Behavioral engagement patterns and psychosocial outcomes in web-based interpretation bias training for anxiety.

IF 7.7
PLOS digital health Pub Date : 2025-07-24 eCollection Date: 2025-07-01 DOI:10.1371/journal.pdig.0000945
Jeremy William Eberle, Sonia Baee, Emma Catherine Wolfe, Mehdi Boukhechba, Daniel Harold Funk, Bethany Ann Teachman, Laura Elizabeth Barnes
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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.

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基于网络的焦虑解释偏差训练的行为参与模式和社会心理结果。
数字心理健康干预措施(DMHIs)有可能扩大焦虑治疗的可及性,但用户参与度往往较低。本研究在一个免费的基于网络的认知偏见修正口译(CBM-I)项目中分析了不同行为参与模式的社会心理结果差异。CBM-I旨在通过以不那么具有威胁性的方式提供对情感模糊情况的思考练习,从而改变焦虑中常见的解释偏见。在一项临床试验中,697名焦虑的社区成年人每周接受5次CBM-I治疗,我们根据任务完成率和花在培训和评估任务上的时间提取了项目使用标记。在对这些标记进行探索性聚类分析后,我们创建了两个参与小组(他们的模式最终反映了在任务上花费的时间通常更多或更少),我们使用多层模型来测试解释偏差和焦虑结果的小组差异。出乎意料的是,参与组并没有显著预测积极解释偏见或焦虑的差异变化。此外,通常在项目上花费较少时间的参与者(包括培训和评估任务)在负面解释偏差(两种测量之一)方面的改善在培训阶段显著高于那些花费更多时间的参与者(事后测试发现,他们明显年龄较大,受教育程度略低)。然而,在2个月的随访中,通常花费较少时间的参与者因负偏倚(两项测量)而在训练收益上有显著损失。研究结果强调了将花费的时间解释为投入的标志所面临的挑战,除了行为标志外,还需要考虑投入的认知和情感标志。进一步了解参与模式有望改善焦虑的DMHIs。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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