The Needs and Experiences of People With Early-Stage Dementia Using an Application for Cognitive and Physical Activation in Germany: Qualitative Study
Bibliographic record
Abstract
Background: The demand for support among people with dementia is increasing, while caregiving capacity is declining. As the trend of aging at home continues, technologies can help maintain the autonomy of people with dementia, enabling them to live independently for as long as possible. Furthermore, digital applications can have numerous positive biopsychosocial effects on the health of people with dementia, enhancing their physical, cognitive, and social functioning. Objective: This study aims to investigate the needs and experiences of people with dementia regarding a prototype tablet-based application designed to promote cognitive and physical activity. Methods: We conducted a methodical triangulation by combining semistructured interviews with people with dementia and external overt participant observation while testing a tablet-based application. A qualitative content analysis, as outlined by Kuckartz, was used to analyze the data. Results: Participants demonstrated varying levels of ability and prior experience with technology. While most were initially hesitant to use the tablet independently, they were more willing to try it after receiving encouragement. Some individuals required more assistance than others, indicating the need for individualized adjustments. Personal relevance to the content appeared to be crucial for cognitive tasks, as it helped to minimize overload for people with dementia. The participants appreciated social interaction with researchers and direct communication. Therefore, it is important to consider the role of personal support when developing and implementing technology. Conclusions: The successful implementation and use of technology requires acceptance and an effective interaction between people with dementia, technology, and caregivers or caring relatives providing personal support. The acceptance of the application was found to be less influenced by the types and presentation of tasks and more by content relevance and social interaction. Ideally, one-on-one support will be provided during use, though this requires additional time and financial resources, which are often limited in caregiving settings.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".