Abstract
BackgroundInternet-delivered cognitive-behavior treatment for insomnia (iCBT-I) has the potential to fill the gap created by the discrepancy between insomnia cases and number of trained professionals. Although the effectiveness of this method was proven in multiple studies conducted in research settings, its feasibility in routine care is still unclear. Predictors, mediators, and moderators of treatment effect remain uncertain since previous studies often give contradictory results. The present study aims to investigate clinical effectiveness and cost-effectiveness of an internet-based CBT-I program Sleepsy in comparison with care as usual (CAU) among patients with CI recruited from clinical settings. Baseline data will be further analyzed to find predictors of treatment outcome.Methods/DesignThe proposed study is a parallel-group randomized controlled trial comparing CAU plus iCBT-I with CAU in a clinical setting. One hundred ten participants will be referred from the medical doctors in Moscow. Both groups will have access to CAU, which corresponds to the treatment prescribed by the referring doctor. Patients of the first group will additionally get access to the iCBT-I program with the opportunity to contact a specialist (guidance on request) in a secured environment. The primary outcome is insomnia severity change from pre- to post-treatment. Secondary outcomes include change of subjective sleep characteristics, life quality, fatigue, daytime sleepiness, comorbid affective disorders, dysfunctional beliefs about sleep, sleep hygiene, healthcare consumption, productivity losses, and longer term outcomes at 3 months follow-up. Predictor analysis will include baseline scores of the aforementioned outcomes along with treatment expectancies and personality traits.DiscussionThe proposed study is one of the first studies evaluating whether iCBT-I also works in routine care. We expect that recruitment of the participants let us determine the target group more precisely and exclude health problems interfering with treatment. Using CAU as control condition may result in a loss of power to detect a meaningful difference. Nevertheless, this approach is reasonable since it reconstructs the clinical situation faced by practicing doctors.
Highlights
Chronic insomnia (CI) is characterized by subjective perception of sleep complaints including difficulties initiating sleep, nighttime awakening, and disability to get back to sleep at night, non-restorative sleep, waking up too early in the morning, associated with any daytime repercussions, occurring 3 nights per week or more and persisting not less than 3 months [1]
The proposed study is one of the first studies evaluating whether Internet-delivered cognitive-behavior treatment for insomnia (iCBT-I) works in routine care
We expect that recruitment of the participants let us determine the target group more precisely and exclude health problems interfering with treatment
Summary
Chronic insomnia (CI) is characterized by subjective perception of sleep complaints including difficulties initiating sleep, nighttime awakening, and disability to get back to sleep at night, non-restorative sleep, waking up too early in the morning, associated with any daytime repercussions, occurring 3 nights per week or more and persisting not less than 3 months [1]. Apart from deterioration in the quality of life of each patient, their families, and friends, CI imposes a non-negligible social and economic burden. This burden includes resources used for treatment, as well as reduced or lost work productivity. The effectiveness of this method was proven in multiple studies conducted in research settings, its feasibility in routine care is still unclear. Predictors, mediators, and moderators of treatment effect remain uncertain since previous studies often give contradictory results. The present study aims to investigate clinical effectiveness and cost-effectiveness of an internet-based CBT-I program Sleepsy in comparison with care as usual (CAU) among patients with CI recruited from clinical settings. Baseline data will be further analyzed to find predictors of treatment outcome
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