Understanding heterogeneity, comorbidity, and variability in depression: Idiographic models and depression outcomes

Abstract

This study uses time-intensive, item-level assessment to examine individual depressive and co-occurring symptom dynamics. Participants experiencing moderate-severe depression (N = 31) completed ecological momentary assessment (EMA) four times per day for 20 days (total observations = 2480). We estimated idiographic networks using MDD, anxiety, and ED items. ED items were most frequently included in individual networks relative to depression and anxiety items. We built ridge and logistic regression ensembles to explore how idiographic network centrality metrics performed at predicting between-subject depression outcomes (PHQ-9 change score and clinical deterioration, respectively) at 6-months follow-up. For predicting PHQ-9 change score, R² ranged between 0.13 and 0.28. Models predicting clinical deterioration ranged from no better than chance to 80 % accuracy. This pilot study shows how co-occurring anxiety and ED symptoms may contribute to the maintenance of depressive symptoms. Future work should assess the predictive utility of psychological networks to develop understanding of how idiographic models may inform clinical decisions.

Keywords: Anxiety; Depression; Eating disorders; Ecological momentary assessment; Idiographic; Network analysis; Predictive modeling.