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Built Environment, Selected Risk Factors and Major Cardiovascular Disease Outcomes: A Systematic Review

  • Pasmore Malambo ,

    Affiliation University of Western Cape, School of Public Health, Robert Sobukwe Rd, Bellville, Cape Town, 7535, South Africa

  • Andre P. Kengne,

    Affiliation Non-communicable disease Unit, South African Medical Research Council, Francie van Zijl Drive, Parowvallei, P.O. Box 19070, 7505 Tygerberg, Cape Town, South Africa

  • Anniza De Villiers,

    Affiliation Non-communicable disease Unit, South African Medical Research Council, Francie van Zijl Drive, Parowvallei, P.O. Box 19070, 7505 Tygerberg, Cape Town, South Africa

  • Estelle V. Lambert,

    Affiliation Division of Exercise Science and Sports Medicine, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Boundary Road, Newlands, 7700, Cape Town, South Africa

  • Thandi Puoane

    Affiliation University of Western Cape, School of Public Health, Robert Sobukwe Rd, Bellville, Cape Town, 7535, South Africa



Built environment attributes have been linked to cardiovascular disease (CVD) risk. Therefore, identifying built environment attributes that are associated with CVD risk is relevant for facilitating effective public health interventions.


To conduct a systematic review of literature to examine the influence of built environmental attributes on CVD risks.

Data Source

Multiple database searches including Science direct, CINAHL, Masterfile Premier, EBSCO and manual scan of reference lists were conducted.

Inclusion Criteria

Studies published in English between 2005 and April 2015 were included if they assessed one or more of the neighborhood environmental attributes in relation with any major CVD outcomes and selected risk factors among adults.

Data Extraction

Author(s), country/city, sex, age, sample size, study design, tool used to measure neighborhood environment, exposure and outcome assessments and associations were extracted from eligible studies.


Eighteen studies met the inclusion criteria. Most studies used both cross-sectional design and Geographic Information System (GIS) to assess the neighborhood environmental attributes. Neighborhood environmental attributes were significantly associated with CVD risk and CVD outcomes in the expected direction. Residential density, safety from traffic, recreation facilities, street connectivity and high walkable environment were associated with physical activity. High walkable environment, fast food restaurants, supermarket/grocery stores were associated with blood pressure, body mass index, diabetes mellitus and metabolic syndrome. High density traffic, road proximity and fast food restaurants were associated with CVDs outcomes.


This study confirms the relationship between neighborhood environment attributes and CVDs and risk factors. Prevention programs should account for neighborhood environmental attributes in the communities where people live.


Current global mortality rates from non-communicable diseases (NCDs) remain unacceptably high and are increasing [1]. More than 70% of global cardiovascular disease (CVD), are attributable to modifiable risk factors [2]. Rapidly globalization is accompanied by increasing urbanization, population growth and changes in demographics and promotes trends towards unhealthy lifestyles [3]. The ecological model, however, states that an individual’s behaviour is influenced by multiple level factors such as social, neighborhood environment, and policy factors [4,5]. One of these factors, the neighborhood environment, and its link to health have been the focus of an increasing number of studies in recent years [6]. These studies are from a variety of disciplines, including urban planning and transportation planning [7].

Despite increases in the number of studies on the relationship between the neighborhood environment and health, the potential impact of the neighborhood environment across a range of health outcomes has not been fully explored. For instance, existing studies have focused on specific CVD risk factors such as obesity [79], metabolic syndrome [10], physical activity [11,12] and walking [13]. In addition, a recent study reviewed obesity-related outcomes [14]. Although Mayne et al. 2015[14] used quasi-experiment in their review, the study centered on obesity and related risk factors. Previously, the association between built environment and obesity has received wide publication. However, no study has broadly reviewed the relationship of neighborhood environment with major CVD outcomes and risk factors, while such a review is necessary to guide future research and policy formulation in this sector [15]. Therefore, the purpose of this study is to synthesize the studies on the association between a number of neighborhood environment attributes and CVD risks.


