Passive heat therapy for cardiovascular disease: current evidence and future directions
Bibliographic record
Abstract
Passive heat therapy is gaining popularity as an intervention to promote cardiovascular, physiological, and, to a lesser degree, thermoregulatory adaptations in patients with cardiovascular disease. Despite this, the efficacy of heat therapy to elicit these adaptations remains unknown. We searched five databases for original research, screening 2913 studies and identifying 18 eligible studies. Heat therapies included Waon therapy, balneotherapy, water perfused trousers, Finnish sauna, and foot immersion. Interventions were administered across various time frames (20-90 min) and performed 3-7 times per week, for durations of 2-8 weeks. The studies collectively involved a diverse population (mean age: 67 (10) years) with cardiovascular diseases. Heat therapy was consistently shown to improve ejection fraction, flow-mediated dilation, brain natriuretic peptide levels, New York Heart Association classification, and 6 min walk distance. However, positive effects on resting heart rate and blood pressure were infrequently observed, and thermoregulatory responses scarcely reported. Heat therapy may increase sweat rate during heat exposure and reduce resting core temperature, but adaptive skin blood flow responses were not observed. Passive heat therapy shows promising utility in patients with cardiovascular disease, while secondary benefits such as markers of thermoregulatory adaptation may also be observed, these require further investigation.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".