Erik Forsell1, Susanna Jernelöv1, Kerstin Blom1, Martin Kraepelien1, Cecilia Svanborg1, Gerhard Andersson1, Nils Lindefors1, Viktor Kaldo1. 1. Center for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, and Stockholm Health Care Services, Stockholm County Council, Huddinge Hospital, Stockholm (Forsell, Jernelöv, Blom, Kraepelien, Svanborg, Andersson, Lindefors, Kaldo); Division of Psychology, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm (Jernelöv); Department of Behavioral Sciences and Learning, Linköping University, Linköping, Sweden (Andersson); and Department of Psychology, Faculty of Health and Life Sciences, Linnaeus University, Växjö, Sweden (Kaldo).
Abstract
OBJECTIVE: This study aimed to demonstrate proof of concept for an adaptive treatment strategy in Internet-delivered cognitive-behavioral therapy (ICBT), where risk of treatment failure is assessed early in treatment and treatment for at-risk patients is adapted to prevent treatment failure. METHODS: A semiautomated algorithm assessed risk of treatment failure early in treatment in 251 patients undergoing ICBT for insomnia with therapist guidance. At-risk patients were randomly assigned to continue standard ICBT or to receive adapted ICBT. The primary outcome was self-rated insomnia symptoms using the Insomnia Severity Index in a linear mixed-effects model. The main secondary outcome was treatment failure (having neither responded nor remitted at the posttreatment assessment). RESULTS: A total of 102 patients were classified as at risk and randomly assigned to receiveadapted ICBT (N=51) or standard ICBT (N=51); 149 patients were classified as not at risk. Patients not at risk had significantly greater score reductions on the Insomnia Severity Index than at-risk patients given standard ICBT. Adapted ICBT for at-risk patients was significantly more successful in reducing symptoms compared with standard ICBT, and it decreased the risk of failing treatment (odds ratio=0.33). At-risk patients receiving adapted ICBT were not more likely to experience treatment failure than those not at risk (odds ratio=0.51), though they were less likely to experience remission. Adapted treatment required, on average, 14 more minutes of therapist-patient time per remaining week. CONCLUSIONS: An adaptive treatment strategy can increase treatment effects for at-risk patients and reduce the number of failed treatments. Future studies should improve accuracy in classification algorithms and identify key factors that boost the effect of adapted treatments.
RCT Entities:
OBJECTIVE: This study aimed to demonstrate proof of concept for an adaptive treatment strategy in Internet-delivered cognitive-behavioral therapy (ICBT), where risk of treatment failure is assessed early in treatment and treatment for at-risk patients is adapted to prevent treatment failure. METHODS: A semiautomated algorithm assessed risk of treatment failure early in treatment in 251 patients undergoing ICBT for insomnia with therapist guidance. At-risk patients were randomly assigned to continue standard ICBT or to receive adapted ICBT. The primary outcome was self-rated insomnia symptoms using the Insomnia Severity Index in a linear mixed-effects model. The main secondary outcome was treatment failure (having neither responded nor remitted at the posttreatment assessment). RESULTS: A total of 102 patients were classified as at risk and randomly assigned to receive adapted ICBT (N=51) or standard ICBT (N=51); 149 patients were classified as not at risk. Patients not at risk had significantly greater score reductions on the Insomnia Severity Index than at-risk patients given standard ICBT. Adapted ICBT for at-risk patients was significantly more successful in reducing symptoms compared with standard ICBT, and it decreased the risk of failing treatment (odds ratio=0.33). At-risk patients receiving adapted ICBT were not more likely to experience treatment failure than those not at risk (odds ratio=0.51), though they were less likely to experience remission. Adapted treatment required, on average, 14 more minutes of therapist-patient time per remaining week. CONCLUSIONS: An adaptive treatment strategy can increase treatment effects for at-risk patients and reduce the number of failed treatments. Future studies should improve accuracy in classification algorithms and identify key factors that boost the effect of adapted treatments.
Entities:
Keywords:
Behavior Therapy; Computers; Diagnosis And Classification; Psychotherapy; Sleep
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