7 Which affects reduced aerobic power more in patients with CHD and atrial fibrillation, age or bodyweight?
Bibliographic record
Abstract
Background Obesity is a key risk factor for atrial fibrillation (AF) and CHD patients with AF are normally older than those patients without AF. Ageing and body mass both have an impact on an individual’s aerobic functional capacity (aerobic power; VO2max/peak). Aim The aim of the study was to evaluate the proportional influences of both age and body mass on VO2 peak in CHD rehabilitation (CR) participants with and without AF. Methods Retrospective analysis of cardiopulmonary exercise test data from previous studies, involving CR participants with and without persistent AF, were analysed in relation to age, body mass, relative VO2peak (ml/kg/min) and absolute VO2peak (ml/min). Differences between the two populations were assessed via independent T-tests with alpha set at p < .05, calculated in SPSS software version 23. Results CR participants with AF (n = 30; 70.7 years) vs. those without AF (n = 68; 56.9 years) were 14 years older (p <0.0001), had a greater body mass (94.6 vs 80.5 kg; p = 0.001) and a lower VO2peak (relative VO2peak: 17.8 vs 26.7 ml/kg/min; absolute VO2 peak: 1684 ml/min vs 2149 ml/min; p <0.0001). The relative and absolute VO2peaks in AF participants vs non-AF participants were lower by 33% and 22%, respectively. Given that ageing is known to contribute to a 1% per year decline in aerobic power, the AF participants would already be expected to have a 14% lower VO2peak than the non-AF participants. Conclusion Compared to non-AF the age-corrected aerobic power of the AF participants explained two-thirds of their lower aerobic power. Whilst it is important to focus on weight-management of AF populations, their observed lower functional capacity was still more strongly related to their older age than their body mass. These results support the prime importance of increased physical activity over weight-loss to mitigate the loss of ‘true’ aerobic power in those with CHD and 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".