Effects of different exercise types on quality of life for patients with atrial fibrillation: a systematic review and meta-analysis
Bibliographic record
Abstract
AIM: To investigate the effectiveness of exercise and the most effective types of exercise for patients with atrial fibrillation (AF) to improve health-related quality of life (HRQoL) and exercise capacity, and reduce AF burden, AF recurrence and adverse events. METHODS AND RESULTS: Systematic search in PubMed, Cochrane Central Register of Controlled Trials, MEDLINE, CINAHL Plus, and SPORTDiscus for randomized controlled trials (RCTs) and nonrandomized pre-post intervention studies investigating the effect of different types of exercise on AF patients. After exclusion, 12 studies (11 RCTs, 1 prepost) with a total of 670 participants were included. Exercise interventions consisted of aerobic exercise, aerobic interval training (AIT), Qigong, yoga, and exercise-based cardiac rehabilitation (CR). There were significant positive effects of exercise on general health {mean difference [MD] = 6.42 [95% confidence interval (CI): 2.90, 9.93]; P = 0.0003; I2 = 17%} and vitality [MD = 6.18 (95% CI: 1.94, 10.41); P = 0.004; I2 = 19%)] sub-scales of the Short Form 36-item questionnaire (SF-36). Qigong resulted in a significant improvement in the 6-min walk test [MD = 105.00m (95% CI: 19.53, 190.47)]. Exercise-based CR and AIT were associated with a significant increment in V̇O2peak, and AIT significantly reduced AF burden. Adverse events were few and one intervention-related serious adverse event was reported for exercise-based CR. CONCLUSION: Exercise led to improvements in HRQoL, exercise capacity, and reduced AF burden. The available exercise interventions for AF patients are few and heterogeneous. Future studies are needed for all types of exercise intervention in this patient group to (co-)develop an optimized exercise training intervention for AF patients.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.035 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".