Determination of Optimal Sleep Treatment Elements (The DOSE Project) - DISA

Overview

Insomnia is a widespread public health challenge considering its impact on daily life, comorbidity with other disorders, and socio-economic costs. Previous research has shown the efficacy of cognitive behavioral therapy for insomnia (CBTI), and recent research indicates that digitally delivered CBTI (eCBTI) is highly efficacious, and statistically significantly equivalent to in-person delivered CBTI (ipCBTI) for treating insomnia. However, research is limited as to how eCBTI can be integrated into general practice as a non-pharmacological alternative to hypnotics. This study aims to evaluate the feasibility, acceptability, and effectiveness of a fully automated mobile application for treating insomnia in general practice. The secondary aims are to examine effects on psychological and physical comorbidities, possible moderators and mediators of the effect of eCBTI, and cost-effectiveness.

Insomnia is prevalent in the general population (10%) and particularly so among patients in general practice (30-50%), with considerable costs to the individual and society. Hypnotics, which remain the most common treatment option in general practice, are usually not curative and are associated with risks of side effects, dependence, tolerance, and increased mortality. In contrast, cognitive behavioral therapy for insomnia (CBTI) has been shown to be highly efficacious and is recommended as the first-line treatment for insomnia by organizations such as the American Academy of Sleep Medicine, the American College of Physicians, and the European Sleep Research Society. However, the challenge remains to make CBTI available to meet population needs due to several barriers, including a limited number of trained therapists, the costs of delivering CBTI face-to-face, and physical and geographical constraints. Digitally delivered CBTI (eCBTI) has been shown to be a possible approach to overcoming these challenges, but research on the effectiveness of eCBTI in a general practice setting remains limited. Given the current lack of non-pharmacological treatment options for insomnia in general practice and the considerable potential of eCBTI to treat insomnia, the primary aim of the proposed study is to evaluate the feasibility, acceptability, and short- and longer-term efficacy of eCBTI for the treatment of insomnia in general practice. Our secondary aims are: a) to evaluate the possible benefits of treating insomnia on psychological and physical symptoms and comorbidities, b) to explore for whom the intervention works by examining the possible moderating effects of information technology proficiency and socio-demographic, clinical, and work-related factors, c) to investigate the possible working mechanisms, including changes in sleep-related cognitions and behaviors, and d) to assess the cost-effectiveness of the intervention. The study is designed as a cluster-randomized controlled trial, randomizing general practitioners (GPs) from three Danish regions to screen patients for insomnia and offer either hvil®, a mobile-based program for delivering CBTI (eCBTI), or care as usual to those with moderate to-severe insomnia (ISI ≥ 10). A total of 2 X 50 GPs are expected to recruit a minimum of 2 X 250 patients who will complete the intervention. The intervention lasts 10 weeks, including an initial one-week assessment period. The primary outcome is insomnia severity, assessed with the Insomnia Severity Index (ISI). Secondary sleep diary-based outcomes include sleep onset latency (SOL), wake after sleep onset (WASO), total sleep time (TST), time in bed (TiB), and sleep efficiency (SE). Secondary non-sleep outcomes include quality-of-life (QoL) and psychological and physical symptoms such as anxiety, depression, fatigue, and pain. Cost-effectiveness will be assessed using data on healthcare utilization, social benefits, and employment from Danish national registries. Outcomes will be assessed at baseline (week 0) (T1), halfway through the intervention (week 5), post-intervention (week 11) (T3), and follow-up (6 months) (T4). Baseline group differences (concerning socio-demographic, disease-related, and psychosocial data) will be explored to test the success of the randomization. If differences are found, sensitivity analyses will be made to evaluate their possible influence on the results. Main effects will be analysed using Mixed Linear Models (MLMs) based on the intent-to-treat sample. MLMs account for the hierarchical, non-independent nature of the data (i.e., repeated measures nested within patients and treatment conditions), testing the time\*group interaction effect, reflecting the effect of treatment. Moderation analyses will evaluate whether individual differences in various baseline variables (e.g., physical function, expectations, computer proficiency, chronotype, etc.) or treatment adherence influence intervention effects.