Physical Activity and Food Environment Assessments

Eyler, Amy A.; Blanck, Heidi M.; Gittelsohn, Joel; Karpyn, Allison; McKenzie, Thomas L.; Partington, Susan; Slater, Sandy J.; Winters, Meghan

doi:10.1016/j.amepre.2014.10.008

Cited by 13 publications

(7 citation statements)

References 27 publications

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“…The metrics developed by Walk Score (Walk Score, Bike Score, and Transit Score) are intuitive, easy to use, and available online, fulfilling many of the recommendations for making built environment measures relevant to practice [ 36 ]. The Bike Score methodology was informed by empirical research, however, the specific algorithms and decay functions are proprietary.…”

Section: Discussionmentioning

confidence: 99%

Bike Score®: Associations between urban bikeability and cycling behavior in 24 cities

Winters

Teschke

Bräuer

et al. 2016

Int J Behav Nutr Phys Act

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BackgroundThere is growing interest in designing cities that support not only walking, but also cycling. Bike Score® is a metric capturing environmental characteristics associated with cycling that is now available for over 160 US and Canadian cities. Our aim was to determine if Bike Score was associated with between and within-city variability in cycling behavior.MethodsWe used linear regression to model associations between Bike Score and journey to work cycling mode share (US: American Community Survey, 2013 or 2012 5-year estimates; Canada: 2011 National Household Survey) for 5664 census tracts in 24 US and Canadian cities.ResultsAt the city level, the correlation between mean Bike Score and mean journey to work cycling mode share was moderate (r = 0.52). At the census tract level, the correlation was 0.35; a ten-unit increase in Bike Score was associated with a 0.5 % (95 % CI: 0.5 to 0.6) increase in the proportion of population cycling to work, a meaningful difference given the low modal shares (mean = 1.9 %) in many North American cities. Census tracts with the highest Bike Scores (>90 to 100) had mode shares 4.0 % higher (β = 4.0, 95 % CI: 2.9 to 5.0) than the lowest Bike Score areas (0–25). City specific analyses indicated between-city variability in associations, with regression estimates between Bike Score and mode share ranging from 0.2 to 3.5 %.ConclusionsThe Bike Score metric was associated bicycle mode share between and within cities, suggesting its utility for planning bicycle infrastructure.Electronic supplementary materialThe online version of this article (doi:10.1186/s12966-016-0339-0) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Bike Score®: Associations between urban bikeability and cycling behavior in 24 cities

Winters

Teschke

Bräuer

et al. 2016

Int J Behav Nutr Phys Act

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“…availability of healthy and unhealthy foods, food prices, promotions/marketing, etc.). These can be measured from simple to complex data collection involving perceived, observed and geographic data (Eyler et al , 2015). …”

Section: Is the “Differential Susceptibility” Model Relevant For Omentioning

confidence: 99%

Gene and environment interaction: Is the differential susceptibility hypothesis relevant for obesity?

Molle

Fatemi

Levitan

et al. 2017

Neuroscience & Biobehavioral Reviews

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The differential susceptibility model states that a given genetic variant is associated with an increased risk of pathology in negative environments but greater than average resilience in enriched ones. While this theory was first implemented in psychiatric-genetic research, it may also help us to unravel the complex ways that genes and environments interact to influence feeding behavior and obesity. We reviewed evidence on gene vs. environment interactions that influence obesity development, aiming to support the applicability of the differential susceptibility model for this condition, and propose that various environmental “layers” relevant for human development should be considered when bearing the differential susceptibility model in mind. Mother-child relationship, socioeconomic status and individual's response are important modifiers of BMI and food intake when interacting with gene variants, “for better and for worse”. While only a few studies to date have investigated obesity outcomes using this approach, we propose that the differential susceptibility hypothesis is in fact highly applicable to the study of genetic and environmental influences on feeding behavior and obesity risk.

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“…In recent years, we have seen an explosion of research on facets of the built environment in topic areas that range from physical activity, 37 to the food environment, 38 to mental health, 39 among others. Beyond actual ‘space’, the built environment may also encompass aspects such as children’s access to physical activity equipment 40 and access to outlets that sell fruits and vegetables.…”

Section: Exploring a Basic Environmental Asset Assessment Framework Fmentioning

confidence: 99%

Assessing environmental assets for health promotion program planning: a practical framework for health promotion practitioners

Springer¹,

Evans²

2016

Health Promot Perspect

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Conducting a health needs assessment is an important if not essential first step for health promotion planning. This paper explores how health needs assessments may be further strengthened for health promotion planning via an assessment of environmental assets rooted in the multiple environments (policy, information, social and physical environments) that shape health and behavior. Guided by a behavioral-ecological perspective- one that seeks to identify environmental assets that can influence health behavior, and an implementation science perspective- one that seeks to interweave health promotion strategies into existing environmental assets, we present a basic framework for assessing environmental assets and review examples from the literature to illustrate the incorporation of environmental assets into health program design. Health promotion practitioners and researchers implicitly identify and apply environmental assets in the design and implementation of health promotion interventions;this paper provides foundation for greater intentionality in assessing environmental assets for health promotion planning.

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Physical Activity and Food Environment Assessments

Cited by 13 publications

References 27 publications

Bike Score®: Associations between urban bikeability and cycling behavior in 24 cities

Bike Score®: Associations between urban bikeability and cycling behavior in 24 cities

Gene and environment interaction: Is the differential susceptibility hypothesis relevant for obesity?

Assessing environmental assets for health promotion program planning: a practical framework for health promotion practitioners

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