Thermoneutral immersion exercise accelerates heart rate recovery: A potential novel training modality
Bibliographic record
Abstract
Abstract This study compared heart rate recovery (HRR) after incremental maximal exercise performed at the same external power output (Pext) on dry land ergocycle (DE) vs. immersible ergocycle (IE). Fifteen young healthy participants (30 ± 7 years, 13 men and 2 women) performed incremental maximal exercise tests on DE and on IE. The initial Pext on DE was 25 W and was increased by 25 W/min at a pedalling cadence between 60 and 80 rpm, while during IE immersion at chest level in thermoneutral water (30°C), the initial Pext deployment was at a cadence of 40 rpm which was increased by 10 rpm until 70 rpm and thereafter by 5 rpm until exhaustion. Gas exchange and heart rate (HR) were measured continuously during exercise and recovery for 5 min. Maximal HR (DE: 176 ± 15 vs. IE 169 ± 12 bpm) reached by the subjects in the two conditions did not differ (P > .05). Parasympathetic reactivation parameters (ΔHR from 10 to 300 s) were compared during the DE and IE HR recovery recordings. During the IE recovery, parasympathetic reactivation in the early phase was more predominant (HRR at Δ10–Δ60 s, P < .05), but similar in the late phase (HRR at Δ120–Δ300 s, P > .05) when compared to the DE condition. In conclusion, incremental maximal IE exercise at chest level immersion in thermoneutral water accelerates the early phase parasympathetic reactivation compared to DE in healthy young participants.
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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".