Usability Study of a Multicomponent Exergame Training for Older Adults with Mobility Limitations
Bibliographic record
Abstract
The global population aged 60 years and over rises due to increasing life expectancy. More older adults suffer from "geriatric giants". Mobility limitations, including immobility and instability, are usually accompanied by physical and cognitive decline, and can be further associated with gait changes. Improvements in physical and cognitive functions can be achieved with virtual reality exergame environments. This study investigated the usability of the newly developed VITAAL exergame in mobility-impaired older adults aged 60 years and older. Usability was evaluated with a mixed-methods approach including a usability protocol, the System Usability Scale, and a guideline-based interview. Thirteen participants (9 female, 80.5 ± 4.9 years, range: 71-89) tested the exergame and completed the measurement. The System Usability Scale was rated in a marginal acceptability range (58.3 ± 16.5, range: 30-85). The usability protocol and the guideline-based interview revealed general positive usability. The VITAAL exergame prototype received positive feedback and can be considered usable by older adults with mobility limitations. However, minor improvements to the system in terms of design, instructions, and technical aspects should be taken into account. The results warrant testing of the feasibility of the adapted multicomponent VITAAL exergame, and its effects on physical and cognitive functions, in comparison with conventional training, should be studied.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 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".