Predicting Engagement While Playing Computer Games in Older Adults With and Without Dementia
Bibliographic record
Abstract
Abstract The purpose of this study was to determine what factors predict the level of engagement of older adults, with and without dementia, while playing computer games. Fourteen older adults with and without dementia (60%/40%) played a computer game over 16 sessions, each for 30 minutes. Variables included participants’ demographics, game-play data and environmental factors. Mixed fixed model for longitudinal data analysis design was used to determine how these variables predicted engagement. Five variables predicted engagement at a statistically significant level: Participant’s performance (B1=+0.16, p<0.03), age (B2=+0.20, p<0.00), previous experience with computer games (B3=+1.021, p<0.02), positive emotions (B4=+0.16, p<0.00), and distractions (noise) during gameplay (B5=-1.07, p<0.05). Cognitive impairment and general health status were correlated with engagement, but these correlations were not statistically significant. Previous experience using computer games and distractions during gameplay were the most important predictors of engagement while older adults with and without dementia played computer games.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".