Acceptability and effects on mental health of a music-based virtual reality intervention in geriatric outpatients: results from a pilot randomized controlled trial
Bibliographic record
Abstract
Background Although the benefits of music on mental health are well established, few studies have investigated the impact of delivering it through virtual reality (VR) technologies. VR offers immersive experiences that can enhance mental health benefits in geriatric patients. However, accessibility to VR music-based interventions for geriatric outpatients remains uncertain. This study aimed to evaluate the acceptability and effects of a music-based VR intervention on emotion, wellbeing and mood in geriatric outpatients living in Montreal (Quebec, Canada). Methods A single-center randomized controlled trial (RCT) with two parallel arms (i.e., control versus intervention) was conducted at the Montreal Geriatric University Institute (Quebec, Canada). A total of 41 outpatients from the geriatric and memory clinics were recruited and randomly assigned in the control group ( n = 20; music listening via headphones) and in the intervention group ( n = 21; VR-based music experience). The primary outcome was the acceptability of the intervention assessed using three complementary criteria: adoption defined as a retention rate ≥80%, satisfaction defined as willingness to reuse the intervention and perceived mental health benefits, and tolerance using the Simulator Sickness Questionnaire [SSQ] score (high tolerance defined by a score ≤9). Secondary outcomes were the effect on emotional state assessed with the Positive and Negative Affect Schedule (PANAS), on wellbeing assessed with the Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS) and on mood states assessed with the Visual Analog Mood Scale (VAMS). Results High retention (95.2%), satisfaction (85.0%) and tolerance (95%) rates were observed in the intervention group. The PANAS positive score significantly improved in the VR group ( β = 15.9, 95% CI [6.8, 25.1], p = 0.001). No significant intergroup differences were observed for wellbeing and mood. Interpretation This study demonstrates that a music-based VR intervention was highly acceptable and led to a significant improvement in positive emotional state among older adults in geriatric outpatients. Clinical trial registration NCT06296199; https://clinicaltrials.gov/study/NCT06296199 .
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".