EXPLORING THE USE OF GROUP DIGITAL ACTIVITIES FOR PEOPLE LIVING WITH DEMENTIA
Bibliographic record
Abstract
Meaningful leisure activities are an important part of well-being, although these activities become less accessible for people with dementia. While digital technologies provide an accessible form of entertainment, the way the technology is introduced, how people are taught to use it, and how players are supported during these activities is important to consider when working with this population. In this study, we explore the use of a motion-based technology (Xbox Kinect) as a group activity for people living with dementia. The study was conducted in a community-based adult day program for people living with dementia. Participants were observed during a virtual bowling activity over a period of 12 weeks. Observations focused on; 1) methods for introducing, teaching, and supporting people to use this technology, 2) effects of repeated exposure on mastery of learned skill, and 3) the influence of the group dynamic on the activity. The findings highlight the importance of training staff how to introduce, teach and support people with dementia during these digital activities. Approaches must be tailored to each individual’s skills and abilities, including the use of verbal prompts, gesture demonstrations, and/or physical support for clients with mobility impairments (e.g. wheelchair users). Over time mastery was evident through reduced prompting, with some participants even offering cues to newer players. The group bowling activity provided a social activity, with participants engaging through positive encouragement, friendly competition, and reminiscing. The findings suggest motion-based digital activities have huge potential for people living with dementia to enjoy meaningful leisure activities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".