking and MVPA outcomes using sociodemographic and BE predictors. Backward elimination identified variables included in final models, and comparison of receiver operating characteristic (ROC) curves determined model fit improvements.ResultsBuilt environment variables associated with physical activity were reduced from 86 to 5 or fewer. Sociodemographic and BE variables from all four BE domains were associated with activity outcomes but differed by activity type and neighborhood size. For the study population, ROC comparisons indicated that adding BE variables to a base model of sociodemographic factors did not improve the ability to predict walking or MVPA.ConclusionsUsing sociodemographic and built environment factors, the proposed approach can guide the estimation of activity prediction models for different activity types, neighborhood sizes, and discrete BE characteristics. Variables associated with walking and MVPA are population and neighborhood BE-specific.
Keywords
Walking; Confidence Intervals; Research Funding; Transportation; Logistic Regression Analysis; Built Environment; Socioeconomic Factors; Predictive Validity; Receiver Operating Characteristic Curves; Data Analysis Software; Descriptive Statistics; Psychology; Washington (state); Active Travel; Home Neighborhood Domains; Physical Activity; Physical-activity; United-states; Life Stage; Adults; Attributes; Health; Associations; Destination; Pitfalls
Moudon, Anne Vernez; Drewnowski, Adam; Duncan, Glen E.; Hurvitz, Philip M.; Saelens, Brian E.; Scharnhorst, Eric. (2013). Characterizing the Food Environment: Pitfalls and Future Directions. Public Health Nutrition, 16(7), 1238 – 1243.
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Abstract
Objective: To assess a county population's exposure to different types of food sources reported to affect both diet quality and obesity rates. Design: Food permit records obtained from the local health department served to establish the full census of food stores and restaurants. Employing prior categorization schemes which classified the relative healthfulness of food sources based on establishment type (i.e. supermarkets v. convenience stores, or full-service v. fast-food restaurants), food establishments were assigned to the healthy, unhealthy or undetermined groups. Setting: King County, WA, USA. Subjects: Full census of food sources. Results: According to all categorization schemes, most food establishments in King County fell into the unhealthy and undetermined groups. Use of the food permit data showed that large stores, which included supermarkets as healthy food establishments, contained a sizeable number of bakery/delis, fish/meat, ethnic and standard quick-service restaurants and coffee shops, all food sources that, when housed in a separate venue or owned by a different business establishment, were classified as either unhealthy or of undetermined value to health. Conclusions: To fully assess the potential health effects of exposure to the extant food environment, future research would need to establish the health value of foods in many such common establishments as individually owned grocery stores and ethnic food stores and restaurants. Within-venue exposure to foods should also be investigated.
Keywords
Food Chemistry; Obesity; Health Boards; Dietary Supplements; Food Cooperatives; Restaurant Reviews; Coffee Shops; Food Consumption; Food Quality; Census Of Food Sources; Exposure; Health Value; Neighborhood Characteristics; Store Availability; Racial Composition; Physical-activity; Weight Status; Restaurants; Association; Proximity; Access; Business Enterprises; Fast Food Restaurants; Fish; Grocery Stores; Healthy Diet; Meat; Nutritional Adequacy; Supermarkets
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