Identifying and predicting posttraumatic stress symptom states in adults with posttraumatic stress disorder

AbstractBetween-person heterogeneity of posttraumatic stress disorder (PTSD) is well established. Within-person analyses and theDSM-5 suggest that heterogeneity may also be evident within individuals across time as they move through social contexts and biological cycles. Modeling within-person symptom-level fluctuations may confirm such heterogeneity, elucidate mechanisms of disorder maintenance, and inform time- and person-specific interventions. The present study aimed to identify and predict discrete within-person disorder presentations, orsymptom states, and explore group-level patterns of these states. Adults (N = 20, 60.0% male,M age = 38.25 years) with PTSD responded to symptom surveys four times per day for 30 days. We subjected each individual's dataset to Gaussian finite mixture modeling (GFMM) to uncover latent, within-person classes of symptom levels (i.e., states) and predicted those states with idiographic elastic net regularized regression using a set of time-based and behavioral predictors. Next, we conducted a GFMM of the within-person GFMM outputs and tested idiographic prediction models of these states. Multiple within-person states were revealed for 19 of 20 participants (Mdn = 4; 66 for the full sample). Prediction models were moderately successful,M AUC = .66 (d = 0.58), range: .50 –1.00. The GFMM of the within-person model outputs revealed two states: one with above-average and one with below-average symptom levels. Prediction models were, again, mode...
Source: Journal of Traumatic Stress - Category: Psychiatry & Psychology Authors: Tags: RESEARCH ARTICLE Source Type: research