Predictors of treatment dropout in self-guided web-based interventions for depression: an ‘individual patient data’ meta-analysis
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Altres documents de l'autoria: Karyotaki, Eirini; Kleiboer, A.; Smit, F.; Turner, D. T.; Mira, Adriana; Andersson, Gerhard; Berger, Thomas; Botella, Cristina; Bretón-López, Juana; Carlbring, Per; Christensen, Helen; De Graaf, E.; Griffiths, K.; Donker, T.; Farrer, Louise; Huibers, M. J. H.; Lenndin, J.; Mackinnon, Andrew; Meyer, B.; Moritz, S.; Riper, H.; Spek, V.; Vernmark, K.; Cuijpers, Pim
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Predictors of treatment dropout in self-guided web-based interventions for depression: an ‘individual patient data’ meta-analysisAutoria
Data de publicació
2015Editor
Cambridge University PressISSN
0033-2917; 1469-8978Cita bibliogràfica
KARYOTAKI, Eirini, et al. Predictors of treatment dropout in self-guided web-based interventions for depression: an ‘individual patient data’meta-analysis. Psychological medicine, 2015, vol. 45, no 13, p. 2717-2726Tipus de document
info:eu-repo/semantics/articleVersió de l'editorial
http://search.proquest.com/docview/1710157654/fulltext/EDC68FE0BAC2403FPQ/6?acco ...Paraules clau / Matèries
Resum
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 interv ... [+]
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 [-]
Publicat a
Psychological Medicine (2015), 45, 2717–2726Drets d'accés
© Cambridge University Press 2015
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