IMPACT OF GROUP-BASED DIGITAL GAMING INTERVENTION AMONG INDIVIDUALS LIVING WITH EARLY TO MODERATE DEMENTIA
Bibliographic record
Abstract
Abstract Slowing the progression of dementia and maintaining good behavioral and psychological symptoms are important aspects of life for individuals living with dementia and their caregivers. This study involved a group-based digital gaming system called Obie Technology as an interactive intervention for individuals living with dementia at a local adult day health center for 20 weeks, from mid-November 2023 to mid-April 2024. This gaming system promotes movement, stimulates cognitive activity, and encourages socialization. For example, colorful balloons float around the board or floor as clients try to hit as many as they can. A mixed methods approach with a pre-post design was employed to examine the effects of the intervention on cognitive function, mood, and behaviors in 24 individuals with early or moderate-level dementia. The average scores of the Montreal Cognitive Assessment (MoCA) at the baseline and midpoint (after 10 weeks) were 15.65 and 15.60, respectively, indicating no significant progression of dementia between these time periods. Participants’ depression level and neuropsychiatric symptoms presentation assessed by standardized instruments also showed no significant changes. The results from qualitative observations demonstrate that the group of people living with early-stage dementia in this study were more engaged and enjoyed the intervention compared to a group with moderate-stage dementia. They often comment that the intervention was fun, interesting, and enjoyable while actively engaging in the game with enhanced movement. In this presentation, we will present the findings based on the completed data, including the post-assessments.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".