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Record W6990672078

Effect of an immersive virtual reality serious game to promote physical activity and cognition among older adults.

2023· article· en· W6990672078 on OpenAlexaboutno aff

Bibliographic record

VenueDIAL (Catholic University of Leuven) · 2023
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionVirtual realityPhysical activityRepeated measures designRehabilitationCalorieCognitive trainingVideo game
DOInot available

Abstract

fetched live from OpenAlex

Background: To halt functional and cognitive decline that tends to augment with the age, there are increasing needs for new rehabilitation methods such as virtual reality (VR). Methods: A dozen of older adults (age>65 years) were recruited and asked to wear a fitness tracker watch for three consecutive weeks. During the first and third week, participants were instructed not to use immersive VR devices. During the second week, they were required to autonomously interact with a self-adaptive immersive VR serious game, for at least 15 minutes per day. ANOVA repeated measures tests were undertaken to compare participants’ number of steps, distance covered and number of consumed calories between week one, two and three. Older adults’ cognition and reaction time was also evaluated using the Montreal Cognitive Assessment and a smartphone application. Results: Older adults did not significantly improved their number of steps, distance covered and number of consumed calories between week one, two and three (p>0.05). Cognition was improved between baseline and week 2 (p=0.026), and between baseline and week 3 (p=0.046) whereas no between weeks difference was found for reaction time (p=0.814). Conclusion: One week of interaction with a self-adaptive serious game in immersive VR seems to enhance cognition with a retention at one-week post-intervention but does not lead to an improved activity nor reaction time among 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 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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.942
Threshold uncertainty score0.522

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 science0.0000.000
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 head0.010
GPT teacher head0.258
Teacher spread0.248 · 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 designOther design
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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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