App-delivered Cognitive-behavioral Therapy for Insomnia Among Patients With Comorbid Musculoskeletal Complaints and Insomnia Referred to 4-week Inpatient Multimodal Rehabilitation
App-delivered Cognitive-behavioral Therapy for Insomnia Among Patients With Comorbid Musculoskeletal Complaints and Insomnia Referred to 4-week Inpatient Multimodal Rehabilitation
The aim of this randomized clinical trial is to evaluate the effectiveness of app-delivered cognitive-behavioral therapy for insomnia adjunct to inpatient multimodal rehabilitation for individuals with comorbid insomnia and chronic pain, compared with rehabilitation (usual care) only.
Insomnia is highly prevalent among patients receiving treatment for long-term musculoskeletal complains. Cognitive-behavioral therapy for insomnia (CBT-I) may be effective for improving sleep quality and pain-related outcomes in these patients, but the availability of this therapy is limited by few trained therapists. In this randomized clinical trial we will evaluate the effectiveness of app-delivered CBT-I adjunct to inpatients multimodal rehabilitation for individuals with comorbid insomnia and chronic pain, compared with rehabilitation (usual care) only. Patients referred to Unicare Helsefort (Norway) with long-term chronic musculoskeletal complains and insomnia are invited to the study. The rehabilitation program consists of 2+2 weeks of inpatient multimodal rehabilitation, where the patients are at home for two weeks between the rehabilitation stays. Participants in the intervention group will receive the (CBT-I) while participating in rehabilitation, while the control group will receive rehabilitation without the app (usual care).
Based on sample size calculation recruiting 15 clusters with 2:1 randomization will achieve 80 % power to detect a 4-point difference between the intervention and usual treatment group on the insomnia severity index (ISI).
Edit 25.08.23: We noted in the review process of a protocol paper that the description of our sample size calculation implied that 150 participants would be included in the study, rather than that 150 potential participants would be screened and that we anticipated that 5 from each group of the 15 clusters would be eligible (i.e. 75 included in the study). We have also revisited the sample size calculations to account for dropouts and low adherence rates known to be a problem in digital interventions. We expect a dropout rate of approximately 20 % and that around 50 % of the participants in the intervention group will complete at least four of the dCBT-I modules. Given that the per-protocol analysis is of particular interest in this study, we aim to recruit 21 clusters to account for the fact that we expect an average of four participants per cluster in the control group to complete the follow-up, and an average of two participants in the intervention group to complete at least four of the modules and attend the follow-up. As total sample size is hard to predict and the sample size is based on number of clusters we have not changed the total enrollment number.
Inclusion Criteria:
Exclusion Criteria: