The Effects of a Novel Deep Breathing Program for Weight Reduction and Cholesterol Metabolism
Bibliographic record
Abstract
Background: Previous studies have demonstrated the positive impact of yoga and deep breathing exercises on overall physical wellness. The effects of deep breathing alone on metabolic rate, weight, and cholesterol management have not been studied well. Objective: This study assessed changes in weight and metabolic markers following a 30-day intervention of a 12-minute deep breathing program. Methods: Sixty-six participants with a BMI >27 kg/m2 and 18-70 years were enrolled in this study. Participants were assigned to either the control or intervention group in a single-blinded manner. The intervention group followed the novel deep breathing program, while the control group did not modify their lifestyle or exercise. Anthropometric measurements and metabolic markers were evaluated and compared between the two groups after 30 days. Results: After 30 days, the control group exhibited no significant change in mean weight. The intervention group showed a significant mean weight reduction of 1.3 kg. The intervention group experienced a remarkable average decrease of 22.53 mg/dL in cholesterol levels, while the control group had an increase of 7.2 mg/dL. The 12-minute deep breathing sessions were associated with a significant increase in growth hormone and lung function. It was also associated with decreased glycosylated haemoglobin, creatine, inflammatory markers, and cholesterol levels. Conclusion: A guided deep breathing program can lead to reductions in markers of obesity among clinically overweight patients, even without additional lifestyle modifications. Further investigations are warranted to explore the effects of novel deep breathing programs on metabolic markers and elucidate the underlying mechanisms of weight loss.
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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 | 0.002 | 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) | 0.000 | 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".