Obesity and the Built Environment: A Reappraisal

Abstract

The built environment (BE) has been viewed as an important determinant of health. Numerous studies have linked BE exposure, captured using a variety of methods, to diet quality and to area prevalence of obesity, diabetes, and cardiovascular disease. First‐generation studies defined the neighborhood BE as the area around the home. Second‐generation studies turned from home‐centric to person‐centric BE measures, capturing an individual’s movements in space and time. Those studies made effective uses of global positioning system tracking devices and mobile phones, sometimes coupled with accelerometers and remote sensors. Activity space metrics explored travel paths, modes, and destinations to assess BE exposure that was both person and context specific. However, as measures of the contextual exposome have become ever more fine‐grained and increasingly complex, connections to long‐term chronic diseases with complex etiologies, such as obesity, are in danger of being lost. Furthermore, few studies on obesity and the BE have included intermediate energy balance behaviors, such as diet and physical activity, or explored the potential roles of social interactions or psychosocial pathways. Emerging survey‐based applications that identify habitual destinations and associated travel patterns may become the third generation of tools to capture health‐relevant BE exposures in the long term.

The Varying Effects of the Food, Built, and Socioeconomic Environment on BMI

“…Although all five furthered our understanding of the potential effects envisaged by intervening on the food, built, and socioeconomic environment, the one overarching takeaway from this research is that BMI varied by the type of intervention implemented and for each intervention; the impact of BMI appears to be further affected by geography (likely interactions with other interventions) and by the circumstances or contexts at the community level. This heterogeneity in findings is consistent with an emerging body of evidence suggesting that there is a need for additional study of strategy interventions on obesity outcomes, with a focus on understanding the interactions of interventions implemented at multiple levels and on the combining of these interventions to achieve aggregate effects (Table ) . Some of this work is already under way, seeking to confirm the collective health impact of using combination strategies to halt the obesity epidemic .…”

Associations between the built environment and obesity: an umbrella review

“…One might argue that the association between the built environment and weight is a bridge too far, and that it would be logical to review the epidemiological evidence on associations with intermediate, more proximal outcomes, such as obesogenic behaviours [ 54 ]. These behaviours are conceptualized mainly through energy imbalance: higher obesogenicity means a surplus of energy intake and a deficiency of energy expenditure.…”

“…Even though it is out of the scope of our study to examine subjective assessment of the built environment (i.e. self-perception), some studies suggest that their role in modifying health behaviours should not be underestimated [ 54 ]. Moreover, it is increasingly being acknowledged that the interactions between humans and their environments take place in a complex system that continuously interacts and adapts, and where the built environment is only one of the many nodes in this interactive network [ 58 ].…”

“…Fortunately, there has been a recent recognition that the low effect size in the current environmental epidemiological studies might be attributed to the isolated single-exposure single-outcome and linear modelling method, neither of which is realistic in terms of human interactions with the environment [ 67 , 68 ]. Traditional epidemiology however could already benefit from bolder exploration of mediation and interaction effects of dietary or physical activity behaviours to further strengthen the causal inferences to the current associations [ 54 , 69 ]. Moving forward, innovative methodologies such as agent-based modelling and other self-learning algorithms could be used to improve our understanding, by allowing interactions in the forms of various simulated scenarios in environmental changes and their consequences on weight [ 70 ].…”

A cross‐sectional analysis of physical activity and weight misreporting in diverse populations: The Seattle Obesity Study III

“…The collection of in-person anthropometric data, by trained research staff, and PA data via accelerometry is resource intensive for researchers and can place undue burden on participants. 3,36 Moreover, in-person data collection risks interruption in the event of largescale disasters. The accuracy afforded by objectively measured PA and anthropometrics often comes at the cost of not having a large sample size due to the feasibility of scaling up such resource intensive data collection.…”