Early post-stroke rehabilitation for upper limb motor function using virtual reality and exoskeleton: equally efficient in older patients
Bibliographic record
Abstract
AIM OF THE STUDY: To evaluate the effectiveness of virtual reality therapy (VRT) Armeo Spring® upper limb exoskeleton (Armeo), in early post-stroke rehabilitation with a focus on the elderly. CLINICAL RATIONALE FOR THE STUDY: Convalescence from a stroke is a complex process driven by a spontaneous recovery supported by multifactorial activation. Novel technology-based rehabilitation methods are being introduced to support brain plasticity. MATERIALS AND METHODS: Using a randomised controlled study design, participants within 30 days after stroke with arm paresis were, in addition to a daily rehabilitation programme, assigned to an intervention group (45 minutes Armeo IG n = 25; mean age 66.5 years) performing VRT, or to a conventional physiotherapy (45 minutes) control group (Armeo CG, n = 25, mean age 68.1 years). Montreal Cognitive Assessment (MoCA), Functional Independence Measure (FIM) and Fugl Mayer Assessment Upper Extremity Scale (FMA-UE) were performed before and after the three-week therapy with 12 therapeutic sessions. Results of participants < 65 and ≥ 65 years old were compared. RESULTS: Paretic upper arm function improved significantly in both the IG and CG groups, the improvement in FMA-UE was significantly higher in the IG compared to the CG (p = 0.02), and patients ≥ 65 years old presented an equal magnitude of improvement in paretic arm function compared to younger patients. CONCLUSIONS AND CLINICAL IMPLICATIONS: Early post-stroke rehabilitation strategies using, in addition to the daily rehabilitation programme, VRT with visual biofeedback is more effective on upper extremity motor performance than conventional physiotherapy, and the effectiveness does not diminish with patient age. This may be a promising addition to conventional physiotherapy in older stroke patients as well as in younger.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".