Implementing Accessibility Settings in Touchscreen Apps for People Living with Dementia
Bibliographic record
Abstract
BACKGROUND: Accessibility options within apps can enable customisation and improve usability. The consideration of accessibility for people living with dementia has not been explored but is necessary to prevent a "digital divide" in our society. This study set out to examine whether the introduction of accessibility settings for people with dementia in two mainstream gaming apps (Solitaire and Bubble Explode) could improve the user experience. OBJECTIVES: To evaluate the effectiveness of tailored accessibility settings for people living with dementia by comparing the gameplay experience with and without the settings and measure the impact on their ability to initiate gameplay, play independently and experience enjoyment. METHODS: Thirty participants were recruited to test one of the two apps that had been adapted to include accessibility features. These features were derived from an analysis of gameplay in a previous study, from which the design of the present study was replicated. The results were compared with those from the earlier study (i.e., pre-adapted apps). RESULTS: The accessibility features significantly improved usability in Solitaire, which had been the more problematic of the two apps when evaluated in its pre-adapted form. Bubble Explode retained the high level of usability without further improvements. Initiation of gameplay was significantly improved in the adapted version of Solitaire, with no significant differences to progression or enjoyment for either app. CONCLUSIONS: This study represents the first implementation of accessibility settings for dementia in mainstream apps, whilst demonstrating the feasibility and positive impact of the approach. The findings reveal core principles of touchscreen interaction and design for dementia that can inform future app development.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".