Effects of High-Resolution Natural Sound with Inaudible High-Frequency Components on Healing, Symptoms, and Sleep Satisfaction in Terminally Ill Cancer Patients
Bibliographic record
Abstract
Objectives: This study aimed to assess the effects of high-resolution natural sound with inaudible high-frequency components (HNIH) on healing, symptoms, sleep satisfaction, and autonomic nerve function among terminally ill cancer patients. Methods: We conducted a single-arm, open-label study of 4-hour HNIH for 20 terminally ill cancer patients. We evaluated the healing state, symptoms (Japanese version of the Edmonton Symptom Assessment System-Revised, ESAS-r-J), global impression, and heart rate variability at 30 minutes (T2) and 4 hours (T3) after starting HNIH and sleep satisfaction the next morning (T4). Results: A total of 18 participants were evaluated (mean age: 69.4 years; 33.3% female). Post-intervention, there was a nonsignificant increase in Healing Scale scores at T2 (mean difference: 5.3, 95% confidence interval [CI]: −1.2 to 11.8, p = 0.106), but a significant increase at T3 (mean difference: 6.6, 95% CI: 1.0 to 12.3, p = 0.024). Specific ESAS-r-J scores demonstrated significant improvements in anxiety (mean difference at T2: −1.2, 95% CI: −1.99 to −0.34, p = 0.008; T3: −1.2, 95% CI: −1.99 to −0.34, p = 0.008), tiredness (mean difference at T2: −0.6, 95% CI: −1.18 to −0.04, p = 0.037), and shortness of breath (mean difference at T2: −1.0, 95% CI: −1.72 to −0.28, p = 0.010). Moreover, 66.7% of participants reported improved general conditions at T2 and T3, whereas 50% reported enhanced sleep satisfaction at T4. Heart rate variability analysis revealed a decreased low-frequency/high-frequency ratio in 55.6% of participants at T2 and 44.4% at T3. Conclusions: The present single-arm study showed that HNIH potentially enhanced healing, alleviated symptoms such as anxiety, tiredness, and shortness of breath, and improved sleep satisfaction in terminally ill cancer patients.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".