ArticleInternet interventions2022
Clinically sufficient classification accuracy and key predictors of treatment failure in a randomized controlled trial of Internet-delivered Cognitive Behavior Therapy for Insomnia.
Article in Internet interventions, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic models for predicting insomnia treatment outcomes: A systematic review.Journal of psychiatric research · 2024Pooled it
- The Academic Threat Appraisal Ratio Scale (ATARS): Insights into attainment, academic progression, and retention in higher education.The British journal of educational psychology · 2026Article
- Predicting individual differences in digital alcohol intervention effectiveness through multimodal data.NPJ digital medicine · 2026Article
- The Prognostic Value of Early Treatment Factors for Cognitive Behavioral Therapy for Insomnia (CBT-I) Outcomes.Nature and science of sleep · 2026Article
- Natural language processing models for predicting treatment outcomes in internet-delivered cognitive behavioral therapy.Internet interventions · 2025Article
- Temporal subtypes and characteristics of insomnia treatment responses.Scientific reports · 2025Article
- An Interpretable Model With Probabilistic Integrated Scoring for Mental Health Treatment Prediction: Design Study.JMIR medical informatics · 2025Article
- Predicting Therapy Outcomes in Patients With Stress-Related Disorders: Protocol for a Predictive Modeling Study.JMIR research protocols · 2025Article
- Methodological choices and clinical usefulness for machine learning predictions of outcome in Internet-based cognitive behavioural therapy.Communications medicine · 2024Article
- Dataset size versus homogeneity: A machine learning study on pooling intervention data in e-mental health dropout predictions.Digital healthArticle
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4 authors.
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Abstract
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.
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