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Optimising and personalising task-shared psychosocial interventions for common mental disorders: a Bayesian component network meta-analysis of individual participant data

dc.contributor.coauthorPapola, D.
dc.contributor.coauthorTedeschi, F.
dc.contributor.coauthorEfthimiou, O.
dc.contributor.coauthorHarrer, M.
dc.contributor.coauthorRamia, J. A.
dc.contributor.coauthorAcarturk, C.
dc.contributor.coauthorAkhtar, A.
dc.contributor.coauthorAlkneme, M. S.
dc.contributor.coauthorBryant, R.
dc.contributor.coauthorBurchert, S.
dc.contributor.coauthorChibanda, D.
dc.contributor.coauthorDe Graaff, A. M.
dc.contributor.coauthorEl Chammay, R.
dc.contributor.coauthorIlkkursun, Z.
dc.contributor.coauthorJordans, M.
dc.contributor.coauthorKnaevelsrud, C.
dc.contributor.coauthorKohrt, B. A.
dc.contributor.coauthorKurt, G.
dc.contributor.coauthorLehti, V.
dc.contributor.coauthorLeku, M. R.
dc.contributor.coauthorMarkkula, N.
dc.contributor.coauthorMatsuzaka, C.
dc.contributor.coauthorMello, M. F.
dc.contributor.coauthorMorina, N.
dc.contributor.coauthorMurphy, J. K.
dc.contributor.coauthorNakimuli-Mpungu, E.
dc.contributor.coauthorRahman, A.
dc.contributor.coauthorSpaaij, J.
dc.contributor.coauthorTol, W. A.
dc.contributor.coauthorVu, N. C.
dc.contributor.coauthorWaqas, A.
dc.contributor.coauthorvan ‘t Hof, E.
dc.contributor.coauthorKaryotaki, E.
dc.contributor.coauthorPurgato, M.
dc.contributor.coauthorSijbrandij, M.
dc.contributor.coauthorCuijpers, P.
dc.contributor.coauthorFurukawa, T. A.
dc.contributor.coauthorPatel, V.
dc.contributor.coauthorBarbui, C.
dc.date.accessioned2026-08-31T12:31:31Z
dc.date.issued2026
dc.description.abstractAlthough psychosocial interventions delivered by non-specialist providers effectively treat common mental disorders such as depression and anxiety, the treatment-specific elements driving their efficacy remain largely unknown. Our aim was to compare and rank the efficacy of the individual active components of task-shared psychosocial interventions and to predict efficacy using individual participant characteristics. Methods We performed a systematic review and Bayesian individual participant data component network meta-analysis of RCTs comparing different task-shared psychosocial interventions with control conditions for the treatment of adults with common mental disorders. We searched MEDLINE, Embase, PsycINFO, and CENTRAL from database inception to March 15, 2023. We included additional datasets published up to May 29, 2026 and unpublished RCTs. We sought individual participant data from the trial authors. A purpose-built taxonomy of treatment-specific elements was used to dismantle interventions into their constituent components. The primary outcome was efficacy in reducing symptoms of common mental disorders, as measured at study endpoint. We used incremental mean difference to indicate the added benefit (or detrimental effect) of adding a component to a treatment. We assessed the risk of bias of the included studies with the revised Cochrane Risk of Bias tool. The protocol was published in a peer-reviewed journal. Findings We included 34 RCTs, from which 30 trials (89%) contributed individual participant data from 10 612 participants. Of these participants, 7662 were women (72·2%) and 2950 were men (27·8%). The mean age of participants was 36·7 years (SD 5·5). Findings identified strengthening social support (incremental mean difference –9·48; 95% credible interval [CrI] –13·29 to –6·60), behavioural activation (–4·15; –7·48 to –0·05), and problem management (–4·08; –5·37 to –2·83) as the most beneficial components. Relaxation (7·97; 3·35 to 12·11) and, less clearly, cognitive reframing (5·17; 0·78 to 9·15) were identified as detrimental components. Interaction analyses revealed that component effects vary systematically by baseline severity and other sociodemographic characteristics. Ethnicity data were not available. Individuals with lived experience contributed to the interpretation of results. Personalised effect estimates can be computed via a freely accessible web application. Interpretation Strengthening social support, problem management, and behavioural activation were associated with the greatest incremental benefit within task-shared psychosocial interventions for adults with depression or anxiety. Funding European Commission.
dc.description.harvestedfromManual
dc.description.indexedbyPubMed
dc.description.indexedbyScopus
dc.description.publisherscopeInternational
dc.description.readpublishN/A
dc.description.sponsoredbyTubitakEuEU
dc.description.sponsorshipEuropean Commission
dc.description.versionPublished Version
dc.identifier.ScopusQuartileN/A
dc.identifier.WoSPercentileN/A
dc.identifier.WoSQuartileN/A
dc.identifier.doi10.1016/s2215-0366(26)00193-8
dc.identifier.eissn2215-0374
dc.identifier.embargoN/A
dc.identifier.endpage760
dc.identifier.grantnoN/A
dc.identifier.issn2215-0366
dc.identifier.issue9
dc.identifier.pubmed42546730
dc.identifier.scopus2-s2.0-105047053903
dc.identifier.startpage747
dc.identifier.urihttp://dx.doi.org/10.1016/s2215-0366(26)00193-8
dc.identifier.urihttps://hdl.handle.net/20.500.14288/34800
dc.identifier.volume13
dc.keywordsPsychosocial
dc.keywordsPsychological intervention
dc.keywordsComponent (thermodynamics)
dc.keywordsBayesian network
dc.keywordsBayesian probability
dc.languageeng
dc.publisherElsevier BV
dc.relation.affiliationKoç University
dc.relation.collectionKoç University Institutional Repository
dc.relation.ispartofThe Lancet Psychiatry
dc.subjectSocial sciences
dc.subjectPsychology
dc.subjectExperimental and cognitive psychology
dc.subjectApplied psychology
dc.subjectLife sciences
dc.subjectNeuroscience
dc.subjectCognitive neuroscience
dc.titleOptimising and personalising task-shared psychosocial interventions for common mental disorders: a Bayesian component network meta-analysis of individual participant data
dc.typeJournal Article
dspace.entity.typePublication

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