Influence of <i>Rhodiola rosea</i> on the heat acclimation process in young healthy men
Bibliographic record
Abstract
The adaptogen Rhodiola rosea (RR) may mitigate stress responses and have beneficial effects on endurance capacity (EC) and mental performance. Heat acclimation (HA) improves EC in the heat, but the potential impact of RR on the HA process is unknown. Therefore, our intent was to determine if RR has a positive impact on HA. Twenty male subjects (age, 22.5 ± 3.0 years) completed 2 EC tests involving walking (6 km·h−1) until volitional exhaustion in a climate chamber (air temperature, 42 °C; relative humidity, 18%) before (H1) and after (H2) an 8-day HA period. One group (SHR; n = 10) ingested standardised extract SHR-5 of RR (a single daily dose of 432 mg), while a second group (PLC; n = 10) administered a placebo prior to each HA session. Efficacy of HA was evaluated on the basis of changes that occurred from H1 to H2 in the time to exhaustion (TTE), exercise heart rate (HR), core and skin temperatures (Tc, Tsk), stress hormones, ratings of perceived exertion (RPE) and fatigue (RPF), and thermal sensation (TS). HA significantly increased TTE (133.1 ± 44.1 min in H1; 233.4 ± 59.8 min in H2; p < 0.0001) and decreased (p < 0.0001) HR, Tc, Tsk, stress hormones as well as RPE, RPF, and TS. However, the magnitude of all these changes was similar (p > 0.05) in the SHR and PLC groups. These results suggest that the use of RR during HA has no beneficial performance, physiological, or perceptual effects in young healthy males.
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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 | 0.000 |
| 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) | 0.001 | 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".