|Spatial accessibility to physical activity facilities and to food outlets and overweight in French youth.|
|Jump to Full Text|
|PMID: 22310474 Owner: NLM Status: MEDLINE|
|OBJECTIVE: Some characteristics of the built environment have been associated with obesity in youth. Our aim was to determine whether individual and environmental socio-economic characteristics modulate the relation between youth overweight and spatial accessibility to physical activity (PA) facilities and to food outlets.
DESIGN: Cross-sectional study.Subjects:3293 students, aged 12 ± 0.6 years, randomly selected from eastern France middle schools.
MEASUREMENTS AND METHODS: Using geographical information systems (GIS), spatial accessibility to PA facilities (urban and nature) was assessed using the distance to PA facilities at the municipality level; spatial accessibility to food outlets (general food outlets, bakeries and fast-food outlets) was calculated at individual level using the student home address and the food outlets addresses. Relations of weight status with spatial accessibility to PA facilities and to food outlets were analysed using mixed logistic models, testing potential direct and interaction effects of individual and environmental socio-economic characteristics.
RESULTS: Individual socio-economic status modulated the relation between spatial accessibility to PA facilities and to general food outlets and overweight. The likelihood of being overweight was higher when spatial accessibility to urban PA facilities and to general food outlets was low, but in children of blue-collar-workers only. The odds ratio (OR) (95% confidence interval) for being overweight of blue-collar-workers children compared with non-blue-collar-workers children was 1.76 (1.25-2.49) when spatial accessibility to urban PA facilities was low. This OR was 1.86 (1.20-2.86) when spatial accessibility to general food outlets was low. There was no significant relationship of overweight with either nature PA facilities or other food outlets (bakeries and fast-food outlets).
CONCLUSION: These results indicate that disparities in spatial accessibility to PA facilities and to general food outlets may amplify the risk of overweight in socio-economically disadvantaged youth. These data should be relevant for influencing health policies and urban planning at both a national and local level.
|R Casey; B Chaix; C Weber; B Schweitzer; H Charreire; P Salze; D Badariotti; A Banos; J-M Oppert; C Simon|
Related Documents :
|24896294 - Erythritol, a non-nutritive sugar alcohol sweetener and the main component of truvia®,...
22709644 - Changes in a middle school food environment affect food behavior and food choices.
22897554 - How to identify food deserts: measuring physical and economic access to supermarkets in...
21787914 - Anaerobic digestion of dairy manure influenced by the waste milk from milking operations.
17182874 - Analysis of responses to individual items on the tinnitus handicap inventory according ...
9713754 - Development of a model for evaluation of microbial cross-contamination in the kitchen.
|Type: Journal Article; Research Support, Non-U.S. Gov't Date: 2012-02-07|
|Title: International journal of obesity (2005) Volume: 36 ISSN: 1476-5497 ISO Abbreviation: Int J Obes (Lond) Publication Date: 2012 Jul|
|Created Date: 2012-07-10 Completed Date: 2012-11-30 Revised Date: 2013-06-26|
Medline Journal Info:
|Nlm Unique ID: 101256108 Medline TA: Int J Obes (Lond) Country: England|
|Languages: eng Pagination: 914-9 Citation Subset: IM|
|CARMEN, INSERM U1060/University of Lyon/INRA U1235, CRNH Rhône-Alpes, Oullins, France.|
|APA/MLA Format Download EndNote Download BibTex|
Body Mass Index
Fast Foods / adverse effects*
France / epidemiology
Health Services Accessibility / statistics & numerical data*
Healthcare Disparities / statistics & numerical data*
Obesity / economics, epidemiology, prevention & control*
Journal ID (nlm-ta): Int J Obes (Lond)
Journal ID (iso-abbrev): Int J Obes (Lond)
Publisher: Nature Publishing Group
Copyright © 2012 Macmillan Publishers Limited
Received Day: 28 Month: 04 Year: 2011
Revision Received Day: 04 Month: 01 Year: 2012
Accepted Day: 10 Month: 01 Year: 2012
Print publication date: Month: 07 Year: 2012
Electronic publication date: Day: 07 Month: 02 Year: 2012
Volume: 36 Issue: 7
First Page: 914 Last Page: 919
PubMed Id: 22310474
Publisher Item Identifier: ijo201210
|Spatial accessibility to physical activity facilities and to food outlets and overweight in French youth Alternate Title:Built environment and overweight in youth|
1CARMEN, INSERM U1060/University of Lyon/INRA U1235, CRNH Rhône-Alpes, Oullins, France
