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Record W2606094056 · doi:10.2196/games.7025

What Older People Like to Play: Genre Preferences and Acceptance of Casual Games

2017· article· en· W2606094056 on OpenAlexvenueno aff
Alvin Chesham, Patric Wyss, René M. Müri, Urs P. Mosimann, Tobias Nef

Bibliographic record

VenueJMIR Serious Games · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsCasualVideo gameAction (physics)PreferencePsychologyCognitionGame mechanicsVideo game designGame designSocial psychologyCognitive psychologyMultimediaComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: In recent computerized cognitive training studies, video games have emerged as a promising tool that can benefit cognitive function and well-being. Whereas most video game training studies have used first-person shooter (FPS) action video games, subsequent studies found that older adults dislike this type of game and generally prefer casual video games (CVGs), which are a subtype of video games that are easy to learn and use simple rules and interfaces. Like other video games, CVGs are organized into genres (eg, puzzle games) based on the rule-directed interaction with the game. Importantly, game genre not only influences the ease of interaction and cognitive abilities CVGs demand, but also affects whether older adults are willing to play any particular genre. To date, studies looking at how different CVG genres resonate with older adults are lacking. OBJECTIVE: The aim of this study was to investigate how much older adults enjoy different CVG genres and how favorably their CVG characteristics are rated. METHODS: A total of 16 healthy adults aged 65 years and above playtested 7 CVGs from 4 genres: casual action, puzzle, simulation, and strategy video games. Thereafter, they rated casual game preference and acceptance of casual game characteristics using 4 scales from the Core Elements of the Gaming Experience Questionnaire (CEGEQ). For this, participants rated how much they liked the game (enjoyment), understood the rules of the game (game-play), learned to manipulate the game (control), and make the game their own (ownership). RESULTS: Overall, enjoyment and acceptance of casual game characteristics was high and significantly above the midpoint of the rating scale for all CVG genres. Mixed model analyses revealed that ratings of enjoyment and casual game characteristics were significantly influenced by CVG genre. Participants' mean enjoyment of casual puzzle games (mean 0.95 out of 1.00) was significantly higher than that for casual simulation games (mean 0.75 and 0.73). For casual game characteristics, casual puzzle and simulation games were given significantly higher game-play ratings than casual action games. Similarly, participants' control ratings for casual puzzle games were significantly higher than that for casual action and simulation games. Finally, ownership was rated significantly higher for casual puzzle and strategy games than for casual action games. CONCLUSIONS: The findings of this study show that CVGs have characteristics that are suitable and enjoyable for older adults. In addition, genre was found to influence enjoyment and ratings of CVG characteristics, indicating that puzzle games are particularly easy to understand, learn, and play, and are enjoyable. Future studies should continue exploring the potential of CVG interventions for older adults in improving cognitive function, everyday functioning, and well-being. We see particular potential for CVGs in people suffering from cognitive impairment due to dementia or brain injury.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.017
GPT teacher head0.322
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations92
Published2017
Admission routes1
Has abstractyes

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