High Intensity Interval Training Leads to Similar Inflammatory Activation as Seen With Traditional Training in Chronic Heart Failure
Bibliographic record
Abstract
BACKGROUND: Inflammatory activation has been associated with the severity and progression of chronic heart failure (CHF). Although cardiac rehabilitation is an important therapy, acute bouts of exercise may lead to increases in pro-inflammatory cytokines with exercise intensity mediating these changes. OBJECTIVE: To evaluate the acute inflammatory response in patients living with CHF during a randomized trial following Steady State (SS) or High Intensity Interval (HIIT) training. METHODS: = 14) were stratified (for body mass and aerobic power) and randomized into SS and HIIT cycle exercise. The HIIT exercise training involved 2 min work:recovery phases at 90:40% heart rate reserve. The SS exercise training involved continuous exercise at 65% of heart rate reserve (matched total work). Acute inflammatory markers were evaluated (via ELISA) at baseline, immediately following the bout, and at 6, 24, and 48 h post-exercise. RESULTS: < 0.05) change in Interleukin-6 immediately post-exercise. CONCLUSIONS: A single bout of HIIT or SS does not result in excessive inflammatory activation in CHF patients. Acute HIIT and SS result in similar changes in inflammatory markers. These findings have important implications for exercise training and rehabilitation programs in persons living with CHF.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".