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

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  • Ioana A Cristea
  • Robin N Kok
  • Pim Cuijpers

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.

Suggested Citation

  • Ioana A Cristea & Robin N Kok & Pim Cuijpers, 2016. "The Effectiveness of Cognitive Bias Modification Interventions for Substance Addictions: A Meta-Analysis," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-19, September.
    • Handle: RePEc:plo:pone00:0162226
    DOI: 10.1371/journal.pone.0162226
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    1. Nicola Orsini & Matteo Bottai & Julian Higgins & Iain Buchan, 2005. "HETEROGI: Stata module to quantify heterogeneity in a meta-analysis," Statistical Software Components S449201, Boston College Department of Economics, revised 25 Jan 2006.
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    3. David Moher & Alessandro Liberati & Jennifer Tetzlaff & Douglas G Altman & The PRISMA Group, 2009. "Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-6, July.
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    Cited by:

    1. Melvyn W. B. Zhang & Helen E. Smith, 2020. "Challenges When Evaluating Cognitive Bias Modification Interventions for Substance Use Disorder," IJERPH, MDPI, vol. 17(21), pages 1-7, October.
    2. Melvyn Zhang & Jiangbo Ying & Tracey Wing & Guo Song & Daniel S. S. Fung & Helen Smith, 2018. "A Systematic Review of Attention Biases in Opioid, Cannabis, Stimulant Use Disorders," IJERPH, MDPI, vol. 15(6), pages 1-12, June.
    3. Yi Zhuang Tan & Melvyn W.B. Zhang & Carol C. Choo, 2019. "Perspectives on Modifying Attentional Biases Amongst Individuals with Tobacco Use Disorder Using Technology: A Review," IJERPH, MDPI, vol. 16(15), pages 1-11, July.
    4. Melvyn Weibin Zhang & Jiang Bo Ying & Guo Song & Daniel S. S. Fung & Helen E. Smith, 2018. "Recent Advances in Attention Bias Modification for Substance Addictions," IJERPH, MDPI, vol. 15(4), pages 1-8, April.
    5. Melvyn W. B. Zhang & Sandor Heng & Syidda B. Amron & Zaakira Mahreen & Guo Song & Daniel S. S. Fung & Helen E. Smith, 2020. "Gamified M-Health Attention Bias Modification Intervention for Individuals with Opioid Use Disorder: Protocol for a Pilot Randomised Study," IJERPH, MDPI, vol. 17(3), pages 1-10, January.
    6. Di Pietro Giorgio & European Commission & IZA, 2022. "Studying abroad and earnings: A meta‐analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 36(4), pages 1096-1129, September.
    7. Melvyn W.B. Zhang & Jiangbo Ying, 2019. "Incorporating Participatory Action Research in Attention Bias Modification Interventions for Addictive Disorders: Perspectives," IJERPH, MDPI, vol. 16(5), pages 1-11, March.
    8. Doris X.Y. Chia & Melvyn W.B. Zhang, 2020. "A Scoping Review of Cognitive Bias in Internet Addiction and Internet Gaming Disorders," IJERPH, MDPI, vol. 17(1), pages 1-11, January.

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