2INSERM, U707, Paris, France
3CNRS Image et Ville, University of Strasbourg, Strasbourg, France
4Promotion of Students' Health, School Academy of Bas-Rhin, Strasbourg, France
5UMR U557 INSERM/U1125 INRA/CNAM/University of Paris 13, CRNH IdF, Bobigny, France
6University of Paris-Est Créteil, Lab′Urba – Urbanism Institut of Paris, Paris, France
7Service de Nutrition GH Pitié-Salpêtrière (AP-HP), University Pierre et Marie Curie-Paris 6, Paris, France
|*Service d'Endocrinologie, Diabètes, Nutrition, Centre Hospitalier Lyon Sud, 165 chemin du Grand Revoyet, F69310 Pierre Benite, France. E-mail: email@example.com
The prevalence of childhood obesity has dramatically increased worldwide over the last few decades.1 Although obesity is a complex health issue related to genetic and lifestyle factors, there is growing evidence linking overweight to ‘obesogenic' environments.2, 3 One environmental dimension that is receiving increased attention is the built environment, and more specifically, spatial accessibility to build and service structures that may influence physical activity (PA) patterns or dietary behaviour, such as parks, playgrounds, sports clubs, land-use types, transportation systems and food outlets.4, 5
Several studies have demonstrated that some characteristics of the built environment are associated with healthy/unhealthy dietary behaviours6 and PA.7 However, the relationships with weight status, in particular that of youth, are less consistent.8, 9, 10, 11, 12 In some studies,13, 14, 15, 16 but not in others,17 youth overweight was inversely related to indices of high walkability and spatial accessibility to PA facilities.8, 10 Similarly, the studies that investigated the relationships between access to food outlets or fast-food outlets and youth overweight have given mixed results.18, 19, 20
Individual characteristics may be correlated with those of the place of residence, indicating that individual and environmental characteristics probably interact to jointly affect weight status in various ways. First, an inverse relationship between individual or environmental socio-economic status (SES) and health-related behaviour or body mass index (BMI) has been found in numerous studies.21, 22 Second, data indicate that neighbourhoods in which low-income children live often have lower accessibility to healthy food or PA facilities than do wealthier neighbourhoods.23 Third, in line with what has been called ‘deprivation amplification',24 environmental factors may have greater effects on disadvantaged children than on their more favoured counterparts.25 Thus, it has been suggested that eco-epidemiological models of obesity should consider both individual and environmental demographic and SES characteristics, at least as confounders or modifiers of built environment effects.26 Despite this, most available studies have focused on direct or independent contributions of specific built environmental factors.
The aim of this study was to assess the risk of overweight in a representative sample of French 12-year-old students according to their spatial accessibility to PA facilities and to food outlets, taking into account various individual and environmental characteristics. We hypothesised that individual and environmental SES characteristics can moderate the association of built environment variables with overweight and with obesity-related behaviours (supervised PA and dietary habits). Using geographical information system (GIS) methods, we analysed spatial accessibility to different PA facilities and to food outlets in relation to the risk of youth overweight, testing potential direct and interaction effects of individual and environmental SES characteristics.
Participants included a representative sample of middle-school first-level students (aged 12.0±0.6 years) residing in the Bas-Rhin department (eastern France). Briefly, as previously described,21, 27 one-third of first-level classes from the 88 schools in the department were randomly selected. Written informed parental consent was obtained for volunteer participants (77% of the 4421 eligible students). Anthropometric and lifestyle data were collected in 2001 by trained research assistants. Students' home addresses and occupational status of both parents were obtained from the school administration. The study was approved by the French National Committee for Informatics and Liberties. The study sample broadly matched the family socio-occupational background of the targeted population. A total of 77 students were excluded from analyses because of missing data. Thus, 3327 students constituted the final study population sample.
