The Effects of Mind-Body Interventions on Sleep in Cancer Patients
Bibliographic record
Abstract
OBJECTIVE: To examine the effect of mind-body interventions (MBIs) on sleep quality among cancer patients, the moderating effects of the intervention components, subject characteristics, and methodological features of the relationship between MBIs and sleep. DATA SOURCES: Electronic databases, including PubMed, Cochrane Library, PsycINFO, and CINAHL, containing data with English-language restriction recorded up to September 15, 2013 were searched thoroughly using keywords related to various types of MBI and sleep. STUDY SELECTION: Of the 114 identified citations, 99 were ineligible. Fifteen studies that followed 1,405 patients with cancer met the inclusion criteria and were analyzed. DATA EXTRACTION: The primary outcome was change in the sleep parameter. Other variables related to components of MBIs, subject characteristics, and methodological features of the studies were also extracted. DATA SYNTHESIS: The weighted mean effect size (ES) was -0.43 (95% confidence interval [CI], -0.24 to -0.62) and the long-term effect size (up to 3 months) was -0.29 (95% CI, -0.52 to -0.06). The sensitivity analysis revealed that MBIs had a significant effect on sleep (g = -0.33, P < .001). The moderating effects of components of the intervention, methodological features, subject characteristics, and quality of the studies on the relationship between MBIs and sleep were not found (all P values > .05). CONCLUSIONS: This meta-analysis confirms that the MBIs yielded a medium effect size on sleep quality and the effect was maintained for up to 3 months. The findings support the implementation of MBIs into the multimodal approach to managing sleep quality in patients with 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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".