A Digital Therapeutic to Improve Insomnia in Multiple Sclerosis: A Randomized Controlled Trial.

Insomnia is prevalent among individuals with Multiple Sclerosis (MS). Improving sleep is an important therapeutic goal, but there is currently a lack of effective treatment options. Cognitive Behavioral Therapy for Insomnia (CBT-I) has been widely studied in other patient groups and is currently recommended as first- line treatment for chronic insomnia. Overall, the availability of CBT-I has been limited, as the number of patients in need of treatment far exceeds the number of available therapists. Therefore, fully automated digital adaptations of CBT-I (dCBT-I) have been developed that contain both screening and intervention. Whether this treatment is effective for a clinical sample of patients diagnosed with MS, or if improved sleep can lead to improved daytime functioning in MS, is however, currently unknown. This is a novel approach to a digital treatment of a common disorder in MS, and that may result in improved implementation of a low-threshold intervention. Update August 28th, 2024 We aim to increase the target sample size from 260 to 550 to increase the statistical power to detect differences between the intervention group and control group on the secondary outcomes, e.g., fatigue, cognitive functioning, mental health, and movement measures measured with actigraphy. Few treatment options have shown effects on these outcomes for people with MS but are a significant problem for this patient group. Small effects from this trial may have substantial scientific and clinical value and are important to test with adequate statistical power. Based on previous RCTs investigating the effectiveness of dCBT-I we aim to have a sample size large enough to detect small to moderate effects (Cohen's d = 0.3 til 0.5) on the secondary outcome measures fatigue, cognitive function, mental health and movement measures measured with actigraphy. As the planned RCT involves limited contact between researchers and participants, we have predicted that the study dropout rate will likely reach about 50%. Therefore, we aim to recruit 550 participants, to enable us to retain 275 participants (137 in each treatment arm) at the end of the RCT. For a two-sample t-test with alpha=0.05, this sample size gives a power of 90% of detecting a difference of Cohen's d = 0.40.