Preparing the Heart for Duty: Virtual Reality Biofeedback in an Arousing Action Game Improves in-Action Voluntary Heart Rate Variability Control in Experienced Police
Bibliographic record
Abstract
Adequate control over evolutionary engrained bodily stress reactions is essential to avoid disproportionate responses during highly arousing situations in police. This regulation can be trained via heart rate variability (HRV)-biofeedback, a widely used intervention aiming to improve stress regulation, but typically conducted under passive, low arousing conditions. We integrated closed-loop HRV-biofeedback in a newly designed engaging Virtual Reality (VR) action game containing the behavioral elements typically compromised under stress. Specifically, we aimed to train in-action physiological self-control under high arousal to allow improved transfer to real-life. A pre-registered (https://osf.io/cdsbx) quasi-randomized controlled trial in 109 police trainers demonstrated highly significant increases in HRV (32% average), through the engaging and gamified closed loop biofeedback. This ability to voluntarily upregulate in-action HRV transferred to game sessions without biofeedback (near transfer). Critically, we could additionally demonstrate transfer to a professional shooting performance assessment outside VR (far transfer). These results suggest that real time-biofeedback in stressful and active action contexts can help train professionals such as police in real-life stress regulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".