High-intensity interval training is not just for athletes – it’s for stroke survivors too
Bibliographic record
Abstract
PURPOSE: High-intensity interval training (HIIT) is an effective exercise intervention for promoting health and recovery outcomes after stroke, but the perspectives and experiences of people post-stroke who have participated in HIIT programs are currently unknown. This study explored the perspectives of individuals post-stroke who participated in a 12-week HIIT intervention. METHODS: An interpretive description methodology was followed. One-on-one semi-structured interviews were conducted on a Criterion-I purposive sample of participants who were 6 to 60 months post-stroke and participated in a 12-week HIIT intervention. A reflexive thematic analysis was undertaken to analyze the transcripts. RESULTS: = 6 women, 3 men) participated in a semi-structured interview. Three main themes were identified: 1) Rewards of being pushed beyond your comfort zone, 2) Valuable learnings through the HIIT program, and 3) Meaningfully integrating HIIT into an exercise routine. DISCUSSION: Participants described HIIT as a rewarding intervention after stroke, providing many intended and unanticipated benefits. Participants emphasized the importance of a collaborative relationship with their providers, which can increase confidence in one's physical capabilities and support self-management strategies to maintain newly developed exercise behaviors. Knowledge translation initiatives are needed to help support the clinical implementation of HIIT as a beneficial exercise intervention after stroke.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".