A Novel Auditory-Cognitive Training App for Delaying or Preventing the Onset of Dementia: Participatory Design With Stakeholders
Bibliographic record
Abstract
BACKGROUND: Multiple gaming apps exist under the dementia umbrella for skills such as navigation; however, an app to specifically investigate the role of hearing loss in the process of cognitive decline is yet to be designed. There is a demonstrable gap in the utilization of games to further the knowledge of the potential relationship between hearing loss and dementia. OBJECTIVE: This study aims to identify the needs, facilitators, and barriers in designing a novel auditory-cognitive training gaming app. METHODS: A participatory design approach was used to engage key stakeholders across audiology and cognitive disorder specialties. Two rounds, including paired semistructured interviews and focus groups, were completed and thematically analyzed. RESULTS: A total of 18 stakeholders participated, and 6 themes were identified to inform the next stage of app development. These included congruence with hobbies, life getting in the way, motivational challenge, accessibility, addictive competition, and realism. CONCLUSIONS: The findings can now be implemented in the development of the app. The app will be evaluated against outcome measures of speech listening in noise, cognitive and attentional tasks, quality of life, and usability.
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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 | 0.028 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".