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Record W3095264846 · doi:10.1186/s40814-020-00708-9

Introducing virtual reality therapy for inpatients with dementia admitted to an acute care hospital: learnings from a pilot to pave the way to a randomized controlled trial

2020· article· en· W3095264846 on OpenAlexafffund
Lora Appel, Erika Kisonas, E Appel, Jennifer Klein, Deanna Bartlett, Jarred Rosenberg, Christopher Smith

Bibliographic record

VenuePilot and Feasibility Studies · 2020
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsToronto East General HospitalYork UniversityUniversity Health Network
FundersToronto East General Hospital Foundation
KeywordsDementiaRandomized controlled trialMedicinePhysical therapySurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Behavioural and psychological symptoms of dementia (BPSD) are difficult to manage, particularly in acute care settings. As virtual reality (VR) technology becomes increasingly accessible and affordable, there is growing interest among clinicians to evaluate VR therapy in hospitalized patients, as an alternative to administering antipsychotics/sedatives or using physical restraints associated with negative side effects. OBJECTIVES: Validate and refine the proposed research protocol for a randomized controlled trial (RCT) that evaluates the impact of VR therapy on managing BPSD in acute care hospitals. Special attention was given to ascertain the processes of introducing non-pharmacological interventions in acute care hospitals. METHODS: Ten patients 65 years or older (mean = 87) previously diagnosed with dementia, admitted to an acute care hospital, were recruited over 3-month period into a prospective longitudinal pilot study. The intervention consisted of viewing 20-min of immersive 360° VR using a head-mounted display. Baseline and outcomes data were collected from the hospital electronic medical records, pre/post mood-state questionnaires, Neuropsychiatric Inventory (NPI) score, and standardized qualitative observations. Comprehensive process data and workflow were documented, including timestamps for each study task and detailed notes on personnel requirements and challenges encountered. RESULTS: Of 516 patients admitted during the study, 67 met the inclusion/exclusion criteria. In total, 234 calls were initiated to substitute decision makers (SDM) of the 67 patients for the consenting process. Nearly half (45.6%) of SDMs declined participation, and 40% could not be reached in time before patients being discharged, resulting in 57 eligible patients not being enrolled. Ten consented participants were enrolled and completed the study. The initial VR session averaged 53.6 min, largely due to the administration of NPI (mean = 19.5 min). Only four participants were able to respond reliably to questions. Seven participants opted for additional VR therapy sessions; of those providing feedback regarding the VR content, they wanted more varied scenery (animals, fields of flowers, holiday themes). Few sessions (4/18) encountered technical difficulties. CONCLUSION: The pilot was instrumental in identifying issues and providing recommendations for the RCT. Screening, inclusion criteria, consenting, data collection, and interaction with SDMs and hospital staff were all processes requiring changes and optimizations. Overall, patients with dementia appear to tolerate immersive VR, and with suggested protocol alterations, it is feasible to evaluate this non-pharmacological intervention in acute care hospitals.

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)0.022
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.022
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0050.003
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 head0.099
GPT teacher head0.356
Teacher spread0.257 · 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 designNon-randomized trial
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

Citations38
Published2020
Admission routes2
Has abstractyes

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