Web-Based Relaxation Intervention for Stress During Social Isolation: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Relaxation practices might be helpful exercises for coping with anxiety and stressful sensations. They may be of particular utility when used in web-based interventions during periods of social isolation. OBJECTIVE: This randomized study aimed to test whether web-based relaxation practices like natural sounds, deep respiration, and body scans can promote relaxation and a positive emotional state, and reduce psychomotor activation and preoccupation related to the COVID-19 pandemic. METHODS: Participants were randomly assigned to one of three experimental conditions. Each condition was characterized by a single online session of a guided square breathing exercise, a guided body scan exercise, or natural sounds. The participants listened to one of the fully automated audio clips for 7 minutes and pre-post completed self-assessed scales on perceived relaxation, psychomotor activation, level of preoccupation associated with COVID-19, and emotional state. At the end of the session, qualitative reports on subjective experience were also collected. RESULTS: Overall, 294 participants completed 75% of the survey and 240 completed the entire survey as well as one of three randomly assigned interventions. Perceived relaxation, psychomotor activation/stress, and preoccupation related to COVID-19 showed a positive improvement after participants listened to the audio clips. The same pattern was observed for the valence and perceived dominance of the emotional state. The square breathing and body scan exercises yielded superior results compared to natural sounds in lowering perceived stress. CONCLUSIONS: This study provides a novel insight that can guide the development of future low-cost web-based interventions to reduce preoccupation and stress in the general population. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/19236.
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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) | 0.001 | 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) | 0.001 | 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".