Effectiveness of an online recovery training for employees exposed to blurred boundaries between work and non-work: Bayesian analysis of a randomised controlled trial.

BACKGROUND: Blurred work-non-work boundaries can have negative effects on mental health, including sleep. OBJECTIVES: In a randomised control trial, we aimed to assess the effectiveness of an online recovery training programme designed to improve symptoms of insomnia in a working population exposed to blurred boundaries. METHODS: 128 participants with severe insomnia symptoms (Insomnia Severity Index ≥15) and working under blurred work and non-work conditions (segmentation supplies <2.25) were randomly assigned to either the recovery intervention or a waitlist control group (WLC). The primary outcome was insomnia severity, assessed at baseline, after 2 months (T2) and 6 months (T3). FINDINGS: A greater reduction in insomnia was observed in the intervention compared with the WLC group at both T2 (d=1.51; 95% CI=1.12 o 1.91) and T3 (d=1.63; 95% CI=1.23 to 2.03]. This was shown by Bayesian analysis of covariance (ANCOVA), whereby the ANCOVA model yielded the highest Bayes factor (BF 10=3.23×e60] and a 99.99% probability. Likewise, frequentist analysis revealed significantly reduced insomnia at both T2 and T3. Beneficial effects were found for secondary outcomes including depression, work-related rumination, and mental detachment from work. Study attrition was 16% at T2 and 44% at T3. CONCLUSIONS: The recovery training was effective in reducing insomnia symptoms, work related and general indicators of mental health in employees exposed to blurred boundaries, both at T2 and T3. CLINICAL IMPLICATIONS: In addition to demonstrating the intervention's effectiveness, this study exemplifies the utilisation of the Bayesian approach in a clinical context and shows its potential to empower recipients of interventional research by offering insights into result probabilities, enabling them to draw informed conclusions. TRIAL REGISTRATION NUMBER: German Clinical Trial Registration (DRKS): DRKS00006223, https://drks.de/search/de/trial/DRKS00006223.

Digital cognitive behavioural therapy for insomnia reduces insomnia in nurses suffering from shift work disorder: A randomised-controlled pilot trial.

Insomnia is a primary symptom of shift work disorder, yet it remains undertreated. This randomised-controlled pilot trial examined the efficacy of a digital, guided cognitive behavioural therapy for insomnia adapted to shift work (SleepCare) in nurses with shift work disorder. The hypothesis was that SleepCare reduces insomnia severity compared with a waitlist control condition. A total of 46 unmedicated nurses suffering from shift work disorder with insomnia (age: 39.7 ± 12.1 years; 80.4% female) were randomised to the SleepCare group or the waitlist control group. The primary outcome measure was the Insomnia Severity Index. Other questionnaires on sleep, mental health and occupational functioning, sleep diary data and actigraphy data were analysed as secondary outcomes. Assessments were conducted before (T0), after the intervention/waitlist period (T1), and 6 months after treatment completion (T2). The SleepCare group showed a significant reduction in insomnia severity from T0 to T1 compared with the control condition (ß = -4.73, SE = 1.12, p < 0.001). Significant improvements were observed in sleepiness, dysfunctional beliefs about sleep, pre-sleep arousal, sleep effort, self-reported sleep efficiency and sleep onset latency. No significant effect was found in actigraphy data. Depressive and anxiety symptoms, cognitive irritation and work ability improved significantly. Overall, satisfaction and engagement with the intervention was high. SleepCare improved insomnia severity, sleep, mental health and occupational functioning. This is the first randomised-controlled trial investigating the efficacy of digital cognitive behavioural therapy for insomnia in a population suffering from shift work disorder with insomnia. Future research should further explore these effects with larger sample sizes and active control conditions.

Who benefits from indirect prevention and treatment of depression using an online intervention for insomnia? Results from an individual-participant data meta-analysis.

BACKGROUND: Major depressive disorder (MDD) is highly prevalent and burdensome for individuals and society. While there are psychological interventions able to prevent and treat MDD, uptake remains low. To overcome structural and attitudinal barriers, an indirect approach of using online insomnia interventions seems promising because insomnia is less stigmatized, predicts MDD onset, is often comorbid and can outlast MDD treatment. This individual-participant-data meta-analysis evaluated the potential of the online insomnia intervention GET.ON Recovery as an indirect treatment to reduce depressive symptom severity (DSS) and potential MDD onset across a range of participant characteristics. METHODS: Efficacy on depressive symptom outcomes was evaluated using multilevel regression models controlling for baseline severity. To identify potential effect moderators, clinical, sociodemographic, and work-related variables were investigated using univariable moderation and random-forest methodology before developing a multivariable decision tree. RESULTS: IPD were obtained from four of seven eligible studies (N = 561); concentrating on workers with high work-stress. DSS was significantly lower in the intervention group both at post-assessment (d = -0.71 [95% CI-0.92 to -0.51]) and at follow-up (d = -0.84 [95% CI -1.11 to -0.57]). In the subsample (n = 121) without potential MDD at baseline, there were no significant group differences in onset of potential MDD. Moderation analyses revealed that effects on DSS differed significantly across baseline severity groups with effect sizes between d = -0.48 and -0.87 (post) and d = - 0.66 to -0.99 (follow-up), while no other sociodemographic, clinical, or work-related characteristics were significant moderators. CONCLUSIONS: An online insomnia intervention is a promising approach to effectively reduce DSS in a preventive and treatment setting.