Vocal Chanting and Relaxation Provide Psychosocial Benefits for Individuals Living with Self-Reported Breathing Difficulties
Bibliographic record
Abstract
Individuals with breathing difficulties such as asthma, chronic obstructive pulmonary disease (COPD), and long COVID often experience mood disturbances and isolation. Previous research on musical care has considered the potential of music and singing to support people with respiratory symptoms, but the possible benefits of group chanting have not been explored. Group chanting is a global practice involving controlled breathing through rhythmic vocalizations, but unlike singing or music participation it requires no musical skill. This study examined if 10 minutes of vocal chanting improves psychological wellbeing, social wellbeing, and breathing function. Participants were 41 Australian adults with dysfunctional breathing and 25 with normal respiratory function, who were randomly allocated to group chanting ( n = 32) or group guided relaxation ( n = 34), with measures taken before and after. Qualitative interviews were undertaken to support the findings and understand the phenomenology of chanting. The results showed that chanting increased positive mood more than relaxation. Both chanting and relaxation improved flourishing and social connection, while decreasing negative mood, with greater and more consistent benefits observed for participants with dysfunctional breathing. Non-attachment, autonomy, and breathing function improved similarly for all participants. These findings suggest that chanting and relaxation may provide psychosocial and breathing-related benefits, particularly for those with dysfunctional breathing. Despite reduced statistical power to detect group differences, the current findings offer promising evidence that warrants replication in future studies.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".