Mobile Game Design Guide to Improve Gaming Experience for the Middle-Aged and Older Adult Population: User-Centered Design Approach
Bibliographic record
Abstract
BACKGROUND: The number of older adult gamers who play mobile games is growing worldwide. Earlier studies have reported that digital games provide cognitive, physical, and socioemotional benefits for older adults. However, current mobile games that understand older adults' gameplay experience and reflect their needs are very scarce. Furthermore, studies that have analyzed older adults' game experience in a holistic manner are rare. OBJECTIVE: The purpose of this study was to suggest mobile game design guidelines for adults older than 50 years from a holistic gaming experience perspective. Adopting a human-centric approach, this study analyzes middle-aged and older adults' gameplay experience and suggests practical design guides to increase accessibility and satisfaction. METHODS: We organized a living laboratory project called the "Intergenerational Play Workshop." In this workshop, 40 middle-aged and older adults (mean age 66.75 years, age range 50-85 years) played commercial mobile games of various genres with young adult partners for 1 month (8 sessions). Using a convergent parallel mixed-method design, we conducted a qualitative analysis of dialogue, game diaries, and behavioral observations during the workshop and a quantitative analysis of the satisfaction level of the game elements for the mobile games that they played. RESULTS: This project was active from April 2019 to December 2021, and the data were collected at the workshops from July 1 to August 28, 2019. Based on the identified themes of positive and negative experiences from the qualitative data, we proposed 45 design guides under 3 categories: (1) cognitive and physical elements, (2) psychological and socioemotional elements, and (3) consumption contextual elements. Our empirical research could reaffirm the proposals from previous studies and provide new guidelines for improving the game design. In addition, we demonstrate how existing commercial games can be evaluated quantitatively by using the satisfaction level of each game's elements and overall satisfaction level. CONCLUSIONS: The final guidelines were presented to game designers to easily find related information and enhance the overall understanding of the game experience of middle-aged and older adults.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".