Predictors of treatment dropout in self-guided web-based interventions for depression

An 'individual patient data' meta-analysis

E. Karyotaki, A. Kleiboer, F. Smit, D. T. Turner, A. M. Pastor, G. Andersson, T. Berger, C. Botella, J. M. Breton, P. Carlbring, H. Christensen, E. de Graaf, K. Griffiths, T. Donker, L. Farrer, M. J. H. Huibers, J. Lenndin, A. Mackinnon, B. Meyer, S. Moritz & 4 others H. Riper, V. Spek, K. Vernmark, P. Cuijpers

Research output: Contribution to journalArticleScientificpeer-review

Abstract

It is well known that web-based interventions can be effective treatments for depression. However, dropout rates in web-based interventions are typically high, especially in self-guided web-based interventions. Rigorous empirical evidence regarding factors influencing dropout in self-guided web-based interventions is lacking due to small study sample sizes. In this paper we examined predictors of dropout in an individual patient data meta-analysis to gain a better understanding of who may benefit from these interventions.
A comprehensive literature search for all randomized controlled trials (RCTs) of psychotherapy for adults with depression from 2006 to January 2013 was conducted. Next, we approached authors to collect the primary data of the selected studies. Predictors of dropout, such as socio-demographic, clinical, and intervention characteristics were examined.
Data from 2705 participants across ten RCTs of self-guided web-based interventions for depression were analysed. The multivariate analysis indicated that male gender [relative risk (RR) 1.08], lower educational level (primary education, RR 1.26) and co-morbid anxiety symptoms (RR 1.18) significantly increased the risk of dropping out, while for every additional 4 years of age, the risk of dropping out significantly decreased (RR 0.94).
Dropout can be predicted by several variables and is not randomly distributed. This knowledge may inform tailoring of online self-help interventions to prevent dropout in identified groups at risk.
Original languageEnglish
Pages (from-to)2717-2726
JournalPsychological medicine: A journal for research in psychiatry and the allied sciences
Volume45
Issue number13
DOIs
Publication statusPublished - 2015

Keywords

  • Adherence
  • depression
  • eHealth
  • self-help
  • treatment
  • treatment dropout
  • web-based interventions

Cite this

Karyotaki, E. ; Kleiboer, A. ; Smit, F. ; Turner, D. T. ; Pastor, A. M. ; Andersson, G. ; Berger, T. ; Botella, C. ; Breton, J. M. ; Carlbring, P. ; Christensen, H. ; de Graaf, E. ; Griffiths, K. ; Donker, T. ; Farrer, L. ; Huibers, M. J. H. ; Lenndin, J. ; Mackinnon, A. ; Meyer, B. ; Moritz, S. ; Riper, H. ; Spek, V. ; Vernmark, K. ; Cuijpers, P. / Predictors of treatment dropout in self-guided web-based interventions for depression : An 'individual patient data' meta-analysis. In: Psychological medicine: A journal for research in psychiatry and the allied sciences. 2015 ; Vol. 45, No. 13. pp. 2717-2726.
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title = "Predictors of treatment dropout in self-guided web-based interventions for depression: An 'individual patient data' meta-analysis",
abstract = "It is well known that web-based interventions can be effective treatments for depression. However, dropout rates in web-based interventions are typically high, especially in self-guided web-based interventions. Rigorous empirical evidence regarding factors influencing dropout in self-guided web-based interventions is lacking due to small study sample sizes. In this paper we examined predictors of dropout in an individual patient data meta-analysis to gain a better understanding of who may benefit from these interventions.A comprehensive literature search for all randomized controlled trials (RCTs) of psychotherapy for adults with depression from 2006 to January 2013 was conducted. Next, we approached authors to collect the primary data of the selected studies. Predictors of dropout, such as socio-demographic, clinical, and intervention characteristics were examined.Data from 2705 participants across ten RCTs of self-guided web-based interventions for depression were analysed. The multivariate analysis indicated that male gender [relative risk (RR) 1.08], lower educational level (primary education, RR 1.26) and co-morbid anxiety symptoms (RR 1.18) significantly increased the risk of dropping out, while for every additional 4 years of age, the risk of dropping out significantly decreased (RR 0.94).Dropout can be predicted by several variables and is not randomly distributed. This knowledge may inform tailoring of online self-help interventions to prevent dropout in identified groups at risk.",
keywords = "Adherence, depression, eHealth, self-help, treatment, treatment dropout, web-based interventions",
author = "E. Karyotaki and A. Kleiboer and F. Smit and Turner, {D. T.} and Pastor, {A. M.} and G. Andersson and T. Berger and C. Botella and Breton, {J. M.} and P. Carlbring and H. Christensen and {de Graaf}, E. and K. Griffiths and T. Donker and L. Farrer and Huibers, {M. J. H.} and J. Lenndin and A. Mackinnon and B. Meyer and S. Moritz and H. Riper and V. Spek and K. Vernmark and P. Cuijpers",
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Karyotaki, E, Kleiboer, A, Smit, F, Turner, DT, Pastor, AM, Andersson, G, Berger, T, Botella, C, Breton, JM, Carlbring, P, Christensen, H, de Graaf, E, Griffiths, K, Donker, T, Farrer, L, Huibers, MJH, Lenndin, J, Mackinnon, A, Meyer, B, Moritz, S, Riper, H, Spek, V, Vernmark, K & Cuijpers, P 2015, 'Predictors of treatment dropout in self-guided web-based interventions for depression: An 'individual patient data' meta-analysis', Psychological medicine: A journal for research in psychiatry and the allied sciences, vol. 45, no. 13, pp. 2717-2726. https://doi.org/10.1017/S0033291715000665

