Haptic-Driven Serious Card Games for Older Adults: User Preferences Study
Bibliographic record
Abstract
BACKGROUND: Haptic feedback technology is widely used, including in serious games. It is an additional sensory method that supplements audio and vision, provides a novel user experience through a physical control layer, and enhances the immersion of virtual reality, thereby improving the user's cognitive state while alleviating dependence on visual information. However, there is limited research on haptic feedback preferences for mobile serious card games designed for older adults. OBJECTIVE: The study aims to investigate older adults' preferences for haptic feedback in mobile serious card games. METHODS: This study recruited a total of 250 participants from Dangtu County, Anhui Province, China, with an average age of 64.78 (SD 4.23) years. A descriptive survey was conducted among older adults, structured questionnaires were distributed, and data were collected via the Wenjuanxing (Changsha Ranxing Information Technology Co Ltd) mobile app. Reliability and validity analyses were performed using SPSS (IBM Corp) software. The questionnaire investigated older adults' basic understanding of card games and serious games, the integration of card games with mobile devices, the potential impact of combining card games with haptic feedback technology, as well as suggestions and opinions. RESULTS: The results showed that 63.2% (158/250) of the older adults liked the slight haptic feedback mode, and 78.4% (196/250) of the participants believed that combining haptic feedback technology with mobile card games would help improve cognitive abilities. The study found that 73.6% (184/250) of the older adults believed that this technology could reduce their reliance on visual information. This confirms that the combination of serious card games and haptic feedback can alleviate sensory impairments in older adults. Qualitative analysis revealed the potential of haptic feedback to reduce visual fatigue and provide an engaging cognitive training experience. CONCLUSIONS: Older adults have shown great interest in incorporating haptic feedback into mobile serious card games, believing that this could enhance their cognitive abilities while reducing their reliance on visual information. However, limitations include sample size and geographic restrictions, differences in digital literacy, self-reported data, and lack of longitudinal assessment. Longitudinal studies are recommended to evaluate the long-term effects of mobile serious card games with haptic feedback on cognitive abilities. Such investigations could provide valuable insights for game developers, rehabilitation institutions, and the development of cognitive training tools for older adults.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".