Dismantling, optimising, and personalising internet cognitive behavioural therapy for depression: a systematic review and component network meta-analysis using individual participant data

TY - JOUR

T1 - Dismantling, optimising, and personalising internet cognitive behavioural therapy for depression: a systematic review and component network meta-analysis using individual participant data

AU - Furukawa, Toshi A

AU - Suganuma, Aya

AU - Ostinelli, Edoardo G

AU - Andersson, Gerhard

AU - Beevers, Christopher G

AU - Shumake, Jason

AU - Berger, Thomas

AU - Boele, Florien Willemijn

AU - Buntrock, Claudia

AU - Carlbring, Per

AU - Choi, Isabella

AU - Christensen, Helen

AU - Mackinnon, Andrew

AU - Dahne, Jennifer

AU - Huibers, Marcus J H

AU - Ebert, David D

AU - Farrer, Louise

AU - Forand, Nicholas R

AU - Strunk, Daniel R

AU - Ezawa, Iony D

AU - Forsell, Erik

AU - Kaldo, Viktor

AU - Geraedts, Anna

AU - Gilbody, Simon

AU - Littlewood, Elizabeth

AU - Brabyn, Sally

AU - Hadjistavropoulos, Heather D

AU - Schneider, Luke H

AU - Johansson, Robert

AU - Kenter, Robin

AU - Kivi, Marie

AU - Björkelund, Cecilia

AU - Kleiboer, Annet

AU - Riper, Heleen

AU - Klein, Jan Philipp

AU - Schröder, Johanna

AU - Meyer, Björn

AU - Moritz, Steffen

AU - Bücker, Lara

AU - Lintvedt, Ove

AU - Johansson, Peter

AU - Lundgren, Johan

AU - Milgrom, Jeannette

AU - Gemmill, Alan W

AU - Mohr, David C

AU - Montero-Marin, Jesus

AU - Garcia-Campayo, Javier

AU - Nobis, Stephanie

AU - Zarski, Anna-Carlotta

AU - O'Moore, Kathleen

AU - Williams, Alishia D

AU - Newby, Jill M

AU - Perini, Sarah

AU - Phillips, Rachel

AU - Schneider, Justine

AU - Pots, Wendy

AU - Pugh, Nicole E

AU - Richards, Derek

AU - Rosso, Isabelle M

AU - Rauch, Scott L

AU - Sheeber, Lisa B

AU - Smith, Jessica

AU - Spek, Viola

AU - Pop, Victor J

AU - Ünlü, Burçin

AU - van Bastelaar, Kim M P

AU - van Luenen, Sanne

AU - Garnefski, Nadia

AU - Kraaij, Vivian

AU - Vernmark, Kristofer

AU - Warmerdam, Lisanne

AU - van Straten, Annemieke

AU - Zagorscak, Pavle

AU - Knaevelsrud, Christine

AU - Heinrich, Manuel

AU - Miguel, Clara

AU - Cipriani, Andrea

AU - Efthimiou, Orestis

AU - Karyotaki, Eirini

AU - Cuijpers, Pim

N1 - Copyright © 2021 Elsevier Ltd. All rights reserved.

