Control of cutaneous vascular conductance and sweating during recovery from dynamic exercise in humans
Bibliographic record
Abstract
The purpose of the study was to examine the effect of 1) passive (assisted pedaling), 2) active (loadless pedaling), and 3) inactive (motionless) recovery modes on mean arterial pressure (MAP), skin blood flow (SkBF), and sweating during recovery after 15 min of dynamic exercise. It was hypothesized that an active recovery mode would be most effective in attenuating the fall in MAP, SkBF, and sweating during exercise recovery. Six male subjects performed 15 min of cycle ergometer exercise at 70% of their predetermined peak oxygen consumption followed by 15 min of 1) active, 2) passive, or 3) inactive recovery. Mean skin temperature (T(sk)), esophageal temperature (T(es)), SkBF, sweating, cardiac output (CO), stroke volume (SV), heart rate (HR), total peripheral resistance (TPR), and MAP were recorded at baseline, end exercise, and 2, 5, 8, 12, and 15 min postexercise. Cutaneous vascular conductance (CVC) was calculated as the ratio of laser-Doppler blood flow to MAP. In the active and passive recovery modes, CVC, sweat rate, MAP, CO, and SV remained elevated over inactive values (P < 0.05). The passive mode was equally as effective as the active mode in maintaining CO, SV, MAP, CVC, and sweat rate above inactive recovery. Sweat rate was different among all modes after 8 min of recovery (P < 0.05). TPR during active recovery remained significantly lower than during recovery in the passive and inactive modes (P < 0.05). No differences in either T(es) or T(sk) were observed among conditions. Given that MAP was higher during passive and active recovery modes than during inactive recovery suggests differences in CVC may be due to differences in baroreceptor unloading and not factors attributed to central command. However, differences in sweat rate may be influenced by factors such as central command and mechanoreceptor stimulation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".