Development and feasibility testing of an online virtual reality platform for delivering therapeutic group singing interventions for people living with spinal cord injury
Bibliographic record
Abstract
People with quadriplegia have a high risk for respiratory illness, social isolation and depression. Previous research has demonstrated that therapeutic singing interventions can not only improve breathing function and speech loudness, but also improve mood and social connectedness for people with quadriplegia. Face-to-face group attendance is difficult for this population due to difficulties with distance and travel. Online environments offer an accessible and cost-effective solution for people to connect with others without leaving their home. In a two-phase iterative design, we explored and tested different approaches for delivering online music therapy sessions with 12 patients from an inpatient spinal cord injury rehabilitation service. Six participants in Phase 1 trialled different virtual reality headsets and completed a short interview about their experience of the equipment and online singing trials. Outcomes from Phase 1 testing led to the development of a custom-built virtual reality application for online group music therapy sessions with low-latency audio. We tested the acceptability and feasibility of this platform in comparison to face-to-face and teleconference options for music therapy with six different patients. These participants completed three validated questionnaires: System Usability Scale, Quebec User Evaluation of Satisfaction with assistive Technology, and Psychosocial Impact of Assistive Devices Scale, and an interview about their experience. Questionnaire scores were good with mean ratings of 4.4 for Quebec User Evaluation of Satisfaction with assistive Technology, 53 for System Usability Scale and positive mean Psychosocial Impact of Assistive Devices Scale scores of 1.5 for competence, 2 for adaptability and 1.5 for self-esteem. Thematic analysis of post-session qualitative interviews revealed five themes: virtual reality was a positive experience, virtual reality was immersive and transportative, virtual reality reduced inhibitions about singing in front of others, virtual reality may reduce social cues, and the virtual reality equipment was comfortable, accessible and easy to use. Telehealth options, including a custom-designed virtual reality program, with low-latency audio are an acceptable and feasible mode of delivery for therapeutic singing interventions for people with spinal cord injury. Future non-inferiority research is needed to test online delivery modes for music therapy in comparison to face-to-face treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".