Data sources/ search strategy

A comprehensive search was conducted to identify all research articles published from 2005 to 2015 that examine neighborhood environment, major CVD outcomes and selected risk factors (Table 1). English language articles were identified from the following databases: EBSCO (including: Academic Search, CINAHL, Global Health, Health Source: Nursing/academic and Medline) and Science Direct. Significant studies were identified using any of the following keywords: neighbourhood environment, perceived neighborhood environment, perceived built environment, land use mix diversity, physical activity, social environment, overweight or obesity, hypertension, diabetes mellitus, metabolic syndrome, coronary heart disease and myocardial infarction.

Study selection

Titles and abstracts of all identified articles were assessed for their potential eligibility. Full texts of potentially eligible articles were then retrieved and their eligibility was verified against the study eligibility criteria. Fig 1 (a flow chart of included studies; see appendix) represents the flow of the literature review conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [16], S1 Table (PRISMA 2009 checklist). Studies published in English were included if: 1) they used a Geographic Information System (GIS) [17] or subjectively assessed one or more of the built environment factors categorized according to the validated and reliably tested ‘Neighborhood Environment Walkability Scale’ (NEWS) which is a better questionnaire to assess the local environment [18]; 2) examined the relationship with any of the major CVD outcomes including myocardial infarction, coronary heart disease and stroke; 3) examined selected risk factors including physical activity (categorized in domains were considered), overweight or obesity, hypertension and diabetes mellitus; 4) were original reports on studies conducted among subjects aged 18 years and above; and 5) if the purpose of the studies were to explore the association between the variables of interest using multivariate analyses. Exclusion criteria were as follows: 1) Studies exclusively conducted on adolescents; 2) studies that employed a qualitative design; 3) systematic review papers; 4) publications from studies where subjects had difficulty with walking and 5) studies that did not meet the criteria for current review.

Fig 1. Flow Chart of included studies.

This figure represents the flow of the literature review conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [16].

Data extraction

The information extracted included the first authors’ name, publication year, the sample size, gender, age range of the subjects, country and city where the study was conducted, study design, study tool (assess neighborhood environment), exposure assessment (any of the neighborhood environment attributes), outcome assessment (CVD outcomes or risk factors), and measures of association. Data abstraction, classification, and quality assessment of each study were conducted by two reviewers independently. A third reviewer was consulted if there was disagreement.

Quality appraisal of the studies

In order to assess the methodological quality for each study selected, the ‘Strengthening the reporting of observational studies in epidemiology’ (STROBE) checklist [19] was adapted in accordance with the objectives of this study. For instance, this included the: sample size, setting, design, study tool (assessing neighborhood environment), exposure, outcome measure and association according to the area of this study. The final PRISMA checklist included I8 items that assessed the quality of this study. Each item scored one point if full reporting was met, or zero if not or partially reported.

Data synthesis

Due to differences in research questions, exposure measurements, outcome measurements and methods across studies, a formal meta-analysis was not possible. Thus, the current review applied a semi-quantitative procedure [7]. The aim of this semi-quantitative procedure was to allow a rapid assessment of the strength of the evidence of an association between the exposure and the outcomes of interest by reducing a range of results from heterogeneous analytical designs to two binary questions [20]: a) did the study under review show a positive or negative association between the built environmental attributes and the outcome of interest? b) and, if so, was this finding statistically significant (p<0.05)? Hence, estimates of associations between neighborhood environment attributes, CVD risk factors and major outcomes were extracted from the eligible studies according to their substantive relevance and methodological findings and results summarized (Table 2). However, to take into account potential publication bias, we did not limit our analysis on papers published in peer-reviewed journals. References of finally included records were additionally checked. Built environment studies assessing relationship with CVD risks and outcomes are relatively recent. Therefore, this study restricted the search for a specific time period and database. Contrary, no quantitative assessment for risk of bias in individual studies was performed. However, in each study sample size, number of observations per built environment and total number of considered CVD risks and outcomes were checked, because small sample sizes result in biased effect estimates.


