Grouping of mood symptoms by time series dynamics.

Background: Understanding how symptoms of mood disorders vary over time in relation to each other is potentially valuable for diagnosis and predicting episodes of illness. In this paper, we characterize the degree of similarity of time series of different mood disorder symptoms.

Methods: We collected 32,215 mood disorder symptom questionnaires, administered twice-daily over 18 months to (n = 19) subjects with rapidly cycling bipolar disorder and (n = 20) healthy control subjects, using visual analog scales to rate 11 sets of symptom severity ratings plus a control item. We used Dynamic Time Warping to calculate similarity ratings between all within-subject pairs of severity ratings followed by Exploratory Factor Analysis (EFA) to identify latent factors of symptom time series across all subjects.

Results: Two latent factors were identified: one with depression and anxiety; and a second, with concentration, energy, irritability, fatigue, appetite, euphoria/elation and overall mood. Restlessness, racing thoughts, and the control item (daily hours of daylight) did not cluster with any of the others.

Limitations: Limited sample size dictated that we pool bipolar and healthy patients and use an iterative EFA procedure.

Conclusion: This analysis suggests that, in a pooled sample of individuals with bipolar disorder and in healthy controls, severity ratings of overall depression and overall anxiety vary jointly as one dynamic factor, while some but not all other DSM mood symptoms vary jointly along with overall mood rating as a second dynamic factor. Further investigation may determine if these findings can simplify subjective symptom reporting in mood-monitoring studies.