Engaging cognitive domains of people with dementia during tablet game play
Bibliographic record
Abstract
Abstract Background Keeping cognitively active is important for people living with dementia or MCI but finding engaging activities can be challenging. Playing digital games has been shown to engage cognitive processes such as visual attention, working memory, and visuomotor skills, in children and adults. This study explored whether these cognitive domains are engaged when people living with dementia or MCI play digital games. Method Participants (n = 32) with dementia or MCI, mean MoCA 13.9, were recruited from adult day programs to play four mainstream games on touchscreen tablets. Each session was recorded and a video coding scheme was developed using behavioral analysis software to map observable behaviors, including eye gaze, hand movements, and game strategy, to five DSM cognitive domains : complex attention, executive function, learning and memory, language, and perceptual motor (DSM‐V, 2013). Five minutes from each initial gameplay session were analyzed to explore the engagement of cognitive domains. Results All five cognitive domains were engaged but varied across the games. For example, a word search games engaged language much more than a jigsaw puzzle game which was higher on perceptual‐motor skills. The most engaged cognitive domain was ‘complex attention’ and the least engaged cognitive domain was ‘language’. All participants demonstrated cognitive engagement whilst playing the games. Conclusion Playing digital games can provide cognitive engagement for people living with dementia or MCI. Using mainstream games increases accessibility of opportunities for cognitive engagement, in a way which is also immersing and rewarding. Future research incorporating eye tracking will increase understanding of how people living with dementia learn to play digital games and respond to prompts. This study presents huge potential for low cost cognitive activity for people with dementia or MCI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".