Ethan M. Berke

Association of the Built Environment With Physical Activity and Obesity in Older Persons

OBJECTIVE We examined whether older persons who live in areas that are conducive to walking are more active or less obese than those living in areas where walking is more difficult. METHODS We used data from the Adult Changes in Thought cohort study for a cross-sectional analysis of 936 participants aged 65 to 97 years. The Walkable and Bikable Communities Project previously formulated a walkability score to predict the probability of walking in King County, Washington. Data from the cohort study were linked to the walkability score at the participant level using a geographic information system. Analyses tested for associations between walkability score and activity and body mass index. RESULTS Higher walkability scores were associated with significantly more walking for exercise across buffers (circular zones around each respondents home) of varying radii (for men, odds ratio [OR]=5.86; 95% confidence interval [CI]=1.01, 34.17 to OR=9.14; CI=1.23, 68.11; for women, OR=1.63; CI=0.94, 2.83 to OR=1.77; CI=1.03, 3.04). A trend toward lower body mass index in men living in more walkable neighborhoods did not reach statistical significance. CONCLUSIONS Findings suggest that neighborhood characteristics are associated with the frequency of walking for physical activity in older people. Whether frequency of walking reduces obesity prevalence is less clear.

Protective association between neighborhood walkability and depression in older men

OBJECTIVES: To evaluate the association between neighborhood walkability and depression in older adults.

End-Of-Life Care For Medicare Beneficiaries With Cancer Is Highly Intensive Overall And Varies Widely

Studies have shown that cancer care near the end of life is more aggressive than many patients prefer. Using a cohort of deceased Medicare beneficiaries with poor-prognosis cancer, meaning that they were likely to die within a year, we examined the association between hospital characteristics and eleven end-of-life care measures, such as hospice use and hospitalization. Our study revealed a relatively high intensity of care in the last weeks of life. At the same time, there was more than a twofold variation within hospital groups with common features, such as cancer center designation and for-profit status. We found that these hospital characteristics explained little of the observed variation in intensity of end-of-life cancer care and that none reliably predicted a specific pattern of care. These findings raise questions about what factors may be contributing to this variation. They also suggest that best practices in end-of-life cancer care can be found in many settings and that efforts to improve the quality of end-of-life care should include every hospital category.

Passive and In-Situ assessment of mental and physical well-being using mobile sensors

The idea of continuously monitoring well-being using mobile-sensing systems is gaining popularity. In-situ measurement of human behavior has the potential to overcome the short comings of gold-standard surveys that have been used for decades by the medical community. However, current sensing systems have mainly focused on tracking physical health; some have approximated aspects of mental health based on proximity measurements but have not been compared against medically accepted screening instruments. In this paper, we show the feasibility of a multi-modal mobile sensing system to simultaneously assess mental and physical health. By continuously capturing fine-grained motion and privacy-sensitive audio data, we are able to derive different metrics that reflect the results of commonly used surveys for assessing well-being by the medical community. In addition, we present a case study that highlights how errors in assessment due to the subjective nature of the responses could potentially be avoided by continuous mobile sensing.

Using built environment characteristics to predict walking for exercise

BackgroundEnvironments 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 ones 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.ResultsFor 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.ConclusionNone 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.

Alcohol Retail Density and Demographic Predictors of Health Disparities: A Geographic Analysis

OBJECTIVES We examined whether the geographic density of alcohol retailers was greater in geographic areas with higher levels of demographic characteristics that predict health disparities. METHODS We obtained the locations of all alcohol retailers in the continental United States and created a map depicting alcohol retail outlet density at the US Census tract level. US Census data provided tract-level measures of poverty, education, crowding, and race/ethnicity. We used multiple linear regression to assess relationships between these variables and retail alcohol density. RESULTS In urban areas, retail alcohol density had significant nonlinear relationships with Black race, Latino ethnicity, poverty, and education, with slopes increasing substantially throughout the highest quartile for each predictor. In high-proportion Latino communities, retail alcohol density was twice as high as the median density. Retail alcohol density had little or no relationship with the demographic factors of interest in suburban, large town, or rural census tracts. CONCLUSIONS Greater density of alcohol retailers was associated with higher levels of poverty and with higher proportions of Blacks and Latinos in urban census tracts. These disparities could contribute to higher morbidity in these geographic areas.

