Biofeedback to treat anxiety in young people at clinical high risk for developing psychosis
Bibliographic record
Abstract
AIM: Anxiety is a common presenting concern for individuals at clinical high risk (CHR) for psychosis. Treatment for CHR is still in the early stages and has focused on transition to psychosis and positive symptom reduction, but little is known about what may be effective in reducing anxiety for these young people. One treatment that may be effective for anxiety is heart rate variability (HRV) biofeedback. The aim of this study was to test the efficacy and feasibility of using HRV biofeedback to reduce anxiety and distress in those at CHR. METHODS: Twenty participants who met minimum scores for anxiety and distress completed 4 weeks of an HRV biofeedback intervention and received pre- and post-intervention assessments. Repeated measures were used to examine changes in scores over time. RESULTS: There was a significant decrease in impaired ability to tolerate normal stressors (P ≤ 0.001) and dysphoric mood (P ≤ 0.001) over time. There was no change on self-reported measures of anxiety and distress. However, when two outliers were removed there was a trend towards improvement in self-reported anxiety (P = 0.07). These results were not impacted by including usage time as a covariate. Feedback and adherence were significant. CONCLUSIONS: HRV biofeedback may be a feasible treatment option for individuals at CHR who have concerns with impaired stress tolerance and dysphoric mood. Future studies with a randomized controlled trial design will be necessary to further determine efficacy.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".