A scoping review of video gaming in rehabilitation
Bibliographic record
Abstract
PURPOSE: To examine the scope of the peer-reviewed literature on the use of commercially available video gaming in rehabilitation. METHODS: Five databases (SCOPUS, Cochrane, PsycINFO, PubMed and CINAHL) were searched for articles published between January 1990 and January 2014. The reference lists of selected articles were also reviewed to identify other relevant studies. RESULTS: Thirty articles met the inclusion criteria. Commercially available video gaming in rehabilitation was most commonly recommended by physiotherapists (50% or 15/30 studies) for populations at risk for falls or with decreased balance (67% or 19/30 studies). The most commonly used target outcomes were those assessing balance and/or fall prevention, with the Berg Balance Scale being the most frequently used (53% or 16/30 studies) outcome measure. The Nintendo Wii was the most prevalent gaming system (90% or 27/30 studies) used in the identified studies. CONCLUSION: Video gaming in rehabilitation is widely used by clinicians. Preliminary findings show that video gaming technology can be applied across a wide variety of rehabilitation populations, with some evidence showing clinical gains in physical functioning (e.g. gait and balance). There is a need for more robust clinical trials evaluating the efficacy of using video game systems as an adjunct to conventional rehabilitation. Implications for Rehabilitation Video gaming is a readily available technology that has been suggested as an enjoyable and motivating activity that engages patients in rehabilitation programming. Video gaming is becoming an increasingly popular adjunct to traditional therapy. Video gaming is most commonly used by physical therapists in a hospital setting for those with balance impairments. Video gaming has been shown to improve functional 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 | 0.018 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.032 | 0.025 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| 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".