Continuous forearm cooling attenuates gastrointestinal temperature increase during cycling
Bibliographic record
Abstract
Hot environmental conditions can challenge thermoregulation resulting in exacerbated heat strain. This study evaluated the influence of continuous inner forearm cooling on gastrointestinal temperature (TGI) and physiological responses to exercise in hot (30°C) and humid (relative humidity: 70%) conditions. Eleven trained cyclists (seven male age: 37±12 years; four female age: 41±15 years; mean±standard deviation) performed two experimental trials, cycling at 66% of their self-reported functional threshold power (average work rate over an hour of maximum effort cycling; 175±34W) for 45 minutes in an environmental chamber. One trial employed continuous inner forearm cooling (COOL) with 5°C water passing through aluminum heat exchangers, while the other had no cooling (CONTROL). Heat was removed from the body at an average rate of 30.3±6.6W during the COOL trial resulting in an attenuation of TGI rise (CONTROL: 2.46±0.70, COOL: 2.03±0.63°C·h−1; p=0.002). The change in heart rate from the 10th minute to the end of exercise, as an indicator of cardiovascular drift, was reduced (CONTROL: 20±7, COOL: 17±6beats·min−1; p=0.050) and end-exercise thermal comfort was improved in the COOL trial with a trend for reduced rating of perceived exertion (p=0.055). Findings suggest that continuous cooling of the inner forearms can attenuate the rise of TGI and help mitigate the risk of heat injury during exercise in hot and humid conditions.
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 | 0.000 | 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".