Combining Virtual Reality Motor Rehabilitation With Cognitive Strategy Use in Chronic Stroke
Bibliographic record
Abstract
IMPORTANCE: Rehabilitation interventions for chronic stroke are largely impairment based, with results confined to the level of impairment instead of function. In contrast, cognitive strategy training interventions have demonstrated clinically meaningful improvements in functional outcomes. Integration of these approaches has yet to be explored. OBJECTIVE: To evaluate acceptability, recruitment, and retention rate and determine which outcome measures best capture the effect of the intervention. DESIGN: Single-group, pre-post design. SETTING: Research laboratory. PARTICIPANTS: Adults with chronic stroke and hemiparesis (N = 10). INTERVENTION: A 12-wk intervention integrating cognitive strategy training with upper extremity motor training. Two weekly sessions used Kinect-based virtual reality to encourage high numbers of upper extremity movement repetitions. The third weekly session focused on the use of cognitive strategies with practice of client-centered goals. OUTCOMES AND MEASURES: Upper extremity motor performance was measured with the Fugl-Meyer Assessment. Occupational performance on trained and untrained goals was measured via the Performance Quality Rating Scale and the Canadian Occupational Performance Measure. Outcome data were gathered preintervention, postintervention, and at 3-mo follow-up. RESULTS: The intervention was perceived as acceptable. Recruitment rate was 15%, and retention rate was 100%. Large effects were found on outcomes of upper extremity motor performance, occupational performance, and participation at follow-up. CONCLUSION AND RELEVANCE: MetacogVR is feasible for adults with chronic stroke. The effect of MetacogVR is best captured through measures of upper extremity motor performance, occupational performance, and participation. WHAT THIS ARTICLES ADDS: Traditional, impairment-based approaches to chronic stroke rehabilitation may require integration with cognitive-strategy training to affect performance on meaningful goals.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".