Effects of Bikram Yoga on Body Composition, Blood Pressure, and Sleep Patterns in Adult Practitioners
Bibliographic record
Abstract
Studies have demonstrated positive results for people who practice traditional yoga, specifically in body mass index, depression, cancer, sleeping patterns, and diabetes. Most forms of traditional yoga are held in a temperate climate between 293 Kelvin (K) to 295 K; a temperature that is calming and places little stress on the body. Bikram yoga, however, is a more intense form of yoga performed in a hotter climate, typically at 314 K with 40% humidity. The purpose of this study was to determine how Bikram yoga affected blood pressure, body composition, and sleep patterns in beginner and intermediate/experienced practitioners. Participants (N=16) completed 8 weeks of sessions, ranging between 2 to 7 classes per week in a local Bikram yoga program conducted by certified instructors. All participants were assessed prior to the study and again at the end of the eight weeks. A BOD POD (an air displacement plethysmography) was used for body composition assessments, while a digital blood pressure cuff was used to assess blood pressure. In addition, participants were also surveyed on sleep parameters pre and post yoga participation. In combining all subjects, results showed there was a significant improvement (p=0.054) in faster time to fall asleep (27.66 min pre and 23.967 min post), and a trend (p=0.057) towards improved mean arterial pressure (92.20 mmHg pre and 88.33 mm Hg post). There were no differences in weight loss or percent body fat in subjects. This study indicates that there is a trend towards improved blood pressure and significant improvement in sleep parameters after 8 weeks of Bikram yoga for both intermediate/experienced and beginners, but no differences in body composition.
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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 | 0.000 | 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) | 0.002 | 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".