Exergame (ExerG)-Based Physical-Cognitive Training for Rehabilitation in Adults With Motor and Balance Impairments: Usability Study
Bibliographic record
Abstract
Background: Exergames are increasingly used in rehabilitation, yet their usability and user experience for patients and therapists, particularly for functional model systems, are underresearched. The diverse needs and preferences of users make conducting usability studies challenging, emphasizing the need for further investigation in real-world settings. Objective: This study aimed to evaluate the usability, safety, and user experience of a novel exergame functional model, the ExerG, from the perspectives of patients and therapists in a rehabilitation setting. Methods: In this mixed methods study, 15 patients undergoing rehabilitation (primary end users [PEUs]) and 20 therapists (secondary end users [SEUs]) from 2 rehabilitation centers in Switzerland and Austria participated in exercising and observation sessions with the ExerG. SEUs received training on system use and technical issue management, enabling them to fulfill their therapist roles while treating patients or mock patients. Rapid Iterative Testing and Evaluation was used and the training software adjusted based on participant feedback. Usability was assessed with questionnaires, semistructured interviews, and through observations during the ExerG testing. System acceptability was evaluated using specific quantitative thresholds based on PEU performance and feedback. An observation protocol tracked SEUs' correct use, errors, hesitations, task completion time, and needed assistance across scenarios. Results: Patients and therapists reported overall good usability and positive experiences with the exergame. PEUs rated 23/29 (79%) instructions as acceptable, showed good-to-very-good exercise performance in 19/29 (65%) tasks, and completed 28/29 (97%) tasks. Patients reported no adverse events, showing improved performance and enjoyment across ExerG exercising rounds, with 79/90 (88%) expressing positive emotions and reporting median scores of 9 (IQR 7.5-10) on a 1-10 user satisfaction scale. Patients were willing to continue using the device if the graphic design was improved (5/15), tracking systems and projector quality were enhanced (each 3/15), instructions clarified (12/15), and the game variety increased (2/15). PEUs felt secure in the safety harness (15/15) but recommended swivel arm movement enhancements (5/15). SEUs effectively executed scenarios, with hesitation and difficulties observed in only 14/41 tasks and 2/41 tasks, across all 20 therapists, accounting for 1.7% and 0.2% of the 820 total task cases, respectively. Therapists' quantitative usability ratings were high (median System Usability Scale score 82.5, IQR 65-95). All SEUs expressed their willingness to use the ExerG (20/20) and reported being able to operate the system using the user handbook (20/20). They emphasized the motivation-enhancing effect of video-game based training (12/20) and considered the activities supportive for physical and cognitive skills (20/20). They suggested incorporating daily living task simulations (13/20), more customizable options (6/20), more targeted motivational feedback (9/20), clearer performance ratings (9/20), and more concise activity instructions (6/20). Conclusions: The interdisciplinary, iterative ExerG development approach shows promise. The findings will inform future optimizations. Future work will assess long-term impact.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".