Measured weight and height were used to define overweight according to International Obesity Task Force age and gender BMI cutoffs.28 Students were asked to complete a food frequency questionnaire, as previously described.27 Four variables were considered here, as dichotomous indicators of unhealthy/healthy diet: consumption of fruits/vegetables/fruit juice, consumption of French fries/potatoes chips and nibbling while watching television the day before the survey, and soft drinks versus water as the most frequent beverage. PA was assessed using the Modifiable Activity Questionnaire for Adolescents.29 Regular (at least once per week) participation in supervised PA (i.e., PA performed by adolescents under supervision by adults) outside of school was reported (dichotomous indicator yes/no). The highest occupational category of either parent, as determined by French socio-occupational nomenclature,30 was used to group individual SES into two categories: children of blue-collar-workers or children of higher socio-occupational categories (non-blue-collar-workers). Student home address was geocoded using the Google Map API.31
Spatial accessibility to PA facilities was assessed using the distance to PA facilities at the municipality level using the 1998 French National Institute of Statistics and Economic Studies inventory database.30 This database includes 11 PA facilities (6 urban facilities: athletic tracks, open-space playgrounds, large collective playgrounds, indoor PA facilities, tennis courts and swimming pools; 5 nature facilities: hiking trails, outdoor recreational parks, boating centres, ski resorts and beaches). For each facility, the distance was set at 0 if the facility was located within the municipality. If there was no facility within the municipality, we used the Euclidian distance to the nearest municipality, which owned that facility. Two variables were defined, one for the urban facilities, the other for the nature facilities, using the mean standardized distance. Each variable was categorized in tertiles (low, medium and high).
Spatial accessibility to food outlets was calculated at individual level using the student home address and the food outlets addresses obtained from Dun and Bradstreet business lists for year 2000. All food outlets were geocoded22 and categorized in three groups using their French activity codes:30 bakeries (n=1053), general food retail (groceries, fresh fruit/vegetable stores, supermarkets and hypermarkets; n=1024) and fast-food outlets (n=371).
Spatial accessibility to each of these three food outlet groups was calculated using an interaction potential model.32 One advantage of GIS-based methods is that they overcome arbitrary limits associated with administrative boundaries.33 The model used here attempted to represent a potential accessibility at any one home address by combining the number of facilities and their proximity, discounting the potential with increasing distance; food outlets within a 1000 m radius were considered. Each variable was divided into three categories: absence of food outlet in the 1000 m radius (low-spatial accessibility), spatial accessibility below the median (medium-spatial accessibility) and spatial accessibility over the median (high-spatial accessibility).
Median tax income and educational level were obtained at IRIS level from 1999 and 2001 French census data. French IRIS23 represent neighbourhoods of a scale comparable to a census block group in the United States. Degree of urbanisation was obtained from a regional land use database.34 Values for each student home address was estimated by means of kernel density method using the Spatial Analyst extension of ArcGIS, version 9.2 (ESRI, Inc., Redlands, CA, USA) with a radius of 1000 m for median tax income and educational level and 2000 m for urbanisation. Urbanisation estimates were subsequently aggregated into three classes.
Baseline descriptive statistics were expressed as means (s.d.) or percentages.
Analyses examined the relations of (1) adolescent weight status with spatial accessibility to urban PA facilities, nature PA facilities and the three categories of food outlets (general food outlets, bakeries and fast-food outlets), (2) regular supervised PA with spatial accessibility to urban and nature PA facilities and (3) the four dietary behaviours with each of the three types of food outlets. We used logistic mixed models (multilevel models) taking into account the hierarchical structure (students nested within schools), with adjustment for individual variables (gender, age and SES) and environmental variables (urbanisation, tax income, educational level and county). In these models, the random effect defined at the school level allowed taking into account the residual correlation in the outcomes within school persisting after adjustment for the covariates. For regular supervised PA and dietary behaviours as outcome, analyses were also run with overweight as additional fixed effect. Interactions between measures of spatial accessibility and fixed effects were tested. Only significant interactions are presented. Results are presented with odds ratios (OR) with 95% confidence interval. Statistical analyses were conducted using SAS software (SAS, version 9.2, SAS Institute Inc., Cary, NC, USA). Significance was set at a P-value of 0.05.
Characteristics of the 3327 students aged 12.0±0.6 years, with a sex ratio of about 1, are presented in Table 1. One-fifth was classified as blue-collar-workers children, one-fifth were overweight (19.7%) and one-third reported no regular supervised PA. Concerning dietary behaviour, one-third had consumed fruits, vegetables or juice more than four times during the preceding 24 h, nearly one-third had eaten French fries or potatoes chips, and one-third had nibbled while watching TV. Soft drinks were the most frequently consumed beverages for 43.0% of the students.
