Gamified Practice Improves Paretic Arm Motor Behavior in Individuals With Stroke
Bibliographic record
Abstract
BACKGROUND: Stroke is a heterogeneous condition, making choice of treatment, and determination of how to structure rehabilitation outcomes difficult. Individualized goal-directed and repetitive physical practice is an important determinant of motor learning. Yet, many investigations of motor learning after stroke deliver task practice without consideration of individual capability of the learner. OBJECTIVE: We developed a gamified arm rehabilitation task for people with stroke that is personalized to individual capacity for paretic arm movement, provides a high dose of practice, progresses through increasingly difficulty levels that are dependent on the performance of the individual, and is practiced in an engaging environment. The objectives of the current study were to determine if 10 days of gamified, object intercept training using the paretic arm would improve arm movement speed and clinical outcome measures of impairment or function. METHODS: Individuals with chronic stroke and age-matched controls engaged in 10 days of gamified, skilled motor practice of a semi-immersive virtual reality-based intercept and release task. The paretic arm was assessed using the Fugl-Meyer Assessment (motor impairment) and Wolf Motor Function Test (motor function) before and after training. RESULTS: Both groups showed faster arm movement speed with practice; individuals with stroke demonstrated reduced paretic arm motor impairment and increased function after the intervention. Age and sex (for both groups), and time post-stroke were not related to changes in movement speed. CONCLUSIONS: Findings indicate that gamified motor training positively affects paretic arm motor behavior in individuals with mild to severe chronic 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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".