Effects of virtual reality guided meditation in older adults: the protocol of a pilot randomized controlled trial
Bibliographic record
Abstract
Background: Virtual reality (VR) based meditation has been shown to help increase relaxation and decrease anxiety and depression in younger adults. However, this has not been studied in Randomized Controlled Trials (RCT) in the older adult population. The aim of this RCT is to assess the feasibility and acceptability of a VR-guided meditation intervention for community-dwelling older adults and its effect on stress and mental health. Methods: We will recruit 30 participants aged ≥ 60 years, whose perceived stress score (PSS) is > 14 (moderate stress), and randomize them 1:1 to the intervention or control waitlist group. The intervention will involve exposure to eight 15-min VR-guided meditation sessions distributed twice weekly for 4-weeks. Two modalities will be offered: in-home and at the hospital. Data analysis: Baseline and post-intervention assessments will evaluate perceived stress, anxiety, depression, sleep quality, quality of life, and mindfulness skills. Analyses will employ mixed methods repeated ANOVA tests. Qualitative analyses through semi-structured interviews and participant observation will be used to assess participants' experiences. Study outcomes include: (A) feasibility and acceptability compared to a waitlist control (B) stress, using the Perceived Stress Scale (PSS); (C) anxiety, and depression, using the Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9); (D) insomnia, quality of life and mindfulness skills, using the Athens Insomnia Scale (AIS), Quality of Life Questionnaire (EQ-5D-5L) and Five Facets Mindfulness Questionnaire Short Forms (FFMQ-SF), respectively. We will also measure immersive tendencies, sickness and sense of presence using the Simulator Sickness Questionnaire (SSQ) and the Presence Questionnaire (PQ). Discussion: at https://clinicaltrials.gov.
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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 | 0.026 | 0.026 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.038 | 0.006 |
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".