The associations of overweight with spatial accessibility to urban (Model 1a) and nature (Model 1b) PA facilities are presented in Table 2. In the two models, the OR of overweight was negatively associated with the median income tax (P=0.02) and with the educational level (P<0.01) of the place of residence but there was no significant relationship with age, gender, county or urbanisation of the place of residence (data not shown). An interaction was found between individual SES and spatial accessibility to urban PA facilities (P=0.02). The likelihood of being overweight was inversely associated with spatial accessibility to urban PA facilities, but in blue-collar-workers children only. The OR of overweight was 1.76 (1.25–2.49) in blue-collar-workers children having low-spatial accessibility to urban PA facilities compared with non-blue-collar-workers children with similar low-spatial accessibility to urban PA facilities as referent category. Overweight was not related to nature PA facilities.
The associations of overweight with spatial accessibility to each type of food outlet (Models 2a, 2b and 2c) are presented in Table 2. In the three models, the OR of overweight was negatively associated with the median income tax (P<0.02) and with the educational level of the place of residence (P<0.01), but there was no significant relationship with age, gender, county or urbanisation of the place of residence (data not shown). There was an interaction between spatial accessibility to general food outlets and individual SES (P=0.05). The likelihood of being overweight was inversely associated with spatial accessibility to general food outlets in blue-collar-workers children only. The OR of overweight was 1.86 (1.21–2.86) for blue-collar-workers children having a low-spatial accessibility to general food outlets compared with non-blue-collar-workers children with similar low-spatial accessibility to general food outlets as referent category. Overweight was not significantly related to the accessibility to bakeries or fast-food outlets.
The associations of regular supervised PA with spatial accessibility to urban and nature PA facilities are presented in Table 3 (Models 3a and 3b). Regular supervised PA was negatively associated with the educational level of the place of residence (P<0.05), but not with either the median tax income or urbanisation of the place of residence (data not shown). There was no significant interaction between individual SES and spatial accessibility to PA facilities. Regular supervised PA was higher in non-blue-collar-workers than in blue-collar-workers children (P<0.001) and was positively associated to spatial accessibility to urban PA facilities (P<0.01) but not to spatial accessibility to nature PA facilities. The OR of regular supervised PA was 1.61 (1.05–2.45) for children having high-spatial accessibility to urban PA facilities compared with children with low-spatial accessibility to urban PA facilities. Further adjustment on overweight did not alter these relationships. Dietary behaviours were not associated with any of the three measures of spatial accessibility to food outlets (data shown in Supplementary Table 1; models 4a, 4b and 4c).
In the present study, we examined the relationship of 12-year-old student overweight and obesity-related behaviour with spatial accessibility to PA facilities and to food outlets, estimated by GIS methods. We specifically explored the hypothesis that such relationships are modulated by individual and environmental SES characteristics. Our main findings provide support for an individual/environment interaction. When spatial accessibility to urban PA facilities or to general food outlets was low, there was an increased likelihood of being overweight in blue-collar-workers children but not in children from higher socio-occupational categories. Consistent with existing evidence,9, 35 the association of spatial accessibility to PA facilities with regular supervised PA, independently of individual and environmental SES characteristics, indicates that PA level might partly mediate the effects of environmental SES on youth overweight.
The strengths of our study lie in the high-participation rate and the only one age group, which limited variability related to age-related behavioural changes observed during adolescence, along with measured BMI and assessment of both PA and food environments. Moreover, we measured environmental characteristics and spatial accessibility to food outlets across continuous space using sophisticated geographical methods to estimate spatial accessibility, without relying on arbitrary administrative boundaries. Some limitations to the study should be mentioned. First, we used cross-sectional data and could not make direct causal inferences. Second, regular supervised PA and dietary measures were self-reported, with relatively crude and non-exhaustive questions concerning diet. Third, this study is based on secondary analysis of pre-existing data. Therefore, information on spatial accessibility to PA facilities was limited and was only available at the municipality level. Although the food outlets variables were more detailed, they were subject to measurement error because of potential inaccuracies in the commercial database used. Moreover, we had no information on other components of accessibility (organisational, economic and qualitative), nor on other PA-related elements, including security and walkability indices.
