Clinical Perceptions and Feasibility Analysis of a Virtual Reality Game for Post-Stroke Rehabilitation
Bibliographic record
Abstract
The increasing prevalence of strokes has led to the search for innovative rehabilitation methods. Immersive virtual reality (VR), especially personalized games, offers an interactive and motivating approach to therapy adherence. The perception and acceptance of physiotherapists are crucial to its implementation and require further investigation. The objective of this research was to evaluate the attitudes and perceptions of physiotherapists regarding the feasibility and effectiveness of a personalized VR game called Motion Health VR for post-stroke rehabilitation. The methodology employed consisted of using three strategies to collect subjective data. First, a multiple-choice questionnaire was administered to 73 physicians and physiatrists during the ISPRM 2023 Conference (International Society of Physical and Rehabilitation Medicine) to collect quantitative data on the utility and feasibility of Motion Health VR. Subsequently, a focus group was conducted with four physiotherapists to obtain qualitative information on the usability, accessibility, and cost-effectiveness of the game. Finally, a feasibility and cost-effectiveness analysis were performed to evaluate the possible long-term benefits and financial implications of implementing Motion Health VR in Colombia. The results obtained were a broad acceptance of VR as a complementary tool in post-stroke rehabilitation and the recognition of personalized games as motivators for patient participation. Physiotherapists highlighted the playability and immersion of the game, although they noted limitations related to costs and spasticity of the patient. The analysis indicated that initial costs, while significant, could be justified by long-term savings and improved patient outcomes. Finally, it is concluded that Motion Health VR demonstrated significant potential to complement post-stroke rehabilitation, receiving positive feedback from physiotherapists. Key challenges include improving access, reducing costs, and providing VR training to optimize rehabilitation outcomes.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".