Virtual Reality for Screening of Cognitive Function in Older Persons: Comparative Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Dementia, which presents as cognitive decline in one or more cognitive domains affecting function, is becoming more prevalent. Traditional cognitive screening tools for dementia have their limitations, with emphasis on memory and to a lesser extent on the cognitive domain of executive function. The use of virtual reality (VR) in screening for cognitive function in older person is promising but evidence for its use is sparse. OBJECTIVE The primary aim is to examine the feasibility and acceptability of using VR to screen for cognitive impairment in older person in a primary care setting, through a VR module. The secondary aim is to assess the module’s ability to discriminate between cognitively normal and cognitively impaired participants. METHODS A comparative study was conducted at a public primary care clinic in Singapore, where 60 older persons were recruited based on a cut-off score of 26 using the Montreal Cognitive Assessment (MoCA) scale. They participated in the VR module to assess their learning and memory, perceptual-motor function and executive function. Each participant was evaluated by a total performance score (range: 0 – 700) upon completion. An assisted questionnaire was also administered to assess their perception of and attitude towards VR. RESULTS 37 participants in Group 1 (cognitively normal; MoCA >= 26) and 23 participants in Group 2 (cognitively impaired; MoCA < 26) were assessed. All participants completed the study with a mean total time of 19.1±3.6 minutes in Group 1 and 20.4±3.4 minutes in Group 2. Mean feedback scores ranged from 3.80 to 4.48 (max=5) in favour of VR. The total performance score in Group 1 (552.0±57.2) was higher than in Group 2 (476.1±61.9) (P < .001), and exhibited moderate positive correlation with scores from other cognitive screening tools: Abbreviated Mental Test (AMT) (0.312), Mini-Mental State Examination (MMSE) (0.373) and MoCA (0.427). A ROC curve analysis, relating total performance score to the presence of cognitive impairment, showed an area under curve of 0.821 (95% confidence interval: 0.714 to 0.928). CONCLUSIONS We demonstrated the feasibility of using an VR-based screening tool for cognitive function in older persons in primary care, who were largely in favour of this tool.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".