Overview of the study selection process

An overview of the types of the articles selected is provided in Table 2, highlighting the author, country, gender, age, sample size, study design, study tools (assess neighborhood environment), exposure measures, outcome measures and their associations. The electronic search yielded 565 articles from the selected databases; MasterFile Premier = 118, CINAHL = 71, Science Direct = 323, EBSCO (including; Academic Search, CINAHL, Global Health, Health Source: Nursing/academic, and Medline) = 47, manual search = 6. After title/abstract screening, 525 articles were excluded for not meeting inclusion criteria. Of the excluded articles, 510 articles were unrelated to neighborhood environmental attributes, CVD risk and CVD outcomes, 5 were systematic reviews, 6 were conducted in a population with clinical conditions (disability), and another 4 were duplicates. The abstracts of 40 citations were then obtained and retrieved. Out of these abstracts, 11 were excluded since 4 were qualitative design and 7 were conducted among adolescents. Thus, 29 full text articles were assessed for eligibility. Of these, 11 were excluded as 7 did not use NEWS, 2 were conducted among adolescents and another 2 did not meet the objective of the review to measure BE (S2 Table, excluded articles). Therefore only 18 articles were finally eligible for inclusion in the current review. The flow chart in Fig 1 shows the process leading to the number of included articles for the review.

Table 2. Studies that have assessed neighborhood environmental attributes and CVD* risk factors and outcomes.

General characteristics of the studies included

Table 2 depicts the descriptive characteristics of the included studies. The year of study ranged between 2005 [21] and 2015 [22], with 27.8% (n = 5) being published in 2012 [2327]. Sample sizes varied across studies, ranging from 102 [21] to 4,319,674 [28]. In all, 55.5% (n = 10) of the studies were conducted in urban [21,23,24,26,2832,33] areas as compared to rural [34], suburban [27] and urban/suburban/rural [35]. Community based studies [22,25,33,36] constituted 22.2% (n = 4) compared to one institution based study [37]. The reported ages of the participants ranged from 18 [25,27,32,33] to 80 years [28]. Most studies included females and males [21, 22,24,25,2736] (88.9%; n = 16) with only 11.1% (n = 2) being in females only [23,26]. Sixteen studies (88.9%) were conducted in high-income countries [2133,34, 36,38], 11.1% (n = 2) in middle income countries [35,37] and 38.9% (n = 7) were conducted in the USA alone [21,23,25,26,31,33,36]. Of all included studies, 94.4% (n = 17) were cross-sectional [2122,26,27,2938] with one being longitudinal [28].

CVD risk factors and outcomes covered across studies

Of the 18 studies reviewed, 44.4% focused on physical activity [21,2325,29,30,35,37], 16.7% on body mass index [23,35], 5.6% on blood pressure [26], 5.6% on diabetes mellitus [33] and 16.7% on metabolic syndrome [27,34,32]. Furthermore, 16.7% of studies [22,28,38] focused on coronary heart disease, stroke and heart failure, Table 2.

Measurement of neighborhood environmental attributes

The majority of the studies (66.7%) used GIS [22,24,26,28,3036] to assess neighbourhood environment attributes, while 33.3% used NEWS questionnaires [21,23,25,27,29,34] (Table 2).

Association between neighborhood environment attributes and CVD risk

The majority of the reported associations of neighborhood environmental attributes with CVD risk factors and outcomes were statistically significant (p < 0.05) with effects estimates in the expected direction, and only two studies with mixed results, comparing neighborhood environmental attributes with transport related physical activity [37] and hypertension [34] respectively, reported no significant association, Table 2. Forty four percent of studies [21,2325,29,30,35,37] reported variety of neighborhood environmental attributes associated with physical activity domains. Conversely, 11.1% of studies reported neighborhood environmental attributes were associated with body mass index [23,36] and blood pressure [26,31]. In addition, 16.6% studies reported metabolic syndrome [27, 32,34] and only one study indicated diabetes mellitus [33] to be related with Built environment attributes. Similarly, 16.6% of studies showed a significant association between neighborhood environmental attributes and myocardial infarction, coronary heart disease, congestive heart failure, angina and stroke [22, 28,38], Table 2.


This review has shown that a variety of neighborhood environmental attributes are associated with physical activity. Furthermore, density of fast food restaurants, supermarkets/grocery stores and high walkable neighborhood environments were associated with body mass index, blood pressure, diabetes mellitus and metabolic syndrome. In addition, high density traffic, road proximity and high density of fast food restaurants were associated with major CVD outcomes.

