Symposia
Translational
Christopher Beevers, Ph.D.
Full Professor
The University of Texas at Austin
Austin, Texas
Kean Hsu, PhD
Assistant Professor
Georgetown University
Washington, District of Columbia
Jasper Smits, Ph.D.
Professor
The University of Texas at Austin
Austin, Texas
David Schnyer, PhD
Professor
University of Texas at Austin
Austin, Texas
Jason Shumake, PhD
Research Assistant Professor
University of Texas at Austin
Austin, Texas
Attention bias modification training (ABMT) is purported to reduce depression by targeting and modifying an attentional bias for sadness-related stimuli. However, few tests of this hypothesis have been completed. This talk will review recent findings from a randomized clinical trial of ABMT for depression where 145 adults (77% female, 62% white) with at least moderate depression severity and a negative attention bias were randomized to active ABMT, sham ABMT, or assessments only. Intent-to-treat analyses indicated that, relative to assessment-only, active ABMT significantly reduced QIDS-SR and HRSD scores by an additional 0.62 ± 0.23 (p= 0.008, d=−0.57) and 0.74 ± 0.31 (p= 0.021, d=−0.49) points per week. Similar results were observed for active versus sham ABMT: a greater symptom reduction of 0.44 ± 0.24 QIDS-SR(p= 0.067,d=−0.41) and 0.69 ± 0.32 HRSD (p= 0.033,d=−0.42) points per week. Sham ABMT did not significantly differ from the assessment-only condition. Contemporaneous longitudinal simplex mediation indicated that change in attentional bias early in treatment partially mediated the effect of ABMT on change in depression symptoms. In this study, depressed individuals with at least modest negative attentional bias benefitted from active ABMT. When effective, ABMT may improve depression in part by reducing an attentional bias for sad stimuli, particularly early on during ABMT.