High‐Intensity Functional Activities Specific Training (FAST) in Post‐Stroke Rehabilitation: A Randomized Trial on Motor Recovery and Quality of Life Improvements
Bibliographic record
Abstract
BACKGROUND: Stroke often leads to long-term disability, impacting motor function, mobility, and quality of life. However, the effectiveness of different intensities of functional training in improving these outcomes in stroke rehabilitation is not well established. PURPOSE: This study aimed to determine the effects of different intensities of functional activities specific training on upper extremity function, mobility, fall risk, and quality of life in stroke patients. METHODS: This randomized controlled trial was conducted in a rehabilitation setting (PRH Islamabad) with age 40-60 years and 3 months post-stroke. Participants (N = 99) were randomly (simple) assigned to conventional therapy, moderate-intensity, or high-intensity training with 33 in each group. Eligibility criteria included a Montreal Cognitive Assessment score of 18-25, Fugl-Meyer Assessment score of 50-70 and a Modified Rankin Scale score of 3 and 4, suggesting moderate motor impairment and moderate to severe disability, respectively. Participants with severe spasticity, inability to follow two-step commands, concurrent stroke treatments, other neurological conditions, and a history of falls were excluded. The 12-week training program utilized the FAST-Table, and outcomes including Fugl-Meyer Assessment for upper extremity, Berg Balance Scale, and Stroke-Specific Quality of Life Urdu scale-were assessed at baseline, 4, 8, and 12 weeks. RESULTS: The mean age of participants were 65.4 ± 7.2 years, gender (52% male, 48% female, ischemic 70%, and hemorrhagic 30%). The high-intensity group showed significant improvements in Fugl-Meyer Assessment-Upper Extremity scores at weeks 8 and 12 (p < 0.05), Timed Up and Go test (p < 0.05), Berg Balance Scale scores (p < 0.05), and Stroke-Specific Quality of Life Urdu scale (p < 0.05). Moderate-intensity and conventional groups also showed improvements individually. DISCUSSION: High-intensity functional training leads to greater improvements in physical performance and quality of life in stroke survivors compared with moderate-intensity functional training and conventional therapies. TRIAL REGISTRATION: https://clinicaltrials.gov/ (NCT05158543).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".