Predictors of tobacco outlet density nationwide: a geographic analysis

Objective To elucidate how demographics of US Census tracts are related to tobacco outlet density (TOD). Method The authors conducted a nationwide assessment of the association between socio-demographic US Census indicators and the density of tobacco outlets across all 64 909 census tracts in the continental USA. Retail tobacco outlet addresses were determined through North American Industry Classification System codes, and density per 1000 population was estimated for each census tract. Independent variables included urban/rural; proportion of the population that was black, Hispanic and women with low levels of education; proportion of families living in poverty and median household size. Results In a multivariate analysis, there was a higher TOD per 1000 population in urban than in rural locations. Furthermore, higher TOD was associated with larger proportions of blacks, Hispanics, women with low levels of education and with smaller household size. Urban–rural differences in the relation between demographics and TOD were found in all socio-demographic categories, with the exception of poverty, but were particularly striking for Hispanics, for whom the relation with TOD was 10 times larger in urban compared with rural census tracts. Conclusions The findings suggest that tobacco outlets are more concentrated in areas where people with higher risk for negative health outcomes reside. Future studies should examine the relation between TOD and smoking, smoking cessation, as well as disease rates.

BeWell: Sensing Sleep, Physical Activities and Social Interactions to Promote Wellbeing

Smartphone sensing and persuasive feedback design is enabling a new generation of wellbeing apps capable of automatically monitoring multiple aspects of physical and mental health. In this article, we present BeWell+ the next generation of the BeWell smartphone wellbeing app, which monitors user behavior along three health dimensions, namely sleep, physical activity, and social interaction. BeWell promotes improved behavioral patterns via feedback rendered as an ambient display on the smartphone’s wallpaper. With BeWell+, we introduce new mechanisms to address key limitations of the original BeWell app; specifically, (1) community adaptive wellbeing feedback, which generalizes to diverse user communities (e.g., elderly, children) by promoting better behavior yet remains realistic to the user’s lifestyle; and, (2) wellbeing adaptive energy allocation, which prioritizes monitoring fidelity and feedback responsiveness on specific health dimensions (e.g., sleep) where the user needs additional help. We evaluate BeWell+ with a 27 person, 19 day field trial. Our findings show that not only can BeWell+ operate successfully on consumer smartphones; but also users understand feedback and respond by taking steps towards leading healthier lifestyles.

Computing travel time when the exact address is unknown: a comparison of point and polygon ZIP code approximation methods

BackgroundTravel time is an important metric of geographic access to health care. We compared strategies of estimating travel times when only subject ZIP code data were available.ResultsUsing simulated data from New Hampshire and Arizona, we estimated travel times to nearest cancer centers by using: 1) geometric centroid of ZIP code polygons as origins, 2) population centroids as origin, 3) service area rings around each cancer center, assigning subjects to rings by assuming they are evenly distributed within their ZIP code, 4) service area rings around each center, assuming the subjects follow the population distribution within the ZIP code. We used travel times based on street addresses as true values to validate estimates. Population-based methods have smaller errors than geometry-based methods. Within categories (geometry or population), centroid and service area methods have similar errors. Errors are smaller in urban areas than in rural areas.ConclusionPopulation-based methods are superior to the geometry-based methods, with the population centroid method appearing to be the best choice for estimating travel time. Estimates in rural areas are less reliable.

Healthcare Cost Differences with Participation in a Community-Based Group Physical Activity Benefit for Medicare Managed Care Health Plan Members

OBJECTIVES: To determine whether participation in a physical activity benefit by Medicare managed care enrollees is associated with lower healthcare utilization and costs.