Neighbourhood typology based on virtual audit of environmental obesogenic characteristics

Assessing the association between food environment and dietary inflammation by community type: a cross-sectional REGARDS study

BACKGROUND

Communities in the United States (US) exist on a continuum of urbanicity, which may inform how individuals interact with their food environment, and thus modify the relationship between food access and dietary behaviors.

OBJECTIVE

This cross-sectional study aims to examine the modifying effect of community type in the association between the relative availability of food outlets and dietary inflammation across the US.

METHODS

Using baseline data from the REasons for Geographic and Racial Differences in Stroke study (2003-2007), we calculated participants' dietary inflammation score (DIS). Higher DIS indicates greater pro-inflammatory exposure. We defined our exposures as the relative availability of supermarkets and fast-food restaurants (percentage of food outlet type out of all food stores or restaurants, respectively) using street-network buffers around the population-weighted centroid of each participant's census tract. We used 1-, 2-, 6-, and 10-mile (~ 2-, 3-, 10-, and 16 km) buffer sizes for higher density urban, lower density urban, suburban/small town, and rural community types, respectively. Using generalized estimating equations, we estimated the association between relative food outlet availability and DIS, controlling for individual and neighborhood socio-demographics and total food outlets. The percentage of supermarkets and fast-food restaurants were modeled together.

RESULTS

Participants (n = 20,322) were distributed across all community types: higher density urban (16.7%), lower density urban (39.8%), suburban/small town (19.3%), and rural (24.2%). Across all community types, mean DIS was - 0.004 (SD = 2.5; min = - 14.2, max = 9.9). DIS was associated with relative availability of fast-food restaurants, but not supermarkets. Association between fast-food restaurants and DIS varied by community type (P for interaction = 0.02). Increases in the relative availability of fast-food restaurants were associated with higher DIS in suburban/small towns and lower density urban areas (p-values < 0.01); no significant associations were present in higher density urban or rural areas.

CONCLUSIONS

The relative availability of fast-food restaurants was associated with higher DIS among participants residing in suburban/small town and lower density urban community types, suggesting that these communities might benefit most from interventions and policies that either promote restaurant diversity or expand healthier food options.

Development of a neighborhood obesogenic built environment characteristics index for the Netherlands

OBJECTIVE

Environmental factors that drive obesity are often studied individually, whereas obesogenic environments are likely to consist of multiple factors from food and physical activity (PA) environments. This study aimed to compose and describe a comprehensive, theory-based, expert-informed index to quantify obesogenicity for all neighborhoods in the Netherlands.

METHODS

The Obesogenic Built Environment CharacterisTics (OBCT) index consists of 17 components. The index was calculated as an average of componential scores across both food and PA environments and was scaled from 0 to 100. The index was visualized and summarized with sensitivity analysis for weighting methods.

RESULTS

The OBCT index for all 12,821 neighborhoods was right-skewed, with a median of 44.6 (IQR = 10.1). Obesogenicity was lower in more urbanized neighborhoods except for the extremely urbanized neighborhoods (>2500 addresses/km² ), where obesogenicity was highest. The overall OBCT index score was moderately correlated with the food environment (Spearman ρ = 0.55, p <0.05) and with the PA environment (ρ = 0.38, p <0.05). Hierarchical weighting increased index correlations with the PA environment but decreased correlations with the food environment.

CONCLUSIONS

The novel OBCT index and its comprehensive environmental scores are potentially useful tools to quantify obesogenicity of neighborhoods.

Assessing physical access to healthy food across United Kingdom: A systematic review of measures and findings

Background

Existing research suggests that physical access to food can affect diet quality and thus obesity rates. When defining retail food environment (RFE) quantitatively, there is a little agreement on how to measure "lack of healthy food" and what parameters to use, resulting in a heterogeneity of study designs and outcome measures. In turn, this leads to a conflicting evidence base being one of the many barriers to using evidence in policy-making.

Aims

This systematic review aimed to identify and describe methods used to assess food accessibility in the United Kingdom (UK) to overcome heterogeneity by providing a classification of measures.

