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Published on: January 22, 2016
Combined Attention Bias Modification and Working Memory Training for Reducing Craving in Methamphetamine Use
Yunfei Duan1, Lijun Xiong1, Yingxin Zhao1
1School of Humanities and Management Science, Southwest Medical University, Luzhou, China.
Negative affect, specifically stress, is linked to methamphetamine craving through attentional bias, moderated by working memory. A digital intervention improved these factors in individuals with methamphetamine use disorder.
Area of Science:
- Neuroscience
- Psychology
- Digital Therapeutics
Background:
- Craving is a key factor in relapse for individuals with methamphetamine use disorder (MUD).
- The affective-cognitive mechanisms underlying craving in MUD are not fully understood.
- Investigating the roles of negative affect, attentional bias, and working memory capacity (WMC) is crucial.
Purpose of the Study:
- To examine the association between negative affect and craving in MUD.
- To determine if attentional bias and WMC mediate or moderate this relationship.
- To evaluate a digital intervention combining attentional bias modification (ABM) and working memory training (WMT).
Main Methods:
- Fifty individuals with MUD completed baseline assessments of negative affect, attentional bias, WMC, and craving.
- Moderated mediation analyses were used to explore the relationships between these variables.
- Forty participants were randomized to a 4-week ABM and WMT intervention or treatment as usual, with outcomes assessed via linear mixed models.
Main Results:
- Stress, a component of negative affect, was positively associated with attentional bias and craving.
- Attentional bias mediated the stress-craving link, and WMC moderated the stress-attentional bias association.
- The intervention group showed significant improvements in stress, negative affect, craving, attentional bias, and WMC compared to the control group.
Conclusions:
- Findings support an affective-cognitive model of craving in MUD.
- A combined ABM and WMT digital intervention shows promise for treating MUD by targeting underlying mechanisms.
- This study provides evidence for mechanism-informed digital therapeutics in addiction treatment.
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