Elsevier

Preventive Medicine

Volume 53, Issues 1–2, July–August 2011, Pages 57-60
Preventive Medicine

The influence of the built environment, social environment and health behaviors on body mass index. Results from RESIDE

https://doi.org/10.1016/j.ypmed.2011.05.004Get rights and content

Abstract

Objective

To examine the individual, behavioral, social and built environment correlates of body mass index (BMI) in an Australian adult population.

Method

Using data from 2003 to 2005 on 1151 participants in the RESIDential Environments project (RESIDE), Perth, Western Australia, linear regression was used to construct multivariate models to examine the variance in BMI explained by significant socio-demographic, environmental and health behavior variables. Both self-report and GIS-derived measures of the built environment were examined.

Results

Age, gender, hours of work, total physical activity, sedentary leisure time and dietary fat were all associated with BMI (p  0.05). BMI was not associated with any objective measures of the built environment or social capital, social cohesion or dog ownership but was independently associated with one perceived environment measure (perceived safety from crime). Overall, 3.3% of the variance in BMI was explained by socio-demographic factors, a further 2.7% by health behaviors and a further 1.5% by perceived environment factors.

Conclusion

Whilst evidence mounts of built environment correlates to physical activity, the demonstrated translation of these effects on BMI remain more elusive. Nevertheless, built environment factors that constrain physical activity warrant further exploration.

Research highlights

► BMI not associated with objective measures of built environment. ► BMI negatively associated with perceived safety from crime. ► Built environment factors that limit physical activity warrant further exploration.

Introduction

Obesity is a major public health concern worldwide. Interest in the relationship between the built environment and obesity is growing (Papas et al., 2007), partly because environmental modifications could have sustained population impact (Sallis et al., 2006). Individual socio-demographic (Ball and Crawford, 2005) and social environment (e.g., overweight partner, low social support for physical activity and healthy eating) (Cohen et al., 2006) factors are consistently associated with body mass index (BMI). Positive associations with fast food outlets and convenience stores, and inverse associations with grocery stores, supermarkets and recreation facilities (Holsten, 2009, Robertson-Wilson and Giles-Corti, 2010) and neighborhood walkability (Doyle et al., 2006, Frank et al., 2006, Saelens et al., 2003) have been reported.

Few studies have tested a comprehensive model of correlates of adult BMI or examined the relative influence of individual, social and built environment factors (Robertson-Wilson and Giles-Corti, 2010). This study examined individual, behavioral, social and built environment correlates of BMI in Australian adults.

Section snippets

Sample

Data were from baseline measures on 1551 participants in the RESIDential Environments project (RESIDE) (Giles-Corti et al., 2008). Participants were adults building a new home in a new housing development who completed a survey prior to moving in (baseline residential locations were distributed throughout metropolitan Perth). Ethics approval was provided by The University of Western Australia's Human Research Ethics Committee.

Measures

BMI (kg/m2) was calculated using self-report height and weight.

Results

Six of the 14 individual factors examined were significantly associated with BMI (Table 1) and collectively explained 3.3% of the variance in BMI.

Table 2 shows associations between each behavioral, social and built environment measure and BMI, adjusted for socio-demographic factors. All 3 health behavior variables were significantly related to BMI, explaining an additional 2.7% of the variance in BMI. None of the objective measures of the built environment—including the presence or density of

Discussion

In this study, age, gender, household composition, education, hours worked, total physical activity, leisure-time sedentary behavior, saturated fat consumption, and perceived safety from crime were significantly associated with BMI. The social environment and objective built environment measures were not associated with BMI. Estimated effect sizes for physical activity and the total variance explained by the models were small. This may be due to the cross-sectional study design and reliance on

Conclusions

Few built environment factors were associated with BMI in this study. However, greater perceived safety from crime was associated with lower BMI, and this association persisted after controlling for socio-demographic factors and obesity-related health behaviors. Future research should examine mediating pathways between the built and social environments, behavior and BMI and incorporate longitudinal designs.

Conflict of interest statement

No conflict of interest was reported by the authors of this paper.

Acknowledgments

This research was funded by a Western Australian Health Promotion Foundation (Healthway) (#11828) and an Australian Research Council (ARC) Linkage grant (#LPO455453). The Western Australian Land Information Authority (© 2003), Western Australian Department of Planning and Sensis Pty Ltd provided spatial data for the objective built environment measures. Hayley Christian and Sarah Foster are supported by an NHMRC Population Health Capacity Building Grant (#458668), Billie Giles-Corti is

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