A Cognitive-Based Board Game With Augmented Reality for Older Adults: Development and Usability Study
Bibliographic record
Abstract
BACKGROUND: Older adults in Taiwan are advised to adopt regular physical and social activities for the maintenance of their cognitive and physical health. Games offer a means of engaging older individuals in these activities. For this study, a collaborative cognitive-based board game, Nostalgic Seekers, was designed and developed with augmented reality technology to support cognitive engagement in older adults. OBJECTIVE: A user study of the board game was conducted to understand how the game facilitates communication, problem solving, and emotional response in older players and whether augmented reality is a suitable technology in game design for these players. METHODS: A total of 23 participants aged 50 to 59 years were recruited to play and evaluate the game. In each session, participants' interactions were observed and recorded, then analyzed through Bales' interaction process analysis. Following each session, participants were interviewed to provide feedback on their experience. RESULTS: The quantitative analysis results showed that the participants engaged in task-oriented communication more frequently than social-emotional communication during the game. In particular, there was a high number of answers relative to questions. The analysis also showed a significant positive correlation between task-oriented acts and the game score. Qualitative analysis indicated that participants found the experience of playing the game enjoyable, nostalgic objects triggered positive emotional responses, and augmented reality technology was widely accepted by participants and provided effective engagement in the game. CONCLUSIONS: Nostalgic Seekers provided cognitive exercise and social engagement to players and demonstrated the positive potential of integrating augmented reality technology into cognitive-based games for older adults. Future game designs could explore strategies for regular and continuous engagement.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".