Do Brief Mindfulness Interventions (BMI) and Health Enhancement Programs (HEP) Improve Sleep in Patients in Hemodialysis with Depression and Anxiety?
Bibliographic record
Abstract
(1) Objective: to determine if a brief mindfulness intervention (BMI) and a health education program (HEP) could improve measures of insomnia in patients undergoing hemodialysis. (2) Methods: this was a planned secondary analysis of a randomized controlled trial of BMI vs. HEP for hemodialysis patients with depression and/or anxiety symptoms. The primary outcome for the analysis was the Athens Insomnia Scale (AIS). The secondary outcome was consolidation of daily inactivity (ConDI), an actigraphy measure that describes sleep continuity and is based on a sleep detection algorithm validated by polysomnography. We also explored whether changes in AIS and ConDI were associated with changes in depression, anxiety, and quality of life scores over 8-week follow-up. (3) Results: BMI and HEP groups did not differ significantly from one another. Exposure to BMI or HEP improved sleep quality (baseline AIS 9.9 (±5.0) vs. 8-week follow-up 6.4 (±3.9), (V = 155.5, p = 0.015)), but not ConDI. Improvements in AIS were associated with lower depression scores (Rho = 0.57, p = 0.01) and higher quality-of-life scores (Rho = 0.46, p = 0.04). (4) Conclusions: mindfulness and HEP may be helpful interventions to improve self-reported sleep quality in patients undergoing hemodialysis. Decreases in insomnia scores were associated with decreased depression symptoms and increased quality of life scores.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".