Incorporating a deep‐learning client outcome prediction tool as feedback in supported internet‐delivered cognitive behavioural therapy for depression and anxiety: A randomised controlled trial within routine clinical practice.
Introduction: Machine learning techniques have been leveraged to predict client psychological treatment outcomes. Few studies, however, have tested whether providing such model predictions as feedback to therapists improves client outcomes. This randomised controlled trial examined (1) the effects o...
| Publicado en: | Counselling & Psychotherapy Research Vol. 25; no. 1; pp. 1 - 15 |
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| Autores principales: | , , , , , , , |
| Formato: | research tables/charts randomized controlled trial Journal Article |
| Publicado: |
Wiley-Blackwell
Mar2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=183991321&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183991321 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14733145 TFX jtl: Counselling & Psychotherapy Research issn: 14733145 maglogo: Y pubinfo: dt: Mar2025 vid: 25 iid: 1 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 183991321 177335893 183991321 183991321 10.1002/capr.12771 183991321 ppf: 1 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Incorporating a deep‐learning client outcome prediction tool as feedback in supported internet‐delivered cognitive behavioural therapy for depression and anxiety: A randomised controlled trial within routine clinical practice. aug: au: Hisler, Garrett C. Young, Katherine S. Cumpanasoiu, Diana Catalina Palacios, Jorge E. Duffy, Daniel Enrique, Angel Keegan, Dessie Richards, Derek affil: Amwell Science, Amwell, Boston MA, , USA sug: subj: Depression Therapy Anxiety Disorders Therapy Cognitive Therapy Methods Telepsychiatry Prediction Models Utilization Deep Learning Treatment Outcomes Evaluation Feedback Psychotherapists Human Questionnaires Randomized Controlled Trials Random Assignment Mental Health Health Status Patient Attitudes Psychotherapist Attitudes ab: Introduction: Machine learning techniques have been leveraged to predict client psychological treatment outcomes. Few studies, however, have tested whether providing such model predictions as feedback to therapists improves client outcomes. This randomised controlled trial examined (1) the effects of implementing therapist feedback via a deep‐learning model (DLM) tool that predicts client treatment response (i.e., reliable improvement on the Patient Health Questionnaire‐9 [PHQ‐9] or Generalized Anxiety Disorder‐7 [GAD‐7]) to internet‐delivered cognitive behavioural therapy (iCBT) in routine clinical care and (2) therapist acceptability of this prediction tool. Methods: Fifty‐one therapists were randomly assigned to access the DLM tool (vs. treatment as usual [TAU]) and oversaw the care of 2394 clients who completed repeated PHQ‐9 and GAD‐7 assessments. Results: Multilevel growth curve models revealed no overall differences between the DLM tool vs. TAU conditions in client clinical outcomes. However, clients of therapists with the DLM tool used more tools, completed more activities and visited more platform pages. In subgroup analyses, clients predicted to be 'not‐on‐track' were statistically significantly more likely to have reliable improvement on the PHQ‐9 in the DLM vs. TAU group. Therapists with access to the DLM tool reported that it was acceptable for use, they had positive attitudes towards it, and reported it prompted greater examination and discussion of clients, particularly those predicted not to improve. Conclusion: Altogether, the DLM tool was acceptable for therapists, and clients engaged more with the platform, with clinical benefits specific to reliable improvement on the PHQ‐9 for not‐on‐track clients. Future applications and considerations for implementing machine learning predictions as feedback tools within iCBT are discussed. pubtype: Academic Journal doctype: research tables/charts randomized controlled trial Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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