Randomized Study on the Efficacy of Cognitive-Behavioral Therapy for Insomnia Secondary to Breast Cancer, Part I: Sleep and Psychological Effects
Bibliographic record
Abstract
PURPOSE: Chronic insomnia is highly prevalent in cancer patients. Cognitive-behavioral therapy (CBT) is considered the treatment of choice for chronic primary insomnia. However, no randomized controlled study has been conducted on its efficacy for insomnia secondary to cancer. Using a randomized controlled design, this study conducted among breast cancer survivors evaluated the effect of CBT on sleep, assessed both subjectively and objectively, and on hypnotic medication use, psychological distress, and quality of life. PATIENTS AND METHODS: Fifty-seven women with insomnia caused or aggravated by breast cancer were randomly assigned to CBT (n = 27) or a waiting-list control condition (n = 30). The treatment consisted of eight weekly sessions administered in a group and combined the use of stimulus control, sleep restriction, cognitive therapy, sleep hygiene, and fatigue management. Follow-up evaluations were carried out 3, 6, and 12 months after the treatment. RESULTS: Participants who received the insomnia treatment had significantly better subjective sleep indices (daily sleep diary, Insomnia Severity Index), a lower frequency of medicated nights, lower levels of depression and anxiety, and greater global quality of life at post-treatment compared with participants of the control group after their waiting period. Results were more equivocal on polysomnographic indices. Therapeutic effects were well maintained up to 12 months after the intervention and generally were clinically significant. CONCLUSION: This study supports the efficacy of CBT for insomnia secondary to breast cancer.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".