Neighborhood walkability and 12-year changes in cardio-metabolic risk: the mediating role of physical activity.


Journal

The international journal of behavioral nutrition and physical activity
ISSN: 1479-5868
Titre abrégé: Int J Behav Nutr Phys Act
Pays: England
ID NLM: 101217089

Informations de publication

Date de publication:
15 10 2019
Historique:
received: 10 05 2019
accepted: 16 09 2019
entrez: 17 10 2019
pubmed: 17 10 2019
medline: 15 1 2020
Statut: epublish

Résumé

Living in walkable neighborhoods may provide long-term cardio-metabolic health benefits to residents. Little empirical research has examined the behavioral mechanisms in this relationship. In this longitudinal study, we examined the potential mediating role of physical activity (baseline and 12-year change) in the relationships of neighborhood walkability with 12-year changes in cardio-metabolic risk markers. The Australian Diabetes, Obesity and Lifestyle study collected data from adults, initially aged 25+ years, in 1999-2000, 2004-05, and 2011-12. We used 12-year follow-up data from 2023 participants who did not change their address during the study period. Outcomes were 12-year changes in waist circumference, weight, systolic and diastolic blood pressure, fasting and 2-h postload plasma glucose, high-density lipoprotein cholesterol, and triglycerides. A walkability index was calculated, using dwelling density, intersection density, and destination density, within 1 km street-network buffers around participants' homes. Spatial data for calculating these measures were sourced around the second follow-up period. Physical activity was assessed by self-reported time spent in moderate-to-vigorous physical activity (including walking). Multilevel models, adjusting for potential confounders, were used to examine the total and indirect relationships. The joint-significance test was used to assess mediation. There was evidence for relationships of higher walkability with smaller increases in weight (P = 0.020), systolic blood pressure (P < 0.001), and high-density lipoprotein cholesterol (P = 0.002); and, for relationships of higher walkability with higher baseline physical activity (P = 0.020), which, in turn, related to smaller increases in waist circumference (P = 0.006), weight (P = 0.020), and a greater increase in high-density lipoprotein cholesterol (P = 0.005). There was no evidence for a relationship of a higher walkability with a change in physical activity during the study period (P = 0.590). Our mediation analysis has shown that the protective effects of walkable neighborhoods against obesity risk may be in part attributable to higher baseline physical activity levels. However, there was no evidence of mediation by increases in physical activity during the study period. Further research is needed to understand other behavioral pathways between walkability and cardio-metabolic health, and to investigate any effects of changes in walkability.

Sections du résumé

BACKGROUND
Living in walkable neighborhoods may provide long-term cardio-metabolic health benefits to residents. Little empirical research has examined the behavioral mechanisms in this relationship. In this longitudinal study, we examined the potential mediating role of physical activity (baseline and 12-year change) in the relationships of neighborhood walkability with 12-year changes in cardio-metabolic risk markers.
METHODS
The Australian Diabetes, Obesity and Lifestyle study collected data from adults, initially aged 25+ years, in 1999-2000, 2004-05, and 2011-12. We used 12-year follow-up data from 2023 participants who did not change their address during the study period. Outcomes were 12-year changes in waist circumference, weight, systolic and diastolic blood pressure, fasting and 2-h postload plasma glucose, high-density lipoprotein cholesterol, and triglycerides. A walkability index was calculated, using dwelling density, intersection density, and destination density, within 1 km street-network buffers around participants' homes. Spatial data for calculating these measures were sourced around the second follow-up period. Physical activity was assessed by self-reported time spent in moderate-to-vigorous physical activity (including walking). Multilevel models, adjusting for potential confounders, were used to examine the total and indirect relationships. The joint-significance test was used to assess mediation.
RESULTS
There was evidence for relationships of higher walkability with smaller increases in weight (P = 0.020), systolic blood pressure (P < 0.001), and high-density lipoprotein cholesterol (P = 0.002); and, for relationships of higher walkability with higher baseline physical activity (P = 0.020), which, in turn, related to smaller increases in waist circumference (P = 0.006), weight (P = 0.020), and a greater increase in high-density lipoprotein cholesterol (P = 0.005). There was no evidence for a relationship of a higher walkability with a change in physical activity during the study period (P = 0.590).
CONCLUSIONS
Our mediation analysis has shown that the protective effects of walkable neighborhoods against obesity risk may be in part attributable to higher baseline physical activity levels. However, there was no evidence of mediation by increases in physical activity during the study period. Further research is needed to understand other behavioral pathways between walkability and cardio-metabolic health, and to investigate any effects of changes in walkability.

Identifiants

pubmed: 31615522
doi: 10.1186/s12966-019-0849-7
pii: 10.1186/s12966-019-0849-7
pmc: PMC6792258
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

86

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Auteurs

Manoj Chandrabose (M)

Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Australia. Manoj.Chandrabose@myacu.edu.au.
Centre for Urban Transitions, Swinburne University of Technology, Melbourne, Australia. Manoj.Chandrabose@myacu.edu.au.

Ester Cerin (E)

Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Australia.
Baker Heart and Diabetes Institute, Melbourne, Australia.
School of Public Health, The University of Hong Kong, Hong Kong, China.

Suzanne Mavoa (S)

Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Australia.
Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.

David Dunstan (D)

Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Australia.
Baker Heart and Diabetes Institute, Melbourne, Australia.

Alison Carver (A)

Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Australia.

Gavin Turrell (G)

Centre for Urban Research, RMIT University, Melbourne, Australia.
School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia.

Neville Owen (N)

Centre for Urban Transitions, Swinburne University of Technology, Melbourne, Australia.
Baker Heart and Diabetes Institute, Melbourne, Australia.
Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.
School of Public Health, The University of Queensland, Brisbane, Australia.
Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.
Institute for Resilient Regions, University of Southern Queensland, Toowoomba, Australia.

Billie Giles-Corti (B)

Centre for Urban Research, RMIT University, Melbourne, Australia.

Takemi Sugiyama (T)

Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Australia.
Centre for Urban Transitions, Swinburne University of Technology, Melbourne, Australia.
Baker Heart and Diabetes Institute, Melbourne, Australia.

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Classifications MeSH