Our results are consistent with other studies [11,39]. In particular, physical activity was associated with safe footpaths and recreational facilities [40,41] and walking [42]. The results indicate that urban attributes such as street connectivity, residential density, recreational facilities and availability of traffic devices improves neighborhood walkability which may promote walking, leisure and transport related to physical activity which, consequently, lowers the incidence of CVDs. For instance, environmental attributes are thought to increase active transportation and lessen the need for private automobile use to accomplish daily tasks, which, in turn, lowers body mass index [43].

This review found that neighborhood environmental attributes such as fast-food restaurants and high walkable neighborhood environment were associated, either positively or negatively with body mass index, blood pressure and metabolic syndrome risk. Previous studies have reported similar results on the association between food environment and BMI [41,44,45] or blood pressure [10]. Greater accessibility to fast food restaurants may encourage people to make food choices at odds with ‘healthy’ dietary recommendations by making these choices easier [46]. Another explanation is that limited access to supermarkets may incentivize visits to convenience stores or fast food restaurants outlets [47] thereby increasing the chance of consuming unhealthy foods, with consequential increases in individual body mass indices and blood pressure levels.

Living in high walkable neighborhoods was associated with a lower prevalence of high body mass index, diabetes mellitus and metabolic syndrome risk. Similar results have been reported elsewhere [10]. Neighbourhood environmental attributes may increase an individual’s active transportation related to the physical activity needed to accomplish daily tasks and thus lower the [43]. For example, a higher population density may support increased recreational opportunities and supermarkets offering a better supply of healthy foods, and so explaining associations between body mass index [48] and metabolic syndrome risk [10]. Moreover, high walkable neighborhood environments are associated with promoting recreational and transport related physical activity [49], participation in which eventually assists in lowering the prevalence of obesity or metabolic syndrome risks. Furthermore, an increase in intersection density in the neighborhood may promote walking through providing more route options and may regulate traffic [48].

Our study also observed that major CVD outcomes are related to built environment attributes. Specifically, a study has reported similar results on proximity to traffic [50]. Environmental attributes include proximity to stores, and access to supermarkets and non-fast food stores which may, consequently, affect the extent to which individuals walk and the food choices they make, which governs their diet and thus links to CVDs [51, 52]. Likewise, high traffic volumes have been associated with noise and air pollution which are linked to major CVDs. In addition, road proximity has been linked with low individual and neighborhood socioeconomic status, both of which have been shown to be associated with CVDs [53].

Limitations of the review

One limitation of this study is the paucity of primary research on the association between neighborhood environmental attributes and CVD risk and major CVDs in an African context. Almost all publications included in the review were cross-sectional, thus causal inferences in the relationships could not be determined. The exclusion of studies not conducted in English also detracts from this study. In addition, this study reviewed few CVD risk factors with selected CVDs. Furthermore, we did not perform meta-analysis to derive pooled estimates of the association across studies. This was due to the much heterogeneity in measures of associations used across included studies, as well as the wide range of outcomes examined across studies. Future studies should explore any association between CVDs and other environmental attributes such as tobacco use, alcohol use and air pollution in order to have a broader understanding of other moderating effects. To our knowledge, this is the first review to document the associations between both objectively and subjectively measured built environment attributes and selected CVD risk and major CVDs. Methods of classification and categorization of the findings in this study follow those of other similar studies, facilitating comparisons. Moreover, this study further contributes to illustrating that studies from developed countries use comparable methodologies to studies from less well developed countries, such as this one.


This study shows that both objective and perceived neighborhood environmental attributes are linked to CVD and its risk factors. The information gathered here from studies that explored neighborhood environmental attributes and their association with CVD risks and major CVD outcomes will help guide policy makers on the neighborhood environmental, transportation, health and education to improve intervention programs by local government and for people at a ‘grass-roots’ level. Future studies should further explore the associations of CVD risk and CVD outcomes with a broad set of neighborhood attributes using a longitudinal approach to better understand the direction of effects.

Supporting Information

S2 Table. Excluded full articles from the review.



The authors would like to acknowledge South African Medical Research Council, Division of Exercise Science and Sports Medicine and School of Public Health for their material support in the study.

