A latent trajectory analysis of inpatient depression treatment.

Abstract

Patients seeking psychotherapy may progress through treatment in varying ways. Modeling multiple treatment trajectories through growth mixture modeling provides a comprehensive way of understanding a patient population. Multiple trajectories may additionally help researchers describe complexities within a patient population, such as those with severe and persistent disorders and comorbid symptoms, to understand characteristics of patients that may be struggling during treatment. We analyzed the depression symptom outcome measures (PHQ-9) for 246 patients receiving inpatient depression treatment. We constructed a growth mixture model of depression symptom changes, allowing the number of treatment trajectories to emerge through the data, and utilized goodness-of-fit indices to select the superior model. Results indicated three classes was the best fitting model, with patients either (a) patients started above the clinical cutoff score for depression and had significant linear change over time, ending therapy just above the clinical cutoff—“Improvement-leveling off-improvement”; (b) patients started therapy well above the clinical cutoff, showed symptom alleviation at the beginning of therapy before the trajectory started to level off—“High symptom pressure”; or (c) patients started therapy just below the clinical cutoff, had steady change throughout therapy, ending well below the clinical cutoff—“continuous improvement.” Implications of the study may include altering the length of treatment based on patient presenting symptoms in order to best serve patients and utilize hospital resources.

The Effectiveness of an Intensive Inpatient Psychotherapy Program for Chronic Depression: A naturalistic comparison with wait list

“…In this study the intensive inpatient psychotherapy treatments provided were affect phobia therapy (APT) and VITA. These specific inpatient treatments have previously been described by Ulvenes et al [ 27 ]. The patients in the wait list control group received TAU in their local communities.…”

Self-compassion and fear of compassion in the treatment of chronic depression: Mechanisms of change?

“…We report on the determination of the sample, all data exclusions, and manipulations. Data from a different, but overlapping sample from the same unit are reported in two separate publications (Melsom et al, 2022; Ulvenes et al, 2022). The analysis was performed with the statistical program R (R Core Team, 2020) using the lavaan package (v.0.6-9; Rosseel, 2012).…”

Emotional clarity and tolerance of emotional distress as mechanisms of change in short-term psychodynamic psychotherapy for chronic depression.

“…We report on the determination of the sample, all data exclusions, and manipulations. Data from a different but overlapping sample from the same unit is reported in a separate publication (Ulvenes et al, 2022). The analysis was conducted in the statistical program R (R Core Team, 2020) using the lavaan package (v0.6-7; Rosseel, 2012).…”

“…Taken together, these results indicate that tolerance of emotional distress may be a mechanism of change in the treatment of depression. Relatedly, Ulvenes et al (2022) analyzed a large sample of chronically depressed patients and found that patients who did not respond to treatment had more severe difficulties with tolerance of emotional distress than those who responded to treatment. Thus, it can be hypothesized that tolerance of emotional distress is a particularly important factor in chronic depression.…”