Effectiveness of Machine Learning-Based Adjustments to an eHealth Intervention Targeting Mild Alcohol Use.

Journal: European addiction research
PMID: 39709958

Abstract

INTRODUCTION: This study aimed to evaluate effects of three machine learning based adjustments made to an eHealth intervention for mild alcohol use disorder, regarding (a) early dropout, (b) participation duration, and (c) success in reaching personal alcohol use goals. Additionally, we aimed to replicate earlier machine learning analyses.

Authors

  • Marloes Derksen
    Arkin Mental Health Care, Amsterdam, The Netherlands.
  • Max van Beek
    Arkin Mental Health Care, Amsterdam, The Netherlands.
  • Matthijs Blankers
    Department of Research, Arkin Mental Health Care, Klaprozenweg 111, 1033NN, Amsterdam, The Netherlands. matthijs.blankers@arkin.nl.
  • Hamed Nasri
    Arkin Mental Health Care, Amsterdam, The Netherlands.
  • Tamara de Bruijn
    Jellinek Prevention, Amsterdam, The Netherlands.
  • Nick Lommerse
    Arkin Mental Health Care, Amsterdam, The Netherlands.
  • Guido van Wingen
    Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Meibergdreef 5, 1105 AZ Amsterdam, The Netherlands. Electronic address: g.a.vanwingen@amsterdamumc.nl.
  • Steffen Pauws
    Department of Communication and Cognition, Tilburg University, Tilburg, The Netherlands.
  • 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.

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