Hot water immersion; potential to improve intermittent running performance and perception of in-game running ability in semi-professional Australian Rules Footballers?
Bibliographic record
Abstract
This study investigated whether hot water immersion (HWI) could heat acclimate athletes and improve intermittent running performance and perception of in-game running ability, during a competitive Australian Rules Football (ARF) season. Fifteen male semi-professional ARF athletes (Mean (SD); age: 22 (3) years, height: 182.3 (6.5) cm, mass: 80.5 (5.1) kg) completed either HWI (HEAT, N = 8, 13 (2) sessions, 322 (69) min exposure, 39.5 (0.3) °C) or acted as a control (CON, N = 7, no water immersion) over 6-weeks. Athletes completed a 30-15 Intermittent Fitness Test pre and post-intervention to assess intermittent running performance (VIFT), with perception of in-game running ability measured. Heat acclimation was determined via change in resting plasma volume, as well as physiological and perceptual responses during HWI. HEAT elicited large PV expansion (mean ± 90% CI: d = 1.03 ± 0.73), large decreases in heart rate (d = -0.89 ± 0.70), thermal sensation (d = -2.30 ± 1.15) and tympanic temperature (d = -1.18 ± 0.77). Large improvements in VIFT were seen in HEAT (d = 1.67 ± 0.93), with HEAT showing a greater improvement in VIFT when compared to CON (d = 0.81 ± 0.88). HEAT also showed greater belief that in-game running ability improved post-intervention (d = 2.15 ± 1.09) compared to CON. A 6-week HWI intervention can elicit heat acclimation, improve perception of in-game running ability, and potentially improve VIFT in semi-professional ARF athletes.
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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".