This section will discuss how the findings of this project related to previous research on obesogenic environments. Previous research defining, measuring and creating obesogenic environments was closely reviewed when conceptualizing this research project. The results of this research project will now be discussed in the context of previous findings for obesogenic environment studies.
This research project was not able to find any significant correlation between obesogenic environment exposure and BMI level. This finding is reflected in the majority of previous research attempting to measure the built environment. No major research study to date has managed to identify a significant correlation between obesogenic environments and obesity outcomes. Jones and Britain (2007) has alluded to the large number of different factors that influence the built environment. This makes the built environment difficult to define, and by extension, difficult to measure. This research project measured both the food and physical characteristics of a participant’s environment. Previous research reviewed elected to measure either the food or physical environment of the area, but not both. This researcher argues that this approach does not fully account for the imbalance that obesity creates between the food and physical components of our individual’s lifestyle. Obesity is defined by an imbalance between energy input and output. This notion is reflected in the exposure to an individual’s surrounding environment. The food environment can be understood as the input, and the physical environment can be understood as the output; an imbalance between input and output is the key identifier of an obesogenic
Page | 109 environment. It is therefore necessary to measure both the food and physical environments when measuring an obesogenic environment. This allows for a comparative analysis between these two factors, which accounts for the imbalances which are typically associated with these areas.
Previous research has used a variety of different indicators within a built area to measure the obesogenic environment. This research project measures the food environment by the number of unhealthy food outlets, and the physical environment by the amount of greenspace. Previous literature has advocated unhealthy food outlets and greenspace as strong indicators of unhealthy or healthy environments respectively. These were the only indicators used to define the food and physical environments of the Hamilton participants, which could explain the lack of significant results achieved through the geospatial analysis. Future research into Hamilton (and indeed other New Zealand cities) should consider a wider range of indicators within the food and physical environments. Previous research by Pomerleau et al. (2013) suggested measuring aspects of physical access within a participants environment such as walkability, which would provide more substantial evidence when measuring the physical environment.
The biggest limitation to using GIS to measure obesogenic environments is in inability to account for an individual’s social environment. Elinder and Jansson (2009) have made note of this, stating the failure to measure the social environment makes it difficult to establish casual relationships between environmental factors and a population’s diet. GIS methods are able to model obesogenic environments based on the built environment, but are unable to model how social indicators of behaviour influence how people interact within the built environment. This research project used specific questions for NZHS to determine if certain social indicators of participants influenced their exposure to an obesogenic environment. The NZHS information was intended to help understand how the participants interact within the GIS modelled environment, i.e. how the
Page | 110 participant got to school, how much unhealthy food they eat etc. Ultimately, the social indicators did not provide enough evidence to suggest a correlation between participants BMI the surrounding environment.
The NZHS variables used did not provide a sufficient amount of information to confidently predict behaviour within the environment. This research method measured obesity exposure within the home, route and school environments. The NZHS information did not demonstrate sufficient evidence to suggest how their social behaviour would be connected to environmental exposure. A key example of this was the nutrition intake variables. These variable detail how many times per week a participant consumed fizzy drinks, takeaways, vegetables and fruit. The GIS modelled food environment created by this research project only accounts for the external community food environment. As is mentioned in Chapter 3, there are three food environments in a community; the home, community and school food environment. The NZHS nutritional information does not state where the participant consumed this food, whether it be at home, in the community or at school. The GIS method is unable to account for the social environment and behaviour of the participant. Participant privacy was another barrier to predicting the social environment of the participant. In order to ensure the privacy and safety of participant’s information, the routes to school were modelled based on the shortest distance from home to an age and gender appropriate school. There was no way of knowing if this route was accurate, which further limits the ability to account for the participant’s social environment.
Ecological indicators in epidemiology are hard to account for. There are a large number of factors that contribute to the health outcomes. Measuring environmental exposure to obesity considers only one of the many different attributes that determine the health outcomes of obesity. The next
Page | 111 section of Chapter 7 will discuss the applications of this research projects key findings to obesity intervention and prevention in New Zealand.