Intensity matters: protocol for a randomized controlled trial exercise intervention for individuals with chronic stroke
Bibliographic record
Abstract
Rationale Cardiovascular exercise is an effective method to improve cardiovascular health outcomes, but also promote neuroplasticity during stroke recovery. Moderate-intensity continuous cardiovascular training (MICT) is an integral part of stroke rehabilitation, yet it may remain a challenge to exercise at sufficiently high intensities to produce beneficial adaptations to neuroplasticity. High-intensity interval training (HIIT) could provide a viable alternative to achieve higher intensities of exercise by using shorter bouts of intense exercise interspersed with periods of recovery. Methods and design This is a two-arm, parallel-group multi-site RCT conducted at the Jewish Rehabilitation Hospital (Laval, Québec, Canada) and McMaster University (Hamilton, Ontario, Canada). Eighty participants with chronic stroke will be recruited at both sites and will be randomly allocated into a HIIT or MICT individualized exercise program on a recumbent stepper, 3 days per week for 12 weeks. Outcomes will be assessed at baseline, at 12 weeks post-intervention, and at an 8-week follow-up. Outcomes The primary outcome is corticospinal excitability, a neuroplasticity marker in brain motor networks, assessed with transcranial magnetic stimulation (TMS). We will also examine additional markers of neuroplasticity, measures of cardiovascular health, motor function, and psychosocial responses to training. Discussion This trial will contribute novel insights into the effectiveness of HIIT to promote neuroplasticity in individuals with chronic stroke. Trial registration ClinicalTrials.gov NCT03614585 . Registered on 3 August 2018
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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 | 0.035 | 0.038 |
| Meta-epidemiology (narrow) | 0.009 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.134 | 0.022 |
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".