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Using kernel density estimation to understand the influence of neighbourhood destinations on BMI

King, Tania L., Bentley, Rebecca J., Thornton, Lukar E. and Kavanagh, Anne M. 2016, Using kernel density estimation to understand the influence of neighbourhood destinations on BMI, BMJ open, vol. 6, no. 2, Article number: e008878, pp. 1-8, doi: 10.1136/bmjopen-2015-008878.

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Title Using kernel density estimation to understand the influence of neighbourhood destinations on BMI
Author(s) King, Tania L.
Bentley, Rebecca J.
Thornton, Lukar E.ORCID iD for Thornton, Lukar E. orcid.org/0000-0001-8759-8671
Kavanagh, Anne M.
Journal name BMJ open
Volume number 6
Issue number 2
Season Article number: e008878
Start page 1
End page 8
Total pages 8
Publisher BMJ Group
Place of publication London, Eng.
Publication date 2016-02-16
ISSN 2044-6055
Keyword(s) epidemiology
public health
statistics & research methods
Summary OBJECTIVES: Little is known about how the distribution of destinations in the local neighbourhood is related to body mass index (BMI). Kernel density estimation (KDE) is a spatial analysis technique that accounts for the location of features relative to each other. Using KDE, this study investigated whether individuals living near destinations (shops and service facilities) that are more intensely distributed rather than dispersed, have lower BMIs.

STUDY DESIGN AND SETTING: A cross-sectional study of 2349 residents of 50 urban areas in metropolitan Melbourne, Australia.

METHODS: Destinations were geocoded, and kernel density estimates of destination intensity were created using kernels of 400, 800 and 1200 m. Using multilevel linear regression, the association between destination intensity (classified in quintiles Q1(least)-Q5(most)) and BMI was estimated in models that adjusted for the following confounders: age, sex, country of birth, education, dominant household occupation, household type, disability/injury and area disadvantage. Separate models included a physical activity variable.

RESULTS: For kernels of 800 and 1200 m, there was an inverse relationship between BMI and more intensely distributed destinations (compared to areas with least destination intensity). Effects were significant at 1200 m: Q4, β -0.86, 95% CI -1.58 to -0.13, p=0.022; Q5, β -1.03 95% CI -1.65 to -0.41, p=0.001. Inclusion of physical activity in the models attenuated effects, although effects remained marginally significant for Q5 at 1200 m: β -0.77 95% CI -1.52, -0.02, p=0.045.

CONCLUSIONS: This study conducted within urban Melbourne, Australia, found that participants living in areas of greater destination intensity within 1200 m of home had lower BMIs. Effects were partly explained by physical activity. The results suggest that increasing the intensity of destination distribution could reduce BMI levels by encouraging higher levels of physical activity.
Language eng
DOI 10.1136/bmjopen-2015-008878
Field of Research 110699 Human Movement and Sports Science not elsewhere classified
Socio Economic Objective 929999 Health not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution non-commercial licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30085278

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.