Digital cognitive behavioural therapy for insomnia reduces insomnia in nurses suffering from shift work disorder: A randomised‐controlled pilot trial
Bibliographic record
Abstract
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 (β = -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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".