A scoping review of video gaming in rehabilitation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it