Does Muscle Mechanical Stimulation After Moderate Exercise Modulate Sweatrate?
Bibliographic record
Abstract
2142 The effects of muscle mechanoreceptor stimulation on sweat rate (SR) during the recovery of exercise are unknown. PURPOSE: This study tested the hypothesis that mechanical stimulation of the previously exercising muscle is capable of modulating SR following exercise. METHODS: Eight healthy adults performed two 20-minute bouts of supine exercise (randomly assigned) on a tandem cycle ergometer (60 rpm at 65% of max heart rate; HR). Following one bout the subject stopped exercise (i.e. rest), while following the other bout the subject's legs passively cycled (at 60 rpm) via a second person cycling the tandem ergometer. This allows for mechanical stimulation of the muscle with minimal activation of central command. Data were obtained throughout exercise and during 10 min of recovery. RESULTS: Exercise bouts were similar in power output, heart rate, esophageal (Tes) and skin (Tsk) temperatures, and oxygen consumption. Tes decreased during recovery but this decrease was not different between recovery modes. Throughout recovery heart rate was higher during passive cycling relative to rest. Early in the recovery period chest and forearm SR were significantly higher during passive cycling relative to resting conditions, whereas later in the recovery period there were no differences between recovery modes. CONCLUSION: These results suggested that mechanical stimulation of the previously exercising muscle is capable of modulating SR following moderate exercise. Study funded in part by JSPS14704020, JSPS15650151, HL61388, HL67422
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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) | 0.002 | 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".