MétaCan
Menu
Back to cohort
Record W1817493136 · doi:10.11575/prism/35530

Is movement better?: comparing sedentary and motion-based game controls for older adults

2013· article· en· W1817493136 on OpenAlexaff
Kathrin Gerling, Kristen Dergousoff, Regan L. Mandryk

Bibliographic record

VenueLincoln Repository (University of Lincoln) · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMotion (physics)Video gameCognitionGame designPhysical medicine and rehabilitationPsychologyFocus (optics)Computer scienceVariety (cybernetics)Cognitive psychologyMultimediaMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

Providing cognitive and physical stimulation for older adults is critical for their well-being. Video games offer the opportunity of engaging seniors, and research has shown a variety of positive effects of motion-based video games for older adults. However, little is known about the suitability of motion-based game controls for older adults and how their use is affected by age-related changes. In this paper, we present a study evaluating sedentary and motion-based game controls with a focus on differences between younger and older adults. Our results show that older adults can apply motion-based game controls efficiently, and that they enjoy motion-based interaction. We present design implications based on our study, and demonstrate how our findings can be applied both to motion-based game design and to general interaction design for older adults.

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)0.002
metaresearch head score (Gemma)0.012
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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.009
GPT teacher head0.219
Teacher spread0.210 · 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

Citations34
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueLincoln Repository (University of Lincoln)Same topicTechnology Use by Older AdultsFrench-language works237,207