A randomized controlled trial of a “Small Changes” behavioral weight loss treatment delivered in cardiac rehabilitation for patients with atrial fibrillation and obesity: study protocol for the BE-WEL in CR-AF study
Bibliographic record
Abstract
BACKGROUND: ) to alleviate symptom burden and improve prognosis, few cardiac rehabilitation (CR) programs include targeted weight loss treatment. AIMS: This RCT protocol will evaluate the efficacy of a "Small Changes" behavioral weight loss treatment (BWLT) to produce clinically relevant (≥ 10%) weight loss among patients with AF and obesity undergoing CR, relative to CR alone. Secondary aims are to establish efficacy of CR + BWLT for improving AF symptoms, AF risk factors, and health-related quality of life. METHODS: Adults (18 +) with AF and obesity will be recruited and randomized to receive CR + BWLT (intervention) or CR-only (control). Controls will receive CR consisting of supervised exercise and risk factor self-management for 12 weeks. The intervention group will receive CR plus BWLT (12 weekly, group-based virtual sessions, followed by 12 weeks of follow-up support). Weight and AF-risk factors will be assessed at pre-randomization, 12 weeks, 24 weeks, and 52 weeks. AF burden will be assessed using 30-s ECGs recorded bidaily and with AF symptoms. The primary endpoint of weight loss will be calculated from baseline to 52 weeks as a percentage of starting weight. Intention-to-treat analyses will compare the proportion in each group achieving ≥ 10% weight loss. Assuming success rates of 5% and 30% among controls and intervention groups, respectively, and a 30% loss to follow-up, 120 patients (60 per group) will provide 80% power to detect a difference using a two-sided independent test of proportions (alpha = 5%). IMPACT: This clinical trial will be the first to demonstrate that adding BWLT to CR promotes clinically meaningful weight loss among patients with AF and comorbid obesity. Findings will inform design and execution of a large efficacy trial of long-term (e.g., 5-year) clinical endpoints (e.g., AF severity, mortality). Implementing weight control interventions designed to target the AF substrate in CR could dramatically reduce morbidity and enhance quality of life among patients living with AF in Canada. TRIAL REGISTRATION: ClinicalTrials.gov registration number: NCT05600829. Registered October 31, 2022.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".