An Integrated Platform Combining Immersive Virtual Reality and Physiological Sensors for Systematic and Individualized Assessment of Stress Response (bWell): Design and Implementation Study
Bibliographic record
Abstract
BACKGROUND: Stress is a pervasive issue in modern society, manifesting in various forms such as emotional, physical, and work-related stress, each with distinct impacts on individuals and society. Traditional stress studies often rely on psychological, performance, or social tests; however, recently, immersive virtual reality (VR), which provides a sense of presence and natural interaction, offers the opportunity to simulate real-world tasks and stressors in controlled environments. Despite its potential, the use of VR to investigate the multifaceted manifestations of stress has not been thoroughly explored. OBJECTIVE: This study aimed to explore the feasibility of using a VR-based platform, bWell, to elicit multifaceted stress responses and measure the resulting behavioral and physiological changes. Specifically, we aimed to design various VR stress exercises based on neurocardiac models to systematically test cardiac functioning within specific contexts of self-regulation (executive functioning, physical efforts, and emotional regulation). METHODS: The development process adhered to guidelines for VR clinical trials and complex health interventions, encompassing 3 phases: preparation, development, and verification. The preparation phase involved a comprehensive literature review to establish links between stress, the heart, and the brain, leading to the formulation of a conceptual model based on the Neurovisceral Integration Model (NVIM) and Vagal Tank Theory (VTT). The development phase involved designing VR exercises targeting specific stressors and integrating physiological sensors such as photoplethysmography (PPG) and electromyography (EMG) to capture heart rate variability (HRV) and facial expressions. The verification phase, conducted with a small number of trials, aimed to design a study and implement a workflow for testing the feasibility, acceptability, and tolerability of the VR exercises. In addition, the potential for capturing physiological measures along with subjective ratings of stress for specific dimensions was assessed. RESULTS: Verification trials demonstrated that the VR exercises were well tolerated, with negligible cybersickness and high user engagement. The different VR exercises successfully elicited the intended stress demands, along with the physiological responses. CONCLUSIONS: The study presents a novel VR-based experimental setup that allows a systematic and individualized assessment of stress responses, paving the way for future research to identify features that confer stress resilience and help individuals manage stress effectively. While our conceptual model highlights the role of HRV in providing valuable insights into stress responses, future research will involve multivariate and machine learning analyses to predict individual stress responses based on comprehensive sensor data, including EMG and the VR-based behavioral data, ultimately guiding personalized stress management interventions.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".