Trial reportPloS one2021
Effectiveness of attentional bias modification training as add-on to regular treatment in alcohol and cannabis use disorder: A multicenter randomized control trial.
Trial report in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07103785 (The Effectiveness of a Mobile Game Intervention in Modifying Attentional Bias Toward Alcohol Cues), which is not on this map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
The Effectiveness of a Mobile Game Intervention in Modifying Attentional Bias Toward Alcohol Cues: A Randomized Controlled Trial
Who cites it
5 citing papers in PubMed.
- Virtual Reality-Based Cue Exposure and Aversion Therapy for Alcohol Dependence: A Randomized Controlled Trial.Addiction biology · 2026Trial
- Cognitive Bias Modification for Individuals With Opioid Use Disorder and Chronic Pain Did Not Predict Changes in Clinical Outcomes.Journal of studies on alcohol and drugs · 2026Article
- Effect of attentional bias modification on social media addiction: a visual dot-probe task.Frontiers in psychology · 2026Article
- Treatments for Cannabis Use Disorder across the Lifespan: A Systematic Review.Brain sciences · 2024Review
- Animal models of compulsion alcohol drinking: Why we love quinine-resistant intake and what we learned from it.Frontiers in psychiatry · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAttentional bias for substance-relevant cues has been found to contribute to the persistence of addiction. Attentional bias modification (ABM) interventions might, therefore, increase positive treatment outcome and reduce relapse rates. The current study investigated the effectiveness of a newly developed home-delivered, multi-session, internet-based ABM intervention, the Bouncing Image Training Task (BITT), as an add-on to treatment as usual (TAU).
methodsParticipants (N = 169), diagnosed with alcohol or cannabis use disorder, were randomly assigned to one of two conditions: the experimental ABM group (50%; TAU+ABM); or the control group (50%; split in two subgroups the TAU+placebo group and TAU-only group, 25% each). Participants completed baseline, post-test, and 6 and 12 months follow-up measures of substance use and craving allowing to assess long-term treatment success and relapse rates. In addition, attentional bias (both engagement and disengagement), as well as secondary physical and psychological complaints (depression, anxiety, and stress) were assessed.
resultsNo significant differences were found between conditions with regard to substance use, craving, relapse rates, attentional bias, or physical and psychological complaints.
conclusionsThe findings may reflect unsuccessful modification of attentional bias, the BITT not targeting the relevant process (engagement vs. disengagement bias), or may relate to the diverse treatment goals of the current sample (i.e., moderation or abstinence). The current findings provide no support for the efficacy of this ABM approach as an add-on to TAU in alcohol or cannabis use disorder. Future studies need to delineate the role of engagement and disengagement bias in the persistence of addiction, and the role of treatment goal in the effectiveness of ABM interventions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.