Anne Vernez Moudon Archives - Page 2 of 9

Differences in Weight Gain Following Residential Relocation in the Moving to Health (M2H) Study

Cruz, Maricela; Drewnowski, Adam; Bobb, Jennifer F.; Hurvitz, Philip M.; Moudon, Anne Vernez; Cook, Andrea; Mooney, Stephen J.; Buszkiewicz, James H.; Lozano, Paula; Rosenberg, Dori E.; Kapos, Flavia; Theis, Mary Kay; Anau, Jane; Arterburn, David. (2022). Differences in Weight Gain Following Residential Relocation in the Moving to Health (M2H) Study. Epidemiology, 33(5), 747-755.

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Abstract

Background: Neighborhoods may play an important role in shaping long-term weight trajectory and obesity risk. Studying the impact of moving to another neighborhood may be the most efficient way to determine the impact of the built environment on health. We explored whether residential moves were associated with changes in body weight. Methods: Kaiser Permanente Washington electronic health records were used to identify 21,502 members aged 18-64 who moved within King County, WA between 2005 and 2017. We linked body weight measures to environment measures, including population, residential, and street intersection densities (800 m and 1,600 m Euclidian buffers) and access to supermarkets and fast foods (1,600 m and 5,000 m network distances). We used linear mixed models to estimate associations between postmove changes in environment and changes in body weight. Results: In general, moving from high-density to moderate- or low-density neighborhoods was associated with greater weight gain postmove. For example, those moving from high to low residential density neighborhoods (within 1,600 m) gained an average of 4.5 (95% confidence interval [CI] = 3.0, 5.9) lbs 3 years after moving, whereas those moving from low to high-density neighborhoods gained an average of 1.3 (95% CI = -0.2, 2.9) lbs. Also, those moving from neighborhoods without fast-food access (within 1600m) to other neighborhoods without fast-food access gained less weight (average 1.6 lbs [95% CI = 0.9, 2.4]) than those moving from and to neighborhoods with fast-food access (average 2.8 lbs [95% CI = 2.5, 3.2]). Conclusions: Moving to higher-density neighborhoods may be associated with reductions in adult weight gain.

Keywords

Body-mass Index; Neighborhood Socioeconomic-status; New-york-city; Built Environment; Physical-activity; Food Environment; Urban Sprawl; Risk-factors; Obesity; Walking; Electronic Medical Records; Fast Foods; Population Density; Residential Density; Residential Moves; Supermarkets

Using Open Data and Open-source Software to Develop Spatial Indicators of Urban Design and Transport Features for Achieving Healthy and Sustainable Cities

Boeing, Geoff; Higgs, Carl; Liu, Shiqin; Giles-corti, Billie; Sallis, James F.; Cerin, Ester; Lowe, Melanie; Adlakha, Deepti; Hinckson, Erica; Moudon, Anne Vernez; Salvo, Deborah; Adams, Marc A.; Barrozo, Ligia, V; Bozovic, Tamara; Delclos-alio, Xavier; Dygryn, Jan; Ferguson, Sara; Gebel, Klaus; Thanh Phuong Ho; Lai, Poh-chin; Martori, Joan C.; Nitvimol, Kornsupha; Queralt, Ana; Roberts, Jennifer D.; Sambo, Garba H.; Schipperijn, Jasper; Vale, David; Van De Weghe, Nico; Vich, Guillem; Arundel, Jonathan. (2022). Using Open Data and Open-source Software to Develop Spatial Indicators of Urban Design and Transport Features for Achieving Healthy and Sustainable Cities. Lancet Global Health, 10(6), E907-E918.

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Abstract

Benchmarking and monitoring of urban design and transport features is crucial to achieving local and international health and sustainability goals. However, most urban indicator frameworks use coarse spatial scales that either only allow between-city comparisons, or require expensive, technical, local spatial analyses for within-city comparisons. This study developed a reusable, open-source urban indicator computational framework using open data to enable consistent local and global comparative analyses. We show this framework by calculating spatial indicators-for 25 diverse cities in 19 countries-of urban design and transport features that support health and sustainability. We link these indicators to cities' policy contexts, and identify populations living above and below critical thresholds for physical activity through walking. Efforts to broaden participation in crowdsourcing data and to calculate globally consistent indicators are essential for planning evidence-informed urban interventions, monitoring policy effects, and learning lessons from peer cities to achieve health, equity, and sustainability goals.

Keywords

Systems; Access; Care

What Next? Expanding Our View of City Planning and Global Health, and Implementing and Monitoring Evidence-informed Policy

Giles-corti, Billie; Moudon, Anne Vernez; Lowe, Melanie; Cerin, Ester; Boeing, Geoff; Frumkin, Howard; Salvo, Deborah; Foster, Sarah; Kleeman, Alexandra; Bekessy, Sarah; De Sa, Thiago Herick; Nieuwenhuijsen, Mark; Higgs, Carl; Hinckson, Erica; Adlakha, Deepti; Arundel, Jonathan; Liu, Shiqin; Oyeyemi, Adewale L.; Nitvimol, Kornsupha; Sallis, James F. (2022). What Next? Expanding Our View of City Planning and Global Health, and Implementing and Monitoring Evidence-informed Policy. Lancet Global Health, 10(6), E919-E926.

