The Effects of Hot Yoga on Kidney Function: An Observational Pilot and Feasibility Study
Bibliographic record
Abstract
Yoga has been shown to have health benefits, whereas exercising in a hot environment has deleterious effects on kidney function. There are no long-term studies on the physiological effects of hot yoga. The purpose of this study was to investigate changes in renal function acutely and over time between practitioners of hot and non-hot yoga. Urine and capillary samples were collected for urinalysis, albumin-creatinine ratio, and serum creatinine at yoga studios preand postexercise over 1 year. Thirty-two participants in non-hot yoga and 19 participants in hot yoga were recruited. Difference in blood capillary creatinine (post-yoga minus pre-yoga) showed a 7.52 μmol/L (SD 11.46) increase for practitioners of hot yoga and a 4.07 μmol/L (SD 9.94) increase for practitioners of non-hot yoga, with a between-group difference of 3.45 μmol/L (95% CI -0.42, 7.32; p = 0.08). Over 1 year, the mean difference in blood capillary creatinine for the hot group increased by 0.91 μmol/L (SD 11.00) and by 3.08 μmol/L (SD 9.96) for the non-hot group, with a between-group difference of -2.17 μmol/L (95% CI -10.20, 5.86; p = 0.58). Over 1 year, the mean difference in albumin-creatinine ratio for the hot group was -0.16 mg/mmol creatinine (SD = 0.74); for the non-hot group the difference was -0.20 mg/μmol (SD = 0.80). The difference in difference between the hot and non-hot groups was 0.04 mg/μmol (95% CI -0.60, 0.68; p = 0.90). Urine collected for urinalysis could not be analyzed due to too many 0 values. This pragmatic observational study did not find a statistically significant change in renal function between participants in non-hot and hot yoga either acutely or over 1 year. A larger and longer study focusing on blood creatinine over time would help to inform the long-term effects of hot yoga on the kidneys.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".