Numerous physical and built environment features have been associated with PA in a growing body of literature.13, 36 Few studies have examined the impact of the built environment on youth obesity, and the possible relationship between the built environment and weight status remains subject to debate.5, 8, 10, 11, 12 In a recent review,9 we found that the inverse relationship between youth weight and indices indicating higher walkability was the most consistent. Contrary to observations in adults, but consistent with our results showing no association between overweight and spatial accessibility to nature PA facilities, parks and ‘green areas' were generally not strongly associated with youth BMI. Recent data showing that park users are primarily very young children and adults suggest that this type of setting may be less important for PA in older children.37
A beneficial relationship between weight and spatial accessibility to PA facilities has been reported in only four out of nine papers.9 However, we should emphasise that about half of the negative papers did not control for environmental or individual SES. Only one study15 adjusted for both, and it found that the number of schools (as potential sites of PA practice) inaccessible on weekends was higher in neighbourhoods with lower SES and was independently associated with significantly higher BMI. Yet, as emphasised by socio-ecological models of behaviour, spatial accessibility is only one of multiple determinants of a healthy lifestyle and health itself. On the other hand, a more favourable socio-economic context and denser social networks might influence the built environment.23 Previous studies38, 39 have found a higher number of PA facilities in more affluent places of residence. However, such relationships are not clear-cut and may be context specific. In two European studies,24, 40 associations between environmental SES and the presence of PA facilities, or comfort-specific characteristics of such facilities, were found to vary from positive to negative depending on the facility. Nevertheless, inter-relationships between built and socio-economic characteristics may result in confounding the association between differing environmental exposures,41 and, as such, both categories of environmental determinants should be included in the analyses.
The current study extends previous research on the effects upon health of spatial accessibility to different facilities by specifically examining cross-level interactions among built environmental characteristics and environmental or individual SES. To our knowledge, no previous study had specifically addressed this issue in relation to PA facilities and youth overweight. Although more research is needed to delineate the exact mechanisms underlying the interactions observed here and to better identify policy-relevant target populations and determinants, our results suggest that disadvantaged children may be more dependent on local environmental determinants than their more favoured counterparts.
Interestingly, we found an analogous interaction between individual SES and spatial accessibility to general food outlets. These results are consistent with existing data indicating that an inverse relationship between overweight and the density of supermarkets, thought to offer a higher variety of healthy food choices, depends on individual characteristics that might reflect SES, such as ethnic origin and mothers employment conditions.42 Similarly, a positive relationship with convenience stores in low-income towns was found in one study,20 whereas in another study, a positive relationship with fast-food outlets availability was found for a low SES population living in East Harlem, New York.19 In contrast to data on the association between spatial accessibility to PA facilities and regular supervised PA, we found no relationship between spatial accessibility to general food outlets and dietary behaviours. Our crude measurement of dietary behaviour may explain, at least in part, this negative result. It may also reflect the fact that the food environment is complex. Organisational characteristics, such as store opening hours, food quality, prices and parental choices, may be as important as spatial accessibility to food outlets and should be taken into account in future research. These characteristics vary according to country and culture, indicating that future studies should be carried out in various geographical and socio-economic settings. Moreover, we cannot exclude the possibility that the relationship of PA and food environment variables with weight is indirect and may reflect other contextual obesity determinants, such as higher walkability or more dense social networks in commercially attractive neighbourhoods.43
In conclusion, our data add to the growing body of evidence documenting relationships between the built environment and health outcomes, including youth overweight, by demonstrating specific interactions between individual and environmental factors in shaping health and health-related inequalities. Although more research is necessary to determine whether these interactions are context-specific, present results may be relevant for influencing health policies and urban planning at both a national and local level.
Supplementary Information accompanies the paper on International Journal of Obesity website (http://www.nature.com/ijo)
This work is part of the ELIANE (Environmental LInks to physical Activity, Nutrition and hEalth) study. ELIANE is a supported by the French National Research Agency (Agence Nationale de la Recherche, ANR-07- PNRA-004). We thank the schools' medical staffs for their technical assistance.
CS is the principal investigator and coordinated the cross-sectional survey. CS and RC performed the analyses and drafted the manuscript. J-MO, BC, BS, HC, PS, AB, DB and CW assisted with the literature search and writing of the manuscript. J-MO is the coordinator of the ELIANE study; CS, BC and CW are the principal investigators in the ELIANE study.
