The role of gaming technology for upper extremity rehabilitation of musculoskeletal conditions—A scoping review with expert insights
Bibliographic record
Abstract
BACKGROUND: Serious games and gaming are increasingly used as therapeutic interventions in the rehabilitation of the upper extremities (UE) among individuals with musculoskeletal (MSK) conditions. PURPOSE: To describe and summarize the characteristics of gaming technology used for UE rehabilitation of MSK conditions. STUDY DESIGN: Scoping review. METHODS: We searched four databases. Three reviewers screened articles and included articles that i) had at least one participant with an MSK condition, ii) used a gaming intervention, and iii) focused on UE rehabilitation, indicated by using an UE outcome measure. Reviewers extracted data on the study, patient, and gaming characteristics, and descriptively summarized the data. Stakeholder consultations were conducted with experts and therapists to receive feedback on the results and on reporting them. RESULTS: We included 41 articles. Gaming technology was reported to have participation (eg, to motivate, increase adherence) and therapeutic (eg, improving range of motion, muscle strength) benefits. The conditions included fractures (n = 8), amputation (n = 8), arthritis (n = 5), shoulder impingement syndrome (n = 3), hand injuries, etc. Gaming technologies such as Myo (n = 4), Leap Motion (n = 4), Nintendo Wii (n = 4), Kinect (n = 6), and Oculus (n = 5) were frequently used. Gaming was used as an adjunct in 49% of the articles and as a standalone intervention in 51%. Joint range of motion and muscle power were commonly evaluated, along with other constructs related to activities, participation, quality of life, compliance, and adherence. Limitations of gaming technology were related to the technology, hardware, games, therapeutics, and costs. CONCLUSIONS: A variety of gaming technology has been used for UE rehabilitation of MSK conditions. This review summarizing the characteristics of these gaming technology can help therapists and researchers make decisions on which ones to use, although some may be currently unavailable.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".