The key objective of the Hamilton geospatial analysis was to determine whether aspects of the NZHS participant’s food and physical environments contributed to BMI outcomes. The results of the regression analysis conclude that this research method was unable to find any statistically significant connection between BMI and the participants surrounding environment. Table 5 and 7 suggest there is a minor positive correlation between BMI and the surrounding environment. As the correlation is so minor, it does not suggest a significant trend. Participants that live in areas
Page | 105 with a high number of unhealthy food outlets and low amounts of greenspace are expected to have a higher BMI, due to their exposure to an obesogenic environment. Adversely, participants that live in areas with a low number of unhealthy food outlets and high amounts of greenspace are expected to have lower BMI due to their lack of exposure to obesogenic environments. The results of this research were unable to support these expectations. The number of unhealthy food outlets or amount of greenspace did not significantly influence the BMI of the Hamilton participants.
Similar results were found in the analysis between mode and transport. The results of the regression analysis suggest that participants who bike and skate are exposed to the most greenspace. According to previous studies, this should make these participants more physically active. The nationwide analysis results showed that skate has a lowest mean BMI of the five transport modes sampled. The NZHS skate option did accommodate other forms of active transport. The results of this analysis can suggest that participants that skate are the most physically active, as they have the lowest BMI. Their exposure to greenspace is more significant that the other modes of transport sampled. However, as the r squared value of skate is very low, there is not enough evidence to suggest that the physical environment is influencing the participants to skate. There was no significant findings in regards to the other modes of transport. One might expect active transport modes to have a higher greenspace exposure than passive transport modes, as greenspace can increase the access and availability of active transport facilities, such as walkways. The results of this analysis did not support this expectation, with really little variation in the R squared values of active and passive modes of transport. Age and BMI were used as controls in this model. These two variables did not help to improve the statistical significance of the regression model for mode of transport. The connection between BMI and mode of transport were already found to be insignificant based on the results from the nationwide
Page | 106 analysis. BMI made no significant difference to the R squared value when introduced within the regression model. This suggests that participant’s food and physical environments in Hamilton did not contribute to mode of transport that participants use. It is important to understand how the food and physical environments can influence mode of transport. A more physically active environment could encourage school children to use active transport to and from school. Many previous studies have referenced the benefits of active transport for school children. The results of this analysis have failed to establish a connection between mode of transport and the food and physical environments of Hamilton school children. Mode of transport was in turn found to have no bearing on BMI status.
The results of the nutrition intake analysis were not statistically significant, due to the low R squared values of the regression model. The R squared value of the regression analysis between fizzy drinks intake and the food environment was the most interesting finding. Despite the R squared value still being low (0.014), this was pointedly higher than the R squared values of takeaway, vegetable and fruit. Of the four nutritional categories sampled, fizzy was represented highest. Over three quarters of Hamilton participants consumed fizzy drinks at least once in the sampled week, more than any of the other three categories. Fizzy drinks have a high sugar content, making them highly unhealthy, yet highly appealing to children. Chapter 4 of this research project referenced the high level of sugar consumption in New Zealand. This finding among Hamilton children is consistent with high sugar consumption. As is mentioned, the R square value between fizzy intake and the food environment is too low to suggest a significant trend. However, in the context of the regression analysis between nutrition intake and the environment of Hamilton participants, fizzy drinks are more significantly represented than the others. BMI and age were once again used as controls in this research. The regression analysis between environment and
Page | 107 nutrition intake found no correlation between participants that eat unhealthy food and an obesogenic environment. A higher consumption of fizzy and takeaways was not connected to participants having a higher BMI, nor the composition of the food and physical environments of participants. The purpose of this analysis was to establish if the food and physical characteristics of a participant’s environment influence the nutritional value of their diet. The results indicate no reason to suggest this is the case. Overall, the linear regression modelled used to measure the correlation between the participant’s environment and obesity did not produce any statistically significant findings.
Hamilton geospatial results summary
The GIS method used for this research project failed to establish any significant correlation between the food and physical environments of the Hamilton participants and any NZHS variables used to measure obesity exposure. Linear regression analysis models were used to test the correlation between the Hamilton participant’s environment and BMI, mode of transport and nutrition intake. The results do not suggest that the surrounding food and physical environments in Hamilton contributed to any of the NZHS variables used within this analysis. This research method measured the environment by the number of unhealthy food outlets and amount of greenspace contained within a participant’s home, route and school environment. The environment was defined by euclidean buffer zones, which indicated the participant’s exposure area. Obesogenic environments were characterised in this research method as areas with a high number of unhealthy food outlets and low amount of greenspace. This research project expected that school children who were exposed to obesogenic environments (as defined by the research method) were more likely to have a higher BMI. Based on the results of the Hamilton geospatial analysis, this research project can conclude that the surrounding food and physical environments
Page | 108 do not influence BMI levels. School children that live in the obesogenic environments were not shown to have a higher BMI levels than school children from non-obesogenic environments. Obesogenic environments in Hamilton do not contribute to the obesity outcomes of NZHS participants.