Materials & Methods

The literature search included electronic and manual searches of peer-reviewed literature and was restricted to studies published in English between January 2010 and March 2021. A total of 9365 articles were assessed for eligibility, of which 44 articles were included in the review. All included studies were analysed with regards to their main characteristics (e.g., associations between variables of interest, setting, sample, design, etc.) and definition of RFE and its metrics. When defining these metrics, the present review distinguishes between a point of origin (centroid, address) from which distance was calculated, summary statistic of accessibility (proximity, buffer, Kernel), and definition of distance (Euclidean, network distance). Trends, gaps and limitations are identified and recommendations made for food accessibility research in UK.

Results

Multiple theoretical and methodological constructs are currently used, mostly quantifying distance by means of Euclidean and ring-buffer distance, using both proximity- and density-based approaches, and ranging from absolute to relative measures. The association between RFE and diet and health in rural areas, as well as a spatiotemporal domain of food access, remains largely unaccounted.

Discussion

Evidence suggests that the duration of exposure may bear a greater importance than the level of exposure and that density-based measures may better capture RFE when compared with proximity-based measures, however, using more complex measures not necessarily produce better results. To move the field forward, studies have called for a greater focus on causality, individual access and the use of various measures, neighbourhood definitions and potential confounders to capture different aspects and dimensions of the RFE, which requires using univariate measures of accessibility and considering the overall context in terms of varying types of neighbourhoods.

Conclusion

In order to render ongoing heterogeneity in measuring RFE, researchers should prioritise measures that may provide a more accurate and realistic account of people's lives and follow an intuitive approach based on convergence of results until consensus could be reached on using some useful standards.

Building a Global Food Systems Typology: A New Tool for Reducing Complexity in Food Systems Analysis

Food systems have a profound impact on diets, nutrition, health, economic development, and environmental sustainability. Yet their complexity poses a persistent challenge in identifying the policy actions that are needed to improve human and planetary health outcomes. Typologies are a useful classification tool to identify similarities and differences among food systems, while reducing this analytical complexity. This study presents a new food system typology, implemented at the country level using parsimonious data that characterize food supply chains, food environments, consumer-related factors, and key outcomes, including dietary intake, nutritional status, health, and environmental impacts. Five food system types are identified: rural and traditional; informal and expanding; emerging and diversifying; modernizing and formalizing; and industrial and consolidated. Patterns across the five system types in key outcome variables align with narratives provided by the food systems and nutrition transition literature, demonstrating the usefulness of this classification method. Substantial heterogeneity nonetheless still exists within individual food system types. Therefore, the recommended use of the typology is in early stages of hypothesis generation, to identify potential risk factors or constraints in the food system that can be explored further at national and sub-national levels.

Panoramic Street-Level Imagery in Data-Driven Urban Research: A Comprehensive Global Review of Applications, Techniques, and Practical Considerations

The release of Google Street View in 2007 inspired several new panoramic street-level imagery platforms including Apple Look Around, Bing StreetSide, Baidu Total View, Tencent Street View, Naver Street View, and Yandex Panorama. The ever-increasing global capture of cities in 360° provides considerable new opportunities for data-driven urban research. This paper provides the first comprehensive, state-of-the-art review on the use of street-level imagery for urban analysis in five research areas: built environment and land use; health and wellbeing; natural environment; urban modelling and demographic surveillance; and area quality and reputation. Panoramic street-level imagery provides advantages in comparison to remotely sensed imagery and conventional urban data sources, whether manual, automated, or machine learning data extraction techniques are applied. Key advantages include low-cost, rapid, high-resolution, and wide-scale data capture, enhanced safety through remote presence, and a unique pedestrian/vehicle point of view for analyzing cities at the scale and perspective in which they are experienced. However, several limitations are evident, including limited ability to capture attribute information, unreliability for temporal analyses, limited use for depth and distance analyses, and the role of corporations as image-data gatekeepers. Findings provide detailed insight for those interested in using panoramic street-level imagery for urban research.

