JMIR Publications

ABSTRACT

Background:

Interest in the measurement of the temporal dynamics of people’s emotional lives has risen substantially in psychological and medical research. Emotions fluctuate and change over time, and measuring the ebb and flow of people’s affective experiences promises enhanced insights into people’s health and functioning. Researchers have used a variety of intensive longitudinal assessment (ILA) methods to create measures of emotion dynamics, including Ecological Momentary Assessments (EMA), end-of-day (EOD) diaries, and the Day Reconstruction Method (DRM). To date, it is unclear whether they can be used interchangeably or whether ostensibly similar emotion dynamics captured by the methods differ in meaningful ways.

Objective:

The purpose of this study was to examine the extent to which different ILA methods yield comparable measures of intraindividual emotion dynamics.

Methods:

Data were collected from 90 participants 50+ years in a probability-based Internet panel, the Understanding America Study. Participants provided positive affect (PA) and negative affect (NA) ratings using three ILA methods: (1) EMA, administered 6 times per day over one week via smartphones; (2) EOD diaries, administered daily online over the same week; (3) the DRM, administered online once during that week. Eleven measures of emotion dynamics (mean PA/NA emotion levels, PA/NA variability, PA/NA instability, PA/NA inertia, emotion network density, mixed emotions, and emotional dialecticism) were calculated from each ILA method. Analyses examined mean differences and correlations of scores addressing the same emotion dynamic across the ILA methods. We also compared the patterns of intercorrelations among the emotion dynamics as well as their relationships with health outcomes (general health, pain, and fatigue), across ILA methods.

Results:

Emotion dynamics derived from EMA and EOD demonstrated moderate to high correspondence for measures of mean emotion levels (rs ≥ .95), variability (rs ≥ .68), instability (rs ≥ .51), mixed emotions (r = .92), and emotional dialecticism (r = .57), and low correspondence for measures of inertia (rs ≥ .17) and emotion network density (r = .36). DRM-derived measures showed high correlations with EMA and EOD for mean emotion levels and mixed emotions (rs ≥ .74), and low to moderate correlations for other measures (rs .17 to .54). Intercorrelations among the emotion dynamics showed high convergence across EMA and EOD, and moderate convergence between DRM and EMA/EOD. Emotion dynamics from all three ILA methods produced very similar patterns of relationships with the health outcomes.

Conclusions:

EMA, EOD, and DRM provide moderately corresponding and meaningful information about individual differences in several emotion dynamics. At the same time, our results caution researchers to view the use of these ILA methods as universally interchangeable. Nevertheless, our study provides preliminary evidence that all three ILA methods can be useful to investigate the role of various emotion dynamics in health and medical research.