IDEAS home Printed from https://ideas.repec.org/a/wly/camsys/v20y2024i1ne1376.html
   My bibliography  Save this article

A comparison of the effectiveness of cognitive behavioural interventions based on delivery features for elevated symptoms of depression in adolescents: A systematic review

Author

Listed:
  • Gretchen Bjornstad
  • Shreya Sonthalia
  • Benjamin Rouse
  • Leanne Freeman
  • Natasha Hessami
  • Jo Hickman Dunne
  • Nick Axford

Abstract

Background Depression is a public health problem and common amongst adolescents. Cognitive behavioural therapy (CBT) is widely used to treat adolescent depression but existing research does not provide clear conclusions regarding the relative effectiveness of different delivery modalities. Objectives The primary aim is to estimate the relative efficacy of different modes of CBT delivery compared with each other and control conditions for reducing depressive symptoms in adolescents. The secondary aim is to compare the different modes of delivery with regard to intervention completion/attrition (a proxy for intervention acceptability). Search Methods The Cochrane Depression, Anxiety and Neurosis Clinical Trials Register was searched in April 2020. MEDLINE, PsycInfo, EMBASE, four other electronic databases, the CENTRAL trial registry, Google Scholar and Google were searched in November 2020, together with reference checking, citation searching and hand‐searching of two databases. Selection Criteria Randomised controlled trials (RCTs) of CBT interventions (irrespective of delivery mode) to reduce symptoms of depression in young people aged 10–19 years with clinically relevant symptoms or diagnosis of depression were included. Data Collection and Analysis Screening and data extraction were completed by two authors independently, with discrepancies addressed by a third author. CBT interventions were categorised as follows: group CBT, individual CBT, remote CBT, guided self‐help, and unguided self‐help. Effect on depressive symptom score was estimated across validated self‐report measures using Hedges' g standardised mean difference. Acceptability was estimated based on loss to follow‐up as an odds ratio. Treatment rankings were developed using the surface under the cumulative ranking curve (SUCRA). Pairwise meta‐analyses were conducted using random effects models where there were two or more head‐to‐head trials. Network analyses were conducted using random effects models. Main Results Sixty‐eight studies were included in the review. The mean age of participants ranged from 10 to 19.5 years, and on average 60% of participants were female. The majority of studies were conducted in schools (28) or universities (6); other settings included primary care, clinical settings and the home. The number of CBT sessions ranged from 1 to 16, the frequency of delivery from once every 2 weeks to twice a week and the duration of each session from 20 min to 2 h. The risk of bias was low across all domains for 23 studies, 24 studies had some concerns and the remaining 21 were assessed to be at high risk of bias. Sixty‐two RCTs (representing 6435 participants) were included in the pairwise and network meta‐analyses for post‐intervention depressive symptom score at post‐intervention. All pre‐specified treatment and control categories were represented by at least one RCT. Although most CBT approaches, except remote CBT, demonstrated superiority over no intervention, no approaches performed clearly better than or equivalent to another. The highest and lowest ranking interventions were guided self‐help (SUCRA 83%) and unguided self‐help (SUCRA 51%), respectively (very low certainty in treatment ranking). Nineteen RCTs (3260 participants) were included in the pairwise and network meta‐analyses for 6 to 12 month follow‐up depressive symptom score. Neither guided self‐help nor remote CBT were evaluated in the RCTs for this time point. Effects were generally attenuated for 6‐ to 12‐month outcomes compared to posttest. No interventions demonstrated superiority to no intervention, although unguided self‐help and group CBT both demonstrated superiority compared to TAU. No CBT approach demonstrated clear superiority over another. The highest and lowest ranking approaches were unguided self‐help and individual CBT, respectively. Sixty‐two RCTs (7347 participants) were included in the pairwise and network meta‐analyses for intervention acceptability. All pre‐specified treatment and control categories were represented by at least one RCT. Although point estimates tended to favour no intervention, no active treatments were clearly inferior. No CBT approach demonstrated clear superiority over another. The highest and lowest ranking active interventions were individual CBT and group CBT respectively. Pairwise meta‐analytic findings were similar to those of the network meta‐analysis for all analyses. There may be age‐based subgroup effects on post‐intervention depressive symptoms. Using the no intervention control group as the reference, the magnitudes of effects appear to be larger for the oldest age categories compared to the other subgroups for each given comparison. However, they were generally less precise and formal testing only indicated a significant difference for group CBT. Findings were robust to pre‐specified sensitivity analyses separating out the type of placebo and excluding cluster‐RCTs, as well as an additional analysis excluding studies where we had imputed standard deviations. Authors' Conclusions At posttreatment, all active treatments (group CBT, individual CBT, guided self‐help, and unguided self‐help) except for remote CBT were more effective than no treatment. Guided self‐help was the most highly ranked intervention but only evaluated in trials with the oldest adolescents (16–19 years). Moreover, the studies of guided self‐help vary in the type and amount of therapist support provided and longer‐term results are needed to determine whether effects persist. The magnitude of effects was generally attenuated for 6‐ to 12‐month outcomes. Although unguided self‐help was the lowest‐ranked active intervention at post‐intervention, it was the highest ranked at follow‐up. This suggests the need for further research into whether interventions with self‐directed elements enable young people to maintain effects by continuing or revisiting the intervention independently, and whether therapist support would improve long‐term outcomes. There was no clear evidence that any active treatments were more acceptable to participants than any others. The relative effectiveness of intervention delivery modes must be taken into account in the context of the needs and preferences of individual young people, particularly as the differences between effect sizes were relatively small. Further research into the type and amount of therapist support that is most acceptable to young people and most cost‐effective would be particularly useful.