Unhealthy environments are associated with adverse mental health and psychological distress: Cross-sectional evidence from nationally representative data in New Zealand

This study combines data on the location of health-constraining 'bads' (i: fast-food outlets, ii: takeaway outlets, iii: dairy outlets and convenience stores, iv: alcohol outlets, and v: gaming venues) and health-promoting 'goods' (i: green spaces, ii: blue spaces, iii: physical activity facilities, and iv: fruit and vegetable outlets) into a nationwide Healthy Living Index. This was applied to pooled (2015/16-2017/18) nationally representative New Zealand Health Survey data, with mental health conditions (depression, bipolar, and anxiety) and psychological distress as population-level outcomes. Mental health was associated with proximity to environmental 'goods' and 'bads'. Compared to those individuals who reside within the unhealthiest environments, there was a steady reduction in the odds of adverse mental health outcomes and psychological distress as the environment became more health-promoting.

Adapting the SPOTLIGHT Virtual Audit Tool to assess food and activity environments relevant for adolescents: a validity and reliability study

Background

Physical inactivity and unhealthy diet are key behavioral determinants underlying obesity. The neighborhood environment represents an important arena for modifying these behaviors, and hence reliable and valid tools to measure it are needed. Most existing virtual audit tools have been designed to assess either food or activity environments deemed relevant for adults. Thus, there is a need for a tool that combines the assessment of food and activity environments, and which focuses on aspects of the environment relevant for youth.

Objective

The aims of the present study were: (a) to adapt the SPOTLIGHT Virtual Audit Tool (S-VAT) developed to assess characteristics of the built environment deemed relevant for adults for use in an adolescent population, (b) to assess the tool’s inter- and intra-rater reliability, and (c) to assess its criterion validity by comparing the virtual audit to a field audit.

Methods

The tool adaptation was based on literature review and on results of a qualitative survey investigating how adolescents perceived the influence of the environment on dietary and physical activity behaviors. Sixty streets (148 street segments) in six neighborhoods were randomly selected as the study sample. Two raters assessed the inter- and intra-rater reliability and criterion validity, comparing the virtual audit tool to a field audit. The results were presented as percentage agreement and Cohen’s kappa (κ).

Results

Intra-rater agreement was found to be moderate to almost perfect (κ = 0.44–0.96) in all categories, except in the category aesthetics (κ = 0.40). Inter-rater agreement between auditors ranged from fair to substantial for all categories (κ = 0.24–0.80). Criterion validity was found to be moderate to almost perfect (κ = 0.56–0.82) for most categories, except aesthetics and grocery stores (κ = 0.26–0.35).

Conclusion

The adapted version of the S-VAT can be used to provide reliable and valid data on built environment characteristics deemed relevant for physical activity and dietary behavior among adolescents.

Psychological and Biological Pathways Linking Perceived Neighborhood Characteristics and Body Mass Index

BACKGROUND

Perceived neighborhood characteristics are linked to obesity, however, the mechanisms linking these two factors remain unknown.

PURPOSE

This study aimed to examine associations between perceived neighborhood characteristics and body mass index (BMI), establish whether indirect pathways through psychological distress and inflammation are important, and determine whether these associations vary by race/ethnicity.

METHODS

Participants were 1,112 adults enrolled in the Texas City Stress and Health Study. Perceived neighborhood characteristics were measured using the Perceived Neighborhood Scale. Psychological distress was measured with the Center for Epidemiological Studies Depression Scale, Perceived Stress Scale and mental health subscale of the Short Form Health Survey-36. Markers of inflammation included C-reactive protein, interleukin-6, and tumor necrosis factor receptor-1. Associations were examined with Structural Equation Modeling.

RESULTS

A model linking neighborhood characteristics with BMI through direct and indirect (i.e., psychological distress and inflammation) paths demonstrated good fit with the data. Less favorable perceived neighborhood characteristics were associated with greater psychological distress (B = -0.87, β = -0.31, p < .001) and inflammation (B = -0.02, β = -0.10, p = .035). Psychological distress and inflammation were also significantly associated with BMI (Bdistress = 0.06, β = 0.08, p = .006; Binflammation = 4.65, β = 0.41, p < .001). Indirect paths from neighborhood characteristics to BMI via psychological distress (B = -0.05, β = -0.03, p = .004) and inflammation (B = -0.08, β = -0.04, p = .045) were significant. In multiple group analysis, a model with parameters constrained equal across race/ethnicity showed adequate fit suggesting associations were comparable across groups.

