Bagheri, Nasser , DÍAZ MILANÉS, DIEGO, Learnihan, Vincent , GARCÍA ALONSO, CARLOS, Daniel, Mark , Mazumdar, Soumya , Tabatabaei-Jafari, Hossein , Leigh, Gweneth , Wangdi, Kinley , Salvador-Carulla, Luis
No
Cities
Article
Científica
1
1
01/11/2026
001818948000001
Background: The built environment plays a pivotal role in shaping cardiometabolic risk by influencing lifestyle behaviours. The 20-Minute Neighbourhood (20MN) concept, promoting accessible, walkable urban areas, has gained traction as a strategy to improve community health. Objectives: This study examined whether neighbourhood built environment features, operationalised within a 20min, are associated with cardiometabolic risk through indirect behavioural pathways over time, using Bayesian Network Analysis (BNA). Our aim was to investigate causal pathways linking the built environment with cardiometabolic risk. Methods: A Bayesian network model was constructed using longitudinal data from the North-West Adelaide Health Study (NWAHS), spanning three waves over ten years. Built environment indices were developed using expert input, fuzzy logic, and GIS data within a 1600 m street-network buffer of participants' residences. Results: The BNA identified indirect pathways linking built environment variables, particularly the food facilities and public facilities indices, to cardiometabolic risk through modifiable behavioural mediators and downstream biometric change. Specifically, food and public facilities were linked to HbA1c indirectly through fruit and vegetable intake, physical activity, and BMI. The model explained 32.1% of HbA1c variance, with BMI serving as a strong predictor. Centrality measures also identified physical activity and BMI as key bridging nodes within the network. Discussion: These findings show that the contribution of neighbourhood built environment features to cardiometabolic risk may operate primarily through indirect behavioural and biometric pathways rather than through simple direct associations. BNA provided a useful framework for identifying these pathways in a longitudinal, policy-relevant urban health context.
Cardiometabolic; Built environment; Bayesian network; Complexity; Ecosystem