Biomarkers in exposure-based treatment of anxiety in virtual reality: a systematic review
Bibliographic record
Abstract
Background A large proportion of individuals with anxiety-related disorders refrain from seeking treatment. This may be because traditional exposure treatments induce anxiety. However, advances in exposure using virtual reality technology may encourage more individuals to seek treatment. Furthermore, using biomarkers with VR-based exposure may enable clinicians to assess anxiety levels objectively and collect data in a naturalistic setting. Methods: Here, we conduct a systematic review of the literature on the use of biomarkers in VR-based exposure treatment for anxiety. Twenty-seven studies were included, with a total of 1046 participants. Results We found that heart rate was the only biomarker that tentatively could identify changes within (75% of instances) and between sessions (60% of instances). The levels of synchrony between the findings for overall biomarkers and the results from questionnaires showed inconclusive results. Regarding the levels of synchrony between the findings for particular biomarkers and the results from questionnaires, only skin conductance level was highly synchronous for differences between groups (87% of instances). Conclusion Based on the present review, biomarkers cannot yet be used reliably to distinguish differences in self-reported symptoms of anxiety in VR-based exposure treatments.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".