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Record W2007381949 · doi:10.1682/jrrd.2013.10.0231

Two-week virtual reality training for dementia: Single case feasibility study

2014· article· en· W2007381949 on OpenAlexaff
Daniel McEwen, Anne Taillon‐Hobson, Martin Bilodeau, Heidi Sveistrup, Hillel M. Finestone

Bibliographic record

VenueThe Journal of Rehabilitation Research and Development · 2014
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsBruyère
Fundersnot available
KeywordsDementiaVirtual realityPhysical medicine and rehabilitationBalance (ability)Balance trainingPhysical therapyIntervention (counseling)CognitionPsychologyCognitive trainingFocus groupMedicineApplied psychologyComputer scienceNursingHuman–computer interaction

Abstract

fetched live from OpenAlex

Persons with dementia (PWD) are known to have difficulty with participation and focus during physical activity. Virtual reality (VR) offers a unique medium for motor learning but has only been used previously for cognitive assessment for PWD. Our study had two objectives: (1) investigate the feasibility and safety of an exercise-based VR training program in PWD, and (2) investigate its effects on balance and mobility. The intervention consisted of daily (5 d/wk, 1 h each) VR training sessions for 2 wk for a single research participant. Clinical balance and mobility measures were assessed 1 wk prior to, during, 1 wk following, and 1 mo after the intervention. Postintervention interviews provided qualitative feedback from the participant and his caregivers. Results indicate that VR training is feasible, safe, and enjoyable for PWD. However, balance and mobility measures were unaffected. VR training is well tolerated in a single research participant with dementia and is an engaging medium for participation in exercise.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.236
GPT teacher head0.427
Teacher spread0.191 · 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 designCase report
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

Citations63
Published2014
Admission routes1
Has abstractyes

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