The authors declare no conflict of interest.
|WHO. Obesity: preventing and managing the global epidemic. Report of a WHO consultationWorld Health Organ Tech Rep SerYear: 2000894ixii1–253.11234459|
|Johnson-Taylor WL,Everhart JE. Modifiable Environmental and Behavioral Determinants of Overweight among Children and Adolescents: Report of a Workshop[ast]ObesityYear: 20061492996616861599|
|Swinburn B,Egger G,Raza F. Dissecting obesogenic environments: the development and application of a framework for identifying and prioritizing environmental interventions for obesityPrev MedYear: 199929(6 Pt 156357010600438|
|Charreire H,Casey R,Salze P,Simon C,Chaix B,Banos A,et al. Measuring the food environment using geographical information systems: a methodological reviewPublic Health NutrYear: 2010131773178520409354|
|Papas MA,Alberg AJ,Ewing R,Helzlsouer KJ,Gary TL,Klassen AC. The Built Environment and ObesityEpidemiol RevYear: 20072912914317533172|
|van der Horst K,Oenema A,Ferreira I,Wendel-Vos W,Giskes K,van Lenthe F,et al. A systematic review of environmental correlates of obesity-related dietary behaviors in youthHealth Educ ResYear: 20062220322616861362|
|Giles-Corti B,Kelty SF,Zubrick SR,Villanueva KP. Encouraging walking for transport and physical activity in children and adolescents: how important is the built environmentSports MedYear: 200939995100919902982|
|Carter MA,Dubois L. Neighbourhoods and child adiposity: a critical appraisal of the literatureHealth PlaceYear: 20101661662820106712|
|Casey R,Oppert JM,Weber C,Charreire H,Salze P,Badariotti D,et al. Determinants of childhood obesity: what can we learn from built environment studiesFood Qual PreferenceYear: 2011e-pub ahead of print 22 June 2011; doi:10.1016/j.foodqual.2011.06.003|
|Dunton GF,Kaplan J,Wolch J,Jerrett M,Reynolds KD. Physical environmental correlates of childhood obesity: a systematic reviewObesity RevYear: 200910393402|
|Feng J,Glass TA,Curriero FC,Stewart WF,Schwartz BS. The built environment and obesity: a systematic review of the epidemiologic evidenceHealth PlaceYear: 20091617519019880341|
|Galvez MP,Pearl M,Yen IH. Childhood obesity and the built environmentCurr Opin PediatrYear: 20102220220720090524|
|Gordon-Larsen P,Nelson MC,Page P,Popkin BM. Inequality in the built environment underlies key health disparities in physical activity and obesityPediatricsYear: 200611741742416452361|
|Oreskovic NM,Winickoff JP,Kuhlthau KA,Romm D,Perrin JM. Obesity and the built environment among Massachusetts childrenClin PediatrYear: 200948904912|
|Scott MM,Cohen DA,Evenson KR,Elder J,Catellier D,Ashwood JS,et al. Weekend schoolyard accessibility, physical activity, and obesity: the trial of activity in Adolescent Girls (TAAG) studyPrev MedYear: 20074439840317292958|
|Timperio A,Jeffery RW,Crawford D,Roberts R,Giles-Corti B,Ball K. Neighbourhood physical activity environments and adiposity in children and mothers: a three-year longitudinal studyInt J Behav Nutr Phys ActYear: 201071820170507|
|Spence JC,Cutumisu N,Edwards J,Evans J. Influence of neighbourhood design and access to facilities on overweight among preschool childrenInt J Pediatr ObesityYear: 20083109116|
|Burdette HL,Whitaker RC. Neighborhood playgrounds, fast food restaurants, and crime: relationships to overweight in low-income preschool childrenPrev MedYear: 200438576314672642|
|Galvez MP,Hong L,Choi E,Liao L,Godbold J,Brenner B. Childhood obesity and neighborhood food-store availability in an inner-city communityAcad PediatrYear: 2009933934319560992|
|Oreskovic NM,Kuhlthau KA,Romm D,Perrin JM. Built environment and weight disparities among children in high- and low-income townsAcad PediatrYear: 2009931532119477705|
|Klein-Platat C,Wagner A,Haan MC,Arveiler D,Schlienger JL,Simon C. Prevalence and sociodemographic determinants of overweight in young French adolescentsDiabetes Metab Res RevYear: 20031915315812673784|
|Organization WH. Obesity: preventing and managing the global epidemic: report of a WHO consultation (WHO Technical Report Series 894)World Health Organization: Geneva, SwitzerlandYear: 2004|
