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Predictors and moderators of cognitive therapy and behavior therapy for insomnia disorder
Rikard Sunnhed1, Hugo Hesser2, Per Carlbring3
1Department of Psychology, Stockholm University, Stockholm, Sweden; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.
Introduction:
Little is known about what pretreatment patient characteristics the outcome of Cognitive Therapy (CT) and Behavioral Therapy (BT) for insomnia disorder depends on. Identifying for whom treatment is most useful is essential for treatment optimization and personalized care. Therefore, this investigation aimed to examine both theory-driven constructs and insomnia-associated clinical variables as potential predictors and moderators of outcomes in CT and BT.
Materials And Methods:
One hundred forty-five participants diagnosed with insomnia disorder were randomly assigned to 10 weekly internet-delivered modules of CT or BT, along with 15 min of weekly telephone support. General clinical predictors and theory-driven moderators (cognitive and behavioral processes) assessed in a previous randomized controlled trial were analyzed using multiple linear regression, with insomnia severity as the outcome.
Results:
Bedtime variability and early morning awakening interacted with treatment and indicated that lower bedtime variability and early morning awakening were associated with a higher effect for CT, whereas the opposite was true for BT. Wake time after sleep onset, insomnia severity index, and sleep efficiency emerged as predictors, indicating prognostic value for treatment outcome.
Conclusions:
In addition to identifying three insomnia-associated variables as predictors of outcome across both treatments, this trial showed that CT and BT could be differentially effective based on patient insomnia heterogeneity at baseline. The differential moderator findings are in line with the theoretical models of CT and BT and might clinically implicate the ability to match therapy to patient features to optimize outcomes.
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