A clinical survey about commercial games in lower limb prosthetic rehabilitation
Bibliographic record
Abstract
BACKGROUND: Despite the popularity of commercial games in lower limb prosthetic rehabilitation, data about their prevalence of use as well as therapists' perspectives about these games are still lacking. OBJECTIVES: To learn about the prevalence of use of commercial games in lower limb prosthetic rehabilitation and therapists' perspectives about these games. STUDY DESIGN: Cross-sectional. METHODS: An online survey was sent to physical and occupational therapists across prosthetic rehabilitation facilities in Canada. The survey had questions about the use of commercial games and therapists' perspectives. RESULTS: Data were collected from 82 therapists. Overall, 46.3% (38/82) reported that they use commercial games; of those, 94.7% (36/38) used the Nintendo Wii Fit. The most reported perceived benefits were the Wii Fit helping to improve weight shifting ( n = 76/82, 92.7%) and balance ( n = 75/82, 91.5%), and being motivating and complementing traditional therapy ( n = 75/82, 91.5%). The most reported perceived barriers/challenges were lack of time and familiarity with the games ( n = 58/82, 70.7%). CONCLUSION: Commercial games, particularly the Wii Fit, are commonly used in lower prosthetic rehabilitation in Canada. Most of the queried therapists view the Wii Fit positively. Knowledge translation activities and developing standard treatment protocols would be helpful in minimizing the barriers identified in this study. Clinical relevance The Wii Fit is prevalent in lower limb prosthetic rehabilitation in Canada and it is viewed positively by therapists as having the potential to improve balance and weight bearing, making rehabilitation more motivating, and complementing traditional therapy. Future studies should investigate the efficacy of the Wii Fit in prosthetic rehabilitation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".