The role of virtual reality-based cognitive training in enhancing motivation and cognitive functions in individuals with chronic stroke
Bibliographic record
Abstract
Stroke represents a major health challenge worldwide, often resulting in significant long-term disability that affects cognitive, motor, and emotional functions. Rehabilitation strategies that enhance patient motivation are crucial for improving outcomes. This randomized controlled trial investigated the impact of Virtual Reality Rehabilitation Systems (VRRS) compared to traditional cognitive training on motivation, cognitive recovery, and emotional state in post-stroke patients. Fifty-four adults with chronic stroke were randomized into two equal groups (27 participants per group). The experimental group received 24 sessions of Virtual Reality (VR) cognitive training, while the control group underwent 24 sessions of traditional cognitive rehabilitation. Motivation was assessed using the McClelland test, while cognitive and emotional states were evaluated using the Montreal Cognitive Assessment (MoCA) and Hamilton Rating Scales for Anxiety and Depression (HAM-A, HAM-D). The experimental group exhibited significant improvements in motivation, with marked increases in Achievement (T0: 68.41 ± 15.81, T1: 68.93 ± 15.80; p < 0.001) and Affiliation(T0: 60.67 ± 14.64, T1: 60.93 ± 15.59; p = 0.006) dimensions, alongside enhanced cognitive function (T0: 24.781 ± 1.89, T1: 26 (24.5-27); p = 0.001), reduced depressive (T0: 41 ± 2.32, T1: 6 (4-8); p = 0.003) and anxiety symptoms (T0: 4.26 ± 1.99, T1: 3.30 ± 1.94; p < 0.001). The Control Group showed significant differences only in MOCA (T0: 25 (23-26.5), T1: 25 (24-27); p < 0.001). Between-group analysis revealed no significant differences between the two groups. These findings underscore the potential of VR as a multifaceted tool to boost motivation, facilitate cognitive recovery, and improve emotional state, offering a comprehensive approach to post-stroke rehabilitation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".