CONCLUSION

Our study extends the literature by demonstrating the importance of neighborhood perceptions as correlates of BMI across race/ethnicity, and highlights the role of psychological and physiological pathways.

Associations between body mass index, physical activity and the built environment in disadvantaged, minority neighborhoods: Predictive validity of GigaPan® imagery

BACKGROUND

The built environment has been shown to influence health in studies of disadvantaged populations using different measurement methods. This study determined whether environmental exposures derived from GigaPan® images could serve as valid predictors of body mass index (BMI), walking and moderate to vigorous physical activity (MVPA) in a longitudinal study of low-income adults living in two primarily African American neighborhoods in Pittsburgh, Pennsylvania, USA. GigaPan® is a robotic system used to obtain high-resolution, panoramic images of environments.

METHODS

Microscale environmental features along 481 streets were audited in 2015-2016 using an audit form. Environmental exposures were estimated for 731 adult participants, using a sample of street segments within a 0.4 km (0.25 mile) network distance from each participant's residential address. Summary environmental exposures were constructed using factor analysis. We tested associations between participant-level environmental exposures and objectively measured BMI, self-reported walking and objectively measured MVPA in regression models controlling for baseline health and demographic variables.

RESULTS

Three factors representing participants' environmental exposures were constructed: pedestrian bicycle-amenities; hilly-vacant-boarded; physical activity-recreation/low housing density. Environments with infrastructure and amenities supportive of walking and bicycling were associated with lower BMI (Coef. = ‒0.47, p = 0.02). Frequent walking was less likely in environments with more physical activity and recreation venues/low housing density (OR = 0.81, 95% CI [0.67, 0.96]). MVPA was not associated with any of the environmental measures and the hilly-vacant-boarded factor was not associated with any of the outcomes.

CONCLUSIONS

Predictive validity was demonstrated for an environmental exposure factor that captured features supportive of walking and cycling in a model predicting BMI, using built environment audit data from GigaPan® imagery. A complementary analysis found lower odds of frequent walking in the neighborhood among participants with exposure to more physical activity and recreational features, but fewer types and lower density of housing.

A COMPARATIVE CASE STUDY OF WALKING ENVIRONMENT IN MADRID AND PHILADELPHIA USING MULTIPLE SAMPLING METHODS AND STREET VIRTUAL AUDITS

The objective of this study is to quantify, using virtual audits in Madrid and Philadelphia, cross-city differences in the walking environment and to test whether differences vary by sampling method. We used two sampling methods; first, a contiguous area combining census units (~15.000 population area for each setting) was selected using the Median Neighborhood Index (MNI). MNI is a summary index that averages Euclidean distances of sociodemographic and urban form features, used to select the median neighborhood for a given city. Second, we selected a population-density stratified sampling of the same number of census units as above. M-SPACES audit tool was deployed, using street virtual audits to measure function, safety, aesthetics, and destinations along each street segment. Madrid streets had lower scores for function (b=-0.29 CI95% -0.55;-0.31) and safety (b=-0.38 CI95% -0.61;-0.14). Madrid had a greater proportion of streets having at least one walking destination in the street segment (PR=1.92 95% CI 1.55; 2.39). We did not find a significant difference between Madrid and Philadelphia in aesthetics. We found an interaction between safety and sampling methods. This approach can reveal which elements of the built environment account for between-city differences, key to mass influences that operate at the city level.

An Exploratory Multivariate Statistical Analysis to Assess Urban Diversity

Understanding diversity in complex urban systems is fundamental in facing current and future sustainability challenges. In this article, we apply an exploratory multivariate statistical analysis (i.e., Principal Component Analysis (PCA) and Multiple Factor Analysis (MFA)) to an urban system’s abstraction of the city’s functioning. Specifically, we relate the environmental, economical, and social characters of the city in a multivariate system of indicators by collecting measurements of those variables at the district scale. Statistical methods are applied to reduce the dimensionality of the multivariate dataset, such that, hidden relationships between the districts of the city are exposed. The methodology has been mainly designed to display diversity, being understood as differentiated attributes of the districts in their dimensionally-reduced description, and to measure it with Euclidean distances. Differentiated characters and distinctive functions of districts are identifiable in the exploratory analysis of a case study of Barcelona (Spain). The distances allow for the identification of clustered districts, as well as those that are separated, exemplifying dissimilarity. Moreover, the temporal dependency of the dataset reveals information about the district’s differentiation or homogenization trends between 2003 and 2015.

