Arterial roads and area socioeconomic status are predictors of fast food restaurant density in King County, WA

<p>Abstract</p> <p>Background</p> <p>Fast food restaurants reportedly target specific populations by locating in lower-income and in minority neighborhoods. Physical proximity to fast food restaurants has been associated with higher obesity rates.</p> <p>Objective</p> <p>To examine possible associations, at the census tract level, between area demographics, arterial road density, and fast food restaurant density in King County, WA, USA.</p> <p>Methods</p> <p>Data on median household incomes, property values, and race/ethnicity were obtained from King County and from US Census data. Fast food restaurant addresses were obtained from Public Health-Seattle & King County and were geocoded. Fast food density was expressed per tract unit area and per capita. Arterial road density was a measure of vehicular and pedestrian access. Multivariate logistic regression models containing both socioeconomic status and road density were used in data analyses.</p> <p>Results</p> <p>Over one half (53.1%) of King County census tracts had at least one fast food restaurant. Mean network distance from dwelling units to a fast food restaurant countywide was 1.40 km, and 1.07 km for census tracts containing at least one fast food restaurant. Fast food restaurant density was significantly associated in regression models with low median household income (p < 0.001) and high arterial road density (p < 0.001) but not with percent of residents who were nonwhite.</p> <p>Conclusion</p> <p>No significant association was observed between census tract minority status and fast food density in King County. Although restaurant density was linked to low household incomes, that effect was attenuated by arterial road density. Fast food restaurants in King County are more likely to be located in lower income neighborhoods and higher traffic areas.</p

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

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

Using built environment characteristics to predict walking for exercise

Background: Environments conducive to walking may help people avoid sedentary lifestyles and associated diseases. Recent studies developed walkability models combining several built environment characteristics to optimally predict walking. Developing and testing such models with the same data could lead to overestimating one's ability to predict walking in an independent sample of the population. More accurate estimates of model fit can be obtained by splitting a single study population into training and validation sets (holdout approach) or through developing and evaluating models in different populations. We used these two approaches to test whether built environment characteristics near the home predict walking for exercise. Study participants lived in western Washington State and were adult members of a health maintenance organization. The physical activity data used in this study were collected by telephone interview and were selected for their relevance to cardiovascular disease. In order to limit confounding by prior health conditions, the sample was restricted to participants in good self-reported health and without a documented history of cardiovascular disease. Results: For 1,608 participants meeting the inclusion criteria, the mean age was 64 years, 90 percent were white, 37 percent had a college degree, and 62 percent of participants reported that they walked for exercise. Single built environment characteristics, such as residential density or connectivity, did not significantly predict walking for exercise. Regression models using multiple built environment characteristics to predict walking were not successful at predicting walking for exercise in an independent population sample. In the validation set, none of the logistic models had a C-statistic confidence interval excluding the null value of 0.5, and none of the linear models explained more than one percent of the variance in time spent walking for exercise. We did not detect significant differences in walking for exercise among census areas or postal codes, which were used as proxies for neighborhoods. Conclusion: None of the built environment characteristics significantly predicted walking for exercise, nor did combinations of these characteristics predict walking for exercise when tested using a holdout approach. These results reflect a lack of neighborhood-level variation in walking for exercise for the population studied.University of Washington Royalty Research fund award; by contracts R01-HL043201, R01-HL068639, and T32-HL07902 from the National Heart, Lung, and Blood Institute; and by grant R01-AG09556 from the National Institute on Aging

Capturing fine-scale travel behaviors: a comparative analysis between personal activity location measurement system (PALMS) and travel diary

Abstract Background Device-collected data from GPS and accelerometers for identifying active travel behaviors have dramatically changed research methods in transportation planning and public health. Automated algorithms have helped researchers to process large datasets with likely fewer errors than found in other collection methods (e.g., self-report travel diary). In this study, we compared travel modes identified by a commonly used automated algorithm (PALMS) that integrates GPS and accelerometer data with those obtained from travel diary estimates. Methods Sixty participants, who made 2100 trips during seven consecutive days of data collection, were selected from among the baseline sample of a project examining the travel behavior impact of a new light rail system in the greater Seattle, WA (USA) area. GPS point level analyses were first conducted to compare trip/place and travel mode detection results using contingency tables. Trip level analyses were then performed to investigate the effect of proportions of time overlap between travel logs and device-collected data on agreement rates. Global performance (with all subjects’ data combined) and subject-level performance of the algorithm were compared at the trip level. Results At the GPS point level, the overall agreement rate of travel mode detection was 77.4% between PALMS and the travel diary. The agreement rate for vehicular trip detection (84.5%) was higher than for bicycling (53.5%) and walking (58.2%). At the trip level, the global performance and subject-level performance of the PALMS algorithm were 46.4% and 42.4%, respectively. Vehicular trip detection showed highest agreement rates in all analyses. Study participants’ primary travel mode and car ownership were significantly related to the subject-level mode agreement rates. Conclusions The PALMS algorithm showed moderate identification power at the GPS point level. However, trip level analyses found lower agreement rates between PALMS and travel diary data, especially for active transportation. Testing different PALMS parameter settings may serve to improve the detection of active travel and help expand PALMS’s applicability in geographically different urbanized areas with a variety of travel modes

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

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

A cross‐sectional analysis of physical activity and weight misreporting in diverse populations: The Seattle Obesity Study III

Summary Background In‐person assessments of physical activity (PA) and body weight can be burdensome for participants and cost prohibitive for researchers. This study examined self‐reported PA and weight accuracy and identified patterns of misreporting in a diverse sample. Methods King, Pierce and Yakima county residents, aged 21–59 years (n = 728), self‐reported their moderate‐to‐vigorous PA (MVPA) and weight, in kilograms. Self‐reports were compared with minutes of bout‐level MVPA, from 3 days of accelerometer data, and measured weights. Regression models examined characteristics associated with underreporting and overreporting of MVPA and weight, the potential bias introduced using each measure and the relation between perceived and measured PA and weight. Results MVPA underreporting was higher among males and college educated participants; however, there was no differential MVPA overreporting. Weight underreporting was higher among males, those age 40–49 years and persons with obesity. Weight overreporting was higher among Hispanic participants and those reporting stress, unhappiness and fair or poor health. The estimated PA–obesity relation was similar using measured and self‐reported PA but not self‐reported weight. Perceived PA and weight predicted measured values. Conclusion Self‐reported PA and weight may be useful should objective measurement be infeasible; however, though population‐specific adjustment for differential reporting should be considered