Urban Form Lab Archives - Page 6 of 10

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.

View Publication

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

Associations between Fast-Food Consumption and Body Mass Index: A Cross-sectional Study in Adult Twins

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.

View Publication

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

The Residential Effect Fallacy in Neighborhood and Health Studies Formal Definition, Empirical Identification, and Correction

Chaix, Basile; Duncan, Dustin; Vallee, Julie; Vernez-moudon, Anne; Benmarhnia, Tarik; Kestens, Yan. (2017). The Residential Effect Fallacy in Neighborhood and Health Studies Formal Definition, Empirical Identification, and Correction. Epidemiology, 28(6), 789 – 797.

View Publication

Abstract

Background: Because of confounding from the urban/rural and socioeconomic organizations of territories and resulting correlation between residential and nonresidential exposures, classically estimated residential neighborhood-outcome associations capture nonresidential environment effects, overestimating residential intervention effects. Our study diagnosed and corrected this residential effect fallacy bias applicable to a large fraction of neighborhood and health studies. Methods: Our empirical application investigated the effect that hypothetical interventions raising the residential number of services would have on the probability that a trip is walked. Using global positioning systems tracking and mobility surveys over 7 days (227 participants and 7440 trips), we employed a multilevel linear probability model to estimate the trip-level association between residential number of services and walking to derive a naive intervention effect estimate and a corrected model accounting for numbers of services at the residence, trip origin, and trip destination to determine a corrected intervention effect estimate (true effect conditional on assumptions). Results: There was a strong correlation in service densities between the residential neighborhood and nonresidential places. From the naive model, hypothetical interventions raising the residential number of services to 200, 500, and 1000 were associated with an increase by 0.020, 0.055, and 0.109 of the probability of walking in the intervention groups. Corrected estimates were of 0.007, 0.019, and 0.039. Thus, naive estimates were overestimated by multiplicative factors of 3.0, 2.9, and 2.8. Conclusions: Commonly estimated residential intervention-outcome associations substantially overestimate true effects. Our somewhat paradoxical conclusion is that to estimate residential effects, investigators critically need information on nonresidential places visited.

Keywords

Self-rated Health; Record Cohort; Physical-activity; Transportation Modes; Built Environment; Activity Spaces; Research Agenda; Risk-factors; Associations; Exposure

Cohort Profile: Twins Study of Environment, Lifestyle Behaviours and Health

Duncan, Glen E.; Avery, Ally; Hurvitz, Philip M.; Moudon, Anne Vernez; Tsang, Siny; Turkheimer, Eric. (2019). Cohort Profile: Twins Study of Environment, Lifestyle Behaviours and Health. International Journal Of Epidemiology, 48(4), 1041.

View Publication

Keywords

Twin Studies; Neighborhoods; Native Americans; Normalized Difference Vegetation Index; Life Style; Twins; Body-mass Index; Physical-activity; Neighborhood Walkability; Waist Circumference; Built Environment; Causal Inference; Deprivation; Validation; Registry; Obesity

Associations between Neighborhood Greenspace and Brain Imaging Measures in Non-Demented Older Adults: The Cardiovascular Health Study

Besser, Lilah M.; Lovasi, Gina S.; Michael, Yvonne L.; Garg, Parveen; Hirsch, Jana A.; Siscovick, David; Hurvitz, Phil; Biggs, Mary L.; Galvin, James E.; Bartz, Traci M.; Longstreth, W. T. (2021). Associations between Neighborhood Greenspace and Brain Imaging Measures in Non-Demented Older Adults: The Cardiovascular Health Study. Social Psychiatry And Psychiatric Epidemiology, 56(9), 1575 – 1585.

