Parasympathetic Reactivation Is Improved After Maximal Cycling Exercise In Immersion As Compared To Dryland Condition
Bibliographic record
Abstract
PURPOSE: This aim of this study was to compare post-exercise parasympathetic reactivation after maximal incremental exercise performed at the same external power output (Pext) on dryland ergocycle (DE) vs. immersible ergocycle (IE). METHODS: Fifteen young healthy participants (30±7 years, 13 males and 2 females) performed in a random order an incremental maximal exercise tests on DE and another one on IE. On DE, the initial external power was 25 W and was increased by 25 W/min. On IE, initial external power was 40 rpm and 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 5-min recovery period. Parasympathetic reactivation parameters (ie: T30, τ, ΔHR from 10 to 300 sec) were compared during the IE and DE recovery. RESULTS: During the IE recovery, parasympathetic reactivation in the short-term phase was more predominant (ie: T30, HRR at Δ10, Δ20, Δ30, Δ60 sec, P<0.05), but similar in the long-term phase (HRR at Δ120, Δ180, Δ240 and Δ300 sec, P>0.05) as compared to the DE condition. CONCLUSION: Our study showed that recovery in immersion to the chest level following maximal exercise can accelerate parasympathetic reactivation during the short-term phase, as compared with recovery after maximal exercise on 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".