Using data from online geocoding services for the assessment of environmental obesogenic factors: a feasibility study

BACKGROUND

The increasing prevalence of obesity is a major public health problem in many countries. Built environment factors are known to be associated with obesity, which is an important risk factor for type 2 diabetes. Online geocoding services could be used to identify regions with a high concentration of obesogenic factors. The aim of our study was to examine the feasibility of integrating information from online geocoding services for the assessment of obesogenic environments.

METHODS

We identified environmental factors associated with obesity from the literature and translated these factors into variables from the online geocoding services Google Maps and OpenStreetMap (OSM). We tested whether spatial data points can be downloaded from these services and processed and visualized on maps. True- and false-positive values, false-negative values, sensitivities and positive predictive values of the processed data were determined using search engines and in-field inspections within four pilot areas in Bavaria, Germany.

RESULTS

Several environmental factors could be identified from the literature that were either positively or negatively correlated with weight outcomes in previous studies. The diversity of query variables was higher in OSM compared with Google Maps. In each pilot area, query results from Google showed a higher absolute number of true-positive hits and of false-positive hits, but a lower number of false-negative hits during the validation process. The positive predictive value of database hits was higher in OSM and ranged between 81 and 100% compared with a range of 63-89% for Google Maps. In contrast, sensitivities were higher in Google Maps (between 59 and 98%) than in OSM (between 20 and 64%).

CONCLUSIONS

It was possible to operationalize obesogenic factors identified from the literature with data and variables available from geocoding services. The validity of Google Maps and OSM was reasonable. The assessment of environmental obesogenic factors via geocoding services could potentially be applied in diabetes surveillance.

Using Google Street View to examine associations between built environment characteristics and U.S. health outcomes

Neighborhood attributes have been shown to influence health, but advances in neighborhood research has been constrained by the lack of neighborhood data for many geographical areas and few neighborhood studies examine features of nonmetropolitan locations. We leveraged a massive source of Google Street View (GSV) images and computer vision to automatically characterize national neighborhood built environments. Using road network data and Google Street View API, from December 15, 2017-May 14, 2018 we retrieved over 16 million GSV images of street intersections across the United States. Computer vision was applied to label each image. We implemented regression models to estimate associations between built environments and county health outcomes, controlling for county-level demographics, economics, and population density. At the county level, greater presence of highways was related to lower chronic diseases and premature mortality. Areas characterized by street view images as 'rural' (having limited infrastructure) had higher obesity, diabetes, fair/poor self-rated health, premature mortality, physical distress, physical inactivity and teen birth rates but lower rates of excessive drinking. Analyses at the census tract level for 500 cities revealed similar adverse associations as was seen at the county level for neighborhood indicators of less urban development. Possible mechanisms include the greater abundance of services and facilities found in more developed areas with roads, enabling access to places and resources for promoting health. GSV images represents an underutilized resource for building national data on neighborhoods and examining the influence of built environments on community health outcomes across the United States.

How has big data contributed to obesity research? A review of the literature

There has been growing interest in the potential of ‘big data’ to enhance our understanding in medicine and public health. Although there is no agreed definition of big data, accepted critical components include greater volume, complexity, coverage and speed of availability. Much of these data are ‘found’ (as opposed to ‘made’), in that they have been collected for non-research purposes, but could include valuable information for research. The aim of this paper is to review the contribution of ‘found’ data to obesity research to date, and describe the benefits and challenges encountered. A narrative review was conducted to identify and collate peer-reviewed research studies. Database searches conducted up to September 2017 found original studies using a variety of data types and sources. These included: retail sales, transport, geospatial, commercial weight management data, social media, and smartphones and wearable technologies. The narrative review highlights the variety of data uses in the literature: describing the built environment, exploring social networks, estimating nutrient purchases or assessing the impact of interventions. The examples demonstrate four significant ways in which ‘found’ data can complement conventional ‘made’ data: firstly, in moving beyond constraints in scope (coverage, size and temporality); secondly, in providing objective, quantitative measures; thirdly, in reaching hard-to-access population groups; and lastly in the potential for evaluating real-world interventions. Alongside these opportunities, ‘found’ data come with distinct challenges, such as: ethical and legal questions around access and ownership; commercial sensitivities; costs; lack of control over data acquisition; validity; representativeness; finding appropriate comparators; and complexities of data processing, management and linkage. Despite widespread recognition of the opportunities, the impact of ‘found’ data on academic obesity research has been limited. The merit of such data lies not in their novelty, but in the benefits they could add over and above, or in combination with, conventionally collected data.