|Taylor WC,Poston WSC,Jones L,Kraft KM. Environmental justice: obesity, physical activity, and healthy eatingJPAHYear: 20063(Suppl 1S30S54|
|Macintyre S,Macdonald L,Ellaway A. Do poorer people have poorer access to local resources and facilities? The distribution of local resources by area deprivation in Glasgow, ScotlandSoc Sci MedYear: 20086790091418599170|
|Humbert ML. Factors that influence physical activity participation among high- and low-SES youthQualitative Health ResYear: 200616467483|
|Chaix B. Geographic life environments and coronary heart disease: a literature review, theoretical contributions, methodological updates, and a research agendaAnn Rev Public HealthYear: 2009308110519705556|
|Platat C,Perrin AE,Oujaa M,Wagner A,Haan MC,Schlienger JL,et al. Diet and physical activity profiles in French preadolescentsBr J NutrYear: 20069650150716925855|
|Cole T,Bellizzi M,Flegal K,Dietz W. Establishing a standard definition for child overweight and obesity worldwide: international surveyBmjYear: 20003201240124310797032|
|Pereira MA,FitzerGerald SJ,Gregg EW,Joswiak ML,Ryan WJ,Suminski RR,et al. A collection of physical activity questionnaires for health-related researchMed Sci Sports ExercYear: 199729(6 SupplS1S2059243481|
|InseeFrench National Institute of Statistics and Economic Studies [Institut national de la statistique et des études économiques]. Available from:http://www.insee.fr/fr/ .|
|Google IGoogle Maps API - Google Code. 2009; Available from:http://code.google.com/intl/fr/apis/maps/ .|
|Salze P,Banos A,Oppert JM,Charreire H,Casey R,Simon C,et al. Estimating spatial accessibility to facilities on the regional scale: an extended commuting-based interaction potential modelInt J Health GeogrYear: 201110221219597|
|Chaix B,Merlo J,Subramanian SV,Lynch J,Chauvin P. Comparison of a spatial perspective with the multilevel analytical approach in neighborhood studies: the case of mental and behavioral disorders due to psychoactive substance use in Malmo, Sweden, 2001Am J EpidemiolYear: 200516217118215972939|
|CigalCoopération pour l′Information Géographique en ALsace.Base de données d'occupation du sol BDOCS 2000. 2003; Available from:http://www.cigalsace.org/ produits_cigal.htm#bd_ocs_2000 .|
|Giles-Corti B,Broomhall MH,Knuiman M,Collins C,Douglas K,Ng K,et al. Increasing walking: how important is distance to, attractiveness, and size of public open spaceAm J Prev MedYear: 200528(2 Suppl 216917615694525|
|Ferreira I,van der Horst K,Wendel-Vos W,Kremers S,van Lenthe FJ,Brug J. Environmental correlates of physical activity in youth ? a review and updateObesity RevYear: 20078129154|
|Cohen DA,McKenzie TL,Sehgal A,Williamson S,Golinelli D,Lurie N. Contribution of public parks to physical activityAm J Public HealthYear: 20079750951417267728|
|Estabrooks PA,Lee RE,Gyurcsik NC. Resources for physical activity participation: does availability and accessibility differ by neighborhood socioeconomic statusAnn Behav MedYear: 20032510010412704011|
|Moore LV,Diez Roux AV,Evenson KR,McGinn AP,Brines SJ. Availability of recreational resources in minority and low socioeconomic status areasAm J Prev MedYear: 200834162218083446|
|Billaudeau N,Oppert JM,Simon C,Charreire H,Casey R,Salze P,et al. Investigating disparities in spatial accessibility to and characteristics of sport facilities: Direction, strength, and spatial scale of associations with area incomeHealth PlaceYear: 201117114121|
|Boone-Heinonen J,Evenson KR,Song Y,Gordon-Larsen P. Built and socioeconomic environments: patterning and associations with physical activity in U.S. adolescentsInt J Behav Nutr Phys ActYear: 201074520487564|
|Powell LM,Bao Y. Food prices, access to food outlets and child weightEcon Human BiolYear: 20097647219231301|
|Diez Roux AV. Residential environments and cardiovascular riskJ Urban HealthYear: 20038056958914709706|
Keywords: overweight, built environment, physical activity, youth, geographic information system.
Previous Document: SIRT1 and CLOCK 3111T>C combined genotype is associated with evening preference and weight loss resi...
Next Document: A gene variant of 11?-hydroxysteroid dehydrogenase type 1 is associated with obesity in children.