A Relaxation App (HeartBot) for Stress and Emotional Well-Being Over a 21-Day Challenge: Randomized Survey Study
Bibliographic record
Abstract
BACKGROUND: HeartBot is an app designed to enable people 14 years and older to use relaxation tools offered by Heartfulness Institute to deal with daily stress and anxiety in a healthy, productive manner. These tools have proven effective in stress management and mental wellness when administered in a controlled environment by a certified proctor. OBJECTIVE: This study aimed to explore the app's effectiveness and evaluate the implementation of the tools. METHODS: In this study, 88 participants were recruited and randomly sorted into 2 groups, the HeartBot intervention group (n=46) and the waitlist control group (n=42). Pre- and postsurveys measured participants' stress levels using the Perceived Stress Scale (PSS) and their social-emotional well-being using the EPOCH (Engagement, Perseverance, Optimism, Connectedness, and Happiness) Measure of Adolescent Well-Being before and after they used the app for 21 days for 30 minutes every day. RESULTS: The study received institutional review board approval on August 18, 2019. Participant recruitment lasted from the approval date until September 30, 2019. The 21-day challenge started on October 1, 2019. Of the 135 people who signed up, 88 completed the study. There was a statistically significant difference in the mean PSS scores before and after the intervention (from 18.3 to 7.89; P<.001). The paired Wilcoxon rank sum test on the EPOCH scores indicated a significant difference in the medians of the total scores (W=411.5, P<.001). CONCLUSIONS: Evidence from this study shows that HeartBot is an effective app that can be used to manage stress and improve positive characteristics of emotional wellness. Future research and widespread usage of the app under this study are encouraged based on this preliminary evidence of its effectiveness. TRIAL REGISTRATION: ClinicalTrials.gov NCT04589520; https://clinicaltrials.gov/ct2/show/NCT04589520.
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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 | 0.007 | 0.001 |
| 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) | 0.001 | 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".