Systematic review of the use of Google Street View in health research: Major themes, strengths, weaknesses and possibilities for future research

We systematically reviewed the current use of Google Street View (GSV) in health research and characterized major themes, strengths and weaknesses in order to highlight possibilities for future research. Of 54 qualifying studies, we found that most used GSV to assess the neighborhood built environment, followed by health policy compliance, study site selection, and disaster preparedness. Most studies were conducted in urban areas of North America, Europe, or New Zealand, with few studies from South America or Asia and none from Africa or rural areas. Health behaviors and outcomes of interest in these studies included injury, alcohol and tobacco use, physical activity and mental health. Major strengths of using GSV imagery included low cost, ease of use, and time saved. Identified weaknesses were image resolution and spatial and temporal availability, largely in developing regions of the world. Despite important limitations, GSV is a promising tool for automated environmental assessment for health research. Currently untapped areas of health research using GSV include identification of sources of air, soil or water pollution, park design and usage, amenity design and longitudinal research on neighborhood conditions.

Exploring the cross-sectional association between outdoor recreational facilities and leisure-time physical activity: the role of usage and residential self-selection

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.

Electronic supplementary material

The online version of this article (10.1186/s12966-018-0689-x) contains supplementary material, which is available to authorized users.

Patterning of neighbourhood food outlets and longitudinal associations with children's eating behaviours

This study examined cross-sectional and prospective associations between typologies of neighbourhood food environment and dietary patterns among 10-12 year-old children. Baseline data were collected in 2003 and follow-up data in 2006 from children in Melbourne or Geelong. Parents completed a food frequency questionnaire at both time points. 'Healthful' and 'energy-dense' dietary pattern scores were computed. A Geographic Information System was used to determine the presence or absence of food outlets (cafés/restaurant; fast food; supermarkets/grocery stores; convenience store; greengrocer; and butcher, seafood or poultry retailer) within an 800 m road network buffer of home. Three typologies were identified: 1-variety of food outlets, including those selling core/fresh foods (n = 96); 2-café/restaurant and convenience (n = 160); 3-few types of outlets (n = 208). Latent class analysis was used to identify underlying unobservable typologies of neighbourhood food outlet availability. Linear mixed models were fitted to determine cross-sectional (n = 439) and longitudinal (n = 173) associations between the three identified neighbourhood typologies and each (log-transformed) dietary pattern, accounting for clustering within families and schools. There was little evidence of cross-sectional associations. The longitudinal analyses showed that compared to those with a variety of food outlets, those with few types had 25% lower scores for the healthful dietary pattern (p < 0.05) three years later. For optimal dietary patterns, availability of a variety of food outlets close to home, particularly those where core/fresh foods are available, may be important.

Neighbourhood typologies and associations with body mass index and obesity: A cross-sectional study

