Effect of an Exercise and Nutrition Program on Quality of Life in Patients With Atrial Fibrillation: The Atrial Fibrillation Lifestyle Project (ALP)
Bibliographic record
Abstract
Background Studies of separate exercise and weight loss interventions have reported improvements in quality of life (QoL) or reduction in atrial fibrillation (AF) burden. We investigated the impact of a structured exercise, nutrition, and risk-factor-modification program on QoL and AF burden. Methods In this trial, 81 successive patients with body mass index > 27 kg/m 2 and nonpermanent AF were randomized to an intervention (n = 41) or control group (n = 40). The intervention consisted of cardiovascular risk management and a 6-month nutrition and exercise program, followed by a 6-month maintenance program. All participants received usual AF care. The primary end-point was QoL at 6 and 12 months. Results At 6 months, we observed improved QoL among patients in the intervention group, relative to that among control-group patients (intervention (I) n = 34, control (C) n = 38) in the 36-item Short Form Survey Instrument scores on the subscales of vitality (I: 13.2 ± 20.4; C: 1.0 ± 14.9, P < 0.001), social functioning (I: 14.7 ± 24.1; C: 2.4 ± 21.2, P = 0.018), emotional well-being (I: 5.5 ± 14.1 ; C: –1.0 ± 13.3, P = 0.017), and general health perceptions (I: 8.1 ± 12.3; C: 2.7 ± 13.3, P = 0.009). At the 6-month follow-up, improvement in the scores on the subscales of vitality ( P = 0.021) and emotional well-being ( P = 0.036) remained significant. The burden of AF as measured by Holter monitor and Toronto AF symptom score was not significantly changed. Conclusions A structured exercise and nutrition program resulted in significant sustained improvements in QoL, without reduction in AF burden. This type of program may provide an additional treatment for people with impaired QoL due to AF.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".