Biofeedback‐based training for stress management in daily hassles: an intervention study
Bibliographic record
Abstract
BACKGROUND: The day-to-day causes of stress are called daily hassles. Daily hassles are correlated with ill health. Biofeedback (BF) is one of the tools used for acquiring stress-coping skills. However, the anatomical correlates of the effects of BF with long training periods remain unclear. In this study, we aimed to investigate this. METHODS: PARTICIPANTS WERE ASSIGNED RANDOMLY TO TWO GROUPS: the intervention group and the control group. Participants in the intervention group performed a biofeedback training (BFT) task (a combination task for heart rate and cerebral blood flow control) every day, for about 5 min once a day. The study outcomes included MRI, psychological tests (e.g., Positive and Negative Affect Schedule, Center for Epidemiologic Studies Depression Scale, and Brief Job Stress Questionnaire), and a stress marker (salivary cortisol levels) before (day 0) and after (day 28) the intervention. RESULTS: We observed significant improvements in the psychological test scores and salivary cortisol levels in the intervention group compared to the control group. Furthermore, voxel-based morphometric analysis revealed that compared to the control group, the intervention group had significantly increased regional gray matter (GM) volume in the right lateral orbitofrontal cortex, which is an anatomical cluster that includes mainly the left hippocampus, and the left subgenual anterior cingulate cortex. The GM regions are associated with the stress response, and, in general, these regions seem to be the most sensitive to the detrimental effects of stress. CONCLUSIONS: Our findings suggest that our BFT is effective against the GM structures vulnerable to stress.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".