Search

Background: Car driving is a form of passive transportation associated with higher sedentary behaviour, which is associated with morbidity. The decision to drive a car is likely to be influenced by the 'drivability' of the built environment, but there is lack of scientific evidence regarding the relative contribution of environmental characteristics of car driving in Europe, compared to individual characteristics. This study aimed to determine which neighbourhood- and individual-level characteristics were associated with car driving in adults of five urban areas across Europe. Second, the study aimed to determine the percentage of variance in car driving explained by individual- and neighbourhood-level characteristics., Methods: Neighbourhood environment characteristics potentially related to car use were identified from the literature. These characteristics were subsequently assessed using a Google Street View audit and available GIS databases, in 59 administrative residential neighbourhoods in five European urban areas. Car driving (min/week) and individual level characteristics were self-reported by study participants (analytic sample n = 4258). We used linear multilevel regression analyses to assess cross-sectional associations of individual and neighbourhood-level characteristics with weekly minutes of car driving, and assessed explained variance at each level and for the total model., Results: Higher residential density (β:-2.61, 95%CI: - 4.99; -0.22) and higher land-use mix (β:-3.73, 95%CI: - 5.61; -1.86) were significantly associated with fewer weekly minutes of car driving. At the individual level, higher age (β: 1.47, 95%CI: 0.60; 2.33), male sex (β: 43.2, 95%CI:24.7; 61.7), being employed (β:80.1, 95%CI: 53.6; 106.5) and ≥ 3 person household composition (β: 47.4, 95%CI: 20.6; 74.2) were associated with higher weekly minutes of car driving. Individual and neighbourhood characteristics contributed about equally to explained variance in minutes of weekly car driving, with 2 and 3% respectively, but total explained variance remained low., Conclusions: Residential density and land-use mix were neighbourhood characteristics consistently associated with minutes of weekly car driving, besides age, sex, employment and household composition. Although total explained variance was low, both individual- and neighbourhood-level characteristics were similarly important in their associations with car use in five European urban areas. This study suggests that more, higher quality, and longitudinal data are needed to increase our understanding of car use and its effects on determinants of health.

The definition of neighborhoods as areas of exposure to the food environment is a challenge in food environment research. We aimed to test the association of density of restaurants with home cooking using four different definitions of residential neighborhoods. We also tested effect modification by age, length of residency, education, and income. This innovative cross-sectional study was conducted in the Netherlands ( N = 1245 adults). We calculated geographic information system-based measures of restaurant density using residential administrative neighborhood boundaries, 800 m and 1600 m buffers around the home and respondents' self-defined boundaries (drawn by the respondents on a map of their residential area). We used adjusted Poisson regression to test associations of restaurant density (tertiles) and the outcome "weekly consumption of home-cooked meals" (six to seven as compared to five days per week (day/week) or fewer). Most respondents reported eating home-cooked meals for at least 6 day/week (74.2%). Regardless of the neighborhood definition used, no association between food environment and home cooking was observed. No effect modification was found. Although exposure in terms of density of restaurants was different according to the four different neighborhood definitions, we found no evidence that the area under study influences the association between density of restaurants and home cooking among Dutch adults.

Background: This paper investigated the independent and joint associations between aspects of the physical neighbourhood environment and social neighbourhood factors with BMI and overweight status in European adults., Methods: Data from 5,199 participants in the SPOTLIGHT survey were analysed. Participants reported on their height, weight and perceptions of the neighbourhood. Objectively measured aspects of the physical neighbourhood environment included: presence of recreational facilities, features of the active transportation environment, neighbourhood aesthetics and presence of different types of food outlets. Social factors included the self-reported variables social network, social cohesion, social trust and perceived crime and the census variable neighbourhood socioeconomic status. Outcome measures were BMI and overweight status. Main associations between physical and social factors and BMI/overweight status were analysed using multilevel regression analyses adjusted for confounders. Moderation analysis was conducted by adding the interaction terms between physical and social neighbourhood factors one by one to the multivariable models. Significant interaction terms were then stratified., Results: Significant associations with BMI/overweight status were found for features of the active transportation environment and all social factors, except perceived crime. Several significant interaction terms were detected, but no significant associations between the physical neighbourhood environment and BMI/overweight status were found after stratification., Conclusion: We did not find consistent interactions between physical and social neighbourhood factors to explain BMI and overweight status., (© 2019 The Author(s) Published by S. Karger AG, Basel.)