Suggested Citation

  • Gretchen Bjornstad & Shreya Sonthalia & Benjamin Rouse & Leanne Freeman & Natasha Hessami & Jo Hickman Dunne & Nick Axford, 2024. "A comparison of the effectiveness of cognitive behavioural interventions based on delivery features for elevated symptoms of depression in adolescents: A systematic review," Campbell Systematic Reviews, John Wiley & Sons, vol. 20(1), March.
  • Handle: RePEc:wly:camsys:v:20:y:2024:i:1:n:e1376
    DOI: 10.1002/cl2.1376
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/cl2.1376
    Download Restriction: no

    File URL: https://libkey.io/10.1002/cl2.1376?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Lu, Guobing & Ades, A.E., 2006. "Assessing Evidence Inconsistency in Mixed Treatment Comparisons," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 447-459, June.
    2. Georgia Salanti & Cinzia Del Giovane & Anna Chaimani & Deborah M Caldwell & Julian P T Higgins, 2014. "Evaluating the Quality of Evidence from a Network Meta-Analysis," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-14, July.
    3. Gretchen J. Bjornstad & Shreya Sonthalia & Benjamin Rouse & Luke Timmons & Laura Whybra & Nick Axford, 2020. "PROTOCOL: A comparison of the effectiveness of cognitive behavioural interventions based on delivery features for elevated symptoms of depression in adolescents," Campbell Systematic Reviews, John Wiley & Sons, vol. 16(1), March.
    4. Anna Chaimani & Georgia Salanti, 2015. "Visualizing assumptions and results in network meta-analysis: The network graphs package," Stata Journal, StataCorp LP, vol. 15(4), pages 905-950, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chunhu Shi & Jo C Dumville & Nicky Cullum, 2018. "Support surfaces for pressure ulcer prevention: A network meta-analysis," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-29, February.
    2. Gretchen J. Bjornstad & Shreya Sonthalia & Benjamin Rouse & Luke Timmons & Laura Whybra & Nick Axford, 2020. "PROTOCOL: A comparison of the effectiveness of cognitive behavioural interventions based on delivery features for elevated symptoms of depression in adolescents," Campbell Systematic Reviews, John Wiley & Sons, vol. 16(1), March.
    3. Chao Zhang & Jiancheng Guan, 2017. "How to identify metaknowledge trends and features in a certain research field? Evidences from innovation and entrepreneurial ecosystem," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(2), pages 1177-1197, November.
    4. David M. Phillippo & Sofia Dias & A. E. Ades & Mark Belger & Alan Brnabic & Alexander Schacht & Daniel Saure & Zbigniew Kadziola & Nicky J. Welton, 2020. "Multilevel network meta‐regression for population‐adjusted treatment comparisons," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1189-1210, June.
    5. Dan Jackson & Sylwia Bujkiewicz & Martin Law & Richard D. Riley & Ian R. White, 2018. "A matrix†based method of moments for fitting multivariate network meta†analysis models with multiple outcomes and random inconsistency effects," Biometrics, The International Biometric Society, vol. 74(2), pages 548-556, June.
    6. Cho-Hao Lee & Po-Huang Chen & Chin Lin & Chieh-Yung Wang & Ching-Liang Ho, 2020. "A network meta-analysis of maintenance therapy in chronic lymphocytic leukemia," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-18, January.
