A Preliminary Study on the Development of a Virtual Reality‐Based Financial Decision‐Making Training Tool for Investment Trusts: Usability and Acceptability Among Community‐Dwelling Healthy Older People
Bibliographic record
Abstract
BACKGROUND: As global population ages, financial gerontology is becoming increasingly important in supporting older adults' financial decision-making. The decline in the real asset value of financial assets driven by inflation and social security reforms necessitates investment strategies beyond traditional savings. However, age-related cognitive decline can impair financial decision-making capacity, highlighting the need for effective interventions. Virtual reality (VR)-based training appears promising in enhancing cognitive function, making it an engaging tool for financial education. This study aimed to evaluate the usability and acceptability of a VR-based financial decision-making training tool for investment trust among community-dwelling older adults. METHODS: Twenty-eight cognitively healthy older adults (≥ 60 years, Japanese version of the Montreal Cognitive Assessment ≥ 26) participated in a usability study using the Oculus Quest 2. The VR training involved interacting with an avatar that explained investment trust and answered decision-making quizzes. Usability and acceptability were assessed using a System Usability Scale, a Simulator Sickness Questionnaire, Likert-scale ratings on content clarity and content analysis of open-ended responses. RESULTS: The median training session lasted 21.8 min, and most participants (89.3%) completed the task with minimal or no support. The System Usability Scale score was 63.8, slightly below the usability benchmark, whereas Simulator Sickness Questionnaire scores indicated minimal VR-induced discomfort (overall score: median 3.7). Content analysis revealed three major strengths-cognitive support, emotional engagement and learner autonomy-and four key challenges including interface usability, content difficulty, physical comfort, and auditory pacing. CONCLUSIONS: This preliminary study suggests that VR-based financial decision-making training is a promising tool for financial education in older adults, as it may reduce psychological barriers to financial learning. However, improvements in usability, including intuitive and interactive feedback, are required. Future studies should explore the long-term impact of VR training on financial decision-making, potential adaptations for vulnerable populations, and its role within hybrid financial education programs.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".