Urban health indicator (UHI) tools provide evidence about the health impacts of the physical urban environment which can be used in built environment policy and decision-making. Where UHI tools provide data at the neighborhood (and lower) scale they can provide valuable information about health inequalities and environmental deprivation. This review performs a census of UHI tools and explores their nature and characteristics (including how they represent, simplify or address complex systems) to increase understanding of their potential use by municipal built environment policy and decision-makers. We searched seven bibliographic databases, four key journals and six practitioner websites and conducted Google searches between January 27, 2016 and February 24, 2016 for UHI tools. We extracted data from primary studies and online indicator systems. We included 198 documents which identified 145 UHI tools comprising 8006 indicators, from which we developed a taxonomy. Our taxonomy classifies the significant diversity of UHI tools with respect to topic, spatial scale, format, scope and purpose. The proportions of UHI tools which measure data at the neighborhood and lower scale, and present data via interactive maps, have both increased over time. This is particularly relevant to built environment policy and decision-makers, reflects growing analytical capability and offers the potential for improved understanding of the complexity of influences on urban health (an aspect noted as a particular challenge by some indicator producers). The relation between urban health indicators and health impacts attributable to modifiable environmental characteristics is often indirect. Furthermore, the use of UHI tools in policy and decision-making appears to be limited, thus raising questions about the continued development of such tools by multiple organisations duplicating scarce resources. Further research is needed to understand the requirements of built environment policy and decision-makers, public health professionals and local communities regarding the form and presentation of indicators which support their varied objectives.

Background: The availability of outdoor recreational facilities is associated with increased leisure-time physical activity (PA). We investigated how much of this association is attributable to selection effects, and explored whether usage of recreational facilities was an explanatory mechanism., Methods: We analysed data from 5199 participants in the SPOTLIGHT survey residing in five European urban regions. Adults completed a survey and a Google Street View-based virtual audit was conducted to objectively measure the availability of outdoor recreational facilities in the residential neighbourhood. We used negative binomial GEE models to examine the association between objective and subjective availability of outdoor recreational facilities and leisure-time PA, and explored whether this association was attenuated after adjustment for socioeconomic status and preference for neighbourhoods with recreational facilities (as indicators of self-selection). We examined whether reported use of recreational facilities was associated with leisure-time PA (as explanatory mechanism), and summarized the most important motivations for (not) using recreational facilities., Results: Subjective - but not objective - availability of outdoor recreational facilities was associated with higher levels of total leisure-time PA. After adjustment for self-selection (which attenuated the association by 25%), we found a 25% difference in weekly minutes of total leisure-time PA between individuals with and without self-reported availability of outdoor recreational facilities. For our study population, this translates to about 28 min per week. Participants who reported outdoor recreational facilities to be present but indicated not to use them (RR = 1.19, 95% CI = 1.03;1.22), and those reporting outdoor recreational facilities to be present and to use them (RR = 1.33, 95% CI = 1.22, 1.45) had higher levels of total leisure-time PA than those who reported outdoor recreational facilities not to be present. Proximity to outdoor recreational facilities was the most important motivation for use., Conclusion: The modest attenuation in the association between availability of outdoor recreational facilities and self-reported leisure-time PA suggests that individuals' higher activity levels may be due more to the perceived availability of outdoor recreational facilities than to self-selection. The use of these facilities seemed to be an important underlying mechanism, and proximity was the main motivator for using recreational facilities.

