Person:

Oreskovic, Nicolas

Background: Previous studies testing the association between the built environment and walking behavior have been largely cross-sectional and have yielded mixed results. This study reports on a natural experiment in which changes to the built environment were implemented at a university campus in Hong Kong. Longitudinal data on walking behaviors were collected using surveys, one before and one after changes to the built environment, to test the influence of changes to the built environment on walking behavior. Methods: Built environment data are from a university campus in Hong Kong, and include land use, campus bus services, pedestrian network, and population density data collected from campus maps, the university developmental office, and field surveys. Walking behavior data were collected at baseline in March 2012 (n = 198) and after changes to the built environment from the same cohort of subjects in December 2012 (n = 169) using a walking diary. Geographic information systems (GIS) was used to map walking routes and built environment variables, and compare each subject’s walking behaviors and built environment exposure before and after the changes to the built environment. Walking behavior outcomes were changes in: i) walking distance, ii) destination-oriented walking, and iii) walked altitude range. Multivariable linear regression models were used to test for associations between changes to the built environment and changes in walking behaviors. Results: Greater pedestrian network connectivity predicted longer walking distances and an increased likelihood of walking as a means of transportation. The increased use of recreational (vs. work) buildings, largely located at mid-range altitudes, as well as increased population density predicted greater walking distances.Having more bus services and a greater population density encouraged people to increase their walked altitude range. Conclusions: In this longitudinal study, changes to the built environment were associated with changes in walking behaviors. Use of GIS combined with walking diaries presents a practical method for mapping and measuring changes in the built environment and walking behaviors, respectively. Additional longitudinal studies can help clarify the relationships between the built environment and walking behaviors identified in this natural experiment.

Purpose: To assess if dispositional optimism is associated with adiposity and explore if dispositional optimism mediates the relationship between parent education and adiposity (BMI z score). Methods: Multivariable regression analyses of data collected from 1298 non-Hispanic black and white 12–19 year olds from a single Midwestern public school district. Results: Less optimistic adolescents had higher BMI z scores (r = −0.09, p < 0.001). Addition of dispositional optimism to the regression model caused an approximately 10% attenuation of the parent education and BMI z score relationship. Sobel tests confirmed that this attenuation indicated partial mediation. Conclusion: Lower dispositional optimism is associated with higher adiposity and this association accounts for some of the influence of parent education on adolescent adiposity.

Background: Physical activity is a health-enhancing behavior, but few adolescents achieve the recommended levels of moderate-to-vigorous physical activity. Understanding how adolescents use different built environment spaces for physical activity and activity varies by location could help in designing effective interventions to promote moderate-to-vigorous physical activity. The objective of this study was to describe the locations where adolescents engage in physical activity and compare traditional intensity-based measures with continuous activity when describing built environment use patterns among adolescents. Methods: Eighty adolescents aged 11–14 years recruited from community health and recreation centers. Adolescents wore accelerometers (Actigraph GT3X) and global positioning system receivers (QStarz BT-Q1000XT) for two separate weeks to record their physical activity levels and locations. Accelerometer data provided a continuous measure of physical activity and intensity-based measures (sedentary time, moderate-to-vigorous physical activity). Physical activity was mapped by land-use classification (home, school, park, playground, streets & sidewalks, other) using geographic information systems and this location-based activity was assessed for both continuous and intensity-based physical activity derived from mixed-effects models which accounted for repeated measures and clustering effects within person, date, school, and town. Results: Mean daily moderate-to-vigorous physical activity was 22 minutes, mean sedentary time was 134 minutes. Moderate-to-vigorous physical activity occurred in bouts lasting up to 15 minutes. Compared to being at home, being at school, on the streets and sidewalks, in parks, and playgrounds were all associated with greater odds of being in moderate-to-vigorous physical activity and achieving higher overall activity levels. Playground use was associated with the highest physical activity level (β = 172 activity counts per minute, SE = 4, p < 0.0001) and greatest odds of being in moderate-to-vigorous physical activity (odds ratio 8.3, 95% confidence interval 4.8-14.2). Conclusion: Adolescents were more likely to engage in physical activity, and achieved their highest physical activity levels, when using built environments located outdoors. Novel objective methods for determining physical activity can provide insight into adolescents’ spatial physical activity patterns, which could help guide physical activity interventions. Promoting zoning and health policies that encourage the design and regular use of outdoor spaces may offer another promising opportunity for increasing adolescent physical activity.

