High-Intensity Interval Training After Stroke: An Opportunity to Promote Functional Recovery, Cardiovascular Health, and Neuroplasticity
Bibliographic record
Abstract
INTRODUCTION: Stroke is the leading cause of adult disability. Individuals poststroke possess less than half of the cardiorespiratory fitness (CRF) as their nonstroke counterparts, leading to inactivity, deconditioning, and an increased risk of cardiovascular events. Preserving cardiovascular health is critical to lower stroke risk; however, stroke rehabilitation typically provides limited opportunity for cardiovascular exercise. Optimal cardiovascular training parameters to maximize recovery in stroke survivors also remains unknown. While stroke rehabilitation recommendations suggest the use of moderate-intensity continuous exercise (MICE) to improve CRF, neither is it routinely implemented in clinical practice, nor is the intensity always sufficient to elicit a training effect. High-intensity interval training (HIIT) has emerged as a potentially effective alternative that encompasses brief high-intensity bursts of exercise interspersed with bouts of recovery, aiming to maximize cardiovascular exercise intensity in a time-efficient manner. HIIT may provide an alternative exercise intervention and invoke more pronounced benefits poststroke. OBJECTIVES: To provide an updated review of HIIT poststroke through ( a) synthesizing current evidence; ( b) proposing preliminary considerations of HIIT parameters to optimize benefit; ( c) discussing potential mechanisms underlying changes in function, cardiovascular health, and neuroplasticity following HIIT; and ( d) discussing clinical implications and directions for future research. RESULTS: Preliminary evidence from 10 studies report HIIT-associated improvements in functional, cardiovascular, and neuroplastic outcomes poststroke; however, optimal HIIT parameters remain unknown. CONCLUSION: Larger randomized controlled trials are necessary to establish ( a) effectiveness, safety, and optimal training parameters within more heterogeneous poststroke populations; (b) potential mechanisms of HIIT-associated improvements; and ( c) adherence and psychosocial outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".