Applying Multiple Statistical Methods to Derive an Index of Dietary Behaviors Most Related to Obesity

To evaluate the success of dietary interventions, we need measures that are more easily assessed and that closely align with intervention messaging. An index of obesogenic dietary behaviors (e.g., consumption of fast food and soft drinks, low fruit and vegetable consumption, and task eating (eating while engaging in other activities)) may serve this purpose and could be derived via data-driven methods typically used to describe nutrient intake. We used behavioral and physical measurement (i.e., body mass index, waist circumference) data from a subset of 2 independent cross-sectional samples of employees enrolled in the Promoting Activity and Changes in Eating (PACE) Study (Seattle, Washington) who were selected at baseline (2005-2007) (n = 561) and during follow-up (2007-2009) (n = 155). Index derivation methods, including principal components regression, partial least squares regression, and reduced rank regression, were compared. The best-fitting index for predicting physical measurements included consumption of fast food, French fries, and soft drinks. In linear mixed models, each 1-quartile increase in index score was associated with a 5% higher baseline body mass index (ratio of geometric means = 1.053, 95% confidence interval: 1.031, 1.075) and an approximately 4% higher baseline waist circumference (ratio = 1.036, 95% confidence interval: 1.019, 1.054) after adjustment for covariates. Results were similar at follow-up before and after adjustment for baseline measures. This index may be useful in evaluating public health or clinic-based dietary interventions to reduce obesity, especially given the ubiquity of these behaviors in the general population.