Mindfulness-Based Stress Reduction Compared With Cognitive Behavioral Therapy for the Treatment of Insomnia Comorbid With Cancer: A Randomized, Partially Blinded, Noninferiority Trial
Bibliographic record
Abstract
PURPOSE: Our study examined whether mindfulness-based stress reduction (MBSR) is noninferior to cognitive behavioral therapy for insomnia (CBT-I) for the treatment of insomnia in patients with cancer. PATIENTS AND METHODS: This was a randomized, partially blinded, noninferiority trial involving patients with cancer with insomnia recruited from a tertiary cancer center in Calgary, Alberta, Canada, from September 2008 to March 2011. Assessments were conducted at baseline, after the program, and after 3 months of follow-up. The noninferiority margin was 4 points measured by the Insomnia Severity Index. Sleep diaries and actigraphy measured sleep onset latency (SOL), wake after sleep onset (WASO), total sleep time (TST), and sleep efficiency. Secondary outcomes included sleep quality, sleep beliefs, mood, and stress. RESULTS: Of 327 patients screened, 111 were randomly assigned (CBT-I, n = 47; MBSR, n = 64). MBSR was inferior to CBT-I for improving insomnia severity immediately after the program (P = .35), but MBSR demonstrated noninferiority at follow-up (P = .02). Sleep diary-measured SOL was reduced by 22 minutes in the CBT-I group and by 14 minutes in the MBSR group at follow-up. Similar reductions in WASO were observed for both groups. TST increased by 0.60 hours for CBT-I and 0.75 hours for MBSR. CBT-I improved sleep quality (P < .001) and dysfunctional sleep beliefs (P < .001), whereas both groups experienced reduced stress (P < .001) and mood disturbance (P < .001). CONCLUSION: Although MBSR produced a clinically significant change in sleep and psychological outcomes, CBT-I was associated with rapid and durable improvement and remains the best choice for the nonpharmacologic treatment of insomnia.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".