Utilization of Immersive Virtual Reality in Cognitive Stimulation Therapy (IVR-CST) for elderly with mild cognitive impairment: A randomized controlled pilot study protocol
Bibliographic record
Abstract
OBJECTIVES: Mild cognitive impairment (MCI) affects about 11.4% of the elderly population in Hong Kong. This study mainly investigates the feasibility and efficacy of immersive virtual reality-based cognitive stimulation therapy (IVR-CST) on MCI, and the use of eye-tracking technology in studying treatment outcome. HYPOTHESIS TO BE TESTED: 1) Whether IVR-CST is a feasible intervention for the elderly with MCI. 2) Whether IVR-CST is efficacious (and more efficacious than conventional CST) in improving cognition. 3) Whether changes in eye movements across therapy and treatment outcome are associated. DESIGN AND SUBJECTS: An open-label, two-armed, assessor-blinded, randomized controlled trial will be conducted. Sixty-six elderly individuals with MCI will be recruited and randomly allocated to either the IVR-CST or the conventional CST group. Their cognition will be measured before and immediately after therapy and 4 weeks post-therapy. INTERVENTIONS: A 14-session IVR-CST or conventional CST, with content adapted from the Chinese-translated manual of CST, will be carried out twice per week in groups of three to four individuals. OUTCOME MEASURES AND DATA ANALYSIS: The Hong Kong Montreal Cognitive Assessment and measures on executive functions/working memory will serve as primary outcomes. The within-subject (before and after therapy) and between-subject (IVR-CST vs. conventional CST) differences will be examined. Besides, eye movements during therapy in the IVR-CST group will be collected and its correlation with primary outcomes will be studied. EXPECTED RESULTS: Positive changes in cognition are expected after therapy in both treatment groups, which may be maintained four weeks post-therapy. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT06838494.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".