Yoga in Virtual Reality Increases Experience of Presence but May Be Less Satisfying, More Difficult, and More Nausea-Unbalance Inducing
Bibliographic record
Abstract
As a result of the COVID-19 pandemic, yoga classes have increasingly been delivered in virtual formats via online videoconferencing. Virtual reality (VR) guided meditations have been found to be a satisfactory means of teaching meditation, but satisfaction with practicing yoga in VR has not been researched. The present study evaluated whether participants experience a greater sense of presence and satisfaction with yoga instruction provided through 360-degree videos when the videos are viewed in three dimensions through a VR headset compared to a standard two-dimensional display. Forty female undergraduate students first participated in a brief in-person yoga class, then two classes of equivalent length that were prerecorded 360-degree videos; all lessons took place during a single 1-hour session. One of the videos was viewed by 3D VR headset, and the other was viewed on a standard 2D laptop, with viewing order randomized across participants. After each of the three yoga classes, a questionnaire assessing spatial, interpersonal, and temporal presence; satisfaction; nausea-unbalance; and difficulty was administered. Split-plot repeated-measures multivariate analysis of variance revealed a significant main effect of delivery format, whereas the main effect for order of the virtual delivery formats and the interaction between the order and format were both nonsignificant. Follow-up univariate analyses revealed that the in-person yoga classes were associated with the greatest satisfaction and sense of presence compared to the two virtual delivery formats. Although the yoga instruction viewed through 3D VR headset was associated with a higher sense of presence than the 2D laptop viewing condition, it was also associated with more nausea-unbalance and difficulty and overall lower satisfaction.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".