Background: Care coordination programs for high-risk, high-cost patients are a critical component of population health management. These programs aim to improve outcomes and reduce costs and have proliferated over the last decade. Some programs, originally designed for Medicare patients, are now transitioning to also serve Medicaid populations. However, there are still gaps in the understanding of what barriers to care Medicaid patients experience, and what supports will be most effective for providing them care coordination. Methods: We conducted two focus groups (n = 13) and thematic analyses to assess the outcomes drivers and programmatic preferences of Medicaid patients enrolled in a high-risk care coordination program at a major academic medical center in Boston, MA. Findings: Two focus groups identified areas where care coordination efforts were having a positive impact, as well as areas of unmet needs among the Medicaid population. Six themes emerged from the focus groups that clustered in three groupings: In the first group (1) enrollment in an existing medical care coordination programs, and (2) provider communication largely presented as positive accounts of assistance, and good relationships with providers, though participants also pointed to areas where these efforts fell short. In the second group (3) trauma histories, (4) mental health challenges, and (5) executive function difficulties all presented challenges faced by high-risk Medicaid patients that would likely require redress through additional programmatic supports. Finally, in the third group, (6) peer-to-peer support tendencies among patients suggested an untapped resource for care coordination programs. Conclusions: Programs aimed at high-risk Medicaid patients will want to consider programmatic adjustments to attend to patient needs in five areas: (1) provider connection/care coordination, (2) trauma, (3) mental health, (4) executive function/paperwork and coaching support, and (5) peer-to-peer support.

Background: Numerous definitions of neighborhood exist, yet few studies have considered youth’s perceptions of neighborhood boundaries. This study compared youth-identified neighborhood (YIN) boundaries to census-defined neighborhood (CDN) boundaries, and determined how the amount of time spent and moderate-to-vigorous physical activity (MVPA) levels compared within both boundary types. Methods: Adolescents aged 11–14 years were asked to identify their neighborhood boundaries using a map. Objective location and physical activity data collected using Global Positioning System (GPS) devices and accelerometers were used to calculate the amount of time spent and MVPA within youth-identified and census-defined neighborhood boundaries. Paired bivariate analyses compared mean area (meters squared), percent of total time, daily MVPA (minutes), time density (minutes/m2) and MVPA density (minutes/m2) for both boundary types. Results: Youth-identified neighborhoods (1,821,705 m2) and census-defined neighborhoods (1,277,181 m2) were not significantly different in area, p = 0.30. However, subjects spent more time in youth-identified neighborhoods (80.3%) than census-defined neighborhoods (58.4%), p < 0.0001, and engaged in more daily MVPA within youth-identified neighborhoods (14.7 minutes) than census-defined neighborhoods (9.5 minutes), p < 0.0001. After adjusting for boundary area, MVPA density (minutes of MVPA per squared meter of area) remained significantly greater for youth-identified neighborhoods (2.4 × 10-4 minutes/m2) than census-defined neighborhoods (1.4 × 10-4 minutes/m2), p = 0.02. Conclusions: Adolescents perceive their neighborhoods to be similar in size to census-defined neighborhoods. However, youth-identified neighborhoods better capture the locations in which adolescents spend time and engage in physical activity. Asking adolescents to identify their neighborhood boundaries is a feasible and valuable method for identifying the spaces that adolescents are exposed to and use to be physically active.