Predicting cannabis use moderation among a sample of digital self-help subscribers: A machine learning study.

Journal: Drug and alcohol dependence
PMID: 39293354

Abstract

BACKGROUND: For individuals who wish to reduce their cannabis use without formal help, there are a variety of self-help tools available. Although some are proven to be effective in reducing cannabis use, effect sizes are typically small. More insight into predictors of successful reduction of use among individuals who frequently use cannabis and desire to reduce/quit could help identify factors that contribute to successful cannabis use moderation.

Authors

  • Marleen I A Olthof
    Trimbos Institute, Netherlands Institute of Mental Health and Addiction, Utrecht, the Netherlands; Amsterdam UMC, Department of Psychiatry, University of Amsterdam, Amsterdam, the Netherlands. Electronic address: molthof@trimbos.nl.
  • Lucas A Ramos
    Amsterdam UMC, University of Amsterdam, Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam Cardiovascular Sciences, Meibergdreef 9, Amsterdam, the Netherlands.
  • Margriet W van Laar
    Trimbos Institute, Netherlands Institute of Mental Health and Addiction, Utrecht, the Netherlands. Electronic address: mlaar@trimbos.nl.
  • Anna E Goudriaan
    Amsterdam UMC, Department of Psychiatry, University of Amsterdam, Amsterdam, the Netherlands; Arkin Mental Health Care, Amsterdam, the Netherlands; Amsterdam Public Health Research Institute, Amsterdam, the Netherlands. Electronic address: a.e.goudriaan@amsterdamumc.nl.
  • Matthijs Blankers
    Department of Research, Arkin Mental Health Care, Klaprozenweg 111, 1033NN, Amsterdam, The Netherlands. matthijs.blankers@arkin.nl.

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