Exploring Relationships Between Fitness Predictors and Activity Opportunities Using Novel Geographic Information Systems Measurements

Abstract

0519 PURPOSE: To explore the unique characteristics of the continuous distance data from Geographic Information Systems (GIS) from participants' homes to develop a predictive tool using precise measures between GIS and opportunities for activity benefits. METHODS: 242 subjects over 55yrs from a registry at the Canadian Centre for Activity and Aging in the London area who lived less than 4000 meters of any of the identified 11 physical activity opportunity (PAO) GIS distances. Baseline physiologic outcomes were used. Standard linear modeling methods including examining correlations with independent and dependent variables and descriptive results were used to determine which related methods to employ. We used observations from our interventions and evidence from literature indicating important relationships between fitness, distance and density of PAO to guide exploration. GIS PAO distances are dominant forces with different densities across the range of PAOs. Linear models using the continuous GIS measures were generated for PAOs within three density modes of between 300m, 300 to 1000m, and over 1000m. We examined multicollinearity and non-linear influences within each distribution. Data reduction methods aided in exploring similar factors to unify some variances. Quantitative information guided the final multivariate predictive model building. Logical interactions were introduced in the model building to reduce the effect of some multicollinearity amongst a few PAOs. RESULTS: All distances were significantly correlated with each other. No significant optimal model could explain a direct relationship between the PAO and the outcomes in any of the three densities. The interactions indicated a few of the PAO variables should be functionally grouped to reduce random error when modelling. CONCLUSIONS: This systematic investigation into a new tool provides some insight into the power that the continuous data from GIS can provide. It also generates additional challenges to determine the best way to use this continuous data to determine how the associated characteristics influence population fitness. The covariate influences on the amount of explained variance generates the need for secondary investigations. Continued exploration into how to redirect the covariate influence and better explain the systematic variance and find the influential confounders when including continuous measures in our investigations of fitness promotion ecology is required.