Randomized Controlled Trial of Virtually Delivered Cognitive Behavioral Therapy for Insomnia to Address Perceived Cancer-Related Cognitive Impairment in Cancer Survivors
Bibliographic record
Abstract
PURPOSE Comorbid insomnia and cancer-related cognitive impairment (CRCI) are experienced by up to 26% of individuals diagnosed with cancer. This study examined the efficacy and durability of cognitive behavioral therapy for insomnia (CBT-I) on perceived CRCI in cancer survivors. METHODS Atlantic Canadian cancer survivors with insomnia and CRCI were randomly assigned to receive seven weekly virtual CBT-I sessions (n = 63) or placed in a waitlist control group (n = 69) to receive treatment after the waiting period. Participants completed assessments at baseline, 1 month (mid-treatment), and 2 months (post-treatment). Age- and education-adjusted mixed-effects models using intention-to-treat principles assessed change at post-treatment. Data from both groups were then pooled to assess the durability of effects at 3 and 6 months. A mediation analysis examined whether change in insomnia symptoms mediated the effect of CBT-I on cognitive outcomes. RESULTS The mean age of the sample was 60 years, 77% were women, and breast cancer was the most common diagnosis (41%). The treatment group reported an 11.35-point reduction in insomnia severity, compared with a 2.67-point reduction in the waitlist control group ( P < .001). The treatment group had a greater overall improvement than the waitlist control on perceived cognitive impairment ( P < .001; d = 0.75), cognitive abilities ( P < .001; d = 0.92), and impact on quality of life ( P < .001; d = 1.01). These improvements were maintained at follow-up. Change in insomnia symptoms fully mediated the effect of CBT-I on subjective cognitive outcomes. CONCLUSION Treating insomnia with CBT-I produces clinically meaningful and durable improvements in CRCI. There is an urgent need increase access to evidence-based treatment for insomnia in cancer centers and the community.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".