The effects of music & auditory beat stimulation on anxiety: A randomized clinical trial
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Music and auditory beat stimulation (ABS) in the theta frequency range (4-7 Hz) are sound-based anxiety treatments that have been independently investigated in prior studies. Here, the anxiety-reducing potential of calm music combined with theta ABS was examined in a large sample of participants. METHODS: An open-label randomized controlled trial was conducted with participants taking anxiolytics (n = 163). Participants were randomly assigned using the Qualtrics randomizer algorithm, to a single session of sound-based treatment in one of four parallel arms: combined (music & ABS; n = 39), music-alone (n = 36), ABS-alone (n = 41), or pink noise (control; n = 47). Pre- and post-intervention somatic and cognitive state anxiety measures were collected along with trait anxiety, personality measures and musical preferences. The study was completed online using a custom application. RESULTS: Based on trait anxiety scores participants were separated into moderate and high trait anxiety sub-groups. Among participants with moderate trait anxiety, we observed reductions in somatic anxiety that were greater in combined and music-alone conditions than in the pink noise condition; and reductions in cognitive state anxiety that were greater in the combined condition than in the music-alone, ABS-alone, and pink noise conditions. While we also observed reductions in somatic and cognitive state anxiety in participants with high trait anxiety, the conditions were not well differentiated. CONCLUSIONS: Sound-based treatments are effective in reducing somatic and cognitive state anxiety. For participants with moderate trait anxiety, combined conditions were most efficacious.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".