STATISTICAL MODELS AND METHODS OF ANALYSIS: Eligible participants who decline to participate will be compared with those enrolled on demographic characteristics to check for sample bias. Descriptive statistics will be used to identify the distribution of data and total instrument scale scores for each of the study surveys. Means and standard deviations will be examined for continuous variables and proportions for categorical variables. Linear regression (SAS) will be used to examine Aim 1 of the study. A growth model with time varying covariates (using MPlus) will be used to examine Aim 2 of this study. Growth modeling allows for examination of individuals over time on one or more outcome variables (e.g., IRs, treatment decisions, and asthma control). Random effects are specified in the model to capture individual differences in development. MPlus uses a multivariate approach and maximum likelihood estimation for this type of model. The time scores for the slope growth factor are fixed to 0, 1, 2, 3, and 4 to define a linear growth model with equidistant time points. The zero time score for the slope growth factor defines the intercept growth factor, the coefficients of the intercept growth factor are fixed at one, the residual variances of the outcomes are estimated and allowed to be different across time, and the residuals are not correlated. Illness representations, CAM, and controller medication use are time varying variables in the model.