Gamification-based tele-rehabilitation for physical therapy in patients with Parkinson’s disease: A scoping review
Bibliographic record
Abstract
OBJECTIVE: A scoping review was conducted to explore what is known about gamification-based tele-rehabilitation (GBT) to enable physical therapy in home settings for people with Parkinson's disease (PD). METHODS: The peer-reviewed literature (OVID Medline, OVID EMBASE, CINAHL EBSCO, and Scopus databases) was searched from January 2010 to May 2024, and 24 articles met the inclusion criteria. The methodological quality of the studies was assessed using the Downs and Black evaluation tool, and levels of evidence were assigned using a modified Sackett scale. RESULTS: The majority of the 24 studies were of poor to fair methodological quality (83%), and all used a quantitative design with small sample sizes. The focus of the included studies was to enhance whole-body rehabilitation, with most addressing the upper extremities. Thirteen studies customized their games, whereas others utilized strictly commercial systems (n = 11). Eight studies reported no adverse events while the rest did not report on these. Eight studies indicated that participants maintained high levels of motivation and adherence in home settings. CONCLUSION: GBT has the potential to offer a safe, engaging and effective physical therapy to the PD population in home settings, but further research is warranted to help delineate clearer implementation considerations.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".