Implementing a Smartphone and Wearable-Based Stress Management Intervention in Women with Coronary artery spasms to Reduce Cardiac Symptoms: A Multicenter, Single-Arm, One-way Crossover Study (Preprint)
Bibliographic record
Abstract
BACKGROUND: Mental stress is a well-known trigger of cardiac symptoms in patients with coronary artery spasms. Hence, stress management is recommended along with medical therapy. However, specific programs for patients with coronary spasms are lacking. We collaborated with patients to develop a smartphone-based app as a tailored solution. The app provides biofeedback based on heart rate variability-driven stress level estimations. OBJECTIVE: This study aimed to evaluate the effect of a biofeedback-driven smartphone stress management app on cardiac symptoms in women with coronary vasospasms. METHODS: We enrolled 117 women aged 18 to 70 years diagnosed with coronary vasospasms, as confirmed by a gold standard coronary function test. A multicenter, single-sequence, 2-period crossover study was conducted, comprising a 4-week control period followed by a 4-week period using the Wavy intervention app. The intervention comprised breathing-based exercises that were prompted when measured stress levels were too high. The primary outcome was the Seattle Angina Questionnaire Summary Score, and the secondary outcomes included the 36-Item Short Form Health Survey and the Perceived Stress Scale-10 items. Additionally, the user experience and the impact of the breathing exercise on heart rate variability were evaluated. RESULTS: A total of 102 patients completed the study, yet no significant improvements were observed in the Seattle Angina Questionnaire following the intervention period, with the control group scores at 50.4 (SD 15.4) and the intervention group scores at 50.9 (SD 15.0; P=.92). Similarly, no differences were found in the 36-Item Short Form Health Survey and the Perceived Stress Score. However, the 1236 breathing relaxation exercises performed during the study led to a significant improvement in heart rate variability, as indicated by a median root mean square of successive differences increase from 18.59 (IQR 8.38-36.58) milliseconds before the exercises to 34.93 (IQR 24.19-50.64) milliseconds after the exercises (z=-13.72; P<.001). Furthermore, two-thirds of participants (68/102, 66.7%) indicated that they would use a more refined version of the app in the future. CONCLUSIONS: A 4-week intervention using the Wavy app did not significantly alleviate anginal symptoms in female patients with coronary vasospasms. However, the breathing exercises demonstrated a notable improvement in heart rate variability, suggesting a reduction in stress levels. Patient feedback indicated broad support for the app and its wearable-based functionality, emphasizing the need for substantial refinements. Future development should incorporate patient perspectives to optimize the app as a comprehensive lifestyle management tool. TRIAL REGISTRATION: ClinicalTrials.gov NCT06171893; https://classic.clinicaltrials.gov/ct2/show/NCT06171893.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".