ABSTRACT
Given that over 20 million adults each year do not receive care for their mental health difficulties, it is imperative to improve system-level capacity issues by increasing treatment efficiency. The present study aimed to collect feasibility/acceptability data on two strategies for increasing the efficiency of cognitive behavioral therapy: (1) personalized skill sequences and (2) personalized skill selections. Participants (N = 70) with anxiety and depressive disorders were enrolled in a pilot sequential multiple assignment randomized trial (SMART). Patients were randomly assigned to receive skill modules from the Unified Protocol in one of three sequencing conditions: standard, sequences that prioritized patients’ relative strengths, and sequences that prioritized relative deficits. Participants also underwent a second-stage randomization to either receive 6 sessions or 12 sessions of treatment. Participants were generally satisfied with the treatment they received, though significant differences favored the Capitalization and Full duration conditions. There were no differences in trajectories of improvement as a function of sequencing condition. There were also no differences in end-of-study outcomes between brief personalized treatment and full standard treatment. Thus, it may be feasible to deliver CBT for personalized durations, though this may not substantially impact trajectories of change in anxiety or depressive symptoms.
Disclosure statement
The first author (SSZ) receives royalties from Oxford University Press from sales of the Unified Protocol, the treatment under study in the present manuscript.
Notes
1. The Countering Emotional Behaviors module in this study consists of two modules from the standard UP: Countering Emotional Behaviors and Emotion Exposures. We linked these two modules because both address aversive reactions to emotions by engaging in behaviors explicitly designed to approach emotional experiences.
2. Participants completed the clinician-rated components of the major assessments in-person at our treatment center prior to COVID-19 modifications, and via telehealth after the implementation of COVID-19 study modifications.
3. Krippendorff’s αs ≥ .80 indicate reliable variables; αs between .67 and .80 indicate tentative reliability (Krippendorff, Citation2004).
4. Post-hoc power analyses using the PowerAnalysisIL package (Lafit et al., Citation2021) in R (Version 3.6.1; R Core Team, Citation2019) suggest that a sample size of 50 per sequencing condition would be needed to detect a medium-to-large effect.