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Record W4411202891 · doi:10.2196/65252

Evaluating the User Experience and Usability of Game-Based Cognitive Assessments for Older People: Systematic Review

2025· review· en· W4411202891 on OpenAlexvenueno aff
Rhys Mantell, Ye In Hwang, Matthew Dark, Kylie Radford, Michael M. Kasumovic, Lauren A. Monds, Peter W. Schofield, Tony Butler, Adrienne Withall

Bibliographic record

VenueJMIR Aging · 2025
Typereview
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsUsabilityComputer scienceHuman–computer interactionCognitionUser experience designPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Game-based cognitive assessments (GBCAs) have the potential to transform the field of cognitive testing by enabling more effective screening of age-related cognitive decline. However, we lack a strong understanding of the usability and overall user experience of these games. This is a risk because the primary target users for GBCAs, older people, are seldom involved in game design research and development. OBJECTIVE: This study aims to address this gap by investigating the usability, acceptability, and enjoyability of GBCAs for older people. METHODS: This study followed established practices for undertaking evidence-based systematic reviews. RESULTS: The initial database search returned 15,232 records. After a thorough screening process, 8 studies remained for extraction and analysis. A synthesis of the included papers identified 2 overlapping yet distinct areas of focus: system usability and subjective user experience. Usability scores were mostly positive across the studies included. However, in several of the game studies, older adults and those with cognitive impairment tended to find GBCAs less usable. This trend was observed even when the games were explicitly designed for these populations, and the tasks were simplistic and representative of basic daily activities. In our second focus area, user experience, we identified the importance of perceived challenge in mediating gameplay experience across groups. That is, generating the appropriate level of difficulty for each user is important for positive user experiences, specifically enjoyment. CONCLUSIONS: On the basis of these findings, we identified key learnings for researchers interested in designing and developing GBCAs. These include (1) recognizing that validity is essential but not sufficient on its own; (2) clearly defining the intended user; (3) designing games that align with the unique preferences and needs of older people; and (4), whenever possible, providing each user with their optimal level of challenge. TRIAL REGISTRATION: PROSPERO CRD42023433298; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023433298.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.116
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.094
GPT teacher head0.509
Teacher spread0.415 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations2
Published2025
Admission routes1
Has abstractyes

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