Biomarkers in exposure-based treatment of anxiety in virtual reality: a systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it