Conceptualizing Analyses of Ecological Momentary Assessment Data

Abstract

Ecological momentary assessment (EMA) methods, which involve collection of real-time data in subjects' real-world environments, are particularly well suited to studying tobacco use. Analyzing EMA datasets can be challenging, as the datasets include a large and varied number of observations per subject and are relatively unstructured. This paper suggests that time is typically a key organizing principle in EMA data and that conceptualizing the data as a timeline of events, behaviors, and experiences can help define analytic approaches. EMA datasets lend themselves to answering a diverse array of research questions, and the research question must drive how data are arranged for analysis and the kinds of statistical models that are applied. This is illustrated with brief examples of diverse analyses applied to answer different questions from an EMA study of tobacco use and relapse.

Within-person associations between daily motivation and self-efficacy and drinking among problem drinkers in treatment.

“…To the best of our knowledge, no study has examined the momentary influence of motivation in AUD treatment. Study findings that self-efficacy predicted next day’s drinking at both the between and within-person levels are consistent with similar findings in the smoking literature (Shiffman, 2014). It seems likely that feelings of competence to succeed in changing behavior may have stable as well as momentary features, perhaps similar to self-esteem.…”

Real-time predictors of smoking among sexual minority and heterosexual young adults: An ecological momentary assessment study

“…To examine situational predictors of smoking, we employed generalized estimating equations (GEE) to account for the nesting of multiple observations within each participant (Zeger et al, 1988). As recommended by Shiffman, GEE is an appropriate analysis method to contrast, across persons, the likelihood of the presence of a particular situational predictor (e.g., other smokers, alcohol consumption) over multiple smoking and nonsmoking occasions (Shiffman, 2014). First, we fit unadjusted GEE models to assess crude relationships between predictors and the outcome in a given EMA report (data not shown).…”

A New Frontier: Integrating Behavioral and Digital Technology to Promote Health Behavior

“…Thus far, no technology-based study of health behavior has included idiographic assessment of consequences. Encouragingly, however, and perhaps by virtue of advances in technology, several health behavior researchers have advocated for within-subject analysis of the antecedent and consequent variables governing healthy and unhealthy behavior (Kanning et al 2013; Riley et al 2011; Shiffman 2014). Behavior analysts have expertise in using methods to not only detect instances of antecedents, behavior, and consequences, but also to assess whether these instances are functionally related to one another and not merely correlated (e.g., Beavers et al 2013; Hanley et al 2003; McComas et al 2009).…”