Addition of high-intensity interval training to a moderate intensity continuous training cardiovascular rehabilitation program after ischemic cerebrovascular disease: A randomized controlled trial
Bibliographic record
Abstract
Introduction: Moderate intensity continuous training (MICT) is usually recommended for stroke or transient ischemic attack (TIA) patients. High intensity interval training (HIIT) has emerged as a potentially effective method for increasing cardiorespiratory fitness (CRF) among clinical populations. Its effectiveness remains to be demonstrated after stroke. A combined program of HIIT and MICT was designed to create a realistic exercise program implemented for a clinical setting to help patients become more active. Purpose: This study aimed to compare the effects of a 6-month exercise program with either MICT only or a combination of HIIT and MICT and a control group in terms of CRF, cardiovascular risk factors, functionality, cognitive function (Montreal Cognitive Assessment) and depression markers (Hospital Anxiety and Depression Scale). Methods: This randomized controlled trial started with 52 participants (33 men and 19 women, mean age: 69.2 ± 10.7) divided into three groups: HIIT + MICT combined, MICT, and control. Both exercise groups consisted of 4 weekly sessions including supervised and at-home exercise. Outcomes were assessed at T0 (baseline measure), T6 (end of exercise protocols), and T12 (follow-up), 40 participants having completed the 12-month follow-up. Results: < 0.05). The two exercise programs induced a comparable increase in self-reported physical activity and a decrease in anxiety and depression markers. Participants in HIIT + MICT and MICT programs declared a good degree of acceptability assessed by the Acceptability and Preferences Questionnaire. Conclusion: A 6-month HIIT + MICT combined program and a standard MICT program induced similar improvements in CRF, self-reported physical activity and anxiety and depression markers among patients with prior ischemic stroke or TIA compared with a control group. These effects appear to persist over time. Addition of HIIT was safe and considered acceptable by participants. Our results do not support any superiority of the combination HIIT + MICT nor disadvantage vs. MICT in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".