Little research has investigated associations between a combined measure of the food and physical activity (PA) environment, BMI (body-mass-index) and obesity. Cross-sectional data (n=22,889, age 18-86years) from the Yorkshire Health Study were used [2010-2013]. BMI was calculated using self-reported height and weight; obesity=BMI≥30. Neighbourhood was defined as a 2km radial buffer. Food outlets and PA facilities were sourced from Ordnance Survey Points of Interest (PoI) and categorised into 'fast-food', 'large supermarkets', 'convenience and other food retail outlets' and 'physical activity facilities'. Parks were sourced from Open Street Map. Latent class analysis was conducted on these five environmental variables and availability was defined by quartiles of exposure. Linear and logistic regressions were then conducted for BMI and obesity respectively for different neighbourhood types. Models adjusted for age, gender, ethnicity, area-level deprivation, and rural/urban classification. A five-class solution demonstrated best fit and was interpretable. Neighbourhood typologies were defined as; 'low availability', 'moderate availability', 'moderate PA, limited food', 'saturated' and 'moderate PA, ample food'. Compared to low availability, one typology demonstrated lower BMI (saturated, b=-0.50, [95% CI=-0.76, -0.23]), while three showed higher BMI (moderate availability, b=0.49 [0.27, 0.72]; moderate PA, limited food, b=0.30 [0.01, 0.59]; moderate PA, ample food, b=0.32 [0.08, 0.57]). Furthermore, compared to the low availability, saturated neighbourhoods showed lower odds of obesity (OR=0.86 [0.75, 0.99]) while moderate availability showed greater odds of obesity (OR=1.18 [1.05, 1.32]). This study supports population-level approaches to tackling obesity however neighbourhoods contained features that were health-promoting and -constraining.

Contextual correlates of happiness in European adults

OBJECTIVES

We aimed to examine the associations of both objectively assessed and perceived physical and social neighborhood characteristics with happiness in European adults. In addition, we aimed to study how these associations differed among subgroups.

METHODS

Participants (N = 6037) of the cross-sectional SPOTLIGHT survey reported on their level of happiness using a 5-point Likert scale, and on perceived physical and social environmental neighborhood characteristics. Objective physical environmental characteristics were assessed using a Google Street View-based neighborhood audit. Associations of 14 physical and social environmental characteristics with happiness were analyzed using multivariable multinomial regression analyses with clustered standard errors.

RESULTS

Living in neighborhoods with higher levels of aesthetics and more water and green space was associated with being very happy. Individuals who perceived their neighborhood to be safer, more functional and more aesthetic were more likely to be very happy. The associations of functionality and aesthetics with happiness were strongest in the Ghent region (Belgium), the Randstad (the Netherlands) and Greater London (United Kingdom). Perceived absence of air pollution was only associated with higher levels of happiness in more highly educated participants. Individuals with a larger social network, more social cohesion and who trusted their neighbors were more likely to be very happy. The association between social networks and happiness was somewhat stronger in men than in women. In general, the associations between environmental characteristics and happiness had similar directions and sizes across socio-economic and socio-demographic subgroups.

CONCLUSIONS

This European study provided evidence that both objectively assessed and perceived physical and social characteristics of the neighborhood environment are associated with the happiness of its residents.

Objectively measured physical environmental neighbourhood factors are not associated with accelerometer-determined total sedentary time in adults

Background

The physical neighbourhood environment may influence adults’ sedentary behaviour. Yet, most studies examining the association between the physical neighbourhood environment and sedentary behaviour rely on self-reported data of either the physical neighbourhood environment and/or sedentary behaviour. The aim of this study was to investigate the associations between objectively measured physical environmental neighbourhood factors and accelerometer-determined total sedentary time in adults.

Methods

In total, 219 Dutch and 128 Belgian adults (mean age ± SD: 55.8 ± 15.4 years) were recruited between March and August 2014 as part of the European SPOTLIGHT project. Physical environmental neighbourhood factors, grouped into eight domains, i.e. walking, cycling, public transport, aesthetics, land use mix, grocery stores, food outlets and recreational facilities, were assessed using the SPOTLIGHT Virtual Audit Tool. Sedentary time was collected using ActiGraph GT3X+ accelerometers. General linear mixed models were conducted to examine associations between physical environmental neighbourhood factors and total sedentary time.

Results

Participants were sedentary, on average, for 542.9 min/day (SD: 84.3), or 9.1 h/day. None of the examined physical environmental neighbourhood factors were significantly related to total sedentary time.

Conclusions

Our findings do not support associations of objectively measured physical environmental neighbourhood factors with adults’ objectively sedentary time in Dutch and Belgian adults. More research on sedentary behaviours in settings such as the home and work setting is needed to examine the influence of more specific physical environmental factors on these context-specific sedentary behaviours.

