Augmented and mixed reality for rehabilitation of people living with Parkinson’s disease: a scoping review
Bibliographic record
Abstract
Abstract Background Extended reality technology, including virtual, augmented, and/or mixed reality, has the potential to create an engaging rehabilitation experience for people living with Parkinson’s disease (PwPD). Systematic reviews have explored the use of immersive virtual reality in rehabilitation for PwPD, however, studies employing mixed and augmented reality have yet to be systematically identified and mapped. As opposed to fully immersive virtual reality, mixed or augmented reality users can see their physical environments alongside digital elements, which may be beneficial in rehabilitation contexts for PwPD to reduce fall risk. Methods: Studies examining mixed or augmented reality in rehabilitation for PwPD were identified via a scoping review. Systematic searches were conducted of Scopus, Embase, PubMed, PsycInfo, and CINAHL, followed by grey literature searches of Google Scholar, ProQuest Dissertations and Theses/Conference Proceedings, Canada’s Drg Agency Grey Matters Tool, and the Overton Index. Results were screened independently by two reviewers, with two additional reviewers available for cross-checking and discussion. Results 26 studies met the inclusion criteria. 19 studies used head-mounted displays, while seven used treadmills with sensory feedback. Interventions focused most often on addressing motor symptoms via gait, balance, and task-specific training, and took place in settings including laboratories, clinical environments, and the home. Notably, long-term engagement with mixed or augmented reality technology was under-explored, with 12 out of 26 studies examining only a single session. Only one study included qualitative methods, and none of the included studies examined early-onset PD. Conclusion This scoping review identified a need for qualitative work on mixed and augmented reality in rehabilitation for PwPD, in order to better understand user experience. Additionally, longer studies are needed to examine potential long-term benefits and issues surrounding engagement with this technology over time. Future research could also explore applications of this technology for addressing non-motor symptoms, and use for people with early-onset PD.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".