Immersible ergocycle prescription as a function of relative exercise intensity
Bibliographic record
Abstract
The purpose of this study was to establish the relationship between various expressions of relative exercise intensity percentage of maximal oxygen uptake (%VO2max), percentage of maximal heart rate (%HRmax), %VO2 reserve (%VO2R), and %HR reserve (%HRR)) in order to obtain the more appropriate method for exercise intensity prescription when using an immersible ergocycle (IE) and to propose a prediction equation to estimate oxygen consumption (VO2) based on IE pedaling rate (rpm) for an individualized exercise training prescription. Thirty-three healthy participants performed incremental exercise tests on IE and dryland ergocycle (DE) at equal external power output (Pext). Exercise on IE began at 40 rpm and was increased by 10 rpm until exhaustion. Exercise on DE began with an initial load of 25 W and increased by 25 W/min until exhaustion. VO2 was measured with a portable gas analyzer (COSMED K4b2) during both incremental tests. On IE and DE, %VO2R, %HRmax, and %HRR at equal Pext did not differ (p > 0.05). The %HRR vs. %VO2R regression for both IE and DE did not differ from the identity line %VO2R IE = 0.99 × HRR IE (%) + 0.01 (r2 = 0.91, SEE = 11%); %VO2R DE = 0.94 × HRR DE (%) + 0.01 (r2 = 0.94, SEE = 8%). Similar mean values for %HRmax, %VO2R, and %HRR at equal Pext were observed on IE and DE. Predicted VO2 obtained according to rpm on IE is represented by: VO2 (L/min) = 0.000542 × rpm2 − 0.026 × rpm + 0.739 (r = 0.91, SEE = 0.319 L/min). The %HRR–%VO2R relationship appears to be the most accurate for exercise training prescription on IE. This study offers new tools to better prescribe, control, and individualize exercise intensity on IE.
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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 | 0.002 |
| 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".