Author Contributions

  1. Conceptualization: PM APK.
  2. Data curation: PM APK.
  3. Formal analysis: PM.
  4. Funding acquisition: TP EVL.
  5. Investigation: PM.
  6. Methodology: PM APK.
  7. Project administration: PM.
  8. Supervision: APK ADV EVL TP.
  9. Visualization: PM APK.
  10. Writing – original draft: PM APK.
  11. Writing – review & editing: PM APK.


  1. 1. WHO. Noncommunicable Diseases Country Profiles 2014. Geneva; 2014.
  2. 2. Ezzati M, Hoorn SV, Rodgers A, Lopez AD, Mathers CD, Murray CJ. Estimates of global and regional potential health gains from reducing multiple major risk factors. Lancet. 2003; 362(9380):271–80. pmid:12892956
  3. 3. Maher D, Ford N, Unwin N. Priorities for developing countries in the global response to non-communicable diseases. Global Health. 2012 Jun.11; 8:14. pmid:22686126
  4. 4. Sallis JF, Floyd MF, Rodriguez DA, Saelens BE. Role of built environments in physical activity, obesity, and cardiovascular disease. Circulation. 2012 Feb; 125(5):729–37. pmid:22311885
  5. 5. Bracy NL, Millstein RA, Carlson JA, Conway TL, Sallis JF, Saelens BE, et al. Is the relationship between the built environment and physical activity moderated by perceptions of crime and safety? Int J Behav Nutr Phys Act [Internet]; 2014 Feb. [cited 2014 Oct 27]; 11(1):24. pmid:24564971
  6. 6. Sallis JF, Linton LS, Kraft MK, Cutter CL, Kerr J, Weitzel J,et al. The active living research program: six years of grantmaking. Am J Prev Med. 2009 Feb; 36:S10–21. pmid:19147053
  7. 7. Feng J, Glass TA, Curriero FC, Stewart WF, Schwartz BS. The built environment and obesity: A systematic review of the epidemiologic evidence. Health Place. 2010 Sep; 16(2):175–90. pmid:19880341
  8. 8. Papas MA, Alberg AJ, Ewing R, Helzlsouer KJ, Gary TL, Klassen AC. The built environment and obesity. Epidemiol Rev. 2007 May; 29(1):129–43.
  9. 9. Ding D, Gebel K. Built environment, physical activity, and obesity: what have we learned from reviewing the literature? Health Place. 2012 Sep; 18(1):100–5. pmid:21983062
  10. 10. Leal C, Chaix B. The influence of geographic life environments on cardiometabolic risk factors: A systematic review, a methodological assessment and a research agenda. Obes Rev. 2011 Jan; 12(3):1–14.
  11. 11. Arango CM, Páez DC, Reis RS, Brownson RC, Parra DC. Association between the perceived environment and physical activity among adults in Latin America: a systematic review. Int. J. Behav. Nutr. Phys. Act. 2013; 10(1): 122. pmid:24171897
  12. 12. Van Cauwenberg J, De Bourdeaudhuij I, De Meester F, Van Dyck D, Salmon J, Clarys P, et al. Relationship between the physical environment and physical activity in older adults: a systematic review. Health Place. 2011 Mar; 17(2):458–69. pmid:21257333
  13. 13. Saelens BE, Handy SL. Built Environment Correlates of Walking: A Review. Med Sci Sport Exerc. 2008 Jul; 40(S7): S550–S566.
  14. 14. Mayne SL, Auchincloss AH, Michael YL. Impact of policy and built environment changes on obesity-related outcomes: A systematic review of naturally-occurring experiments. Obes Rev. 2015 May; 16(5):362–375. pmid:25753170
  15. 15. Dunton GF, Kaplan J, Wolch J, Jerrett M, Reynolds KD. Physical environmental correlates of childhood obesity: A systematic review. Obes Rev. 2009 Jul; 10(4):393–402. pmid:19389058
  16. 16. Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA group. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. Phys Ther. 2009 Jun; 89(9):873–80. pmid:19723669
  17. 17. Thornton LE, Pearce JR, Kavanagh AM. Using Geographic Information Systems (GIS) to assess the role of the built environment in influencing obesity: a glossary. Int J Behav Nutr Phys Act; 2011 Jan [cited 2014 Oct 27]; 8(1):71. pmid:21722367
