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Record W2893132725 · doi:10.1177/1541931218621120

Virtual Reality Exergames for People Living with Dementia Based on Exercise Therapy Best Practices

2018· article· en· W2893132725 on OpenAlexaff
Mahzar Eisapour, Shi Cao, Laura Domenicucci, Jennifer Boger

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2018
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDementiaVirtual realityQuality of life (healthcare)PsychologyApplied psychologyPhysical activityPhysical medicine and rehabilitationPhysical therapyHuman–computer interactionComputer scienceMedicinePsychotherapist

Abstract

fetched live from OpenAlex

Exercise is an important factor for people living with dementia as it improves physical fitness and quality of life; however, it can be challenging for them to engage in exercise. This research created two virtual reality environments using Oculus Rift head-mounted display and Oculus touch controllers, with the goal of increasing the accessibility to exercise for people living with dementia. A three-week evaluation was conducted with six persons living with dementia to compare the virtual programs with human/therapist-guided exercise. The results showed that both virtual exercise programs were comparable to the therapist-guided exercise in terms of subjective enjoyment, comfort, and difficulty level of the activities. All the participants completed all the tasks designed for them in each day and five wanted to continue using virtual reality exercises. This research demonstrates promising potential of virtual reality exergames for people living with dementia. Future studies are needed to expand the available tasks, increase the available environments, and to examine clinical impact.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.029
GPT teacher head0.288
Teacher spread0.259 · 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 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

Citations61
Published2018
Admission routes1
Has abstractyes

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