Personalized music and hypnosis interventions in palliative and end-of-life care at home: A randomized controlled pilot study
Bibliographic record
Abstract
To evaluate the preliminary effectiveness of a personalized intervention integrating pre-recorded hypnosis and/or music in palliative and end-of-life care, and explore patient preferences and experiences. Forty patients receiving palliative and end-of-life care at home were recruited and randomly assigned to the experimental group or the control/delayed intervention group. Two intervention sessions were conducted within one week, featuring the following modalities tailored to patient preferences: pre-recorded music, hypnosis, or a combination of music and hypnosis. Participants provided self-ratings of their symptoms and distress at predetermined time points using the Edmonton Symptom Assessment Scale and the Distress Thermometer. We used a mixed-effects model to address the quantitative objectives, and we conducted a content analysis to meet the qualitative objectives. The intervention program significantly reduced participants’ distress, showing a medium effect size when comparing the intervention sessions to the control sessions. We also found a medium effect size for improved well-being between groups. Intervention modality did not appear to affect the responses. Participants reported calmness and well-being. The voluntary use of these interventions post-experiment emphasizes their relevance for palliative and end-of-life care. The qualitative findings were consistent with the quantitative results, and revealed additional potential uses and ways to improve the intervention. The personalized pre-recorded music and hypnosis interventions appear to be effective in reducing distress and show great potential for enhancing the overall well-being of individuals in palliative and end-of-life care. Further studies are needed to determine how these findings can be applied to a broader population. • MuzHyp program decreased distress and enhanced participants’ overall well-being. • Caregiver presence contributed to symptom relief, highlighting the human role. • Most participants chose an intervention with music, likely due to its familiarity. • Some continued MuzHyp post-study, reporting better sleep, comfort, and pain control. • The program is cost-effective, easy to use, and adaptable for independent applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 teacher head, 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".