Guided or self-guided internet-based cognitive-behavioural therapy (iCBT) for depression? Study protocol of an individual participant data network meta-analysis

Eirini Karyotaki, Toshi A. Furukawa, Orestis Efthimiou, Heleen Riper, Pim Cuijpers

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Introduction Although guided forms of internet-based cognitive-behavioural therapy (iCBT) result in a substantial reduction in depression, it seems that the most scalable way to deliver iCBT is without guidance. However, direct evidence on the comparison between guided and self-guided iCBT is scarce. Moreover, it is unclear which types of patients may benefit more from each of these two forms of iCBT. Network meta-analysis (NMA) using individual participant data (IPD) offers a way to assess the relative efficacy of multiple (>2) interventions. Moreover, it maximises our power to detect patient-level characteristics (covariates) that have an important effect on the efficacy of interventions. This protocol describes the procedures of an IPD-NMA, which aims at examining the relative efficacy of guided compared with self-guided iCBT and at identifying predictors and moderators of treatment outcome. Methods and analysis We will use an existing database on psychotherapies for adult depression to identify eligible studies. This database has been updated up to 1 January 2018, through literature searches in PubMed, Embase, PsycINFO and Cochrane Library. The outcome of this IPD-NMA is reduction in depressive symptoms severity. We will fit the model in a Bayesian setting. After fitting the model, we will report the relative treatment effects for different types of patients, and we will discuss the clinical implications of our findings. Based on the results from the IPD-NMA model, we will develop and validate a personalised prediction model, aiming to provide patient-level predictions about the effects of the interventions. Ethics and dissemination An ethical approval is not required for this study. The results will be published in a peer-review journal. These results will guide clinical decisions about the most efficient way to allocate iCBT resources, thereby increasing the scalability of this innovative therapeutic approach.

Original languageEnglish
Article numbere026820
JournalBMJ Open
Issue number6
Publication statusPublished - 1 Jun 2019


  • depression
  • e-health
  • individual patient data
  • network meta-analysis
  • prediction models

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