Immediate Effect of Physiotherapist-demonstrated Action Observation with Execution for Improving Upper Extremity Motor Function in Stroke: a Pre-post Pilot Study
Bibliographic record
Abstract
BACKGROUND: Video-demonstrated action-observation-execution is an effective intervention for motor re-learning in stroke rehabilitation. But customization of video for each task repeatedly questions its feasibility within limited resources, particularly for daily routine practice and in community settings. Physiotherapist-demonstrated action-observation-execution is a practical intervention based on the principle of observation and consecutive repetitions of observed real, live movements. The main objective of this study was to investigate the immediate effect of Physiotherapist-demonstrated action-observation-execution in upper extremity motor training in stroke. METHODS: Individuals with stroke were screened and 5 eligible participants were recruited. The research was a pre-post. A single session of Physiotherapist-demonstrated action-observation-execution was administered. A functional "Drinking" task was subdivided into simpler acts and trained. Pre and post intervention assessment of movement time using five hand-and-arm items of Nepali Wolf Motor Function Test were carried out. Global recovery was assessed in the form of Visual Analogue Scale. RESULTS: Paired t-test provided statistically significant difference in total movement time (mean difference=5.04 seconds, standard deviation=1.92, p=0.004) with larger effect size (0.95) indicating impressive improvement in movement time with the training. Substantial difference in global recovery score was noted (mean difference=17.40, standard deviation=3.65, p<0.0001, effect size=1.00) signifying the increased confidence and improved performance of upper extremity post treatment. CONCLUSIONS: The findings indicated that Physiotherapist-demonstrated action-observation-execution could be a feasible intervention to train motor functions in participants with stroke. Large-scale studies are recommended to establish the effectiveness of the intervention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".