Acute cardiorespiratory responses in participants with heart disease during cycling at different immersion levels
Bibliographic record
Abstract
Summary Physical activity is often avoided or practised at a low‐intensity level because of the limited ability of patients with heart disease (HD) to provide sustained effort. Immersible training has been suggested as a possible alternative as hydrostatic pressure can modify some hemodynamic parameters in healthy patients and potentially increase the exercise capacity in patients with HD. The purpose of this study was to examine the acute cardiorespiratory adaptations at different levels of immersion using an immersible ergometer (IE) in patients with HD. Twenty‐one patients and 13 healthy controls (HC) participated in this study. Several cardiorespiratory parameters were assessed at two levels of immersion (hips and xiphoid) for five different pedalling rates (40, 50, 60, 70 and peak rpm). At submaximal intensity, HD and HC participants did not differ significantly for most variables. However, for nearly and/or maximal workload, HD participants showed significantly lower values for VO2 and higher values for VE/VO2 and VE/VCO2 for both immersion levels. The increase in immersion level from hips to xiphoid resulted in a significant decrease in VO2 in both HC and HD groups at the same exercise intensity. In addition, the increase in the effects of size values based on the exercise workload indicates that group differences are accentuated with the highest pedalling rates. Our data suggest that participants with HD may benefit from the hydrostatic effect during IE cycling by allowing them to alleviate their submaximal efforts and increasing their maximal cardiorespiratory capacity during hip immersion.
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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 | 0.001 |
| 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 | 0.001 | 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".