Habitual Hot-Tub Bathing and Cardiovascular Risk Factors in Patients With Type 2 Diabetes Mellitus: A Cross-Sectional Study
Bibliographic record
Abstract
Background: Several studies suggested that heat therapy, including sauna or hot-tub bathing, was associated with improved glycemia and other risk factors for cardiovascular diseases. This study aimed to assess the influences of the habit of hot-tub bathing on cardiovascular risk factors in patients with type 2 diabetes in a real-world setting. Methods: In this cross-sectional study, we enrolled the patients with type 2 diabetes who regularly visited the outpatient clinic between October 2018 and March 2019. We obtained the information on the habit of hot-tub bathing by using a self-reported questionnaire. The results of anthropometric measurements, blood tests and medications were obtained from the medical charts. We divided the patients into three groups according to the frequency of hot-tub bathing as follows; group 1: ≥ 4 times a week, group 2: < 4 times a week, ≥ 1 time a week, group 3: < 1 time a week. The biomarkers were compared among the groups by one-way analysis of variance. Multiple linear regression analyses were performed to adjust for confounding variables. Results: We enrolled 1,297 patients. There were significant differences in body mass index (group1: 25.5 ± 5.0, group 2: 26.0 ± 5.4, group 3: 26.7 ± 6.0, P = 0.025), diastolic blood pressure (73 ± 12, 75 ± 12, 77 ± 13, P = 0.001) and hemoglobin A1c (7.10 ± 0.97, 7.20 ± 1.11, 7.36 ± 1.67, P = 0.012). Multiple regression analysis revealed that the frequency of hot-tub bathing was a significant determinant of hemoglobin A1c, body mass index and diastolic blood pressure. Conclusions: In this real-world study, habitual hot-tub bathing was associated with slight improvements in glycemia, obesity and diastolic blood pressure, and thus, can be a possible lifestyle intervention in patients with type 2 diabetes.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".