  18. 18. Cerin E, Conway TL, Saelens BE, Frank LD, Sallis JF. Cross-validation of the factorial structure of the Neighborhood Environment Walkability Scale (NEWS) and its abbreviated form (NEWS-A). Int J Behav Nutr Phys Act. 2009 Jun; 6:32. pmid:19508724
  19. 19. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Int J Surg. 2014 Jul;12(12):1495–9 pmid:25046131
  20. 20. Williams J, Scarborough P, Matthews A, Cowburn G, Foster C, Roberts N, et al. A systematic review of the influence of the retail food environment around schools on obesity-related outcomes. Obes Rev. 2014 May; 15(5):359–74. pmid:24417984
  21. 21. Atkinson JL, Sallis JF, Saelens BE, Cain KL, Black JB. Recreational Environments With Physical Activity. Am J Heal Promot. 2005 Apr; 19:304–9.
  22. 22. Chum A, O’Campo P. Cross-sectional associations between residential environmental exposures and cardiovascular diseases. BMC Public Health. 2015 Apr;15(438). pmid:25924669
  23. 23. Adams MA, Sallis JF, Conway TL, Frank LD, Saelens BE, Kerr J, et al. Physical activity among older adults health problems than are their inactive. J Heal Behav. 2012; 36(6):757–69.
  24. 24. Witten K. Neighbourhood built environment is associated with residents’ transport and leisure physical activity: findings from New Zealand using objective exposure and outcome measures. Env Heal Persp. 2012 Mar; 120(7):971–7.
  25. 25. Martinez SM, Ayala GX, Patrick K, Arredondo EM, Roesch S, Elder J. Associated pathways between neighborhood environment, community resource factors, and leisure-time physical activity among Mexican-American adults in San Diego, California. Am J Health Promot. 2012 May/Jun; 26(5):281–8. pmid:22548422
  26. 26. Drewnowski A, Aggarwal A, Hurvitz PM, Monsivais P, Moudon AV. Obesity and supermarket access: proximity or price? Am J Public Health. 2012 Aug; 102(8):74–81.
  27. 27. Baldock K, Paquet C, Howard N, Coffee N, Hugo G, Taylor A, et al. Associations between resident perceptions of the local residential environment and metabolic syndrome. J Environ Public Health. 2012 Aug; 2012. pmid:23049574
  28. 28. Hamano T, Kawakami N, Li X, Sundquist K. Neighbourhood Environment and Stroke: A Follow-Up Study in Sweden. PLoS One. 2013 Feb; 8(2): e56680. pmid:23457603
  29. 29. Heesch KC, Giles-Corti B, Turrell G. Cycling for transport and recreation: Associations with socio-economic position, environmental perceptions, and psychological disposition. Prev Med. 2014; 63:29–35. pmid:24625925
  30. 30. Wilson L-AM, Giles-Corti B, Burton NW, Giskes K, Haynes M, Turrell G. The association between objectively measured neighborhood features and walking in middle-aged adults. Am J Health Promot 2011 Mar/Apr; 25(4):e12–e21. pmid:21476324
  31. 31. Li F, Harmer P, Cardinal BJ, Vongjaturapat N. Built environment and changes in blood pressure in middle aged and older adults. Prev Med. 2009 March; 48(3): 237–241 pmid:19297686
  32. 32. Coffee NT, Howard N, Paquet C, Hugo G, Daniel M. Is walkability associated with a lower cardiometabolic risk? Health Place. 2013 Feb; 21:163–169. pmid:23501378
  33. 33. Sundquist K, Eriksson U, Mezuk B, Ohlsson H. Neighborhood walkability, deprivation and incidence of type 2 diabetes: A population-based study on 512,061 Swedish adults. Health Place. 2014 Nov; 31(2015):24–30.
  34. 34. Müller-Riemenschneider F, Pereira G, Villanueva K, Christian H, Knuiman M, Giles-Corti B, Fiona CB. Neighborhood walkability and cardiometabolic risk factors in australian adults: an observational study. BMC Public Health. 2013; 13:755. pmid:23947939
  35. 35. Hanibuchi T, Kawachi I, Nakaya T, Hirai H, Kondo K. Neighborhood built environment and physical activity of Japanese older adults: results from the Aichi Gerontological Evaluation Study (AGES). BMC Public Health. 2011; 11:657. pmid:21854598
