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Record W4414836392 · doi:10.1145/3748603

ExerCube vs. Virtual Reality: A Comparative Study of Exergame Technologies for Older Adults

2025· article· en· W4414836392 on OpenAlexafffund
Sukran Karaosmanoglu, Sebastian Cmentowski, Lennart E. Nacke, Frank Steinicke

Bibliographic record

VenueProceedings of the ACM on Human-Computer Interaction · 2025
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsUniversity of Waterloo
FundersCanadian Institutes of Health ResearchUniversity of WaterlooEuropean CommissionHORIZON EUROPE Framework ProgrammeBundesministerium für Bildung und ForschungAustralian Government
KeywordsVirtual realityAffect (linguistics)Physical activityImmersion (mathematics)Perceived exertionOlder people

Abstract

fetched live from OpenAlex

Insufficient physical activity is a major challenge in our aging society. Although exergames can provide enjoyable exercise opportunities for older adults, it remains unclear which display technology is best suited to reach this goal. This paper compares two popular exergame technologies with different immersion levels: (i) a virtual reality head-mounted display (VR-HMD) and (ii) the ExerCube, a commercial projection-based system. We conducted a within-participants study ( N =34) with older adults to evaluate player experience, presence, cybersickness, game performance, and physical exertion. Both display types provided a comparably high player experience and physical exertion that can benefit older adults’ physical well-being. The VR-HMD offered superior presence, while the ExerCube led to higher performance and physical activity. Our findings advance the understanding of how different exergame technologies affect older adults’ experiences. We present research and design implications to guide the future development of age-appropriate exergames.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.702
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.001
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 head0.062
GPT teacher head0.367
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations4
Published2025
Admission routes2
Has abstractyes

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