PY - 2021/6

Y1 - 2021/6

N2 - BACKGROUND: Internet cognitive behavioural therapy (iCBT) is a viable delivery format of CBT for depression. However, iCBT programmes include training in a wide array of cognitive and behavioural skills via different delivery methods, and it remains unclear which of these components are more efficacious and for whom.METHODS: We did a systematic review and individual participant data component network meta-analysis (cNMA) of iCBT trials for depression. We searched PubMed, PsycINFO, Embase, and the Cochrane Library for randomised controlled trials (RCTs) published from database inception to Jan 1, 2019, that compared any form of iCBT against another or a control condition in the acute treatment of adults (aged ≥18 years) with depression. Studies with inpatients or patients with bipolar depression were excluded. We sought individual participant data from the original authors. When these data were unavailable, we used aggregate data. Two independent researchers identified the included components. The primary outcome was depression severity, expressed as incremental mean difference (iMD) in the Patient Health Questionnaire-9 (PHQ-9) scores when a component is added to a treatment. We developed a web app that estimates relative efficacies between any two combinations of components, given baseline patient characteristics. This study is registered in PROSPERO, CRD42018104683.FINDINGS: We identified 76 RCTs, including 48 trials contributing individual participant data (11 704 participants) and 28 trials with aggregate data (6474 participants). The participants' weighted mean age was 42·0 years and 12 406 (71%) of 17 521 reported were women. There was suggestive evidence that behavioural activation might be beneficial (iMD -1·83 [95% credible interval (CrI) -2·90 to -0·80]) and that relaxation might be harmful (1·20 [95% CrI 0·17 to 2·27]). Baseline severity emerged as the strongest prognostic factor for endpoint depression. Combining human and automated encouragement reduced dropouts from treatment (incremental odds ratio, 0·32 [95% CrI 0·13 to 0·93]). The risk of bias was low for the randomisation process, missing outcome data, or selection of reported results in most of the included studies, uncertain for deviation from intended interventions, and high for measurement of outcomes. There was moderate to high heterogeneity among the studies and their components.INTERPRETATION: The individual patient data cNMA revealed potentially helpful, less helpful, or harmful components and delivery formats for iCBT packages. iCBT packages aiming to be effective and efficient might choose to include beneficial components and exclude ones that are potentially detrimental. Our web app can facilitate shared decision making by therapist and patient in choosing their preferred iCBT package.FUNDING: Japan Society for the Promotion of Science.

AB - BACKGROUND: Internet cognitive behavioural therapy (iCBT) is a viable delivery format of CBT for depression. However, iCBT programmes include training in a wide array of cognitive and behavioural skills via different delivery methods, and it remains unclear which of these components are more efficacious and for whom.METHODS: We did a systematic review and individual participant data component network meta-analysis (cNMA) of iCBT trials for depression. We searched PubMed, PsycINFO, Embase, and the Cochrane Library for randomised controlled trials (RCTs) published from database inception to Jan 1, 2019, that compared any form of iCBT against another or a control condition in the acute treatment of adults (aged ≥18 years) with depression. Studies with inpatients or patients with bipolar depression were excluded. We sought individual participant data from the original authors. When these data were unavailable, we used aggregate data. Two independent researchers identified the included components. The primary outcome was depression severity, expressed as incremental mean difference (iMD) in the Patient Health Questionnaire-9 (PHQ-9) scores when a component is added to a treatment. We developed a web app that estimates relative efficacies between any two combinations of components, given baseline patient characteristics. This study is registered in PROSPERO, CRD42018104683.FINDINGS: We identified 76 RCTs, including 48 trials contributing individual participant data (11 704 participants) and 28 trials with aggregate data (6474 participants). The participants' weighted mean age was 42·0 years and 12 406 (71%) of 17 521 reported were women. There was suggestive evidence that behavioural activation might be beneficial (iMD -1·83 [95% credible interval (CrI) -2·90 to -0·80]) and that relaxation might be harmful (1·20 [95% CrI 0·17 to 2·27]). Baseline severity emerged as the strongest prognostic factor for endpoint depression. Combining human and automated encouragement reduced dropouts from treatment (incremental odds ratio, 0·32 [95% CrI 0·13 to 0·93]). The risk of bias was low for the randomisation process, missing outcome data, or selection of reported results in most of the included studies, uncertain for deviation from intended interventions, and high for measurement of outcomes. There was moderate to high heterogeneity among the studies and their components.INTERPRETATION: The individual patient data cNMA revealed potentially helpful, less helpful, or harmful components and delivery formats for iCBT packages. iCBT packages aiming to be effective and efficient might choose to include beneficial components and exclude ones that are potentially detrimental. Our web app can facilitate shared decision making by therapist and patient in choosing their preferred iCBT package.FUNDING: Japan Society for the Promotion of Science.

KW - Cognitive Behavioral Therapy

KW - Depressive Disorder/psychology

KW - Humans

KW - Internet

KW - Network Meta-Analysis

KW - Outcome Assessment, Health Care

KW - Randomized Controlled Trials as Topic

KW - Systems Analysis

U2 - 10.1016/S2215-0366(21)00077-8

DO - 10.1016/S2215-0366(21)00077-8

M3 - SCORING: Journal article

C2 - 33957075

VL - 8

SP - 500

EP - 511

JO - LANCET PSYCHIAT

JF - LANCET PSYCHIAT

SN - 2215-0374

IS - 6

ER -