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Clinically sufficient classification accuracy and key predictors of treatment failure in a randomized controlled trial of Internet-delivered Cognitive Behavior Therapy for Insomnia
Karolinska Institutet, Sweden;Stockholm County Council, Sweden.
Karolinska Institutet, Sweden;Stockholm County Council, Sweden.
Karolinska Institutet, Sweden;Stockholm County Council, Sweden.
Linnaeus University, Faculty of Health and Life Sciences, Department of Psychology. Karolinska Institutet, Sweden;Stockholm County Council, Sweden. (DISA ; DISA-IDP)ORCID iD: 0000-0002-6443-5279
2022 (English)In: Internet Interventions, ISSN 2214-7829, Vol. 29, article id 100554Article in journal (Refereed) Published
Sustainable development
SDG 3: Ensure healthy lives and promote well-being for all at all ages
Abstract [en]

Background: In Adaptive Treatment Strategies, each patient's outcome is predicted early in treatment, and treatment is adapted for those at risk of failure. It is unclear what minimum accuracy is needed for a classifier to be clinically useful. This study aimed to establish a empirically supported benchmark accuracy for an Adaptive Treatment Strategy and explore the relative value of input predictors. Method: Predictions from 200 patients receiving Internet-delivered cognitive-behavioral therapy in an RCT was analyzed. Correlation and logistic regression was used to explore all included predictors and the predictive capacity of different models. Results: The classifier had a Balanced accuracy of 67 %. Eleven out of the 21 predictors correlated significantly with Failure. A model using all predictors explained 56 % of the outcome variance, and simpler models between 16 and 47 %. Important predictors were patient rated stress, treatment credibility, depression change, and insomnia symptoms at week 3 as well as clinician rated attitudes towards homework and sleep medication. Conclusions: The accuracy (67 %) found in this study sets a minimum benchmark for when prediction accuracy could be clinically useful. Key predictive factors were mainly related to insomnia, depression or treatment involvement. Simpler predictive models showed some promise and should be developed further, possibly using machine learning methods.

Place, publisher, year, edition, pages
Elsevier, 2022. Vol. 29, article id 100554
Keywords [en]
Insomnia, Personalized medicine, Adaptive treatment strategy, Prediction, Internet-delivered Cognitive Behavior Therapy
National Category
Psychology
Research subject
Social Sciences, Psychology
Identifiers
URN: urn:nbn:se:lnu:diva-116365DOI: 10.1016/j.invent.2022.100554ISI: 000841809600006PubMedID: 35799973Scopus ID: 2-s2.0-85132857294OAI: oai:DiVA.org:lnu-116365DiVA, id: diva2:1697079
Available from: 2022-09-20 Created: 2022-09-20 Last updated: 2023-04-06Bibliographically approved

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Kaldo, Viktor

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