MétaCan
Menu
← Back to cohort
Record W4242598351 · doi:10.2196/preprints.25017

The Effects of 3D Immersion Technology (3Scape) on Mental Health in Outpatients From a Short-Term Assessment, Rehabilitation, and Treatment Program: Feasibility Protocol for a Randomized Controlled Trial (Preprint)

2020· preprint· en· W4242598351 on OpenAlexaboutno aff
Antonio Miguel Cruz, Anna-Maria Ladurner, Megan Kohls-Wiebe, David Rawani

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthAnxietyMoodDementiaRehabilitationQuality of life (healthcare)Randomized controlled trialDepression (economics)MedicineCognitionPsychiatryPsychologyGerontologyPhysical therapyNursing

Abstract

fetched live from OpenAlex

BACKGROUND Mental health conditions are prevalent among Canadians and are a leading cause of disability. Each year, 1 in 5 Canadians experiences a mental health issue. A total of 5% of people aged ≥65 years perceive their mental health as fair or poor, and 6.3% of them have mood disorders. Regarding older adults with cognitive impairments such as dementia, up to 40%-50% of them experience depression at some point. We believe that older adults can benefit significantly from information and telecommunication technologies as a strategy for improving mental health conditions such as depression and anxiety, while simultaneously improving their quality of life. 3Scape Systems Inc is an Alberta-based private company that has produced a series of specialized 3D videos designed to simulate real-life events and engage individuals living with mental health disorders and cognitive impairments such as dementia. OBJECTIVE This study aims to explore the trial design and effects of 3Scape videos on older adults’ symptoms of depression and anxiety and the efficacy of this technology in improving the quality of life of patients attending the Short-Term Assessment, Rehabilitation, and Treatment Psychiatry Day Hospital program at Glenrose Rehabilitation Hospital and to provide data to estimate the parameters required to design a definitive randomized controlled trial. METHODS The trial will use a randomized controlled design comprising 15 intervention participants and 15 control group participants. The participants will be adults aged ≥65 years who are cognitively intact or have minimal cognitive impairment (ie, Montreal Cognitive Assessment score ≥18), and are clients of the Short-Term Assessment, Rehabilitation, and Treatment Psychiatry Day Hospital program at Glenrose Rehabilitation Hospital. This study’s primary outcome variables are related to clients’ depressive and anxiety symptoms and their quality of life. The control group will receive the standard of care (ie, the Short-Term Assessment, Rehabilitation, and Treatment Psychiatry Day Hospital program at Glenrose Rehabilitation Hospital). The intervention group will receive the same standard of care as the control group and will use 3Scape Systems videos for therapeutic activities. RESULTS Our study is currently on hold because of the COVID-19 pandemic. The recruitment process is expected to resume by November 2021, and the primary impact analysis is expected to be conducted by February 2022. CONCLUSIONS This study will provide valuable information such as the measurement of comparative intervention effects, perception of older adults and mental health therapists about the 3Scape Systems, the associated costs of treatment, and product costs. This will contribute to the evidence planning process, which will be crucial for the future adoption of 3Scape Systems. CLINICALTRIAL International Standard Randomized Controlled Trial Number (ISRCTN): 93685907; https://www.isrctn.com/ISRCTN93685907. INTERNATIONAL REGISTERED REPORT PRR1-10.2196/25017

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)0.021
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.049
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.021
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0030.003
Open science0.0030.002
Research integrity0.0070.005
Insufficient payload (model declined to judge)0.0490.007

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 head0.036
GPT teacher head0.439
Teacher spread0.403 · 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 designRandomized trial
Domainnot available
GenreProtocol

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
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicTelemedicine and Telehealth Implementation→French-language works237,207→