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Predictors of treatment dropout in self-guided web-based interventions for depression: an ‘individual patient data’ meta-analysis
Open accessAbstract
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.
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“In contrast to previous studies on adherence in depression and anxiety interventions, we found no evidence for many of the previously identified predictive factors for adherence including baseline symptom levels, gender, age, or education (Christensen et al., 2009; Farrer et al., 2014; Karyotaki et al., 2015; Beatty et al., 2017).”
From Predictors of acceptability and engagement in a self-guided online program for depression and anxiety (2021) · cites this paper as Karyotaki et al., 2015
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“Our results coincide with the literature indicating that people treated with Internet-based programs report high levels of acceptability and high satisfaction (58, 73–78).”
From Exploring the relationship between the acceptability of an internet-based intervention for depression in primary care and clinical outcomes: Secondary analysis of a randomized controlled trial (2019) · cites this paper as [78]
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“Although available research shows that the effect of eHealth interventions does not depend on age and that older adults are even more adherent than younger adults [58], several precautions will be taken to reduce dropout and heighten compliance throughout this study.”
From An Internet-based Acceptance and Commitment Therapy intervention for older adults with anxiety complaints: study protocol for a cluster randomized controlled trial. (2018) · cites this paper as [58]
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“although previous efforts to identify nontechnological factors influencing attrition in online interventions have yielded inconclusive results [71-75].”
From Short-term effectiveness of web-based guided self-help for phobic outpatients: randomized controlled trial. (2014) · cites this paper as [74]
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“Nearly 40% of participants withdrew before completing at least 25% of all treatment modules in self-guided W-MHIs”
From Young People’s Barriers and Facilitators of Engagement with Web-Based Mental Health Interventions for Anxiety and Depression: A Qualitative Study (2024) · cites this paper as [13]
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“and more than 83% of participants withdrew before completing the entire programmes [13].”
From Young People’s Barriers and Facilitators of Engagement with Web-Based Mental Health Interventions for Anxiety and Depression: A Qualitative Study (2024) · cites this paper as [13]
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“Adherence is a common challenge in web-based interventions (Sieverink et al., 2017), particularly in self-guided interventions (Karyotaki et al., 2015).”
From Efficacy of a web-based psychoeducational intervention, Fex-can sex, for young adult childhood cancer survivors with sexual dysfunction: A randomized controlled trial (2024) · cites this paper as Karyotaki et al., 2015
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“A recent IPDM showed that a lower educational level significantly increased the risk of dropping out (78).”
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“The IDPM, mentioned above (78), revealed that men had a greater risk of dropping out.”
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“Moreover, there is also evidence supporting the idea that women are more aware of health problems than men (78, 79).”
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“According to a recent individual patient data meta-analysis (IPDM) (78), younger ages were related to more dropouts.”
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“It should also be taken into account that women try harder to cope with depression than men (78), which would indicate a greater willingness to continue and complete IBIs and be more satisfied with them.”
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“Furthermore, the literature shows that in unguided web-based interventions for depression, compared to guided web-based interventions, average levels of adherence were estimated at 26% in unguided interventions and 72% in guided interventions (22, 78).”
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“unguided interventions have been shown to be much easier to implement and less costly than guided web-based interventions [72]”
From An Internet-based treatment for flying phobia (NO-FEAR Airlines): Study protocol for a randomized controlled trial (2016) · cites this paper as [72]
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“Because there was systematic attrition in this trial, the significant differences between completers and dropouts may provide valuable information to identify focal points for targeted attrition reduction, although previous efforts to identify nontechnological factors influencing attrition in online interventions have yielded inconclusive results [71-75].”
From Short-term effectiveness of web-based guided self-help for phobic outpatients: randomized controlled trial. (2014) · cites this paper as [74]
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“Their effects on engagement have not yet been demonstrated (Karyotaki et al., 2015).”
From Predictors of acceptability and engagement in a self-guided online program for depression and anxiety (2021) · cites this paper as Karyotaki et al., 2015
Experimental — accuracy not yet measuredIn the citing paper
“Considering only unguided digital interventions, a meta-analytic study by Karyotaki and colleagues [42] showed that almost 70% of participants dropped out before completing 75% of the intervention.”
From Use of the Principles of Design Thinking to Address Limitations of Digital Mental Health Interventions for Youth: Viewpoint (2019) · cites this paper as [42]
In the citing paper
“Attrition and low adherence are even bigger challenges among children and adolescents; the younger the participant sample, the greater the dropout rate [37,42].”
From Use of the Principles of Design Thinking to Address Limitations of Digital Mental Health Interventions for Youth: Viewpoint (2019) · cites this paper as [42]
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“A review of empirical studies has found collectively low rates of DMHI completion, with over 70% of users failing to complete all treatment modules, and more than 50% disengaging before completing half of all treatment modules (15).”
BackgroundSelf-citation
Metadata
- 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.
- Affiliations
- Amsterdam Public Health (NL)
- Trimbos Institute (NL)
- Universitat Jaume I (ES)
- Linköping University (SE)
- University of Bern (CH)
- Stockholm University (SE)
- Black Dog Institute (AU)
- Australian National University (AU)
- The University of Melbourne (AU)
- City, University of London (GB)
- University Medical Center Hamburg-Eppendorf (DE)
- Avans University of Applied Sciences (NL)
- Corresponding author
- Karyotaki, E.
- ORCID
- Venue
- Psychological Medicine
- Publisher
- Cambridge University Press (CUP)
- Published
- 2015-04-17
- Volume
- 45
- Issue
- 13
- Pages
- 2717-2726
- ISSN
- 0033-2917 (ISSN-L)
- 1469-8978
- OpenAlex
- W2133074847
- Cited by
- 408
- References
- 37
- Citations by year
- 2026: 38 · 2025: 47 · 2024: 57 · 2023: 46 · 2022: 39 · 2021: 44 · 2020: 33 · 2019: 32 · 2018: 29 · 2017: 23 · 2016: 14 · 2015: 4
- FWCI
- 3.25
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Psychological Medicine (publishedVersion)
- Research portal (Tilburg University) (publishedVersion)
- PubMed (publishedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (publishedVersion, other-oa)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- VU Research Portal (publishedVersion, other-oa)
- Repositori UJI (Universitat Jaume I) (acceptedVersion)
- reroDoc Digital Library (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- BORIS (University Library Bern) (submittedVersion, other-oa)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- ANU Open Research (Australian National University) (submittedVersion)
- Pure Amsterdam UMC (submittedVersion, other-oa)
- Research Publications (Maastricht University) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Open Access CRIS of the University of Bern
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Health Sciences › Medicine › Psychiatry and Mental health › Mental Health Treatment and Access
- Keywords
- treatment discontinuation, depression, predictors of dropout, online self-help, individual patient data, gender differences, educational attainment, anxiety comorbidity, age, intervention tailoring
- MeSH terms
- Anxiety, Depression, Humans, Patient Dropouts, Prognosis, Psychotherapy, Randomized Controlled Trials as Topic, Internet
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