A Novel Virtual Reality-Based Nature Meditation Program for Older Adults’ Mental Health: Results from a Pilot Randomized Controlled Trial
Bibliographic record
Abstract
OBJECTIVES: The rapid aging of the global population necessitates innovative interventions to address older adults' mental health. This pilot study evaluated the feasibility, acceptability, and preliminary efficacy of a 4-week, 8-session nature-mindfulness-compassion program using immersive virtual reality (Embodied-and-Embedded-Mindfulness-Compassion-Framework - Virtual-Reality (EEMCF-VR)) for older adults' mental health. METHODS: = 12). Participants completed self-report assessments of stress, positive and negative emotions, coping self-efficacy, psychosocial well-being, mindfulness, and nature connectedness at baseline (T1), midpoint (T2), post-intervention (T3), and 4-week follow-up (T4). Additionally, the EEMCF-VR group completed program feedback (T3) and simulator sickness (T1-T3) questionnaires. RESULTS: EEMCF-VR met feasibility benchmarks (recruitment targets achieved, attrition < 15%) and was well-tolerated (minimal simulator sickness). Participant feedback indicated high acceptability. The EEMCF-VR group reported significantly lower stress and negative emotions at T2 and T4 compared to controls. Qualitative analysis highlighted perceived benefits and components to retain (e.g. video content) or refine (e.g. headset weight). CONCLUSIONS: EEMCF-VR demonstrated feasibility and acceptability, with promising effects on stress and mood, warranting investigation in larger trials. CLINICAL IMPLICATIONS: EEMCF-VR shows potential as a scalable intervention to reduce older adults' emotional distress.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".