An Assessment of the Effects of Iyengar Yoga Practice on the Health‐Related Quality of Life of Patients with Chronic Respiratory Diseases: A Pilot Study
Bibliographic record
Abstract
OBJECTIVE: To assess the effects of an Iyengar yoga program (IYP) on patients with chronic respiratory diseases. METHODS: Patients attending lung transplant clinics in a tertiary institution were invited to participate in a two-phase, 12-week IYP that included 2 h biweekly classes. Doctors completed a formal physical and clinical assessment on candidates before enrollment. Patients with New York Association Class III or IV, or dyspnea grade IV were excluded. At baseline and at the end of 12-weeks, patients completed the Hospital Anxiety and Depression Scale (HADS), Chronic Respiratory Questionnaire (CRQ) and Health Utilities Index (HUI). Medication(s), 6 min walk test results and other clinical parameters were also recorded. Patients recorded the effects of the IYP on their daily living in journals. Nonparametric and qualitative methods were used to analyze the data. RESULTS: Twenty-five patients diagnosed with pulmonary arterial hypertension and chronic obstructive pulmonary disease (mean age 60 years) were invited to participate. At the end of the 12-week period, changes in HADS anxiety and CRQ fatigue scores were statistically significant (P<0.05) and changes in HUI ambulation, pain, emotion and overall score were clinically important. The content of the journals revealed patients' improvement in breathing capacity, mobility, energy, sleep and included positive feedback such as: "increased tidal volume with slowing expiration", "I have an overall feeling of wellbeing" and "excellent amount of energy". CONCLUSIONS: The findings suggest that yoga has significant potential to produce benefits. Potential benefits will be further explored in a national multisite study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".