A considerable proportion of European adults report little or no interest in physical activity. Identifying individual-level and environmental-level characteristics of these individuals can help designing effective interventions and policies to promote physical activity. This cross-sectional study additionally explored associations between level of interest and physical activity, after controlling for other individual and environmental variables. Measures of objective and perceived features of the physical environment of residence, self-reported physical activity and other lifestyle behaviors, barriers towards physical activity, general health, and demographics were obtained from 5205 European adults participating in the 2014 online SPOTLIGHT survey. t-Tests, chi-square tests, and generalized estimating equations with negative binomial log-link function were conducted. Adults not interested in physical activity reported a higher BMI and a lower self-rated health, were less educated, and to a smaller extent female and less frequently employed. They were more prone to have less healthy eating habits, and to perceive more barriers towards physical activity. Only minor differences were observed in environmental attributes: the non-interested were slightly more likely to live in neighborhoods objectively characterized as less aesthetic and containing more destinations, and perceived as less functional, safe, and aesthetic. Even after controlling for other individual and environmental factors, interest in physical activity remained a significant correlate of physical activity, supporting the importance of this association. This study is among the first to describe characteristics of individuals with reduced interest in physical activity, suggesting that (lack of) interest is a robust correlate of physical activity in several personal and environmental conditions., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Background: Little is known about the relation between the neighbourhood food environment and home cooking. We explored the independent and combined associations between residential neighbourhood spatial access to restaurants and grocery stores with home cooking in European adults., Methods: Data of 5076 participants of the SPOTLIGHT study were collected across five European countries in 2014. Food retailers were classified into grocery stores (supermarkets and local food shops) and restaurants (full-service restaurants, fast food and take-away restaurants, café/bars). We used multinomial logistic regression models to test the associations between tertiles of spatial access to restaurants and spatial to access grocery stores and the outcome 'frequency of home cooking' categorized into 0-3; 4-5; and 6-7 days/week. Additive interaction analysis was used to test the combined association between access to grocery stores and to restaurants with home cooking., Results: Mean age was 52.3 years; most participants were women (55.5%) and completed higher education (53.8%). Residents with highest access to restaurants had a reduced likelihood of home cooking 6-7 days/week (vs. 0-3 days/week) (relative risk ratio (RRR) 0.42; 95%CI = 0.23-0.76) when compared with lowest access to restaurants. No association was found for spatial access to grocery stores. Additive interaction analysis showed that individuals with medium access to grocery stores and highest access to restaurants had the lowest likelihood (RRR = 0.29, 95%CI = 0.10-0.84) of cooking 6-7 days/week when compared to individuals with lowest access to restaurants and highest access to grocery stores., Conclusion: Greater neighbourhood spatial access to restaurants was associated with lower frequency of home cooking, largely independent of access to grocery stores.

Background: Neighbourhood income inequality may contribute to differences in body weight. We explored whether neighbourhood social capital mediated the association of neighbourhood income inequality with individual body mass index (BMI)., Methods: A total of 4126 adult participants from 48 neighbourhoods in France, Hungary, the Netherlands and the UK provided information on their levels of income, perceptions of neighbourhood social capital and BMI. Factor analysis of the 13-item social capital scale revealed two social capital constructs: social networks and social cohesion. Neighbourhood income inequality was defined as the ratio of the amount of income earned by the top 20% and the bottom 20% in a given neighbourhood. Two single mediation analyses-using multilevel linear regression analyses-with neighbourhood social networks and neighbourhood social cohesion as possible mediators-were conducted using MacKinnon's product-of-coefficients method, adjusted for age, gender, education and absolute household income., Results: Higher neighbourhood income inequality was associated with elevated levels of BMI and lower levels of neighbourhood social networks and neighbourhood social cohesion. High levels of neighbourhood social networks were associated with lower BMI. Results stratified by country demonstrate that social networks fully explained the association between income inequality and BMI in France and the Netherlands. Social cohesion was only a significant mediating variable for Dutch participants., Conclusion: The results suggest that in some European urban regions, neighbourhood social capital plays a large role in the association between neighbourhood income inequality and individual BMI., (© The Author 2016. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.)

Objectives: This study aimed to examine the mediating effects of energy-balance related behaviors on the association of neighborhood socio-economic status (SES) and neighborhood residential area density (RAD) with body mass index (BMI)., Methods: In total, 6037 adults from four neighborhood types (high SES/high RAD, high SES/low RAD, low SES/high RAD, and low SES/low RAD) in five Mid-European urban regions completed an online survey asking about their energy-balance related behaviors (physical activity [PA], sedentary behavior, and dietary behavior), determinants of these behaviors and their body weight and height. MacKinnon's product-of-coefficients test was used to assess mediating effects., Results: Transport-related PA, leisure-time PA and vegetable intake seemed to mediate the association between neighborhood type and BMI. Residents from low SES/low RAD neighborhoods reported less transport-related PA, less leisure-time PA and less vegetable intake than high SES/high RAD residents, and these behaviors (i.e. transport-related PA, leisure-time PA and vegetable intake) were related to having a higher BMI., Conclusion: The association between neighborhood type and BMI can be explained, at least in part, by energy-balance related behaviors., (Copyright © 2016 Elsevier Inc. All rights reserved.)

The neighbourhood is recognized as an important unit of analysis in research on the relation between obesogenic environments and development of obesity. One important challenge is to define the limits of the residential neighbourhood, as perceived by study participants themselves, in order to improve our understanding of the interaction between contextual features and patterns of obesity. An innovative tool was developed in the framework of the SPOTLIGHT project to identify the boundaries of neighbourhoods as defined by participants in five European urban regions. The aims of this study were (i) to describe self-defined neighbourhood (size and overlap with predefined residential area) according to the characteristics of the sampling administrative neighbourhoods (residential density and socioeconomic status) within the five study regions and (ii) to determine which individual or/and environmental factors are associated with variations in size of self-defined neighbourhoods. Self-defined neighbourhood size varies according to both individual factors (age, educational level, length of residence and attachment to neighbourhood) and contextual factors. These findings have consequences for how residential neighbourhoods are defined and operationalized and can inform how self-defined neighbourhoods may be used in research on associations between contextual characteristics and health outcomes such as obesity., (© 2016 World Obesity.)

