Gamification Integration in Technological Devices for Motor Rehabilitation in Parkinson Disease: Scoping Review
Bibliographic record
Abstract
Background: Parkinson disease (PD) is a rapidly growing neurological condition worldwide. While physiotherapy and exercise are effective interventions, the addition of motivational aspects that improve adherence could be beneficial for people with PD. Incorporating technological devices into motor rehabilitation, coupled with gamification elements, could enhance the relevance of rehabilitation and alleviate motor symptoms. Objective: The aim of this scoping review was to identify and classify the technological devices that integrate gamification elements used in motor rehabilitation in PD, and to describe the justification behind the use of these devices and elements in this context. Methods: We conducted a scoping review following the framework proposed by Joanna Briggs Institute, along with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Major health science databases (MEDLINE, EMBASE, Scopus, Cochrane, Web of Science, PsycINFO, and Epistemonikos) were systematically searched. Relevant studies were included if they used technological interventions with gamification elements for motor symptom rehabilitation in PD. Gamification elements were extracted and categorized based on established frameworks, and content analysis was used to review the justifications for the use of technologies integrating gamification. Results: A total of 4451 studies were retrieved from the search. After the abstract and full-text screening, 81 studies were eligible for data extraction. The analysis identified 453 gamification elements across studies, with development and accomplishment being the most prominent core drive. Progress/feedback was the most frequently used element (79/81, 98% of studies), followed by points (70/81, 86%) and levels/progression (66/81, 81%). Other notable elements included badges, leaderboards, and customization, while several core drives, like ownership and possession, lacked reported elements. Most interventions were delivered through commercial video game consoles (33/81, 41%), followed by computer-based systems (32/81, 40%). Tablet-based applications and integrated rehabilitation platforms were used in 11% (9/81) and 10% (8/81) of the studies, respectively. The expected roles of technology were clear, but intentional use of gamification was scarce. Conclusions: This scoping review highlights the widespread adoption of technologies integrating gamification elements for motor symptom rehabilitation in individuals with PD. However, it also underscores a critical gap in understanding and justifying gamification mechanics. The current landscape relies heavily on commercial video games and emphasizes performance-based experiences, lacking theoretical grounding.
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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 | 0.019 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".