Predictors of treatment dropout in self-guided web-based interventions for depression : An 'individual patient data' meta-analysis. / Karyotaki, E.; Kleiboer, A.; Smit, F.; Turner, D. T.; Pastor, A. M.; Andersson, G.; Berger, T.; Botella, C.; Breton, J. M.; Carlbring, P.; Christensen, H.; de Graaf, E.; Griffiths, K.; Donker, T.; Farrer, L.; Huibers, M. J. H.; Lenndin, J.; Mackinnon, A.; Meyer, B.; Moritz, S.; Riper, H.; Spek, V.; Vernmark, K.; Cuijpers, P.

In: Psychological medicine: A journal for research in psychiatry and the allied sciences, Vol. 45, No. 13, 2015, p. 2717-2726.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Predictors of treatment dropout in self-guided web-based interventions for depression

T2 - An 'individual patient data' meta-analysis

AU - Karyotaki, E.

AU - Kleiboer, A.

AU - Smit, F.

AU - Turner, D. T.

AU - Pastor, A. M.

AU - Andersson, G.

AU - Berger, T.

AU - Botella, C.

AU - Breton, J. M.

AU - Carlbring, P.

AU - Christensen, H.

AU - de Graaf, E.

AU - Griffiths, K.

AU - Donker, T.

AU - Farrer, L.

AU - Huibers, M. J. H.

AU - Lenndin, J.

AU - Mackinnon, A.

AU - Meyer, B.

AU - Moritz, S.

AU - Riper, H.

AU - Spek, V.

AU - Vernmark, K.

AU - Cuijpers, P.

PY - 2015

Y1 - 2015

N2 - It is well known that web-based interventions can be effective treatments for depression. However, dropout rates in web-based interventions are typically high, especially in self-guided web-based interventions. Rigorous empirical evidence regarding factors influencing dropout in self-guided web-based interventions is lacking due to small study sample sizes. In this paper we examined predictors of dropout in an individual patient data meta-analysis to gain a better understanding of who may benefit from these interventions.A comprehensive literature search for all randomized controlled trials (RCTs) of psychotherapy for adults with depression from 2006 to January 2013 was conducted. Next, we approached authors to collect the primary data of the selected studies. Predictors of dropout, such as socio-demographic, clinical, and intervention characteristics were examined.Data from 2705 participants across ten RCTs of self-guided web-based interventions for depression were analysed. The multivariate analysis indicated that male gender [relative risk (RR) 1.08], lower educational level (primary education, RR 1.26) and co-morbid anxiety symptoms (RR 1.18) significantly increased the risk of dropping out, while for every additional 4 years of age, the risk of dropping out significantly decreased (RR 0.94).Dropout can be predicted by several variables and is not randomly distributed. This knowledge may inform tailoring of online self-help interventions to prevent dropout in identified groups at risk.

AB - It is well known that web-based interventions can be effective treatments for depression. However, dropout rates in web-based interventions are typically high, especially in self-guided web-based interventions. Rigorous empirical evidence regarding factors influencing dropout in self-guided web-based interventions is lacking due to small study sample sizes. In this paper we examined predictors of dropout in an individual patient data meta-analysis to gain a better understanding of who may benefit from these interventions.A comprehensive literature search for all randomized controlled trials (RCTs) of psychotherapy for adults with depression from 2006 to January 2013 was conducted. Next, we approached authors to collect the primary data of the selected studies. Predictors of dropout, such as socio-demographic, clinical, and intervention characteristics were examined.Data from 2705 participants across ten RCTs of self-guided web-based interventions for depression were analysed. The multivariate analysis indicated that male gender [relative risk (RR) 1.08], lower educational level (primary education, RR 1.26) and co-morbid anxiety symptoms (RR 1.18) significantly increased the risk of dropping out, while for every additional 4 years of age, the risk of dropping out significantly decreased (RR 0.94).Dropout can be predicted by several variables and is not randomly distributed. This knowledge may inform tailoring of online self-help interventions to prevent dropout in identified groups at risk.

KW - Adherence

KW - depression

KW - eHealth

KW - self-help

KW - treatment

KW - treatment dropout

KW - web-based interventions

U2 - 10.1017/S0033291715000665

DO - 10.1017/S0033291715000665

M3 - Article

VL - 45

SP - 2717

EP - 2726

JO - Psychological Medicine

JF - Psychological Medicine

SN - 0033-2917

IS - 13

ER -