A Cloud-Based Virtual Reality App for a Novel Telemindfulness Service: Rationale, Design and Feasibility Evaluation
Bibliographic record
Abstract
BACKGROUND: Worldwide, there has been a marked increase in stress and anxiety, also among patients with traumatic brain injury (TBI). Access to psychology services is limited, with some estimates suggesting that over 50% of sufferers are not accessing the existing services available to them for reasons such as inconvenience, embarrassment, or stigmatization concerns around mental health. Health service providers have increasingly been turning to drug-free therapies, such as mindfulness programs, as complementary treatments. OBJECTIVE: Virtual reality (VR) as a new delivery method for meditation-based stress and anxiety reduction therapy offers configurable environments and privacy protection. Our objective was to design a serious learning-meditation environment and to test the feasibility of the developed telemindfulness approach based on cloud technologies. METHODS: We developed a cloud-based system, which consisted of a Web interface for the mindfulness instructor and remote clients, who had 3D VR headsets. The mindfulness instructor could communicate over the Web interface with the participants using the headset. Additionally, the Web app enabled group sessions in virtual rooms, 360-degree videos, and real interactions or standalone meditation. The mindfulness program was designed as an 8-week Mindfulness-Based Stress Reduction course specifically for the developed virtual environments. The program was tested with four employees and four patients with TBI. The effects were measured with psychometric tests, the Mindful Attention Awareness Scale (MAAS) and the Satisfaction With Life Scale (SWLS). Patients also carried out the Mini-Mental State Examination (MMSE). An additional objective evaluation has also been carried out by tracking head motion. Additionally, the power spectrum analyses of similar tasks between sessions were tested. RESULTS: The patients achieved a higher level of life satisfaction during the study (SWLS: mean 23.0, SD 1.8 vs mean 18.3, SD 3.9) and a slight increase of the MAAS score (mean 3.4, SD 0.6 vs mean 3.3, SD 0.4). Particular insight into the MAAS items revealed that one patient had a lower MAAS score (mean 2.3). Employees showed high MAAS scores (mean 4.3, SD 0.7) and although their SWLS dropped to mean 26, their SWLS was still high (mean 27.3, SD 2.8). The power spectrum showed that the employees had a considerable reduction in high-frequency movements less than 0.34 Hz, particularly with the 360-degree video. As expected, the patients demonstrated a gradual decrease of high-frequency movements while sitting during the mindfulness practices in the virtual environment. CONCLUSIONS: With such a small sample size, it is too early to make any specific conclusions, but the presented results may accelerate the use of innovative technologies and challenge new ideas in research and development in the field of mindfulness/telemindfulness.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
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".