Developing Home-Based Virtual Reality Therapy Interventions
Bibliographic record
Abstract
OBJECTIVE: Stroke is one of the leading causes of serious long-term disability. However, home exercise programs given at rehabilitation often lack in motivational aspects. The purposes of this pilot study were (1) create individualized virtual reality (VR) games and (2) determine the effectiveness of VR games for improving movement in upper extremities in a 6-week home therapy intervention for persons with stroke. SUBJECTS AND METHODS: Participants were two individuals with upper extremity hemiparesis following a stroke. VR games were created using the Looking Glass programming language and modified based on personal interests, goals, and abilities. Participants were asked to play 1 hour each day for 6 weeks. Assessments measured upper extremity movement (range of motion and Action Research Arm Test [ARAT]) and performance in functional skills (Canadian Occupational Performance Measure [COPM] and Motor Activity Log [MAL]). RESULTS: Three VR games were created by a supervised occupational therapist student. The participants played approximately four to six times a week and performed over 100 repetitions of movements each day. Participants showed improvement in upper extremity movement and participation in functional tasks based on results from the COPM, ARAT, and MAL. CONCLUSIONS: Further development in the programming environment is needed to be plausible in a rehabilitation setting. Suggestions include graded-level support and continuation of creating a natural programming language, which will increase the ability to use the program in a rehabilitation setting. However, the VR games were shown to be effective as a home therapy intervention for persons with stroke. VR has the potential to advance therapy services by creating a more motivating home-based therapy service.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".