Improvement of pain experience and changes in heart rate variability through music-imaginative pain treatment
Bibliographic record
Abstract
Music-imaginative Pain Treatment (MIPT) is a form of music therapy addressing pain experience and affective attitudes toward pain. It includes two self-composed music pieces: one dedicated to the pain experience (pain music, PM) and the other to healing imagination (healing music, HM). Our non-experimental study addresses patients with chronic somatoform pain disorders participating in MIPT. The goal is to gain insight into the direct effect mechanisms of MIPT by combining outcome measures on both the objective physiological and subjective perception levels. The research questions are directed toward changes in pain experience and heart rate variability and their correlations. Thirty-seven hospitalized patients with chronic or somatoform pain disorders receiving MIPT participated in this study. Demographic data and psychometric measures (Symptom Check List SCL90, Childhood Trauma Questionnaire CTQ) were collected to characterize the sample. Subjective pain experience was measured by McGill Pain Questionnaire (SF-MPQ), and Heart Rate Variability by 24 h-ECG. Data analysis shows a reduction of reported pain from MT1 = 19.1 (SD = 7.3) to MT2 = 10.6 (SD = 8.0) in all dimensions of the SF-MPQ. HRV analyses shows a reduced absolute power during PM and HM, while a relative shift in the autonomic system toward higher vagal activity appears during HM. Significant correlations between HRV and MPQ could not be calculated. Findings are interpreted as a physiological correlate to the psychological processes of the patients. Future studies with more participants, a control-group design, and the integration of medium- and long-term effects are recommended.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".