Modeling mood variation associated with smoking: an application of a heterogeneous mixed-effects model for analysis of ecological momentary assessment (EMA) data

Mixed models are used increasingly for analysis of ecological momentary assessment (EMA) data. The variance parameters of the random effects, which indicate the degree of heterogeneity in the population of subjects, are considered usually to be homogeneous across subjects. Modeling these variances can shed light on interesting hypotheses in substance abuse research. We describe how these variances can be modeled in terms of covariates to examine the covariate effects on between-subjects variation, focusing on positive and negative mood and the degree to which these moods change as a function of smoking. The data are drawn from an EMA study of adolescent smoking. Participants were 234 adolescents, either in 9th or 10th grades, who provided EMA mood reports from both random prompts and following smoking events. We focused on two mood outcomes: measures of the subject's negative and positive affect and several covariates: gender, grade, negative mood regulation and smoking level. Following smoking, adolescents experienced higher positive affect and lower negative affect than they did at random, non-smoking times. Our analyses also indicated an increased consistency of subjective mood responses as smoking experience increased and a diminishing of mood change.