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Dropout in self-guided web-based interventions for depression can be predicted by several variables
  1. Ma. Asunción Lara,
  2. Marcela Tiburcio Sainz
  1. Department of Social Sciences in Health, Ramón de la Fuente Muñiz National Institute of Psychiatry, Mexico City, Mexico
  1. Correspondence to Dr Ma. Asunción Lara, Ramón de la Fuente Muñiz National Institute of Psychiatry, Mexico City, DF 14370, Mexico; laracan{at}

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ABSTRACT FROM: Karyotaki E, Kleiboer A, Smit F, et al. Predictors of treatment dropout in self-guided web-based interventions for depression: an ‘individual patient data’ meta-analysis. Psychol Med 2015;45:2717–26.

What is already known on this topic

Web-based interventions may constitute an effective form of treatment for depression compared with face-to-face treatments.1 However, self-guided interventions show less-promising results and higher dropout rates than guided web-based interventions,2 since human support increases treatment adherence through accountability to a therapist. It is therefore essential to identify the characteristics of individuals and interventions related to treatment dropout in unguided interventions to increase their efficacy and foster adherence.

Methods of the study

The study brings together data from separate studies to undertake an individual patient data (IPD) meta-analysis to identify sociodemographic, clinical and intervention characteristics that predict dropout in self-guided web-based interventions for people with depressive symptoms. The primary studies were selected from an existing database of randomised controlled trials (RCTs) ( The …

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  • Competing interests None declared.

  • Provenance and peer review Commissioned; internally peer reviewed.

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