Home-Based Augmented Reality Exercise For People With Parkinson Disease: Qualitative Acceptability Study
Bibliographic record
Abstract
BACKGROUND: The rising prevalence of Parkinson disease and the growing demand on the health care system underscore the need for accessible and innovative care solutions, such as Reality Digital Therapeutics (Reality DTx)-an augmented reality neurorehabilitation program that delivers remotely prescribed gait and balance exercises for people with Parkinson disease to perform at home. OBJECTIVE: At a preimplementation stage, this qualitative study aimed to explore the acceptability of Reality DTx. METHODS: An exploratory qualitative study design was used. We conducted semistructured interviews, guided by the theoretical framework of acceptability, with 22 people with Parkinson disease who used Reality DTx at home for 6 weeks as part of a feasibility trial. We conducted a reflexive thematic analysis using an inductive, semantic approach informed by critical realism. RESULTS: The results of the reflexive thematic analysis are described in 3 themes and 9 subthemes. The 3 themes are "there was considerable interindividual variation," "the intervention is complementary to supervised physical therapy," and "adherence in the long term is crucial." Participants reported variable perceptions of effectiveness and variable experiences of effort to complete the Reality DTx program. They viewed Reality DTx as a valuable complement to supervised physical therapy and emphasized the indispensable role of the physical therapist for external control of long-term exercise adherence and for meaningful feedback on motor performance, as well as the desire for social connection. Flexibility in time and location was mentioned as a very important program characteristic, supporting long-term exercise adherence. Suggestions for improvement included enhanced visibility of progression in scores, increased variation in games, and the integration of competitive elements. CONCLUSIONS: Remotely prescribed, gamified, augmented reality exercises at home, complementary to supervised physical therapy, are acceptable to people with Parkinson disease. The findings inform future Reality DTx development and implementation from the perspective of people with Parkinson disease, which should be weighted with the perspectives of other stakeholders such as clinicians and other key decision-makers.
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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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".