Built environmental correlates of cycling for transport across Europe

This cross-sectional study aimed to determine which objective built environmental factors, identified using a virtual neighbourhood audit, were associated with cycling for transport in adults living in five urban regions across Europe. The moderating role of age, gender, socio-economic status and country on these associations was also investigated. Overall, results showed that people living in neighbourhoods with a preponderance of speed limits below 30km/h, many bicycle lanes, with less traffic calming devices, more trees, more litter and many parked cars forming an obstacle on the road were more likely to cycle for transport than people living in areas with lower prevalence of these factors. Evidence was only found for seven out of 56 possible moderators of these associations. These results suggest that reducing speed limits for motorized vehicles and the provision of more bicycle lanes may be effective interventions to promote cycling in Europe.

Lifestyle correlates of overweight in adults: a hierarchical approach (the SPOTLIGHT project)

BACKGROUND

Obesity-related lifestyle behaviors usually co-exist but few studies have examined their simultaneous relation with body weight. This study aimed to identify the hierarchy of lifestyle-related behaviors associated with being overweight in adults, and to examine subgroups so identified.

METHODS

Data were obtained from a cross-sectional survey conducted across 60 urban neighborhoods in 5 European urban regions between February and September 2014. Data on socio-demographics, physical activity, sedentary behaviors, eating habits, smoking, alcohol consumption, and sleep duration were collected by questionnaire. Participants also reported their weight and height. A recursive partitioning tree approach (CART) was applied to identify both main correlates of overweight and lifestyle subgroups.

RESULTS

In 5295 adults, mean (SD) body mass index (BMI) was 25.2 (4.5) kg/m2, and 46.0 % were overweight (BMI ≥25 kg/m2). CART analysis showed that among all lifestyle-related behaviors examined, the first identified correlate was sitting time while watching television, followed by smoking status. Different combinations of lifestyle-related behaviors (prolonged daily television viewing, former smoking, short sleep, lower vegetable consumption, and lower physical activity) were associated with a higher likelihood of being overweight, revealing 10 subgroups. Members of four subgroups with overweight prevalence >50 % were mainly males, older adults, with lower education, and living in greener neighborhoods with low residential density.

CONCLUSION

Sedentary behavior while watching television was identified as the most important correlate of being overweight. Delineating the hierarchy of correlates provides a better understanding of lifestyle-related behavior combinations which may assist in targeting preventative strategies aimed at tackling obesity.

Physical Environmental Correlates of Domain-Specific Sedentary Behaviours across Five European Regions (the SPOTLIGHT Project)

Background

The relation between neighbourhood environmental factors and domain-specific sedentary behaviours among adults remains unclear. This study firstly aims to examine the association of perceived and objectively measured neighbourhood safety, aesthetics, destinations and functionality with transport-related, work-related and leisure-time sedentary behaviour. Secondly, the study aims to assess whether these associations are moderated by age, gender or educational level.

Methods

In 60 randomly sampled neighbourhoods from 5 urban regions in Europe (Ghent and suburbs, Paris and inner suburbs, Budapest and suburbs, the Randstad, and Greater London), a virtual audit with Google Street View was performed to assess environmental characteristics. A total of 5,205 adult inhabitants of these neighbourhoods reported socio-demographic characteristics, sedentary behaviours, and neighbourhood perceptions in an online survey. Generalized linear mixed models were conducted to examine associations between physical environmental neighbourhood factors and sedentary behaviours. Interaction terms were added to test the moderating role of individual-level socio-demographic variables.

Results

Lower levels of leisure-time sedentary behaviour (i.e. all leisure activities except television viewing and computer use) were observed among adults who perceived greater numbers of destinations such as supermarkets, recreational facilities, or restaurants in their neighbourhood, and among adults who lived in a neighbourhood with more objectively measured aesthetic features, such as trees, water areas or public parks. Lower levels of work-related sedentary behaviour were observed among adults who perceived less aesthetic features in their neighbourhood, and among adults who lived in a neighbourhood with less objectively measured destinations. Both age, gender and educational level moderated the associations between neighbourhood environmental factors and sedentary behaviours.

Conclusion

Preliminary evidence was found for associations between neighbourhood environmental factors and domain-specific sedentary behaviours among adults. However, these associations varied according to objective or subjective environmental measures. More research is needed to confirm and clarify the associations.

Mismatch between perceived and objectively measured environmental obesogenic features in European neighbourhoods

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.