    7. Giammarco Alderotti & Daniele Vignoli & Michela Baccini & Anna Matysiak, 2019. "Employment Uncertainty and Fertility: A Network Meta-Analysis of European Research Findings," Econometrics Working Papers Archive 2019_06, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    8. Pepijn Vemer & Maiwenn J Al & Mark Oppe & Maureen P M H Rutten-van Mölken, 2017. "Mix and match. A simulation study on the impact of mixed-treatment comparison methods on health-economic outcomes," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-20, February.
    9. Dong Hyuk Kang & Kang Su Cho & Won Sik Ham & Hyungmin Lee & Jong Kyou Kwon & Young Deuk Choi & Joo Yong Lee, 2016. "Comparison of High, Intermediate, and Low Frequency Shock Wave Lithotripsy for Urinary Tract Stone Disease: Systematic Review and Network Meta-Analysis," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-22, July.
    10. Cho Naing & Maxine A Whittaker & Norah Htet Htet & Saint Nway Aye & Joon Wah Mak, 2019. "Efficacy of antimalarial drugs for treatment of uncomplicated falciparum malaria in Asian region: A network meta-analysis," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-16, December.
    11. Jing Zhang & Yiping Yuan & Haitao Chu, 2016. "The Impact of Excluding Trials from Network Meta-Analyses – An Empirical Study," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-17, December.
    12. J.Jaime Caro & K. Ishak, 2010. "No Head-to-Head Trial? Simulate the Missing Arms," PharmacoEconomics, Springer, vol. 28(10), pages 957-967, October.
    13. van Valkenhoef, Gert & de Brock, E.O. & Hillege, Hans & Zhao, Jing, 2012. "Addis," Research Report 12007-Other, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    14. Loukia M. Spineli, 2022. "A Revised Framework to Evaluate the Consistency Assumption Globally in a Network of Interventions," Medical Decision Making, , vol. 42(5), pages 637-648, July.
    15. Tamara Kerber Tedesco & Thais Gimenez & Isabela Floriano & Anelise Fernandes Montagner & Lucila Basto Camargo & Ana Flávia Bissoto Calvo & Susana Morimoto & Daniela Prócida Raggio, 2018. "Scientific evidence for the management of dentin caries lesions in pediatric dentistry: A systematic review and network meta-analysis," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-20, November.
    16. David Lunn & Jessica Barrett & Michael Sweeting & Simon Thompson, 2013. "Fully Bayesian hierarchical modelling in two stages, with application to meta-analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(4), pages 551-572, August.
    17. David M. Phillippo & Sofia Dias & A. E. Ades & Vanessa Didelez & Nicky J. Welton, 2018. "Sensitivity of treatment recommendations to bias in network meta‐analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 843-867, June.
    18. Stefanie Reken & Sibylle Sturtz & Corinna Kiefer & Yvonne-Beatrice Böhler & Beate Wieseler, 2016. "Assumptions of Mixed Treatment Comparisons in Health Technology Assessments - Challenges and Possible Steps for Practical Application," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-16, August.
    19. Fernanda S Tonin & Helena H Borba & Antonio M Mendes & Astrid Wiens & Fernando Fernandez-Llimos & Roberto Pontarolo, 2019. "Description of network meta-analysis geometry: A metrics design study," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-14, February.
    20. Hillege, Hans & Brock, Bert de & Valkenhoef, Gert van & Zhao, Jing, 2012. "ADDIS: an automated way to do network meta-analysis," Research Report 12007-0ther, University of Groningen, Research Institute SOM (Systems, Organisations and Management).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:camsys:v:20:y:2024:i:1:n:e1376. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1111/(ISSN)1891-1803 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.