The Effectiveness of Cognitive Bias Modification Interventions for Substance Addictions: A Meta-Analysis

Abstract

Background and aims: Cognitive bias modification (CBM) interventions, presumably targeting automatic processes, are considered particularly promising for addictions. We conducted a meta-analysis examining randomized controlled trials (RCTs) of CBM for substance addiction outcomes.

Methods: Studies were identified through systematic searches in bibliographical databases. We included RCTs of CBM interventions, alone or in combination with other treatments, for any type of addiction. We examined trial risk of bias, publication bias and possible moderators. Effects sizes were computed for post-test and follow-up, using a random-effects model. We grouped outcome measures and reported results for addiction (all related measures), craving and cognitive bias.

Results: We identified 25 trials, 18 for alcohol problems, and 7 for smoking. At post-test, there was no significant effect of CBM for addiction, g = 0.08 (95% CI -0.02 to 0.18) or craving, g = 0.05 (95% CI -0.06 to 0.16), but there was a significant, moderate effect on cognitive bias, g = 0.60 (95% CI 0.39 to 0.79). Results were similar for alcohol and smoking outcomes taken separately. Follow-up addiction outcomes were reported in 7 trials, resulting in a small but significant effect of CBM, g = 0.18 (95% CI 0.03 to 0.32). Results for addiction and craving did not differ by substance type, sample type, delivery setting, bias targeted or number of sessions. Risk of bias was high or uncertain in most trials, for most criteria considered. Meta-regression analyses revealed significant inverse relationships between risk of bias and effect sizes for addiction outcomes and craving. The relationship between cognitive bias and respectively addiction ESs was not significant. There was consistent evidence of publication bias in the form of funnel plot asymmetry.

Conclusions: Our results cast serious doubts on the clinical utility of CBM interventions for addiction problems, but sounder methodological trials are necessary before this issue can be settled. We found no indication that positive effects on biases translate into effects on addiction outcomes.

Fig 1. Flowchart of selection and inclusion process, following the PRISMA statement.

Fig 2. Risk of bias graph: review authors' judgments about each risk of bias item presented as percentages across all included studies.

Fig 3. Standardized effect sizes of CBM interventions for addiction (all measures).

Fig 4. Meta-regression analyses for the effects of low risk of bias score on effects sizes for 1) addiction (all measures), and 2) craving.