Perceived barriers towards physical activity and healthy eating as well as local availability of opportunities (destinations in the neighbourhood) are important determinants of obesity-related behaviours in adults. Little is known, however, about how these factors interact with the behaviours. Data were analysed from 5,205 participants of the SPOTLIGHT survey, conducted in 60 neighbourhoods in urban regions of five different countries across Europe. A virtual audit was conducted to collect data on the presence of destinations in each neighbourhood. Direct associations of, and interactions between, the number of individual perceived barriers and presence of destinations with obesity-related behaviours (physical activity and dietary behaviours) were analysed using multilevel regression analyses, adjusted for key covariates. Perceiving more individual barriers towards physical activity and healthy eating was associated with lower odds of physical activity and healthy eating. The presence of destinations such as bicycle lanes, parks and supermarkets was associated with higher levels of physical activity and healthier dietary behaviours. Analyses of additive interaction terms suggested that the interaction of destinations and barriers was competitive, such that the presence of destinations influenced obesity-related behaviours most among those perceiving more barriers. These explorative findings emphasize the interest and importance of combining objective (e.g. virtual neighbourhood audit) methods and subjective (e.g. individual perceived barriers collected in a survey) to better understand how the characteristics of the residential built environment can shape obesity-related behaviours depending on individual characteristics., (© 2016 World Obesity.)

Virtual audit (using tools such as Google Street View) can help assess multiple characteristics of the physical environment. This exposure assessment can then be associated with health outcomes such as obesity. Strengths of virtual audit include collection of large amount of data, from various geographical contexts, following standard protocols. Using data from a virtual audit of obesity-related features carried out in five urban European regions, the current study aimed to (i) describe this international virtual audit dataset and (ii) identify neighbourhood patterns that can synthesize the complexity of such data and compare patterns across regions. Data were obtained from 4,486 street segments across urban regions in Belgium, France, Hungary, the Netherlands and the UK. We used multiple factor analysis and hierarchical clustering on principal components to build a typology of neighbourhoods and to identify similar/dissimilar neighbourhoods, regardless of region. Four neighbourhood clusters emerged, which differed in terms of food environment, recreational facilities and active mobility features, i.e. the three indicators derived from factor analysis. Clusters were unequally distributed across urban regions. Neighbourhoods mostly characterized by a high level of outdoor recreational facilities were predominantly located in Greater London, whereas neighbourhoods characterized by high urban density and large amounts of food outlets were mostly located in Paris. Neighbourhoods in the Randstad conurbation, Ghent and Budapest appeared to be very similar, characterized by relatively lower residential densities, greener areas and a very low percentage of streets offering food and recreational facility items. These results provide multidimensional constructs of obesogenic characteristics that may help target at-risk neighbourhoods more effectively than isolated features., (© 2016 World Obesity.)

We compared ecometric neighbourhood scores of social capital (contextual variation) to mean neighbourhood scores (individual and contextual variation), using several health-related outcomes (i.e. self-rated health, weight status and obesity-related behaviours). Data were analysed from 5,900 participants in the European SPOTLIGHT survey. Factor analysis of the 13-item social capital scale revealed two social capital constructs: social networks and social cohesion. The associations of ecometric and mean neighbourhood-level scores of these constructs with self-rated health, weight status and obesity-related behaviours were analysed using multilevel regression analyses, adjusted for key covariates. Analyses using ecometric and mean neighbourhood scores, but not mean neighbourhood scores adjusted for individual scores, yielded similar regression coefficients. Higher levels of social network and social cohesion were not only associated with better self-rated health, lower odds of obesity and higher fruit consumption, but also with prolonged sitting and less transport-related physical activity. Only associations with transport-related physical activity and sedentary behaviours were associated with mean neighbourhood scores adjusted for individual scores. As analyses using ecometric scores generated the same results as using mean neighbourhood scores, but different results when using mean neighbourhood scores adjusted for individual scores, this suggests that the theoretical advantage of the ecometric approach (i.e. teasing out individual and contextual variation) may not be achieved in practice. The different operationalisations of social network and social cohesion were associated with several health outcomes, but the constructs that appeared to represent the contextual variation best were only associated with two of the outcomes., (© 2016 World Obesity.)

