 Behavioral engagement patterns and psychosocial outcomes in web-based interpretation bias training for anxietyÁngel Francisco Vela de la Garza Evia, Jeremy William Eberle, Sonia Baee, Emma Catherine Wolfe, Mehdi Boukhechba, Daniel Harold Funk, Bethany Ann Teachman and Laura Elizabeth Barnes PLOS Digital Health, 2025, vol. 4, issue 7, 1-23 Abstract: Digital mental health interventions (DMHIs) have the potential to expand treatment access for anxiety but often have low user engagement. The present study analyzed differences in psychosocial outcomes for different behavioral engagement patterns in a free web-based cognitive bias modification for interpretation (CBM-I) program. CBM-I is designed to shift interpretation biases common in anxiety by providing practice thinking about emotionally ambiguous situations in less threatening ways. Using data from 697 anxious community adults undergoing five weekly sessions of CBM-I in a clinical trial, we extracted program use markers based on task completion rate and time spent on training and assessment tasks. After using an exploratory cluster analysis of these markers to create two engagement groups (whose patterns ended up reflecting generally more vs. less time spent across tasks), we used multilevel models to test for group differences in interpretation bias and anxiety outcomes. Unexpectedly, engagement group did not significantly predict differential change in positive interpretation bias or anxiety. Further, participants who generally spent less time on the program (including both training and assessment tasks) improved in negative interpretation bias (on one of two measures) significantly more during the training phase than those who spent more time (and post hoc tests found were significantly older and slightly less educated). However, participants who generally spent less time had a significant loss in training gains for negative bias (on both measures) by 2-month follow-up. Findings highlight the challenge of interpreting time spent as a marker of engagement and the need to consider cognitive and affective markers of engagement in addition to behavioral markers. Further understanding engagement patterns holds promise for improving DMHIs for anxiety.Author summary: Digital mental health interventions can increase access to care for the many people with anxiety facing treatment barriers. For example, online programs that train people to interpret ambiguous situations in less threatening ways can reduce anxiety symptoms, without requiring a psychotherapist. However, many who start such programs do not engage with them much (based on tasks completed and time invested), which may limit program effectiveness. We assessed the impact of anxious adults’ engagement patterns in a five-session web-based interpretation bias training program on their improvements in anxious thinking and anxiety symptoms. We used behavioral engagement data (i.e., number of tasks users completed, time users spent on tasks) to create two user groups, reflecting those who generally spent more versus less time on both training and assessment tasks. Unexpectedly, for most treatment outcomes, users who spent more time did not improve significantly more than users who spent less time, and for one outcome users who spent less time improved more. Findings highlight the challenge of interpreting time spent as a measure of engagement and the need to also measure how users think and feel about the program as they complete tasks to more fully understand how engagement patterns impact treatment outcomes. Date: 2025 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pdig00:0000945 DOI: 10.1371/journal.pdig.0000945 Access Statistics for this article More articles in PLOS Digital Health from Public Library of Science |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.