Abstract
While depressive symptom levels have been well-studied and linked to diverse deleterious outcomes including mortality risk, how symptom levels vary over time, and whether this variability is similarly linked to adverse outcomes, has received little attention. This study examined three methods of calculating variability in depressive symptom levels over time, in a diverse community sample of 184 participants who were followed from age 13 to 36. Simulations identified the method most appropriate for measuring depressive symptom variability in this study, mean absolute successive difference (MASD), based on associations with types of variability of interest (i.e., frequent/large change around an overall trajectory). Analyses then explored the stability of depressive symptom variability over time, gender differences in depressive symptom variability, and associations with social vulnerability factors in adolescence, emotion regulation challenges in adulthood, and measures of physical health and biological aging. There was some support for the hypothesis that depressive symptom variability is associated with greater emotion regulation challenges, above and beyond the effects of overall mean levels of symptoms. However, depressive symptom variability was not reliably linked to other outcomes of interest, and variability was not stable over time beyond the stability of mean levels. There were also no significant gender effects in depressive symptom variability. Findings suggest that depressive symptom variability, as calculated from annual depressive symptom sum scores, may be a random process driven by environmental factors (e.g., response to life events) rather than a stable trait that contributes to significant emotional difficulties and aging and health risks. These findings provide evidence that it may not be useful to monitor depressive symptom variability, as symptom levels remain more closely linked to internal processes like emotion regulation challenges. Future directions are provided to guide research in capturing meaningful depressive symptom variability.