Findings from research on the association between the built environment and obesity remain equivocal but may be partly explained by differences in approaches used to characterize the built environment. Findings obtained using subjective measures may differ substantially from those measured objectively. We investigated the agreement between perceived and objectively measured obesogenic environmental features to assess (1) the extent of agreement between individual perceptions and observable characteristics of the environment and (2) the agreement between aggregated perceptions and observable characteristics, and whether this varied by type of characteristic, region or neighbourhood. Cross-sectional data from the SPOTLIGHT project (n = 6037 participants from 60 neighbourhoods in five European urban regions) were used. Residents' perceptions were self-reported, and objectively measured environmental features were obtained by a virtual audit using Google Street View. Percent agreement and Kappa statistics were calculated. The mismatch was quantified at neighbourhood level by a distance metric derived from a factor map. The extent to which the mismatch metric varied by region and neighbourhood was examined using linear regression models. Overall, agreement was moderate (agreement < 82%, kappa < 0.3) and varied by obesogenic environmental feature, region and neighbourhood. Highest agreement was found for food outlets and outdoor recreational facilities, and lowest agreement was obtained for aesthetics. In general, a better match was observed in high-residential density neighbourhoods characterized by a high density of food outlets and recreational facilities. Future studies should combine perceived and objectively measured built environment qualities to better understand the potential impact of the built environment on health, particularly in low residential density neighbourhoods., (© 2016 World Obesity.)

Residents of socioeconomically deprived areas perceive their neighbourhood as less conducive to healthy behaviours than residents of more affluent areas. Whether these unfavourable perceptions are based on objective neighbourhood features or other factors is poorly understood. We examined individual and contextual correlates of socioeconomic inequalities in neighbourhood perceptions across five urban regions in Europe. Data were analysed from 5205 participants of the SPOTLIGHT survey. Participants reported perceptions of their neighbourhood environment with regard to aesthetics, safety, the presence of destinations and functionality of the neighbourhood, which were summed into an overall neighbourhood perceptions score. Multivariable multilevel regression analyses were conducted to investigate whether the following factors were associated with socioeconomic inequalities in neighbourhood perceptions: objectively observed neighbourhood features, neighbourhood social capital, exposure to the neighbourhood, self-rated health and lifestyle behaviours. Objectively observed traffic safety, aesthetics and the presence of destinations in the neighbourhood explained around 15% of differences in neighbourhood perceptions between residents of high and low neighbourhoods; levels of neighbourhood social cohesion explained around 52%. Exposure to the neighbourhood, self-rated health and lifestyle behaviours were significant correlates of neighbourhood perceptions but did not contribute to socioeconomic differences. This cross-European study provided evidence that socioeconomic differences in neighbourhood perceptions are not only associated with objective neighbourhood features but also with social cohesion. Levels of physical activity, sleep duration, self-rated health, happiness and neighbourhood preference were also associated with neighbourhood perceptions., (© 2016 World Obesity.)

Objectives: The authors designed an instrument to measure objectively aspects of the built and food environments in urban areas, the EURO-PREVOB Community Questionnaire, within the EU-funded project 'Tackling the social and economic determinants of nutrition and physical activity for the prevention of obesity across Europe' (EURO-PREVOB). This paper describes its development, reliability, validity, feasibility and relevance to public health and obesity research., Study Design: The Community Questionnaire is designed to measure key aspects of the food and built environments in urban areas of varying levels of affluence or deprivation, within different countries. The questionnaire assesses (1) the food environment and (2) the built environment., Methods: Pilot tests of the EURO-PREVOB Community Questionnaire were conducted in five to 10 purposively sampled urban areas of different socio-economic status in each of Ankara, Brno, Marseille, Riga, and Sarajevo. Inter-rater reliability was compared between two pairs of fieldworkers in each city centre using three methods: inter-observer agreement (IOA), kappa statistics, and intraclass correlation coefficients (ICCs)., Results: Data were collected successfully in all five cities. Overall reliability of the EURO-PREVOB Community Questionnaire was excellent (inter-observer agreement (IOA) > 0.87; intraclass correlation coefficients (ICC)s > 0.91 and kappa statistics > 0.7. However, assessment of certain aspects of the quality of the built environment yielded slightly lower IOA coefficients than the quantitative aspects., Conclusions: The EURO-PREVOB Community Questionnaire was found to be a reliable and practical observational tool for measuring differences in community-level data on environmental factors that can impact on dietary intake and physical activity. The next step is to evaluate its predictive power by collecting behavioural and anthropometric data relevant to obesity and its determinants., (Copyright © 2013 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.)