Web-based cognitive-behavioral therapy for insomnia in cancer survivors: The OncoSleep randomized trial
Bibliographic record
Abstract
PURPOSE: Insomnia is highly prevalent among cancer survivors and can have serious implications if inadequately treated. Cognitive-behavioral therapy for Insomnia (CBT-I) is recommended as the first-line treatment for insomnia but is rarely available to cancer survivors. We tested the effectiveness of a web-based CBT-I program, OncoSleep, in cancer survivors. METHODS: Cancer survivors with insomnia (n = 154) were randomly assigned (1:1) to digital CBT-I (6 weekly self-guided modules plus online clinician support) or a waitlist control group. Patient-reported outcome measures of insomnia severity (primary outcome), daytime functioning, and sleep diaries were administered online at baseline and post-treatment (8 weeks). Intention-to-treat analyses were performed using mixed-effects models. Statistical tests were two-sided. RESULTS: The treatment group reported an average 11.0-point reduction in the Insomnia Severity index (ISI), compared to a 1.4-point reduction in the control group (p<.001). Statistically significant group-by-time interactions were observed: web-based CBT-I produced significant, large effects for improvements in insomnia severity (d = -2.56), cognitive functioning (d = 0.95), physical (d = 1.24) and psychological quality of life (d = 0.80), and fatigue (d = -1.35). Small-to-large effect sizes were found for reductions in anxiety (d = -0.77), depression (d = -0.71), and pain (d = -0.40). Change in insomnia severity mediated the effect of digital CBT-I on daytime outcomes. CONCLUSIONS: Web-based CBT-I with clinician support appears to be an effective treatment for insomnia in cancer survivors, offering meaningful benefits for comorbid symptoms and quality of life. Further studies with active comparisons and longer follow-up periods are needed to confirm these findings. Digital CBT-I could be integrated into cancer rehabilitation programs to reduce the burden of insomnia. [ClinicalTrials.gov: NCT04898855].
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".