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Predictors of Adherence to Psychological Treatment for Insomnia and Pain: Analysis from a Randomized Trial.

Koffel E, Vitiello MV, McCurry SM, Rybarczyk B, Von Korff M. Predictors of Adherence to Psychological Treatment for Insomnia and Pain: Analysis from a Randomized Trial. The Clinical Journal of Pain. 2018 Apr 1; 34(4):375-382.

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OBJECTIVES: Poor adherence to psychological treatment for insomnia is common and limits treatment gains. Very little is known about predictors of adherence among patients with chronic pain, although adherence is theorized to be more critical and more challenging for these patients. This secondary data analysis examines predictors of drop-out and therapy nonattendance in an osteoarthritis population receiving psychological treatment for insomnia and pain. METHODS: Data were analyzed from the "Lifestyles" trial, a randomized controlled trial of a 6-week group cognitive behavioral pain coping skills intervention (CBT-P), group cognitive-behavioral therapy for pain and insomnia (CBT-PI), and an education only attention control group (EOC). The current analysis focuses on 122 participants randomized to CBT-PI from 6 primary care clinics. Measures of treatment acceptability, demographics, and symptoms were collected at baseline. Factor analysis was used to clarify the boundaries of these domains, and hierarchical regression was used to examine the incremental predictive power of these patient characteristics on therapy attendance. RESULTS: Ratings of treatment acceptability were distinct from demographic and medical variables and baseline symptoms. Treatment acceptability was significantly related to session attendance and drop-out (rs ranging from 0.24 to 0.32) and was also one of the strongest predictors of session attendance (ß = 0.20; P < 0.05). DISCUSSION: Perceptions of treatment acceptability early in treatment represent a potentially modifiable target to enhance adherence to psychological treatment for insomnia and pain among patients with chronic pain. This work represents an important step towards understanding how to best maximize sleep treatments for this patient population.

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