Predictors of treatment dropout in self-guided web-based interventions for depression: an 'individual patient data' meta-analysis. Issue 13 (17th April 2015)

• Record Type: Journal Article
• Authors: 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.
• Is Part Of: Psychological medicine. Volume 45:Issue 13(2015)
• Page Start: 2717
• Page End: 2726
• Publication Date: 2015-04-17
• Subjects: Adherence, -- depression, -- eHealth, -- self-help, -- treatment, -- treatment dropout, -- web-based interventions; Psychiatry -- Periodicals; Medicine and psychology -- Periodicals; Clinical psychology -- Periodicals; 616.89
• DOI: 10.1017/S0033291715000665
• Languages: English
• ISSNs: 0033-2917

Abstract

Abstract : Background: 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. Method: 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. Results: 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). Conclusions: 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.