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 this with brief examples of diverse analyses applied to answer different questions from an EMA study of tobacco use and relapse.

Figure 1.

The figure shows the data stream for five subjects in the study, indicated as horizontal blocks A–E. Each point represents an observation of a particular type (see legend). The x-axis is continuous time, marked by days in the study. The end of Day 17, indicated by the vertical dotted line, was designated as the Target Quit, the time after which subjects were expected to abstain. The different markers indicate the different kinds of records, as identified in the legend. Thus, the graph shows the timeline of events for each subject. The bottom-most subject (A) did not quit, as indicated by the presence of cigarette records in the dates following the quit date. Subject B quit, as indicated by the absence of cigarette records after the quit date, but then frequently lapsed, as indicated by many lapse records, and eventually relapsed, as indicated by the return of smoking records around Day 28. Subject C quit, and lapsed, but did not relapse while in the study. Subject D did not lapse until Day 23 but had frequent temptations in the preceding days. Subject E quit and did not report any lapses, though some temptations were reported, at progressively sparser frequency. Daily milestones of waking up and going to bed and daily waking and evening assessments are not shown.

Figure 2.

The figure shows the relative frequency of smoking in each of eight 2-hr blocks defined by time of day, with approximate times reported below the axis. Actual times and span of each block differed by subject and day, according to when they woke up and went to bed. Smoking frequency is indicated on a standard scale, relative to each subject’s average. The lines represent patterns seen in different clusters defined by their circadian patterns of smoking. Figure from Chandra et al. (2007), reprinted with APA permission.

Figure 3.

The figure shows the negative affect reported by subjects who lapsed, by time prior to the first lapse; the final point represents the negative affect reported to have been experienced just before the lapse. The data are limited to subjects who attributed their lapse to negative affect or stress. The left panel shows data aggregated to daily averages, for the 4 days preceding the first lapse. The right panel is based on reports on the day of the lapse itself, but preceding the lapse. The points represent a smoothed moving average of affect reported at individual assessment occasions. Based on Shiffman and Waters (2004), reprinted with APA permission.

Figure 4.

The figure shows the lag between successive smoking lapses, as subjects progressed from one lapse to the next. The x-axis represents successive lapses from 1 to 25. The y-axis represents the median time between lapses, for successive lapses. Data are shown separately for subjects randomized to treatment with active nicotine patch or placebo. Figure from Kirchner, Shiffman et al. (2012), reprinted with APA permission.

Figure 5.

A graphical summary of slope functions of the time-varying effects of self-efficacy (SE) on smoking urges for relapsers (solid line, N = 40) and successful quitters (dashed line, N = 207) over the course of 2 weeks postquit. Each curve shows the estimated slope for smoking urges versus SE (confidence); negative values indicated that higher SE is associated with lower urges. The grey lines (solid and dashed) surrounding each line represent the 95% CI for each curve. Figure from Shiyko et al. (2012), reprinted with permission of Springer Science and Business Media.