Effectiveness of a Virtual Reality Open-Air Bath Program in Reducing Loneliness and Improving Brain Function for Dementia Prevention in Older Adults: Protocol for a Prospective Randomized Crossover Study
Bibliographic record
Abstract
BACKGROUND: Older adults often face loneliness due to chronic illness or loss of close ones, a situation worsened by the COVID-19 pandemic. Increased loneliness heightens the risk of diseases, especially dementia, necessitating urgent action. OBJECTIVE: This study aims to assess the impact of a virtual reality (VR)-based open-air bath program on depression and loneliness in older individuals with subjective cognitive decline/mild cognitive impairment attending the Dementia Medical Center in Kyoto, Japan. We further aim to evaluate the feasibility of the program (participant recruitment and adherence) and to measure program enjoyment and satisfaction. METHODS: The study design is a crossover trial with a 1:1 ratio, wherein 12 participants will be randomly assigned to groups 1 and 2, with group 2 serving as a waitlist control and group 1 receiving the VR program from the onset for 6 months; the VR program will be conducted 6 times (monthly). Program completion for group 1 will be followed by an observation period from months 7 to 12. Group 2 will participate in the VR program from months 7 to 12, with an observation period from months 1 to 6. Cognitive tests, psychiatric assessments, and the University of California, Los Angeles Loneliness Scale will be conducted before the study, at 6 months, and at 12 months. Results will be analyzed using repeated-measures ANOVA. Head magnetic resonance imaging and single-photon emission computed tomography scans will be performed before and after the VR program to evaluate changes and effects on brain regions. RESULTS: Recruitment began in September 2023 and data collection is expected to be completed by March 2025. Complete study results will be published by September 2025. CONCLUSIONS: This study examines the preliminary effects of VR on loneliness in older adults with predementia through open-air bath simulations. VR experiences could benefit this population, particularly those with limited outdoor activities. Quantifying VR's impact will aid in determining the size for a larger clinical trial. Qualitative results will inform participation mechanisms and guide the implementation and design of future trials. TRIAL REGISTRATION: University hospital Medical Information Network UMIN000052667; https://tinyurl.com/3yaccay5. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/57101.
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".