  36. 36. Pruchno R, Wilson-Genderson M, Gupta AK. Neighborhood food environment and obesity in community-dwelling older adults: Individual and neighborhood effects. Am J Public Health. 2014 May;104(5):924– pmid:24625148
  37. 37. Zhou R, Li Y, Umezaki M, Ding Y, Jiang H, Comber A, Fu H. Association between physical activity and neighborhood environment among middle-aged adults in Shanghai. 2013 Feb; 2013. pmid:23690800
  38. 38. Kan H, Heiss G, Rose KM, Whitsel EA, Lurmann F, London SJ. Prospective analysis of traffic exposure as a risk factor for incident coronary heart disease: the atherosclerosis risk in communities (ARIC) study. Environ Health Perspect. 2008 Jul; 116(11):1463–8. pmid:19057697
  39. 39. McCormack GR, Shiell A. In search of causality: a systematic review of the relationship between the built environment and physical activity among adults. Int J Behav Nutr Phys Act. 2011 Nov; 8(1):125. pmid:22077952
  40. 40. Cunningham GO & Micheal YL. Concepts guiding the study of the impact of the built environment on physical activity for older adults: a review of the literature. J Health Promot. 2004 Oct; 8(6):435–43.
  41. 41. Ferdinand AO, Sen B, Rahurkar S, Engler S, Menachemi N. The relationship between built environments and physical activity: a systematic review. Am J Public Health. 2012 Oct; 102(10):e7–e13. pmid:22897546
  42. 42. Kaczynski AT, Henderson KA. Parks and Recreation Settings and Active Living: A Review of Associations With Physical Activity Function and Intensity. J Phys Act Health. 2008; 5(4):619–32. pmid:18648125
  43. 43. Frank LD, Schmid TL, Sallis JF, Chapman J, Saelens BE. Linking objectively measured physical activity with objectively measured urban form: Findings from SMARTRAQ. Am J Prev Med. 2005; 28(2S2):117–25.
  44. 44. Mackenbach JD, Rutter H, Compernolle S, Glonti K, Oppert J-M, Charreire H, et al. Obesogenic environments: a systematic review of the association between the physical environment and adult weight status, the SPOTLIGHT project. BMC Public Health. 2014; 14(1):233. pmid:24602291
  45. 45. Holsten JE. Obesity and the community food environment: a systematic review. Public Health Nutr. 2008; 12(3), 397–405. pmid:18477414
  46. 46. Burns CM, Inglis AD. Measuring food access in Melbourne: Access to healthy and fast foods by car, bus and foot in an urban municipality in Melbourne. Heal Place. 2007; 13(4):877–85.
  47. 47. Inagami S, Cohen DA, Finch BK, Asch SM. You are where you shop. Grocery store locations, weight, and neighborhoods. Am J Prev Med. 2006; 31(1):10–7. pmid:16777537
  48. 48. Rundle A, Roux AV, Free LM, Miller D, Neckerman KM, Weiss CC. The urban built environment and obesity in New York City. Am J Heal Promot. 2007; 21(4 Suppl.):326–34.
  49. 49. Badland H, Schofield G. The built environment and transport-related physical activity: what we do and do not know. J Phys Act Health. 2005; 2(4):433–42.
  50. 50. Tonne C, Melly S, Mittleman M, Coull B, Goldberg R, Schwartz J. A case-control analysis of exposure to traffic and acute myocardial infarction. Environ Health Perspect. 2007; 115(1):53–7. pmid:17366819
  51. 51. Pasala SK, Rao AA, Sridhar GR. Built environment and diabetes. Int J Diabetes Dev Ctries. 2010; 30(2):63–8. pmid:20535308
  52. 52. Diez Roux AV. Residentail environments and cardiovascular risk. J Urban Heal. 2003; 80(4):569–89.
  53. 53. Hoffmann B, Moebus S, Dragano N, Möhlenkamp S, Memmesheimer M, Erbel R, et al. Residential traffic exposure and coronary heart disease: results from the Heinz Nixdorf Recall Study. Biomarkers. 2009; 14(S1):74–8.