View Publication

Abstract

Purpose Greater neighborhood greenspace has been associated with brain health, including better cognition and lower odds of Alzheimer's disease in older adults. We investigated associations between neighborhood greenspace and brain-based magnetic resonance imaging (MRI) measures and potential effect modification by sex or apolipoprotein E genotype (APOE), a risk factor for Alzheimer's disease. Methods We obtained a sample of non-demented participants 65 years or older (n = 1125) from the longitudinal, population-based Cardiovascular Health Study (CHS). Greenspace data were derived from the National Land Cover Dataset. Adjusted multivariable linear regression estimated associations between neighborhood greenspace five years prior to the MRI and left and right hippocampal volume and 10-point grades of ventricular size and burden of white matter hyperintensity. Interaction terms tested effect modification by APOE genotype and sex. CHS data (1989-1999) were obtained/analyzed in 2020. Results Participants were on average 79 years old [standard deviation (SD) = 4], 58% were female, and 11% were non-white race. Mean neighborhood greenspace was 38% (SD = 28%). Greater proportion of greenspace in the neighborhood five years before MRI was borderline associated with lower ventricle grade (estimate: - 0.30; 95% confidence interval: - 0.61, 0.00). We observed no associations between greenspace and the other MRI outcome measures and no evidence of effect modification by APOE genotype and sex. Conclusion This study suggests a possible association between greater greenspace and less ventricular enlargement, a measure reflecting global brain atrophy. If confirmed in other longitudinal cohort studies, interventions and policies to improve community greenspaces may help to maintain brain health in older age.

Keywords

Mild Cognitive Impairment; Ventricular Enlargement; Residential Greenness; Hippocampal Atrophy; Volume; Disease; Environment; Progression; Symptoms; Dementia; Neighborhood; Green Space; Mri; Brain Volume; Hippocampal; White Matter

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.

View Publication

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

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.

View Publication

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

Utilitarian and Recreational Walking Among Spanish- and English-Speaking Latino Adults in Micropolitan US Towns

Doescher, Mark P.; Lee, Chanam; Saelens, Brian E.; Lee, Chunkuen; Berke, Ethan M.; Adachi-mejia, Anna M.; Patterson, Davis G.; Moudon, Anne Vernez. (2017). Utilitarian and Recreational Walking Among Spanish- and English-Speaking Latino Adults in Micropolitan US Towns. Journal Of Immigrant & Minority Health, 19(2), 237 – 245.

View Publication

Abstract

Walking among Latinos in US Micropolitan towns may vary by language spoken. In 2011-2012, we collected telephone survey and built environment (BE) data from adults in six towns located within micropolitan counties from two states with sizable Latino populations. We performed mixed-effects logistic regression modeling to examine relationships between ethnicity-language group [Spanish-speaking Latinos (SSLs); English-speaking Latinos (ESLs); and English-speaking non-Latinos (ENLs)] and utilitarian walking and recreational walking, accounting for socio-demographic, lifestyle and BE characteristics. Low-income SSLs reported higher amounts of utilitarian walking than ENLs (p = 0.007), but utilitarian walking in this group decreased as income increased. SSLs reported lower amounts of recreational walking than ENLs (p = 0.004). ESL-ENL differences were not significant. We identified no statistically significant interactions between ethnicity-language group and BE characteristics. Approaches to increase walking in micropolitan towns with sizable SSL populations may need to account for this group's differences in walking behaviors.

Keywords

Walking; Confidence Intervals; Ecology; Ethnic Groups; Hispanic Americans; Income; Language & Languages; Metropolitan Areas; Population; Public Health; Recreation; Rural Conditions; White People; Logistic Regression Analysis; Socioeconomic Factors; Social Context; Body Mass Index; Acquisition Of Data; Physical Activity; Data Analysis Software; Odds Ratio; United States; Environment Design; Ethnicity; Rural Populations; Physical-activity; Built Environment; United-states; Postmenopausal Women; Acculturation; Risk; Transportation; Mortality; Health; Associations; Studies; Demographic Aspects; Telephone Surveys; Minority & Ethnic Groups; Physical Fitness; Low Income Groups; Urban Environments; Demographics; Language; Accounting; Statistical Analysis; Urban Areas; Towns; Populations; Adults; Lifestyles; Latin American Cultural Groups; Sociodemographics; Landscape Architecture; Population Growth; Pediatrics; Leisure; Health Care; Noncitizens; Preventive Medicine; United States--us

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.

View Publication

Abstract

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.

View Publication

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