SingStrong—A singing and breathing retraining intervention for respiratory and other common symptoms of long COVID: A pilot study
Bibliographic record
Abstract
Background Management of Long COVID (LC) is hugely challenging for clinicians. This pilot study evaluated a breathing retraining and singing programme (SingStrong for LC) to address common LC symptoms. The study hypothesized that this intervention would improve symptoms impacting disordered breathing and participant wellbeing. Methods The 10-week, bi-weekly online programme was comprised of a 45-min class of mindfulness, breathing retraining, vocal exercises, and singing. Sessions were recorded for non-attenders and conducted by a trained vocal coach experienced in respiratory cohorts. Persons with a confirmed COVID-19 diagnosis and persisting symptoms were invited to participate. Demographic and COVID-19 data were collected, and the DePaul Symptom Questionnaire Short Form (DSQ- SF) and COVID-19 Yorkshire Rehab Screen questionnaires were administered. Post-intervention focus groups were also conducted. Results Of 27 (F = 23(85%)) participants recruited, data from 21 who completed at least 10 (50%) classes were analysed. Participants showed significant pre--post-intervention improvements in all breathlessness symptoms (at rest: P \< 0.001; dressing: P = 0.01; stairs: P \< 0.001), fatigue (P = 0.03), usual activities (P = 0.04), pain/disability (P = 0.03), voice quality (P = 0.01), and communication/cognition (P = 0.04). Pre--post number of instances meeting DSQ-SF criteria for myalgic encephalomyelitis (ME) and chronic fatigue syndrome (CFS) decreased by a net of nine cases (14.3%). No association between COVID-19 hospitalisation status and diagnosis of ME/CFS was identified. Qualitative feedback from eight participants was overwhelmingly positive with all reporting improvements in breathing and general well-being. Conclusion The SingStrong programme shows promise as a viable treatment option for LC sufferers. Future studies are required to further investigate the efficacy of this intervention.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".