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Abstract

This Series on urban design, transport, and health aimed to facilitate development of a global system of health-related policy and spatial indicators to assess achievements and deficiencies in urban and transport policies and features. This final paper in the Series summarises key findings, considers what to do next, and outlines urgent key actions. Our study of 25 cities in 19 countries found that, despite many well intentioned policies, few cities had measurable standards and policy targets to achieve healthy and sustainable cities. Available standards and targets were often insufficient to promote health and wellbeing, and health-supportive urban design and transport features were often inadequate or inequitably distributed. City planning decisions affect human and planetary health and amplify city vulnerabilities, as the COVID-19 pandemic has highlighted. Hence, we offer an expanded framework of pathways through which city planning affects health, incorporating 11 integrated urban system policies and 11 integrated urban and transport interventions addressing current and emerging issues. Our call to action recommends widespread uptake and further development of our methods and open-source tools to create upstream policy and spatial indicators to benchmark and track progress; unmask spatial inequities; inform interventions and investments; and accelerate transitions to net zero, healthy, and sustainable cities.

Associations between Neighborhood Built Environment, Residential Property Values, and Adult BMI Change: The Seattle Obesity Study III

Buszkiewicz, James H.; Rose, Chelsea M.; Ko, Linda K.; Mou, Jin; Moudon, Anne Vernez; Hurvitz, Philip M.; Cook, Andrea J.; Drewnowski, Adam. (2022). Associations between Neighborhood Built Environment, Residential Property Values, and Adult BMI Change: The Seattle Obesity Study III. SSM-Population Health, 19.

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Abstract

Objective: To examine associations between neighborhood built environment (BE) variables, residential property values, and longitudinal 1-and 2-year changes in body mass index (BMI). Methods: The Seattle Obesity Study III was a prospective cohort study of adults with geocoded residential addresses, conducted in King, Pierce, and Yakima Counties in Washington State. Measured heights and weights were obtained at baseline (n = 879), year 1 (n = 727), and year 2 (n = 679). Tax parcel residential property values served as proxies for individual socioeconomic status. Residential unit and road intersection density were captured using Euclidean-based SmartMaps at 800 m buffers. Counts of supermarket (0 versus. 1+) and fast-food restaurant availability (0, 1-3, 4+) were measured using network based SmartMaps at 1600 m buffers. Density measures and residential property values were categorized into tertiles. Linear mixed-effects models tested whether baseline BE variables and property values were associated with differential changes in BMI at year 1 or year 2, adjusting for age, gender, race/ethnicity, education, home ownership, and county of residence. These associations were then tested for potential disparities by age group, gender, race/ethnicity, and education. Results: Road intersection density, access to food sources, and residential property values were inversely associated with BMI at baseline. At year 1, participants in the 3rd tertile of density metrics and with 4+ fast-food restaurants nearby showed less BMI gain compared to those in the 1st tertile or with 0 restaurants. At year 2, higher residential property values were predictive of lower BMI gain. There was evidence of differential associations by age group, gender, and education but not race/ethnicity. Conclusion: Inverse associations between BE metrics and residential property values at baseline demonstrated mixed associations with 1-and 2-year BMI change. More work is needed to understand how individual-level sociodemographic factors moderate associations between the BE, property values, and BMI change.

Keywords

Body-mass Index; Physical-activity; Food Environment; Socioeconomic-status; Weight-gain; Health; Quality

City Planning Policies to Support Health and Sustainability: An International Comparison of Policy Indicators for 25 Cities

Lowe, Melanie; Adlakha, Deepti; Sallis, James F.; Salvo, Deborah; Cerin, Ester; Moudon, Anne Vernez; Higgs, Carl; Hinckson, Erica; Arundel, Jonathan; Boeing, Geoff; Liu, Shiqin; Mansour, Perla; Gebel, Klaus; Puig-ribera, Anna; Mishra, Pinki Bhasin; Bozovic, Tamara; Carson, Jacob; Dygryn, Jan; Florindo, Alex A.; Ho, Thanh Phuong; Hook, Hannah; Hunter, Ruth F.; Lai, Poh-chin; Molina-garcia, Javier; Nitvimol, Kornsupha; Oyeyemi, Adewale L.; Ramos, Carolina D. G.; Resendiz, Eugen; Troelsen, Jens; Witlox, Frank; Giles-corti, Billie. (2022). City Planning Policies to Support Health and Sustainability: An International Comparison of Policy Indicators for 25 Cities. Lancet Global Health, 10(6), E882-E894.

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Abstract

City planning policies influence urban lifestyles, health, and sustainability. We assessed policy frameworks for city planning for 25 cities across 19 lower-middle-income countries, upper-middle-income countries, and high-income countries to identify whether these policies supported the creation of healthy and sustainable cities. We systematically collected policy data for evidence-informed indicators related to integrated city planning, air pollution, destination accessibility, distribution of employment, demand management, design, density, distance to public transport, and transport infrastructure investment. Content analysis identified strengths, limitations, and gaps in policies, allowing us to draw comparisons between cities. We found that despite common policy rhetoric endorsing healthy and sustainable cities, there was a paucity of measurable policy targets in place to achieve these aspirations. Some policies were inconsistent with public health evidence, which sets up barriers to achieving healthy and sustainable urban environments. There is an urgent need to build capacity for health-enhancing city planning policy and governance, particularly in low-income and middle-income countries.

Keywords

Physical-activity; Population Health; Walkability

Walking Objectively Measured: Classifying Accelerometer Data with GPS and Travel Diaries

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

Residential Property Values Predict Prevalent Obesity but Do Not Predict 1-year Weight Change

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

Differences in Behavior, Time, Location, and Built Environment between Objectively Measured Utilitarian and Recreational Walking

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

Probabilistic Walking Models Using Built Environment and Sociodemographic Predictors

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

Characterizing the Food Environment: Pitfalls and Future Directions

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