Longitudinal mixture modeling allows for estimation of person-level patterns when there is heterogeneity in how people change over time. We demonstrate two modeling approaches: latent class growth analysis/growth mixture modeling (LCGA/GMM) and repeated measures latent profile analysis (RMLPA). The data originated from a randomized trial examining mechanisms of exercise behavior maintenance. We previously reported that average affective response remained stable during exercise training. The present study tests whether affective response over time could be best described through the estimation of latent subpopulations. Secondary analysis of women ( n = 201, mean age = 37.4; baseline mean BMI = 29.3) recruited for a 16-week randomized trial of exercise intensity/duration. Affective response was measured within exercise bout (minutes 0, 10, 20, 30, and 40) over four waves (weeks 1, 4, 8, and 16). LCGA/GMM was the primary approach for average-bout affective response (4 time points; “wave-level”), where a 3-class solution emerged of “stable,” “high, increasing,” and “decreasing” affective response patterns over time. RMLPA was used for minute-interval analyses where a four-class solution emerged. Weighted analyses examined theoretical outcomes (e.g., change in VO 2 max, posttest Theory of Planned Behavior constructs) of latent class membership. Person-centered methodologies demonstrated heterogeneity in affective response over time and within specific exercise bouts. The rich longitudinal data structure facilitated illustration and comparison between methods in terms of: (1) assumptions about functional form, missing data, and random effects; (2) consideration of across wave versus within bout changes; and (3) implications of modeling choice on theory development. Supplemental materials include annotated MPlus and R code for data visualization and model estimation.
Schmiege et al. (Tue,) studied this question.