Lower Dynamic Cerebral Autoregulation following Acute Bout of Low-Volume High-Intensity Interval Exercise in Chronic Stroke Compared to Healthy Adults
Bibliographic record
Abstract
Abstract Fluctuating blood pressure during high-intensity interval exercise (HIIT) may challenge dynamic cerebral autoregulation (dCA), specifically post-stroke after an injury to the cerebrovasculature. We hypothesized dCA would be attenuated at rest and during a sit-to-stand, immediately following and 30 min after HIIT in individuals post-stroke compared to age- and sex-matched controls (CON). HIIT switched every minute between 70% and 10% estimated maximal watts for 10 min. Mean arterial pressure (MAP) and middle cerebral artery blood velocity (MCAv) were recorded. Resting dCA measured spontaneous fluctuations in MAP and MCAv via transfer function analysis. For sit-to-stand, time delay before an increase in cerebrovascular conductance index (CVCi = MCAv/MAP), rate of regulation, and %change in MCAv and MAP were measured. Twenty-two individuals post-stroke (age 60±12 yrs, 31±16 months) and twenty-four CON (age 60±13 yrs) completed the study. VLF gain (p=0.02, η 2 =0.18) and normalized gain (p=0.01, η 2 =0.43) had a group-by-time interaction, with CON improving immediately and 30 min after HIIT. Individuals post-stroke had impaired VLF phase (p=0.03, η 2 =0.22) immediately following HIIT compared to CON. We found no differences in the sit-to-stand. Our study showed lower resting dCA up to 30 min after HIIT in individuals post-stroke compared to CON while the sit-to-stand response was maintained.
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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 | 0.000 | 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".