The effects of high‐intensity exercise training and detraining with and without active recovery on postexercise hypotension in young men
Bibliographic record
Abstract
Whether high-intensity exercise training and detraining combined with skeletal muscle pump (MP) could alter the magnitude of postexercise hypotension has not been investigated. We therefore sought to determine whether the combination of MP (unloaded back-pedaling) with 4 weeks of high-intensity exercise training and detraining could alter the magnitude of postexercise hypotension. Fourteen healthy men underwent 4 weeks of high-intensity exercise training (5 consecutive days per week for 15 min per session at 40% of the difference between the gas exchange threshold and maximal oxygen uptake [i.e., Δ40%]) followed by detraining for 4 weeks. Assessments were conducted at Pre-training (Pre), Post-training (Post) and after Detraining with (MP) and without MP (Con). The exercise test in the Pre, Post and the Detraining consisted of 15 min exercise at Δ40% followed by 1 h of recovery. At all time-points, the postexercise reduction in mean arterial pressure (MAP) was reduced in MP compared to Con (all p < 0.01). Four weeks of high-intensity exercise training resulted in a reduction in the magnitude of postexercise hypotension (i.e., the change in MAP from baseline was mitigated) across both trials (All p < 0.01) when compared to Pre and Detraining. Following Detraining, the reduction of MAP from baseline was reduced compared to Pre, but was not different from Post. We conclude that high-intensity exercise training combined with skeletal MP reduces the magnitude of postexercise hypotension, and this effect is partially retained for 4 weeks following the complete cessation of high-intensity exercise training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".